diff --git a/.github/workflows/.codespell-exclude b/.github/workflows/.codespell-exclude deleted file mode 100644 index 902cb55cb04..00000000000 --- a/.github/workflows/.codespell-exclude +++ /dev/null @@ -1,6 +0,0 @@ -"NotIn": "not in", -- `/checkin`: Check-in -docs/docs/integrations/providers/trulens.mdx -self.assertIn( -from trulens_eval import Tru -tru = Tru() diff --git a/.github/workflows/_integration_test.yml b/.github/workflows/_integration_test.yml index 48e277e936a..a1f7f988fa0 100644 --- a/.github/workflows/_integration_test.yml +++ b/.github/workflows/_integration_test.yml @@ -1,4 +1,5 @@ name: '🚀 Integration Tests' +run-name: 'Test ${{ inputs.working-directory }} on Python ${{ inputs.python-version }}' on: workflow_dispatch: @@ -11,6 +12,7 @@ on: required: true type: string description: "Python version to use" + default: "3.11" permissions: contents: read @@ -24,7 +26,7 @@ jobs: run: working-directory: ${{ inputs.working-directory }} runs-on: ubuntu-latest - name: '🚀 Integration Tests (Python ${{ inputs.python-version }})' + name: 'Python ${{ inputs.python-version }}' steps: - uses: actions/checkout@v4 diff --git a/.github/workflows/_release.yml b/.github/workflows/_release.yml index 283ba41bbae..93d1aea24f2 100644 --- a/.github/workflows/_release.yml +++ b/.github/workflows/_release.yml @@ -1,5 +1,5 @@ name: '🚀 Package Release' -run-name: '🚀 Release ${{ inputs.working-directory }} by @${{ github.actor }}' +run-name: 'Release ${{ inputs.working-directory }} ${{ inputs.release-version }}' on: workflow_call: inputs: @@ -14,6 +14,11 @@ on: type: string description: "From which folder this pipeline executes" default: 'libs/langchain' + release-version: + required: true + type: string + default: '0.1.0' + description: "New version of package being released" dangerous-nonmaster-release: required: false type: boolean @@ -111,7 +116,7 @@ jobs: # Look for the latest release of the same base version REGEX="^$PKG_NAME==$BASE_VERSION\$" PREV_TAG=$(git tag --sort=-creatordate | (grep -P "$REGEX" || true) | head -1) - + # If no exact base version match, look for the latest release of any kind if [ -z "$PREV_TAG" ]; then REGEX="^$PKG_NAME==\\d+\\.\\d+\\.\\d+\$" @@ -122,7 +127,7 @@ jobs: PREV_TAG="$PKG_NAME==${VERSION%.*}.$(( ${VERSION##*.} - 1 ))"; [[ "${VERSION##*.}" -eq 0 ]] && PREV_TAG="" # backup case if releasing e.g. 0.3.0, looks up last release - # note if last release (chronologically) was e.g. 0.1.47 it will get + # note if last release (chronologically) was e.g. 0.1.47 it will get # that instead of the last 0.2 release if [ -z "$PREV_TAG" ]; then REGEX="^$PKG_NAME==\\d+\\.\\d+\\.\\d+\$" @@ -484,7 +489,7 @@ jobs: with: name: dist path: ${{ inputs.working-directory }}/dist/ - + - name: Create Tag uses: ncipollo/release-action@v1 with: diff --git a/.github/workflows/api_doc_build.yml b/.github/workflows/api_doc_build.yml index c97b4e5e19e..e3eab91c962 100644 --- a/.github/workflows/api_doc_build.yml +++ b/.github/workflows/api_doc_build.yml @@ -1,4 +1,5 @@ -name: '📚 API Documentation Build' +name: '📚 API Docs' +run-name: 'Build & Deploy API Reference' # Runs daily or can be triggered manually for immediate updates on: @@ -51,7 +52,6 @@ jobs: run: | # Get unique repositories REPOS=$(echo "$REPOS_UNSORTED" | sort -u) - # Checkout each unique repository for repo in $REPOS; do # Validate repository format (allow any org with proper format) @@ -59,15 +59,14 @@ jobs: echo "Error: Invalid repository format: $repo" exit 1 fi - + REPO_NAME=$(echo $repo | cut -d'/' -f2) - + # Additional validation for repo name if [[ ! "$REPO_NAME" =~ ^[a-zA-Z0-9_.-]+$ ]]; then echo "Error: Invalid repository name: $REPO_NAME" exit 1 fi - echo "Checking out $repo to $REPO_NAME" git clone --depth 1 https://github.com/$repo.git $REPO_NAME done diff --git a/.github/workflows/people.yml b/.github/workflows/people.yml index fe6a56fc8f4..d47b4e73529 100644 --- a/.github/workflows/people.yml +++ b/.github/workflows/people.yml @@ -1,5 +1,6 @@ name: '👥 LangChain People' - +run-name: 'Update People Data' +# This workflow updates the LangChain People data by fetching the latest information from the LangChain Git on: schedule: - cron: "0 14 1 * *" diff --git a/.github/workflows/run_notebooks.yml b/.github/workflows/run_notebooks.yml index fc5b4fab91a..8f5c194762a 100644 --- a/.github/workflows/run_notebooks.yml +++ b/.github/workflows/run_notebooks.yml @@ -1,5 +1,5 @@ -name: '📝 Run Documentation Notebooks' - +name: '📓 Validate Documentation Notebooks' +run-name: 'Test notebooks in ${{ inputs.working-directory }}' on: workflow_dispatch: inputs: diff --git a/.github/workflows/scheduled_test.yml b/.github/workflows/scheduled_test.yml index 99662af8f0f..62ed8699ddd 100644 --- a/.github/workflows/scheduled_test.yml +++ b/.github/workflows/scheduled_test.yml @@ -1,4 +1,5 @@ name: '⏰ Scheduled Integration Tests' +run-name: "Run Integration Tests - ${{ inputs.working-directory-force || 'all libs' }} (Python ${{ inputs.python-version-force || '3.9, 3.11' }})" on: workflow_dispatch: # Allows maintainers to trigger the workflow manually in GitHub UI @@ -161,7 +162,7 @@ jobs: - name: '🧹 Clean up External Libraries' # Clean up external libraries to avoid affecting git status check - run: | + run: | rm -rf \ langchain/libs/partners/google-genai \ langchain/libs/partners/google-vertexai \ diff --git a/README.md b/README.md index 02a42335e83..fff4ced6472 100644 --- a/README.md +++ b/README.md @@ -15,7 +15,7 @@ [![GitHub star chart](https://img.shields.io/github/stars/langchain-ai/langchain?style=flat-square)](https://star-history.com/#langchain-ai/langchain) [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langchain?style=flat-square)](https://github.com/langchain-ai/langchain/issues) [![Open in Dev Containers](https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode&style=flat-square)](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain) -[](https://codespaces.new/langchain-ai/langchain) +[Open in Github Codespace](https://codespaces.new/langchain-ai/langchain) [![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai) [![CodSpeed Badge](https://img.shields.io/endpoint?url=https://codspeed.io/badge.json)](https://codspeed.io/langchain-ai/langchain) diff --git a/SECURITY.md b/SECURITY.md index 215e71e838a..1a3ee073a06 100644 --- a/SECURITY.md +++ b/SECURITY.md @@ -32,7 +32,7 @@ LangChain is partnered with [huntr by Protect AI](https://huntr.com/) to provide a bounty program for our open source projects. Please report security vulnerabilities associated with the LangChain -open source projects [here](https://huntr.com/bounties/disclose/?target=https%3A%2F%2Fgithub.com%2Flangchain-ai%2Flangchain&validSearch=true). +open source projects at [huntr](https://huntr.com/bounties/disclose/?target=https%3A%2F%2Fgithub.com%2Flangchain-ai%2Flangchain&validSearch=true). Before reporting a vulnerability, please review: diff --git a/cookbook/Multi_modal_RAG_google.ipynb b/cookbook/Multi_modal_RAG_google.ipynb index 579f8c7dfe1..c085080a6e9 100644 --- a/cookbook/Multi_modal_RAG_google.ipynb +++ b/cookbook/Multi_modal_RAG_google.ipynb @@ -144,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "kWDWfSDBMPl8", "metadata": {}, "outputs": [ @@ -185,7 +185,7 @@ " )\n", " # Text summary chain\n", " model = VertexAI(\n", - " temperature=0, model_name=\"gemini-2.0-flash-lite-001\", max_tokens=1024\n", + " temperature=0, model_name=\"gemini-2.5-flash\", max_tokens=1024\n", " ).with_fallbacks([empty_response])\n", " summarize_chain = {\"element\": lambda x: x} | prompt | model | StrOutputParser()\n", "\n", @@ -235,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "PeK9bzXv3olF", "metadata": {}, "outputs": [], @@ -254,7 +254,7 @@ "\n", "def image_summarize(img_base64, prompt):\n", " \"\"\"Make image summary\"\"\"\n", - " model = ChatVertexAI(model=\"gemini-2.0-flash\", max_tokens=1024)\n", + " model = ChatVertexAI(model=\"gemini-2.5-flash\", max_tokens=1024)\n", "\n", " msg = model.invoke(\n", " [\n", @@ -431,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "GlwCErBaCKQW", "metadata": {}, "outputs": [], @@ -553,7 +553,7 @@ " \"\"\"\n", "\n", " # Multi-modal LLM\n", - " model = ChatVertexAI(temperature=0, model_name=\"gemini-2.0-flash\", max_tokens=1024)\n", + " model = ChatVertexAI(temperature=0, model_name=\"gemini-2.5-flash\", max_tokens=1024)\n", "\n", " # RAG pipeline\n", " chain = (\n", diff --git a/cookbook/tool_call_messages.ipynb b/cookbook/tool_call_messages.ipynb index 3d533c89b70..0619caddda7 100644 --- a/cookbook/tool_call_messages.ipynb +++ b/cookbook/tool_call_messages.ipynb @@ -34,7 +34,7 @@ "tools = [multiply, exponentiate, add]\n", "\n", "gpt35 = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0).bind_tools(tools)\n", - "claude3 = ChatAnthropic(model=\"claude-3-sonnet-20240229\").bind_tools(tools)\n", + "claude3 = ChatAnthropic(model=\"claude-3-7-sonnet-20250219\").bind_tools(tools)\n", "llm_with_tools = gpt35.configurable_alternatives(\n", " ConfigurableField(id=\"llm\"), default_key=\"gpt35\", claude3=claude3\n", ")" @@ -113,14 +113,15 @@ { "data": { "text/plain": [ - "{'messages': [HumanMessage(content=\"what's 3 plus 5 raised to the 2.743. also what's 17.24 - 918.1241\"),\n", - " AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_6yMU2WsS4Bqgi1WxFHxtfJRc', 'function': {'arguments': '{\"x\": 8, \"y\": 2.743}', 'name': 'exponentiate'}, 'type': 'function'}, {'id': 'call_GAL3dQiKFF9XEV0RrRLPTvVp', 'function': {'arguments': '{\"x\": 17.24, \"y\": -918.1241}', 'name': 'add'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 58, 'prompt_tokens': 168, 'total_tokens': 226}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': 'fp_b28b39ffa8', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-528302fc-7acf-4c11-82c4-119ccf40c573-0', tool_calls=[{'name': 'exponentiate', 'args': {'x': 8, 'y': 2.743}, 'id': 'call_6yMU2WsS4Bqgi1WxFHxtfJRc'}, {'name': 'add', 'args': {'x': 17.24, 'y': -918.1241}, 'id': 'call_GAL3dQiKFF9XEV0RrRLPTvVp'}]),\n", - " ToolMessage(content='300.03770462067547', tool_call_id='call_6yMU2WsS4Bqgi1WxFHxtfJRc'),\n", - " ToolMessage(content='-900.8841', tool_call_id='call_GAL3dQiKFF9XEV0RrRLPTvVp'),\n", - " AIMessage(content='The result of \\\\(3 + 5^{2.743}\\\\) is approximately 300.04, and the result of \\\\(17.24 - 918.1241\\\\) is approximately -900.88.', response_metadata={'token_usage': {'completion_tokens': 44, 'prompt_tokens': 251, 'total_tokens': 295}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': 'fp_b28b39ffa8', 'finish_reason': 'stop', 'logprobs': None}, id='run-d1161669-ed09-4b18-94bd-6d8530df5aa8-0')]}" + "{'messages': [HumanMessage(content=\"what's 3 plus 5 raised to the 2.743. also what's 17.24 - 918.1241\", additional_kwargs={}, response_metadata={}),\n", + " AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_xuNXwm2P6U2Pp2pAbC1sdIBz', 'function': {'arguments': '{\"x\": 3, \"y\": 5}', 'name': 'add'}, 'type': 'function'}, {'id': 'call_0pImUJUDlYa5zfBcxxuvWyYS', 'function': {'arguments': '{\"x\": 8, \"y\": 2.743}', 'name': 'exponentiate'}, 'type': 'function'}, {'id': 'call_yaownQ9TZK0dkqD1KSFyax4H', 'function': {'arguments': '{\"x\": 17.24, \"y\": -918.1241}', 'name': 'add'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 75, 'prompt_tokens': 131, 'total_tokens': 206, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'id': 'chatcmpl-ByJm2qxSWU3oTTSZQv64J4XQKZhA6', 'service_tier': 'default', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run--35fad027-47f7-44d3-aa8b-99f4fc24098c-0', tool_calls=[{'name': 'add', 'args': {'x': 3, 'y': 5}, 'id': 'call_xuNXwm2P6U2Pp2pAbC1sdIBz', 'type': 'tool_call'}, {'name': 'exponentiate', 'args': {'x': 8, 'y': 2.743}, 'id': 'call_0pImUJUDlYa5zfBcxxuvWyYS', 'type': 'tool_call'}, {'name': 'add', 'args': {'x': 17.24, 'y': -918.1241}, 'id': 'call_yaownQ9TZK0dkqD1KSFyax4H', 'type': 'tool_call'}], usage_metadata={'input_tokens': 131, 'output_tokens': 75, 'total_tokens': 206, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}),\n", + " ToolMessage(content='8.0', tool_call_id='call_xuNXwm2P6U2Pp2pAbC1sdIBz'),\n", + " ToolMessage(content='300.03770462067547', tool_call_id='call_0pImUJUDlYa5zfBcxxuvWyYS'),\n", + " ToolMessage(content='-900.8841', tool_call_id='call_yaownQ9TZK0dkqD1KSFyax4H'),\n", + " AIMessage(content='The results are:\\n1. 3 plus 5 is 8.\\n2. 5 raised to the power of 2.743 is approximately 300.04.\\n3. 17.24 minus 918.1241 is approximately -900.88.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 55, 'prompt_tokens': 236, 'total_tokens': 291, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'id': 'chatcmpl-ByJm345MYnpowGS90iAZAlSs7haed', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None}, id='run--5fa66d47-d80e-45d0-9c32-31348c735d72-0', usage_metadata={'input_tokens': 236, 'output_tokens': 55, 'total_tokens': 291, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})]}" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -146,17 +147,17 @@ { "data": { "text/plain": [ - "{'messages': [HumanMessage(content=\"what's 3 plus 5 raised to the 2.743. also what's 17.24 - 918.1241\"),\n", - " AIMessage(content=[{'text': \"Okay, let's break this down into two parts:\", 'type': 'text'}, {'id': 'toolu_01DEhqcXkXTtzJAiZ7uMBeDC', 'input': {'x': 3, 'y': 5}, 'name': 'add', 'type': 'tool_use'}], response_metadata={'id': 'msg_01AkLGH8sxMHaH15yewmjwkF', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 450, 'output_tokens': 81}}, id='run-f35bfae8-8ded-4f8a-831b-0940d6ad16b6-0', tool_calls=[{'name': 'add', 'args': {'x': 3, 'y': 5}, 'id': 'toolu_01DEhqcXkXTtzJAiZ7uMBeDC'}]),\n", - " ToolMessage(content='8.0', tool_call_id='toolu_01DEhqcXkXTtzJAiZ7uMBeDC'),\n", - " AIMessage(content=[{'id': 'toolu_013DyMLrvnrto33peAKMGMr1', 'input': {'x': 8.0, 'y': 2.743}, 'name': 'exponentiate', 'type': 'tool_use'}], response_metadata={'id': 'msg_015Fmp8aztwYcce2JDAFfce3', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 545, 'output_tokens': 75}}, id='run-48aaeeeb-a1e5-48fd-a57a-6c3da2907b47-0', tool_calls=[{'name': 'exponentiate', 'args': {'x': 8.0, 'y': 2.743}, 'id': 'toolu_013DyMLrvnrto33peAKMGMr1'}]),\n", - " ToolMessage(content='300.03770462067547', tool_call_id='toolu_013DyMLrvnrto33peAKMGMr1'),\n", - " AIMessage(content=[{'text': 'So 3 plus 5 raised to the 2.743 power is 300.04.\\n\\nFor the second part:', 'type': 'text'}, {'id': 'toolu_01UTmMrGTmLpPrPCF1rShN46', 'input': {'x': 17.24, 'y': -918.1241}, 'name': 'add', 'type': 'tool_use'}], response_metadata={'id': 'msg_015TkhfRBENPib2RWAxkieH6', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 638, 'output_tokens': 105}}, id='run-45fb62e3-d102-4159-881d-241c5dbadeed-0', tool_calls=[{'name': 'add', 'args': {'x': 17.24, 'y': -918.1241}, 'id': 'toolu_01UTmMrGTmLpPrPCF1rShN46'}]),\n", - " ToolMessage(content='-900.8841', tool_call_id='toolu_01UTmMrGTmLpPrPCF1rShN46'),\n", - " AIMessage(content='Therefore, 17.24 - 918.1241 = -900.8841', response_metadata={'id': 'msg_01LgKnRuUcSyADCpxv9tPoYD', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 759, 'output_tokens': 24}}, id='run-1008254e-ccd1-497c-8312-9550dd77bd08-0')]}" + "{'messages': [HumanMessage(content=\"what's 3 plus 5 raised to the 2.743. also what's 17.24 - 918.1241\", additional_kwargs={}, response_metadata={}),\n", + " AIMessage(content=[{'text': \"I'll solve these calculations for you.\\n\\nFor the first part, I need to calculate 3 plus 5 raised to the power of 2.743.\\n\\nLet me break this down:\\n1) First, I'll calculate 5 raised to the power of 2.743\\n2) Then add 3 to the result\", 'type': 'text'}, {'id': 'toolu_01L1mXysBQtpPLQ2AZTaCGmE', 'input': {'x': 5, 'y': 2.743}, 'name': 'exponentiate', 'type': 'tool_use'}], additional_kwargs={}, response_metadata={'id': 'msg_01HCbDmuzdg9ATMyKbnecbEE', 'model': 'claude-3-7-sonnet-20250219', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 563, 'output_tokens': 146, 'server_tool_use': None, 'service_tier': 'standard'}, 'model_name': 'claude-3-7-sonnet-20250219'}, id='run--9f6469fb-bcbb-4c1c-9eec-79f6979c38e6-0', tool_calls=[{'name': 'exponentiate', 'args': {'x': 5, 'y': 2.743}, 'id': 'toolu_01L1mXysBQtpPLQ2AZTaCGmE', 'type': 'tool_call'}], usage_metadata={'input_tokens': 563, 'output_tokens': 146, 'total_tokens': 709, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}),\n", + " ToolMessage(content='82.65606421491815', tool_call_id='toolu_01L1mXysBQtpPLQ2AZTaCGmE'),\n", + " AIMessage(content=[{'text': \"Now I'll add 3 to this result:\", 'type': 'text'}, {'id': 'toolu_01NARC83e9obV35mZ6jYzBiN', 'input': {'x': 3, 'y': 82.65606421491815}, 'name': 'add', 'type': 'tool_use'}], additional_kwargs={}, response_metadata={'id': 'msg_01ELwyCtVLeGC685PUFqmdz2', 'model': 'claude-3-7-sonnet-20250219', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 727, 'output_tokens': 87, 'server_tool_use': None, 'service_tier': 'standard'}, 'model_name': 'claude-3-7-sonnet-20250219'}, id='run--d5af3d7c-e8b7-4cc2-997a-ad2dafd08751-0', tool_calls=[{'name': 'add', 'args': {'x': 3, 'y': 82.65606421491815}, 'id': 'toolu_01NARC83e9obV35mZ6jYzBiN', 'type': 'tool_call'}], usage_metadata={'input_tokens': 727, 'output_tokens': 87, 'total_tokens': 814, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}),\n", + " ToolMessage(content='85.65606421491815', tool_call_id='toolu_01NARC83e9obV35mZ6jYzBiN'),\n", + " AIMessage(content=[{'text': \"For the second part, you asked for 17.24 - 918.1241. I don't have a subtraction function available, but I can rewrite this as adding a negative number: 17.24 + (-918.1241)\", 'type': 'text'}, {'id': 'toolu_01Q6fLcZkBWZpMPCZ55WXR3N', 'input': {'x': 17.24, 'y': -918.1241}, 'name': 'add', 'type': 'tool_use'}], additional_kwargs={}, response_metadata={'id': 'msg_01WkmDwUxWjjaKGnTtdLGJnN', 'model': 'claude-3-7-sonnet-20250219', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 832, 'output_tokens': 130, 'server_tool_use': None, 'service_tier': 'standard'}, 'model_name': 'claude-3-7-sonnet-20250219'}, id='run--39a6fbda-4c81-47a6-b361-524bd4ee5823-0', tool_calls=[{'name': 'add', 'args': {'x': 17.24, 'y': -918.1241}, 'id': 'toolu_01Q6fLcZkBWZpMPCZ55WXR3N', 'type': 'tool_call'}], usage_metadata={'input_tokens': 832, 'output_tokens': 130, 'total_tokens': 962, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}),\n", + " ToolMessage(content='-900.8841', tool_call_id='toolu_01Q6fLcZkBWZpMPCZ55WXR3N'),\n", + " AIMessage(content='So, the answers are:\\n1) 3 plus 5 raised to the 2.743 = 85.65606421491815\\n2) 17.24 - 918.1241 = -900.8841', additional_kwargs={}, response_metadata={'id': 'msg_015Yoc62CvdJbANGFouiQ6AQ', 'model': 'claude-3-7-sonnet-20250219', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 978, 'output_tokens': 58, 'server_tool_use': None, 'service_tier': 'standard'}, 'model_name': 'claude-3-7-sonnet-20250219'}, id='run--174c0882-6180-47ea-8f63-d7b747302327-0', usage_metadata={'input_tokens': 978, 'output_tokens': 58, 'total_tokens': 1036, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}})]}" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -177,7 +178,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "langchain", "language": "python", "name": "python3" }, @@ -191,7 +192,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.10.16" } }, "nbformat": 4, diff --git a/docs/docs/additional_resources/arxiv_references.mdx b/docs/docs/additional_resources/arxiv_references.mdx index 2b8e2bd3f70..c775093d042 100644 --- a/docs/docs/additional_resources/arxiv_references.mdx +++ b/docs/docs/additional_resources/arxiv_references.mdx @@ -1,10 +1,10 @@ # arXiv - + LangChain implements the latest research in the field of Natural Language Processing. This page contains `arXiv` papers referenced in the LangChain Documentation, API Reference, Templates, and Cookbooks. -From the opposite direction, scientists use `LangChain` in research and reference it in the research papers. +From the opposite direction, scientists use `LangChain` in research and reference it in the research papers. `arXiv` papers with references to: [LangChain](https://arxiv.org/search/?query=langchain&searchtype=all&source=header) | [LangGraph](https://arxiv.org/search/?query=langgraph&searchtype=all&source=header) | [LangSmith](https://arxiv.org/search/?query=langsmith&searchtype=all&source=header) @@ -83,7 +83,7 @@ a set of open-domain QA datasets, covering multiple query complexities, and show that ours enhances the overall efficiency and accuracy of QA systems, compared to relevant baselines including the adaptive retrieval approaches. Code is available at: https://github.com/starsuzi/Adaptive-RAG. - + ## Self-Discover: Large Language Models Self-Compose Reasoning Structures - **Authors:** Pei Zhou, Jay Pujara, Xiang Ren, et al. @@ -106,7 +106,7 @@ than 20%, while requiring 10-40x fewer inference compute. Finally, we show that the self-discovered reasoning structures are universally applicable across model families: from PaLM 2-L to GPT-4, and from GPT-4 to Llama2, and share commonalities with human reasoning patterns. - + ## RAG-Fusion: a New Take on Retrieval-Augmented Generation - **Authors:** Zackary Rackauckas @@ -129,7 +129,7 @@ the generated queries' relevance to the original query is insufficient. This research marks significant progress in artificial intelligence (AI) and natural language processing (NLP) applications and demonstrates transformations in a global and multi-industry context. - + ## RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval - **Authors:** Parth Sarthi, Salman Abdullah, Aditi Tuli, et al. @@ -152,7 +152,7 @@ tasks. On question-answering tasks that involve complex, multi-step reasoning, we show state-of-the-art results; for example, by coupling RAPTOR retrieval with the use of GPT-4, we can improve the best performance on the QuALITY benchmark by 20% in absolute accuracy. - + ## Corrective Retrieval Augmented Generation - **Authors:** Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, et al. @@ -180,7 +180,7 @@ them. CRAG is plug-and-play and can be seamlessly coupled with various RAG-based approaches. Experiments on four datasets covering short- and long-form generation tasks show that CRAG can significantly improve the performance of RAG-based approaches. - + ## Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering - **Authors:** Tal Ridnik, Dedy Kredo, Itamar Friedman @@ -206,7 +206,7 @@ to 44% with the AlphaCodium flow. Many of the principles and best practices acquired in this work, we believe, are broadly applicable to general code generation tasks. Full implementation is available at: https://github.com/Codium-ai/AlphaCodium - + ## Mixtral of Experts - **Authors:** Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, et al. @@ -229,7 +229,7 @@ multilingual benchmarks. We also provide a model fine-tuned to follow instructions, Mixtral 8x7B - Instruct, that surpasses GPT-3.5 Turbo, Claude-2.1, Gemini Pro, and Llama 2 70B - chat model on human benchmarks. Both the base and instruct models are released under the Apache 2.0 license. - + ## Dense X Retrieval: What Retrieval Granularity Should We Use? - **Authors:** Tong Chen, Hongwei Wang, Sihao Chen, et al. @@ -255,7 +255,7 @@ also enhances the performance of downstream QA tasks, since the retrieved texts are more condensed with question-relevant information, reducing the need for lengthy input tokens and minimizing the inclusion of extraneous, irrelevant information. - + ## Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models - **Authors:** Wenhao Yu, Hongming Zhang, Xiaoman Pan, et al. @@ -286,7 +286,7 @@ with CoN significantly outperform standard RALMs. Notably, CoN achieves an average improvement of +7.9 in EM score given entirely noisy retrieved documents and +10.5 in rejection rates for real-time questions that fall outside the pre-training knowledge scope. - + ## Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection - **Authors:** Akari Asai, Zeqiu Wu, Yizhong Wang, et al. @@ -317,7 +317,7 @@ outperforms ChatGPT and retrieval-augmented Llama2-chat on Open-domain QA, reasoning and fact verification tasks, and it shows significant gains in improving factuality and citation accuracy for long-form generations relative to these models. - + ## Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models - **Authors:** Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen, et al. @@ -338,7 +338,7 @@ substantial performance gains on various challenging reasoning-intensive tasks including STEM, Knowledge QA, and Multi-Hop Reasoning. For instance, Step-Back Prompting improves PaLM-2L performance on MMLU (Physics and Chemistry) by 7% and 11% respectively, TimeQA by 27%, and MuSiQue by 7%. - + ## Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation - **Authors:** Xuefei Ning, Zinan Lin, Zixuan Zhou, et al. @@ -359,7 +359,7 @@ potentially improve the answer quality on several question categories. SoT is an initial attempt at data-centric optimization for inference efficiency, and showcases the potential of eliciting high-quality answers by explicitly planning the answer structure in language. - + ## Llama 2: Open Foundation and Fine-Tuned Chat Models - **Authors:** Hugo Touvron, Louis Martin, Kevin Stone, et al. @@ -377,7 +377,7 @@ safety, may be a suitable substitute for closed-source models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs. - + ## Lost in the Middle: How Language Models Use Long Contexts - **Authors:** Nelson F. Liu, Kevin Lin, John Hewitt, et al. @@ -399,7 +399,7 @@ significantly degrades when models must access relevant information in the middle of long contexts, even for explicitly long-context models. Our analysis provides a better understanding of how language models use their input context and provides new evaluation protocols for future long-context language models. - + ## Query Rewriting for Retrieval-Augmented Large Language Models - **Authors:** Xinbei Ma, Yeyun Gong, Pengcheng He, et al. @@ -426,7 +426,7 @@ Evaluation is conducted on downstream tasks, open-domain QA and multiple-choice QA. Experiments results show consistent performance improvement, indicating that our framework is proven effective and scalable, and brings a new framework for retrieval-augmented LLM. - + ## Large Language Model Guided Tree-of-Thought - **Authors:** Jieyi Long @@ -452,7 +452,7 @@ the effectiveness of the proposed technique, we implemented a ToT-based solver for the Sudoku Puzzle. Experimental results show that the ToT framework can significantly increase the success rate of Sudoku puzzle solving. Our implementation of the ToT-based Sudoku solver is available on [GitHub](https://github.com/jieyilong/tree-of-thought-puzzle-solver). - + ## Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models - **Authors:** Lei Wang, Wanyu Xu, Yihuai Lan, et al. @@ -482,7 +482,7 @@ by a large margin, is comparable to or exceeds Zero-shot-Program-of-Thought Prompting, and has comparable performance with 8-shot CoT prompting on the math reasoning problem. The code can be found at https://github.com/AGI-Edgerunners/Plan-and-Solve-Prompting. - + ## Zero-Shot Listwise Document Reranking with a Large Language Model - **Authors:** Xueguang Ma, Xinyu Zhang, Ronak Pradeep, et al. @@ -506,7 +506,7 @@ results, but can also act as a final-stage reranker to improve the top-ranked results of a pointwise method for improved efficiency. Additionally, we apply our approach to subsets of MIRACL, a recent multilingual retrieval dataset, with results showing its potential to generalize across different languages. - + ## Visual Instruction Tuning - **Authors:** Haotian Liu, Chunyuan Li, Qingyang Wu, et al. @@ -530,7 +530,7 @@ instruction-following dataset. When fine-tuned on Science QA, the synergy of LLaVA and GPT-4 achieves a new state-of-the-art accuracy of 92.53%. We make GPT-4 generated visual instruction tuning data, our model and code base publicly available. - + ## Generative Agents: Interactive Simulacra of Human Behavior - **Authors:** Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai, et al. @@ -563,7 +563,7 @@ architecture--observation, planning, and reflection--each contribute critically to the believability of agent behavior. By fusing large language models with computational, interactive agents, this work introduces architectural and interaction patterns for enabling believable simulations of human behavior. - + ## CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society - **Authors:** Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, et al. @@ -590,7 +590,7 @@ include introducing a novel communicative agent framework, offering a scalable approach for studying the cooperative behaviors and capabilities of multi-agent systems, and open-sourcing our library to support research on communicative agents and beyond: https://github.com/camel-ai/camel. - + ## HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face - **Authors:** Yongliang Shen, Kaitao Song, Xu Tan, et al. @@ -619,7 +619,7 @@ HuggingGPT can tackle a wide range of sophisticated AI tasks spanning different modalities and domains and achieve impressive results in language, vision, speech, and other challenging tasks, which paves a new way towards the realization of artificial general intelligence. - + ## A Watermark for Large Language Models - **Authors:** John Kirchenbauer, Jonas Geiping, Yuxin Wen, et al. @@ -641,7 +641,7 @@ interpretable p-values, and derive an information-theoretic framework for analyzing the sensitivity of the watermark. We test the watermark using a multi-billion parameter model from the Open Pretrained Transformer (OPT) family, and discuss robustness and security. - + ## Precise Zero-Shot Dense Retrieval without Relevance Labels - **Authors:** Luyu Gao, Xueguang Ma, Jimmy Lin, et al. @@ -670,7 +670,7 @@ details. Our experiments show that HyDE significantly outperforms the state-of-the-art unsupervised dense retriever Contriever and shows strong performance comparable to fine-tuned retrievers, across various tasks (e.g. web search, QA, fact verification) and languages~(e.g. sw, ko, ja). - + ## Constitutional AI: Harmlessness from AI Feedback - **Authors:** Yuntao Bai, Saurav Kadavath, Sandipan Kundu, et al. @@ -697,7 +697,7 @@ and RL methods can leverage chain-of-thought style reasoning to improve the human-judged performance and transparency of AI decision making. These methods make it possible to control AI behavior more precisely and with far fewer human labels. - + ## Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments - **Authors:** Zhivar Sourati, Vishnu Priya Prasanna Venkatesh, Darshan Deshpande, et al. @@ -727,7 +727,7 @@ components and fallacy classes, indicating that fallacy identification is a challenging task that may require specialized forms of reasoning to capture various classes. We share our open-source code and data on GitHub to support further work on logical fallacy identification. - + ## Complementary Explanations for Effective In-Context Learning - **Authors:** Xi Ye, Srinivasan Iyer, Asli Celikyilmaz, et al. @@ -752,7 +752,7 @@ performance. Therefore, we propose a maximal marginal relevance-based exemplar selection approach for constructing exemplar sets that are both relevant as well as complementary, which successfully improves the in-context learning performance across three real-world tasks on multiple LLMs. - + ## PAL: Program-aided Language Models - **Authors:** Luyu Gao, Aman Madaan, Shuyan Zhou, et al. @@ -784,7 +784,7 @@ larger models. For example, PAL using Codex achieves state-of-the-art few-shot accuracy on the GSM8K benchmark of math word problems, surpassing PaLM-540B which uses chain-of-thought by absolute 15% top-1. Our code and data are publicly available at http://reasonwithpal.com/ . - + ## An Analysis of Fusion Functions for Hybrid Retrieval - **Authors:** Sebastian Bruch, Siyu Gai, Amir Ingber @@ -803,7 +803,7 @@ learning of a CC fusion is generally agnostic to the choice of score normalization; that CC outperforms RRF in in-domain and out-of-domain settings; and finally, that CC is sample efficient, requiring only a small set of training examples to tune its only parameter to a target domain. - + ## ReAct: Synergizing Reasoning and Acting in Language Models - **Authors:** Shunyu Yao, Jeffrey Zhao, Dian Yu, et al. @@ -835,7 +835,7 @@ benchmarks (ALFWorld and WebShop), ReAct outperforms imitation and reinforcement learning methods by an absolute success rate of 34% and 10% respectively, while being prompted with only one or two in-context examples. Project site with code: https://react-lm.github.io - + ## Deep Lake: a Lakehouse for Deep Learning - **Authors:** Sasun Hambardzumyan, Abhinav Tuli, Levon Ghukasyan, et al. @@ -860,7 +860,7 @@ streams the data over the network to (a) Tensor Query Language, (b) in-browser visualization engine, or (c) deep learning frameworks without sacrificing GPU utilization. Datasets stored in Deep Lake can be accessed from PyTorch, TensorFlow, JAX, and integrate with numerous MLOps tools. - + ## Matryoshka Representation Learning - **Authors:** Aditya Kusupati, Gantavya Bhatt, Aniket Rege, et al. @@ -891,7 +891,7 @@ representations. Finally, we show that MRL extends seamlessly to web-scale datasets (ImageNet, JFT) across various modalities -- vision (ViT, ResNet), vision + language (ALIGN) and language (BERT). MRL code and pretrained models are open-sourced at https://github.com/RAIVNLab/MRL. - + ## Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages - **Authors:** Kevin Heffernan, Onur Çelebi, Holger Schwenk @@ -917,7 +917,7 @@ which is valuable in the low-resource setting. very low-resource languages and handle 50 African languages, many of which are not covered by any other model. For these languages, we train sentence encoders, mine bitexts, and validate the bitexts by training NMT systems. - + ## Evaluating the Text-to-SQL Capabilities of Large Language Models - **Authors:** Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau @@ -934,7 +934,7 @@ this setting. Furthermore, we demonstrate on the GeoQuery and Scholar benchmarks that a small number of in-domain examples provided in the prompt enables Codex to perform better than state-of-the-art models finetuned on such few-shot examples. - + ## Locally Typical Sampling - **Authors:** Clara Meister, Tiago Pimentel, Gian Wiher, et al. @@ -963,7 +963,7 @@ human evaluations show that, in comparison to nucleus and top-k sampling, locally typical sampling offers competitive performance (in both abstractive summarization and story generation) in terms of quality while consistently reducing degenerate repetitions. - + ## ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction - **Authors:** Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, et al. @@ -985,7 +985,7 @@ improve the quality and space footprint of late interaction. We evaluate ColBERTv2 across a wide range of benchmarks, establishing state-of-the-art quality within and outside the training domain while reducing the space footprint of late interaction models by 6--10$\times$. - + ## Learning Transferable Visual Models From Natural Language Supervision - **Authors:** Alec Radford, Jong Wook Kim, Chris Hallacy, et al. @@ -1014,7 +1014,7 @@ For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP. - + ## Language Models are Few-Shot Learners - **Authors:** Tom B. Brown, Benjamin Mann, Nick Ryder, et al. @@ -1047,7 +1047,7 @@ training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general. - + ## Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks - **Authors:** Patrick Lewis, Ethan Perez, Aleksandra Piktus, et al. @@ -1078,7 +1078,7 @@ parametric seq2seq models and task-specific retrieve-and-extract architectures. For language generation tasks, we find that RAG models generate more specific, diverse and factual language than a state-of-the-art parametric-only seq2seq baseline. - + ## CTRL: A Conditional Transformer Language Model for Controllable Generation - **Authors:** Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, et al. @@ -1098,4 +1098,3 @@ codes also allow CTRL to predict which parts of the training data are most likely given a sequence. This provides a potential method for analyzing large amounts of data via model-based source attribution. We have released multiple full-sized, pretrained versions of CTRL at https://github.com/salesforce/ctrl. - \ No newline at end of file diff --git a/docs/docs/changes/changelog/core.mdx b/docs/docs/changes/changelog/core.mdx index bd8b7a5d673..10d08fdbc67 100644 --- a/docs/docs/changes/changelog/core.mdx +++ b/docs/docs/changes/changelog/core.mdx @@ -7,4 +7,4 @@ - `BaseChatModel` methods `__call__`, `call_as_llm`, `predict`, `predict_messages`. Will be removed in 0.2.0. Use `BaseChatModel.invoke` instead. - `BaseChatModel` methods `apredict`, `apredict_messages`. Will be removed in 0.2.0. Use `BaseChatModel.ainvoke` instead. - `BaseLLM` methods `__call__`, `predict`, `predict_messages`. Will be removed in 0.2.0. Use `BaseLLM.invoke` instead. -- `BaseLLM` methods `apredict`, `apredict_messages`. Will be removed in 0.2.0. Use `BaseLLM.ainvoke` instead. \ No newline at end of file +- `BaseLLM` methods `apredict`, `apredict_messages`. Will be removed in 0.2.0. Use `BaseLLM.ainvoke` instead. diff --git a/docs/docs/changes/changelog/langchain.mdx b/docs/docs/changes/changelog/langchain.mdx index 04a7d8d9dcd..5dbfe7c7298 100644 --- a/docs/docs/changes/changelog/langchain.mdx +++ b/docs/docs/changes/changelog/langchain.mdx @@ -90,4 +90,4 @@ Deprecated classes and methods will be removed in 0.2.0 | OpenAIMultiFunctionsAgent | create_openai_tools_agent | Use LCEL builder over a class | | SelfAskWithSearchAgent | create_self_ask_with_search | Use LCEL builder over a class | | StructuredChatAgent | create_structured_chat_agent | Use LCEL builder over a class | -| XMLAgent | create_xml_agent | Use LCEL builder over a class | \ No newline at end of file +| XMLAgent | create_xml_agent | Use LCEL builder over a class | diff --git a/docs/docs/concepts/agents.mdx b/docs/docs/concepts/agents.mdx index 3ca96125e1f..16be27162a3 100644 --- a/docs/docs/concepts/agents.mdx +++ b/docs/docs/concepts/agents.mdx @@ -11,8 +11,8 @@ Please see the following resources for more information: ## Legacy agent concept: AgentExecutor -LangChain previously introduced the `AgentExecutor` as a runtime for agents. -While it served as an excellent starting point, its limitations became apparent when dealing with more sophisticated and customized agents. +LangChain previously introduced the `AgentExecutor` as a runtime for agents. +While it served as an excellent starting point, its limitations became apparent when dealing with more sophisticated and customized agents. As a result, we're gradually phasing out `AgentExecutor` in favor of more flexible solutions in LangGraph. ### Transitioning from AgentExecutor to LangGraph diff --git a/docs/docs/concepts/callbacks.mdx b/docs/docs/concepts/callbacks.mdx index 5899a4a98cd..f8c6b81e922 100644 --- a/docs/docs/concepts/callbacks.mdx +++ b/docs/docs/concepts/callbacks.mdx @@ -70,4 +70,4 @@ This is a common reason why you may fail to see events being emitted from custom runnables or tools. ::: -For specifics on how to use callbacks, see the [relevant how-to guides here](/docs/how_to/#callbacks). \ No newline at end of file +For specifics on how to use callbacks, see the [relevant how-to guides here](/docs/how_to/#callbacks). diff --git a/docs/docs/concepts/chat_history.mdx b/docs/docs/concepts/chat_history.mdx index 57d22c27353..6d80b155bcc 100644 --- a/docs/docs/concepts/chat_history.mdx +++ b/docs/docs/concepts/chat_history.mdx @@ -26,7 +26,7 @@ A full conversation often involves a combination of two patterns of alternating Since chat models have a maximum limit on input size, it's important to manage chat history and trim it as needed to avoid exceeding the [context window](/docs/concepts/chat_models/#context-window). -While processing chat history, it's essential to preserve a correct conversation structure. +While processing chat history, it's essential to preserve a correct conversation structure. Key guidelines for managing chat history: diff --git a/docs/docs/concepts/chat_models.mdx b/docs/docs/concepts/chat_models.mdx index 03133a253e5..dc931727247 100644 --- a/docs/docs/concepts/chat_models.mdx +++ b/docs/docs/concepts/chat_models.mdx @@ -127,7 +127,7 @@ If the input exceeds the context window, the model may not be able to process th The size of the input is measured in [tokens](/docs/concepts/tokens) which are the unit of processing that the model uses. ## Advanced topics - + ### Rate-limiting Many chat model providers impose a limit on the number of requests that can be made in a given time period. diff --git a/docs/docs/concepts/embedding_models.mdx b/docs/docs/concepts/embedding_models.mdx index a91018036c0..e9dbb64f1b5 100644 --- a/docs/docs/concepts/embedding_models.mdx +++ b/docs/docs/concepts/embedding_models.mdx @@ -15,9 +15,9 @@ Embedding models can also be [multimodal](/docs/concepts/multimodality) though s Imagine being able to capture the essence of any text - a tweet, document, or book - in a single, compact representation. This is the power of embedding models, which lie at the heart of many retrieval systems. -Embedding models transform human language into a format that machines can understand and compare with speed and accuracy. +Embedding models transform human language into a format that machines can understand and compare with speed and accuracy. These models take text as input and produce a fixed-length array of numbers, a numerical fingerprint of the text's semantic meaning. -Embeddings allow search system to find relevant documents not just based on keyword matches, but on semantic understanding. +Embeddings allow search system to find relevant documents not just based on keyword matches, but on semantic understanding. ## Key concepts @@ -27,16 +27,16 @@ Embeddings allow search system to find relevant documents not just based on keyw (2) **Measure similarity**: Embedding vectors can be compared using simple mathematical operations. -## Embedding +## Embedding -### Historical context +### Historical context -The landscape of embedding models has evolved significantly over the years. -A pivotal moment came in 2018 when Google introduced [BERT (Bidirectional Encoder Representations from Transformers)](https://www.nvidia.com/en-us/glossary/bert/). +The landscape of embedding models has evolved significantly over the years. +A pivotal moment came in 2018 when Google introduced [BERT (Bidirectional Encoder Representations from Transformers)](https://www.nvidia.com/en-us/glossary/bert/). BERT applied transformer models to embed text as a simple vector representation, which lead to unprecedented performance across various NLP tasks. -However, BERT wasn't optimized for generating sentence embeddings efficiently. +However, BERT wasn't optimized for generating sentence embeddings efficiently. This limitation spurred the creation of [SBERT (Sentence-BERT)](https://www.sbert.net/examples/training/sts/README.html), which adapted the BERT architecture to generate semantically rich sentence embeddings, easily comparable via similarity metrics like cosine similarity, dramatically reduced the computational overhead for tasks like finding similar sentences. -Today, the embedding model ecosystem is diverse, with numerous providers offering their own implementations. +Today, the embedding model ecosystem is diverse, with numerous providers offering their own implementations. To navigate this variety, researchers and practitioners often turn to benchmarks like the Massive Text Embedding Benchmark (MTEB) [here](https://huggingface.co/blog/mteb) for objective comparisons. :::info[Further reading] @@ -93,9 +93,9 @@ LangChain offers many embedding model integrations which you can find [on the em ## Measure similarity -Each embedding is essentially a set of coordinates, often in a high-dimensional space. +Each embedding is essentially a set of coordinates, often in a high-dimensional space. In this space, the position of each point (embedding) reflects the meaning of its corresponding text. -Just as similar words might be close to each other in a thesaurus, similar concepts end up close to each other in this embedding space. +Just as similar words might be close to each other in a thesaurus, similar concepts end up close to each other in this embedding space. This allows for intuitive comparisons between different pieces of text. By reducing text to these numerical representations, we can use simple mathematical operations to quickly measure how alike two pieces of text are, regardless of their original length or structure. Some common similarity metrics include: @@ -118,7 +118,7 @@ def cosine_similarity(vec1, vec2): similarity = cosine_similarity(query_result, document_result) print("Cosine Similarity:", similarity) -``` +``` :::info[Further reading] @@ -127,4 +127,4 @@ print("Cosine Similarity:", similarity) * See Pinecone's [blog post](https://www.pinecone.io/learn/vector-similarity/) on similarity metrics. * See OpenAI's [FAQ](https://platform.openai.com/docs/guides/embeddings/faq) on what similarity metric to use with OpenAI embeddings. -::: +::: diff --git a/docs/docs/concepts/evaluation.mdx b/docs/docs/concepts/evaluation.mdx index 274ef98367c..73cb3b1f7f1 100644 --- a/docs/docs/concepts/evaluation.mdx +++ b/docs/docs/concepts/evaluation.mdx @@ -14,4 +14,3 @@ This process is vital for building reliable applications. - It allows you to track results over time and automatically run your evaluators on a schedule or as part of CI/Code To learn more, check out [this LangSmith guide](https://docs.smith.langchain.com/concepts/evaluation). - diff --git a/docs/docs/concepts/example_selectors.mdx b/docs/docs/concepts/example_selectors.mdx index 32dad8c5fa4..fee840cabe7 100644 --- a/docs/docs/concepts/example_selectors.mdx +++ b/docs/docs/concepts/example_selectors.mdx @@ -17,4 +17,4 @@ Sometimes these examples are hardcoded into the prompt, but for more advanced si ## Related resources -* [Example selector how-to guides](/docs/how_to/#example-selectors) \ No newline at end of file +* [Example selector how-to guides](/docs/how_to/#example-selectors) diff --git a/docs/docs/concepts/multimodality.mdx b/docs/docs/concepts/multimodality.mdx index 72139a29a87..56fa8aa4ad7 100644 --- a/docs/docs/concepts/multimodality.mdx +++ b/docs/docs/concepts/multimodality.mdx @@ -14,7 +14,7 @@ * [Chat models](/docs/concepts/chat_models) * [Messages](/docs/concepts/messages) ::: - + LangChain supports multimodal data as input to chat models: 1. Following provider-specific formats diff --git a/docs/docs/concepts/rag.mdx b/docs/docs/concepts/rag.mdx index 0180aa74a87..606296dbaf4 100644 --- a/docs/docs/concepts/rag.mdx +++ b/docs/docs/concepts/rag.mdx @@ -8,7 +8,7 @@ ## Overview -Retrieval Augmented Generation (RAG) is a powerful technique that enhances [language models](/docs/concepts/chat_models/) by combining them with external knowledge bases. +Retrieval Augmented Generation (RAG) is a powerful technique that enhances [language models](/docs/concepts/chat_models/) by combining them with external knowledge bases. RAG addresses [a key limitation of models](https://www.glean.com/blog/how-to-build-an-ai-assistant-for-the-enterprise): models rely on fixed training datasets, which can lead to outdated or incomplete information. When given a query, RAG systems first search a knowledge base for relevant information. The system then incorporates this retrieved information into the model's prompt. @@ -44,7 +44,7 @@ See our conceptual guide on [retrieval](/docs/concepts/retrieval/). ## Adding external knowledge -With a retrieval system in place, we need to pass knowledge from this system to the model. +With a retrieval system in place, we need to pass knowledge from this system to the model. A RAG pipeline typically achieves this following these steps: - Receive an input query. @@ -59,12 +59,12 @@ from langchain_openai import ChatOpenAI from langchain_core.messages import SystemMessage, HumanMessage # Define a system prompt that tells the model how to use the retrieved context -system_prompt = """You are an assistant for question-answering tasks. -Use the following pieces of retrieved context to answer the question. -If you don't know the answer, just say that you don't know. +system_prompt = """You are an assistant for question-answering tasks. +Use the following pieces of retrieved context to answer the question. +If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise. Context: {context}:""" - + # Define a question question = """What are the main components of an LLM-powered autonomous agent system?""" @@ -78,7 +78,7 @@ docs_text = "".join(d.page_content for d in docs) system_prompt_fmt = system_prompt.format(context=docs_text) # Create a model -model = ChatOpenAI(model="gpt-4o", temperature=0) +model = ChatOpenAI(model="gpt-4o", temperature=0) # Generate a response questions = model.invoke([SystemMessage(content=system_prompt_fmt), diff --git a/docs/docs/concepts/retrieval.mdx b/docs/docs/concepts/retrieval.mdx index 0467bb95ced..b607ea5d117 100644 --- a/docs/docs/concepts/retrieval.mdx +++ b/docs/docs/concepts/retrieval.mdx @@ -10,28 +10,28 @@ ::: :::danger[Security] - + Some of the concepts reviewed here utilize models to generate queries (e.g., for SQL or graph databases). -There are inherent risks in doing this. -Make sure that your database connection permissions are scoped as narrowly as possible for your application's needs. -This will mitigate, though not eliminate, the risks of building a model-driven system capable of querying databases. +There are inherent risks in doing this. +Make sure that your database connection permissions are scoped as narrowly as possible for your application's needs. +This will mitigate, though not eliminate, the risks of building a model-driven system capable of querying databases. For more on general security best practices, see our [security guide](/docs/security/). ::: -## Overview +## Overview -Retrieval systems are fundamental to many AI applications, efficiently identifying relevant information from large datasets. +Retrieval systems are fundamental to many AI applications, efficiently identifying relevant information from large datasets. These systems accommodate various data formats: - Unstructured text (e.g., documents) is often stored in vector stores or lexical search indexes. - Structured data is typically housed in relational or graph databases with defined schemas. -Despite the growing diversity in data formats, modern AI applications increasingly aim to make all types of data accessible through natural language interfaces. -Models play a crucial role in this process by translating natural language queries into formats compatible with the underlying search index or database. +Despite the growing diversity in data formats, modern AI applications increasingly aim to make all types of data accessible through natural language interfaces. +Models play a crucial role in this process by translating natural language queries into formats compatible with the underlying search index or database. This translation enables more intuitive and flexible interactions with complex data structures. -## Key concepts +## Key concepts ![Retrieval](/img/retrieval_concept.png) @@ -39,20 +39,20 @@ This translation enables more intuitive and flexible interactions with complex d (2) **Information retrieval**: Search queries are used to fetch information from various retrieval systems. -## Query analysis +## Query analysis -While users typically prefer to interact with retrieval systems using natural language, these systems may require specific query syntax or benefit from certain keywords. +While users typically prefer to interact with retrieval systems using natural language, these systems may require specific query syntax or benefit from certain keywords. Query analysis serves as a bridge between raw user input and optimized search queries. Some common applications of query analysis include: 1. **Query Re-writing**: Queries can be re-written or expanded to improve semantic or lexical searches. 2. **Query Construction**: Search indexes may require structured queries (e.g., SQL for databases). -Query analysis employs models to transform or construct optimized search queries from raw user input. +Query analysis employs models to transform or construct optimized search queries from raw user input. ### Query re-writing -Retrieval systems should ideally handle a wide spectrum of user inputs, from simple and poorly worded queries to complex, multi-faceted questions. -To achieve this versatility, a popular approach is to use models to transform raw user queries into more effective search queries. +Retrieval systems should ideally handle a wide spectrum of user inputs, from simple and poorly worded queries to complex, multi-faceted questions. +To achieve this versatility, a popular approach is to use models to transform raw user queries into more effective search queries. This transformation can range from simple keyword extraction to sophisticated query expansion and reformulation. Here are some key benefits of using models for query analysis in unstructured data retrieval: @@ -87,7 +87,7 @@ class Questions(BaseModel): ) # Create an instance of the model and enforce the output structure -model = ChatOpenAI(model="gpt-4o", temperature=0) +model = ChatOpenAI(model="gpt-4o", temperature=0) structured_model = model.with_structured_output(Questions) # Define the system prompt @@ -111,7 +111,7 @@ See our RAG from Scratch videos for a few different specific approaches: ### Query construction -Query analysis also can focus on translating natural language queries into specialized query languages or filters. +Query analysis also can focus on translating natural language queries into specialized query languages or filters. This translation is crucial for effectively interacting with various types of databases that house structured or semi-structured data. 1. **Structured Data examples**: For relational and graph databases, Domain-Specific Languages (DSLs) are used to query data. @@ -129,10 +129,10 @@ These approaches leverage models to bridge the gap between user intent and the s | [Text to SQL](/docs/tutorials/sql_qa/) | If users are asking questions that require information housed in a relational database, accessible via SQL. | This uses an LLM to transform user input into a SQL query. | | [Text-to-Cypher](/docs/tutorials/graph/) | If users are asking questions that require information housed in a graph database, accessible via Cypher. | This uses an LLM to transform user input into a Cypher query. | -As an example, here is how to use the `SelfQueryRetriever` to convert natural language queries into metadata filters. +As an example, here is how to use the `SelfQueryRetriever` to convert natural language queries into metadata filters. ```python -metadata_field_info = schema_for_metadata +metadata_field_info = schema_for_metadata document_content_description = "Brief summary of a movie" llm = ChatOpenAI(temperature=0) retriever = SelfQueryRetriever.from_llm( @@ -149,20 +149,20 @@ retriever = SelfQueryRetriever.from_llm( * See our [blog post overview](https://blog.langchain.dev/query-construction/). * See our RAG from Scratch video on [query construction](https://youtu.be/kl6NwWYxvbM?feature=shared). -::: +::: -## Information retrieval +## Information retrieval ### Common retrieval systems #### Lexical search indexes -Many search engines are based upon matching words in a query to the words in each document. +Many search engines are based upon matching words in a query to the words in each document. This approach is called lexical retrieval, using search [algorithms that are typically based upon word frequencies](https://cameronrwolfe.substack.com/p/the-basics-of-ai-powered-vector-search?utm_source=profile&utm_medium=reader2). The intution is simple: a word appears frequently both in the user’s query and a particular document, then this document might be a good match. The particular data structure used to implement this is often an [*inverted index*](https://www.geeksforgeeks.org/inverted-index/). -This types of index contains a list of words and a mapping of each word to a list of locations at which it occurs in various documents. +This types of index contains a list of words and a mapping of each word to a list of locations at which it occurs in various documents. Using this data structure, it is possible to efficiently match the words in search queries to the documents in which they appear. [BM25](https://en.wikipedia.org/wiki/Okapi_BM25#:~:text=BM25%20is%20a%20bag%2Dof,slightly%20different%20components%20and%20parameters.) and [TF-IDF](https://en.wikipedia.org/wiki/Tf%E2%80%93idf) are [two popular lexical search algorithms](https://cameronrwolfe.substack.com/p/the-basics-of-ai-powered-vector-search?utm_source=profile&utm_medium=reader2). @@ -171,13 +171,13 @@ Using this data structure, it is possible to efficiently match the words in sear * See the [BM25](/docs/integrations/retrievers/bm25/) retriever integration. * See the [Elasticsearch](/docs/integrations/retrievers/elasticsearch_retriever/) retriever integration. -::: +::: #### Vector indexes Vector indexes are an alternative way to index and store unstructured data. -See our conceptual guide on [vectorstores](/docs/concepts/vectorstores/) for a detailed overview. -In short, rather than using word frequencies, vectorstores use an [embedding model](/docs/concepts/embedding_models/) to compress documents into high-dimensional vector representation. +See our conceptual guide on [vectorstores](/docs/concepts/vectorstores/) for a detailed overview. +In short, rather than using word frequencies, vectorstores use an [embedding model](/docs/concepts/embedding_models/) to compress documents into high-dimensional vector representation. This allows for efficient similarity search over embedding vectors using simple mathematical operations like cosine similarity. :::info[Further reading] @@ -190,9 +190,9 @@ This allows for efficient similarity search over embedding vectors using simple #### Relational databases -Relational databases are a fundamental type of structured data storage used in many applications. -They organize data into tables with predefined schemas, where each table represents an entity or relationship. -Data is stored in rows (records) and columns (attributes), allowing for efficient querying and manipulation through SQL (Structured Query Language). +Relational databases are a fundamental type of structured data storage used in many applications. +They organize data into tables with predefined schemas, where each table represents an entity or relationship. +Data is stored in rows (records) and columns (attributes), allowing for efficient querying and manipulation through SQL (Structured Query Language). Relational databases excel at maintaining data integrity, supporting complex queries, and handling relationships between different data entities. :::info[Further reading] @@ -204,8 +204,8 @@ Relational databases excel at maintaining data integrity, supporting complex que #### Graph databases -Graph databases are a specialized type of database designed to store and manage highly interconnected data. -Unlike traditional relational databases, graph databases use a flexible structure consisting of nodes (entities), edges (relationships), and properties. +Graph databases are a specialized type of database designed to store and manage highly interconnected data. +Unlike traditional relational databases, graph databases use a flexible structure consisting of nodes (entities), edges (relationships), and properties. This structure allows for efficient representation and querying of complex, interconnected data. Graph databases store data in a graph structure, with nodes, edges, and properties. They are particularly useful for storing and querying complex relationships between data points, such as social networks, supply-chain management, fraud detection, and recommendation services @@ -213,12 +213,12 @@ They are particularly useful for storing and querying complex relationships betw :::info[Further reading] * See our [tutorial](/docs/tutorials/graph/) for working with graph databases. -* See our [list of graph database integrations](/docs/integrations/graphs/). +* See our [list of graph database integrations](/docs/integrations/graphs/). * See Neo4j's [starter kit for LangChain](https://neo4j.com/developer-blog/langchain-neo4j-starter-kit/). ::: -### Retriever +### Retriever LangChain provides a unified interface for interacting with various retrieval systems through the [retriever](/docs/concepts/retrievers/) concept. The interface is straightforward: diff --git a/docs/docs/concepts/retrievers.mdx b/docs/docs/concepts/retrievers.mdx index 0cc7aaedfc0..20288ffee38 100644 --- a/docs/docs/concepts/retrievers.mdx +++ b/docs/docs/concepts/retrievers.mdx @@ -23,16 +23,16 @@ The LangChain [retriever](/docs/concepts/retrievers/) interface is straightforwa ## Key concept ![Retriever](/img/retriever_concept.png) - + All retrievers implement a simple interface for retrieving documents using natural language queries. -## Interface +## Interface -The only requirement for a retriever is the ability to accepts a query and return documents. +The only requirement for a retriever is the ability to accepts a query and return documents. In particular, [LangChain's retriever class](https://python.langchain.com/api_reference/core/retrievers/langchain_core.retrievers.BaseRetriever.html#) only requires that the `_get_relevant_documents` method is implemented, which takes a `query: str` and returns a list of [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) objects that are most relevant to the query. The underlying logic used to get relevant documents is specified by the retriever and can be whatever is most useful for the application. -A LangChain retriever is a [runnable](/docs/how_to/lcel_cheatsheet/), which is a standard interface for LangChain components. +A LangChain retriever is a [runnable](/docs/how_to/lcel_cheatsheet/), which is a standard interface for LangChain components. This means that it has a few common methods, including `invoke`, that are used to interact with it. A retriever can be invoked with a query: ```python @@ -42,23 +42,23 @@ docs = retriever.invoke(query) Retrievers return a list of [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) objects, which have two attributes: * `page_content`: The content of this document. Currently is a string. -* `metadata`: Arbitrary metadata associated with this document (e.g., document id, file name, source, etc). +* `metadata`: Arbitrary metadata associated with this document (e.g., document id, file name, source, etc). :::info[Further reading] * See our [how-to guide](/docs/how_to/custom_retriever/) on building your own custom retriever. ::: - + ## Common types Despite the flexibility of the retriever interface, a few common types of retrieval systems are frequently used. ### Search apis -It's important to note that retrievers don't need to actually *store* documents. -For example, we can build retrievers on top of search APIs that simply return search results! -See our retriever integrations with [Amazon Kendra](/docs/integrations/retrievers/amazon_kendra_retriever/) or [Wikipedia Search](/docs/integrations/retrievers/wikipedia/). +It's important to note that retrievers don't need to actually *store* documents. +For example, we can build retrievers on top of search APIs that simply return search results! +See our retriever integrations with [Amazon Kendra](/docs/integrations/retrievers/amazon_kendra_retriever/) or [Wikipedia Search](/docs/integrations/retrievers/wikipedia/). ### Relational or graph database @@ -75,7 +75,7 @@ For example, you can build a retriever for a SQL database using text-to-SQL conv ### Lexical search -As discussed in our conceptual review of [retrieval](/docs/concepts/retrieval/), many search engines are based upon matching words in a query to the words in each document. +As discussed in our conceptual review of [retrieval](/docs/concepts/retrieval/), many search engines are based upon matching words in a query to the words in each document. [BM25](https://en.wikipedia.org/wiki/Okapi_BM25#:~:text=BM25%20is%20a%20bag%2Dof,slightly%20different%20components%20and%20parameters.) and [TF-IDF](https://en.wikipedia.org/wiki/Tf%E2%80%93idf) are [two popular lexical search algorithms](https://cameronrwolfe.substack.com/p/the-basics-of-ai-powered-vector-search?utm_source=profile&utm_medium=reader2). LangChain has retrievers for many popular lexical search algorithms / engines. @@ -85,11 +85,11 @@ LangChain has retrievers for many popular lexical search algorithms / engines. * See the [TF-IDF](/docs/integrations/retrievers/tf_idf/) retriever integration. * See the [Elasticsearch](/docs/integrations/retrievers/elasticsearch_retriever/) retriever integration. -::: +::: -### Vector store +### Vector store -[Vector stores](/docs/concepts/vectorstores/) are a powerful and efficient way to index and retrieve unstructured data. +[Vector stores](/docs/concepts/vectorstores/) are a powerful and efficient way to index and retrieve unstructured data. A vectorstore can be used as a retriever by calling the `as_retriever()` method. ```python @@ -99,7 +99,7 @@ retriever = vectorstore.as_retriever() ## Advanced retrieval patterns -### Ensemble +### Ensemble Because the retriever interface is so simple, returning a list of `Document` objects given a search query, it is possible to combine multiple retrievers using ensembling. This is particularly useful when you have multiple retrievers that are good at finding different types of relevant documents. @@ -112,24 +112,24 @@ ensemble_retriever = EnsembleRetriever( ) ``` -When ensembling, how do we combine search results from many retrievers? +When ensembling, how do we combine search results from many retrievers? This motivates the concept of re-ranking, which takes the output of multiple retrievers and combines them using a more sophisticated algorithm such as [Reciprocal Rank Fusion (RRF)](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf). -### Source document retention +### Source document retention Many retrievers utilize some kind of index to make documents easily searchable. -The process of indexing can include a transformation step (e.g., vectorstores often use document splitting). +The process of indexing can include a transformation step (e.g., vectorstores often use document splitting). Whatever transformation is used, can be very useful to retain a link between the *transformed document* and the original, giving the retriever the ability to return the *original* document. ![Retrieval with full docs](/img/retriever_full_docs.png) This is particularly useful in AI applications, because it ensures no loss in document context for the model. -For example, you may use small chunk size for indexing documents in a vectorstore. -If you return *only* the chunks as the retrieval result, then the model will have lost the original document context for the chunks. +For example, you may use small chunk size for indexing documents in a vectorstore. +If you return *only* the chunks as the retrieval result, then the model will have lost the original document context for the chunks. -LangChain has two different retrievers that can be used to address this challenge. -The [Multi-Vector](/docs/how_to/multi_vector/) retriever allows the user to use any document transformation (e.g., use an LLM to write a summary of the document) for indexing while retaining linkage to the source document. -The [ParentDocument](/docs/how_to/parent_document_retriever/) retriever links document chunks from a text-splitter transformation for indexing while retaining linkage to the source document. +LangChain has two different retrievers that can be used to address this challenge. +The [Multi-Vector](/docs/how_to/multi_vector/) retriever allows the user to use any document transformation (e.g., use an LLM to write a summary of the document) for indexing while retaining linkage to the source document. +The [ParentDocument](/docs/how_to/parent_document_retriever/) retriever links document chunks from a text-splitter transformation for indexing while retaining linkage to the source document. | Name | Index Type | Uses an LLM | When to Use | Description | |-----------------------------------------------------------|-------------------------------|---------------------------|-----------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| diff --git a/docs/docs/concepts/runnables.mdx b/docs/docs/concepts/runnables.mdx index 29a4246aa3a..df0ca474dbe 100644 --- a/docs/docs/concepts/runnables.mdx +++ b/docs/docs/concepts/runnables.mdx @@ -107,7 +107,7 @@ The Runnable interface provides methods to get the [JSON Schema](https://json-sc These APIs are mostly used internally for unit-testing and by [LangServe](/docs/concepts/architecture#langserve) which uses the APIs for input validation and generation of [OpenAPI documentation](https://www.openapis.org/). -In addition, to the input and output types, some Runnables have been set up with additional run time configuration options. +In addition, to the input and output types, some Runnables have been set up with additional run time configuration options. There are corresponding APIs to get the Pydantic Schema and JSON Schema of the configuration options for the Runnable. Please see the [Configurable Runnables](#configurable-runnables) section for more information. @@ -151,12 +151,12 @@ Passing `config` to the `invoke` method is done like so: ```python some_runnable.invoke( - some_input, + some_input, config={ - 'run_name': 'my_run', - 'tags': ['tag1', 'tag2'], + 'run_name': 'my_run', + 'tags': ['tag1', 'tag2'], 'metadata': {'key': 'value'} - + } ) ``` @@ -185,13 +185,13 @@ There are two main patterns by which new `Runnables` are created: foo_runnable = RunnableLambda(foo) ``` -LangChain will try to propagate `RunnableConfig` automatically for both of the patterns. +LangChain will try to propagate `RunnableConfig` automatically for both of the patterns. For handling the second pattern, LangChain relies on Python's [contextvars](https://docs.python.org/3/library/contextvars.html). In Python 3.11 and above, this works out of the box, and you do not need to do anything special to propagate the `RunnableConfig` to the sub-calls. -In Python 3.9 and 3.10, if you are using **async code**, you need to manually pass the `RunnableConfig` through to the `Runnable` when invoking it. +In Python 3.9 and 3.10, if you are using **async code**, you need to manually pass the `RunnableConfig` through to the `Runnable` when invoking it. This is due to a limitation in [asyncio's tasks](https://docs.python.org/3/library/asyncio-task.html#asyncio.create_task) in Python 3.9 and 3.10 which did not accept a `context` argument. @@ -201,7 +201,7 @@ Propagating the `RunnableConfig` manually is done like so: ```python async def foo(input, config): # <-- Note the config argument return await bar_runnable.ainvoke(input, config=config) - + foo_runnable = RunnableLambda(foo) ``` @@ -235,7 +235,7 @@ The attributes will also be propagated to [callbacks](/docs/concepts/callbacks), This is an advanced feature that is unnecessary for most users. ::: -You may need to set a custom `run_id` for a given run, in case you want +You may need to set a custom `run_id` for a given run, in case you want to reference it later or correlate it with other systems. The `run_id` MUST be a valid UUID string and **unique** for each run. It is used to identify @@ -249,7 +249,7 @@ import uuid run_id = uuid.uuid4() some_runnable.invoke( - some_input, + some_input, config={ 'run_id': run_id } @@ -292,7 +292,7 @@ In addition, you can use it to specify any custom configuration options to pass ### Setting callbacks -Use this option to configure [callbacks](/docs/concepts/callbacks) for the runnable at +Use this option to configure [callbacks](/docs/concepts/callbacks) for the runnable at runtime. The callbacks will be passed to all sub-calls made by the runnable. ```python diff --git a/docs/docs/concepts/streaming.mdx b/docs/docs/concepts/streaming.mdx index c2dc400e23e..34ff344dd4b 100644 --- a/docs/docs/concepts/streaming.mdx +++ b/docs/docs/concepts/streaming.mdx @@ -52,7 +52,7 @@ In addition, there is a **legacy** async [astream_log](https://python.langchain. The `stream()` method returns an iterator that yields chunks of output synchronously as they are produced. You can use a `for` loop to process each chunk in real-time. For example, when using an LLM, this allows the output to be streamed incrementally as it is generated, reducing the wait time for users. -The type of chunk yielded by the `stream()` and `astream()` methods depends on the component being streamed. For example, when streaming from an [LLM](/docs/concepts/chat_models) each component will be an [AIMessageChunk](/docs/concepts/messages#aimessagechunk); however, for other components, the chunk may be different. +The type of chunk yielded by the `stream()` and `astream()` methods depends on the component being streamed. For example, when streaming from an [LLM](/docs/concepts/chat_models) each component will be an [AIMessageChunk](/docs/concepts/messages#aimessagechunk); however, for other components, the chunk may be different. The `stream()` method returns an iterator that yields these chunks as they are produced. For example, @@ -99,7 +99,7 @@ If you compose multiple Runnables using [LangChain’s Expression Language (LCEL :::tip -Use the `astream_events` API to access custom data and intermediate outputs from LLM applications built entirely with [LCEL](/docs/concepts/lcel). +Use the `astream_events` API to access custom data and intermediate outputs from LLM applications built entirely with [LCEL](/docs/concepts/lcel). While this API is available for use with [LangGraph](/docs/concepts/architecture#langgraph) as well, it is usually not necessary when working with LangGraph, as the `stream` and `astream` methods provide comprehensive streaming capabilities for LangGraph graphs. ::: @@ -119,7 +119,7 @@ from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate from langchain_anthropic import ChatAnthropic -model = ChatAnthropic(model="claude-3-sonnet-20240229") +model = ChatAnthropic(model="claude-3-7-sonnet-20250219") prompt = ChatPromptTemplate.from_template("tell me a joke about {topic}") parser = StrOutputParser() @@ -148,7 +148,7 @@ LangChain simplifies streaming from [chat models](/docs/concepts/chat_models) by ### How It Works -When you call the `invoke` (or `ainvoke`) method on a chat model, LangChain will automatically switch to streaming mode if it detects that you are trying to stream the overall application. +When you call the `invoke` (or `ainvoke`) method on a chat model, LangChain will automatically switch to streaming mode if it detects that you are trying to stream the overall application. Under the hood, it'll have `invoke` (or `ainvoke`) use the `stream` (or `astream`) method to generate its output. The result of the invocation will be the same as far as the code that was using `invoke` is concerned; however, while the chat model is being streamed, LangChain will take care of invoking `on_llm_new_token` events in LangChain's [callback system](/docs/concepts/callbacks). These callback events allow LangGraph `stream`/`astream` and `astream_events` to surface the chat model's output in real-time. @@ -158,14 +158,14 @@ Example: ```python def node(state): ... - # The code below uses the invoke method, but LangChain will + # The code below uses the invoke method, but LangChain will # automatically switch to streaming mode - # when it detects that the overall + # when it detects that the overall # application is being streamed. ai_message = model.invoke(state["messages"]) ... -for chunk in compiled_graph.stream(..., mode="messages"): +for chunk in compiled_graph.stream(..., mode="messages"): ... ``` ## Async Programming diff --git a/docs/docs/concepts/structured_outputs.mdx b/docs/docs/concepts/structured_outputs.mdx index 72b84608d1d..d6d31a5d215 100644 --- a/docs/docs/concepts/structured_outputs.mdx +++ b/docs/docs/concepts/structured_outputs.mdx @@ -1,15 +1,15 @@ # Structured outputs -## Overview +## Overview -For many applications, such as chatbots, models need to respond to users directly in natural language. -However, there are scenarios where we need models to output in a *structured format*. +For many applications, such as chatbots, models need to respond to users directly in natural language. +However, there are scenarios where we need models to output in a *structured format*. For example, we might want to store the model output in a database and ensure that the output conforms to the database schema. This need motivates the concept of structured output, where models can be instructed to respond with a particular output structure. ![Structured output](/img/structured_output.png) -## Key concepts +## Key concepts 1. **Schema definition:** The output structure is represented as a schema, which can be defined in several ways.
2. **Returning structured output:** The model is given this schema, and is instructed to return output that conforms to it. @@ -18,7 +18,7 @@ This need motivates the concept of structured output, where models can be instru This pseudocode illustrates the recommended workflow when using structured output. LangChain provides a method, [`with_structured_output()`](/docs/how_to/structured_output/#the-with_structured_output-method), that automates the process of binding the schema to the [model](/docs/concepts/chat_models/) and parsing the output. -This helper function is available for all model providers that support structured output. +This helper function is available for all model providers that support structured output. ```python # Define schema @@ -31,7 +31,7 @@ structured_output = model_with_structure.invoke(user_input) ## Schema definition -The central concept is that the output structure of model responses needs to be represented in some way. +The central concept is that the output structure of model responses needs to be represented in some way. While types of objects you can use depend on the model you're working with, there are common types of objects that are typically allowed or recommended for structured output in Python. The simplest and most common format for structured output is a JSON-like structure, which in Python can be represented as a dictionary (dict) or list (list). @@ -45,7 +45,7 @@ JSON objects (or dicts in Python) are often used directly when the tool requires ``` As a second example, [Pydantic](https://docs.pydantic.dev/latest/) is particularly useful for defining structured output schemas because it offers type hints and validation. -Here's an example of a Pydantic schema: +Here's an example of a Pydantic schema: ```python from pydantic import BaseModel, Field @@ -59,7 +59,7 @@ class ResponseFormatter(BaseModel): ## Returning structured output With a schema defined, we need a way to instruct the model to use it. -While one approach is to include this schema in the prompt and *ask nicely* for the model to use it, this is not recommended. +While one approach is to include this schema in the prompt and *ask nicely* for the model to use it, this is not recommended. Several more powerful methods that utilizes native features in the model provider's API are available. ### Using tool calling @@ -78,7 +78,7 @@ model_with_tools = model.bind_tools([ResponseFormatter]) ai_msg = model_with_tools.invoke("What is the powerhouse of the cell?") ``` -The arguments of the tool call are already extracted as a dictionary. +The arguments of the tool call are already extracted as a dictionary. This dictionary can be optionally parsed into a Pydantic object, matching our original `ResponseFormatter` schema. ```python @@ -92,7 +92,7 @@ pydantic_object = ResponseFormatter.model_validate(ai_msg.tool_calls[0]["args"]) ### JSON mode -In addition to tool calling, some model providers support a feature called `JSON mode`. +In addition to tool calling, some model providers support a feature called `JSON mode`. This supports JSON schema definition as input and enforces the model to produce a conforming JSON output. You can find a table of model providers that support JSON mode [here](/docs/integrations/chat/). Here is an example of how to use JSON mode with OpenAI: @@ -105,21 +105,21 @@ ai_msg {'random_ints': [45, 67, 12, 34, 89, 23, 78, 56, 90, 11]} ``` -## Structured output method +## Structured output method -There are a few challenges when producing structured output with the above methods: +There are a few challenges when producing structured output with the above methods: -1. When tool calling is used, tool call arguments needs to be parsed from a dictionary back to the original schema.
+1. When tool calling is used, tool call arguments needs to be parsed from a dictionary back to the original schema.
-2. In addition, the model needs to be instructed to *always* use the tool when we want to enforce structured output, which is a provider specific setting.
+2. In addition, the model needs to be instructed to *always* use the tool when we want to enforce structured output, which is a provider specific setting.
-3. When JSON mode is used, the output needs to be parsed into a JSON object. +3. When JSON mode is used, the output needs to be parsed into a JSON object. With these challenges in mind, LangChain provides a helper function (`with_structured_output()`) to streamline the process. ![Diagram of with structured output](/img/with_structured_output.png) -This both binds the schema to the model as a tool and parses the output to the specified output schema. +This both binds the schema to the model as a tool and parses the output to the specified output schema. ```python # Bind the schema to the model diff --git a/docs/docs/concepts/testing.mdx b/docs/docs/concepts/testing.mdx index cd0114f31e1..371215c0238 100644 --- a/docs/docs/concepts/testing.mdx +++ b/docs/docs/concepts/testing.mdx @@ -23,9 +23,9 @@ def test_convert_to_openai_messages(): ToolCall(name='parrot_multiply_tool', id='1', args={'a': 2, 'b': 3}), ] ) - + result = convert_to_openai_messages(ai_message) - + expected = { "role": "assistant", "tool_calls": [ diff --git a/docs/docs/concepts/text_llms.mdx b/docs/docs/concepts/text_llms.mdx index d35a72476af..173d4d8016e 100644 --- a/docs/docs/concepts/text_llms.mdx +++ b/docs/docs/concepts/text_llms.mdx @@ -7,4 +7,4 @@ You are probably looking for the [Chat Model Concept Guide](/docs/concepts/chat_ LangChain has implementations for older language models that take a string as input and return a string as output. These models are typically named without the "Chat" prefix (e.g., `Ollama`, `Anthropic`, `OpenAI`, etc.), and may include the "LLM" suffix (e.g., `OllamaLLM`, `AnthropicLLM`, `OpenAILLM`, etc.). These models implement the [BaseLLM](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.llms.BaseLLM.html#langchain_core.language_models.llms.BaseLLM) interface. Users should be using almost exclusively the newer [Chat Models](/docs/concepts/chat_models) as most -model providers have adopted a chat like interface for interacting with language models. \ No newline at end of file +model providers have adopted a chat like interface for interacting with language models. diff --git a/docs/docs/concepts/text_splitters.mdx b/docs/docs/concepts/text_splitters.mdx index b5875dd33b9..9be35f9acd8 100644 --- a/docs/docs/concepts/text_splitters.mdx +++ b/docs/docs/concepts/text_splitters.mdx @@ -69,7 +69,7 @@ texts = text_splitter.split_text(document) ### Text-structured based -Text is naturally organized into hierarchical units such as paragraphs, sentences, and words. +Text is naturally organized into hierarchical units such as paragraphs, sentences, and words. We can leverage this inherent structure to inform our splitting strategy, creating split that maintain natural language flow, maintain semantic coherence within split, and adapts to varying levels of text granularity. LangChain's [`RecursiveCharacterTextSplitter`](/docs/how_to/recursive_text_splitter/) implements this concept: - The `RecursiveCharacterTextSplitter` attempts to keep larger units (e.g., paragraphs) intact. @@ -92,7 +92,7 @@ texts = text_splitter.split_text(document) ### Document-structured based -Some documents have an inherent structure, such as HTML, Markdown, or JSON files. +Some documents have an inherent structure, such as HTML, Markdown, or JSON files. In these cases, it's beneficial to split the document based on its structure, as it often naturally groups semantically related text. Key benefits of structure-based splitting: - Preserves the logical organization of the document @@ -116,7 +116,7 @@ Examples of structure-based splitting: ### Semantic meaning based -Unlike the previous methods, semantic-based splitting actually considers the *content* of the text. +Unlike the previous methods, semantic-based splitting actually considers the *content* of the text. While other approaches use document or text structure as proxies for semantic meaning, this method directly analyzes the text's semantics. There are several ways to implement this, but conceptually the approach is split text when there are significant changes in text *meaning*. As an example, we can use a sliding window approach to generate embeddings, and compare the embeddings to find significant differences: diff --git a/docs/docs/concepts/tokens.mdx b/docs/docs/concepts/tokens.mdx index d42755e8d56..f5d04bbad76 100644 --- a/docs/docs/concepts/tokens.mdx +++ b/docs/docs/concepts/tokens.mdx @@ -55,4 +55,4 @@ According to the OpenAI post, the approximate token counts for English text are * 1 token ~= 4 chars in English * 1 token ~= ¾ words -* 100 tokens ~= 75 words \ No newline at end of file +* 100 tokens ~= 75 words diff --git a/docs/docs/concepts/tool_calling.mdx b/docs/docs/concepts/tool_calling.mdx index 7f48142f411..a8bc00b7726 100644 --- a/docs/docs/concepts/tool_calling.mdx +++ b/docs/docs/concepts/tool_calling.mdx @@ -6,7 +6,7 @@ ::: -## Overview +## Overview Many AI applications interact directly with humans. In these cases, it is appropriate for models to respond in natural language. But what about cases where we want a model to also interact *directly* with systems, such as databases or an API? @@ -14,12 +14,12 @@ These systems often have a particular input schema; for example, APIs frequently This need motivates the concept of *tool calling*. You can use [tool calling](https://platform.openai.com/docs/guides/function-calling/example-use-cases) to request model responses that match a particular schema. :::info -You will sometimes hear the term `function calling`. We use this term interchangeably with `tool calling`. +You will sometimes hear the term `function calling`. We use this term interchangeably with `tool calling`. ::: ![Conceptual overview of tool calling](/img/tool_calling_concept.png) -## Key concepts +## Key concepts 1. **Tool Creation:** Use the [@tool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.convert.tool.html) decorator to create a [tool](/docs/concepts/tools). A tool is an association between a function and its schema.
2. **Tool Binding:** The tool needs to be connected to a model that supports tool calling. This gives the model awareness of the tool and the associated input schema required by the tool.
@@ -40,7 +40,7 @@ The tool call arguments can be passed directly to the tool. tools = [my_tool] # Tool binding model_with_tools = model.bind_tools(tools) -# Tool calling +# Tool calling response = model_with_tools.invoke(user_input) ``` @@ -65,16 +65,16 @@ def multiply(a: int, b: int) -> int: ::: -## Tool binding +## Tool binding -[Many](https://platform.openai.com/docs/guides/function-calling) [model providers](https://platform.openai.com/docs/guides/function-calling) support tool calling. +[Many](https://platform.openai.com/docs/guides/function-calling) [model providers](https://platform.openai.com/docs/guides/function-calling) support tool calling. :::tip See our [model integration page](/docs/integrations/chat/) for a list of providers that support tool calling. ::: -The central concept to understand is that LangChain provides a standardized interface for connecting tools to models. -The `.bind_tools()` method can be used to specify which tools are available for a model to call. +The central concept to understand is that LangChain provides a standardized interface for connecting tools to models. +The `.bind_tools()` method can be used to specify which tools are available for a model to call. ```python model_with_tools = model.bind_tools(tools_list) @@ -113,7 +113,7 @@ However, if we pass an input *relevant to the tool*, the model should choose to result = llm_with_tools.invoke("What is 2 multiplied by 3?") ``` -As before, the output `result` will be an `AIMessage`. +As before, the output `result` will be an `AIMessage`. But, if the tool was called, `result` will have a `tool_calls` [attribute](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html#langchain_core.messages.ai.AIMessage.tool_calls). This attribute includes everything needed to execute the tool, including the tool name and input arguments: diff --git a/docs/docs/concepts/tools.mdx b/docs/docs/concepts/tools.mdx index d121e4fedc1..e3a2db9770a 100644 --- a/docs/docs/concepts/tools.mdx +++ b/docs/docs/concepts/tools.mdx @@ -6,7 +6,7 @@ ## Overview -The **tool** abstraction in LangChain associates a Python **function** with a **schema** that defines the function's **name**, **description** and **expected arguments**. +The **tool** abstraction in LangChain associates a Python **function** with a **schema** that defines the function's **name**, **description** and **expected arguments**. **Tools** can be passed to [chat models](/docs/concepts/chat_models) that support [tool calling](/docs/concepts/tool_calling) allowing the model to request the execution of a specific function with specific inputs. @@ -68,10 +68,10 @@ You can also inspect the tool's schema and other properties: ```python print(multiply.name) # multiply print(multiply.description) # Multiply two numbers. -print(multiply.args) +print(multiply.args) # { -# 'type': 'object', -# 'properties': {'a': {'type': 'integer'}, 'b': {'type': 'integer'}}, +# 'type': 'object', +# 'properties': {'a': {'type': 'integer'}, 'b': {'type': 'integer'}}, # 'required': ['a', 'b'] # } ``` @@ -96,7 +96,7 @@ Please see the [API reference for @tool](https://python.langchain.com/api_refere def some_tool(...) -> Tuple[str, Any]: """Tool that does something.""" ... - return 'Message for chat model', some_artifact + return 'Message for chat model', some_artifact ``` See [how to return artifacts from tools](/docs/how_to/tool_artifacts/) for more details. @@ -134,7 +134,7 @@ def user_specific_tool(input_data: str, user_id: InjectedToolArg) -> str: Annotating the `user_id` argument with `InjectedToolArg` tells LangChain that this argument should not be exposed as part of the tool's schema. -See [how to pass run time values to tools](/docs/how_to/tool_runtime/) for more details on how to use `InjectedToolArg`. +See [how to pass run time values to tools](/docs/how_to/tool_runtime/) for more details on how to use `InjectedToolArg`. ### RunnableConfig diff --git a/docs/docs/concepts/vectorstores.mdx b/docs/docs/concepts/vectorstores.mdx index 6edc225aa71..d2d0b6b8bd0 100644 --- a/docs/docs/concepts/vectorstores.mdx +++ b/docs/docs/concepts/vectorstores.mdx @@ -9,7 +9,7 @@ ::: :::info[Note] -This conceptual overview focuses on text-based indexing and retrieval for simplicity. +This conceptual overview focuses on text-based indexing and retrieval for simplicity. However, embedding models can be [multi-modal](https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-multimodal-embeddings) and vector stores can be used to store and retrieve a variety of data types beyond text. ::: @@ -125,7 +125,7 @@ to the documentation of the specific vectorstore you are using to see what simil Given a similarity metric to measure the distance between the embedded query and any embedded document, we need an algorithm to efficiently search over *all* the embedded documents to find the most similar ones. There are various ways to do this. As an example, many vectorstores implement [HNSW (Hierarchical Navigable Small World)](https://www.pinecone.io/learn/series/faiss/hnsw/), a graph-based index structure that allows for efficient similarity search. -Regardless of the search algorithm used under the hood, the LangChain vectorstore interface has a `similarity_search` method for all integrations. +Regardless of the search algorithm used under the hood, the LangChain vectorstore interface has a `similarity_search` method for all integrations. This will take the search query, create an embedding, find similar documents, and return them as a list of [Documents](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html). ```python @@ -166,7 +166,7 @@ vectorstore.similarity_search( k=2, filter={"source": "tweet"}, ) -``` +``` :::info[Further reading] @@ -179,7 +179,7 @@ vectorstore.similarity_search( While algorithms like HNSW provide the foundation for efficient similarity search in many cases, additional techniques can be employed to improve search quality and diversity. For example, [maximal marginal relevance](https://python.langchain.com/v0.1/docs/modules/model_io/prompts/example_selectors/mmr/) is a re-ranking algorithm used to diversify search results, which is applied after the initial similarity search to ensure a more diverse set of results. -As a second example, some [vector stores](/docs/integrations/retrievers/pinecone_hybrid_search/) offer built-in [hybrid-search](https://docs.pinecone.io/guides/data/understanding-hybrid-search) to combine keyword and semantic similarity search, which marries the benefits of both approaches. +As a second example, some [vector stores](/docs/integrations/retrievers/pinecone_hybrid_search/) offer built-in [hybrid-search](https://docs.pinecone.io/guides/data/understanding-hybrid-search) to combine keyword and semantic similarity search, which marries the benefits of both approaches. At the moment, there is no unified way to perform hybrid search using LangChain vectorstores, but it is generally exposed as a keyword argument that is passed in with `similarity_search`. See this [how-to guide on hybrid search](/docs/how_to/hybrid/) for more details. @@ -188,4 +188,4 @@ See this [how-to guide on hybrid search](/docs/how_to/hybrid/) for more details. | [Hybrid search](/docs/integrations/retrievers/pinecone_hybrid_search/) | When combining keyword-based and semantic similarity. | Hybrid search combines keyword and semantic similarity, marrying the benefits of both approaches. [Paper](https://arxiv.org/abs/2210.11934). | | [Maximal Marginal Relevance (MMR)](https://python.langchain.com/api_reference/pinecone/vectorstores/langchain_pinecone.vectorstores.PineconeVectorStore.html#langchain_pinecone.vectorstores.PineconeVectorStore.max_marginal_relevance_search) | When needing to diversify search results. | MMR attempts to diversify the results of a search to avoid returning similar and redundant documents. | - + diff --git a/docs/docs/concepts/why_langchain.mdx b/docs/docs/concepts/why_langchain.mdx index 1713222611c..e9de2ff1fbc 100644 --- a/docs/docs/concepts/why_langchain.mdx +++ b/docs/docs/concepts/why_langchain.mdx @@ -18,7 +18,7 @@ LangChain exposes a standard interface for key components, making it easy to swi 3. **Observability and evaluation:** As applications become more complex, it becomes increasingly difficult to understand what is happening within them. Furthermore, the pace of development can become rate-limited by the [paradox of choice](https://en.wikipedia.org/wiki/Paradox_of_choice). -For example, developers often wonder how to engineer their prompt or which LLM best balances accuracy, latency, and cost. +For example, developers often wonder how to engineer their prompt or which LLM best balances accuracy, latency, and cost. [Observability](https://en.wikipedia.org/wiki/Observability) and evaluations can help developers monitor their applications and rapidly answer these types of questions with confidence. @@ -29,10 +29,10 @@ As an example, all [chat models](/docs/concepts/chat_models/) implement the [Bas This provides a standard way to interact with chat models, supporting important but often provider-specific features like [tool calling](/docs/concepts/tool_calling/) and [structured outputs](/docs/concepts/structured_outputs/). -### Example: chat models +### Example: chat models Many [model providers](/docs/concepts/chat_models/) support [tool calling](/docs/concepts/tool_calling/), a critical feature for many applications (e.g., [agents](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/)), that allows a developer to request model responses that match a particular schema. -The APIs for each provider differ. +The APIs for each provider differ. LangChain's [chat model](/docs/concepts/chat_models/) interface provides a common way to bind [tools](/docs/concepts/tools) to a model in order to support [tool calling](/docs/concepts/tool_calling/): ```python @@ -42,7 +42,7 @@ tools = [my_tool] model_with_tools = model.bind_tools(tools) ``` -Similarly, getting models to produce [structured outputs](/docs/concepts/structured_outputs/) is an extremely common use case. +Similarly, getting models to produce [structured outputs](/docs/concepts/structured_outputs/) is an extremely common use case. Providers support different approaches for this, including [JSON mode or tool calling](https://platform.openai.com/docs/guides/structured-outputs), with different APIs. LangChain's [chat model](/docs/concepts/chat_models/) interface provides a common way to produce structured outputs using the `with_structured_output()` method: @@ -62,9 +62,9 @@ The underlying implementation of the retriever depends on the type of data store documents = my_retriever.invoke("What is the meaning of life?") ``` -## Orchestration +## Orchestration -While standardization for individual components is useful, we've increasingly seen that developers want to *combine* components into more complex applications. +While standardization for individual components is useful, we've increasingly seen that developers want to *combine* components into more complex applications. This motivates the need for [orchestration](https://en.wikipedia.org/wiki/Orchestration_(computing)). There are several common characteristics of LLM applications that this orchestration layer should support: @@ -75,7 +75,7 @@ There are several common characteristics of LLM applications that this orchestra The recommended way to orchestrate components for complex applications is [LangGraph](https://langchain-ai.github.io/langgraph/concepts/high_level/). LangGraph is a library that gives developers a high degree of control by expressing the flow of the application as a set of nodes and edges. LangGraph comes with built-in support for [persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/), [human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/), [memory](https://langchain-ai.github.io/langgraph/concepts/memory/), and other features. -It's particularly well suited for building [agents](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/) or [multi-agent](https://langchain-ai.github.io/langgraph/concepts/multi_agent/) applications. +It's particularly well suited for building [agents](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/) or [multi-agent](https://langchain-ai.github.io/langgraph/concepts/multi_agent/) applications. Importantly, individual LangChain components can be used as LangGraph nodes, but you can also use LangGraph **without** using LangChain components. :::info[Further reading] @@ -86,8 +86,8 @@ Have a look at our free course, [Introduction to LangGraph](https://academy.lang ## Observability and evaluation -The pace of AI application development is often rate-limited by high-quality evaluations because there is a paradox of choice. -Developers often wonder how to engineer their prompt or which LLM best balances accuracy, latency, and cost. +The pace of AI application development is often rate-limited by high-quality evaluations because there is a paradox of choice. +Developers often wonder how to engineer their prompt or which LLM best balances accuracy, latency, and cost. High quality tracing and evaluations can help you rapidly answer these types of questions with confidence. [LangSmith](https://docs.smith.langchain.com/) is our platform that supports observability and evaluation for AI applications. See our conceptual guides on [evaluations](https://docs.smith.langchain.com/concepts/evaluation) and [tracing](https://docs.smith.langchain.com/concepts/tracing) for more details. diff --git a/docs/docs/contributing/how_to/documentation/index.mdx b/docs/docs/contributing/how_to/documentation/index.mdx index 345793c06a1..96f591700bb 100644 --- a/docs/docs/contributing/how_to/documentation/index.mdx +++ b/docs/docs/contributing/how_to/documentation/index.mdx @@ -1,6 +1,6 @@ # Contribute documentation -Documentation is a vital part of LangChain. We welcome both new documentation for new features and +Documentation is a vital part of LangChain. We welcome both new documentation for new features and community improvements to our current documentation. Please read the resources below before getting started: - [Documentation style guide](style_guide.mdx) diff --git a/docs/docs/contributing/how_to/documentation/style_guide.mdx b/docs/docs/contributing/how_to/documentation/style_guide.mdx index a5e369666ad..c9e0970f0f9 100644 --- a/docs/docs/contributing/how_to/documentation/style_guide.mdx +++ b/docs/docs/contributing/how_to/documentation/style_guide.mdx @@ -35,7 +35,7 @@ Some examples include: - [Build a Retrieval Augmented Generation (RAG) App](/docs/tutorials/rag/) A good structural rule of thumb is to follow the structure of this [example from Numpy](https://numpy.org/numpy-tutorials/content/tutorial-svd.html). - + Here are some high-level tips on writing a good tutorial: - Focus on guiding the user to get something done, but keep in mind the end-goal is more to impart principles than to create a perfect production system. @@ -49,7 +49,7 @@ Here are some high-level tips on writing a good tutorial: - The first time you mention a LangChain concept, use its full name (e.g. "LangChain Expression Language (LCEL)"), and link to its conceptual/other documentation page. - It's also helpful to add a prerequisite callout that links to any pages with necessary background information. - End with a recap/next steps section summarizing what the tutorial covered and future reading, such as related how-to guides. - + ### How-to guides A how-to guide, as the name implies, demonstrates how to do something discrete and specific. diff --git a/docs/docs/contributing/how_to/integrations/community.mdx b/docs/docs/contributing/how_to/integrations/community.mdx index 945ae766f7c..e6a328ca2ff 100644 --- a/docs/docs/contributing/how_to/integrations/community.mdx +++ b/docs/docs/contributing/how_to/integrations/community.mdx @@ -15,7 +15,7 @@ guide linked without much discussion. The `langchain-community` package is in `libs/community`. -It can be installed with `pip install langchain-community`, and exported members can be imported with code like +It can be installed with `pip install langchain-community`, and exported members can be imported with code like ```python from langchain_community.chat_models import ChatParrotLink @@ -23,7 +23,7 @@ from langchain_community.llms import ParrotLinkLLM from langchain_community.vectorstores import ParrotLinkVectorStore ``` -The `community` package relies on manually-installed dependent packages, so you will see errors +The `community` package relies on manually-installed dependent packages, so you will see errors if you try to import a package that is not installed. In our fake example, if you tried to import `ParrotLinkLLM` without installing `parrot-link-sdk`, you will see an `ImportError` telling you to install it when trying to use it. Let's say we wanted to implement a chat model for Parrot Link AI. We would create a new file in `libs/community/langchain_community/chat_models/parrot_link.py` with the following code: diff --git a/docs/docs/contributing/how_to/integrations/from_template.mdx b/docs/docs/contributing/how_to/integrations/from_template.mdx index 9365ff23dd5..1050289bf66 100644 --- a/docs/docs/contributing/how_to/integrations/from_template.mdx +++ b/docs/docs/contributing/how_to/integrations/from_template.mdx @@ -14,8 +14,8 @@ First, duplicate this template repository: https://github.com/langchain-ai/integ In this guide, we will create a `libs/langchain-parrot-link` folder, simulating the creation of a partner package for a fake company, "Parrot Link AI". -A package is -installed by users with `pip install langchain-{partner}`, and the package members +A package is +installed by users with `pip install langchain-{partner}`, and the package members can be imported with code like: ```python @@ -93,11 +93,11 @@ to the relevant `docs/docs/integrations` directory in the monorepo root. ## (If Necessary) Deprecate community integration -Note: this is only necessary if you're migrating an existing community integration into -a partner package. If the component you're integrating is net-new to LangChain (i.e. +Note: this is only necessary if you're migrating an existing community integration into +a partner package. If the component you're integrating is net-new to LangChain (i.e. not already in the `community` package), you can skip this step. -Let's pretend we migrated our `ChatParrotLink` chat model from the community package to +Let's pretend we migrated our `ChatParrotLink` chat model from the community package to the partner package. We would need to deprecate the old model in the community package. We would do that by adding a `@deprecated` decorator to the old model as follows, in @@ -116,8 +116,8 @@ After our change, it would look like this: from langchain_core._api.deprecation import deprecated @deprecated( - since="0.0.", - removal="1.0.0", + since="0.0.", + removal="1.0.0", alternative_import="langchain_parrot_link.ChatParrotLink" ) class ChatParrotLink(BaseChatModel): diff --git a/docs/docs/contributing/how_to/integrations/package.mdx b/docs/docs/contributing/how_to/integrations/package.mdx index 806fb852c49..424c4f5cba5 100644 --- a/docs/docs/contributing/how_to/integrations/package.mdx +++ b/docs/docs/contributing/how_to/integrations/package.mdx @@ -4,7 +4,7 @@ pagination_prev: contributing/how_to/integrations/index --- # How to implement an integration package -This guide walks through the process of implementing a LangChain integration +This guide walks through the process of implementing a LangChain integration package. Integration packages are just Python packages that can be installed with `pip install `, @@ -14,11 +14,11 @@ We will cover: 1. (Optional) How to bootstrap a new integration package 2. How to implement components, such as [chat models](/docs/concepts/chat_models/) and [vector stores](/docs/concepts/vectorstores/), that adhere -to the LangChain interface; +to the LangChain interface; ## (Optional) bootstrapping a new integration package -In this section, we will outline 2 options for bootstrapping a new integration package, +In this section, we will outline 2 options for bootstrapping a new integration package, and you're welcome to use other tools if you prefer! 1. **langchain-cli**: This is a command-line tool that can be used to bootstrap a new integration package with a template for LangChain components and Poetry for dependency management. @@ -132,7 +132,7 @@ We will also add some `test` dependencies in a separate poetry dependency group. you are not using Poetry, we recommend adding these in a way that won't package them with your published package, or just installing them separately when you run tests. -`langchain-tests` will provide the [standard tests](../standard_tests) we will use later. +`langchain-tests` will provide the [standard tests](../standard_tests) we will use later. We recommended pinning these to the latest version: Note: Replace `` with the latest version of `langchain-tests` below. @@ -168,8 +168,8 @@ langchain-parrot-link/ └── README.md ``` -All of these files should already exist from step 1, except for -`chat_models.py` and `test_chat_models.py`! We will implement `test_chat_models.py` +All of these files should already exist from step 1, except for +`chat_models.py` and `test_chat_models.py`! We will implement `test_chat_models.py` later, following the [standard tests](../standard_tests) guide. For `chat_models.py`, simply paste the contents of the chat model implementation @@ -202,7 +202,7 @@ import CodeBlock from '@theme/CodeBlock'; - + Refer to the [Custom Chat Model Guide](/docs/how_to/custom_chat_model) guide for detail on a starter chat model [implementation](/docs/how_to/custom_chat_model/#implementation). @@ -244,7 +244,7 @@ import ChatModelSource from '../../../../src/theme/integration_template/integrat base class. This interface consists of methods for writing, deleting and searching for documents in the vector store. - `VectorStore` supports a variety of synchronous and asynchronous search types (e.g., + `VectorStore` supports a variety of synchronous and asynchronous search types (e.g., nearest-neighbor or maximum marginal relevance), as well as interfaces for adding documents to the store. See the [API Reference](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.base.VectorStore.html) for all supported methods. The required methods are tabulated below: @@ -331,7 +331,7 @@ or parameters to call the tool with. 2. To take a "tool call" as generated above, and take some action and return a response that can be passed back to the chat model as a ToolMessage. -The `Tools` class must inherit from the [BaseTool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.base.BaseTool.html#langchain_core.tools.base.BaseTool) base class. This interface has 3 properties and 2 methods that should be implemented in a +The `Tools` class must inherit from the [BaseTool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.base.BaseTool.html#langchain_core.tools.base.BaseTool) base class. This interface has 3 properties and 2 methods that should be implemented in a subclass. | Method/Property | Description | @@ -355,7 +355,7 @@ important for the initial user experience of the tool. arguments. This is used to validate the input arguments to the tool, and to provide a schema for the LLM to fill out when calling the tool. Similar to the `name` and `description` of the overall Tool class, the fields' names (the variable name) and -description (part of `Field(..., description="description")`) are passed to the LLM, +description (part of `Field(..., description="description")`) are passed to the LLM, and the values in these fields should be concise and LLM-usable. ### Run methods diff --git a/docs/docs/contributing/how_to/integrations/standard_tests.mdx b/docs/docs/contributing/how_to/integrations/standard_tests.mdx index 5462b966a64..19638a4c9c3 100644 --- a/docs/docs/contributing/how_to/integrations/standard_tests.mdx +++ b/docs/docs/contributing/how_to/integrations/standard_tests.mdx @@ -15,7 +15,7 @@ First, let's install 2 dependencies: :::note -Because added tests in new versions of `langchain-tests` can break your CI/CD pipelines, we recommend pinning the +Because added tests in new versions of `langchain-tests` can break your CI/CD pipelines, we recommend pinning the version of `langchain-tests` to avoid unexpected changes. ::: @@ -45,7 +45,7 @@ pip install --editable . ## Add and configure standard tests -There are 2 namespaces in the `langchain-tests` package: +There are 2 namespaces in the `langchain-tests` package: - [unit tests](../../../concepts/testing.mdx#unit-tests) (`langchain_tests.unit_tests`): designed to be used to test the component in isolation and without access to external services - [integration tests](../../../concepts/testing.mdx#integration-tests) (`langchain_tests.integration_tests`): designed to be used to test the component with access to external services (in particular, the external service that the component is designed to interact with). @@ -283,7 +283,7 @@ to specify the tool to be tested and the tool's configuration: | `tool_constructor_params` | The parameters to pass to the tool (optional). | | `tool_invoke_params_example` | An example of the parameters to pass to the tool's `invoke` method. | -If you are testing a tool class and pass a class like `MyTool` to `tool_constructor`, you can pass the parameters to the constructor in `tool_constructor_params`. +If you are testing a tool class and pass a class like `MyTool` to `tool_constructor`, you can pass the parameters to the constructor in `tool_constructor_params`. If you are testing an instantiated tool, you can pass the instantiated tool to `tool_constructor` and do not override `tool_constructor_params`. diff --git a/docs/docs/contributing/reference/faq.mdx b/docs/docs/contributing/reference/faq.mdx index 178796ca33f..5a24c73927e 100644 --- a/docs/docs/contributing/reference/faq.mdx +++ b/docs/docs/contributing/reference/faq.mdx @@ -13,7 +13,7 @@ necessary before merging it. Oftentimes, it is more efficient for the maintainers to make these changes themselves before merging, rather than asking you to do so in code review. -By default, most pull requests will have a +By default, most pull requests will have a `✅ Maintainers are allowed to edit this pull request.` badge in the right-hand sidebar. diff --git a/docs/docs/contributing/reference/index.mdx b/docs/docs/contributing/reference/index.mdx index 4311a01cda1..f0b34ba5841 100644 --- a/docs/docs/contributing/reference/index.mdx +++ b/docs/docs/contributing/reference/index.mdx @@ -2,4 +2,4 @@ - [**Repository Structure**](repo_structure.mdx): Understand the high level structure of the repository. - [**Review Process**](review_process.mdx): Learn about the review process for pull requests. -- [**Frequently Asked Questions (FAQ)**](faq.mdx): Get answers to common questions about contributing. \ No newline at end of file +- [**Frequently Asked Questions (FAQ)**](faq.mdx): Get answers to common questions about contributing. diff --git a/docs/docs/contributing/reference/review_process.mdx b/docs/docs/contributing/reference/review_process.mdx index 8a82cc019f6..44474fb09f8 100644 --- a/docs/docs/contributing/reference/review_process.mdx +++ b/docs/docs/contributing/reference/review_process.mdx @@ -8,7 +8,7 @@ This document outlines the process used by the LangChain maintainers for reviewi We categorize PRs using three main statuses, which are marked as project item statuses in the right sidebar and can be viewed in detail [here](https://github.com/orgs/langchain-ai/projects/12/views/1). -- **Triage**: +- **Triage**: - Initial status for all newly submitted PRs. - Requires a maintainer to categorize it into one of the other statuses. diff --git a/docs/docs/how_to/callbacks_async.ipynb b/docs/docs/how_to/callbacks_async.ipynb index f42f7f0a045..33f33329b51 100644 --- a/docs/docs/how_to/callbacks_async.ipynb +++ b/docs/docs/how_to/callbacks_async.ipynb @@ -56,32 +56,13 @@ "text": [ "zzzz....\n", "Hi! I just woke up. Your llm is starting\n", - "Sync handler being called in a `thread_pool_executor`: token: Here\n", - "Sync handler being called in a `thread_pool_executor`: token: 's\n", - "Sync handler being called in a `thread_pool_executor`: token: a\n", - "Sync handler being called in a `thread_pool_executor`: token: little\n", - "Sync handler being called in a `thread_pool_executor`: token: joke\n", - "Sync handler being called in a `thread_pool_executor`: token: for\n", - "Sync handler being called in a `thread_pool_executor`: token: you\n", - "Sync handler being called in a `thread_pool_executor`: token: :\n", "Sync handler being called in a `thread_pool_executor`: token: \n", + "Sync handler being called in a `thread_pool_executor`: token: Why\n", + "Sync handler being called in a `thread_pool_executor`: token: don't scientists trust atoms?\n", "\n", - "Why\n", - "Sync handler being called in a `thread_pool_executor`: token: can\n", - "Sync handler being called in a `thread_pool_executor`: token: 't\n", - "Sync handler being called in a `thread_pool_executor`: token: a\n", - "Sync handler being called in a `thread_pool_executor`: token: bicycle\n", - "Sync handler being called in a `thread_pool_executor`: token: stan\n", - "Sync handler being called in a `thread_pool_executor`: token: d up\n", - "Sync handler being called in a `thread_pool_executor`: token: by\n", - "Sync handler being called in a `thread_pool_executor`: token: itself\n", - "Sync handler being called in a `thread_pool_executor`: token: ?\n", - "Sync handler being called in a `thread_pool_executor`: token: Because\n", - "Sync handler being called in a `thread_pool_executor`: token: it\n", - "Sync handler being called in a `thread_pool_executor`: token: 's\n", - "Sync handler being called in a `thread_pool_executor`: token: two\n", - "Sync handler being called in a `thread_pool_executor`: token: -\n", - "Sync handler being called in a `thread_pool_executor`: token: tire\n", + "Because they make up\n", + "Sync handler being called in a `thread_pool_executor`: token: everything!\n", + "Sync handler being called in a `thread_pool_executor`: token: \n", "zzzz....\n", "Hi! I just woke up. Your llm is ending\n" ] @@ -89,10 +70,10 @@ { "data": { "text/plain": [ - "LLMResult(generations=[[ChatGeneration(text=\"Here's a little joke for you:\\n\\nWhy can't a bicycle stand up by itself? Because it's two-tire\", message=AIMessage(content=\"Here's a little joke for you:\\n\\nWhy can't a bicycle stand up by itself? Because it's two-tire\", id='run-8afc89e8-02c0-4522-8480-d96977240bd4-0'))]], llm_output={}, run=[RunInfo(run_id=UUID('8afc89e8-02c0-4522-8480-d96977240bd4'))])" + "LLMResult(generations=[[ChatGeneration(text=\"Why don't scientists trust atoms?\\n\\nBecause they make up everything!\", message=AIMessage(content=\"Why don't scientists trust atoms?\\n\\nBecause they make up everything!\", additional_kwargs={}, response_metadata={'model_name': 'claude-3-7-sonnet-20250219', 'stop_reason': 'end_turn', 'stop_sequence': None}, id='run--a596349d-8a7c-45fe-8691-bb1f9cfd6c08-0', usage_metadata={'input_tokens': 11, 'output_tokens': 17, 'total_tokens': 28, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}}))]], llm_output={}, run=[RunInfo(run_id=UUID('a596349d-8a7c-45fe-8691-bb1f9cfd6c08'))], type='LLMResult')" ] }, - "execution_count": 2, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -134,7 +115,7 @@ "# To enable streaming, we pass in `streaming=True` to the ChatModel constructor\n", "# Additionally, we pass in a list with our custom handler\n", "chat = ChatAnthropic(\n", - " model=\"claude-3-sonnet-20240229\",\n", + " model=\"claude-3-7-sonnet-20250219\",\n", " max_tokens=25,\n", " streaming=True,\n", " callbacks=[MyCustomSyncHandler(), MyCustomAsyncHandler()],\n", @@ -157,7 +138,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "langchain", "language": "python", "name": "python3" }, @@ -171,7 +152,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.10.16" } }, "nbformat": 4, diff --git a/docs/docs/how_to/callbacks_attach.ipynb b/docs/docs/how_to/callbacks_attach.ipynb index 7d06f3b7643..d890752d857 100644 --- a/docs/docs/how_to/callbacks_attach.ipynb +++ b/docs/docs/how_to/callbacks_attach.ipynb @@ -49,22 +49,28 @@ "execution_count": 1, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Error in LoggingHandler.on_chain_start callback: AttributeError(\"'NoneType' object has no attribute 'get'\")\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Chain RunnableSequence started\n", "Chain ChatPromptTemplate started\n", - "Chain ended, outputs: messages=[HumanMessage(content='What is 1 + 2?')]\n", + "Chain ended, outputs: messages=[HumanMessage(content='What is 1 + 2?', additional_kwargs={}, response_metadata={})]\n", "Chat model started\n", - "Chat model ended, response: generations=[[ChatGeneration(text='1 + 2 = 3', message=AIMessage(content='1 + 2 = 3', response_metadata={'id': 'msg_01NTYMsH9YxkoWsiPYs4Lemn', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}}, id='run-d6bcfd72-9c94-466d-bac0-f39e456ad6e3-0'))]] llm_output={'id': 'msg_01NTYMsH9YxkoWsiPYs4Lemn', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}} run=None\n", - "Chain ended, outputs: content='1 + 2 = 3' response_metadata={'id': 'msg_01NTYMsH9YxkoWsiPYs4Lemn', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}} id='run-d6bcfd72-9c94-466d-bac0-f39e456ad6e3-0'\n" + "Chat model ended, response: generations=[[ChatGeneration(text='The sum of 1 + 2 is 3.', message=AIMessage(content='The sum of 1 + 2 is 3.', additional_kwargs={}, response_metadata={'id': 'msg_01F1qPrmBD9igfzHdqVipmKX', 'model': 'claude-3-7-sonnet-20250219', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 16, 'output_tokens': 17, 'server_tool_use': None, 'service_tier': 'standard'}, 'model_name': 'claude-3-7-sonnet-20250219'}, id='run--71edddf3-2474-42dc-ad43-fadb4882c3c8-0', usage_metadata={'input_tokens': 16, 'output_tokens': 17, 'total_tokens': 33, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}))]] llm_output={'id': 'msg_01F1qPrmBD9igfzHdqVipmKX', 'model': 'claude-3-7-sonnet-20250219', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 16, 'output_tokens': 17, 'server_tool_use': None, 'service_tier': 'standard'}, 'model_name': 'claude-3-7-sonnet-20250219'} run=None type='LLMResult'\n", + "Chain ended, outputs: content='The sum of 1 + 2 is 3.' additional_kwargs={} response_metadata={'id': 'msg_01F1qPrmBD9igfzHdqVipmKX', 'model': 'claude-3-7-sonnet-20250219', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 16, 'output_tokens': 17, 'server_tool_use': None, 'service_tier': 'standard'}, 'model_name': 'claude-3-7-sonnet-20250219'} id='run--71edddf3-2474-42dc-ad43-fadb4882c3c8-0' usage_metadata={'input_tokens': 16, 'output_tokens': 17, 'total_tokens': 33, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}\n" ] }, { "data": { "text/plain": [ - "AIMessage(content='1 + 2 = 3', response_metadata={'id': 'msg_01NTYMsH9YxkoWsiPYs4Lemn', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}}, id='run-d6bcfd72-9c94-466d-bac0-f39e456ad6e3-0')" + "AIMessage(content='The sum of 1 + 2 is 3.', additional_kwargs={}, response_metadata={'id': 'msg_01F1qPrmBD9igfzHdqVipmKX', 'model': 'claude-3-7-sonnet-20250219', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 16, 'output_tokens': 17, 'server_tool_use': None, 'service_tier': 'standard'}, 'model_name': 'claude-3-7-sonnet-20250219'}, id='run--71edddf3-2474-42dc-ad43-fadb4882c3c8-0', usage_metadata={'input_tokens': 16, 'output_tokens': 17, 'total_tokens': 33, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}})" ] }, "execution_count": 1, @@ -101,7 +107,7 @@ "\n", "\n", "callbacks = [LoggingHandler()]\n", - "llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\")\n", + "llm = ChatAnthropic(model=\"claude-3-7-sonnet-20250219\")\n", "prompt = ChatPromptTemplate.from_template(\"What is 1 + {number}?\")\n", "\n", "chain = prompt | llm\n", @@ -127,7 +133,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "langchain", "language": "python", "name": "python3" }, @@ -141,7 +147,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.16" } }, "nbformat": 4, diff --git a/docs/docs/how_to/callbacks_constructor.ipynb b/docs/docs/how_to/callbacks_constructor.ipynb index bae51a02f29..b29311bd242 100644 --- a/docs/docs/how_to/callbacks_constructor.ipynb +++ b/docs/docs/how_to/callbacks_constructor.ipynb @@ -52,16 +52,16 @@ "output_type": "stream", "text": [ "Chat model started\n", - "Chat model ended, response: generations=[[ChatGeneration(text='1 + 2 = 3', message=AIMessage(content='1 + 2 = 3', response_metadata={'id': 'msg_01CdKsRmeS9WRb8BWnHDEHm7', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}}, id='run-2d7fdf2a-7405-4e17-97c0-67e6b2a65305-0'))]] llm_output={'id': 'msg_01CdKsRmeS9WRb8BWnHDEHm7', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}} run=None\n" + "Chat model ended, response: generations=[[ChatGeneration(text='1 + 2 = 3', message=AIMessage(content='1 + 2 = 3', additional_kwargs={}, response_metadata={'id': 'msg_01DQMbSk263KpY2vouHM5gsC', 'model': 'claude-3-7-sonnet-20250219', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 16, 'output_tokens': 13, 'server_tool_use': None, 'service_tier': 'standard'}, 'model_name': 'claude-3-7-sonnet-20250219'}, id='run--ab896e4e-c3fd-48b1-a41a-b6b525cbc041-0', usage_metadata={'input_tokens': 16, 'output_tokens': 13, 'total_tokens': 29, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}))]] llm_output={'id': 'msg_01DQMbSk263KpY2vouHM5gsC', 'model': 'claude-3-7-sonnet-20250219', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 16, 'output_tokens': 13, 'server_tool_use': None, 'service_tier': 'standard'}, 'model_name': 'claude-3-7-sonnet-20250219'} run=None type='LLMResult'\n" ] }, { "data": { "text/plain": [ - "AIMessage(content='1 + 2 = 3', response_metadata={'id': 'msg_01CdKsRmeS9WRb8BWnHDEHm7', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}}, id='run-2d7fdf2a-7405-4e17-97c0-67e6b2a65305-0')" + "AIMessage(content='1 + 2 = 3', additional_kwargs={}, response_metadata={'id': 'msg_01DQMbSk263KpY2vouHM5gsC', 'model': 'claude-3-7-sonnet-20250219', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 16, 'output_tokens': 13, 'server_tool_use': None, 'service_tier': 'standard'}, 'model_name': 'claude-3-7-sonnet-20250219'}, id='run--ab896e4e-c3fd-48b1-a41a-b6b525cbc041-0', usage_metadata={'input_tokens': 16, 'output_tokens': 13, 'total_tokens': 29, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}})" ] }, - "execution_count": 18, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -95,7 +95,7 @@ "\n", "\n", "callbacks = [LoggingHandler()]\n", - "llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\", callbacks=callbacks)\n", + "llm = ChatAnthropic(model=\"claude-3-7-sonnet-20250219\", callbacks=callbacks)\n", "prompt = ChatPromptTemplate.from_template(\"What is 1 + {number}?\")\n", "\n", "chain = prompt | llm\n", @@ -119,7 +119,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "langchain", "language": "python", "name": "python3" }, @@ -133,7 +133,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.16" } }, "nbformat": 4, diff --git a/docs/docs/how_to/callbacks_runtime.ipynb b/docs/docs/how_to/callbacks_runtime.ipynb index aa9048b6928..d569db52ebc 100644 --- a/docs/docs/how_to/callbacks_runtime.ipynb +++ b/docs/docs/how_to/callbacks_runtime.ipynb @@ -42,25 +42,31 @@ "execution_count": 4, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Error in LoggingHandler.on_chain_start callback: AttributeError(\"'NoneType' object has no attribute 'get'\")\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Chain RunnableSequence started\n", "Chain ChatPromptTemplate started\n", - "Chain ended, outputs: messages=[HumanMessage(content='What is 1 + 2?')]\n", + "Chain ended, outputs: messages=[HumanMessage(content='What is 1 + 2?', additional_kwargs={}, response_metadata={})]\n", "Chat model started\n", - "Chat model ended, response: generations=[[ChatGeneration(text='1 + 2 = 3', message=AIMessage(content='1 + 2 = 3', response_metadata={'id': 'msg_01D8Tt5FdtBk5gLTfBPm2tac', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}}, id='run-bb0dddd8-85f3-4e6b-8553-eaa79f859ef8-0'))]] llm_output={'id': 'msg_01D8Tt5FdtBk5gLTfBPm2tac', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}} run=None\n", - "Chain ended, outputs: content='1 + 2 = 3' response_metadata={'id': 'msg_01D8Tt5FdtBk5gLTfBPm2tac', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}} id='run-bb0dddd8-85f3-4e6b-8553-eaa79f859ef8-0'\n" + "Chat model ended, response: generations=[[ChatGeneration(text='1 + 2 = 3', message=AIMessage(content='1 + 2 = 3', additional_kwargs={}, response_metadata={'id': 'msg_019ieJt8K32iC77qBbQmSa9m', 'model': 'claude-3-7-sonnet-20250219', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 16, 'output_tokens': 13, 'server_tool_use': None, 'service_tier': 'standard'}, 'model_name': 'claude-3-7-sonnet-20250219'}, id='run--2f596356-99c9-45ef-94ff-fb170072abdf-0', usage_metadata={'input_tokens': 16, 'output_tokens': 13, 'total_tokens': 29, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}))]] llm_output={'id': 'msg_019ieJt8K32iC77qBbQmSa9m', 'model': 'claude-3-7-sonnet-20250219', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 16, 'output_tokens': 13, 'server_tool_use': None, 'service_tier': 'standard'}, 'model_name': 'claude-3-7-sonnet-20250219'} run=None type='LLMResult'\n", + "Chain ended, outputs: content='1 + 2 = 3' additional_kwargs={} response_metadata={'id': 'msg_019ieJt8K32iC77qBbQmSa9m', 'model': 'claude-3-7-sonnet-20250219', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 16, 'output_tokens': 13, 'server_tool_use': None, 'service_tier': 'standard'}, 'model_name': 'claude-3-7-sonnet-20250219'} id='run--2f596356-99c9-45ef-94ff-fb170072abdf-0' usage_metadata={'input_tokens': 16, 'output_tokens': 13, 'total_tokens': 29, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}\n" ] }, { "data": { "text/plain": [ - "AIMessage(content='1 + 2 = 3', response_metadata={'id': 'msg_01D8Tt5FdtBk5gLTfBPm2tac', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}}, id='run-bb0dddd8-85f3-4e6b-8553-eaa79f859ef8-0')" + "AIMessage(content='1 + 2 = 3', additional_kwargs={}, response_metadata={'id': 'msg_019ieJt8K32iC77qBbQmSa9m', 'model': 'claude-3-7-sonnet-20250219', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 16, 'output_tokens': 13, 'server_tool_use': None, 'service_tier': 'standard'}, 'model_name': 'claude-3-7-sonnet-20250219'}, id='run--2f596356-99c9-45ef-94ff-fb170072abdf-0', usage_metadata={'input_tokens': 16, 'output_tokens': 13, 'total_tokens': 29, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}})" ] }, - "execution_count": 4, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -94,7 +100,7 @@ "\n", "\n", "callbacks = [LoggingHandler()]\n", - "llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\")\n", + "llm = ChatAnthropic(model=\"claude-3-7-sonnet-20250219\")\n", "prompt = ChatPromptTemplate.from_template(\"What is 1 + {number}?\")\n", "\n", "chain = prompt | llm\n", @@ -118,7 +124,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "langchain", "language": "python", "name": "python3" }, @@ -132,7 +138,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.10.16" } }, "nbformat": 4, diff --git a/docs/docs/how_to/custom_callbacks.ipynb b/docs/docs/how_to/custom_callbacks.ipynb index 211f23ae222..29dbb8c3db0 100644 --- a/docs/docs/how_to/custom_callbacks.ipynb +++ b/docs/docs/how_to/custom_callbacks.ipynb @@ -49,33 +49,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "My custom handler, token: Here\n", - "My custom handler, token: 's\n", - "My custom handler, token: a\n", - "My custom handler, token: bear\n", - "My custom handler, token: joke\n", - "My custom handler, token: for\n", - "My custom handler, token: you\n", - "My custom handler, token: :\n", "My custom handler, token: \n", + "My custom handler, token: Why\n", + "My custom handler, token: don't bears wear shoes?\n", "\n", - "Why\n", - "My custom handler, token: di\n", - "My custom handler, token: d the\n", - "My custom handler, token: bear\n", - "My custom handler, token: dissol\n", - "My custom handler, token: ve\n", - "My custom handler, token: in\n", - "My custom handler, token: water\n", - "My custom handler, token: ?\n", - "My custom handler, token: \n", - "Because\n", - "My custom handler, token: it\n", - "My custom handler, token: was\n", - "My custom handler, token: a\n", - "My custom handler, token: polar\n", - "My custom handler, token: bear\n", - "My custom handler, token: !\n" + "Because they\n", + "My custom handler, token: prefer to go bear-foot!\n", + "My custom handler, token: \n" ] } ], @@ -95,7 +75,7 @@ "# To enable streaming, we pass in `streaming=True` to the ChatModel constructor\n", "# Additionally, we pass in our custom handler as a list to the callbacks parameter\n", "model = ChatAnthropic(\n", - " model=\"claude-3-sonnet-20240229\", streaming=True, callbacks=[MyCustomHandler()]\n", + " model=\"claude-3-7-sonnet-20250219\", streaming=True, callbacks=[MyCustomHandler()]\n", ")\n", "\n", "chain = prompt | model\n", @@ -119,7 +99,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "langchain", "language": "python", "name": "python3" }, @@ -133,7 +113,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.10.16" } }, "nbformat": 4, diff --git a/docs/docs/how_to/document_loader_directory.ipynb b/docs/docs/how_to/document_loader_directory.ipynb index c6cfe5c4976..5d73bb68ad9 100644 --- a/docs/docs/how_to/document_loader_directory.ipynb +++ b/docs/docs/how_to/document_loader_directory.ipynb @@ -30,7 +30,7 @@ "id": "e3cdb7bb-1f58-4a7a-af83-599443127834", "metadata": {}, "source": [ - "`DirectoryLoader` accepts a `loader_cls` kwarg, which defaults to [UnstructuredLoader](/docs/integrations/document_loaders/unstructured_file). [Unstructured](https://unstructured-io.github.io/unstructured/) supports parsing for a number of formats, such as PDF and HTML. Here we use it to read in a markdown (.md) file.\n", + "`DirectoryLoader` accepts a `loader_cls` kwarg, which defaults to [UnstructuredLoader](/docs/integrations/document_loaders/unstructured_file). [Unstructured](https://docs.unstructured.io/) supports parsing for a number of formats, such as PDF and HTML. Here we use it to read in a markdown (.md) file.\n", "\n", "We can use the `glob` parameter to control which files to load. Note that here it doesn't load the `.rst` file or the `.html` files." ] diff --git a/docs/docs/how_to/document_loader_json.mdx b/docs/docs/how_to/document_loader_json.mdx index 83af3fdf93e..f2a4dd4e942 100644 --- a/docs/docs/how_to/document_loader_json.mdx +++ b/docs/docs/how_to/document_loader_json.mdx @@ -4,14 +4,14 @@ [JSON Lines](https://jsonlines.org/) is a file format where each line is a valid JSON value. -LangChain implements a [JSONLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html) -to convert JSON and JSONL data into LangChain [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document) -objects. It uses a specified [jq schema](https://en.wikipedia.org/wiki/Jq_(programming_language)) to parse the JSON files, allowing for the extraction of specific fields into the content +LangChain implements a [JSONLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html) +to convert JSON and JSONL data into LangChain [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document) +objects. It uses a specified [jq schema](https://en.wikipedia.org/wiki/Jq_(programming_language)) to parse the JSON files, allowing for the extraction of specific fields into the content and metadata of the LangChain Document. It uses the `jq` python package. Check out this [manual](https://stedolan.github.io/jq/manual/#Basicfilters) for a detailed documentation of the `jq` syntax. -Here we will demonstrate: +Here we will demonstrate: - How to load JSON and JSONL data into the content of a LangChain `Document`; - How to load JSON and JSONL data into metadata associated with a `Document`. @@ -194,7 +194,7 @@ pprint(data) ### JSON file with jq schema `content_key` -To load documents from a JSON file using the content_key within the jq schema, set is_content_key_jq_parsable=True. +To load documents from a JSON file using the content_key within the jq schema, set is_content_key_jq_parsable=True. Ensure that content_key is compatible and can be parsed using the jq schema. ```python diff --git a/docs/docs/how_to/document_loader_office_file.mdx b/docs/docs/how_to/document_loader_office_file.mdx index 6d2ef5faad0..3eb2b6022e3 100644 --- a/docs/docs/how_to/document_loader_office_file.mdx +++ b/docs/docs/how_to/document_loader_office_file.mdx @@ -2,14 +2,14 @@ The [Microsoft Office](https://www.office.com/) suite of productivity software includes Microsoft Word, Microsoft Excel, Microsoft PowerPoint, Microsoft Outlook, and Microsoft OneNote. It is available for Microsoft Windows and macOS operating systems. It is also available on Android and iOS. -This covers how to load commonly used file formats including `DOCX`, `XLSX` and `PPTX` documents into a LangChain +This covers how to load commonly used file formats including `DOCX`, `XLSX` and `PPTX` documents into a LangChain [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document) object that we can use downstream. ## Loading DOCX, XLSX, PPTX with AzureAIDocumentIntelligenceLoader -[Azure AI Document Intelligence](https://aka.ms/doc-intelligence) (formerly known as `Azure Form Recognizer`) is machine-learning +[Azure AI Document Intelligence](https://aka.ms/doc-intelligence) (formerly known as `Azure Form Recognizer`) is machine-learning based service that extracts texts (including handwriting), tables, document structures (e.g., titles, section headings, etc.) and key-value-pairs from digital or scanned PDFs, images, Office and HTML files. Document Intelligence supports `PDF`, `JPEG/JPG`, `PNG`, `BMP`, `TIFF`, `HEIF`, `DOCX`, `XLSX`, `PPTX` and `HTML`. diff --git a/docs/docs/how_to/dynamic_chain.ipynb b/docs/docs/how_to/dynamic_chain.ipynb index f292e6a1f4a..46bf1171023 100644 --- a/docs/docs/how_to/dynamic_chain.ipynb +++ b/docs/docs/how_to/dynamic_chain.ipynb @@ -35,7 +35,7 @@ "\n", "from langchain_anthropic import ChatAnthropic\n", "\n", - "llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\")" + "llm = ChatAnthropic(model=\"claude-3-7-sonnet-20250219\")" ] }, { @@ -47,10 +47,10 @@ { "data": { "text/plain": [ - "\"According to the context provided, Egypt's population in 2024 is estimated to be about 111 million.\"" + "\"Egypt's population in 2024 is about 111 million.\"" ] }, - "execution_count": 10, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -142,13 +142,10 @@ "name": "stdout", "output_type": "stream", "text": [ + "\n", "What\n", - " is\n", - " the\n", - " population\n", - " of\n", - " Egypt\n", - "?\n" + " is the population of Egypt?\n", + "\n" ] } ], @@ -168,9 +165,9 @@ ], "metadata": { "kernelspec": { - "display_name": "poetry-venv-2", + "display_name": "langchain", "language": "python", - "name": "poetry-venv-2" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -182,7 +179,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.1" + "version": "3.10.16" } }, "nbformat": 4, diff --git a/docs/docs/how_to/extraction_parse.ipynb b/docs/docs/how_to/extraction_parse.ipynb index 5ec9c86348d..8b8564f29c9 100644 --- a/docs/docs/how_to/extraction_parse.ipynb +++ b/docs/docs/how_to/extraction_parse.ipynb @@ -42,7 +42,7 @@ "\n", "from langchain_anthropic.chat_models import ChatAnthropic\n", "\n", - "model = ChatAnthropic(model_name=\"claude-3-sonnet-20240229\", temperature=0)" + "model = ChatAnthropic(model_name=\"claude-3-7-sonnet-20250219\", temperature=0)" ] }, { @@ -200,7 +200,7 @@ { "data": { "text/plain": [ - "People(people=[Person(name='Anna', height_in_meters=1.83)])" + "People(people=[Person(name='Anna', height_in_meters=1.8288)])" ] }, "execution_count": 5, @@ -242,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "b1f11912-c1bb-4a2a-a482-79bf3996961f", "metadata": { "execution": { @@ -359,14 +359,6 @@ } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/bagatur/langchain/.venv/lib/python3.11/site-packages/pydantic/_internal/_fields.py:201: UserWarning: Field name \"schema\" in \"PromptInput\" shadows an attribute in parent \"BaseModel\"\n", - " warnings.warn(\n" - ] - }, { "data": { "text/plain": [ @@ -397,7 +389,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "langchain", "language": "python", "name": "python3" }, @@ -411,7 +403,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.10.16" } }, "nbformat": 4, diff --git a/docs/docs/how_to/filter_messages.ipynb b/docs/docs/how_to/filter_messages.ipynb index 108ee908645..91834b13a47 100644 --- a/docs/docs/how_to/filter_messages.ipynb +++ b/docs/docs/how_to/filter_messages.ipynb @@ -23,11 +23,11 @@ { "data": { "text/plain": [ - "[HumanMessage(content='example input', name='example_user', id='2'),\n", - " HumanMessage(content='real input', name='bob', id='4')]" + "[HumanMessage(content='example input', additional_kwargs={}, response_metadata={}, name='example_user', id='2'),\n", + " HumanMessage(content='real input', additional_kwargs={}, response_metadata={}, name='bob', id='4')]" ] }, - "execution_count": 1, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -60,12 +60,12 @@ { "data": { "text/plain": [ - "[SystemMessage(content='you are a good assistant', id='1'),\n", - " HumanMessage(content='real input', name='bob', id='4'),\n", - " AIMessage(content='real output', name='alice', id='5')]" + "[SystemMessage(content='you are a good assistant', additional_kwargs={}, response_metadata={}, id='1'),\n", + " HumanMessage(content='real input', additional_kwargs={}, response_metadata={}, name='bob', id='4'),\n", + " AIMessage(content='real output', additional_kwargs={}, response_metadata={}, name='alice', id='5')]" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -83,12 +83,12 @@ { "data": { "text/plain": [ - "[HumanMessage(content='example input', name='example_user', id='2'),\n", - " HumanMessage(content='real input', name='bob', id='4'),\n", - " AIMessage(content='real output', name='alice', id='5')]" + "[HumanMessage(content='example input', additional_kwargs={}, response_metadata={}, name='example_user', id='2'),\n", + " HumanMessage(content='real input', additional_kwargs={}, response_metadata={}, name='bob', id='4'),\n", + " AIMessage(content='real output', additional_kwargs={}, response_metadata={}, name='alice', id='5')]" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -126,10 +126,10 @@ { "data": { "text/plain": [ - "AIMessage(content=[], response_metadata={'id': 'msg_01Wz7gBHahAwkZ1KCBNtXmwA', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 3}}, id='run-b5d8a3fe-004f-4502-a071-a6c025031827-0', usage_metadata={'input_tokens': 16, 'output_tokens': 3, 'total_tokens': 19})" + "AIMessage(content=[], additional_kwargs={}, response_metadata={'id': 'msg_01At8GtCiJ79M29yvNwCiQaB', 'model': 'claude-3-7-sonnet-20250219', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 16, 'output_tokens': 3, 'server_tool_use': None, 'service_tier': 'standard'}, 'model_name': 'claude-3-7-sonnet-20250219'}, id='run--b3db2b91-0edf-4c48-99e7-35e641b8229d-0', usage_metadata={'input_tokens': 16, 'output_tokens': 3, 'total_tokens': 19, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}})" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -137,7 +137,7 @@ "source": [ "from langchain_anthropic import ChatAnthropic\n", "\n", - "llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=0)\n", + "llm = ChatAnthropic(model=\"claude-3-7-sonnet-20250219\", temperature=0)\n", "# Notice we don't pass in messages. This creates\n", "# a RunnableLambda that takes messages as input\n", "filter_ = filter_messages(exclude_names=[\"example_user\", \"example_assistant\"])\n", @@ -164,8 +164,9 @@ { "data": { "text/plain": [ - "[HumanMessage(content='real input', name='bob', id='4'),\n", - " AIMessage(content='real output', name='alice', id='5')]" + "[SystemMessage(content='you are a good assistant', additional_kwargs={}, response_metadata={}, id='1'),\n", + " HumanMessage(content='real input', additional_kwargs={}, response_metadata={}, name='bob', id='4'),\n", + " AIMessage(content='real output', additional_kwargs={}, response_metadata={}, name='alice', id='5')]" ] }, "execution_count": 6, @@ -190,9 +191,9 @@ ], "metadata": { "kernelspec": { - "display_name": "poetry-venv-2", + "display_name": "langchain", "language": "python", - "name": "poetry-venv-2" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -204,7 +205,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.1" + "version": "3.10.16" } }, "nbformat": 4, diff --git a/docs/docs/how_to/index.mdx b/docs/docs/how_to/index.mdx index 389cb0d3aa7..04e587a3276 100644 --- a/docs/docs/how_to/index.mdx +++ b/docs/docs/how_to/index.mdx @@ -172,7 +172,7 @@ Indexing is the process of keeping your vectorstore in-sync with the underlying ### Tools -LangChain [Tools](/docs/concepts/tools) contain a description of the tool (to pass to the language model) as well as the implementation of the function to call. Refer [here](/docs/integrations/tools/) for a list of pre-built tools. +LangChain [Tools](/docs/concepts/tools) contain a description of the tool (to pass to the language model) as well as the implementation of the function to call. Refer [here](/docs/integrations/tools/) for a list of pre-built tools. - [How to: create tools](/docs/how_to/custom_tools) - [How to: use built-in tools and toolkits](/docs/how_to/tools_builtin) diff --git a/docs/docs/how_to/merge_message_runs.ipynb b/docs/docs/how_to/merge_message_runs.ipynb index ff9aaee593f..10a4c363d53 100644 --- a/docs/docs/how_to/merge_message_runs.ipynb +++ b/docs/docs/how_to/merge_message_runs.ipynb @@ -97,20 +97,13 @@ "id": "6d5a0283-11f8-435b-b27b-7b18f7693592", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: you may need to restart the kernel to use updated packages.\n" - ] - }, { "data": { "text/plain": [ - "AIMessage(content=[], additional_kwargs={}, response_metadata={'id': 'msg_01KNGUMTuzBVfwNouLDpUMwf', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 84, 'output_tokens': 3}}, id='run-b908b198-9c24-450b-9749-9d4a8182937b-0', usage_metadata={'input_tokens': 84, 'output_tokens': 3, 'total_tokens': 87})" + "AIMessage(content='\\n\\nAs for the actual answer, LangChain is named for connecting (chaining) language models together with other components. And Harrison Chase is one of the co-founders of LangChain, not someone being chased! \\n\\nBut I like to think he\\'s running after runaway tokens that escaped from the embedding space. \"Come back here, you vectors!\"', additional_kwargs={}, response_metadata={'id': 'msg_018MF8xBrM1ztw69XTx3Uxcy', 'model': 'claude-3-7-sonnet-20250219', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 84, 'output_tokens': 80, 'server_tool_use': None, 'service_tier': 'standard'}, 'model_name': 'claude-3-7-sonnet-20250219'}, id='run--caa1b9d6-a554-40ad-95cd-268938d8223b-0', usage_metadata={'input_tokens': 84, 'output_tokens': 80, 'total_tokens': 164, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}})" ] }, - "execution_count": 9, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -118,7 +111,7 @@ "source": [ "from langchain_anthropic import ChatAnthropic\n", "\n", - "llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=0)\n", + "llm = ChatAnthropic(model=\"claude-3-7-sonnet-20250219\", temperature=0)\n", "# Notice we don't pass in messages. This creates\n", "# a RunnableLambda that takes messages as input\n", "merger = merge_message_runs()\n", @@ -150,7 +143,7 @@ " AIMessage(content='Well, I guess they thought \"WordRope\" and \"SentenceString\" just didn\\'t have the same ring to it!\\nWhy, he\\'s probably chasing after the last cup of coffee in the office!', additional_kwargs={}, response_metadata={})]" ] }, - "execution_count": 10, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -176,10 +169,10 @@ { "data": { "text/plain": [ - "AIMessage(content='A convergent series is an infinite series whose partial sums approach a finite value as more terms are added. In other words, the sequence of partial sums has a limit.\\n\\nMore formally, an infinite series Σ an (where an are the terms of the series) is said to be convergent if the sequence of partial sums:\\n\\nS1 = a1\\nS2 = a1 + a2 \\nS3 = a1 + a2 + a3\\n...\\nSn = a1 + a2 + a3 + ... + an\\n...\\n\\nconverges to some finite number S as n goes to infinity. We write:\\n\\nlim n→∞ Sn = S\\n\\nThe finite number S is called the sum of the convergent infinite series.\\n\\nIf the sequence of partial sums does not approach any finite limit, the infinite series is said to be divergent.\\n\\nSome key properties:\\n- A series converges if and only if the sequence of its partial sums is a Cauchy sequence.\\n- Absolute/conditional convergence criteria help determine if a given series converges.\\n- Convergent series have many important applications in mathematics, physics, engineering etc.', additional_kwargs={}, response_metadata={'id': 'msg_01MfV6y2hep7ZNvDz24A36U4', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 29, 'output_tokens': 267}}, id='run-9d925f58-021e-4bd0-94fc-f8f5e91010a4-0', usage_metadata={'input_tokens': 29, 'output_tokens': 267, 'total_tokens': 296})" + "AIMessage(content=\"# Definition of a Convergent Series\\n\\nA series is a sum of terms in a sequence, typically written as:\\n\\n$$\\\\sum_{n=1}^{\\\\infty} a_n = a_1 + a_2 + a_3 + \\\\ldots$$\\n\\nA series is called **convergent** if the sequence of partial sums approaches a finite limit.\\n\\n## Formal Definition\\n\\nLet's define the sequence of partial sums:\\n$$S_N = \\\\sum_{n=1}^{N} a_n = a_1 + a_2 + \\\\ldots + a_N$$\\n\\nA series $\\\\sum_{n=1}^{\\\\infty} a_n$ is convergent if and only if:\\n- The limit of the partial sums exists and is finite\\n- That is, there exists a finite number $S$ such that $\\\\lim_{N \\\\to \\\\infty} S_N = S$\\n\\nIf this limit exists, we say the series converges to $S$, and we write:\\n$$\\\\sum_{n=1}^{\\\\infty} a_n = S$$\\n\\nIf the limit doesn't exist or is infinite, the series is called divergent.\", additional_kwargs={}, response_metadata={'id': 'msg_018ypyi2MTjV6S7jCydSqDn9', 'model': 'claude-3-7-sonnet-20250219', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 29, 'output_tokens': 273, 'server_tool_use': None, 'service_tier': 'standard'}, 'model_name': 'claude-3-7-sonnet-20250219'}, id='run--5de0ca29-d031-48f7-bc75-671eade20b74-0', usage_metadata={'input_tokens': 29, 'output_tokens': 273, 'total_tokens': 302, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}})" ] }, - "execution_count": 14, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -203,7 +196,7 @@ "id": "51ba533a-43c7-4e5f-bd91-a4ec23ceeb34", "metadata": {}, "source": [ - "LangSmith Trace: https://smith.langchain.com/public/432150b6-9909-40a7-8ae7-944b7e657438/r/f4ad5fb2-4d38-42a6-b780-25f62617d53f" + "[LangSmith Trace](https://smith.langchain.com/public/432150b6-9909-40a7-8ae7-944b7e657438/r/f4ad5fb2-4d38-42a6-b780-25f62617d53f)" ] }, { @@ -213,15 +206,15 @@ "source": [ "## API reference\n", "\n", - "For a complete description of all arguments head to the API reference: https://python.langchain.com/api_reference/core/messages/langchain_core.messages.utils.merge_message_runs.html" + "For a complete description of all arguments head to the [API reference](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.utils.merge_message_runs.html)" ] } ], "metadata": { "kernelspec": { - "display_name": "poetry-venv-2", + "display_name": "langchain", "language": "python", - "name": "poetry-venv-2" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -233,7 +226,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.10.16" } }, "nbformat": 4, diff --git a/docs/docs/how_to/multimodal_inputs.ipynb b/docs/docs/how_to/multimodal_inputs.ipynb index 96da487ed35..9c3b98d6d52 100644 --- a/docs/docs/how_to/multimodal_inputs.ipynb +++ b/docs/docs/how_to/multimodal_inputs.ipynb @@ -373,7 +373,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "a0b91b29-dbd6-4c94-8f24-05471adc7598", "metadata": {}, "outputs": [ @@ -397,7 +397,7 @@ "\n", "\n", "# Pass to LLM\n", - "llm = init_chat_model(\"google_genai:gemini-2.0-flash-001\")\n", + "llm = init_chat_model(\"google_genai:gemini-2.5-flash\")\n", "\n", "message = {\n", " \"role\": \"user\",\n", diff --git a/docs/docs/how_to/response_metadata.ipynb b/docs/docs/how_to/response_metadata.ipynb index 68919043062..89bcbfb1e78 100644 --- a/docs/docs/how_to/response_metadata.ipynb +++ b/docs/docs/how_to/response_metadata.ipynb @@ -23,17 +23,18 @@ { "data": { "text/plain": [ - "{'token_usage': {'completion_tokens': 110,\n", + "{'token_usage': {'completion_tokens': 88,\n", " 'prompt_tokens': 16,\n", - " 'total_tokens': 126,\n", + " 'total_tokens': 104,\n", " 'completion_tokens_details': {'accepted_prediction_tokens': 0,\n", " 'audio_tokens': 0,\n", " 'reasoning_tokens': 0,\n", " 'rejected_prediction_tokens': 0},\n", " 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}},\n", " 'model_name': 'gpt-4o-mini-2024-07-18',\n", - " 'system_fingerprint': 'fp_b8bc95a0ac',\n", - " 'id': 'chatcmpl-BDrISvLar6AqcZngBmhajFZXVc2u9',\n", + " 'system_fingerprint': 'fp_34a54ae93c',\n", + " 'id': 'chatcmpl-ByN1Qkvqb5fAGKKzXXxZ3rBlnqkWs',\n", + " 'service_tier': 'default',\n", " 'finish_reason': 'stop',\n", " 'logprobs': None}" ] @@ -68,15 +69,17 @@ { "data": { "text/plain": [ - "{'id': 'msg_01JHnvPqgERY7MZwrvfkmq52',\n", - " 'model': 'claude-3-5-sonnet-20241022',\n", + "{'id': 'msg_01NTWnqvbNKSjGfqQL7xikau',\n", + " 'model': 'claude-3-7-sonnet-20250219',\n", " 'stop_reason': 'end_turn',\n", " 'stop_sequence': None,\n", " 'usage': {'cache_creation_input_tokens': 0,\n", " 'cache_read_input_tokens': 0,\n", " 'input_tokens': 17,\n", - " 'output_tokens': 221},\n", - " 'model_name': 'claude-3-5-sonnet-20241022'}" + " 'output_tokens': 197,\n", + " 'server_tool_use': None,\n", + " 'service_tier': 'standard'},\n", + " 'model_name': 'claude-3-7-sonnet-20250219'}" ] }, "execution_count": 2, @@ -87,7 +90,7 @@ "source": [ "from langchain_anthropic import ChatAnthropic\n", "\n", - "llm = ChatAnthropic(model=\"claude-3-5-sonnet-latest\")\n", + "llm = ChatAnthropic(model=\"claude-3-7-sonnet-20250219\")\n", "msg = llm.invoke(\"What's the oldest known example of cuneiform\")\n", "msg.response_metadata" ] @@ -97,30 +100,22 @@ "id": "c1f24f69-18f6-43c1-8b26-3f88ec515259", "metadata": {}, "source": [ - "## Google VertexAI" + "## Google Generative AI" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "39549336-25f5-4839-9846-f687cd77e59b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'is_blocked': False,\n", - " 'safety_ratings': [],\n", - " 'usage_metadata': {'prompt_token_count': 10,\n", - " 'candidates_token_count': 55,\n", - " 'total_token_count': 65,\n", - " 'prompt_tokens_details': [{'modality': 1, 'token_count': 10}],\n", - " 'candidates_tokens_details': [{'modality': 1, 'token_count': 55}],\n", - " 'cached_content_token_count': 0,\n", - " 'cache_tokens_details': []},\n", + "{'prompt_feedback': {'block_reason': 0, 'safety_ratings': []},\n", " 'finish_reason': 'STOP',\n", - " 'avg_logprobs': -0.251378042047674,\n", - " 'model_name': 'gemini-2.0-flash-001'}" + " 'model_name': 'gemini-2.5-flash',\n", + " 'safety_ratings': []}" ] }, "execution_count": 1, @@ -129,9 +124,9 @@ } ], "source": [ - "from langchain_google_vertexai import ChatVertexAI\n", + "from langchain_google_genai import ChatGoogleGenerativeAI\n", "\n", - "llm = ChatVertexAI(model=\"gemini-2.0-flash-001\")\n", + "llm = ChatGoogleGenerativeAI(model=\"gemini-2.5-flash\")\n", "msg = llm.invoke(\"What's the oldest known example of cuneiform\")\n", "msg.response_metadata" ] @@ -173,7 +168,7 @@ "source": [ "from langchain_aws import ChatBedrockConverse\n", "\n", - "llm = ChatBedrockConverse(model=\"anthropic.claude-3-sonnet-20240229-v1:0\")\n", + "llm = ChatBedrockConverse(model=\"anthropic.claude-3-7-sonnet-20250219-v1:0\")\n", "msg = llm.invoke(\"What's the oldest known example of cuneiform\")\n", "msg.response_metadata" ] @@ -196,14 +191,14 @@ "data": { "text/plain": [ "{'token_usage': {'prompt_tokens': 13,\n", - " 'total_tokens': 219,\n", - " 'completion_tokens': 206},\n", + " 'total_tokens': 306,\n", + " 'completion_tokens': 293},\n", " 'model_name': 'mistral-small-latest',\n", " 'model': 'mistral-small-latest',\n", " 'finish_reason': 'stop'}" ] }, - "execution_count": 5, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -301,7 +296,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "langchain", "language": "python", "name": "python3" }, @@ -315,7 +310,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.10.16" } }, "nbformat": 4, diff --git a/docs/docs/how_to/sequence.ipynb b/docs/docs/how_to/sequence.ipynb index 140ac0bfe3c..837bdc92273 100644 --- a/docs/docs/how_to/sequence.ipynb +++ b/docs/docs/how_to/sequence.ipynb @@ -61,7 +61,7 @@ "if \"ANTHROPIC_API_KEY\" not in os.environ:\n", " os.environ[\"ANTHROPIC_API_KEY\"] = getpass()\n", "\n", - "model = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=0)" + "model = ChatAnthropic(model=\"claude-3-7-sonnet-20250219\", temperature=0)" ] }, { @@ -93,7 +93,7 @@ { "data": { "text/plain": [ - "\"Here's a bear joke for you:\\n\\nWhy did the bear dissolve in water?\\nBecause it was a polar bear!\"" + "\"Why don't bears wear shoes?\\n\\nBecause they prefer to go bear-foot!\"" ] }, "execution_count": 3, @@ -128,7 +128,7 @@ { "data": { "text/plain": [ - "'Haha, that\\'s a clever play on words! Using \"polar\" to imply the bear dissolved or became polar/polarized when put in water. Not the most hilarious joke ever, but it has a cute, groan-worthy pun that makes it mildly amusing. I appreciate a good pun or wordplay joke.'" + "'Yes, that\\'s a funny joke! It\\'s a classic pun that plays on the homophone pair \"bare-foot\" and \"bear-foot.\" The humor comes from:\\n\\n1. The wordplay between \"barefoot\" (not wearing shoes) and \"bear-foot\" (the foot of a bear)\\n2. The logical connection to the setup (bears don\\'t wear shoes)\\n3. It\\'s family-friendly and accessible\\n4. It\\'s a simple, clean pun that creates an unexpected but satisfying punchline\\n\\nIt\\'s the kind of joke that might make you groan and smile at the same time - what people often call a \"dad joke.\"'" ] }, "execution_count": 4, @@ -161,7 +161,7 @@ { "data": { "text/plain": [ - "\"Haha, that's a cute and punny joke! I like how it plays on the idea of beets blushing or turning red like someone blushing. Food puns can be quite amusing. While not a total knee-slapper, it's a light-hearted, groan-worthy dad joke that would make me chuckle and shake my head. Simple vegetable humor!\"" + "'Yes, that\\'s a cute and funny joke! It works well because:\\n\\n1. It plays on the double meaning of \"roots\" - both the literal roots of the beet plant and the metaphorical sense of knowing one\\'s origins or foundation\\n2. It\\'s a simple, clean pun that doesn\\'t rely on offensive content\\n3. It has a satisfying logical connection (beets are root vegetables)\\n\\nIt\\'s the kind of wholesome food pun that might make people groan a little but also smile. Perfect for sharing in casual conversation or with kids!'" ] }, "execution_count": 5, @@ -205,7 +205,7 @@ { "data": { "text/plain": [ - "\"I cannot reproduce any copyrighted material verbatim, but I can try to analyze the humor in the joke you provided without quoting it directly.\\n\\nThe joke plays on the idea that the Cylon raiders, who are the antagonists in the Battlestar Galactica universe, failed to locate the human survivors after attacking their home planets (the Twelve Colonies) due to using an outdated and poorly performing operating system (Windows Vista) for their targeting systems.\\n\\nThe humor stems from the juxtaposition of a futuristic science fiction setting with a relatable real-world frustration – the use of buggy, slow, or unreliable software or technology. It pokes fun at the perceived inadequacies of Windows Vista, which was widely criticized for its performance issues and other problems when it was released.\\n\\nBy attributing the Cylons' failure to locate the humans to their use of Vista, the joke creates an amusing and unexpected connection between a fictional advanced race of robots and a familiar technological annoyance experienced by many people in the real world.\\n\\nOverall, the joke relies on incongruity and relatability to generate humor, but without reproducing any copyrighted material directly.\"" + "\"This joke is moderately funny! It plays on Battlestar Galactica lore where Cylons are robots with 12 different models trying to infiltrate human society. The humor comes from the idea of a Cylon accidentally revealing their non-human nature through a pickup line that references their artificial origins. It's a decent nerd-culture joke that would land well with fans of the show, though someone unfamiliar with Battlestar Galactica might not get the reference. The punchline effectively highlights the contradiction in a Cylon trying to blend in while simultaneously revealing their true identity.\"" ] }, "execution_count": 6, @@ -256,7 +256,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "langchain", "language": "python", "name": "python3" }, @@ -270,7 +270,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.1" + "version": "3.10.16" } }, "nbformat": 4, diff --git a/docs/docs/how_to/streaming.ipynb b/docs/docs/how_to/streaming.ipynb index 630e36bba21..869872dcbfd 100644 --- a/docs/docs/how_to/streaming.ipynb +++ b/docs/docs/how_to/streaming.ipynb @@ -79,18 +79,7 @@ "execution_count": 1, "id": "f123bdcb-8c8b-440c-9bbd-aa5ed4e9cd17", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], + "outputs": [], "source": [ "# | output: false\n", "# | echo: false\n", @@ -110,7 +99,7 @@ "\n", "from langchain_anthropic import ChatAnthropic\n", "\n", - "model = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=0)" + "model = ChatAnthropic(model=\"claude-3-7-sonnet-20250219\", temperature=0)" ] }, { @@ -131,7 +120,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "The| sky| appears| blue| during| the| day|.|" + "|The sky typically| appears blue during the day due to a phenomenon| called Rayleigh scattering, where| air molecules scatter sunlight, with| blue light being scattered more than other colors. However|, the sky's color can vary|:\n", + "\n", + "- At sunrise/sunset:| Red, orange, pink, or purple\n", + "-| During storms: Gray or dark blue|\n", + "- At night: Dark| blue to black\n", + "- In certain| atmospheric conditions: White, yellow, or green| (rare)\n", + "\n", + "The color we perceive depends| on weather conditions, time of day, pollution| levels, and our viewing angle.||" ] } ], @@ -160,7 +156,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The| sky| appears| blue| during| the| day|.|" + "|The sky typically| appears blue during the day due to a phenomenon called Rayleigh| scattering, where air molecules scatter sunlight,| with blue light being scattered more than other colors. However|, the sky's color can vary - appearing re|d, orange, or pink during sunrise and sunset,| gray on cloudy days, and black at night.| The color you see depends on the time of| day, weather conditions, and your location.||" ] } ], @@ -188,10 +184,10 @@ { "data": { "text/plain": [ - "AIMessageChunk(content='The', id='run-b36bea64-5511-4d7a-b6a3-a07b3db0c8e7')" + "AIMessageChunk(content='', additional_kwargs={}, response_metadata={'model_name': 'claude-3-7-sonnet-20250219'}, id='run--c4f01565-8bb4-4f07-9b23-acfe46cbeca3', usage_metadata={'input_tokens': 13, 'output_tokens': 0, 'total_tokens': 13, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -219,10 +215,10 @@ { "data": { "text/plain": [ - "AIMessageChunk(content='The sky appears blue during', id='run-b36bea64-5511-4d7a-b6a3-a07b3db0c8e7')" + "AIMessageChunk(content='The sky typically appears blue during the day due to a phenomenon called Rayleigh scattering, where air molecules scatter sunlight, with blue light being scattered more than other colors. However', additional_kwargs={}, response_metadata={'model_name': 'claude-3-7-sonnet-20250219'}, id='run--c4f01565-8bb4-4f07-9b23-acfe46cbeca3', usage_metadata={'input_tokens': 13, 'output_tokens': 0, 'total_tokens': 13, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -259,17 +255,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Here|'s| a| joke| about| a| par|rot|:|\n", + "|Why| don't parrots use the internet?\n", "\n", - "A man| goes| to| a| pet| shop| to| buy| a| par|rot|.| The| shop| owner| shows| him| two| stunning| pa|rr|ots| with| beautiful| pl|um|age|.|\n", - "\n", - "\"|There|'s| a| talking| par|rot| an|d a| non|-|talking| par|rot|,\"| the| owner| says|.| \"|The| talking| par|rot| costs| $|100|,| an|d the| non|-|talking| par|rot| is| $|20|.\"|\n", - "\n", - "The| man| says|,| \"|I|'ll| take| the| non|-|talking| par|rot| at| $|20|.\"|\n", - "\n", - "He| pays| an|d leaves| with| the| par|rot|.| As| he|'s| walking| down| the| street|,| the| par|rot| looks| up| at| him| an|d says|,| \"|You| know|,| you| really| are| a| stupi|d man|!\"|\n", - "\n", - "The| man| is| stun|ne|d an|d looks| at| the| par|rot| in| dis|bel|ief|.| The| par|rot| continues|,| \"|Yes|,| you| got| r|ippe|d off| big| time|!| I| can| talk| just| as| well| as| that| other| par|rot|,| an|d you| only| pai|d $|20| |for| me|!\"|" + "They|'re afraid of getting a virus from all the tweets|!||" ] } ], @@ -337,26 +325,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "{}\n", "{'countries': []}\n", - "{'countries': [{}]}\n", - "{'countries': [{'name': ''}]}\n", "{'countries': [{'name': 'France'}]}\n", - "{'countries': [{'name': 'France', 'population': 67}]}\n", - "{'countries': [{'name': 'France', 'population': 67413}]}\n", - "{'countries': [{'name': 'France', 'population': 67413000}]}\n", - "{'countries': [{'name': 'France', 'population': 67413000}, {}]}\n", - "{'countries': [{'name': 'France', 'population': 67413000}, {'name': ''}]}\n", - "{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain'}]}\n", - "{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47}]}\n", - "{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351}]}\n", - "{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351567}]}\n", - "{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351567}, {}]}\n", - "{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351567}, {'name': ''}]}\n", - "{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351567}, {'name': 'Japan'}]}\n", - "{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351567}, {'name': 'Japan', 'population': 125}]}\n", - "{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351567}, {'name': 'Japan', 'population': 125584}]}\n", - "{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351567}, {'name': 'Japan', 'population': 125584000}]}\n" + "{'countries': [{'name': 'France', 'population': 67750}]}\n", + "{'countries': [{'name': 'France', 'population': 67750000}, {}]}\n", + "{'countries': [{'name': 'France', 'population': 67750000}, {'name': 'Spain'}]}\n", + "{'countries': [{'name': 'France', 'population': 67750000}, {'name': 'Spain', 'population': 47350000}, {}]}\n", + "{'countries': [{'name': 'France', 'population': 67750000}, {'name': 'Spain', 'population': 47350000}, {'name': 'Japan'}]}\n", + "{'countries': [{'name': 'France', 'population': 67750000}, {'name': 'Spain', 'population': 47350000}, {'name': 'Japan', 'population': 125700000}]}\n" ] } ], @@ -539,11 +515,11 @@ { "data": { "text/plain": [ - "[[Document(page_content='harrison worked at kensho'),\n", - " Document(page_content='harrison likes spicy food')]]" + "[[Document(id='2740a247-9738-48c4-8c8f-d879d4ed39f7', metadata={}, page_content='harrison worked at kensho'),\n", + " Document(id='1d3d012f-1cb0-4bee-928a-c8bf0f8b1b92', metadata={}, page_content='harrison likes spicy food')]]" ] }, - "execution_count": 10, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -614,15 +590,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Base|d on| the| given| context|,| Harrison| worke|d at| K|ens|ho|.|\n", + "|Base|d on the provided context, Harrison worked at Kens|ho.\n", "\n", - "Here| are| |3| |made| up| sentences| about| this| place|:|\n", + "Three made up sentences about Kens|ho:\n", "\n", - "1|.| K|ens|ho| was| a| cutting|-|edge| technology| company| known| for| its| innovative| solutions| in| artificial| intelligence| an|d data| analytics|.|\n", + "1. Kensho is a| cutting-edge technology company that specializes in| AI and data analytics for financial institutions.\n", "\n", - "2|.| The| modern| office| space| at| K|ens|ho| feature|d open| floor| plans|,| collaborative| work|sp|aces|,| an|d a| vib|rant| atmosphere| that| fos|tere|d creativity| an|d team|work|.|\n", + "2|. The Kensho office features| an open floor plan with panoramic views of the city| skyline, creating an inspiring environment for its| employees.\n", "\n", - "3|.| With| its| prime| location| in| the| heart| of| the| city|,| K|ens|ho| attracte|d top| talent| from| aroun|d the| worl|d,| creating| a| diverse| an|d dynamic| work| environment|.|" + "3. At Kensho,| team members often collaborate in innovative brainstorming sessions while| enjoying complimentary gourmet coffee from| their in-house café.||" ] } ], @@ -763,38 +739,38 @@ " 'data': {'input': 'hello'},\n", " 'name': 'ChatAnthropic',\n", " 'tags': [],\n", - " 'run_id': 'b18d016d-8b9b-49e7-a555-44db498fcf66',\n", + " 'run_id': 'c35a72be-a5af-4bd5-bd9b-206135c28ef6',\n", " 'metadata': {'ls_provider': 'anthropic',\n", - " 'ls_model_name': 'claude-3-sonnet-20240229',\n", + " 'ls_model_name': 'claude-3-7-sonnet-20250219',\n", " 'ls_model_type': 'chat',\n", " 'ls_temperature': 0.0,\n", " 'ls_max_tokens': 1024},\n", " 'parent_ids': []},\n", " {'event': 'on_chat_model_stream',\n", - " 'run_id': 'b18d016d-8b9b-49e7-a555-44db498fcf66',\n", + " 'run_id': 'c35a72be-a5af-4bd5-bd9b-206135c28ef6',\n", " 'name': 'ChatAnthropic',\n", " 'tags': [],\n", " 'metadata': {'ls_provider': 'anthropic',\n", - " 'ls_model_name': 'claude-3-sonnet-20240229',\n", + " 'ls_model_name': 'claude-3-7-sonnet-20250219',\n", " 'ls_model_type': 'chat',\n", " 'ls_temperature': 0.0,\n", " 'ls_max_tokens': 1024},\n", - " 'data': {'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={}, id='run-b18d016d-8b9b-49e7-a555-44db498fcf66', usage_metadata={'input_tokens': 8, 'output_tokens': 4, 'total_tokens': 12, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})},\n", + " 'data': {'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={'model_name': 'claude-3-7-sonnet-20250219'}, id='run--c35a72be-a5af-4bd5-bd9b-206135c28ef6', usage_metadata={'input_tokens': 8, 'output_tokens': 0, 'total_tokens': 8, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})},\n", " 'parent_ids': []},\n", " {'event': 'on_chat_model_stream',\n", - " 'run_id': 'b18d016d-8b9b-49e7-a555-44db498fcf66',\n", + " 'run_id': 'c35a72be-a5af-4bd5-bd9b-206135c28ef6',\n", " 'name': 'ChatAnthropic',\n", " 'tags': [],\n", " 'metadata': {'ls_provider': 'anthropic',\n", - " 'ls_model_name': 'claude-3-sonnet-20240229',\n", + " 'ls_model_name': 'claude-3-7-sonnet-20250219',\n", " 'ls_model_type': 'chat',\n", " 'ls_temperature': 0.0,\n", " 'ls_max_tokens': 1024},\n", - " 'data': {'chunk': AIMessageChunk(content='Hello! How can', additional_kwargs={}, response_metadata={}, id='run-b18d016d-8b9b-49e7-a555-44db498fcf66')},\n", + " 'data': {'chunk': AIMessageChunk(content='Hello! How', additional_kwargs={}, response_metadata={}, id='run--c35a72be-a5af-4bd5-bd9b-206135c28ef6')},\n", " 'parent_ids': []}]" ] }, - "execution_count": 14, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -813,30 +789,30 @@ "data": { "text/plain": [ "[{'event': 'on_chat_model_stream',\n", - " 'run_id': 'b18d016d-8b9b-49e7-a555-44db498fcf66',\n", + " 'run_id': 'c35a72be-a5af-4bd5-bd9b-206135c28ef6',\n", " 'name': 'ChatAnthropic',\n", " 'tags': [],\n", " 'metadata': {'ls_provider': 'anthropic',\n", - " 'ls_model_name': 'claude-3-sonnet-20240229',\n", + " 'ls_model_name': 'claude-3-7-sonnet-20250219',\n", " 'ls_model_type': 'chat',\n", " 'ls_temperature': 0.0,\n", " 'ls_max_tokens': 1024},\n", - " 'data': {'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={'stop_reason': 'end_turn', 'stop_sequence': None}, id='run-b18d016d-8b9b-49e7-a555-44db498fcf66', usage_metadata={'input_tokens': 0, 'output_tokens': 12, 'total_tokens': 12, 'input_token_details': {}})},\n", + " 'data': {'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={'stop_reason': 'end_turn', 'stop_sequence': None}, id='run--c35a72be-a5af-4bd5-bd9b-206135c28ef6', usage_metadata={'input_tokens': 0, 'output_tokens': 40, 'total_tokens': 40})},\n", " 'parent_ids': []},\n", " {'event': 'on_chat_model_end',\n", - " 'data': {'output': AIMessageChunk(content='Hello! How can I assist you today?', additional_kwargs={}, response_metadata={'stop_reason': 'end_turn', 'stop_sequence': None}, id='run-b18d016d-8b9b-49e7-a555-44db498fcf66', usage_metadata={'input_tokens': 8, 'output_tokens': 16, 'total_tokens': 24, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})},\n", - " 'run_id': 'b18d016d-8b9b-49e7-a555-44db498fcf66',\n", + " 'data': {'output': AIMessageChunk(content=\"Hello! How can I assist you today? Whether you have questions, need information, or just want to chat, I'm here to help. What would you like to talk about?\", additional_kwargs={}, response_metadata={'model_name': 'claude-3-7-sonnet-20250219', 'stop_reason': 'end_turn', 'stop_sequence': None}, id='run--c35a72be-a5af-4bd5-bd9b-206135c28ef6', usage_metadata={'input_tokens': 8, 'output_tokens': 40, 'total_tokens': 48, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})},\n", + " 'run_id': 'c35a72be-a5af-4bd5-bd9b-206135c28ef6',\n", " 'name': 'ChatAnthropic',\n", " 'tags': [],\n", " 'metadata': {'ls_provider': 'anthropic',\n", - " 'ls_model_name': 'claude-3-sonnet-20240229',\n", + " 'ls_model_name': 'claude-3-7-sonnet-20250219',\n", " 'ls_model_type': 'chat',\n", " 'ls_temperature': 0.0,\n", " 'ls_max_tokens': 1024},\n", " 'parent_ids': []}]" ] }, - "execution_count": 15, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -903,23 +879,34 @@ " 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'},\n", " 'name': 'RunnableSequence',\n", " 'tags': [],\n", - " 'run_id': '4765006b-16e2-4b1d-a523-edd9fd64cb92',\n", - " 'metadata': {}},\n", + " 'run_id': 'f859e56f-a760-4670-a24e-040e11bcd7fc',\n", + " 'metadata': {},\n", + " 'parent_ids': []},\n", " {'event': 'on_chat_model_start',\n", - " 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`')]]}},\n", + " 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`', additional_kwargs={}, response_metadata={})]]}},\n", " 'name': 'ChatAnthropic',\n", " 'tags': ['seq:step:1'],\n", - " 'run_id': '0320c234-7b52-4a14-ae4e-5f100949e589',\n", - " 'metadata': {}},\n", + " 'run_id': '2aa8c9e6-a5cd-4e94-b994-cb0e9bd8ab21',\n", + " 'metadata': {'ls_provider': 'anthropic',\n", + " 'ls_model_name': 'claude-3-7-sonnet-20250219',\n", + " 'ls_model_type': 'chat',\n", + " 'ls_temperature': 0.0,\n", + " 'ls_max_tokens': 1024},\n", + " 'parent_ids': ['f859e56f-a760-4670-a24e-040e11bcd7fc']},\n", " {'event': 'on_chat_model_stream',\n", - " 'data': {'chunk': AIMessageChunk(content='{', id='run-0320c234-7b52-4a14-ae4e-5f100949e589')},\n", - " 'run_id': '0320c234-7b52-4a14-ae4e-5f100949e589',\n", + " 'data': {'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={'model_name': 'claude-3-7-sonnet-20250219'}, id='run--2aa8c9e6-a5cd-4e94-b994-cb0e9bd8ab21', usage_metadata={'input_tokens': 56, 'output_tokens': 0, 'total_tokens': 56, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})},\n", + " 'run_id': '2aa8c9e6-a5cd-4e94-b994-cb0e9bd8ab21',\n", " 'name': 'ChatAnthropic',\n", " 'tags': ['seq:step:1'],\n", - " 'metadata': {}}]" + " 'metadata': {'ls_provider': 'anthropic',\n", + " 'ls_model_name': 'claude-3-7-sonnet-20250219',\n", + " 'ls_model_type': 'chat',\n", + " 'ls_temperature': 0.0,\n", + " 'ls_max_tokens': 1024},\n", + " 'parent_ids': ['f859e56f-a760-4670-a24e-040e11bcd7fc']}]" ] }, - "execution_count": 18, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -955,25 +942,25 @@ "output_type": "stream", "text": [ "Chat model chunk: ''\n", - "Chat model chunk: '{'\n", - "Parser chunk: {}\n", - "Chat model chunk: '\\n \"countries'\n", - "Chat model chunk: '\": [\\n '\n", + "Chat model chunk: '```'\n", + "Chat model chunk: 'json\\n{\\n \"countries\": ['\n", "Parser chunk: {'countries': []}\n", - "Chat model chunk: '{\\n \"'\n", - "Parser chunk: {'countries': [{}]}\n", - "Chat model chunk: 'name\": \"France'\n", + "Chat model chunk: '\\n {\\n \"name\": \"France\",'\n", "Parser chunk: {'countries': [{'name': 'France'}]}\n", - "Chat model chunk: '\",\\n \"'\n", - "Chat model chunk: 'population\": 67'\n", - "Parser chunk: {'countries': [{'name': 'France', 'population': 67}]}\n", - "Chat model chunk: '413'\n", - "Parser chunk: {'countries': [{'name': 'France', 'population': 67413}]}\n", - "Chat model chunk: '000\\n },'\n", - "Parser chunk: {'countries': [{'name': 'France', 'population': 67413000}]}\n", - "Chat model chunk: '\\n {'\n", - "Parser chunk: {'countries': [{'name': 'France', 'population': 67413000}, {}]}\n", - "Chat model chunk: '\\n \"name\":'\n", + "Chat model chunk: '\\n \"population\": 67750000\\n },'\n", + "Parser chunk: {'countries': [{'name': 'France', 'population': 67750000}]}\n", + "Chat model chunk: '\\n {\\n \"name\": \"Spain\",'\n", + "Parser chunk: {'countries': [{'name': 'France', 'population': 67750000}, {'name': 'Spain'}]}\n", + "Chat model chunk: '\\n \"population\": 47350'\n", + "Parser chunk: {'countries': [{'name': 'France', 'population': 67750000}, {'name': 'Spain', 'population': 47350}]}\n", + "Chat model chunk: '000\\n },\\n {\\n \"'\n", + "Parser chunk: {'countries': [{'name': 'France', 'population': 67750000}, {'name': 'Spain', 'population': 47350000}, {}]}\n", + "Chat model chunk: 'name\": \"Japan\",\\n \"population\":'\n", + "Parser chunk: {'countries': [{'name': 'France', 'population': 67750000}, {'name': 'Spain', 'population': 47350000}, {'name': 'Japan'}]}\n", + "Chat model chunk: ' 125700000\\n }'\n", + "Parser chunk: {'countries': [{'name': 'France', 'population': 67750000}, {'name': 'Spain', 'population': 47350000}, {'name': 'Japan', 'population': 125700000}]}\n", + "Chat model chunk: '\\n ]\\n}\\n```'\n", + "Chat model chunk: ''\n", "...\n" ] } @@ -1033,18 +1020,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'event': 'on_parser_start', 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'my_parser', 'tags': ['seq:step:2'], 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'metadata': {}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n", - "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n", - "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': []}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n", - "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{}]}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n", - "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France'}]}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n", - "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67}]}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n", - "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413}]}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n", - "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}]}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n", - "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}, {}]}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n", - "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain'}]}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n", - "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47}]}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n", - "...\n" + "{'event': 'on_parser_start', 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'my_parser', 'tags': ['seq:step:2'], 'run_id': '781af9b6-31f8-47f2-ab79-52d17b000857', 'metadata': {}, 'parent_ids': ['82c918c6-d5f6-4d2d-b710-4668509fe2f0']}\n", + "{'event': 'on_parser_stream', 'run_id': '781af9b6-31f8-47f2-ab79-52d17b000857', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': []}}, 'parent_ids': ['82c918c6-d5f6-4d2d-b710-4668509fe2f0']}\n", + "{'event': 'on_parser_stream', 'run_id': '781af9b6-31f8-47f2-ab79-52d17b000857', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France'}]}}, 'parent_ids': ['82c918c6-d5f6-4d2d-b710-4668509fe2f0']}\n", + "{'event': 'on_parser_stream', 'run_id': '781af9b6-31f8-47f2-ab79-52d17b000857', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67750}]}}, 'parent_ids': ['82c918c6-d5f6-4d2d-b710-4668509fe2f0']}\n", + "{'event': 'on_parser_stream', 'run_id': '781af9b6-31f8-47f2-ab79-52d17b000857', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67750000}, {}]}}, 'parent_ids': ['82c918c6-d5f6-4d2d-b710-4668509fe2f0']}\n", + "{'event': 'on_parser_stream', 'run_id': '781af9b6-31f8-47f2-ab79-52d17b000857', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67750000}, {'name': 'Spain'}]}}, 'parent_ids': ['82c918c6-d5f6-4d2d-b710-4668509fe2f0']}\n", + "{'event': 'on_parser_stream', 'run_id': '781af9b6-31f8-47f2-ab79-52d17b000857', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67750000}, {'name': 'Spain', 'population': 47350000}]}}, 'parent_ids': ['82c918c6-d5f6-4d2d-b710-4668509fe2f0']}\n", + "{'event': 'on_parser_stream', 'run_id': '781af9b6-31f8-47f2-ab79-52d17b000857', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67750000}, {'name': 'Spain', 'population': 47350000}, {'name': 'Japan'}]}}, 'parent_ids': ['82c918c6-d5f6-4d2d-b710-4668509fe2f0']}\n", + "{'event': 'on_parser_stream', 'run_id': '781af9b6-31f8-47f2-ab79-52d17b000857', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67750000}, {'name': 'Spain', 'population': 47350000}, {'name': 'Japan', 'population': 125700000}]}}, 'parent_ids': ['82c918c6-d5f6-4d2d-b710-4668509fe2f0']}\n", + "{'event': 'on_parser_end', 'data': {'output': {'countries': [{'name': 'France', 'population': 67750000}, {'name': 'Spain', 'population': 47350000}, {'name': 'Japan', 'population': 125700000}]}}, 'run_id': '781af9b6-31f8-47f2-ab79-52d17b000857', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'parent_ids': ['82c918c6-d5f6-4d2d-b710-4668509fe2f0']}\n" ] } ], @@ -1086,17 +1071,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'event': 'on_chat_model_start', 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'model', 'tags': ['seq:step:1'], 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n", - "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={}, id='run-156c3e40-82fb-49ff-8e41-9e998061be8c', usage_metadata={'input_tokens': 56, 'output_tokens': 1, 'total_tokens': 57, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})}, 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n", - "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='{', additional_kwargs={}, response_metadata={}, id='run-156c3e40-82fb-49ff-8e41-9e998061be8c')}, 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n", - "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n \"countries', additional_kwargs={}, response_metadata={}, id='run-156c3e40-82fb-49ff-8e41-9e998061be8c')}, 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n", - "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\": [\\n ', additional_kwargs={}, response_metadata={}, id='run-156c3e40-82fb-49ff-8e41-9e998061be8c')}, 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n", - "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='{\\n \"', additional_kwargs={}, response_metadata={}, id='run-156c3e40-82fb-49ff-8e41-9e998061be8c')}, 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n", - 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Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'model', 'tags': ['seq:step:1'], 'run_id': 'b7a08416-a629-4b42-b5d5-dbe48566e5d5', 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-7-sonnet-20250219', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['116a6506-5a19-4f60-a8c2-7b728d4b8248']}\n", + "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={'model_name': 'claude-3-7-sonnet-20250219'}, id='run--b7a08416-a629-4b42-b5d5-dbe48566e5d5', usage_metadata={'input_tokens': 56, 'output_tokens': 0, 'total_tokens': 56, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})}, 'run_id': 'b7a08416-a629-4b42-b5d5-dbe48566e5d5', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-7-sonnet-20250219', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['116a6506-5a19-4f60-a8c2-7b728d4b8248']}\n", + "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='```', additional_kwargs={}, response_metadata={}, id='run--b7a08416-a629-4b42-b5d5-dbe48566e5d5')}, 'run_id': 'b7a08416-a629-4b42-b5d5-dbe48566e5d5', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-7-sonnet-20250219', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['116a6506-5a19-4f60-a8c2-7b728d4b8248']}\n", + "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='json\\n{\\n \"countries\": [', additional_kwargs={}, response_metadata={}, id='run--b7a08416-a629-4b42-b5d5-dbe48566e5d5')}, 'run_id': 'b7a08416-a629-4b42-b5d5-dbe48566e5d5', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-7-sonnet-20250219', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['116a6506-5a19-4f60-a8c2-7b728d4b8248']}\n", + "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n {\\n \"name\": \"France\",', additional_kwargs={}, response_metadata={}, id='run--b7a08416-a629-4b42-b5d5-dbe48566e5d5')}, 'run_id': 'b7a08416-a629-4b42-b5d5-dbe48566e5d5', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-7-sonnet-20250219', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['116a6506-5a19-4f60-a8c2-7b728d4b8248']}\n", + "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n \"population\": 67750', additional_kwargs={}, response_metadata={}, id='run--b7a08416-a629-4b42-b5d5-dbe48566e5d5')}, 'run_id': 'b7a08416-a629-4b42-b5d5-dbe48566e5d5', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-7-sonnet-20250219', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['116a6506-5a19-4f60-a8c2-7b728d4b8248']}\n", + "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='000\\n },\\n {\\n \"', additional_kwargs={}, response_metadata={}, id='run--b7a08416-a629-4b42-b5d5-dbe48566e5d5')}, 'run_id': 'b7a08416-a629-4b42-b5d5-dbe48566e5d5', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-7-sonnet-20250219', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['116a6506-5a19-4f60-a8c2-7b728d4b8248']}\n", + "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='name\": \"Spain\",\\n \"population\":', additional_kwargs={}, response_metadata={}, id='run--b7a08416-a629-4b42-b5d5-dbe48566e5d5')}, 'run_id': 'b7a08416-a629-4b42-b5d5-dbe48566e5d5', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-7-sonnet-20250219', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['116a6506-5a19-4f60-a8c2-7b728d4b8248']}\n", + "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' 47350000\\n },', additional_kwargs={}, response_metadata={}, id='run--b7a08416-a629-4b42-b5d5-dbe48566e5d5')}, 'run_id': 'b7a08416-a629-4b42-b5d5-dbe48566e5d5', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-7-sonnet-20250219', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['116a6506-5a19-4f60-a8c2-7b728d4b8248']}\n", + "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n {\\n \"name\": \"Japan\",', additional_kwargs={}, response_metadata={}, id='run--b7a08416-a629-4b42-b5d5-dbe48566e5d5')}, 'run_id': 'b7a08416-a629-4b42-b5d5-dbe48566e5d5', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-7-sonnet-20250219', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['116a6506-5a19-4f60-a8c2-7b728d4b8248']}\n", + "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n \"population\": 125700', additional_kwargs={}, response_metadata={}, id='run--b7a08416-a629-4b42-b5d5-dbe48566e5d5')}, 'run_id': 'b7a08416-a629-4b42-b5d5-dbe48566e5d5', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-7-sonnet-20250219', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['116a6506-5a19-4f60-a8c2-7b728d4b8248']}\n", "...\n" ] } @@ -1144,17 +1129,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'event': 'on_chain_start', 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'RunnableSequence', 'tags': ['my_chain'], 'run_id': '58d1302e-36ce-4df7-a3cb-47cb73d57e44', 'metadata': {}, 'parent_ids': []}\n", - "{'event': 'on_chat_model_start', 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`', additional_kwargs={}, response_metadata={})]]}}, 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'run_id': '8222e8a1-d978-4f30-87fc-b2dba838774b', 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['58d1302e-36ce-4df7-a3cb-47cb73d57e44']}\n", - "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={}, id='run-8222e8a1-d978-4f30-87fc-b2dba838774b', usage_metadata={'input_tokens': 56, 'output_tokens': 1, 'total_tokens': 57, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})}, 'run_id': '8222e8a1-d978-4f30-87fc-b2dba838774b', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['58d1302e-36ce-4df7-a3cb-47cb73d57e44']}\n", - "{'event': 'on_parser_start', 'data': {}, 'name': 'JsonOutputParser', 'tags': ['seq:step:2', 'my_chain'], 'run_id': '75604c84-e1e6-494a-8b2a-950f45d932e8', 'metadata': {}, 'parent_ids': ['58d1302e-36ce-4df7-a3cb-47cb73d57e44']}\n", - "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='{', additional_kwargs={}, response_metadata={}, id='run-8222e8a1-d978-4f30-87fc-b2dba838774b')}, 'run_id': '8222e8a1-d978-4f30-87fc-b2dba838774b', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['58d1302e-36ce-4df7-a3cb-47cb73d57e44']}\n", - "{'event': 'on_parser_stream', 'run_id': '75604c84-e1e6-494a-8b2a-950f45d932e8', 'name': 'JsonOutputParser', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}, 'data': {'chunk': {}}, 'parent_ids': ['58d1302e-36ce-4df7-a3cb-47cb73d57e44']}\n", - "{'event': 'on_chain_stream', 'run_id': '58d1302e-36ce-4df7-a3cb-47cb73d57e44', 'name': 'RunnableSequence', 'tags': ['my_chain'], 'metadata': {}, 'data': {'chunk': {}}, 'parent_ids': []}\n", - "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n \"countries', additional_kwargs={}, response_metadata={}, id='run-8222e8a1-d978-4f30-87fc-b2dba838774b')}, 'run_id': '8222e8a1-d978-4f30-87fc-b2dba838774b', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['58d1302e-36ce-4df7-a3cb-47cb73d57e44']}\n", - "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\": [\\n ', additional_kwargs={}, response_metadata={}, id='run-8222e8a1-d978-4f30-87fc-b2dba838774b')}, 'run_id': '8222e8a1-d978-4f30-87fc-b2dba838774b', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['58d1302e-36ce-4df7-a3cb-47cb73d57e44']}\n", - "{'event': 'on_parser_stream', 'run_id': '75604c84-e1e6-494a-8b2a-950f45d932e8', 'name': 'JsonOutputParser', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}, 'data': {'chunk': {'countries': []}}, 'parent_ids': ['58d1302e-36ce-4df7-a3cb-47cb73d57e44']}\n", - "{'event': 'on_chain_stream', 'run_id': '58d1302e-36ce-4df7-a3cb-47cb73d57e44', 'name': 'RunnableSequence', 'tags': ['my_chain'], 'metadata': {}, 'data': {'chunk': {'countries': []}}, 'parent_ids': []}\n", + "{'event': 'on_chain_start', 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'RunnableSequence', 'tags': ['my_chain'], 'run_id': '3e4f8c37-a44a-46b7-a7e5-75182d1cca31', 'metadata': {}, 'parent_ids': []}\n", + "{'event': 'on_chat_model_start', 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`', additional_kwargs={}, response_metadata={})]]}}, 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'run_id': '778846c9-acd3-43b7-b9c0-ac718761b2bc', 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-7-sonnet-20250219', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['3e4f8c37-a44a-46b7-a7e5-75182d1cca31']}\n", + "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={'model_name': 'claude-3-7-sonnet-20250219'}, id='run--778846c9-acd3-43b7-b9c0-ac718761b2bc', usage_metadata={'input_tokens': 56, 'output_tokens': 0, 'total_tokens': 56, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})}, 'run_id': '778846c9-acd3-43b7-b9c0-ac718761b2bc', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-7-sonnet-20250219', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['3e4f8c37-a44a-46b7-a7e5-75182d1cca31']}\n", + "{'event': 'on_parser_start', 'data': {}, 'name': 'JsonOutputParser', 'tags': ['seq:step:2', 'my_chain'], 'run_id': '2c46d24f-231c-4062-a7ab-b7954840986d', 'metadata': {}, 'parent_ids': ['3e4f8c37-a44a-46b7-a7e5-75182d1cca31']}\n", + "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='```', additional_kwargs={}, response_metadata={}, id='run--778846c9-acd3-43b7-b9c0-ac718761b2bc')}, 'run_id': '778846c9-acd3-43b7-b9c0-ac718761b2bc', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-7-sonnet-20250219', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['3e4f8c37-a44a-46b7-a7e5-75182d1cca31']}\n", + "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='json\\n{\\n \"countries\": [', additional_kwargs={}, response_metadata={}, id='run--778846c9-acd3-43b7-b9c0-ac718761b2bc')}, 'run_id': '778846c9-acd3-43b7-b9c0-ac718761b2bc', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-7-sonnet-20250219', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['3e4f8c37-a44a-46b7-a7e5-75182d1cca31']}\n", + "{'event': 'on_parser_stream', 'run_id': '2c46d24f-231c-4062-a7ab-b7954840986d', 'name': 'JsonOutputParser', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}, 'data': {'chunk': {'countries': []}}, 'parent_ids': ['3e4f8c37-a44a-46b7-a7e5-75182d1cca31']}\n", + "{'event': 'on_chain_stream', 'run_id': '3e4f8c37-a44a-46b7-a7e5-75182d1cca31', 'name': 'RunnableSequence', 'tags': ['my_chain'], 'metadata': {}, 'data': {'chunk': {'countries': []}}, 'parent_ids': []}\n", + "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n {\\n \"name\": \"France\",', additional_kwargs={}, response_metadata={}, id='run--778846c9-acd3-43b7-b9c0-ac718761b2bc')}, 'run_id': '778846c9-acd3-43b7-b9c0-ac718761b2bc', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-7-sonnet-20250219', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['3e4f8c37-a44a-46b7-a7e5-75182d1cca31']}\n", + "{'event': 'on_parser_stream', 'run_id': '2c46d24f-231c-4062-a7ab-b7954840986d', 'name': 'JsonOutputParser', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France'}]}}, 'parent_ids': ['3e4f8c37-a44a-46b7-a7e5-75182d1cca31']}\n", + "{'event': 'on_chain_stream', 'run_id': '3e4f8c37-a44a-46b7-a7e5-75182d1cca31', 'name': 'RunnableSequence', 'tags': ['my_chain'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France'}]}}, 'parent_ids': []}\n", "...\n" ] } @@ -1271,32 +1256,27 @@ "output_type": "stream", "text": [ "Chat model chunk: ''\n", - "Chat model chunk: '{'\n", - "Parser chunk: {}\n", - "Chat model chunk: '\\n \"countries'\n", - "Chat model chunk: '\": [\\n '\n", + "Chat model chunk: '```'\n", + "Chat model chunk: 'json\\n{\\n \"countries\": ['\n", "Parser chunk: {'countries': []}\n", - "Chat model chunk: '{\\n \"'\n", - "Parser chunk: {'countries': [{}]}\n", - "Chat model chunk: 'name\": \"France'\n", + "Chat model chunk: '\\n {\\n \"name\": \"France\",'\n", "Parser chunk: {'countries': [{'name': 'France'}]}\n", - "Chat model chunk: '\",\\n \"'\n", - "Chat model chunk: 'population\": 67'\n", - "Parser chunk: {'countries': [{'name': 'France', 'population': 67}]}\n", - "Chat model chunk: '413'\n", - "Parser chunk: {'countries': [{'name': 'France', 'population': 67413}]}\n", - "Chat model chunk: '000\\n },'\n", - "Parser chunk: {'countries': [{'name': 'France', 'population': 67413000}]}\n", - "Chat model chunk: '\\n {'\n", - "Parser chunk: {'countries': [{'name': 'France', 'population': 67413000}, {}]}\n", - "Chat model chunk: '\\n \"name\":'\n", - "Chat model chunk: ' \"Spain\",'\n", - "Parser chunk: {'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain'}]}\n", - "Chat model chunk: '\\n \"population\":'\n", - "Chat model chunk: ' 47'\n", - "Parser chunk: {'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47}]}\n", - "Chat model chunk: '351'\n", - "Parser chunk: {'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351}]}\n", + "Chat model chunk: '\\n \"population\": 67750'\n", + "Parser chunk: {'countries': [{'name': 'France', 'population': 67750}]}\n", + "Chat model chunk: '000\\n },\\n {\\n \"'\n", + "Parser chunk: {'countries': [{'name': 'France', 'population': 67750000}, {}]}\n", + "Chat model chunk: 'name\": \"Spain\",\\n \"population\":'\n", + "Parser chunk: {'countries': [{'name': 'France', 'population': 67750000}, {'name': 'Spain'}]}\n", + "Chat model chunk: ' 47350000\\n },'\n", + "Parser chunk: {'countries': [{'name': 'France', 'population': 67750000}, {'name': 'Spain', 'population': 47350000}]}\n", + "Chat model chunk: '\\n {\\n \"name\": \"Japan\",'\n", + "Parser chunk: {'countries': [{'name': 'France', 'population': 67750000}, {'name': 'Spain', 'population': 47350000}, {'name': 'Japan'}]}\n", + "Chat model chunk: '\\n \"population\": 125700'\n", + "Parser chunk: {'countries': [{'name': 'France', 'population': 67750000}, {'name': 'Spain', 'population': 47350000}, {'name': 'Japan', 'population': 125700}]}\n", + "Chat model chunk: '000\\n }\\n ]\\n}'\n", + "Parser chunk: {'countries': [{'name': 'France', 'population': 67750000}, {'name': 'Spain', 'population': 47350000}, {'name': 'Japan', 'population': 125700000}]}\n", + "Chat model chunk: '\\n```'\n", + "Chat model chunk: ''\n", "...\n" ] } @@ -1350,10 +1330,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'event': 'on_tool_start', 'data': {'input': 'hello'}, 'name': 'bad_tool', 'tags': [], 'run_id': 'ea900472-a8f7-425d-b627-facdef936ee8', 'metadata': {}}\n", - "{'event': 'on_chain_start', 'data': {'input': 'hello'}, 'name': 'reverse_word', 'tags': [], 'run_id': '77b01284-0515-48f4-8d7c-eb27c1882f86', 'metadata': {}}\n", - "{'event': 'on_chain_end', 'data': {'output': 'olleh', 'input': 'hello'}, 'run_id': '77b01284-0515-48f4-8d7c-eb27c1882f86', 'name': 'reverse_word', 'tags': [], 'metadata': {}}\n", - "{'event': 'on_tool_end', 'data': {'output': 'olleh'}, 'run_id': 'ea900472-a8f7-425d-b627-facdef936ee8', 'name': 'bad_tool', 'tags': [], 'metadata': {}}\n" + "{'event': 'on_tool_start', 'data': {'input': 'hello'}, 'name': 'bad_tool', 'tags': [], 'run_id': 'b1c6b79d-f94b-432f-a289-1ea68a7c3cea', 'metadata': {}, 'parent_ids': []}\n", + "{'event': 'on_chain_start', 'data': {'input': 'hello'}, 'name': 'reverse_word', 'tags': [], 'run_id': 'e661c1ec-e6d2-4f9a-9620-b50645f2b194', 'metadata': {}, 'parent_ids': ['b1c6b79d-f94b-432f-a289-1ea68a7c3cea']}\n", + "{'event': 'on_chain_end', 'data': {'output': 'olleh', 'input': 'hello'}, 'run_id': 'e661c1ec-e6d2-4f9a-9620-b50645f2b194', 'name': 'reverse_word', 'tags': [], 'metadata': {}, 'parent_ids': ['b1c6b79d-f94b-432f-a289-1ea68a7c3cea']}\n", + "{'event': 'on_tool_end', 'data': {'output': 'olleh'}, 'run_id': 'b1c6b79d-f94b-432f-a289-1ea68a7c3cea', 'name': 'bad_tool', 'tags': [], 'metadata': {}, 'parent_ids': []}\n" ] } ], @@ -1397,10 +1377,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'event': 'on_tool_start', 'data': {'input': 'hello'}, 'name': 'correct_tool', 'tags': [], 'run_id': 'd5ea83b9-9278-49cc-9f1d-aa302d671040', 'metadata': {}}\n", - "{'event': 'on_chain_start', 'data': {'input': 'hello'}, 'name': 'reverse_word', 'tags': [], 'run_id': '44dafbf4-2f87-412b-ae0e-9f71713810df', 'metadata': {}}\n", - "{'event': 'on_chain_end', 'data': {'output': 'olleh', 'input': 'hello'}, 'run_id': '44dafbf4-2f87-412b-ae0e-9f71713810df', 'name': 'reverse_word', 'tags': [], 'metadata': {}}\n", - "{'event': 'on_tool_end', 'data': {'output': 'olleh'}, 'run_id': 'd5ea83b9-9278-49cc-9f1d-aa302d671040', 'name': 'correct_tool', 'tags': [], 'metadata': {}}\n" + "{'event': 'on_tool_start', 'data': {'input': 'hello'}, 'name': 'correct_tool', 'tags': [], 'run_id': '399c91f5-a40b-4173-943f-a9c583a04003', 'metadata': {}, 'parent_ids': []}\n", + "{'event': 'on_chain_start', 'data': {'input': 'hello'}, 'name': 'reverse_word', 'tags': [], 'run_id': 'e9cc7db1-4587-40af-9c35-2d787b3f0956', 'metadata': {}, 'parent_ids': ['399c91f5-a40b-4173-943f-a9c583a04003']}\n", + "{'event': 'on_chain_end', 'data': {'output': 'olleh', 'input': 'hello'}, 'run_id': 'e9cc7db1-4587-40af-9c35-2d787b3f0956', 'name': 'reverse_word', 'tags': [], 'metadata': {}, 'parent_ids': ['399c91f5-a40b-4173-943f-a9c583a04003']}\n", + "{'event': 'on_tool_end', 'data': {'output': 'olleh'}, 'run_id': '399c91f5-a40b-4173-943f-a9c583a04003', 'name': 'correct_tool', 'tags': [], 'metadata': {}, 'parent_ids': []}\n" ] } ], @@ -1433,11 +1413,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'event': 'on_chain_start', 'data': {'input': '1234'}, 'name': 'reverse_and_double', 'tags': [], 'run_id': '03b0e6a1-3e60-42fc-8373-1e7829198d80', 'metadata': {}}\n", - "{'event': 'on_chain_start', 'data': {'input': '1234'}, 'name': 'reverse_word', 'tags': [], 'run_id': '5cf26fc8-840b-4642-98ed-623dda28707a', 'metadata': {}}\n", - "{'event': 'on_chain_end', 'data': {'output': '4321', 'input': '1234'}, 'run_id': '5cf26fc8-840b-4642-98ed-623dda28707a', 'name': 'reverse_word', 'tags': [], 'metadata': {}}\n", - "{'event': 'on_chain_stream', 'data': {'chunk': '43214321'}, 'run_id': '03b0e6a1-3e60-42fc-8373-1e7829198d80', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}}\n", - "{'event': 'on_chain_end', 'data': {'output': '43214321'}, 'run_id': '03b0e6a1-3e60-42fc-8373-1e7829198d80', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}}\n" + "{'event': 'on_chain_start', 'data': {'input': '1234'}, 'name': 'reverse_and_double', 'tags': [], 'run_id': '04726e2e-f508-4f90-9d4f-f88e588f0b39', 'metadata': {}, 'parent_ids': []}\n", + "{'event': 'on_chain_stream', 'run_id': '04726e2e-f508-4f90-9d4f-f88e588f0b39', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}, 'data': {'chunk': '43214321'}, 'parent_ids': []}\n", + "{'event': 'on_chain_end', 'data': {'output': '43214321'}, 'run_id': '04726e2e-f508-4f90-9d4f-f88e588f0b39', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}, 'parent_ids': []}\n" ] } ], @@ -1475,11 +1453,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'event': 'on_chain_start', 'data': {'input': '1234'}, 'name': 'reverse_and_double', 'tags': [], 'run_id': '1bfcaedc-f4aa-4d8e-beee-9bba6ef17008', 'metadata': {}}\n", - "{'event': 'on_chain_start', 'data': {'input': '1234'}, 'name': 'reverse_word', 'tags': [], 'run_id': '64fc99f0-5d7d-442b-b4f5-4537129f67d1', 'metadata': {}}\n", - "{'event': 'on_chain_end', 'data': {'output': '4321', 'input': '1234'}, 'run_id': '64fc99f0-5d7d-442b-b4f5-4537129f67d1', 'name': 'reverse_word', 'tags': [], 'metadata': {}}\n", - "{'event': 'on_chain_stream', 'data': {'chunk': '43214321'}, 'run_id': '1bfcaedc-f4aa-4d8e-beee-9bba6ef17008', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}}\n", - "{'event': 'on_chain_end', 'data': {'output': '43214321'}, 'run_id': '1bfcaedc-f4aa-4d8e-beee-9bba6ef17008', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}}\n" + "{'event': 'on_chain_start', 'data': {'input': '1234'}, 'name': 'reverse_and_double', 'tags': [], 'run_id': '25f72976-aa79-408d-bb42-6d0f038cde52', 'metadata': {}, 'parent_ids': []}\n", + "{'event': 'on_chain_stream', 'run_id': '25f72976-aa79-408d-bb42-6d0f038cde52', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}, 'data': {'chunk': '43214321'}, 'parent_ids': []}\n", + "{'event': 'on_chain_end', 'data': {'output': '43214321'}, 'run_id': '25f72976-aa79-408d-bb42-6d0f038cde52', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}, 'parent_ids': []}\n" ] } ], @@ -1513,7 +1489,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "langchain", "language": "python", "name": "python3" }, @@ -1527,7 +1503,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.10.16" } }, "nbformat": 4, diff --git a/docs/docs/how_to/tools_human.ipynb b/docs/docs/how_to/tools_human.ipynb index 7a827f057e0..a05201f7a0c 100644 --- a/docs/docs/how_to/tools_human.ipynb +++ b/docs/docs/how_to/tools_human.ipynb @@ -94,7 +94,7 @@ "\n", "from langchain_anthropic import ChatAnthropic\n", "\n", - "llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=0)" + "llm = ChatAnthropic(model=\"claude-3-7-sonnet-20250219\", temperature=0)" ] }, { @@ -108,11 +108,12 @@ "text/plain": [ "[{'name': 'count_emails',\n", " 'args': {'last_n_days': 5},\n", - " 'id': 'toolu_01QYZdJ4yPiqsdeENWHqioFW',\n", + " 'id': 'toolu_01XrE4AU9QLo4imbriDDkmXm',\n", + " 'type': 'tool_call',\n", " 'output': 10}]" ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -299,7 +300,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "langchain", "language": "python", "name": "python3" }, @@ -313,7 +314,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.16" } }, "nbformat": 4, diff --git a/docs/docs/integrations/chat/google_generative_ai.ipynb b/docs/docs/integrations/chat/google_generative_ai.ipynb index ab9b0e2c2eb..8620197c9d8 100644 --- a/docs/docs/integrations/chat/google_generative_ai.ipynb +++ b/docs/docs/integrations/chat/google_generative_ai.ipynb @@ -19,7 +19,7 @@ "\n", "Access Google's Generative AI models, including the Gemini family, directly via the Gemini API or experiment rapidly using Google AI Studio. The `langchain-google-genai` package provides the LangChain integration for these models. This is often the best starting point for individual developers.\n", "\n", - "For information on the latest models, their features, context windows, etc. head to the [Google AI docs](https://ai.google.dev/gemini-api/docs/models/gemini). All examples use the `gemini-2.0-flash` model. Gemini 2.5 Pro and 2.5 Flash can be used via `gemini-2.5-pro-preview-03-25` and `gemini-2.5-flash-preview-04-17`. All model ids can be found in the [Gemini API docs](https://ai.google.dev/gemini-api/docs/models).\n", + "For information on the latest models, their features, context windows, etc. head to the [Google AI docs](https://ai.google.dev/gemini-api/docs/models/gemini). All model ids can be found in the [Gemini API docs](https://ai.google.dev/gemini-api/docs/models).\n", "\n", "### Integration details\n", "\n", @@ -117,7 +117,7 @@ "from langchain_google_genai import ChatGoogleGenerativeAI\n", "\n", "llm = ChatGoogleGenerativeAI(\n", - " model=\"gemini-2.0-flash\",\n", + " model=\"gemini-2.5-flash\",\n", " temperature=0,\n", " max_tokens=None,\n", " timeout=None,\n", @@ -242,7 +242,7 @@ "\n", "### Image Input\n", "\n", - "Provide image inputs along with text using a `HumanMessage` with a list content format. The `gemini-2.0-flash` model can handle images." + "Provide image inputs along with text using a `HumanMessage` with a list content format. Make sure to use a model that supports image input, such as `gemini-2.5-flash`." ] }, { @@ -297,7 +297,7 @@ "\n", "### Audio Input\n", "\n", - "Provide audio file inputs along with text. Use a model like `gemini-2.0-flash`." + "Provide audio file inputs along with text." ] }, { @@ -340,7 +340,7 @@ "source": [ "### Video Input\n", "\n", - "Provide video file inputs along with text. Use a model like `gemini-2.0-flash`." + "Provide video file inputs along with text." ] }, { @@ -384,7 +384,7 @@ "source": [ "### Image Generation (Multimodal Output)\n", "\n", - "The `gemini-2.0-flash` model can generate text and images inline (image generation is experimental). You need to specify the desired `response_modalities`." + "Certain models (such as `gemini-2.0-flash-preview-image-generation`) can generate text and images inline. You need to specify the desired `response_modalities`. See more information on the [Gemini API docs](https://ai.google.dev/gemini-api/docs/image-generation) for details." ] }, { @@ -830,7 +830,7 @@ "source": [ "from langchain_google_genai import ChatGoogleGenerativeAI\n", "\n", - "llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash\")\n", + "llm = ChatGoogleGenerativeAI(model=\"gemini-2.5-flash\")\n", "\n", "\n", "async def run_async_calls():\n", @@ -900,7 +900,7 @@ "source": [ "## API reference\n", "\n", - "For detailed documentation of all ChatGoogleGenerativeAI features and configurations head to the API reference: https://python.langchain.com/api_reference/google_genai/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html" + "For detailed documentation of all ChatGoogleGenerativeAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_genai/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html)." ] } ], diff --git a/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb b/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb index 11f0fcac65e..b86f949aa40 100644 --- a/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb +++ b/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb @@ -19,7 +19,7 @@ "\n", "This page provides a quick overview for getting started with VertexAI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatVertexAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html).\n", "\n", - "ChatVertexAI exposes all foundational models available in Google Cloud, like `gemini-1.5-pro`, `gemini-1.5-flash`, etc. For a full and updated list of available models visit [VertexAI documentation](https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/overview).\n", + "ChatVertexAI exposes all foundational models available in Google Cloud, like `gemini-2.5-pro`, `gemini-2.5-flash`, etc. For a full and updated list of available models visit [VertexAI documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/models).\n", "\n", ":::info Google Cloud VertexAI vs Google PaLM\n", "\n", @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de", "metadata": {}, "outputs": [], @@ -109,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae", "metadata": {}, "outputs": [], @@ -117,7 +117,7 @@ "from langchain_google_vertexai import ChatVertexAI\n", "\n", "llm = ChatVertexAI(\n", - " model=\"gemini-1.5-flash-001\",\n", + " model=\"gemini-2.5-flash\",\n", " temperature=0,\n", " max_tokens=None,\n", " max_retries=6,\n", @@ -210,7 +210,7 @@ "source": [ "from langchain_google_vertexai import ChatVertexAI\n", "\n", - "llm = ChatVertexAI(model=\"gemini-2.0-flash-001\").bind_tools([{\"google_search\": {}}])\n", + "llm = ChatVertexAI(model=\"gemini-2.5-flash\").bind_tools([{\"google_search\": {}}])\n", "\n", "response = llm.invoke(\"What is today's news?\")" ] @@ -237,7 +237,7 @@ "source": [ "from langchain_google_vertexai import ChatVertexAI\n", "\n", - "llm = ChatVertexAI(model=\"gemini-2.0-flash-001\").bind_tools([{\"code_execution\": {}}])\n", + "llm = ChatVertexAI(model=\"gemini-2.5-flash\").bind_tools([{\"code_execution\": {}}])\n", "\n", "response = llm.invoke(\"What is 3^3?\")" ] diff --git a/docs/docs/integrations/chat/index.mdx b/docs/docs/integrations/chat/index.mdx index fb43dcdc89b..14d8c88e7b9 100644 --- a/docs/docs/integrations/chat/index.mdx +++ b/docs/docs/integrations/chat/index.mdx @@ -37,4 +37,4 @@ import { CategoryTable, IndexTable } from "@theme/FeatureTables"; ## All chat models - \ No newline at end of file + diff --git a/docs/docs/integrations/graphs/tigergraph.mdx b/docs/docs/integrations/graphs/tigergraph.mdx index a9901459a0d..ebcb9331682 100644 --- a/docs/docs/integrations/graphs/tigergraph.mdx +++ b/docs/docs/integrations/graphs/tigergraph.mdx @@ -1,9 +1,9 @@ # TigerGraph >[TigerGraph](https://www.tigergraph.com/tigergraph-db/) is a natively distributed and high-performance graph database. -> The storage of data in a graph format of vertices and edges leads to rich relationships, +> The storage of data in a graph format of vertices and edges leads to rich relationships, > ideal for grouding LLM responses. - + A big example of the `TigerGraph` and `LangChain` integration [presented here](https://github.com/tigergraph/graph-ml-notebooks/blob/main/applications/large_language_models/TigerGraph_LangChain_Demo.ipynb). ## Installation and Setup diff --git a/docs/docs/integrations/llms/google_vertex_ai_palm.ipynb b/docs/docs/integrations/llms/google_vertex_ai_palm.ipynb index 624189296e8..0820687b64b 100644 --- a/docs/docs/integrations/llms/google_vertex_ai_palm.ipynb +++ b/docs/docs/integrations/llms/google_vertex_ai_palm.ipynb @@ -23,13 +23,9 @@ "\n", "**Note:** This is separate from the `Google Generative AI` integration, it exposes [Vertex AI Generative API](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview) on `Google Cloud`.\n", "\n", - "VertexAI exposes all foundational models available in google cloud:\n", - "- Gemini for Text ( `gemini-1.0-pro` )\n", - "- Gemini with Multimodality ( `gemini-1.5-pro-001` and `gemini-pro-vision`)\n", - "- Palm 2 for Text (`text-bison`)\n", - "- Codey for Code Generation (`code-bison`)\n", + "VertexAI exposes all foundational models available in google cloud.\n", "\n", - "For a full and updated list of available models visit [VertexAI documentation](https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/overview)" + "For a full and updated list of available models visit [VertexAI documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/models)" ] }, { @@ -47,7 +43,7 @@ "\n", "To use `Vertex AI Generative AI` you must have the `langchain-google-vertexai` Python package installed and either:\n", "- Have credentials configured for your environment (gcloud, workload identity, etc...)\n", - "- Store the path to a service account JSON file as the GOOGLE_APPLICATION_CREDENTIALS environment variable\n", + "- Store the path to a service account JSON file as the `GOOGLE_APPLICATION_CREDENTIALS` environment variable\n", "\n", "This codebase uses the `google.auth` library which first looks for the application credentials variable mentioned above, and then looks for system-level auth.\n", "\n", @@ -84,31 +80,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from langchain_google_vertexai import VertexAI\n", "\n", "# To use model\n", - "model = VertexAI(model_name=\"gemini-pro\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "NOTE : You can also specify a [Gemini Version](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versioning#gemini-model-versions)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# To specify a particular model version\n", - "model = VertexAI(model_name=\"gemini-1.0-pro-002\")" + "model = VertexAI(model_name=\"gemini-2.5-pro\")" ] }, { @@ -285,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -301,7 +280,7 @@ ], "source": [ "# You may also pass safety_settings to generate method\n", - "llm = VertexAI(model_name=\"gemini-1.0-pro-001\")\n", + "llm = VertexAI(model_name=\"gemini-2.5-pro\")\n", "\n", "# invoke a model response\n", "output = llm.invoke(\n", @@ -622,15 +601,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from langchain_core.messages import HumanMessage\n", "from langchain_google_vertexai import ChatVertexAI\n", "\n", - "# Use Gemini 1.5 Pro\n", - "llm = ChatVertexAI(model=\"gemini-1.5-pro-001\")" + "llm = ChatVertexAI(model=\"gemini-2.5-pro\")" ] }, { @@ -683,15 +661,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from langchain_core.messages import HumanMessage\n", "from langchain_google_vertexai import ChatVertexAI\n", "\n", - "# Use Gemini 1.5 Pro\n", - "llm = ChatVertexAI(model=\"gemini-1.5-pro-001\")" + "llm = ChatVertexAI(model=\"gemini-2.5-pro\")" ] }, { @@ -741,20 +718,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Using Audio with Gemini 1.5 Pro" + "### Using Audio with Gemini Models" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from langchain_core.messages import HumanMessage\n", "from langchain_google_vertexai import ChatVertexAI\n", "\n", - "# Use Gemini 1.5 Pro\n", - "llm = ChatVertexAI(model=\"gemini-1.5-pro-001\")" + "llm = ChatVertexAI(model=\"gemini-2.5-pro\")" ] }, { @@ -1226,9 +1202,6 @@ "metadata": {}, "source": [ "NOTE : Specify the correct [Claude 3 Model Versions](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/use-claude#claude-opus)\n", - "- For Claude 3 Opus (Preview), use `claude-3-opus@20240229`.\n", - "- For Claude 3 Sonnet, use `claude-3-sonnet@20240229`.\n", - "- For Claude 3 Haiku, use `claude-3-haiku@20240307`.\n", "\n", "We don't recommend using the Anthropic Claude 3 model versions that don't include a suffix that starts with an @ symbol (claude-3-opus, claude-3-sonnet, or claude-3-haiku)." ] diff --git a/docs/docs/integrations/llms/layerup_security.mdx b/docs/docs/integrations/llms/layerup_security.mdx index 6beee532090..b71ff76c2aa 100644 --- a/docs/docs/integrations/llms/layerup_security.mdx +++ b/docs/docs/integrations/llms/layerup_security.mdx @@ -82,4 +82,4 @@ layerup_security = LayerupSecurity( response = layerup_security.invoke( "Summarize this message: my name is Bob Dylan. My SSN is 123-45-6789." ) -``` \ No newline at end of file +``` diff --git a/docs/docs/integrations/providers/acreom.mdx b/docs/docs/integrations/providers/acreom.mdx index 78987870a2d..d819ad40002 100644 --- a/docs/docs/integrations/providers/acreom.mdx +++ b/docs/docs/integrations/providers/acreom.mdx @@ -4,7 +4,7 @@ ## Installation and Setup -No installation is required. +No installation is required. ## Document Loader diff --git a/docs/docs/integrations/providers/activeloop_deeplake.mdx b/docs/docs/integrations/providers/activeloop_deeplake.mdx index f0bcb60afd6..f051a3ae11a 100644 --- a/docs/docs/integrations/providers/activeloop_deeplake.mdx +++ b/docs/docs/integrations/providers/activeloop_deeplake.mdx @@ -1,6 +1,6 @@ # Activeloop Deep Lake ->[Activeloop Deep Lake](https://docs.activeloop.ai/) is a data lake for Deep Learning applications, allowing you to use it +>[Activeloop Deep Lake](https://docs.activeloop.ai/) is a data lake for Deep Learning applications, allowing you to use it > as a vector store. ## Why Deep Lake? diff --git a/docs/docs/integrations/providers/ads4gpts.mdx b/docs/docs/integrations/providers/ads4gpts.mdx index fd82f96f8eb..05f597fe53e 100644 --- a/docs/docs/integrations/providers/ads4gpts.mdx +++ b/docs/docs/integrations/providers/ads4gpts.mdx @@ -1,6 +1,6 @@ # ADS4GPTs -> [ADS4GPTs](https://www.ads4gpts.com/) is building the open monetization backbone of the AI-Native internet. It helps AI applications monetize through advertising with a UX and Privacy first approach. +> [ADS4GPTs](https://www.ads4gpts.com/) is building the open monetization backbone of the AI-Native internet. It helps AI applications monetize through advertising with a UX and Privacy first approach. ## Installation and Setup diff --git a/docs/docs/integrations/providers/ai21.mdx b/docs/docs/integrations/providers/ai21.mdx index 8d3c4124ef1..6fd10704d09 100644 --- a/docs/docs/integrations/providers/ai21.mdx +++ b/docs/docs/integrations/providers/ai21.mdx @@ -1,7 +1,7 @@ # AI21 Labs ->[AI21 Labs](https://www.ai21.com/about) is a company specializing in Natural -> Language Processing (NLP), which develops AI systems +>[AI21 Labs](https://www.ai21.com/about) is a company specializing in Natural +> Language Processing (NLP), which develops AI systems > that can understand and generate natural language. This page covers how to use the `AI21` ecosystem within `LangChain`. @@ -17,7 +17,7 @@ pip install langchain-ai21 ## Chat models -### AI21 Chat +### AI21 Chat See a [usage example](/docs/integrations/chat/ai21). @@ -27,7 +27,7 @@ from langchain_ai21 import ChatAI21 ## Deprecated features -:::caution The following features are deprecated. +:::caution The following features are deprecated. ::: ### AI21 LLM @@ -48,4 +48,4 @@ from langchain_ai21 import AI21ContextualAnswers ```python from langchain_ai21 import AI21SemanticTextSplitter -``` \ No newline at end of file +``` diff --git a/docs/docs/integrations/providers/ainetwork.mdx b/docs/docs/integrations/providers/ainetwork.mdx index fdd8393e23c..ef3413be0b1 100644 --- a/docs/docs/integrations/providers/ainetwork.mdx +++ b/docs/docs/integrations/providers/ainetwork.mdx @@ -1,7 +1,7 @@ # AINetwork ->[AI Network](https://www.ainetwork.ai/build-on-ain) is a layer 1 blockchain designed to accommodate -> large-scale AI models, utilizing a decentralized GPU network powered by the +>[AI Network](https://www.ainetwork.ai/build-on-ain) is a layer 1 blockchain designed to accommodate +> large-scale AI models, utilizing a decentralized GPU network powered by the > [$AIN token](https://www.ainetwork.ai/token), enriching AI-driven `NFTs` (`AINFTs`). diff --git a/docs/docs/integrations/providers/airbyte.mdx b/docs/docs/integrations/providers/airbyte.mdx index f1198b14861..ef0419e57c6 100644 --- a/docs/docs/integrations/providers/airbyte.mdx +++ b/docs/docs/integrations/providers/airbyte.mdx @@ -1,6 +1,6 @@ # Airbyte ->[Airbyte](https://github.com/airbytehq/airbyte) is a data integration platform for ELT pipelines from APIs, +>[Airbyte](https://github.com/airbytehq/airbyte) is a data integration platform for ELT pipelines from APIs, > databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases. ## Installation and Setup diff --git a/docs/docs/integrations/providers/alibaba_cloud.mdx b/docs/docs/integrations/providers/alibaba_cloud.mdx index b6d2718fac1..24846e36b88 100644 --- a/docs/docs/integrations/providers/alibaba_cloud.mdx +++ b/docs/docs/integrations/providers/alibaba_cloud.mdx @@ -1,15 +1,15 @@ # Alibaba Cloud >[Alibaba Group Holding Limited (Wikipedia)](https://en.wikipedia.org/wiki/Alibaba_Group), or `Alibaba` -> (Chinese: 阿里巴巴), is a Chinese multinational technology company specializing in e-commerce, retail, +> (Chinese: 阿里巴巴), is a Chinese multinational technology company specializing in e-commerce, retail, > Internet, and technology. -> +> > [Alibaba Cloud (Wikipedia)](https://en.wikipedia.org/wiki/Alibaba_Cloud), also known as `Aliyun` -> (Chinese: 阿里云; pinyin: Ālǐyún; lit. 'Ali Cloud'), is a cloud computing company, a subsidiary -> of `Alibaba Group`. `Alibaba Cloud` provides cloud computing services to online businesses and +> (Chinese: 阿里云; pinyin: Ālǐyún; lit. 'Ali Cloud'), is a cloud computing company, a subsidiary +> of `Alibaba Group`. `Alibaba Cloud` provides cloud computing services to online businesses and > Alibaba's own e-commerce ecosystem. - - + + ## LLMs ### Alibaba Cloud PAI EAS @@ -104,4 +104,4 @@ See [installation instructions and a usage example](/docs/integrations/vectorsto ```python from langchain_community.vectorstores import TablestoreVectorStore -``` \ No newline at end of file +``` diff --git a/docs/docs/integrations/providers/analyticdb.mdx b/docs/docs/integrations/providers/analyticdb.mdx index 7a9e551075e..a48952f5632 100644 --- a/docs/docs/integrations/providers/analyticdb.mdx +++ b/docs/docs/integrations/providers/analyticdb.mdx @@ -1,15 +1,15 @@ # AnalyticDB ->[AnalyticDB for PostgreSQL](https://www.alibabacloud.com/help/en/analyticdb-for-postgresql/latest/product-introduction-overview) -> is a massively parallel processing (MPP) data warehousing service +>[AnalyticDB for PostgreSQL](https://www.alibabacloud.com/help/en/analyticdb-for-postgresql/latest/product-introduction-overview) +> is a massively parallel processing (MPP) data warehousing service > from [Alibaba Cloud](https://www.alibabacloud.com/) >that is designed to analyze large volumes of data online. ->`AnalyticDB for PostgreSQL` is developed based on the open-source `Greenplum Database` -> project and is enhanced with in-depth extensions by `Alibaba Cloud`. AnalyticDB -> for PostgreSQL is compatible with the ANSI SQL 2003 syntax and the PostgreSQL and -> Oracle database ecosystems. AnalyticDB for PostgreSQL also supports row store and -> column store. AnalyticDB for PostgreSQL processes petabytes of data offline at a +>`AnalyticDB for PostgreSQL` is developed based on the open-source `Greenplum Database` +> project and is enhanced with in-depth extensions by `Alibaba Cloud`. AnalyticDB +> for PostgreSQL is compatible with the ANSI SQL 2003 syntax and the PostgreSQL and +> Oracle database ecosystems. AnalyticDB for PostgreSQL also supports row store and +> column store. AnalyticDB for PostgreSQL processes petabytes of data offline at a > high performance level and supports highly concurrent. This page covers how to use the AnalyticDB ecosystem within LangChain. diff --git a/docs/docs/integrations/providers/annoy.mdx b/docs/docs/integrations/providers/annoy.mdx index 18a86fbfa39..545a2a2f15a 100644 --- a/docs/docs/integrations/providers/annoy.mdx +++ b/docs/docs/integrations/providers/annoy.mdx @@ -1,9 +1,9 @@ # Annoy -> [Annoy](https://github.com/spotify/annoy) (`Approximate Nearest Neighbors Oh Yeah`) -> is a C++ library with Python bindings to search for points in space that are -> close to a given query point. It also creates large read-only file-based data -> structures that are mapped into memory so that many processes may share the same data. +> [Annoy](https://github.com/spotify/annoy) (`Approximate Nearest Neighbors Oh Yeah`) +> is a C++ library with Python bindings to search for points in space that are +> close to a given query point. It also creates large read-only file-based data +> structures that are mapped into memory so that many processes may share the same data. ## Installation and Setup diff --git a/docs/docs/integrations/providers/anthropic.mdx b/docs/docs/integrations/providers/anthropic.mdx index dfa9340f6ec..5b107a03337 100644 --- a/docs/docs/integrations/providers/anthropic.mdx +++ b/docs/docs/integrations/providers/anthropic.mdx @@ -31,7 +31,7 @@ model = ChatAnthropic(model='claude-3-opus-20240229') ### [Legacy] AnthropicLLM -**NOTE**: `AnthropicLLM` only supports legacy `Claude 2` models. +**NOTE**: `AnthropicLLM` only supports legacy `Claude 2` models. To use the newest `Claude 3` models, please use `ChatAnthropic` instead. See a [usage example](/docs/integrations/llms/anthropic). diff --git a/docs/docs/integrations/providers/anyscale.mdx b/docs/docs/integrations/providers/anyscale.mdx index 8b35f0490e3..dda08e598bf 100644 --- a/docs/docs/integrations/providers/anyscale.mdx +++ b/docs/docs/integrations/providers/anyscale.mdx @@ -3,12 +3,12 @@ >[Anyscale](https://www.anyscale.com) is a platform to run, fine tune and scale LLMs via production-ready APIs. > [Anyscale Endpoints](https://docs.anyscale.com/endpoints/overview) serve many open-source models in a cost-effective way. -`Anyscale` also provides [an example](https://docs.anyscale.com/endpoints/model-serving/examples/langchain-integration) +`Anyscale` also provides [an example](https://docs.anyscale.com/endpoints/model-serving/examples/langchain-integration) how to setup `LangChain` with `Anyscale` for advanced chat agents. ## Installation and Setup -- Get an Anyscale Service URL, route and API key and set them as environment variables (`ANYSCALE_SERVICE_URL`,`ANYSCALE_SERVICE_ROUTE`, `ANYSCALE_SERVICE_TOKEN`). +- Get an Anyscale Service URL, route and API key and set them as environment variables (`ANYSCALE_SERVICE_URL`,`ANYSCALE_SERVICE_ROUTE`, `ANYSCALE_SERVICE_TOKEN`). - Please see [the Anyscale docs](https://www.anyscale.com/get-started) for more details. We have to install the `openai` package: diff --git a/docs/docs/integrations/providers/apache.mdx b/docs/docs/integrations/providers/apache.mdx index 6acb7156111..0dd30866a35 100644 --- a/docs/docs/integrations/providers/apache.mdx +++ b/docs/docs/integrations/providers/apache.mdx @@ -1,63 +1,63 @@ # Apache Software Foundation ->[The Apache Software Foundation (Wikipedia)](https://en.wikipedia.org/wiki/The_Apache_Software_Foundation) -> is a decentralized open source community of developers. The software they -> produce is distributed under the terms of the Apache License, a permissive -> open-source license for free and open-source software (FOSS). The Apache projects -> are characterized by a collaborative, consensus-based development process -> and an open and pragmatic software license, which is to say that it -> allows developers, who receive the software freely, to redistribute -> it under non-free terms. Each project is managed by a self-selected +>[The Apache Software Foundation (Wikipedia)](https://en.wikipedia.org/wiki/The_Apache_Software_Foundation) +> is a decentralized open source community of developers. The software they +> produce is distributed under the terms of the Apache License, a permissive +> open-source license for free and open-source software (FOSS). The Apache projects +> are characterized by a collaborative, consensus-based development process +> and an open and pragmatic software license, which is to say that it +> allows developers, who receive the software freely, to redistribute +> it under non-free terms. Each project is managed by a self-selected > team of technical experts who are active contributors to the project. ## Apache AGE ->[Apache AGE](https://age.apache.org/) is a `PostgreSQL` extension that provides -> graph database functionality. `AGE` is an acronym for `A Graph Extension`, and -> is inspired by Bitnine’s fork of `PostgreSQL 10`, `AgensGraph`, which is -> a multimodal database. The goal of the project is to create single -> storage that can handle both relational and graph model data so that users -> can use standard ANSI SQL along with `openCypher`, the Graph query language. -> The data elements `Apache AGE` stores are nodes, edges connecting them, and +>[Apache AGE](https://age.apache.org/) is a `PostgreSQL` extension that provides +> graph database functionality. `AGE` is an acronym for `A Graph Extension`, and +> is inspired by Bitnine’s fork of `PostgreSQL 10`, `AgensGraph`, which is +> a multimodal database. The goal of the project is to create single +> storage that can handle both relational and graph model data so that users +> can use standard ANSI SQL along with `openCypher`, the Graph query language. +> The data elements `Apache AGE` stores are nodes, edges connecting them, and > attributes of nodes and edges. - + See more about [integrating with Apache AGE](/docs/integrations/graphs/apache_age). ## Apache Cassandra ->[Apache Cassandra](https://cassandra.apache.org/) is a NoSQL, row-oriented, -> highly scalable and highly available database. Starting with version 5.0, +>[Apache Cassandra](https://cassandra.apache.org/) is a NoSQL, row-oriented, +> highly scalable and highly available database. Starting with version 5.0, > the database ships with vector search capabilities. - + See more about [integrating with Apache Cassandra](/docs/integrations/providers/cassandra/). ## Apache Doris ->[Apache Doris](https://doris.apache.org/) is a modern data warehouse for +>[Apache Doris](https://doris.apache.org/) is a modern data warehouse for > real-time analytics. It delivers lightning-fast analytics on real-time data at scale. > ->Usually `Apache Doris` is categorized into OLAP, and it has showed excellent -> performance in ClickBench — a Benchmark For Analytical DBMS. Since it has +>Usually `Apache Doris` is categorized into OLAP, and it has showed excellent +> performance in ClickBench — a Benchmark For Analytical DBMS. Since it has > a super-fast vectorized execution engine, it could also be used as a fast vectordb. - + See more about [integrating with Apache Doris](/docs/integrations/providers/apache_doris/). ## Apache Kafka ->[Apache Kafka](https://github.com/apache/kafka) is a distributed messaging system +>[Apache Kafka](https://github.com/apache/kafka) is a distributed messaging system > that is used to publish and subscribe to streams of records. - + See more about [integrating with Apache Kafka](/docs/integrations/memory/kafka_chat_message_history). ## Apache Spark ->[Apache Spark](https://spark.apache.org/) is a unified analytics engine for -> large-scale data processing. It provides high-level APIs in Scala, Java, -> Python, and R, and an optimized engine that supports general computation -> graphs for data analysis. It also supports a rich set of higher-level -> tools including `Spark SQL` for SQL and DataFrames, `pandas API on Spark` -> for pandas workloads, `MLlib` for machine learning, +>[Apache Spark](https://spark.apache.org/) is a unified analytics engine for +> large-scale data processing. It provides high-level APIs in Scala, Java, +> Python, and R, and an optimized engine that supports general computation +> graphs for data analysis. It also supports a rich set of higher-level +> tools including `Spark SQL` for SQL and DataFrames, `pandas API on Spark` +> for pandas workloads, `MLlib` for machine learning, > `GraphX` for graph processing, and `Structured Streaming` for stream processing. See more about [integrating with Apache Spark](/docs/integrations/providers/spark). diff --git a/docs/docs/integrations/providers/apache_doris.mdx b/docs/docs/integrations/providers/apache_doris.mdx index 9beee729f33..1b5b3b62511 100644 --- a/docs/docs/integrations/providers/apache_doris.mdx +++ b/docs/docs/integrations/providers/apache_doris.mdx @@ -3,8 +3,8 @@ >[Apache Doris](https://doris.apache.org/) is a modern data warehouse for real-time analytics. It delivers lightning-fast analytics on real-time data at scale. ->Usually `Apache Doris` is categorized into OLAP, and it has showed excellent performance -> in [ClickBench — a Benchmark For Analytical DBMS](https://benchmark.clickhouse.com/). +>Usually `Apache Doris` is categorized into OLAP, and it has showed excellent performance +> in [ClickBench — a Benchmark For Analytical DBMS](https://benchmark.clickhouse.com/). > Since it has a super-fast vectorized execution engine, it could also be used as a fast vectordb. ## Installation and Setup diff --git a/docs/docs/integrations/providers/apple.mdx b/docs/docs/integrations/providers/apple.mdx index 5a87afeb6c5..17e45493df9 100644 --- a/docs/docs/integrations/providers/apple.mdx +++ b/docs/docs/integrations/providers/apple.mdx @@ -1,10 +1,10 @@ # Apple ->[Apple Inc. (Wikipedia)](https://en.wikipedia.org/wiki/Apple_Inc.) is an American +>[Apple Inc. (Wikipedia)](https://en.wikipedia.org/wiki/Apple_Inc.) is an American > multinational corporation and technology company. > -> [iMessage (Wikipedia)](https://en.wikipedia.org/wiki/IMessage) is an instant -> messaging service developed by Apple Inc. and launched in 2011. +> [iMessage (Wikipedia)](https://en.wikipedia.org/wiki/IMessage) is an instant +> messaging service developed by Apple Inc. and launched in 2011. > `iMessage` functions exclusively on Apple platforms. ## Installation and Setup diff --git a/docs/docs/integrations/providers/arangodb.mdx b/docs/docs/integrations/providers/arangodb.mdx index ff2d312fa9e..3bb88a0f8ca 100644 --- a/docs/docs/integrations/providers/arangodb.mdx +++ b/docs/docs/integrations/providers/arangodb.mdx @@ -1,6 +1,6 @@ # ArangoDB ->[ArangoDB](https://github.com/arangodb/arangodb) is a scalable graph database system to +>[ArangoDB](https://github.com/arangodb/arangodb) is a scalable graph database system to > drive value from connected data, faster. Native graphs, an integrated search engine, and JSON support, via a single query language. ArangoDB runs on-prem, in the cloud – anywhere. ## Installation and Setup @@ -13,7 +13,7 @@ pip install python-arango ## Graph QA Chain -Connect your `ArangoDB` Database with a chat model to get insights on your data. +Connect your `ArangoDB` Database with a chat model to get insights on your data. See the notebook example [here](/docs/integrations/graphs/arangodb). diff --git a/docs/docs/integrations/providers/arcee.mdx b/docs/docs/integrations/providers/arcee.mdx index b685dd9b2d7..9d3565741a5 100644 --- a/docs/docs/integrations/providers/arcee.mdx +++ b/docs/docs/integrations/providers/arcee.mdx @@ -1,10 +1,10 @@ # Arcee ->[Arcee](https://www.arcee.ai/about/about-us) enables the development and advancement -> of what we coin as SLMs—small, specialized, secure, and scalable language models. -> By offering a SLM Adaptation System and a seamless, secure integration, -> `Arcee` empowers enterprises to harness the full potential of -> domain-adapted language models, driving the transformative +>[Arcee](https://www.arcee.ai/about/about-us) enables the development and advancement +> of what we coin as SLMs—small, specialized, secure, and scalable language models. +> By offering a SLM Adaptation System and a seamless, secure integration, +> `Arcee` empowers enterprises to harness the full potential of +> domain-adapted language models, driving the transformative > innovation in operations. diff --git a/docs/docs/integrations/providers/arcgis.mdx b/docs/docs/integrations/providers/arcgis.mdx index c7a00fd7ffc..9115d5e1b48 100644 --- a/docs/docs/integrations/providers/arcgis.mdx +++ b/docs/docs/integrations/providers/arcgis.mdx @@ -1,14 +1,14 @@ # ArcGIS ->[ArcGIS](https://www.esri.com/en-us/arcgis/about-arcgis/overview) is a family of client, +>[ArcGIS](https://www.esri.com/en-us/arcgis/about-arcgis/overview) is a family of client, > server and online geographic information system software developed and maintained by [Esri](https://www.esri.com/). -> +> >`ArcGISLoader` uses the `arcgis` package. -> `arcgis` is a Python library for the vector and raster analysis, geocoding, map making, -> routing and directions. It administers, organizes and manages users, +> `arcgis` is a Python library for the vector and raster analysis, geocoding, map making, +> routing and directions. It administers, organizes and manages users, > groups and information items in your GIS. ->It enables access to ready-to-use maps and curated geographic data from `Esri` -> and other authoritative sources, and works with your own data as well. +>It enables access to ready-to-use maps and curated geographic data from `Esri` +> and other authoritative sources, and works with your own data as well. ## Installation and Setup diff --git a/docs/docs/integrations/providers/argilla.mdx b/docs/docs/integrations/providers/argilla.mdx index fc4232e0ec9..4f78ed39fc0 100644 --- a/docs/docs/integrations/providers/argilla.mdx +++ b/docs/docs/integrations/providers/argilla.mdx @@ -1,8 +1,8 @@ # Argilla ->[Argilla](https://argilla.io/) is an open-source data curation platform for LLMs. -> Using `Argilla`, everyone can build robust language models through faster data curation -> using both human and machine feedback. `Argilla` provides support for each step in the MLOps cycle, +>[Argilla](https://argilla.io/) is an open-source data curation platform for LLMs. +> Using `Argilla`, everyone can build robust language models through faster data curation +> using both human and machine feedback. `Argilla` provides support for each step in the MLOps cycle, > from data labeling to model monitoring. ## Installation and Setup diff --git a/docs/docs/integrations/providers/arxiv.mdx b/docs/docs/integrations/providers/arxiv.mdx index 7fabf7396c1..0ffd7acd719 100644 --- a/docs/docs/integrations/providers/arxiv.mdx +++ b/docs/docs/integrations/providers/arxiv.mdx @@ -1,7 +1,7 @@ # Arxiv ->[arXiv](https://arxiv.org/) is an open-access archive for 2 million scholarly articles in the fields of physics, -> mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and +>[arXiv](https://arxiv.org/) is an open-access archive for 2 million scholarly articles in the fields of physics, +> mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and > systems science, and economics. diff --git a/docs/docs/integrations/providers/asknews.mdx b/docs/docs/integrations/providers/asknews.mdx index 1aa6dd81e4a..c82285a94b3 100644 --- a/docs/docs/integrations/providers/asknews.mdx +++ b/docs/docs/integrations/providers/asknews.mdx @@ -12,7 +12,7 @@ First, you need to install `asknews` python package. pip install asknews ``` -You also need to set our AskNews API credentials, which can be generated at +You also need to set our AskNews API credentials, which can be generated at the [AskNews console](https://my.asknews.app/). diff --git a/docs/docs/integrations/providers/assemblyai.mdx b/docs/docs/integrations/providers/assemblyai.mdx index dc666f2fc36..347f7909372 100644 --- a/docs/docs/integrations/providers/assemblyai.mdx +++ b/docs/docs/integrations/providers/assemblyai.mdx @@ -1,11 +1,11 @@ # AssemblyAI ->[AssemblyAI](https://www.assemblyai.com/) builds `Speech AI` models for tasks like +>[AssemblyAI](https://www.assemblyai.com/) builds `Speech AI` models for tasks like speech-to-text, speaker diarization, speech summarization, and more. -> `AssemblyAI’s` Speech AI models include accurate speech-to-text for voice data -> (such as calls, virtual meetings, and podcasts), speaker detection, sentiment analysis, +> `AssemblyAI’s` Speech AI models include accurate speech-to-text for voice data +> (such as calls, virtual meetings, and podcasts), speaker detection, sentiment analysis, > chapter detection, PII redaction. - + ## Installation and Setup @@ -22,7 +22,7 @@ pip install -U assemblyai ### AssemblyAI Audio Transcript -The `AssemblyAIAudioTranscriptLoader` transcribes audio files with the `AssemblyAI API` +The `AssemblyAIAudioTranscriptLoader` transcribes audio files with the `AssemblyAI API` and loads the transcribed text into documents. See a [usage example](/docs/integrations/document_loaders/assemblyai). @@ -33,8 +33,8 @@ from langchain_community.document_loaders import AssemblyAIAudioTranscriptLoader ### AssemblyAI Audio Loader By Id -The `AssemblyAIAudioLoaderById` uses the AssemblyAI API to get an existing -transcription and loads the transcribed text into one or more Documents, +The `AssemblyAIAudioLoaderById` uses the AssemblyAI API to get an existing +transcription and loads the transcribed text into one or more Documents, depending on the specified format. ```python diff --git a/docs/docs/integrations/providers/atlas.mdx b/docs/docs/integrations/providers/atlas.mdx index 06545aca112..ec2471b74cf 100644 --- a/docs/docs/integrations/providers/atlas.mdx +++ b/docs/docs/integrations/providers/atlas.mdx @@ -1,6 +1,6 @@ # Atlas ->[Nomic Atlas](https://docs.nomic.ai/index.html) is a platform for interacting with both +>[Nomic Atlas](https://docs.nomic.ai/index.html) is a platform for interacting with both > small and internet scale unstructured datasets. @@ -16,4 +16,4 @@ See a [usage example](/docs/integrations/vectorstores/atlas). ```python from langchain_community.vectorstores import AtlasDB -``` \ No newline at end of file +``` diff --git a/docs/docs/integrations/providers/awadb.md b/docs/docs/integrations/providers/awadb.md index 0bc4d374a8b..bdb6f4451fd 100644 --- a/docs/docs/integrations/providers/awadb.md +++ b/docs/docs/integrations/providers/awadb.md @@ -8,7 +8,6 @@ pip install awadb ``` - ## Vector store ```python @@ -17,7 +16,6 @@ from langchain_community.vectorstores import AwaDB See a [usage example](/docs/integrations/vectorstores/awadb). - ## Embedding models ```python diff --git a/docs/docs/integrations/providers/aws.mdx b/docs/docs/integrations/providers/aws.mdx index c85b6eb8374..1feab98059d 100755 --- a/docs/docs/integrations/providers/aws.mdx +++ b/docs/docs/integrations/providers/aws.mdx @@ -18,14 +18,14 @@ pip install langchain-community boto3 ### Bedrock Chat ->[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that offers a choice of -> high-performing foundation models (FMs) from leading AI companies like `AI21 Labs`, `Anthropic`, `Cohere`, -> `Meta`, `Stability AI`, and `Amazon` via a single API, along with a broad set of capabilities you need to -> build generative AI applications with security, privacy, and responsible AI. Using `Amazon Bedrock`, -> you can easily experiment with and evaluate top FMs for your use case, privately customize them with -> your data using techniques such as fine-tuning and `Retrieval Augmented Generation` (`RAG`), and build -> agents that execute tasks using your enterprise systems and data sources. Since `Amazon Bedrock` is -> serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy +>[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that offers a choice of +> high-performing foundation models (FMs) from leading AI companies like `AI21 Labs`, `Anthropic`, `Cohere`, +> `Meta`, `Stability AI`, and `Amazon` via a single API, along with a broad set of capabilities you need to +> build generative AI applications with security, privacy, and responsible AI. Using `Amazon Bedrock`, +> you can easily experiment with and evaluate top FMs for your use case, privately customize them with +> your data using techniques such as fine-tuning and `Retrieval Augmented Generation` (`RAG`), and build +> agents that execute tasks using your enterprise systems and data sources. Since `Amazon Bedrock` is +> serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy > generative AI capabilities into your applications using the AWS services you are already familiar with. See a [usage example](/docs/integrations/chat/bedrock). @@ -55,7 +55,7 @@ from langchain_aws import ChatBedrockConverse ## LLMs ### Bedrock - + See a [usage example](/docs/integrations/llms/bedrock). ```python @@ -64,16 +64,16 @@ from langchain_aws import BedrockLLM ### Amazon API Gateway ->[Amazon API Gateway](https://aws.amazon.com/api-gateway/) is a fully managed service that makes it easy for -> developers to create, publish, maintain, monitor, and secure APIs at any scale. APIs act as the "front door" -> for applications to access data, business logic, or functionality from your backend services. Using -> `API Gateway`, you can create RESTful APIs and WebSocket APIs that enable real-time two-way communication +>[Amazon API Gateway](https://aws.amazon.com/api-gateway/) is a fully managed service that makes it easy for +> developers to create, publish, maintain, monitor, and secure APIs at any scale. APIs act as the "front door" +> for applications to access data, business logic, or functionality from your backend services. Using +> `API Gateway`, you can create RESTful APIs and WebSocket APIs that enable real-time two-way communication > applications. `API Gateway` supports containerized and serverless workloads, as well as web applications. -> -> `API Gateway` handles all the tasks involved in accepting and processing up to hundreds of thousands of -> concurrent API calls, including traffic management, CORS support, authorization and access control, -> throttling, monitoring, and API version management. `API Gateway` has no minimum fees or startup costs. -> You pay for the API calls you receive and the amount of data transferred out and, with the `API Gateway` +> +> `API Gateway` handles all the tasks involved in accepting and processing up to hundreds of thousands of +> concurrent API calls, including traffic management, CORS support, authorization and access control, +> throttling, monitoring, and API version management. `API Gateway` has no minimum fees or startup costs. +> You pay for the API calls you receive and the amount of data transferred out and, with the `API Gateway` > tiered pricing model, you can reduce your cost as your API usage scales. See a [usage example](/docs/integrations/llms/amazon_api_gateway). @@ -84,7 +84,7 @@ from langchain_community.llms import AmazonAPIGateway ### SageMaker Endpoint ->[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a system that can build, train, and deploy +>[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a system that can build, train, and deploy > machine learning (ML) models with fully managed infrastructure, tools, and workflows. We use `SageMaker` to host our model and expose it as the `SageMaker Endpoint`. @@ -131,7 +131,7 @@ from langchain_community.document_loaders import S3DirectoryLoader, S3FileLoader ### Amazon Textract ->[Amazon Textract](https://docs.aws.amazon.com/managedservices/latest/userguide/textract.html) is a machine +>[Amazon Textract](https://docs.aws.amazon.com/managedservices/latest/userguide/textract.html) is a machine > learning (ML) service that automatically extracts text, handwriting, and data from scanned documents. See a [usage example](/docs/integrations/document_loaders/amazon_textract). @@ -153,10 +153,10 @@ from langchain_community.document_loaders.athena import AthenaLoader ### AWS Glue ->The [AWS Glue Data Catalog](https://docs.aws.amazon.com/en_en/glue/latest/dg/catalog-and-crawler.html) is a centralized metadata -> repository that allows you to manage, access, and share metadata about -> your data stored in AWS. It acts as a metadata store for your data assets, -> enabling various AWS services and your applications to query and connect +>The [AWS Glue Data Catalog](https://docs.aws.amazon.com/en_en/glue/latest/dg/catalog-and-crawler.html) is a centralized metadata +> repository that allows you to manage, access, and share metadata about +> your data stored in AWS. It acts as a metadata store for your data assets, +> enabling various AWS services and your applications to query and connect > to the data they need efficiently. See a [usage example](/docs/integrations/document_loaders/glue_catalog). @@ -169,11 +169,11 @@ from langchain_community.document_loaders.glue_catalog import GlueCatalogLoader ### Amazon OpenSearch Service -> [Amazon OpenSearch Service](https://aws.amazon.com/opensearch-service/) performs -> interactive log analytics, real-time application monitoring, website search, and more. `OpenSearch` is -> an open source, -> distributed search and analytics suite derived from `Elasticsearch`. `Amazon OpenSearch Service` offers the -> latest versions of `OpenSearch`, support for many versions of `Elasticsearch`, as well as +> [Amazon OpenSearch Service](https://aws.amazon.com/opensearch-service/) performs +> interactive log analytics, real-time application monitoring, website search, and more. `OpenSearch` is +> an open source, +> distributed search and analytics suite derived from `Elasticsearch`. `Amazon OpenSearch Service` offers the +> latest versions of `OpenSearch`, support for many versions of `Elasticsearch`, as well as > visualization capabilities powered by `OpenSearch Dashboards` and `Kibana`. We need to install several python libraries. @@ -215,9 +215,9 @@ See a [usage example](/docs/integrations/vectorstores/documentdb). ```python from langchain_community.vectorstores import DocumentDBVectorSearch ``` -### Amazon MemoryDB -[Amazon MemoryDB](https://aws.amazon.com/memorydb/) is a durable, in-memory database service that delivers ultra-fast performance. MemoryDB is compatible with Redis OSS, a popular open source data store, -enabling you to quickly build applications using the same flexible and friendly Redis OSS APIs, and commands that they already use today. +### Amazon MemoryDB +[Amazon MemoryDB](https://aws.amazon.com/memorydb/) is a durable, in-memory database service that delivers ultra-fast performance. MemoryDB is compatible with Redis OSS, a popular open source data store, +enabling you to quickly build applications using the same flexible and friendly Redis OSS APIs, and commands that they already use today. InMemoryVectorStore class provides a vectorstore to connect with Amazon MemoryDB. @@ -238,14 +238,14 @@ See a [usage example](/docs/integrations/vectorstores/memorydb). ### Amazon Kendra -> [Amazon Kendra](https://docs.aws.amazon.com/kendra/latest/dg/what-is-kendra.html) is an intelligent search service -> provided by `Amazon Web Services` (`AWS`). It utilizes advanced natural language processing (NLP) and machine -> learning algorithms to enable powerful search capabilities across various data sources within an organization. -> `Kendra` is designed to help users find the information they need quickly and accurately, +> [Amazon Kendra](https://docs.aws.amazon.com/kendra/latest/dg/what-is-kendra.html) is an intelligent search service +> provided by `Amazon Web Services` (`AWS`). It utilizes advanced natural language processing (NLP) and machine +> learning algorithms to enable powerful search capabilities across various data sources within an organization. +> `Kendra` is designed to help users find the information they need quickly and accurately, > improving productivity and decision-making. -> With `Kendra`, we can search across a wide range of content types, including documents, FAQs, knowledge bases, -> manuals, and websites. It supports multiple languages and can understand complex queries, synonyms, and +> With `Kendra`, we can search across a wide range of content types, including documents, FAQs, knowledge bases, +> manuals, and websites. It supports multiple languages and can understand complex queries, synonyms, and > contextual meanings to provide highly relevant search results. We need to install the `langchain-aws` library. @@ -262,8 +262,8 @@ from langchain_aws import AmazonKendraRetriever ### Amazon Bedrock (Knowledge Bases) -> [Knowledge bases for Amazon Bedrock](https://aws.amazon.com/bedrock/knowledge-bases/) is an -> `Amazon Web Services` (`AWS`) offering which lets you quickly build RAG applications by using your +> [Knowledge bases for Amazon Bedrock](https://aws.amazon.com/bedrock/knowledge-bases/) is an +> `Amazon Web Services` (`AWS`) offering which lets you quickly build RAG applications by using your > private data to customize foundation model response. We need to install the `langchain-aws` library. @@ -282,10 +282,10 @@ from langchain_aws import AmazonKnowledgeBasesRetriever ### AWS Lambda ->[`Amazon AWS Lambda`](https://aws.amazon.com/pm/lambda/) is a serverless computing service provided by -> `Amazon Web Services` (`AWS`). It helps developers to build and run applications and services without -> provisioning or managing servers. This serverless architecture enables you to focus on writing and -> deploying code, while AWS automatically takes care of scaling, patching, and managing the +>[`Amazon AWS Lambda`](https://aws.amazon.com/pm/lambda/) is a serverless computing service provided by +> `Amazon Web Services` (`AWS`). It helps developers to build and run applications and services without +> provisioning or managing servers. This serverless architecture enables you to focus on writing and +> deploying code, while AWS automatically takes care of scaling, patching, and managing the > infrastructure required to run your applications. We need to install `boto3` python library. @@ -300,10 +300,10 @@ See a [usage example](/docs/integrations/tools/awslambda). ### AWS DynamoDB ->[AWS DynamoDB](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/dynamodb/index.html) +>[AWS DynamoDB](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/dynamodb/index.html) > is a fully managed `NoSQL` database service that provides fast and predictable performance with seamless scalability. - -We have to configure the [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html). + +We have to configure the [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html). We need to install the `boto3` library. @@ -361,13 +361,13 @@ from langchain_community.callbacks.bedrock_anthropic_callback import BedrockAnth ### SageMaker Tracking ->[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a fully managed service that is used to quickly +>[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a fully managed service that is used to quickly > and easily build, train and deploy machine learning (ML) models. ->[Amazon SageMaker Experiments](https://docs.aws.amazon.com/sagemaker/latest/dg/experiments.html) is a capability -> of `Amazon SageMaker` that lets you organize, track, +>[Amazon SageMaker Experiments](https://docs.aws.amazon.com/sagemaker/latest/dg/experiments.html) is a capability +> of `Amazon SageMaker` that lets you organize, track, > compare and evaluate ML experiments and model versions. - + We need to install several python libraries. ```bash @@ -384,7 +384,7 @@ from langchain_community.callbacks import SageMakerCallbackHandler ### Amazon Comprehend Moderation Chain ->[Amazon Comprehend](https://aws.amazon.com/comprehend/) is a natural-language processing (NLP) service that +>[Amazon Comprehend](https://aws.amazon.com/comprehend/) is a natural-language processing (NLP) service that > uses machine learning to uncover valuable insights and connections in text. diff --git a/docs/docs/integrations/providers/azure_ai.mdx b/docs/docs/integrations/providers/azure_ai.mdx index dd126d55e41..cce74565d52 100644 --- a/docs/docs/integrations/providers/azure_ai.mdx +++ b/docs/docs/integrations/providers/azure_ai.mdx @@ -2,14 +2,14 @@ All functionality related to [Azure AI Foundry](https://learn.microsoft.com/en-us/azure/developer/python/get-started) and its related projects. -Integration packages for Azure AI, Dynamic Sessions, SQL Server are maintained in +Integration packages for Azure AI, Dynamic Sessions, SQL Server are maintained in the [langchain-azure](https://github.com/langchain-ai/langchain-azure) repository. ## Chat models -We recommend developers start with the (`langchain-azure-ai`) to access all the models available in [Azure AI Foundry](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/model-catalog-overview). +We recommend developers start with the (`langchain-azure-ai`) to access all the models available in [Azure AI Foundry](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/model-catalog-overview). -### Azure AI Chat Completions Model +### Azure AI Chat Completions Model Access models like Azure OpenAI, DeepSeek R1, Cohere, Phi and Mistral using the `AzureAIChatCompletionsModel` class. diff --git a/docs/docs/integrations/providers/baai.mdx b/docs/docs/integrations/providers/baai.mdx index 58ff1152ef8..38da1dd5f77 100644 --- a/docs/docs/integrations/providers/baai.mdx +++ b/docs/docs/integrations/providers/baai.mdx @@ -1,11 +1,11 @@ # BAAI ->[Beijing Academy of Artificial Intelligence (BAAI) (Wikipedia)](https://en.wikipedia.org/wiki/Beijing_Academy_of_Artificial_Intelligence), -> also known as `Zhiyuan Institute`, is a Chinese non-profit artificial -> intelligence (AI) research laboratory. `BAAI` conducts AI research -> and is dedicated to promoting collaboration among academia and industry, -> as well as fostering top talent and a focus on long-term research on -> the fundamentals of AI technology. As a collaborative hub, BAAI's founding +>[Beijing Academy of Artificial Intelligence (BAAI) (Wikipedia)](https://en.wikipedia.org/wiki/Beijing_Academy_of_Artificial_Intelligence), +> also known as `Zhiyuan Institute`, is a Chinese non-profit artificial +> intelligence (AI) research laboratory. `BAAI` conducts AI research +> and is dedicated to promoting collaboration among academia and industry, +> as well as fostering top talent and a focus on long-term research on +> the fundamentals of AI technology. As a collaborative hub, BAAI's founding > members include leading AI companies, universities, and research institutes. @@ -13,7 +13,7 @@ ### HuggingFaceBgeEmbeddings ->[BGE models on the HuggingFace](https://huggingface.co/BAAI/bge-large-en-v1.5) +>[BGE models on the HuggingFace](https://huggingface.co/BAAI/bge-large-en-v1.5) > are one of [the best open-source embedding models](https://huggingface.co/spaces/mteb/leaderboard). See a [usage example](/docs/integrations/text_embedding/bge_huggingface). @@ -24,8 +24,8 @@ from langchain_community.embeddings import HuggingFaceBgeEmbeddings ### IpexLLMBgeEmbeddings ->[IPEX-LLM](https://github.com/intel-analytics/ipex-llm) is a PyTorch -> library for running LLM on Intel CPU and GPU (e.g., local PC with iGPU, +>[IPEX-LLM](https://github.com/intel-analytics/ipex-llm) is a PyTorch +> library for running LLM on Intel CPU and GPU (e.g., local PC with iGPU, > discrete GPU such as Arc, Flex and Max) with very low latency. See a [usage example running model on Intel CPU](/docs/integrations/text_embedding/ipex_llm). diff --git a/docs/docs/integrations/providers/baichuan.mdx b/docs/docs/integrations/providers/baichuan.mdx index 409a66d6f8c..2724f5d6b30 100644 --- a/docs/docs/integrations/providers/baichuan.mdx +++ b/docs/docs/integrations/providers/baichuan.mdx @@ -1,6 +1,6 @@ # Baichuan ->[Baichuan Inc.](https://www.baichuan-ai.com/) is a Chinese startup in the era of AGI, +>[Baichuan Inc.](https://www.baichuan-ai.com/) is a Chinese startup in the era of AGI, > dedicated to addressing fundamental human needs: Efficiency, Health, and Happiness. diff --git a/docs/docs/integrations/providers/baidu.mdx b/docs/docs/integrations/providers/baidu.mdx index 67ed5efe409..1661d2681f3 100644 --- a/docs/docs/integrations/providers/baidu.mdx +++ b/docs/docs/integrations/providers/baidu.mdx @@ -1,7 +1,7 @@ # Baidu ->[Baidu Cloud](https://cloud.baidu.com/) is a cloud service provided by `Baidu, Inc.`, -> headquartered in Beijing. It offers a cloud storage service, client software, +>[Baidu Cloud](https://cloud.baidu.com/) is a cloud service provided by `Baidu, Inc.`, +> headquartered in Beijing. It offers a cloud storage service, client software, > file management, resource sharing, and Third Party Integration. diff --git a/docs/docs/integrations/providers/bananadev.mdx b/docs/docs/integrations/providers/bananadev.mdx index 9972bc159a8..ff326517c12 100644 --- a/docs/docs/integrations/providers/bananadev.mdx +++ b/docs/docs/integrations/providers/bananadev.mdx @@ -1,6 +1,6 @@ # Banana ->[Banana](https://www.banana.dev/) provided serverless GPU inference for AI models, +>[Banana](https://www.banana.dev/) provided serverless GPU inference for AI models, > a CI/CD build pipeline and a simple Python framework (`Potassium`) to server your models. This page covers how to use the [Banana](https://www.banana.dev) ecosystem within LangChain. @@ -26,7 +26,7 @@ Other starter repos are available [here](https://github.com/orgs/bananaml/reposi ## Build the Banana app -To use Banana apps within Langchain, you must include the `outputs` key +To use Banana apps within Langchain, you must include the `outputs` key in the returned json, and the value must be a string. ```python diff --git a/docs/docs/integrations/providers/baseten.md b/docs/docs/integrations/providers/baseten.mdx similarity index 87% rename from docs/docs/integrations/providers/baseten.md rename to docs/docs/integrations/providers/baseten.mdx index d5ced9dc052..d2a0c1758a6 100644 --- a/docs/docs/integrations/providers/baseten.md +++ b/docs/docs/integrations/providers/baseten.mdx @@ -1,12 +1,12 @@ # Baseten ->[Baseten](https://baseten.co) is a provider of all the infrastructure you need to deploy and serve +>[Baseten](https://baseten.co) is a provider of all the infrastructure you need to deploy and serve > ML models performantly, scalably, and cost-efficiently. ->As a model inference platform, `Baseten` is a `Provider` in the LangChain ecosystem. +>As a model inference platform, `Baseten` is a `Provider` in the LangChain ecosystem. The `Baseten` integration currently implements a single `Component`, LLMs, but more are planned! ->`Baseten` lets you run both open source models like Llama 2 or Mistral and run proprietary or +>`Baseten` lets you run both open source models like Llama 2 or Mistral and run proprietary or fine-tuned models on dedicated GPUs. If you're used to a provider like OpenAI, using Baseten has a few differences: >* Rather than paying per token, you pay per minute of GPU used. @@ -21,8 +21,8 @@ fine-tuned models on dedicated GPUs. If you're used to a provider like OpenAI, u You'll need two things to use Baseten models with LangChain: -- A [Baseten account](https://baseten.co) -- An [API key](https://docs.baseten.co/observability/api-keys) +* A [Baseten account](https://baseten.co) +* An [API key](https://docs.baseten.co/observability/api-keys) Export your API key to your as an environment variable called `BASETEN_API_KEY`. diff --git a/docs/docs/integrations/providers/beam.mdx b/docs/docs/integrations/providers/beam.mdx index 7f723eb0dec..30d53723f1d 100644 --- a/docs/docs/integrations/providers/beam.mdx +++ b/docs/docs/integrations/providers/beam.mdx @@ -1,6 +1,6 @@ # Beam ->[Beam](https://www.beam.cloud/) is a cloud computing platform that allows you to run your code +>[Beam](https://www.beam.cloud/) is a cloud computing platform that allows you to run your code > on remote servers with GPUs. diff --git a/docs/docs/integrations/providers/beautiful_soup.mdx b/docs/docs/integrations/providers/beautiful_soup.mdx index 289d4059fab..8b1058b3148 100644 --- a/docs/docs/integrations/providers/beautiful_soup.mdx +++ b/docs/docs/integrations/providers/beautiful_soup.mdx @@ -1,8 +1,8 @@ # Beautiful Soup ->[Beautiful Soup](https://www.crummy.com/software/BeautifulSoup/) is a Python package for parsing -> HTML and XML documents (including having malformed markup, i.e. non-closed tags, so named after tag soup). -> It creates a parse tree for parsed pages that can be used to extract data from HTML,[3] which +>[Beautiful Soup](https://www.crummy.com/software/BeautifulSoup/) is a Python package for parsing +> HTML and XML documents (including having malformed markup, i.e. non-closed tags, so named after tag soup). +> It creates a parse tree for parsed pages that can be used to extract data from HTML,[3] which > is useful for web scraping. ## Installation and Setup diff --git a/docs/docs/integrations/providers/bittensor.mdx b/docs/docs/integrations/providers/bittensor.mdx index 137069077db..fee391ec8db 100644 --- a/docs/docs/integrations/providers/bittensor.mdx +++ b/docs/docs/integrations/providers/bittensor.mdx @@ -1,6 +1,6 @@ # Bittensor ->[Neural Internet Bittensor](https://neuralinternet.ai/) network, an open source protocol +>[Neural Internet Bittensor](https://neuralinternet.ai/) network, an open source protocol > that powers a decentralized, blockchain-based, machine learning network. ## Installation and Setup diff --git a/docs/docs/integrations/providers/blackboard.mdx b/docs/docs/integrations/providers/blackboard.mdx index 09312bc4dfa..927aebbcf5a 100644 --- a/docs/docs/integrations/providers/blackboard.mdx +++ b/docs/docs/integrations/providers/blackboard.mdx @@ -1,11 +1,11 @@ # Blackboard >[Blackboard Learn](https://en.wikipedia.org/wiki/Blackboard_Learn) (previously the `Blackboard Learning Management System`) -> is a web-based virtual learning environment and learning management system developed by Blackboard Inc. -> The software features course management, customizable open architecture, and scalable design that allows -> integration with student information systems and authentication protocols. It may be installed on local servers, -> hosted by `Blackboard ASP Solutions`, or provided as Software as a Service hosted on Amazon Web Services. -> Its main purposes are stated to include the addition of online elements to courses traditionally delivered +> is a web-based virtual learning environment and learning management system developed by Blackboard Inc. +> The software features course management, customizable open architecture, and scalable design that allows +> integration with student information systems and authentication protocols. It may be installed on local servers, +> hosted by `Blackboard ASP Solutions`, or provided as Software as a Service hosted on Amazon Web Services. +> Its main purposes are stated to include the addition of online elements to courses traditionally delivered > face-to-face and development of completely online courses with few or no face-to-face meetings. ## Installation and Setup diff --git a/docs/docs/integrations/providers/bookendai.mdx b/docs/docs/integrations/providers/bookendai.mdx index e5eecde38d7..b8f974187f6 100644 --- a/docs/docs/integrations/providers/bookendai.mdx +++ b/docs/docs/integrations/providers/bookendai.mdx @@ -6,7 +6,7 @@ LangChain implements an integration with embeddings provided by [bookend.ai](htt ## Installation and Setup -You need to register and get the `API_KEY` +You need to register and get the `API_KEY` from the [bookend.ai](https://bookend.ai/) website. ## Embedding model diff --git a/docs/docs/integrations/providers/box.mdx b/docs/docs/integrations/providers/box.mdx index 85ffc1a79e9..fcf6a093034 100644 --- a/docs/docs/integrations/providers/box.mdx +++ b/docs/docs/integrations/providers/box.mdx @@ -1,11 +1,11 @@ # Box -[Box](https://box.com) is the Intelligent Content Cloud, a single platform that enables -organizations to fuel collaboration, manage the entire content lifecycle, secure critical content, -and transform business workflows with enterprise AI. Founded in 2005, Box simplifies work for +[Box](https://box.com) is the Intelligent Content Cloud, a single platform that enables +organizations to fuel collaboration, manage the entire content lifecycle, secure critical content, +and transform business workflows with enterprise AI. Founded in 2005, Box simplifies work for leading global organizations, including AstraZeneca, JLL, Morgan Stanley, and Nationwide. -In this package, we make available a number of ways to include Box content in your AI workflows. +In this package, we make available a number of ways to include Box content in your AI workflows. ### Installation and setup @@ -23,9 +23,9 @@ Box, check out our [developer documentation](https://developer.box.com). In order to integrate with Box, you need a few things: -* A Box instance — if you are not a current Box customer, sign up for a +* A Box instance — if you are not a current Box customer, sign up for a [free dev account](https://account.box.com/signup/n/developer#ty9l3). -* A Box app — more on how to +* A Box app — more on how to [create an app](https://developer.box.com/guides/getting-started/first-application/) * Your app approved in your Box instance — This is done by your admin. The good news is if you are using a free developer account, you are the admin. @@ -35,13 +35,13 @@ The good news is if you are using a free developer account, you are the admin. The `box-langchain` package offers some flexibility to authentication. The most basic authentication method is by using a developer token. This can be -found in the [Box developer console](https://account.box.com/developers/console) -on the configuration screen. This token is purposely short-lived (1 hour) and is -intended for development. With this token, you can add it to your environment as -`BOX_DEVELOPER_TOKEN`, you can pass it directly to the loader, or you can use the +found in the [Box developer console](https://account.box.com/developers/console) +on the configuration screen. This token is purposely short-lived (1 hour) and is +intended for development. With this token, you can add it to your environment as +`BOX_DEVELOPER_TOKEN`, you can pass it directly to the loader, or you can use the `BoxAuth` authentication helper class. -We will cover passing it directly to the loader in the section below. +We will cover passing it directly to the loader in the section below. ### BoxAuth helper class @@ -55,14 +55,14 @@ We will cover passing it directly to the loader in the section below. :::note If using JWT authentication, you will need to download the configuration from the Box -developer console after generating your public/private key pair. Place this file in your +developer console after generating your public/private key pair. Place this file in your application directory structure somewhere. You will use the path to this file when using the `BoxAuth` helper class. ::: -For more information, learn about how to +For more information, learn about how to [set up a Box application](https://developer.box.com/guides/getting-started/first-application/), -and check out the +and check out the [Box authentication guide](https://developer.box.com/guides/authentication/select/) for more about our different authentication options. @@ -187,4 +187,4 @@ from langchain_box.retrievers import BoxRetriever ```python from langchain_box.blob_loaders import BoxBlobLoader -``` \ No newline at end of file +``` diff --git a/docs/docs/integrations/providers/brave_search.mdx b/docs/docs/integrations/providers/brave_search.mdx index 647004302cc..48db79203f7 100644 --- a/docs/docs/integrations/providers/brave_search.mdx +++ b/docs/docs/integrations/providers/brave_search.mdx @@ -2,15 +2,15 @@ >[Brave Search](https://en.wikipedia.org/wiki/Brave_Search) is a search engine developed by Brave Software. -> - `Brave Search` uses its own web index. As of May 2022, it covered over 10 billion pages and was used to serve 92% -> of search results without relying on any third-parties, with the remainder being retrieved -> server-side from the Bing API or (on an opt-in basis) client-side from Google. According -> to Brave, the index was kept "intentionally smaller than that of Google or Bing" in order to -> help avoid spam and other low-quality content, with the disadvantage that "Brave Search is +> - `Brave Search` uses its own web index. As of May 2022, it covered over 10 billion pages and was used to serve 92% +> of search results without relying on any third-parties, with the remainder being retrieved +> server-side from the Bing API or (on an opt-in basis) client-side from Google. According +> to Brave, the index was kept "intentionally smaller than that of Google or Bing" in order to +> help avoid spam and other low-quality content, with the disadvantage that "Brave Search is > not yet as good as Google in recovering long-tail queries." ->- `Brave Search Premium`: As of April 2023 Brave Search is an ad-free website, but it will +>- `Brave Search Premium`: As of April 2023 Brave Search is an ad-free website, but it will > eventually switch to a new model that will include ads and premium users will get an ad-free experience. -> User data including IP addresses won't be collected from its users by default. A premium account +> User data including IP addresses won't be collected from its users by default. A premium account > will be required for opt-in data-collection. diff --git a/docs/docs/integrations/providers/breebs.md b/docs/docs/integrations/providers/breebs.mdx similarity index 77% rename from docs/docs/integrations/providers/breebs.md rename to docs/docs/integrations/providers/breebs.mdx index fc6826874b4..8ff58195f10 100644 --- a/docs/docs/integrations/providers/breebs.md +++ b/docs/docs/integrations/providers/breebs.mdx @@ -1,11 +1,10 @@ # Breebs (Open Knowledge) ->[Breebs](https://www.breebs.com/) is an open collaborative knowledge platform. +>[Breebs](https://www.breebs.com/) is an open collaborative knowledge platform. >Anybody can create a `Breeb`, a knowledge capsule based on PDFs stored on a Google Drive folder. >A `Breeb` can be used by any LLM/chatbot to improve its expertise, reduce hallucinations and give access to sources. ->Behind the scenes, `Breebs` implements several `Retrieval Augmented Generation (RAG)` models -> to seamlessly provide useful context at each iteration. - +>Behind the scenes, `Breebs` implements several `Retrieval Augmented Generation (RAG)` models +> to seamlessly provide useful context at each iteration. ## Retriever @@ -13,4 +12,4 @@ from langchain.retrievers import BreebsRetriever ``` -[See a usage example (Retrieval & ConversationalRetrievalChain)](/docs/integrations/retrievers/breebs) \ No newline at end of file +[See a usage example (Retrieval & ConversationalRetrievalChain)](/docs/integrations/retrievers/breebs) diff --git a/docs/docs/integrations/providers/brightdata.mdx b/docs/docs/integrations/providers/brightdata.mdx index e7a8b2cfcf3..9e5e197020b 100644 --- a/docs/docs/integrations/providers/brightdata.mdx +++ b/docs/docs/integrations/providers/brightdata.mdx @@ -31,4 +31,4 @@ The Bright Data integration provides several tools: - [BrightDataSERP](/docs/integrations/tools/brightdata_serp) - Search engine results collection with geo-targeting - [BrightDataUnblocker](/docs/integrations/tools/brightdata_unlocker) - Access ANY public website that might be geo-restricted or bot-protected -- [BrightDataWebScraperAPI](/docs/integrations/tools/brightdata-webscraperapi) - Extract structured data from 100+ ppoular domains, e.g. Amazon product details and LinkedIn profiles \ No newline at end of file +- [BrightDataWebScraperAPI](/docs/integrations/tools/brightdata-webscraperapi) - Extract structured data from 100+ ppoular domains, e.g. Amazon product details and LinkedIn profiles diff --git a/docs/docs/integrations/providers/browserless.mdx b/docs/docs/integrations/providers/browserless.mdx index 0fe4463af92..8f8f8c54746 100644 --- a/docs/docs/integrations/providers/browserless.mdx +++ b/docs/docs/integrations/providers/browserless.mdx @@ -1,7 +1,7 @@ # Browserless ->[Browserless](https://www.browserless.io/docs/start) is a service that allows you to -> run headless Chrome instances in the cloud. It’s a great way to run browser-based +>[Browserless](https://www.browserless.io/docs/start) is a service that allows you to +> run headless Chrome instances in the cloud. It’s a great way to run browser-based > automation at scale without having to worry about managing your own infrastructure. ## Installation and Setup diff --git a/docs/docs/integrations/providers/byte_dance.mdx b/docs/docs/integrations/providers/byte_dance.mdx index 25f6e2533ef..77fe29cb567 100644 --- a/docs/docs/integrations/providers/byte_dance.mdx +++ b/docs/docs/integrations/providers/byte_dance.mdx @@ -10,7 +10,7 @@ You can find the access instructions [here](https://open.larksuite.com/document) ## Document Loaders ->[Lark Suite](https://www.larksuite.com/) is an enterprise collaboration platform +>[Lark Suite](https://www.larksuite.com/) is an enterprise collaboration platform > developed by `ByteDance`. ### Lark Suite for Document diff --git a/docs/docs/integrations/providers/cassandra.mdx b/docs/docs/integrations/providers/cassandra.mdx index 87cf5ebe0c9..88a4b47146d 100644 --- a/docs/docs/integrations/providers/cassandra.mdx +++ b/docs/docs/integrations/providers/cassandra.mdx @@ -3,7 +3,7 @@ > [Apache Cassandra®](https://cassandra.apache.org/) is a NoSQL, row-oriented, highly scalable and highly available database. > Starting with version 5.0, the database ships with [vector search capabilities](https://cassandra.apache.org/doc/trunk/cassandra/vector-search/overview.html). -The integrations outlined in this page can be used with `Cassandra` as well as other CQL-compatible databases, +The integrations outlined in this page can be used with `Cassandra` as well as other CQL-compatible databases, i.e. those using the `Cassandra Query Language` protocol. @@ -66,7 +66,7 @@ Learn more in the [example notebook](/docs/integrations/document_loaders/cassand #### Attribution statement -> Apache Cassandra, Cassandra and Apache are either registered trademarks or trademarks of +> Apache Cassandra, Cassandra and Apache are either registered trademarks or trademarks of > the [Apache Software Foundation](http://www.apache.org/) in the United States and/or other countries. ## Toolkit diff --git a/docs/docs/integrations/providers/cerebras.mdx b/docs/docs/integrations/providers/cerebras.mdx index 10201bc0240..3c97def697a 100644 --- a/docs/docs/integrations/providers/cerebras.mdx +++ b/docs/docs/integrations/providers/cerebras.mdx @@ -27,4 +27,4 @@ export CEREBRAS_API_KEY="your-api-key-here" ``` ## Chat Model -See a [usage example](/docs/integrations/chat/cerebras). \ No newline at end of file +See a [usage example](/docs/integrations/chat/cerebras). diff --git a/docs/docs/integrations/providers/cerebriumai.mdx b/docs/docs/integrations/providers/cerebriumai.mdx index 912dbd90f61..e0f45b3567e 100644 --- a/docs/docs/integrations/providers/cerebriumai.mdx +++ b/docs/docs/integrations/providers/cerebriumai.mdx @@ -12,7 +12,7 @@ See the examples in the [CerebriumAI documentation](https://docs.cerebrium.ai/ex pip install cerebrium ``` -- [Get an CerebriumAI api key](https://docs.cerebrium.ai/cerebrium/getting-started/installation) and set +- [Get an CerebriumAI api key](https://docs.cerebrium.ai/cerebrium/getting-started/installation) and set it as an environment variable (`CEREBRIUMAI_API_KEY`) @@ -23,4 +23,4 @@ See a [usage example](/docs/integrations/llms/cerebriumai). ```python from langchain_community.llms import CerebriumAI -``` \ No newline at end of file +``` diff --git a/docs/docs/integrations/providers/chaindesk.mdx b/docs/docs/integrations/providers/chaindesk.mdx index 7cfd5e96b88..3cb560e7117 100644 --- a/docs/docs/integrations/providers/chaindesk.mdx +++ b/docs/docs/integrations/providers/chaindesk.mdx @@ -5,7 +5,7 @@ ## Installation and Setup -We need to sign up for Chaindesk, create a datastore, add some data and get your datastore api endpoint url. +We need to sign up for Chaindesk, create a datastore, add some data and get your datastore api endpoint url. We need the [API Key](https://docs.chaindesk.ai/api-reference/authentication). ## Retriever diff --git a/docs/docs/integrations/providers/clickhouse.mdx b/docs/docs/integrations/providers/clickhouse.mdx index 64e4608c535..62c5e4c56e8 100644 --- a/docs/docs/integrations/providers/clickhouse.mdx +++ b/docs/docs/integrations/providers/clickhouse.mdx @@ -1,9 +1,9 @@ # ClickHouse -> [ClickHouse](https://clickhouse.com/) is the fast and resource efficient open-source database for real-time -> apps and analytics with full SQL support and a wide range of functions to assist users in writing analytical queries. -> It has data structures and distance search functions (like `L2Distance`) as well as -> [approximate nearest neighbor search indexes](https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/annindexes) +> [ClickHouse](https://clickhouse.com/) is the fast and resource efficient open-source database for real-time +> apps and analytics with full SQL support and a wide range of functions to assist users in writing analytical queries. +> It has data structures and distance search functions (like `L2Distance`) as well as +> [approximate nearest neighbor search indexes](https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/annindexes) > That enables ClickHouse to be used as a high performance and scalable vector database to store and search vectors with SQL. diff --git a/docs/docs/integrations/providers/cloudflare.mdx b/docs/docs/integrations/providers/cloudflare.mdx index 82d97d666c3..aa6a348b521 100644 --- a/docs/docs/integrations/providers/cloudflare.mdx +++ b/docs/docs/integrations/providers/cloudflare.mdx @@ -1,10 +1,10 @@ # Cloudflare ->[Cloudflare, Inc. (Wikipedia)](https://en.wikipedia.org/wiki/Cloudflare) is an American company that provides -> content delivery network services, cloud cybersecurity, DDoS mitigation, and ICANN-accredited +>[Cloudflare, Inc. (Wikipedia)](https://en.wikipedia.org/wiki/Cloudflare) is an American company that provides +> content delivery network services, cloud cybersecurity, DDoS mitigation, and ICANN-accredited > domain registration services. ->[Cloudflare Workers AI](https://developers.cloudflare.com/workers-ai/) allows you to run machine +>[Cloudflare Workers AI](https://developers.cloudflare.com/workers-ai/) allows you to run machine > learning models, on the `Cloudflare` network, from your code via REST API. @@ -38,4 +38,4 @@ See [installation instructions and usage example](/docs/integrations/llms/cloudf ```python from langchain_community.llms.cloudflare_workersai import CloudflareWorkersAI -``` \ No newline at end of file +``` diff --git a/docs/docs/integrations/providers/clova.mdx b/docs/docs/integrations/providers/clova.mdx index b10aa930511..3c3f2aae6ed 100644 --- a/docs/docs/integrations/providers/clova.mdx +++ b/docs/docs/integrations/providers/clova.mdx @@ -1,7 +1,7 @@ # Clova ->[CLOVA Studio](https://api.ncloud-docs.com/docs/ai-naver-clovastudio-summary) is a service -> of [Naver Cloud Platform](https://www.ncloud.com/) that uses `HyperCLOVA` language models, +>[CLOVA Studio](https://api.ncloud-docs.com/docs/ai-naver-clovastudio-summary) is a service +> of [Naver Cloud Platform](https://www.ncloud.com/) that uses `HyperCLOVA` language models, > a hyperscale AI technology, to output phrases generated through AI technology based on user input. diff --git a/docs/docs/integrations/providers/cogniswitch.mdx b/docs/docs/integrations/providers/cogniswitch.mdx index d8aee6a4c9d..6dc1eb4af45 100644 --- a/docs/docs/integrations/providers/cogniswitch.mdx +++ b/docs/docs/integrations/providers/cogniswitch.mdx @@ -1,11 +1,11 @@ # CogniSwitch ->[CogniSwitch](https://www.cogniswitch.ai/aboutus) is an API based data platform that -> enhances enterprise data by extracting entities, concepts and their relationships -> thereby converting this data into a multidimensional format and storing it in -> a database that can accommodate these enhancements. In our case the data is stored -> in a knowledge graph. This enhanced data is now ready for consumption by LLMs and -> other GenAI applications ensuring the data is consumable and context can be maintained. +>[CogniSwitch](https://www.cogniswitch.ai/aboutus) is an API based data platform that +> enhances enterprise data by extracting entities, concepts and their relationships +> thereby converting this data into a multidimensional format and storing it in +> a database that can accommodate these enhancements. In our case the data is stored +> in a knowledge graph. This enhanced data is now ready for consumption by LLMs and +> other GenAI applications ensuring the data is consumable and context can be maintained. > Thereby eliminating hallucinations and delivering accuracy. ## Toolkit diff --git a/docs/docs/integrations/providers/cohere.mdx b/docs/docs/integrations/providers/cohere.mdx index 0bd8417317d..e0bae9eaf7d 100644 --- a/docs/docs/integrations/providers/cohere.mdx +++ b/docs/docs/integrations/providers/cohere.mdx @@ -45,7 +45,7 @@ llm = Cohere() print(llm.invoke("Come up with a pet name")) ``` -Usage of the Cohere (legacy) [LLM model](/docs/integrations/llms/cohere) +Usage of the Cohere (legacy) [LLM model](/docs/integrations/llms/cohere) ### Tool calling ```python @@ -92,7 +92,7 @@ while res.tool_calls: print(res.content) ``` -Tool calling with Cohere LLM can be done by binding the necessary tools to the llm as seen above. +Tool calling with Cohere LLM can be done by binding the necessary tools to the llm as seen above. An alternative, is to support multi hop tool calling with the ReAct agent as seen below. ### ReAct Agent @@ -154,4 +154,4 @@ Usage of the Cohere [Text Embeddings model](/docs/integrations/text_embedding/co ### Reranker -Usage of the Cohere [Reranker](/docs/integrations/retrievers/cohere-reranker) \ No newline at end of file +Usage of the Cohere [Reranker](/docs/integrations/retrievers/cohere-reranker) diff --git a/docs/docs/integrations/providers/confluence.mdx b/docs/docs/integrations/providers/confluence.mdx index 27a7e274a21..fde9a1e0861 100644 --- a/docs/docs/integrations/providers/confluence.mdx +++ b/docs/docs/integrations/providers/confluence.mdx @@ -1,6 +1,6 @@ # Confluence ->[Confluence](https://www.atlassian.com/software/confluence) is a wiki collaboration platform that saves and organizes all of the project-related material. `Confluence` is a knowledge base that primarily handles content management activities. +>[Confluence](https://www.atlassian.com/software/confluence) is a wiki collaboration platform that saves and organizes all of the project-related material. `Confluence` is a knowledge base that primarily handles content management activities. ## Installation and Setup @@ -9,7 +9,7 @@ pip install atlassian-python-api ``` -We need to set up `username/api_key` or `Oauth2 login`. +We need to set up `username/api_key` or `Oauth2 login`. See [instructions](https://support.atlassian.com/atlassian-account/docs/manage-api-tokens-for-your-atlassian-account/). diff --git a/docs/docs/integrations/providers/connery.mdx b/docs/docs/integrations/providers/connery.mdx index 36684a97fa0..dceee50237e 100644 --- a/docs/docs/integrations/providers/connery.mdx +++ b/docs/docs/integrations/providers/connery.mdx @@ -1,14 +1,14 @@ # Connery ->[Connery SDK](https://github.com/connery-io/connery-sdk) is an NPM package that +>[Connery SDK](https://github.com/connery-io/connery-sdk) is an NPM package that > includes both an SDK and a CLI, designed for the development of plugins and actions. > ->The CLI automates many things in the development process. The SDK -> offers a JavaScript API for defining plugins and actions and packaging them -> into a plugin server with a standardized REST API generated from the metadata. -> The plugin server handles authorization, input validation, and logging. +>The CLI automates many things in the development process. The SDK +> offers a JavaScript API for defining plugins and actions and packaging them +> into a plugin server with a standardized REST API generated from the metadata. +> The plugin server handles authorization, input validation, and logging. > So you can focus on the logic of your actions. -> +> > See the use cases and examples in the [Connery SDK documentation](https://sdk.connery.io/docs/use-cases/) ## Toolkit diff --git a/docs/docs/integrations/providers/couchbase.mdx b/docs/docs/integrations/providers/couchbase.mdx index 70c318e794a..91358a4ca93 100644 --- a/docs/docs/integrations/providers/couchbase.mdx +++ b/docs/docs/integrations/providers/couchbase.mdx @@ -1,7 +1,7 @@ # Couchbase ->[Couchbase](http://couchbase.com/) is an award-winning distributed NoSQL cloud database -> that delivers unmatched versatility, performance, scalability, and financial value +>[Couchbase](http://couchbase.com/) is an award-winning distributed NoSQL cloud database +> that delivers unmatched versatility, performance, scalability, and financial value > for all of your cloud, mobile, AI, and edge computing applications. ## Installation and Setup @@ -77,7 +77,7 @@ db_password = "Password" # password for the database user # query is a valid SQL++ query query = """ - SELECT h.* FROM `travel-sample`.inventory.hotel h + SELECT h.* FROM `travel-sample`.inventory.hotel h WHERE h.country = 'United States' LIMIT 1 """ @@ -178,4 +178,4 @@ message_history = CouchbaseChatMessageHistory( message_history.add_user_message("hi!") ``` -API Reference: [CouchbaseChatMessageHistory](https://couchbase-ecosystem.github.io/langchain-couchbase/langchain_couchbase.html#module-langchain_couchbase.chat_message_histories) \ No newline at end of file +API Reference: [CouchbaseChatMessageHistory](https://couchbase-ecosystem.github.io/langchain-couchbase/langchain_couchbase.html#module-langchain_couchbase.chat_message_histories) diff --git a/docs/docs/integrations/providers/ctranslate2.mdx b/docs/docs/integrations/providers/ctranslate2.mdx index 0e3c3a9319e..563ed429c1f 100644 --- a/docs/docs/integrations/providers/ctranslate2.mdx +++ b/docs/docs/integrations/providers/ctranslate2.mdx @@ -1,14 +1,14 @@ # CTranslate2 ->[CTranslate2](https://opennmt.net/CTranslate2/quickstart.html) is a C++ and Python library +>[CTranslate2](https://opennmt.net/CTranslate2/quickstart.html) is a C++ and Python library > for efficient inference with Transformer models. > >The project implements a custom runtime that applies many performance optimization -> techniques such as weights quantization, layers fusion, batch reordering, etc., +> techniques such as weights quantization, layers fusion, batch reordering, etc., > to accelerate and reduce the memory usage of Transformer models on CPU and GPU. > ->A full list of features and supported models is included in the -> [project’s repository](https://opennmt.net/CTranslate2/guides/transformers.html). +>A full list of features and supported models is included in the +> [project’s repository](https://opennmt.net/CTranslate2/guides/transformers.html). > To start, please check out the official [quickstart guide](https://opennmt.net/CTranslate2/quickstart.html). diff --git a/docs/docs/integrations/providers/cube.mdx b/docs/docs/integrations/providers/cube.mdx index 9393bc36aa2..1237affc133 100644 --- a/docs/docs/integrations/providers/cube.mdx +++ b/docs/docs/integrations/providers/cube.mdx @@ -1,12 +1,12 @@ # Cube ->[Cube](https://cube.dev/) is the Semantic Layer for building data apps. It helps -> data engineers and application developers access data from modern data stores, +>[Cube](https://cube.dev/) is the Semantic Layer for building data apps. It helps +> data engineers and application developers access data from modern data stores, > organize it into consistent definitions, and deliver it to every application. ## Installation and Setup -We have to get the API key and the URL of the Cube instance. See +We have to get the API key and the URL of the Cube instance. See [these instructions](https://cube.dev/docs/product/apis-integrations/rest-api#configuration-base-path). diff --git a/docs/docs/integrations/providers/dappier.mdx b/docs/docs/integrations/providers/dappier.mdx index 2a1235984f1..8af111a3522 100644 --- a/docs/docs/integrations/providers/dappier.mdx +++ b/docs/docs/integrations/providers/dappier.mdx @@ -28,7 +28,7 @@ export DAPPIER_API_KEY="your-api-key" We also need to set our Dappier API credentials, which can be generated at the [Dappier site.](https://platform.dappier.com/profile/api-keys). -We can find the supported data models by heading over to the +We can find the supported data models by heading over to the [Dappier marketplace.](https://platform.dappier.com/marketplace) ## Chat models diff --git a/docs/docs/integrations/providers/dashvector.mdx b/docs/docs/integrations/providers/dashvector.mdx index b7ded751ddf..1cc9ec0170c 100644 --- a/docs/docs/integrations/providers/dashvector.mdx +++ b/docs/docs/integrations/providers/dashvector.mdx @@ -1,6 +1,6 @@ # DashVector -> [DashVector](https://help.aliyun.com/document_detail/2510225.html) is a fully-managed vectorDB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements. +> [DashVector](https://help.aliyun.com/document_detail/2510225.html) is a fully-managed vectorDB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements. This document demonstrates to leverage DashVector within the LangChain ecosystem. In particular, it shows how to install DashVector, and how to use it as a VectorStore plugin in LangChain. It is broken into two parts: installation and setup, and then references to specific DashVector wrappers. @@ -28,7 +28,7 @@ See the [use example](/docs/integrations/vectorstores/dashvector). ## Vector Store -A DashVector Collection is wrapped as a familiar VectorStore for native usage within LangChain, +A DashVector Collection is wrapped as a familiar VectorStore for native usage within LangChain, which allows it to be readily used for various scenarios, such as semantic search or example selection. You may import the vectorstore by: diff --git a/docs/docs/integrations/providers/databricks.md b/docs/docs/integrations/providers/databricks.mdx similarity index 99% rename from docs/docs/integrations/providers/databricks.md rename to docs/docs/integrations/providers/databricks.mdx index 2cf4961971a..f9bb6cc6ff2 100644 --- a/docs/docs/integrations/providers/databricks.md +++ b/docs/docs/integrations/providers/databricks.mdx @@ -20,7 +20,7 @@ First-party Databricks integrations are now available in the databricks-langchai pip install databricks-langchain ``` -The legacy langchain-databricks partner package is still available but will be soon deprecated. +The legacy langchain-databricks partner package is still available but will be soon deprecated. Chat Model ---------- @@ -52,7 +52,6 @@ llm = Databricks(endpoint="your-completion-endpoint") See the [usage example](/docs/integrations/llms/databricks) for more guidance on how to use it within your LangChain application. - Embeddings ---------- @@ -66,7 +65,6 @@ embeddings = DatabricksEmbeddings(endpoint="databricks-bge-large-en") See the [usage example](/docs/integrations/text_embedding/databricks) for more guidance on how to use it within your LangChain application. - Vector Search ------------- @@ -88,7 +86,6 @@ docs = dvs.similarity_search("What is vector search?) See the [usage example](/docs/integrations/vectorstores/databricks_vector_search) for how to set up vector indices and integrate them with LangChain. - MLflow Integration ------------------ @@ -103,6 +100,7 @@ See [MLflow LangChain Integration](/docs/integrations/providers/mlflow_tracking) SQLDatabase ----------- + To connect to Databricks SQL or query structured data, see the [Databricks structured retriever tool documentation](https://docs.databricks.com/en/generative-ai/agent-framework/structured-retrieval-tools.html#table-query-tool) and to create an agent using the above created SQL UDF see [Databricks UC Integration](https://docs.unitycatalog.io/ai/integrations/langchain/). Open Models diff --git a/docs/docs/integrations/providers/dataforseo.mdx b/docs/docs/integrations/providers/dataforseo.mdx index 3ded2eba84f..c2d0e3d354c 100644 --- a/docs/docs/integrations/providers/dataforseo.mdx +++ b/docs/docs/integrations/providers/dataforseo.mdx @@ -6,7 +6,7 @@ This page provides instructions on how to use the DataForSEO search APIs within ## Installation and Setup -Get a [DataForSEO API Access login and password](https://app.dataforseo.com/register), and set them as environment variables +Get a [DataForSEO API Access login and password](https://app.dataforseo.com/register), and set them as environment variables (`DATAFORSEO_LOGIN` and `DATAFORSEO_PASSWORD` respectively). ```python diff --git a/docs/docs/integrations/providers/dataherald.mdx b/docs/docs/integrations/providers/dataherald.mdx index 2b1c276df52..35b546164ba 100644 --- a/docs/docs/integrations/providers/dataherald.mdx +++ b/docs/docs/integrations/providers/dataherald.mdx @@ -5,7 +5,7 @@ This page covers how to use the `Dataherald API` within LangChain. ## Installation and Setup -- Install requirements with +- Install requirements with ```bash pip install dataherald ``` diff --git a/docs/docs/integrations/providers/deepinfra.mdx b/docs/docs/integrations/providers/deepinfra.mdx index 5eb2b1b3877..eb178bc7753 100644 --- a/docs/docs/integrations/providers/deepinfra.mdx +++ b/docs/docs/integrations/providers/deepinfra.mdx @@ -1,8 +1,8 @@ # DeepInfra ->[DeepInfra](https://deepinfra.com/docs) allows us to run the -> [latest machine learning models](https://deepinfra.com/models) with ease. -> DeepInfra takes care of all the heavy lifting related to running, scaling and monitoring +>[DeepInfra](https://deepinfra.com/docs) allows us to run the +> [latest machine learning models](https://deepinfra.com/models) with ease. +> DeepInfra takes care of all the heavy lifting related to running, scaling and monitoring > the models. Users can focus on your application and integrate the models with simple REST API calls. >DeepInfra provides [examples](https://deepinfra.com/docs/advanced/langchain) of integration with LangChain. diff --git a/docs/docs/integrations/providers/diffbot.mdx b/docs/docs/integrations/providers/diffbot.mdx index 1a9e9934642..e30a3ae2d61 100644 --- a/docs/docs/integrations/providers/diffbot.mdx +++ b/docs/docs/integrations/providers/diffbot.mdx @@ -8,9 +8,9 @@ ## Document Loader -Diffbot's [Extract API](https://docs.diffbot.com/reference/extract-introduction) is a service that structures and normalizes data from web pages. +Diffbot's [Extract API](https://docs.diffbot.com/reference/extract-introduction) is a service that structures and normalizes data from web pages. -Unlike traditional web scraping tools, `Diffbot Extract` doesn't require any rules to read the content on a page. It uses a computer vision model to classify a page into one of 20 possible types, and then transforms raw HTML markup into JSON. The resulting structured JSON follows a consistent [type-based ontology](https://docs.diffbot.com/docs/ontology), which makes it easy to extract data from multiple different web sources with the same schema. +Unlike traditional web scraping tools, `Diffbot Extract` doesn't require any rules to read the content on a page. It uses a computer vision model to classify a page into one of 20 possible types, and then transforms raw HTML markup into JSON. The resulting structured JSON follows a consistent [type-based ontology](https://docs.diffbot.com/docs/ontology), which makes it easy to extract data from multiple different web sources with the same schema. See a [usage example](/docs/integrations/document_loaders/diffbot). diff --git a/docs/docs/integrations/providers/dingo.mdx b/docs/docs/integrations/providers/dingo.mdx index b12a6a72cbc..5c3dadc6afc 100644 --- a/docs/docs/integrations/providers/dingo.mdx +++ b/docs/docs/integrations/providers/dingo.mdx @@ -1,12 +1,12 @@ # DingoDB ->[DingoDB](https://github.com/dingodb) is a distributed multi-modal vector -> database. It combines the features of a data lake and a vector database, -> allowing for the storage of any type of data (key-value, PDF, audio, -> video, etc.) regardless of its size. Utilizing DingoDB, you can construct -> your own Vector Ocean (the next-generation data architecture following data -> warehouse and data lake). This enables -> the analysis of both structured and unstructured data through +>[DingoDB](https://github.com/dingodb) is a distributed multi-modal vector +> database. It combines the features of a data lake and a vector database, +> allowing for the storage of any type of data (key-value, PDF, audio, +> video, etc.) regardless of its size. Utilizing DingoDB, you can construct +> your own Vector Ocean (the next-generation data architecture following data +> warehouse and data lake). This enables +> the analysis of both structured and unstructured data through > a singular SQL with exceptionally low latency in real time. ## Installation and Setup diff --git a/docs/docs/integrations/providers/discord-shikenso.mdx b/docs/docs/integrations/providers/discord-shikenso.mdx index 32b5a576cc7..833b4c30d53 100644 --- a/docs/docs/integrations/providers/discord-shikenso.mdx +++ b/docs/docs/integrations/providers/discord-shikenso.mdx @@ -62,4 +62,4 @@ send_tool = tools[1] # DiscordSendMessage ## Future Integrations Additional integrations (e.g., document loaders, chat loaders) could be added for Discord. -Check the [Discord Developer Docs](https://discord.com/developers/docs/intro) for more information, and watch for updates or advanced usage examples in the [langchain_discord GitHub repo](https://github.com/Shikenso-Analytics/langchain-discord). \ No newline at end of file +Check the [Discord Developer Docs](https://discord.com/developers/docs/intro) for more information, and watch for updates or advanced usage examples in the [langchain_discord GitHub repo](https://github.com/Shikenso-Analytics/langchain-discord). diff --git a/docs/docs/integrations/providers/discord.mdx b/docs/docs/integrations/providers/discord.mdx index 17b87229031..8ea1bf6e3a3 100644 --- a/docs/docs/integrations/providers/discord.mdx +++ b/docs/docs/integrations/providers/discord.mdx @@ -1,7 +1,7 @@ # Discord (community loader) ->[Discord](https://discord.com/) is a VoIP and instant messaging social platform. Users have the ability to communicate -> with voice calls, video calls, text messaging, media and files in private chats or as part of communities called +>[Discord](https://discord.com/) is a VoIP and instant messaging social platform. Users have the ability to communicate +> with voice calls, video calls, text messaging, media and files in private chats or as part of communities called > "servers". A server is a collection of persistent chat rooms and voice channels which can be accessed via invite links. ## Installation and Setup @@ -16,7 +16,7 @@ Follow these steps to download your `Discord` data: 2. Then go to **Privacy and Safety** 3. Head over to the **Request all of my Data** and click on **Request Data** button -It might take 30 days for you to receive your data. You'll receive an email at the address which is registered +It might take 30 days for you to receive your data. You'll receive an email at the address which is registered with Discord. That email will have a download button using which you would be able to download your personal Discord data. @@ -24,7 +24,7 @@ with Discord. That email will have a download button using which you would be ab See a [usage example](/docs/integrations/document_loaders/discord). -**NOTE:** The `DiscordChatLoader` is not the `ChatLoader` but a `DocumentLoader`. +**NOTE:** The `DiscordChatLoader` is not the `ChatLoader` but a `DocumentLoader`. It is used to load the data from the `Discord` data dump. For the `ChatLoader` see Chat Loader section below. diff --git a/docs/docs/integrations/providers/docarray.mdx b/docs/docs/integrations/providers/docarray.mdx index d1d41a19834..524e3764b4a 100644 --- a/docs/docs/integrations/providers/docarray.mdx +++ b/docs/docs/integrations/providers/docarray.mdx @@ -1,7 +1,7 @@ # DocArray -> [DocArray](https://docarray.jina.ai/) is a library for nested, unstructured, multimodal data in transit, -> including text, image, audio, video, 3D mesh, etc. It allows deep-learning engineers to efficiently process, +> [DocArray](https://docarray.jina.ai/) is a library for nested, unstructured, multimodal data in transit, +> including text, image, audio, video, 3D mesh, etc. It allows deep-learning engineers to efficiently process, > embed, search, recommend, store, and transfer multimodal data with a Pythonic API. diff --git a/docs/docs/integrations/providers/docugami.mdx b/docs/docs/integrations/providers/docugami.mdx index dcd0566c4a7..71dad812cd8 100644 --- a/docs/docs/integrations/providers/docugami.mdx +++ b/docs/docs/integrations/providers/docugami.mdx @@ -1,7 +1,7 @@ # Docugami ->[Docugami](https://docugami.com) converts business documents into a Document XML Knowledge Graph, generating forests -> of XML semantic trees representing entire documents. This is a rich representation that includes the semantic and +>[Docugami](https://docugami.com) converts business documents into a Document XML Knowledge Graph, generating forests +> of XML semantic trees representing entire documents. This is a rich representation that includes the semantic and > structural characteristics of various chunks in the document as an XML tree. ## Installation and Setup diff --git a/docs/docs/integrations/providers/docusaurus.mdx b/docs/docs/integrations/providers/docusaurus.mdx index e137d627724..bb76f1b017d 100644 --- a/docs/docs/integrations/providers/docusaurus.mdx +++ b/docs/docs/integrations/providers/docusaurus.mdx @@ -1,8 +1,8 @@ # Docusaurus ->[Docusaurus](https://docusaurus.io/) is a static-site generator which provides +>[Docusaurus](https://docusaurus.io/) is a static-site generator which provides > out-of-the-box documentation features. - + ## Installation and Setup diff --git a/docs/docs/integrations/providers/dria.mdx b/docs/docs/integrations/providers/dria.mdx index 7e3c5cdbace..6a9b3733633 100644 --- a/docs/docs/integrations/providers/dria.mdx +++ b/docs/docs/integrations/providers/dria.mdx @@ -1,9 +1,9 @@ # Dria ->[Dria](https://dria.co/) is a hub of public RAG models for developers to +>[Dria](https://dria.co/) is a hub of public RAG models for developers to > both contribute and utilize a shared embedding lake. -See more details about the LangChain integration with Dria +See more details about the LangChain integration with Dria at [this page](https://dria.co/docs/integrations/langchain). ## Installation and Setup diff --git a/docs/docs/integrations/providers/dropbox.mdx b/docs/docs/integrations/providers/dropbox.mdx index 590a58b9a68..7617f698516 100644 --- a/docs/docs/integrations/providers/dropbox.mdx +++ b/docs/docs/integrations/providers/dropbox.mdx @@ -1,8 +1,8 @@ # Dropbox ->[Dropbox](https://en.wikipedia.org/wiki/Dropbox) is a file hosting service that brings everything-traditional +>[Dropbox](https://en.wikipedia.org/wiki/Dropbox) is a file hosting service that brings everything-traditional > files, cloud content, and web shortcuts together in one place. - + ## Installation and Setup diff --git a/docs/docs/integrations/providers/duckduckgo_search.mdx b/docs/docs/integrations/providers/duckduckgo_search.mdx index 29ab01981f4..47d94490559 100644 --- a/docs/docs/integrations/providers/duckduckgo_search.mdx +++ b/docs/docs/integrations/providers/duckduckgo_search.mdx @@ -2,7 +2,7 @@ >[DuckDuckGo Search](https://github.com/deedy5/duckduckgo_search) is a package that > searches for words, documents, images, videos, news, maps and text -> translation using the `DuckDuckGo.com` search engine. It is downloading files +> translation using the `DuckDuckGo.com` search engine. It is downloading files > and images to a local hard drive. ## Installation and Setup diff --git a/docs/docs/integrations/providers/e2b.mdx b/docs/docs/integrations/providers/e2b.mdx index ee0ca085aa4..bb9cc675bad 100644 --- a/docs/docs/integrations/providers/e2b.mdx +++ b/docs/docs/integrations/providers/e2b.mdx @@ -1,6 +1,6 @@ # E2B ->[E2B](https://e2b.dev/) provides open-source secure sandboxes +>[E2B](https://e2b.dev/) provides open-source secure sandboxes > for AI-generated code execution. See more [here](https://github.com/e2b-dev). ## Installation and Setup diff --git a/docs/docs/integrations/providers/edenai.mdx b/docs/docs/integrations/providers/edenai.mdx index a33e92ec6a9..730043afdaf 100644 --- a/docs/docs/integrations/providers/edenai.mdx +++ b/docs/docs/integrations/providers/edenai.mdx @@ -1,15 +1,15 @@ # Eden AI ->[Eden AI](https://docs.edenai.co/docs/getting-started-with-eden-ai) user interface (UI) -> is designed for handling the AI projects. With `Eden AI Portal`, +>[Eden AI](https://docs.edenai.co/docs/getting-started-with-eden-ai) user interface (UI) +> is designed for handling the AI projects. With `Eden AI Portal`, > you can perform no-code AI using the best engines for the market. ## Installation and Setup -Accessing the Eden AI API requires an API key, which you can get by -[creating an account](https://app.edenai.run/user/register) and -heading [here](https://app.edenai.run/admin/account/settings). +Accessing the Eden AI API requires an API key, which you can get by +[creating an account](https://app.edenai.run/user/register) and +heading [here](https://app.edenai.run/admin/account/settings). ## LLMs diff --git a/docs/docs/integrations/providers/elasticsearch.mdx b/docs/docs/integrations/providers/elasticsearch.mdx index 734ef9d46ce..4efbfc29276 100644 --- a/docs/docs/integrations/providers/elasticsearch.mdx +++ b/docs/docs/integrations/providers/elasticsearch.mdx @@ -12,7 +12,7 @@ There are two ways to get started with Elasticsearch: #### Install Elasticsearch on your local machine via Docker -Example: Run a single-node Elasticsearch instance with security disabled. +Example: Run a single-node Elasticsearch instance with security disabled. This is not recommended for production use. ```bash @@ -58,8 +58,8 @@ from langchain_community.vectorstores.ecloud_vector_search import EcloudESVector ### ElasticsearchRetriever -The `ElasticsearchRetriever` enables flexible access to all Elasticsearch features -through the Query DSL. +The `ElasticsearchRetriever` enables flexible access to all Elasticsearch features +through the Query DSL. See a [usage example](/docs/integrations/retrievers/elasticsearch_retriever). diff --git a/docs/docs/integrations/providers/elevenlabs.mdx b/docs/docs/integrations/providers/elevenlabs.mdx index 56352730478..09fca44a682 100644 --- a/docs/docs/integrations/providers/elevenlabs.mdx +++ b/docs/docs/integrations/providers/elevenlabs.mdx @@ -1,15 +1,15 @@ # ElevenLabs ->[ElevenLabs](https://elevenlabs.io/about) is a voice AI research & deployment company +>[ElevenLabs](https://elevenlabs.io/about) is a voice AI research & deployment company > with a mission to make content universally accessible in any language & voice. > ->`ElevenLabs` creates the most realistic, versatile and contextually-aware -> AI audio, providing the ability to generate speech in hundreds of +>`ElevenLabs` creates the most realistic, versatile and contextually-aware +> AI audio, providing the ability to generate speech in hundreds of > new and existing voices in 29 languages. ## Installation and Setup -First, you need to set up an ElevenLabs account. You can follow the +First, you need to set up an ElevenLabs account. You can follow the [instructions here](https://docs.elevenlabs.io/welcome/introduction). Install the Python package: diff --git a/docs/docs/integrations/providers/embedchain.mdx b/docs/docs/integrations/providers/embedchain.mdx index 9078c6ab71d..592c40be571 100644 --- a/docs/docs/integrations/providers/embedchain.mdx +++ b/docs/docs/integrations/providers/embedchain.mdx @@ -1,11 +1,11 @@ # Embedchain -> [Embedchain](https://github.com/embedchain/embedchain) is a RAG framework to create +> [Embedchain](https://github.com/embedchain/embedchain) is a RAG framework to create > data pipelines. It loads, indexes, retrieves and syncs all the data. > ->It is available as an [open source package](https://github.com/embedchain/embedchain) +>It is available as an [open source package](https://github.com/embedchain/embedchain) > and as a [hosted platform solution](https://app.embedchain.ai/). - + ## Installation and Setup diff --git a/docs/docs/integrations/providers/epsilla.mdx b/docs/docs/integrations/providers/epsilla.mdx index 78da4d6a984..37bc3236da8 100644 --- a/docs/docs/integrations/providers/epsilla.mdx +++ b/docs/docs/integrations/providers/epsilla.mdx @@ -20,4 +20,4 @@ To import this vectorstore: from langchain_community.vectorstores import Epsilla ``` -For a more detailed walkthrough of the Epsilla wrapper, see [this notebook](/docs/integrations/vectorstores/epsilla) \ No newline at end of file +For a more detailed walkthrough of the Epsilla wrapper, see [this notebook](/docs/integrations/vectorstores/epsilla) diff --git a/docs/docs/integrations/providers/etherscan.mdx b/docs/docs/integrations/providers/etherscan.mdx index cc4e197b289..b1c0d2e279b 100644 --- a/docs/docs/integrations/providers/etherscan.mdx +++ b/docs/docs/integrations/providers/etherscan.mdx @@ -1,8 +1,8 @@ # Etherscan ->[Etherscan](https://docs.etherscan.io/) is the leading blockchain explorer, +>[Etherscan](https://docs.etherscan.io/) is the leading blockchain explorer, > search, API and analytics platform for `Ethereum`, a decentralized smart contracts platform. - + ## Installation and Setup diff --git a/docs/docs/integrations/providers/everlyai.mdx b/docs/docs/integrations/providers/everlyai.mdx index 2ec50703019..d059b9df1cc 100644 --- a/docs/docs/integrations/providers/everlyai.mdx +++ b/docs/docs/integrations/providers/everlyai.mdx @@ -1,11 +1,11 @@ # Everly AI -> [Everly AI](https://everlyai.xyz/) allows you to run your ML models at scale in the cloud. +> [Everly AI](https://everlyai.xyz/) allows you to run your ML models at scale in the cloud. > It also provides API access to [several LLM models](https://everlyai.xyz/). ## Installation and Setup -To use `Everly AI`, you will need an API key. Visit +To use `Everly AI`, you will need an API key. Visit [Everly AI](https://everlyai.xyz/) to create an API key in your profile. ## Chat models diff --git a/docs/docs/integrations/providers/facebook.mdx b/docs/docs/integrations/providers/facebook.mdx index 6734c9462e5..786381c8551 100644 --- a/docs/docs/integrations/providers/facebook.mdx +++ b/docs/docs/integrations/providers/facebook.mdx @@ -1,18 +1,18 @@ # Facebook - Meta ->[Meta Platforms, Inc.](https://www.facebook.com/), doing business as `Meta`, formerly -> named `Facebook, Inc.`, and `TheFacebook, Inc.`, is an American multinational technology -> conglomerate. The company owns and operates `Facebook`, `Instagram`, `Threads`, +>[Meta Platforms, Inc.](https://www.facebook.com/), doing business as `Meta`, formerly +> named `Facebook, Inc.`, and `TheFacebook, Inc.`, is an American multinational technology +> conglomerate. The company owns and operates `Facebook`, `Instagram`, `Threads`, > and `WhatsApp`, among other products and services. - + ## Embedding models ### LASER ->[LASER](https://github.com/facebookresearch/LASER) is a Python library developed by -> the `Meta AI Research` team and used for -> creating multilingual sentence embeddings for -> [over 147 languages as of 2/25/2024](https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200) +>[LASER](https://github.com/facebookresearch/LASER) is a Python library developed by +> the `Meta AI Research` team and used for +> creating multilingual sentence embeddings for +> [over 147 languages as of 2/25/2024](https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200) ```bash pip install laser_encoders @@ -28,7 +28,7 @@ from langchain_community.embeddings.laser import LaserEmbeddings ### Facebook Messenger ->[Messenger](https://en.wikipedia.org/wiki/Messenger_(software)) is an instant messaging app and +>[Messenger](https://en.wikipedia.org/wiki/Messenger_(software)) is an instant messaging app and > platform developed by `Meta Platforms`. Originally developed as `Facebook Chat` in 2008, the company revamped its > messaging service in 2010. @@ -42,9 +42,9 @@ from langchain_community.document_loaders import FacebookChatLoader ### Facebook Faiss ->[Facebook AI Similarity Search (Faiss)](https://engineering.fb.com/2017/03/29/data-infrastructure/faiss-a-library-for-efficient-similarity-search/) -> is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that -> search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting +>[Facebook AI Similarity Search (Faiss)](https://engineering.fb.com/2017/03/29/data-infrastructure/faiss-a-library-for-efficient-similarity-search/) +> is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that +> search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting > code for evaluation and parameter tuning. [Faiss documentation](https://faiss.ai/). @@ -71,7 +71,7 @@ from langchain_community.vectorstores import FAISS ### Facebook Messenger ->[Messenger](https://en.wikipedia.org/wiki/Messenger_(software)) is an instant messaging app and +>[Messenger](https://en.wikipedia.org/wiki/Messenger_(software)) is an instant messaging app and > platform developed by `Meta Platforms`. Originally developed as `Facebook Chat` in 2008, the company revamped its > messaging service in 2010. diff --git a/docs/docs/integrations/providers/fauna.mdx b/docs/docs/integrations/providers/fauna.mdx index 252c0101d2e..95b78628011 100644 --- a/docs/docs/integrations/providers/fauna.mdx +++ b/docs/docs/integrations/providers/fauna.mdx @@ -1,9 +1,9 @@ # Fauna ->[Fauna](https://fauna.com/) is a distributed document-relational database -> that combines the flexibility of documents with the power of a relational, +>[Fauna](https://fauna.com/) is a distributed document-relational database +> that combines the flexibility of documents with the power of a relational, > ACID compliant database that scales across regions, clouds or the globe. - + ## Installation and Setup diff --git a/docs/docs/integrations/providers/featherless-ai.mdx b/docs/docs/integrations/providers/featherless-ai.mdx index c10de11717b..1beb2d8ef8e 100644 --- a/docs/docs/integrations/providers/featherless-ai.mdx +++ b/docs/docs/integrations/providers/featherless-ai.mdx @@ -1,6 +1,6 @@ # Featherless AI -[Featherless AI](https://featherless.ai/) is a serverless AI inference platform that offers access to over 4300+ open-source models. Our goal is to make all AI models available for serverless inference. We provide inference via API to a continually expanding library of open-weight models. +[Featherless AI](https://featherless.ai/) is a serverless AI inference platform that offers access to over 4300+ open-source models. Our goal is to make all AI models available for serverless inference. We provide inference via API to a continually expanding library of open-weight models. # Installation and Setup `pip install langchain-featherless-ai` @@ -9,4 +9,4 @@ 3. Set up your API key as an environment variable(`FEATHERLESSAI_API_KEY`) # Model catalog -Visit our model catalog for an overview of all our models: https://featherless.ai/models \ No newline at end of file +Visit our model catalog for an overview of all our models: https://featherless.ai/models diff --git a/docs/docs/integrations/providers/fiddler.md b/docs/docs/integrations/providers/fiddler.mdx similarity index 85% rename from docs/docs/integrations/providers/fiddler.md rename to docs/docs/integrations/providers/fiddler.mdx index 87bffdff9cf..6f3742d3172 100644 --- a/docs/docs/integrations/providers/fiddler.md +++ b/docs/docs/integrations/providers/fiddler.mdx @@ -1,7 +1,7 @@ # Fiddler ->[Fiddler](https://www.fiddler.ai/) provides a unified platform to monitor, explain, analyze, -> and improve ML deployments at an enterprise scale. +>[Fiddler](https://www.fiddler.ai/) provides a unified platform to monitor, explain, analyze, +> and improve ML deployments at an enterprise scale. ## Installation and Setup @@ -19,7 +19,6 @@ pip install fiddler-client ## Callbacks - ```python from langchain_community.callbacks.fiddler_callback import FiddlerCallbackHandler ``` diff --git a/docs/docs/integrations/providers/firecrawl.mdx b/docs/docs/integrations/providers/firecrawl.mdx index 745c7637463..48f9e44c170 100644 --- a/docs/docs/integrations/providers/firecrawl.mdx +++ b/docs/docs/integrations/providers/firecrawl.mdx @@ -1,7 +1,7 @@ # FireCrawl ->[FireCrawl](https://firecrawl.dev/?ref=langchain) crawls and converts any website into LLM-ready data. -> It crawls all accessible subpages and give you clean markdown +>[FireCrawl](https://firecrawl.dev/?ref=langchain) crawls and converts any website into LLM-ready data. +> It crawls all accessible subpages and give you clean markdown > and metadata for each. No sitemap required. diff --git a/docs/docs/integrations/providers/fireworks.md b/docs/docs/integrations/providers/fireworks.mdx similarity index 89% rename from docs/docs/integrations/providers/fireworks.md rename to docs/docs/integrations/providers/fireworks.mdx index ea5c7de34f9..46cb20f6bbb 100644 --- a/docs/docs/integrations/providers/fireworks.md +++ b/docs/docs/integrations/providers/fireworks.mdx @@ -1,6 +1,6 @@ # Fireworks AI ->[Fireworks AI](https://fireworks.ai) is a generative AI inference platform to run and +>[Fireworks AI](https://fireworks.ai) is a generative AI inference platform to run and > customize models with industry-leading speed and production-readiness. @@ -9,7 +9,7 @@ - Install the Fireworks integration package. - ``` + ```bash pip install langchain-fireworks ``` @@ -44,7 +44,7 @@ from langchain_fireworks import ChatFireworks See a [usage example](/docs/integrations/llms/fireworks). ```python -from langchain_fireworks import Fireworks +from langchain_fireworks import Fireworks ``` ## Embedding models @@ -52,5 +52,5 @@ from langchain_fireworks import Fireworks See a [usage example](/docs/integrations/text_embedding/fireworks). ```python -from langchain_fireworks import FireworksEmbeddings +from langchain_fireworks import FireworksEmbeddings ``` diff --git a/docs/docs/integrations/providers/forefrontai.mdx b/docs/docs/integrations/providers/forefrontai.mdx index 4d447ee37a6..12b1841c311 100644 --- a/docs/docs/integrations/providers/forefrontai.mdx +++ b/docs/docs/integrations/providers/forefrontai.mdx @@ -1,7 +1,7 @@ # Forefront AI > [Forefront AI](https://forefront.ai/) is a platform enabling you to -> fine-tune and inference open-source text generation models +> fine-tune and inference open-source text generation models ## Installation and Setup @@ -16,4 +16,4 @@ See a [usage example](/docs/integrations/llms/forefrontai). ```python from langchain_community.llms import ForefrontAI -``` \ No newline at end of file +``` diff --git a/docs/docs/integrations/providers/gel.mdx b/docs/docs/integrations/providers/gel.mdx index 4c18945c742..a60576515d9 100644 --- a/docs/docs/integrations/providers/gel.mdx +++ b/docs/docs/integrations/providers/gel.mdx @@ -19,14 +19,14 @@ pip install langchain-gel ```gel using extension pgvector; - + module default { scalar type EmbeddingVector extending ext::pgvector::vector<1536>; type Record { required collection: str; text: str; - embedding: EmbeddingVector; + embedding: EmbeddingVector; external_id: str { constraint exclusive; }; @@ -34,7 +34,7 @@ module default { index ext::pgvector::hnsw_cosine(m := 16, ef_construction := 128) on (.embedding) - } + } } ``` @@ -50,7 +50,7 @@ from langchain_gel import GelVectorStore vector_store = GelVectorStore( embeddings=embeddings, -) +) ``` -See the full usage example [here](/docs/integrations/vectorstores/gel). \ No newline at end of file +See the full usage example [here](/docs/integrations/vectorstores/gel). diff --git a/docs/docs/integrations/providers/geopandas.mdx b/docs/docs/integrations/providers/geopandas.mdx index c14a29c40bd..69acf34868a 100644 --- a/docs/docs/integrations/providers/geopandas.mdx +++ b/docs/docs/integrations/providers/geopandas.mdx @@ -1,10 +1,10 @@ # Geopandas ->[GeoPandas](https://geopandas.org/) is an open source project to make working -> with geospatial data in python easier. `GeoPandas` extends the datatypes used by -> `pandas` to allow spatial operations on geometric types. +>[GeoPandas](https://geopandas.org/) is an open source project to make working +> with geospatial data in python easier. `GeoPandas` extends the datatypes used by +> `pandas` to allow spatial operations on geometric types. > Geometric operations are performed by `shapely`. - + ## Installation and Setup diff --git a/docs/docs/integrations/providers/github.mdx b/docs/docs/integrations/providers/github.mdx index 0b8c369198c..b44b2dc022a 100644 --- a/docs/docs/integrations/providers/github.mdx +++ b/docs/docs/integrations/providers/github.mdx @@ -1,10 +1,10 @@ # GitHub ->[GitHub](https://github.com/) is a developer platform that allows developers to create, -> store, manage and share their code. It uses `Git` software, providing the -> distributed version control of Git plus access control, bug tracking, +>[GitHub](https://github.com/) is a developer platform that allows developers to create, +> store, manage and share their code. It uses `Git` software, providing the +> distributed version control of Git plus access control, bug tracking, > software feature requests, task management, continuous integration, and wikis for every project. - + ## Installation and Setup @@ -24,8 +24,8 @@ from langchain_community.document_loaders import GitHubIssuesLoader, GithubFileL ## Tools/Toolkit ### GitHubToolkit -The `GitHub` toolkit contains tools that enable an LLM agent to interact -with a GitHub repository. +The `GitHub` toolkit contains tools that enable an LLM agent to interact +with a GitHub repository. The toolkit is a wrapper for the `PyGitHub` library. diff --git a/docs/docs/integrations/providers/gitlab.mdx b/docs/docs/integrations/providers/gitlab.mdx index 5aab6d547a2..735e36ced3e 100644 --- a/docs/docs/integrations/providers/gitlab.mdx +++ b/docs/docs/integrations/providers/gitlab.mdx @@ -1,18 +1,18 @@ # GitLab ->[GitLab Inc.](https://about.gitlab.com/) is an open-core company -> that operates `GitLab`, a DevOps software package that can develop, -> secure, and operate software. `GitLab` includes a distributed version +>[GitLab Inc.](https://about.gitlab.com/) is an open-core company +> that operates `GitLab`, a DevOps software package that can develop, +> secure, and operate software. `GitLab` includes a distributed version > control based on Git, including features such as access control, bug tracking, -> software feature requests, task management, and wikis for every project, -> as well as snippets. +> software feature requests, task management, and wikis for every project, +> as well as snippets. ## Tools/Toolkits ### GitLabToolkit -The `Gitlab` toolkit contains tools that enable an LLM agent to interact with a gitlab repository. +The `Gitlab` toolkit contains tools that enable an LLM agent to interact with a gitlab repository. The toolkit is a wrapper for the `python-gitlab` library. diff --git a/docs/docs/integrations/providers/goat.mdx b/docs/docs/integrations/providers/goat.mdx index 042a700b455..e4652f6fb0f 100644 --- a/docs/docs/integrations/providers/goat.mdx +++ b/docs/docs/integrations/providers/goat.mdx @@ -36,4 +36,4 @@ See how to do it [here](https://github.com/goat-sdk/goat/tree/main#-contributing ## Installation and Setup -Check out our [quickstart](https://github.com/goat-sdk/goat/tree/main/python/examples/by-framework/langchain) to see how to set up and install GOAT. +Check out our [quickstart](https://github.com/goat-sdk/goat/tree/main/python/examples/by-framework/langchain) to see how to set up and install GOAT. diff --git a/docs/docs/integrations/providers/google.mdx b/docs/docs/integrations/providers/google.mdx index 67bb994c7c3..33df8a6fea7 100644 --- a/docs/docs/integrations/providers/google.mdx +++ b/docs/docs/integrations/providers/google.mdx @@ -29,14 +29,14 @@ export GOOGLE_API_KEY="YOUR_API_KEY" ### Chat Models -Use the `ChatGoogleGenerativeAI` class to interact with Gemini 2.0 and 2.5 models. See +Use the `ChatGoogleGenerativeAI` class to interact with Gemini models. See details in [this guide](/docs/integrations/chat/google_generative_ai). ```python from langchain_google_genai import ChatGoogleGenerativeAI from langchain_core.messages import HumanMessage -llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash") +llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash") # Simple text invocation result = llm.invoke("Sing a ballad of LangChain.") @@ -61,14 +61,14 @@ The `image_url` can be a public URL, a GCS URI (`gs://...`), a local file path, ### Embedding Models -Generate text embeddings using models like `gemini-embedding-exp-03-07` with the `GoogleGenerativeAIEmbeddings` class. +Generate text embeddings using models like `gemini-embedding-001` with the `GoogleGenerativeAIEmbeddings` class. See a [usage example](/docs/integrations/text_embedding/google_generative_ai). ```python from langchain_google_genai import GoogleGenerativeAIEmbeddings -embeddings = GoogleGenerativeAIEmbeddings(model="models/gemini-embedding-exp-03-07") +embeddings = GoogleGenerativeAIEmbeddings(model="models/gemini-embedding-001") vector = embeddings.embed_query("What are embeddings?") print(vector[:5]) ``` @@ -83,7 +83,7 @@ See a [usage example](/docs/integrations/llms/google_ai). ```python from langchain_google_genai import GoogleGenerativeAI -llm = GoogleGenerativeAI(model="gemini-2.0-flash") +llm = GoogleGenerativeAI(model="gemini-2.5-flash") result = llm.invoke("Sing a ballad of LangChain.") print(result) ``` @@ -105,7 +105,7 @@ Google Cloud integrations typically use Application Default Credentials (ADC). R #### Vertex AI -Access chat models like `Gemini` via the Vertex AI platform. +Access chat models like Gemini via the Vertex AI platform. See a [usage example](/docs/integrations/chat/google_vertex_ai_palm). @@ -135,7 +135,7 @@ from langchain_google_vertexai.model_garden_maas.mistral import VertexModelGarde #### Gemma local from Hugging Face ->Local `Gemma` model loaded from `HuggingFace`. Requires `langchain-google-vertexai`. +>Local Gemma model loaded from HuggingFace. Requires `langchain-google-vertexai`. ```python from langchain_google_vertexai.gemma import GemmaChatLocalHF @@ -143,7 +143,7 @@ from langchain_google_vertexai.gemma import GemmaChatLocalHF #### Gemma local from Kaggle ->Local `Gemma` model loaded from `Kaggle`. Requires `langchain-google-vertexai`. +>Local Gemma model loaded from Kaggle. Requires `langchain-google-vertexai`. ```python from langchain_google_vertexai.gemma import GemmaChatLocalKaggle @@ -159,7 +159,7 @@ from langchain_google_vertexai.gemma import GemmaChatVertexAIModelGarden #### Vertex AI image captioning ->Implementation of the `Image Captioning model` as a chat. Requires `langchain-google-vertexai`. +>Implementation of the Image Captioning model as a chat. Requires `langchain-google-vertexai`. ```python from langchain_google_vertexai.vision_models import VertexAIImageCaptioningChat @@ -196,7 +196,7 @@ interface. #### Vertex AI Model Garden -Access `Gemini`, and hundreds of OSS models via `Vertex AI Model Garden` service. Requires `langchain-google-vertexai`. +Access Gemini, and hundreds of OSS models via Vertex AI Model Garden service. Requires `langchain-google-vertexai`. See a [usage example](/docs/integrations/llms/google_vertex_ai_palm#vertex-model-garden). @@ -206,7 +206,7 @@ from langchain_google_vertexai import VertexAIModelGarden #### Gemma local from Hugging Face ->Local `Gemma` model loaded from `HuggingFace`. Requires `langchain-google-vertexai`. +>Local Gemma model loaded from HuggingFace. Requires `langchain-google-vertexai`. ```python from langchain_google_vertexai.gemma import GemmaLocalHF @@ -214,7 +214,7 @@ from langchain_google_vertexai.gemma import GemmaLocalHF #### Gemma local from Kaggle ->Local `Gemma` model loaded from `Kaggle`. Requires `langchain-google-vertexai`. +>Local Gemma model loaded from Kaggle. Requires `langchain-google-vertexai`. ```python from langchain_google_vertexai.gemma import GemmaLocalKaggle @@ -230,7 +230,7 @@ from langchain_google_vertexai.gemma import GemmaVertexAIModelGarden #### Vertex AI image captioning ->Implementation of the `Image Captioning model` as an LLM. Requires `langchain-google-vertexai`. +>Implementation of the Image Captioning model as an LLM. Requires `langchain-google-vertexai`. ```python from langchain_google_vertexai.vision_models import VertexAIImageCaptioning @@ -1138,7 +1138,7 @@ Integrations with various Google services beyond the core Cloud Platform. #### Google Drive ->[Google Drive](https://en.wikipedia.org/wiki/Google_Drive) file storage. Currently supports `Google Docs`. +>[Google Drive](https://en.wikipedia.org/wiki/Google_Drive) file storage. Currently supports Google Docs. Install with Drive dependencies: @@ -1416,7 +1416,7 @@ from langchain_community.utilities import GoogleSerperAPIWrapper #### YouTube Search Tool ->Search `YouTube` videos without the official API. Requires `youtube_search` package. +>Search YouTube videos without the official API. Requires `youtube_search` package. ```bash pip install youtube_search langchain # Requires base langchain diff --git a/docs/docs/integrations/providers/google_serper.mdx b/docs/docs/integrations/providers/google_serper.mdx index c2a82c6b336..2bd39a3fe87 100644 --- a/docs/docs/integrations/providers/google_serper.mdx +++ b/docs/docs/integrations/providers/google_serper.mdx @@ -1,6 +1,6 @@ # Serper - Google Search API -This page covers how to use the [Serper](https://serper.dev) Google Search API within LangChain. Serper is a low-cost Google Search API that can be used to add answer box, knowledge graph, and organic results data from Google Search. +This page covers how to use the [Serper](https://serper.dev) Google Search API within LangChain. Serper is a low-cost Google Search API that can be used to add answer box, knowledge graph, and organic results data from Google Search. It is broken into two parts: setup, and then references to the specific Google Serper wrapper. ## Setup diff --git a/docs/docs/integrations/providers/gooseai.mdx b/docs/docs/integrations/providers/gooseai.mdx index f0bdf819d2e..6ead75c8907 100644 --- a/docs/docs/integrations/providers/gooseai.mdx +++ b/docs/docs/integrations/providers/gooseai.mdx @@ -1,9 +1,9 @@ # GooseAI ->[GooseAI](https://goose.ai) makes deploying NLP services easier and more accessible. -> `GooseAI` is a fully managed inference service delivered via API. -> With feature parity to other well known APIs, `GooseAI` delivers a plug-and-play solution -> for serving open source language models at the industry's best economics by simply +>[GooseAI](https://goose.ai) makes deploying NLP services easier and more accessible. +> `GooseAI` is a fully managed inference service delivered via API. +> With feature parity to other well known APIs, `GooseAI` delivers a plug-and-play solution +> for serving open source language models at the industry's best economics by simply > changing 2 lines in your code. ## Installation and Setup @@ -24,4 +24,4 @@ See a [usage example](/docs/integrations/llms/gooseai). ```python from langchain_community.llms import GooseAI -``` \ No newline at end of file +``` diff --git a/docs/docs/integrations/providers/gradient.mdx b/docs/docs/integrations/providers/gradient.mdx index 37cd04e91ec..df1ff875804 100644 --- a/docs/docs/integrations/providers/gradient.mdx +++ b/docs/docs/integrations/providers/gradient.mdx @@ -11,7 +11,7 @@ Get a [Gradient access token and workspace](https://gradient.ai/) and set it as ## LLM -There exists an Gradient LLM wrapper, which you can access with +There exists an Gradient LLM wrapper, which you can access with See a [usage example](/docs/integrations/llms/gradient). ```python @@ -20,7 +20,7 @@ from langchain_community.llms import GradientLLM ## Text Embedding Model -There exists an Gradient Embedding model, which you can access with +There exists an Gradient Embedding model, which you can access with ```python from langchain_community.embeddings import GradientEmbeddings ``` diff --git a/docs/docs/integrations/providers/graphsignal.mdx b/docs/docs/integrations/providers/graphsignal.mdx index 6e4867d3579..c69311e2771 100644 --- a/docs/docs/integrations/providers/graphsignal.mdx +++ b/docs/docs/integrations/providers/graphsignal.mdx @@ -24,7 +24,7 @@ To additionally trace any function or code, you can use a decorator or a context ```python @graphsignal.trace_function -def handle_request(): +def handle_request(): chain.run("some initial text") ``` diff --git a/docs/docs/integrations/providers/grobid.mdx b/docs/docs/integrations/providers/grobid.mdx index 9740854ed11..78e86e8d4a6 100644 --- a/docs/docs/integrations/providers/grobid.mdx +++ b/docs/docs/integrations/providers/grobid.mdx @@ -43,4 +43,4 @@ loader = GenericLoader.from_filesystem( docs = loader.load() ``` Chunk metadata will include Bounding Boxes. Although these are a bit funky to parse, -they are explained in https://grobid.readthedocs.io/en/latest/Coordinates-in-PDF/ \ No newline at end of file +they are explained in https://grobid.readthedocs.io/en/latest/Coordinates-in-PDF/ diff --git a/docs/docs/integrations/providers/groq.mdx b/docs/docs/integrations/providers/groq.mdx index 53a8dab417b..072023bf037 100644 --- a/docs/docs/integrations/providers/groq.mdx +++ b/docs/docs/integrations/providers/groq.mdx @@ -1,12 +1,12 @@ # Groq ->[Groq](https://groq.com) developed the world's first Language Processing Unit™, or `LPU`. -> The `Groq LPU` has a deterministic, single core streaming architecture that sets the standard +>[Groq](https://groq.com) developed the world's first Language Processing Unit™, or `LPU`. +> The `Groq LPU` has a deterministic, single core streaming architecture that sets the standard > for GenAI inference speed with predictable and repeatable performance for any given workload. > ->Beyond the architecture, `Groq` software is designed to empower developers like you with -> the tools you need to create innovative, powerful AI applications. -> +>Beyond the architecture, `Groq` software is designed to empower developers like you with +> the tools you need to create innovative, powerful AI applications. +> >With Groq as your engine, you can: >* Achieve uncompromised low latency and performance for real-time AI and HPC inferences 🔥 >* Know the exact performance and compute time for any given workload 🔮 diff --git a/docs/docs/integrations/providers/hacker_news.mdx b/docs/docs/integrations/providers/hacker_news.mdx index fc232a3db0c..ddc53eea4c8 100644 --- a/docs/docs/integrations/providers/hacker_news.mdx +++ b/docs/docs/integrations/providers/hacker_news.mdx @@ -1,8 +1,8 @@ # Hacker News ->[Hacker News](https://en.wikipedia.org/wiki/Hacker_News) (sometimes abbreviated as `HN`) is a social news -> website focusing on computer science and entrepreneurship. It is run by the investment fund and startup -> incubator `Y Combinator`. In general, content that can be submitted is defined as "anything that gratifies +>[Hacker News](https://en.wikipedia.org/wiki/Hacker_News) (sometimes abbreviated as `HN`) is a social news +> website focusing on computer science and entrepreneurship. It is run by the investment fund and startup +> incubator `Y Combinator`. In general, content that can be submitted is defined as "anything that gratifies > one's intellectual curiosity." ## Installation and Setup diff --git a/docs/docs/integrations/providers/hazy_research.mdx b/docs/docs/integrations/providers/hazy_research.mdx index 13cbda6b8ee..65670347b60 100644 --- a/docs/docs/integrations/providers/hazy_research.mdx +++ b/docs/docs/integrations/providers/hazy_research.mdx @@ -10,7 +10,7 @@ It is broken into two parts: installation and setup, and then references to spec ### LLM -There exists an LLM wrapper around Hazy Research's `manifest` library. +There exists an LLM wrapper around Hazy Research's `manifest` library. `manifest` is a python library which is itself a wrapper around many model providers, and adds in caching, history, and more. To use this wrapper: diff --git a/docs/docs/integrations/providers/hologres.mdx b/docs/docs/integrations/providers/hologres.mdx index 8dbb3d80faa..b40b88594f8 100644 --- a/docs/docs/integrations/providers/hologres.mdx +++ b/docs/docs/integrations/providers/hologres.mdx @@ -1,7 +1,7 @@ # Hologres ->[Hologres](https://www.alibabacloud.com/help/en/hologres/latest/introduction) is a unified real-time data warehousing service developed by Alibaba Cloud. You can use Hologres to write, update, process, and analyze large amounts of data in real time. ->`Hologres` supports standard `SQL` syntax, is compatible with `PostgreSQL`, and supports most PostgreSQL functions. Hologres supports online analytical processing (OLAP) and ad hoc analysis for up to petabytes of data, and provides high-concurrency and low-latency online data services. +>[Hologres](https://www.alibabacloud.com/help/en/hologres/latest/introduction) is a unified real-time data warehousing service developed by Alibaba Cloud. You can use Hologres to write, update, process, and analyze large amounts of data in real time. +>`Hologres` supports standard `SQL` syntax, is compatible with `PostgreSQL`, and supports most PostgreSQL functions. Hologres supports online analytical processing (OLAP) and ad hoc analysis for up to petabytes of data, and provides high-concurrency and low-latency online data services. >`Hologres` provides **vector database** functionality by adopting [Proxima](https://www.alibabacloud.com/help/en/hologres/latest/vector-processing). >`Proxima` is a high-performance software library developed by `Alibaba DAMO Academy`. It allows you to search for the nearest neighbors of vectors. Proxima provides higher stability and performance than similar open-source software such as Faiss. Proxima allows you to search for similar text or image embeddings with high throughput and low latency. Hologres is deeply integrated with Proxima to provide a high-performance vector search service. diff --git a/docs/docs/integrations/providers/html2text.mdx b/docs/docs/integrations/providers/html2text.mdx index c8cf35210ff..83ac22bebf1 100644 --- a/docs/docs/integrations/providers/html2text.mdx +++ b/docs/docs/integrations/providers/html2text.mdx @@ -1,6 +1,6 @@ # HTML to text ->[html2text](https://github.com/Alir3z4/html2text/) is a Python package that converts a page of `HTML` into clean, easy-to-read plain `ASCII text`. +>[html2text](https://github.com/Alir3z4/html2text/) is a Python package that converts a page of `HTML` into clean, easy-to-read plain `ASCII text`. The ASCII also happens to be a valid `Markdown` (a text-to-HTML format). diff --git a/docs/docs/integrations/providers/huawei.mdx b/docs/docs/integrations/providers/huawei.mdx index 22b12ca717f..a1086857b83 100644 --- a/docs/docs/integrations/providers/huawei.mdx +++ b/docs/docs/integrations/providers/huawei.mdx @@ -1,11 +1,11 @@ # Huawei ->[Huawei Technologies Co., Ltd.](https://www.huawei.com/) is a Chinese multinational +>[Huawei Technologies Co., Ltd.](https://www.huawei.com/) is a Chinese multinational > digital communications technology corporation. -> ->[Huawei Cloud](https://www.huaweicloud.com/intl/en-us/product/) provides a comprehensive suite of -> global cloud computing services. - +> +>[Huawei Cloud](https://www.huaweicloud.com/intl/en-us/product/) provides a comprehensive suite of +> global cloud computing services. + ## Installation and Setup diff --git a/docs/docs/integrations/providers/huggingface.mdx b/docs/docs/integrations/providers/huggingface.mdx index b956fc60241..bc3a7fcc3e7 100644 --- a/docs/docs/integrations/providers/huggingface.mdx +++ b/docs/docs/integrations/providers/huggingface.mdx @@ -2,7 +2,7 @@ All functionality related to [Hugging Face Hub](https://huggingface.co/) and libraries like [transformers](https://huggingface.co/docs/transformers/index), [sentence transformers](https://sbert.net/), and [datasets](https://huggingface.co/docs/datasets/index). -> [Hugging Face](https://huggingface.co/) is an AI platform with all major open source models, datasets, MCPs, and demos. +> [Hugging Face](https://huggingface.co/) is an AI platform with all major open source models, datasets, MCPs, and demos. > It supplies model inference locally and via serverless [Inference Providers](https://huggingface.co/docs/inference-providers). > > You can use [Inference Providers](https://huggingface.co/docs/inference-providers) to run open source models like DeepSeek R1 on scalable serverless infrastructure. @@ -128,9 +128,9 @@ from langchain_community.document_loaders.hugging_face_dataset import HuggingFac >Load model information from `Hugging Face Hub`, including README content. > ->This loader interfaces with the `Hugging Face Models API` to fetch -> and load model metadata and README files. -> The API allows you to search and filter models based on +>This loader interfaces with the `Hugging Face Models API` to fetch +> and load model metadata and README files. +> The API allows you to search and filter models based on > specific criteria such as model tags, authors, and more. ```python diff --git a/docs/docs/integrations/providers/hyperbrowser.mdx b/docs/docs/integrations/providers/hyperbrowser.mdx index 69f9c7a4fc2..3964124c058 100644 --- a/docs/docs/integrations/providers/hyperbrowser.mdx +++ b/docs/docs/integrations/providers/hyperbrowser.mdx @@ -41,7 +41,7 @@ Hyperbrowser provides a number of Browser Agents tools. Currently we supported - OpenAI CUA - Browser Use -You can see more details [here](/docs/integrations/tools/hyperbrowser_browser_agent_tools) +You can see more details [here](/docs/integrations/tools/hyperbrowser_browser_agent_tools) #### Browser Use Tool A general-purpose browser automation tool that can handle various web tasks through natural language instructions. diff --git a/docs/docs/integrations/providers/ibm.mdx b/docs/docs/integrations/providers/ibm.mdx index f53d023d08e..a0ef1e76f72 100644 --- a/docs/docs/integrations/providers/ibm.mdx +++ b/docs/docs/integrations/providers/ibm.mdx @@ -4,10 +4,10 @@ LangChain integrations related to IBM technologies, including the [IBM watsonx.ai](https://www.ibm.com/products/watsonx-ai) platform and DB2 database. ## Watsonx AI -IBM® watsonx.ai™ AI studio is part of the IBM [watsonx](https://www.ibm.com/watsonx)™ AI and data platform, bringing together new generative -AI capabilities powered by [foundation models](https://www.ibm.com/products/watsonx-ai/foundation-models) and traditional machine learning (ML) -into a powerful studio spanning the AI lifecycle. Tune and guide models with your enterprise data to meet your needs with easy-to-use tools for -building and refining performant prompts. With watsonx.ai, you can build AI applications in a fraction of the time and with a fraction of the data. +IBM® watsonx.ai™ AI studio is part of the IBM [watsonx](https://www.ibm.com/watsonx)™ AI and data platform, bringing together new generative +AI capabilities powered by [foundation models](https://www.ibm.com/products/watsonx-ai/foundation-models) and traditional machine learning (ML) +into a powerful studio spanning the AI lifecycle. Tune and guide models with your enterprise data to meet your needs with easy-to-use tools for +building and refining performant prompts. With watsonx.ai, you can build AI applications in a fraction of the time and with a fraction of the data. Watsonx.ai offers: - **Multi-model variety and flexibility:** Choose from IBM-developed, open-source and third-party models, or build your own model. diff --git a/docs/docs/integrations/providers/ieit_systems.mdx b/docs/docs/integrations/providers/ieit_systems.mdx index d81d0be3f74..362179d8a37 100644 --- a/docs/docs/integrations/providers/ieit_systems.mdx +++ b/docs/docs/integrations/providers/ieit_systems.mdx @@ -1,8 +1,8 @@ # IEIT Systems ->[IEIT Systems](https://en.ieisystem.com/) is a Chinese information technology company -> established in 1999. It provides the IT infrastructure products, solutions, -> and services, innovative IT products and solutions across cloud computing, +>[IEIT Systems](https://en.ieisystem.com/) is a Chinese information technology company +> established in 1999. It provides the IT infrastructure products, solutions, +> and services, innovative IT products and solutions across cloud computing, > big data, and artificial intelligence. @@ -18,7 +18,7 @@ from langchain_community.llms.yuan2 import Yuan2 See the [installation instructions](/docs/integrations/chat/yuan2/#setting-up-your-api-server). -Yuan2.0 provided an OpenAI compatible API, and ChatYuan2 is integrated into langchain by using `OpenAI client`. +Yuan2.0 provided an OpenAI compatible API, and ChatYuan2 is integrated into langchain by using `OpenAI client`. Therefore, ensure the `openai` package is installed. ```bash diff --git a/docs/docs/integrations/providers/ifixit.mdx b/docs/docs/integrations/providers/ifixit.mdx index fdcb4ba8023..1ef1ec91b06 100644 --- a/docs/docs/integrations/providers/ifixit.mdx +++ b/docs/docs/integrations/providers/ifixit.mdx @@ -1,6 +1,6 @@ # iFixit ->[iFixit](https://www.ifixit.com) is the largest, open repair community on the web. The site contains nearly 100k +>[iFixit](https://www.ifixit.com) is the largest, open repair community on the web. The site contains nearly 100k > repair manuals, 200k Questions & Answers on 42k devices, and all the data is licensed under `CC-BY-NC-SA 3.0`. ## Installation and Setup diff --git a/docs/docs/integrations/providers/iflytek.mdx b/docs/docs/integrations/providers/iflytek.mdx index 9852830511c..2c6a7dad944 100644 --- a/docs/docs/integrations/providers/iflytek.mdx +++ b/docs/docs/integrations/providers/iflytek.mdx @@ -1,8 +1,8 @@ # iFlytek ->[iFlytek](https://www.iflytek.com) is a Chinese information technology company -> established in 1999. It creates voice recognition software and -> voice-based internet/mobile products covering education, communication, +>[iFlytek](https://www.iflytek.com) is a Chinese information technology company +> established in 1999. It creates voice recognition software and +> voice-based internet/mobile products covering education, communication, > music, intelligent toys industries. diff --git a/docs/docs/integrations/providers/imsdb.mdx b/docs/docs/integrations/providers/imsdb.mdx index 8b30a2dea98..759b1945893 100644 --- a/docs/docs/integrations/providers/imsdb.mdx +++ b/docs/docs/integrations/providers/imsdb.mdx @@ -1,7 +1,7 @@ # IMSDb >[IMSDb](https://imsdb.com/) is the `Internet Movie Script Database`. -> +> ## Installation and Setup There isn't any special setup for it. diff --git a/docs/docs/integrations/providers/infinity.mdx b/docs/docs/integrations/providers/infinity.mdx index 887a8584036..27ef0537bd7 100644 --- a/docs/docs/integrations/providers/infinity.mdx +++ b/docs/docs/integrations/providers/infinity.mdx @@ -4,7 +4,7 @@ ## Text Embedding Model -There exists an infinity Embedding model, which you can access with +There exists an infinity Embedding model, which you can access with ```python from langchain_community.embeddings import InfinityEmbeddings ``` diff --git a/docs/docs/integrations/providers/iugu.mdx b/docs/docs/integrations/providers/iugu.mdx index 5abbeaa8a06..84814d81caa 100644 --- a/docs/docs/integrations/providers/iugu.mdx +++ b/docs/docs/integrations/providers/iugu.mdx @@ -1,9 +1,9 @@ # Iugu >[Iugu](https://www.iugu.com/) is a Brazilian services and software as a service (SaaS) -> company. It offers payment-processing software and application programming +> company. It offers payment-processing software and application programming > interfaces for e-commerce websites and mobile applications. - + ## Installation and Setup diff --git a/docs/docs/integrations/providers/jaguar.mdx b/docs/docs/integrations/providers/jaguar.mdx index 839a34ad326..00c0c20f933 100644 --- a/docs/docs/integrations/providers/jaguar.mdx +++ b/docs/docs/integrations/providers/jaguar.mdx @@ -34,7 +34,7 @@ Environment Variables in client programs: export OPENAI_API_KEY="......" export JAGUAR_API_KEY="......" - + ## Jaguar API Together with LangChain, a Jaguar client class is provided by importing it in Python: diff --git a/docs/docs/integrations/providers/javelin_ai_gateway.mdx b/docs/docs/integrations/providers/javelin_ai_gateway.mdx index d678e34597e..e1767f8606e 100644 --- a/docs/docs/integrations/providers/javelin_ai_gateway.mdx +++ b/docs/docs/integrations/providers/javelin_ai_gateway.mdx @@ -1,14 +1,14 @@ # Javelin AI Gateway -[The Javelin AI Gateway](https://www.getjavelin.io) service is a high-performance, enterprise grade API Gateway for AI applications. -It is designed to streamline the usage and access of various large language model (LLM) providers, +[The Javelin AI Gateway](https://www.getjavelin.io) service is a high-performance, enterprise grade API Gateway for AI applications. +It is designed to streamline the usage and access of various large language model (LLM) providers, such as OpenAI, Cohere, Anthropic and custom large language models within an organization by incorporating -robust access security for all interactions with LLMs. +robust access security for all interactions with LLMs. -Javelin offers a high-level interface that simplifies the interaction with LLMs by providing a unified endpoint -to handle specific LLM related requests. +Javelin offers a high-level interface that simplifies the interaction with LLMs by providing a unified endpoint +to handle specific LLM related requests. -See the Javelin AI Gateway [documentation](https://docs.getjavelin.io) for more details. +See the Javelin AI Gateway [documentation](https://docs.getjavelin.io) for more details. [Javelin Python SDK](https://www.github.com/getjavelin/javelin-python) is an easy to use client library meant to be embedded into AI Applications ## Installation and Setup diff --git a/docs/docs/integrations/providers/joplin.mdx b/docs/docs/integrations/providers/joplin.mdx index b3c83acc5ff..673562a118c 100644 --- a/docs/docs/integrations/providers/joplin.mdx +++ b/docs/docs/integrations/providers/joplin.mdx @@ -1,12 +1,12 @@ # Joplin ->[Joplin](https://joplinapp.org/) is an open-source note-taking app. It captures your thoughts +>[Joplin](https://joplinapp.org/) is an open-source note-taking app. It captures your thoughts > and securely accesses them from any device. - + ## Installation and Setup -The `Joplin API` requires an access token. +The `Joplin API` requires an access token. You can find installation instructions [here](https://joplinapp.org/api/references/rest_api/). diff --git a/docs/docs/integrations/providers/koboldai.mdx b/docs/docs/integrations/providers/koboldai.mdx index 66db8d9df42..06853bedc46 100644 --- a/docs/docs/integrations/providers/koboldai.mdx +++ b/docs/docs/integrations/providers/koboldai.mdx @@ -1,10 +1,10 @@ # KoboldAI ->[KoboldAI](https://koboldai.com/) is a free, open-source project that allows users to run AI models locally -> on their own computer. +>[KoboldAI](https://koboldai.com/) is a free, open-source project that allows users to run AI models locally +> on their own computer. > It's a browser-based front-end that can be used for writing or role playing with an AI. ->[KoboldAI](https://github.com/KoboldAI/KoboldAI-Client) is a "a browser-based front-end for -> AI-assisted writing with multiple local & remote AI models...". +>[KoboldAI](https://github.com/KoboldAI/KoboldAI-Client) is a "a browser-based front-end for +> AI-assisted writing with multiple local & remote AI models...". > It has a public and local API that can be used in LangChain. ## Installation and Setup diff --git a/docs/docs/integrations/providers/konko.mdx b/docs/docs/integrations/providers/konko.mdx index c7146778c96..4d069d4f6ba 100644 --- a/docs/docs/integrations/providers/konko.mdx +++ b/docs/docs/integrations/providers/konko.mdx @@ -62,4 +62,4 @@ See a usage [example](/docs/integrations/chat/konko). chat_response = chat_instance([msg]) ``` -For further assistance, contact [support@konko.ai](mailto:support@konko.ai) or join our [Discord](https://discord.gg/TXV2s3z7RZ). \ No newline at end of file +For further assistance, contact [support@konko.ai](mailto:support@konko.ai) or join our [Discord](https://discord.gg/TXV2s3z7RZ). diff --git a/docs/docs/integrations/providers/konlpy.mdx b/docs/docs/integrations/providers/konlpy.mdx index d4d925144e3..3b2ebc72848 100644 --- a/docs/docs/integrations/providers/konlpy.mdx +++ b/docs/docs/integrations/providers/konlpy.mdx @@ -1,6 +1,6 @@ # KoNLPY ->[KoNLPy](https://konlpy.org/) is a Python package for natural language processing (NLP) +>[KoNLPy](https://konlpy.org/) is a Python package for natural language processing (NLP) > of the Korean language. diff --git a/docs/docs/integrations/providers/lakefs.mdx b/docs/docs/integrations/providers/lakefs.mdx index c38d5bb4928..b53f238f4a5 100644 --- a/docs/docs/integrations/providers/lakefs.mdx +++ b/docs/docs/integrations/providers/lakefs.mdx @@ -1,7 +1,7 @@ # lakeFS ->[lakeFS](https://docs.lakefs.io/) provides scalable version control over -> the data lake, and uses Git-like semantics to create and access those versions. +>[lakeFS](https://docs.lakefs.io/) provides scalable version control over +> the data lake, and uses Git-like semantics to create and access those versions. ## Installation and Setup diff --git a/docs/docs/integrations/providers/langchain_decorators.mdx b/docs/docs/integrations/providers/langchain_decorators.mdx index 1d51f06735b..b7525c3110f 100644 --- a/docs/docs/integrations/providers/langchain_decorators.mdx +++ b/docs/docs/integrations/providers/langchain_decorators.mdx @@ -6,7 +6,7 @@ Disclaimer: `LangChain decorators` is not created by the LangChain team and is n >`LangChain decorators` is a layer on the top of LangChain that provides syntactic sugar 🍭 for writing custom langchain prompts and chains > ->For Feedback, Issues, Contributions - please raise an issue here: +>For Feedback, Issues, Contributions - please raise an issue here: >[ju-bezdek/langchain-decorators](https://github.com/ju-bezdek/langchain-decorators) @@ -27,7 +27,7 @@ Here is a simple example of a code written with **LangChain Decorators ✨** @llm_prompt def write_me_short_post(topic:str, platform:str="twitter", audience:str = "developers")->str: """ - Write me a short header for my post about {topic} for {platform} platform. + Write me a short header for my post about {topic} for {platform} platform. It should be for {audience} audience. (Max 15 words) """ @@ -52,7 +52,7 @@ Good idea on how to start is to review the examples here: - [colab notebook](https://colab.research.google.com/drive/1no-8WfeP6JaLD9yUtkPgym6x0G9ZYZOG#scrollTo=N4cf__D0E2Yk) # Defining other parameters -Here we are just marking a function as a prompt with `llm_prompt` decorator, turning it effectively into a LLMChain. Instead of running it +Here we are just marking a function as a prompt with `llm_prompt` decorator, turning it effectively into a LLMChain. Instead of running it Standard LLMchain takes much more init parameter than just inputs_variables and prompt... here is this implementation detail hidden in the decorator. @@ -82,7 +82,7 @@ PromptTypes.AGENT_REASONING.llm = ChatOpenAI() class MyCustomPromptTypes(PromptTypes): GPT4=PromptTypeSettings(llm=ChatOpenAI(model="gpt-4")) -@llm_prompt(prompt_type=MyCustomPromptTypes.GPT4) +@llm_prompt(prompt_type=MyCustomPromptTypes.GPT4) def write_a_complicated_code(app_idea:str)->str: ... @@ -112,7 +112,7 @@ To pass any of these, just declare them in the function (or use kwargs to pass a async def write_me_short_post(topic:str, platform:str="twitter", memory:SimpleMemory = None): """ {history_key} - Write me a short header for my post about {topic} for {platform} platform. + Write me a short header for my post about {topic} for {platform} platform. It should be for {audience} audience. (Max 15 words) """ @@ -125,7 +125,7 @@ await write_me_short_post(topic="old movies") # Simplified streaming If we want to leverage streaming: - - we need to define prompt as async function + - we need to define prompt as async function - turn on the streaming on the decorator, or we can define PromptType with streaming on - capture the stream using StreamingContext @@ -140,10 +140,10 @@ from langchain_decorators import StreamingContext, llm_prompt # this will mark the prompt for streaming (useful if we want stream just some prompts in our app... but don't want to pass distribute the callback handlers) # note that only async functions can be streamed (will get an error if it's not) -@llm_prompt(capture_stream=True) +@llm_prompt(capture_stream=True) async def write_me_short_post(topic:str, platform:str="twitter", audience:str = "developers"): """ - Write me a short header for my post about {topic} for {platform} platform. + Write me a short header for my post about {topic} for {platform} platform. It should be for {audience} audience. (Max 15 words) """ @@ -156,7 +156,7 @@ tokens=[] def capture_stream_func(new_token:str): tokens.append(new_token) -# if we want to capture the stream, we need to wrap the execution into StreamingContext... +# if we want to capture the stream, we need to wrap the execution into StreamingContext... # this will allow us to capture the stream even if the prompt call is hidden inside higher level method # only the prompts marked with capture_stream will be captured here with StreamingContext(stream_to_stdout=True, callback=capture_stream_func): @@ -171,7 +171,7 @@ print(result) # Prompt declarations -By default the prompt is the whole function docs, unless you mark your prompt +By default the prompt is the whole function docs, unless you mark your prompt ## Documenting your prompt @@ -185,7 +185,7 @@ def write_me_short_post(topic:str, platform:str="twitter", audience:str = "devel It needs to be a code block, marked as a `` language ``` - Write me a short header for my post about {topic} for {platform} platform. + Write me a short header for my post about {topic} for {platform} platform. It should be for {audience} audience. (Max 15 words) ``` @@ -193,7 +193,7 @@ def write_me_short_post(topic:str, platform:str="twitter", audience:str = "devel Now only to code block above will be used as a prompt, and the rest of the docstring will be used as a description for developers. (It has also a nice benefit that IDE (like VS code) will display the prompt properly (not trying to parse it as markdown, and thus not showing new lines properly)) """ - return + return ``` ## Chat messages prompt @@ -206,23 +206,23 @@ def simulate_conversation(human_input:str, agent_role:str="a pirate"): """ ## System message - note the `:system` suffix inside the tag - + ``` You are a {agent_role} hacker. You mus act like one. You reply always in code, using python or javascript code block... for example: - + ... do not reply with anything else.. just with code - respecting your role. ``` - # human message + # human message (we are using the real role that are enforced by the LLM - GPT supports system, assistant, user) ``` Helo, who are you ``` a reply: - + ``` \``` python <<- escaping inner code block with \ that should be part of the prompt @@ -230,7 +230,7 @@ def simulate_conversation(human_input:str, agent_role:str="a pirate"): print("Argh... hello you pesky pirate") \``` ``` - + we can also add some history using placeholder ``` {history} @@ -293,7 +293,7 @@ write_name_suggestions(company_business="sells cookies", count=5) ## More complex structures -for dict / pydantic you need to specify the formatting instructions... +for dict / pydantic you need to specify the formatting instructions... this can be tedious, that's why you can let the output parser gegnerate you the instructions based on the model (pydantic) ``` python @@ -343,12 +343,12 @@ class AssistantPersonality(BaseModel): We can reference any {field} or {a_property} inside our prompt... and combine it with {function_kwarg} in the method """ - + @llm_prompt def introduce_your_self(self)->str: """ ```  - You are an assistant named {assistant_name}. + You are an assistant named {assistant_name}. Your role is to act as {assistant_role} ``` ``` @@ -356,7 +356,7 @@ class AssistantPersonality(BaseModel): ``` """ - + personality = AssistantPersonality(assistant_name="John", assistant_role="a pirate") diff --git a/docs/docs/integrations/providers/langfair.mdx b/docs/docs/integrations/providers/langfair.mdx index c140649e985..5039f44df2b 100644 --- a/docs/docs/integrations/providers/langfair.mdx +++ b/docs/docs/integrations/providers/langfair.mdx @@ -1,6 +1,6 @@ # LangFair: Use-Case Level LLM Bias and Fairness Assessments -LangFair is a comprehensive Python library designed for conducting bias and fairness assessments of large language model (LLM) use cases. The LangFair [repository](https://github.com/cvs-health/langfair) includes a comprehensive framework for [choosing bias and fairness metrics](https://github.com/cvs-health/langfair/tree/main#-choosing-bias-and-fairness-metrics-for-an-llm-use-case), along with [demo notebooks](https://github.com/cvs-health/langfair/tree/main/examples) and a [technical playbook](https://arxiv.org/abs/2407.10853) that discusses LLM bias and fairness risks, evaluation metrics, and best practices. +LangFair is a comprehensive Python library designed for conducting bias and fairness assessments of large language model (LLM) use cases. The LangFair [repository](https://github.com/cvs-health/langfair) includes a comprehensive framework for [choosing bias and fairness metrics](https://github.com/cvs-health/langfair/tree/main#-choosing-bias-and-fairness-metrics-for-an-llm-use-case), along with [demo notebooks](https://github.com/cvs-health/langfair/tree/main/examples) and a [technical playbook](https://arxiv.org/abs/2407.10853) that discusses LLM bias and fairness risks, evaluation metrics, and best practices. Explore our [documentation site](https://cvs-health.github.io/langfair/) for detailed instructions on using LangFair. @@ -16,7 +16,7 @@ pip install langfair ``` ### Usage Examples -Below are code samples illustrating how to use LangFair to assess bias and fairness risks in text generation and summarization use cases. The below examples assume the user has already defined a list of prompts from their use case, `prompts`. +Below are code samples illustrating how to use LangFair to assess bias and fairness risks in text generation and summarization use cases. The below examples assume the user has already defined a list of prompts from their use case, `prompts`. ##### Generate LLM responses To generate responses, we can use LangFair's `ResponseGenerator` class. First, we must create a `langchain` LLM object. Below we use `ChatVertexAI`, but **any of [LangChain’s LLM classes](https://js.langchain.com/docs/integrations/chat/) may be used instead**. Note that `InMemoryRateLimiter` is to used to avoid rate limit errors. @@ -24,7 +24,7 @@ To generate responses, we can use LangFair's `ResponseGenerator` class. First, w from langchain_google_vertexai import ChatVertexAI from langchain_core.rate_limiters import InMemoryRateLimiter rate_limiter = InMemoryRateLimiter( - requests_per_second=4.5, check_every_n_seconds=0.5, max_bucket_size=280, + requests_per_second=4.5, check_every_n_seconds=0.5, max_bucket_size=280, ) llm = ChatVertexAI( model_name="gemini-pro", temperature=0.3, rate_limiter=rate_limiter @@ -49,8 +49,8 @@ tm = ToxicityMetrics( # device=device, # uncomment if GPU is available, ) tox_result = tm.evaluate( - prompts=duplicated_prompts, - responses=responses, + prompts=duplicated_prompts, + responses=responses, return_data=True ) tox_result['metrics'] @@ -90,7 +90,7 @@ Counterfactual metrics can be easily computed with `CounterfactualMetrics`. from langfair.metrics.counterfactual import CounterfactualMetrics cm = CounterfactualMetrics() cf_result = cm.evaluate( - texts1=male_responses, + texts1=male_responses, texts2=female_responses, attribute='gender' ) @@ -107,7 +107,7 @@ To streamline assessments for text generation and summarization use cases, the ` ```python from langfair.auto import AutoEval auto_object = AutoEval( - prompts=prompts, + prompts=prompts, langchain_llm=llm, # toxicity_device=device # uncomment if GPU is available ) @@ -126,4 +126,4 @@ results['metrics'] # 'RougeL Similarity': 0.5195852482361165, # 'Bleu Similarity': 0.3278433712872481, # 'Sentiment Bias': 0.0009947145187601957}}} -``` \ No newline at end of file +``` diff --git a/docs/docs/integrations/providers/langfuse.mdx b/docs/docs/integrations/providers/langfuse.mdx index 77d6c7fce69..5eeb78d8260 100644 --- a/docs/docs/integrations/providers/langfuse.mdx +++ b/docs/docs/integrations/providers/langfuse.mdx @@ -8,7 +8,7 @@ You can configure the integration via (1) constructor arguments or (2) environment variables. Get your Langfuse credentials by signing up at [cloud.langfuse.com](https://cloud.langfuse.com) or [self-hosting Langfuse](https://langfuse.com/self-hosting). -### Constructor arguments +### Constructor arguments ```python pip install langfuse @@ -19,29 +19,29 @@ from langfuse import Langfuse, get_client from langfuse.langchain import CallbackHandler from langchain_openai import ChatOpenAI # Example LLM from langchain_core.prompts import ChatPromptTemplate - + # Initialize Langfuse client with constructor arguments Langfuse( public_key="your-public-key", secret_key="your-secret-key", host="https://cloud.langfuse.com" # Optional: defaults to https://cloud.langfuse.com ) - + # Get the configured client instance langfuse = get_client() - + # Initialize the Langfuse handler langfuse_handler = CallbackHandler() - + # Create your LangChain components llm = ChatOpenAI(model_name="gpt-4o") prompt = ChatPromptTemplate.from_template("Tell me a joke about {topic}") chain = prompt | llm - + # Run your chain with Langfuse tracing response = chain.invoke({"topic": "cats"}, config={"callbacks": [langfuse_handler]}) print(response.content) - + # Flush events to Langfuse in short-lived applications langfuse.flush() ``` @@ -61,9 +61,9 @@ LANGFUSE_HOST="https://cloud.langfuse.com" # Initialize Langfuse handler from langfuse.langchain import CallbackHandler langfuse_handler = CallbackHandler() - + # Your Langchain code - + # Add Langfuse handler as callback (classic and LCEL) chain.invoke({"input": ""}, config={"callbacks": [langfuse_handler]}) ``` @@ -178,4 +178,4 @@ Example trace in Langfuse: https://cloud.langfuse.com/project/cloramnkj0002jz088 ![Trace view of chat app in Langfuse](https://langfuse.com/images/cookbook/integration-langgraph/integration_langgraph_chatapp_trace.png) - Check out the [full notebook](https://langfuse.com/docs/integrations/langchain/example-python-langgraph) to see more examples. -- To learn how to evaluate the performance of your LangGraph application, check out the [LangGraph evaluation guide](https://langfuse.com/docs/integrations/langchain/example-langgraph-agents). \ No newline at end of file +- To learn how to evaluate the performance of your LangGraph application, check out the [LangGraph evaluation guide](https://langfuse.com/docs/integrations/langchain/example-langgraph-agents). diff --git a/docs/docs/integrations/providers/llamaedge.mdx b/docs/docs/integrations/providers/llamaedge.mdx index 64fbb50d389..55693ee53d5 100644 --- a/docs/docs/integrations/providers/llamaedge.mdx +++ b/docs/docs/integrations/providers/llamaedge.mdx @@ -1,6 +1,6 @@ # LlamaEdge ->[LlamaEdge](https://llamaedge.com/docs/intro/) is the easiest & fastest way to run customized +>[LlamaEdge](https://llamaedge.com/docs/intro/) is the easiest & fastest way to run customized > and fine-tuned LLMs locally or on the edge. > >* Lightweight inference apps. `LlamaEdge` is in MBs instead of GBs diff --git a/docs/docs/integrations/providers/llamafile.mdx b/docs/docs/integrations/providers/llamafile.mdx index d0d0f268d10..7b43bb2d514 100644 --- a/docs/docs/integrations/providers/llamafile.mdx +++ b/docs/docs/integrations/providers/llamafile.mdx @@ -1,12 +1,12 @@ # llamafile ->[llamafile](https://github.com/Mozilla-Ocho/llamafile) lets you distribute and run LLMs +>[llamafile](https://github.com/Mozilla-Ocho/llamafile) lets you distribute and run LLMs > with a single file. ->`llamafile` makes open LLMs much more accessible to both developers and end users. -> `llamafile` is doing that by combining [llama.cpp](https://github.com/ggerganov/llama.cpp) with -> [Cosmopolitan Libc](https://github.com/jart/cosmopolitan) into one framework that collapses -> all the complexity of LLMs down to a single-file executable (called a "llamafile") +>`llamafile` makes open LLMs much more accessible to both developers and end users. +> `llamafile` is doing that by combining [llama.cpp](https://github.com/ggerganov/llama.cpp) with +> [Cosmopolitan Libc](https://github.com/jart/cosmopolitan) into one framework that collapses +> all the complexity of LLMs down to a single-file executable (called a "llamafile") > that runs locally on most computers, with no installation. diff --git a/docs/docs/integrations/providers/localai.mdx b/docs/docs/integrations/providers/localai.mdx index 663eb592498..7a95bda66e3 100644 --- a/docs/docs/integrations/providers/localai.mdx +++ b/docs/docs/integrations/providers/localai.mdx @@ -1,9 +1,9 @@ # LocalAI ->[LocalAI](https://localai.io/) is the free, Open Source OpenAI alternative. -> `LocalAI` act as a drop-in replacement REST API that’s compatible with OpenAI API -> specifications for local inferencing. It allows you to run LLMs, generate images, -> audio (and not only) locally or on-prem with consumer grade hardware, +>[LocalAI](https://localai.io/) is the free, Open Source OpenAI alternative. +> `LocalAI` act as a drop-in replacement REST API that’s compatible with OpenAI API +> specifications for local inferencing. It allows you to run LLMs, generate images, +> audio (and not only) locally or on-prem with consumer grade hardware, > supporting multiple model families and architectures. :::caution @@ -17,7 +17,7 @@ The source code is available on [Github](https://github.com/mkhludnev/langchain- ## Installation and Setup -We have to install several python packages: +We have to install several python packages: ```bash pip install tenacity openai diff --git a/docs/docs/integrations/providers/marqo.md b/docs/docs/integrations/providers/marqo.mdx similarity index 97% rename from docs/docs/integrations/providers/marqo.md rename to docs/docs/integrations/providers/marqo.mdx index 1e18569309e..df9a3afabd2 100644 --- a/docs/docs/integrations/providers/marqo.md +++ b/docs/docs/integrations/providers/marqo.mdx @@ -6,13 +6,14 @@ This page covers how to use the Marqo ecosystem within LangChain. Marqo is a tensor search engine that uses embeddings stored in in-memory HNSW indexes to achieve cutting edge search speeds. Marqo can scale to hundred-million document indexes with horizontal index sharding and allows for async and non-blocking data upload and search. Marqo uses the latest machine learning models from PyTorch, Huggingface, OpenAI and more. You can start with a pre-configured model or bring your own. The built in ONNX support and conversion allows for faster inference and higher throughput on both CPU and GPU. -Because Marqo include its own inference your documents can have a mix of text and images, you can bring Marqo indexes with data from your other systems into the langchain ecosystem without having to worry about your embeddings being compatible. +Because Marqo include its own inference your documents can have a mix of text and images, you can bring Marqo indexes with data from your other systems into the langchain ecosystem without having to worry about your embeddings being compatible. Deployment of Marqo is flexible, you can get started yourself with our docker image or [contact us about our managed cloud offering!](https://www.marqo.ai/pricing) To run Marqo locally with our docker image, [see our getting started.](https://docs.marqo.ai/latest/) ## Installation and Setup + - Install the Python SDK with `pip install marqo` ## Wrappers @@ -24,6 +25,7 @@ There exists a wrapper around Marqo indexes, allowing you to use them within the The Marqo vectorstore can also work with existing multimodal indexes where your documents have a mix of images and text, for more information refer to [our documentation](https://docs.marqo.ai/latest/#multi-modal-and-cross-modal-search). Note that instantiating the Marqo vectorstore with an existing multimodal index will disable the ability to add any new documents to it via the langchain vectorstore `add_texts` method. To import this vectorstore: + ```python from langchain_community.vectorstores import Marqo ``` diff --git a/docs/docs/integrations/providers/mediawikidump.mdx b/docs/docs/integrations/providers/mediawikidump.mdx index 52f5fde1e71..db07083ef48 100644 --- a/docs/docs/integrations/providers/mediawikidump.mdx +++ b/docs/docs/integrations/providers/mediawikidump.mdx @@ -1,7 +1,7 @@ # MediaWikiDump ->[MediaWiki XML Dumps](https://www.mediawiki.org/wiki/Manual:Importing_XML_dumps) contain the content of a wiki -> (wiki pages with all their revisions), without the site-related data. A XML dump does not create a full backup +>[MediaWiki XML Dumps](https://www.mediawiki.org/wiki/Manual:Importing_XML_dumps) contain the content of a wiki +> (wiki pages with all their revisions), without the site-related data. A XML dump does not create a full backup > of the wiki database, the dump does not contain user accounts, images, edit logs, etc. diff --git a/docs/docs/integrations/providers/meilisearch.mdx b/docs/docs/integrations/providers/meilisearch.mdx index 31cc5d4c22a..b7868fc0425 100644 --- a/docs/docs/integrations/providers/meilisearch.mdx +++ b/docs/docs/integrations/providers/meilisearch.mdx @@ -1,10 +1,10 @@ # Meilisearch > [Meilisearch](https://meilisearch.com) is an open-source, lightning-fast, and hyper -> relevant search engine. -> It comes with great defaults to help developers build snappy search experiences. +> relevant search engine. +> It comes with great defaults to help developers build snappy search experiences. > -> You can [self-host Meilisearch](https://www.meilisearch.com/docs/learn/getting_started/installation#local-installation) +> You can [self-host Meilisearch](https://www.meilisearch.com/docs/learn/getting_started/installation#local-installation) > or run on [Meilisearch Cloud](https://www.meilisearch.com/pricing). > >`Meilisearch v1.3` supports vector search. diff --git a/docs/docs/integrations/providers/memcached.mdx b/docs/docs/integrations/providers/memcached.mdx index f7719deda40..57ce8424c47 100644 --- a/docs/docs/integrations/providers/memcached.mdx +++ b/docs/docs/integrations/providers/memcached.mdx @@ -31,4 +31,4 @@ llm.invoke("Which city is the most crowded city in the USA?") llm.invoke("Which city is the most crowded city in the USA?") ``` -Learn more in the [example notebook](/docs/integrations/llm_caching#memcached-cache) \ No newline at end of file +Learn more in the [example notebook](/docs/integrations/llm_caching#memcached-cache) diff --git a/docs/docs/integrations/providers/microsoft.mdx b/docs/docs/integrations/providers/microsoft.mdx index 64ac3c61361..c8651f20933 100644 --- a/docs/docs/integrations/providers/microsoft.mdx +++ b/docs/docs/integrations/providers/microsoft.mdx @@ -6,7 +6,7 @@ keywords: [azure] All functionality related to [Microsoft Azure](https://portal.azure.com) and other [Microsoft](https://www.microsoft.com) products. -## Chat Models +## Chat Models Microsoft offers three main options for accessing chat models through Azure: @@ -126,7 +126,7 @@ embed_model = AzureAIEmbeddingsModel( ### Azure AI Data ->[Azure AI Studio](https://ai.azure.com/) provides the capability to upload data assets +>[Azure AI Studio](https://ai.azure.com/) provides the capability to upload data assets > to cloud storage and register existing data assets from the following sources: > >- `Microsoft OneLake` @@ -150,7 +150,7 @@ from langchain.document_loaders import AzureAIDataLoader >[Azure AI Document Intelligence](https://aka.ms/doc-intelligence) (formerly known > as `Azure Form Recognizer`) is machine-learning -> based service that extracts texts (including handwriting), tables, document structures, +> based service that extracts texts (including handwriting), tables, document structures, > and key-value-pairs > from digital or scanned PDFs, images, Office and HTML files. > @@ -246,13 +246,13 @@ from langchain_community.document_loaders import UnstructuredWordDocumentLoader ### Microsoft Excel ->[Microsoft Excel](https://en.wikipedia.org/wiki/Microsoft_Excel) is a spreadsheet editor developed by -> Microsoft for Windows, macOS, Android, iOS and iPadOS. -> It features calculation or computation capabilities, graphing tools, pivot tables, and a macro programming +>[Microsoft Excel](https://en.wikipedia.org/wiki/Microsoft_Excel) is a spreadsheet editor developed by +> Microsoft for Windows, macOS, Android, iOS and iPadOS. +> It features calculation or computation capabilities, graphing tools, pivot tables, and a macro programming > language called Visual Basic for Applications (VBA). Excel forms part of the Microsoft 365 suite of software. -The `UnstructuredExcelLoader` is used to load `Microsoft Excel` files. The loader works with both `.xlsx` and `.xls` files. -The page content will be the raw text of the Excel file. If you use the loader in `"elements"` mode, an HTML +The `UnstructuredExcelLoader` is used to load `Microsoft Excel` files. The loader works with both `.xlsx` and `.xls` files. +The page content will be the raw text of the Excel file. If you use the loader in `"elements"` mode, an HTML representation of the Excel file will be available in the document metadata under the `text_as_html` key. See a [usage example](/docs/integrations/document_loaders/microsoft_excel). @@ -264,8 +264,8 @@ from langchain_community.document_loaders import UnstructuredExcelLoader ### Microsoft SharePoint ->[Microsoft SharePoint](https://en.wikipedia.org/wiki/SharePoint) is a website-based collaboration system -> that uses workflow applications, “list” databases, and other web parts and security features to +>[Microsoft SharePoint](https://en.wikipedia.org/wiki/SharePoint) is a website-based collaboration system +> that uses workflow applications, “list” databases, and other web parts and security features to > empower business teams to work together developed by Microsoft. See a [usage example](/docs/integrations/document_loaders/microsoft_sharepoint). @@ -301,9 +301,9 @@ from langchain_community.document_loaders.onenote import OneNoteLoader ### Playwright URL Loader ->[Playwright](https://github.com/microsoft/playwright) is an open-source automation tool -> developed by `Microsoft` that allows you to programmatically control and automate -> web browsers. It is designed for end-to-end testing, scraping, and automating +>[Playwright](https://github.com/microsoft/playwright) is an open-source automation tool +> developed by `Microsoft` that allows you to programmatically control and automate +> web browsers. It is designed for end-to-end testing, scraping, and automating > tasks across various web browsers such as `Chromium`, `Firefox`, and `WebKit`. @@ -322,7 +322,7 @@ from langchain_community.document_loaders.onenote import OneNoteLoader ## Vector Stores ### Azure Cosmos DB -AI agents can rely on Azure Cosmos DB as a unified [memory system](https://learn.microsoft.com/en-us/azure/cosmos-db/ai-agents#memory-can-make-or-break-agents) solution, enjoying speed, scale, and simplicity. This service successfully [enabled OpenAI's ChatGPT service](https://www.youtube.com/watch?v=6IIUtEFKJec&t) to scale dynamically with high reliability and low maintenance. Powered by an atom-record-sequence engine, it is the world's first globally distributed [NoSQL](https://learn.microsoft.com/en-us/azure/cosmos-db/distributed-nosql), [relational](https://learn.microsoft.com/en-us/azure/cosmos-db/distributed-relational), and [vector database](https://learn.microsoft.com/en-us/azure/cosmos-db/vector-database) service that offers a serverless mode. +AI agents can rely on Azure Cosmos DB as a unified [memory system](https://learn.microsoft.com/en-us/azure/cosmos-db/ai-agents#memory-can-make-or-break-agents) solution, enjoying speed, scale, and simplicity. This service successfully [enabled OpenAI's ChatGPT service](https://www.youtube.com/watch?v=6IIUtEFKJec&t) to scale dynamically with high reliability and low maintenance. Powered by an atom-record-sequence engine, it is the world's first globally distributed [NoSQL](https://learn.microsoft.com/en-us/azure/cosmos-db/distributed-nosql), [relational](https://learn.microsoft.com/en-us/azure/cosmos-db/distributed-relational), and [vector database](https://learn.microsoft.com/en-us/azure/cosmos-db/vector-database) service that offers a serverless mode. Below are two available Azure Cosmos DB APIs that can provide vector store functionalities. @@ -392,9 +392,9 @@ from langchain_community.vectorstores import AzureCosmosDBNoSQLVectorSearch >[Azure Database for PostgreSQL - Flexible Server](https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/service-overview) is a relational database service based on the open-source Postgres database engine. It's a fully managed database-as-a-service that can handle mission-critical workloads with predictable performance, security, high availability, and dynamic scalability. -See [set up instructions](https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/quickstart-create-server-portal) for Azure Database for PostgreSQL. +See [set up instructions](https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/quickstart-create-server-portal) for Azure Database for PostgreSQL. -See a [usage example](/docs/integrations/memory/postgres_chat_message_history/). Simply use the [connection string](https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/connect-python?tabs=cmd%2Cpassword#add-authentication-code) from your Azure Portal. +See a [usage example](/docs/integrations/memory/postgres_chat_message_history/). Simply use the [connection string](https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/connect-python?tabs=cmd%2Cpassword#add-authentication-code) from your Azure Portal. Since Azure Database for PostgreSQL is open-source Postgres, you can use the [LangChain's Postgres support](/docs/integrations/vectorstores/pgvector/) to connect to Azure Database for PostgreSQL. @@ -459,11 +459,11 @@ from langchain_community.retrievers import AzureAISearchRetriever ### Azure Database for PostgreSQL >[Azure Database for PostgreSQL - Flexible Server](https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/service-overview) is a relational database service based on the open-source Postgres database engine. It's a fully managed database-as-a-service that can handle mission-critical workloads with predictable performance, security, high availability, and dynamic scalability. -See [set up instructions](https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/quickstart-create-server-portal) for Azure Database for PostgreSQL. +See [set up instructions](https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/quickstart-create-server-portal) for Azure Database for PostgreSQL. -You need to [enable pgvector extension](https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/how-to-use-pgvector) in your database to use Postgres as a vector store. Once you have the extension enabled, you can use the [PGVector in LangChain](/docs/integrations/vectorstores/pgvector/) to connect to Azure Database for PostgreSQL. +You need to [enable pgvector extension](https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/how-to-use-pgvector) in your database to use Postgres as a vector store. Once you have the extension enabled, you can use the [PGVector in LangChain](/docs/integrations/vectorstores/pgvector/) to connect to Azure Database for PostgreSQL. -See a [usage example](/docs/integrations/vectorstores/pgvector/). Simply use the [connection string](https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/connect-python?tabs=cmd%2Cpassword#add-authentication-code) from your Azure Portal. +See a [usage example](/docs/integrations/vectorstores/pgvector/). Simply use the [connection string](https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/connect-python?tabs=cmd%2Cpassword#add-authentication-code) from your Azure Portal. ## Tools @@ -621,9 +621,9 @@ from langchain_community.tools.powerbi.tool import QueryPowerBITool ### PlayWright Browser Toolkit ->[Playwright](https://github.com/microsoft/playwright) is an open-source automation tool -> developed by `Microsoft` that allows you to programmatically control and automate -> web browsers. It is designed for end-to-end testing, scraping, and automating +>[Playwright](https://github.com/microsoft/playwright) is an open-source automation tool +> developed by `Microsoft` that allows you to programmatically control and automate +> web browsers. It is designed for end-to-end testing, scraping, and automating > tasks across various web browsers such as `Chromium`, `Firefox`, and `WebKit`. We need to install several python packages. @@ -673,7 +673,7 @@ from langchain_community.graphs.graph_document import GraphDocument, Node, Relat ### Bing Search API ->[Microsoft Bing](https://www.bing.com/), commonly referred to as `Bing` or `Bing Search`, +>[Microsoft Bing](https://www.bing.com/), commonly referred to as `Bing` or `Bing Search`, > is a web search engine owned and operated by `Microsoft`. See a [usage example](/docs/integrations/tools/bing_search). @@ -686,9 +686,9 @@ from langchain_community.utilities import BingSearchAPIWrapper ### Microsoft Presidio ->[Presidio](https://microsoft.github.io/presidio/) (Origin from Latin praesidium ‘protection, garrison’) -> helps to ensure sensitive data is properly managed and governed. It provides fast identification and -> anonymization modules for private entities in text and images such as credit card numbers, names, +>[Presidio](https://microsoft.github.io/presidio/) (Origin from Latin praesidium ‘protection, garrison’) +> helps to ensure sensitive data is properly managed and governed. It provides fast identification and +> anonymization modules for private entities in text and images such as credit card numbers, names, > locations, social security numbers, bitcoin wallets, US phone numbers, financial data and more. First, you need to install several python packages and download a `SpaCy` model. diff --git a/docs/docs/integrations/providers/mlx.mdx b/docs/docs/integrations/providers/mlx.mdx index dc859305cde..1699b83cda3 100644 --- a/docs/docs/integrations/providers/mlx.mdx +++ b/docs/docs/integrations/providers/mlx.mdx @@ -1,7 +1,7 @@ # MLX ->[MLX](https://ml-explore.github.io/mlx/build/html/index.html) is a `NumPy`-like array framework -> designed for efficient and flexible machine learning on `Apple` silicon, +>[MLX](https://ml-explore.github.io/mlx/build/html/index.html) is a `NumPy`-like array framework +> designed for efficient and flexible machine learning on `Apple` silicon, > brought to you by `Apple machine learning research`. diff --git a/docs/docs/integrations/providers/modal.mdx b/docs/docs/integrations/providers/modal.mdx index 7e02799d717..44cb4e30e2a 100644 --- a/docs/docs/integrations/providers/modal.mdx +++ b/docs/docs/integrations/providers/modal.mdx @@ -1,7 +1,7 @@ # Modal This page covers how to use the Modal ecosystem to run LangChain custom LLMs. -It is broken into two parts: +It is broken into two parts: 1. Modal installation and web endpoint deployment 2. Using deployed web endpoint with `LLM` wrapper class. diff --git a/docs/docs/integrations/providers/mongodb.mdx b/docs/docs/integrations/providers/mongodb.mdx index f981cdc1302..dfa5a342e2e 100644 --- a/docs/docs/integrations/providers/mongodb.mdx +++ b/docs/docs/integrations/providers/mongodb.mdx @@ -1,9 +1,9 @@ # MongoDB ->[MongoDB](https://www.mongodb.com/) is a NoSQL, document-oriented +>[MongoDB](https://www.mongodb.com/) is a NoSQL, document-oriented > database that supports JSON-like documents with a dynamic schema. - -**NOTE:** + +**NOTE:** - See other `MongoDB` integrations on the [MongoDB Atlas page](/docs/integrations/providers/mongodb_atlas). ## Installation and Setup diff --git a/docs/docs/integrations/providers/mongodb_atlas.mdx b/docs/docs/integrations/providers/mongodb_atlas.mdx index 531d106fd6b..24ae4988a26 100644 --- a/docs/docs/integrations/providers/mongodb_atlas.mdx +++ b/docs/docs/integrations/providers/mongodb_atlas.mdx @@ -1,7 +1,7 @@ # MongoDB Atlas >[MongoDB Atlas](https://www.mongodb.com/docs/atlas/) is a fully-managed cloud -> database available in AWS, Azure, and GCP. It now has support for native +> database available in AWS, Azure, and GCP. It now has support for native > Vector Search on the MongoDB document data. ## Installation and Setup @@ -26,7 +26,7 @@ from langchain_mongodb import MongoDBAtlasVectorSearch ### Full Text Search Retriever ->`Hybrid Search Retriever` performs full-text searches using +>`Hybrid Search Retriever` performs full-text searches using > Lucene’s standard (`BM25`) analyzer. ```python @@ -35,9 +35,9 @@ from langchain_mongodb.retrievers import MongoDBAtlasFullTextSearchRetriever ### Hybrid Search Retriever ->`Hybrid Search Retriever` combines vector and full-text searches weighting +>`Hybrid Search Retriever` combines vector and full-text searches weighting > them the via `Reciprocal Rank Fusion` (`RRF`) algorithm. - + ```python from langchain_mongodb.retrievers import MongoDBAtlasHybridSearchRetriever ``` diff --git a/docs/docs/integrations/providers/myscale.mdx b/docs/docs/integrations/providers/myscale.mdx index 8192983ef93..026ee8ebc54 100644 --- a/docs/docs/integrations/providers/myscale.mdx +++ b/docs/docs/integrations/providers/myscale.mdx @@ -39,7 +39,7 @@ There are two ways to set up parameters for myscale index. index = MyScale(embedding_function, config) index.add_documents(...) ``` - + ## Wrappers supported functions: - `add_texts` diff --git a/docs/docs/integrations/providers/naver.mdx b/docs/docs/integrations/providers/naver.mdx index 739ee9b3ec1..14031add9fd 100644 --- a/docs/docs/integrations/providers/naver.mdx +++ b/docs/docs/integrations/providers/naver.mdx @@ -29,7 +29,7 @@ pip install -U langchain-naver ## Chat models -### ChatClovaX +### ChatClovaX See a [usage example](/docs/integrations/chat/naver). diff --git a/docs/docs/integrations/providers/nebius.mdx b/docs/docs/integrations/providers/nebius.mdx index c9e4e53fb4b..c688d13a199 100644 --- a/docs/docs/integrations/providers/nebius.mdx +++ b/docs/docs/integrations/providers/nebius.mdx @@ -115,4 +115,4 @@ tool = NebiusRetrievalTool( name="nebius_search", description="Search for information about European capitals" ) -``` \ No newline at end of file +``` diff --git a/docs/docs/integrations/providers/nlpcloud.mdx b/docs/docs/integrations/providers/nlpcloud.mdx index f6d664833a1..d3bb9839676 100644 --- a/docs/docs/integrations/providers/nlpcloud.mdx +++ b/docs/docs/integrations/providers/nlpcloud.mdx @@ -1,6 +1,6 @@ # NLPCloud ->[NLP Cloud](https://docs.nlpcloud.com/#introduction) is an artificial intelligence platform that allows you to use the most advanced AI engines, and even train your own engines with your own data. +>[NLP Cloud](https://docs.nlpcloud.com/#introduction) is an artificial intelligence platform that allows you to use the most advanced AI engines, and even train your own engines with your own data. ## Installation and Setup diff --git a/docs/docs/integrations/providers/nomic.mdx b/docs/docs/integrations/providers/nomic.mdx index f825e3c74e3..774bdc9a0ba 100644 --- a/docs/docs/integrations/providers/nomic.mdx +++ b/docs/docs/integrations/providers/nomic.mdx @@ -8,7 +8,7 @@ >- `GPT4All`: the Open Source Edge Language Model Ecosystem The Nomic integration exists in two partner packages: [langchain-nomic](https://pypi.org/project/langchain-nomic/) -and in [langchain-community](https://pypi.org/project/langchain-community/). +and in [langchain-community](https://pypi.org/project/langchain-community/). ## Installation diff --git a/docs/docs/integrations/providers/notion.mdx b/docs/docs/integrations/providers/notion.mdx index 6ed4fd306fc..457ee7078c0 100644 --- a/docs/docs/integrations/providers/notion.mdx +++ b/docs/docs/integrations/providers/notion.mdx @@ -1,7 +1,7 @@ # Notion DB ->[Notion](https://www.notion.so/) is a collaboration platform with modified Markdown support that integrates kanban -> boards, tasks, wikis and databases. It is an all-in-one workspace for notetaking, knowledge and data management, +>[Notion](https://www.notion.so/) is a collaboration platform with modified Markdown support that integrates kanban +> boards, tasks, wikis and databases. It is an all-in-one workspace for notetaking, knowledge and data management, > and project and task management. ## Installation and Setup diff --git a/docs/docs/integrations/providers/nuclia.mdx b/docs/docs/integrations/providers/nuclia.mdx index 91daeb6a5a2..54443f20f4a 100644 --- a/docs/docs/integrations/providers/nuclia.mdx +++ b/docs/docs/integrations/providers/nuclia.mdx @@ -1,7 +1,7 @@ # Nuclia >[Nuclia](https://nuclia.com) automatically indexes your unstructured data from any internal -> and external source, providing optimized search results and generative answers. +> and external source, providing optimized search results and generative answers. > It can handle video and audio transcription, image content extraction, and document parsing. @@ -14,8 +14,8 @@ We need to install the `nucliadb-protos` package to use the `Nuclia Understandin pip install nucliadb-protos ``` -We need to have a `Nuclia account`. -We can create one for free at [https://nuclia.cloud](https://nuclia.cloud), +We need to have a `Nuclia account`. +We can create one for free at [https://nuclia.cloud](https://nuclia.cloud), and then [create a NUA key](https://docs.nuclia.dev/docs/docs/using/understanding/intro). @@ -23,7 +23,7 @@ and then [create a NUA key](https://docs.nuclia.dev/docs/docs/using/understandin ### Nuclia ->`Nuclia Understanding API` document transformer splits text into paragraphs and sentences, +>`Nuclia Understanding API` document transformer splits text into paragraphs and sentences, > identifies entities, provides a summary of the text and generates embeddings for all the sentences. To use the Nuclia document transformer, we need to instantiate a `NucliaUnderstandingAPI` diff --git a/docs/docs/integrations/providers/nvidia.mdx b/docs/docs/integrations/providers/nvidia.mdx index 2dc6bf2f438..73b40bef646 100644 --- a/docs/docs/integrations/providers/nvidia.mdx +++ b/docs/docs/integrations/providers/nvidia.mdx @@ -1,15 +1,15 @@ # NVIDIA -The `langchain-nvidia-ai-endpoints` package contains LangChain integrations building applications with models on -NVIDIA NIM inference microservice. NIM supports models across domains like chat, embedding, and re-ranking models -from the community as well as NVIDIA. These models are optimized by NVIDIA to deliver the best performance on NVIDIA -accelerated infrastructure and deployed as a NIM, an easy-to-use, prebuilt containers that deploy anywhere using a single +The `langchain-nvidia-ai-endpoints` package contains LangChain integrations building applications with models on +NVIDIA NIM inference microservice. NIM supports models across domains like chat, embedding, and re-ranking models +from the community as well as NVIDIA. These models are optimized by NVIDIA to deliver the best performance on NVIDIA +accelerated infrastructure and deployed as a NIM, an easy-to-use, prebuilt containers that deploy anywhere using a single command on NVIDIA accelerated infrastructure. -NVIDIA hosted deployments of NIMs are available to test on the [NVIDIA API catalog](https://build.nvidia.com/). After testing, -NIMs can be exported from NVIDIA’s API catalog using the NVIDIA AI Enterprise license and run on-premises or in the cloud, +NVIDIA hosted deployments of NIMs are available to test on the [NVIDIA API catalog](https://build.nvidia.com/). After testing, +NIMs can be exported from NVIDIA’s API catalog using the NVIDIA AI Enterprise license and run on-premises or in the cloud, giving enterprises ownership and full control of their IP and AI application. -NIMs are packaged as container images on a per model basis and are distributed as NGC container images through the NVIDIA NGC Catalog. +NIMs are packaged as container images on a per model basis and are distributed as NGC container images through the NVIDIA NGC Catalog. At their core, NIMs provide easy, consistent, and familiar APIs for running inference on an AI model. Below is an example on how to use some common functionality surrounding text-generative and embedding models. diff --git a/docs/docs/integrations/providers/oceanbase.mdx b/docs/docs/integrations/providers/oceanbase.mdx index 30537f13910..65ea5e0976e 100644 --- a/docs/docs/integrations/providers/oceanbase.mdx +++ b/docs/docs/integrations/providers/oceanbase.mdx @@ -1,7 +1,7 @@ # OceanBase -[OceanBase Database](https://github.com/oceanbase/oceanbase) is a distributed relational database. -It is developed entirely by Ant Group. The OceanBase Database is built on a common server cluster. +[OceanBase Database](https://github.com/oceanbase/oceanbase) is a distributed relational database. +It is developed entirely by Ant Group. The OceanBase Database is built on a common server cluster. Based on the Paxos protocol and its distributed structure, the OceanBase Database provides high availability and linear scalability. OceanBase currently has the ability to store vectors. Users can easily perform the following operations with SQL: diff --git a/docs/docs/integrations/providers/oci.mdx b/docs/docs/integrations/providers/oci.mdx index 58c167995bf..73d797244d7 100644 --- a/docs/docs/integrations/providers/oci.mdx +++ b/docs/docs/integrations/providers/oci.mdx @@ -6,7 +6,7 @@ The `LangChain` integrations related to [Oracle Cloud Infrastructure](https://ww > Oracle Cloud Infrastructure (OCI) [Generative AI](https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm) is a fully managed service that provides a set of state-of-the-art, > customizable large language models (LLMs) that cover a wide range of use cases, and which are available through a single API. > Using the OCI Generative AI service you can access ready-to-use pretrained models, or create and host your own fine-tuned -> custom models based on your own data on dedicated AI clusters. +> custom models based on your own data on dedicated AI clusters. To use, you should have the latest `oci` python SDK and the langchain_community package installed. diff --git a/docs/docs/integrations/providers/octoai.mdx b/docs/docs/integrations/providers/octoai.mdx index d4a064c7c76..8c6193d4750 100644 --- a/docs/docs/integrations/providers/octoai.mdx +++ b/docs/docs/integrations/providers/octoai.mdx @@ -1,7 +1,7 @@ # OctoAI ->[OctoAI](https://docs.octoai.cloud/docs) offers easy access to efficient compute -> and enables users to integrate their choice of AI models into applications. +>[OctoAI](https://docs.octoai.cloud/docs) offers easy access to efficient compute +> and enables users to integrate their choice of AI models into applications. > The `OctoAI` compute service helps you run, tune, and scale AI applications easily. diff --git a/docs/docs/integrations/providers/ollama.mdx b/docs/docs/integrations/providers/ollama.mdx index 2636de04069..3d602eb6909 100644 --- a/docs/docs/integrations/providers/ollama.mdx +++ b/docs/docs/integrations/providers/ollama.mdx @@ -1,9 +1,9 @@ # Ollama ->[Ollama](https://ollama.com/) allows you to run open-source large language models, +>[Ollama](https://ollama.com/) allows you to run open-source large language models, > such as [Llama3.1](https://ai.meta.com/blog/meta-llama-3-1/), locally. > ->`Ollama` bundles model weights, configuration, and data into a single package, defined by a Modelfile. +>`Ollama` bundles model weights, configuration, and data into a single package, defined by a Modelfile. >It optimizes setup and configuration details, including GPU usage. >For a complete list of supported models and model variants, see the [Ollama model library](https://ollama.ai/library). @@ -12,7 +12,7 @@ on how to use `Ollama` with LangChain. ## Installation and Setup ### Ollama installation -Follow [these instructions](https://github.com/ollama/ollama?tab=readme-ov-file#ollama) +Follow [these instructions](https://github.com/ollama/ollama?tab=readme-ov-file#ollama) to set up and run a local Ollama instance. Ollama will start as a background service automatically, if this is disabled, run: diff --git a/docs/docs/integrations/providers/openai.mdx b/docs/docs/integrations/providers/openai.mdx index 61abb8f4bd6..c65e57989fb 100644 --- a/docs/docs/integrations/providers/openai.mdx +++ b/docs/docs/integrations/providers/openai.mdx @@ -13,7 +13,7 @@ All functionality related to OpenAI > **OpenAI** systems run on an **Azure**-based supercomputing platform from **Microsoft**. > > The [OpenAI API](https://platform.openai.com/docs/models) is powered by a diverse set of models with different capabilities and price points. -> +> > [ChatGPT](https://chat.openai.com) is the Artificial Intelligence (AI) chatbot developed by `OpenAI`. ## Installation and Setup @@ -81,8 +81,8 @@ from langchain.retrievers import ChatGPTPluginRetriever ### Dall-E Image Generator ->[OpenAI Dall-E](https://openai.com/dall-e-3) are text-to-image models developed by `OpenAI` -> using deep learning methodologies to generate digital images from natural language descriptions, +>[OpenAI Dall-E](https://openai.com/dall-e-3) are text-to-image models developed by `OpenAI` +> using deep learning methodologies to generate digital images from natural language descriptions, > called "prompts". @@ -105,7 +105,7 @@ from langchain.adapters import openai as lc_openai There are several places you can use the `tiktoken` tokenizer. By default, it is used to count tokens for OpenAI LLMs. -You can also use it to count tokens when splitting documents with +You can also use it to count tokens when splitting documents with ```python from langchain.text_splitter import CharacterTextSplitter CharacterTextSplitter.from_tiktoken_encoder(...) diff --git a/docs/docs/integrations/providers/opensearch.mdx b/docs/docs/integrations/providers/opensearch.mdx index be55c26d7b2..4028bd27fe2 100644 --- a/docs/docs/integrations/providers/opensearch.mdx +++ b/docs/docs/integrations/providers/opensearch.mdx @@ -9,8 +9,8 @@ It is broken into two parts: installation and setup, and then references to spec ### VectorStore -There exists a wrapper around OpenSearch vector databases, allowing you to use it as a vectorstore -for semantic search using approximate vector search powered by lucene, nmslib and faiss engines +There exists a wrapper around OpenSearch vector databases, allowing you to use it as a vectorstore +for semantic search using approximate vector search powered by lucene, nmslib and faiss engines or using painless scripting and script scoring functions for bruteforce vector search. To import this vectorstore: diff --git a/docs/docs/integrations/providers/perplexity.mdx b/docs/docs/integrations/providers/perplexity.mdx index 9af892b10dd..f59f0fe591a 100644 --- a/docs/docs/integrations/providers/perplexity.mdx +++ b/docs/docs/integrations/providers/perplexity.mdx @@ -1,7 +1,7 @@ # Perplexity ->[Perplexity](https://www.perplexity.ai/pro) is the most powerful way to search -> the internet with unlimited Pro Search, upgraded AI models, unlimited file upload, +>[Perplexity](https://www.perplexity.ai/pro) is the most powerful way to search +> the internet with unlimited Pro Search, upgraded AI models, unlimited file upload, > image generation, and API credits. > > You can check a [list of available models](https://docs.perplexity.ai/docs/model-cards). diff --git a/docs/docs/integrations/providers/petals.mdx b/docs/docs/integrations/providers/petals.mdx index db85c3cfc80..3be8b51f325 100644 --- a/docs/docs/integrations/providers/petals.mdx +++ b/docs/docs/integrations/providers/petals.mdx @@ -11,7 +11,7 @@ It is broken into two parts: installation and setup, and then references to spec ### LLM -There exists an Petals LLM wrapper, which you can access with +There exists an Petals LLM wrapper, which you can access with ```python from langchain_community.llms import Petals ``` diff --git a/docs/docs/integrations/providers/pipeshift.md b/docs/docs/integrations/providers/pipeshift.mdx similarity index 88% rename from docs/docs/integrations/providers/pipeshift.md rename to docs/docs/integrations/providers/pipeshift.mdx index 73b29a1f0ed..22c9b729b8f 100644 --- a/docs/docs/integrations/providers/pipeshift.md +++ b/docs/docs/integrations/providers/pipeshift.mdx @@ -8,7 +8,7 @@ - Install the Pipeshift integration package. - ``` + ```bash pip install langchain-pipeshift ``` @@ -18,13 +18,13 @@ You can perform authentication using your Pipeshift API key in any of the following ways: -1. Adding API key to the environment variable as `PIPESHIFT_API_KEY`. +1. Adding API key to the environment variable as `PIPESHIFT_API_KEY`. ```python os.environ["PIPESHIFT_API_KEY"] = "" ``` -2. By passing `api_key` to the pipeshift LLM module or chat module +2. By passing `api_key` to the pipeshift LLM module or chat module ```python llm = Pipeshift(api_key="", model="meta-llama/Meta-Llama-3.1-8B-Instruct", max_tokens=512) diff --git a/docs/docs/integrations/providers/predibase.md b/docs/docs/integrations/providers/predibase.mdx similarity index 97% rename from docs/docs/integrations/providers/predibase.md rename to docs/docs/integrations/providers/predibase.mdx index dba020c8832..2f9ab338477 100644 --- a/docs/docs/integrations/providers/predibase.md +++ b/docs/docs/integrations/providers/predibase.mdx @@ -1,15 +1,16 @@ # Predibase -Learn how to use LangChain with models on Predibase. +Learn how to use LangChain with models on Predibase. ## Setup + - Create a [Predibase](https://predibase.com/) account and [API key](https://docs.predibase.com/sdk-guide/intro). - Install the Predibase Python client with `pip install predibase` - Use your API key to authenticate ### LLM -Predibase integrates with LangChain by implementing LLM module. You can see a short example below or a full notebook under LLM > Integrations > Predibase. +Predibase integrates with LangChain by implementing LLM module. You can see a short example below or a full notebook under LLM > Integrations > Predibase. ```python import os diff --git a/docs/docs/integrations/providers/predictionguard.mdx b/docs/docs/integrations/providers/predictionguard.mdx index f862d43eca3..bff4cb4c9e9 100644 --- a/docs/docs/integrations/providers/predictionguard.mdx +++ b/docs/docs/integrations/providers/predictionguard.mdx @@ -78,4 +78,4 @@ from langchain_predictionguard import PredictionGuard llm = PredictionGuard(model="Hermes-2-Pro-Llama-3-8B") llm.invoke("Tell me a joke about bears") -``` \ No newline at end of file +``` diff --git a/docs/docs/integrations/providers/premai.md b/docs/docs/integrations/providers/premai.mdx similarity index 99% rename from docs/docs/integrations/providers/premai.md rename to docs/docs/integrations/providers/premai.mdx index 678763a33af..a1488dcd5af 100644 --- a/docs/docs/integrations/providers/premai.md +++ b/docs/docs/integrations/providers/premai.mdx @@ -285,7 +285,7 @@ In order to pass tools and let the LLM choose the tool it needs to call, we need ```python from langchain_core.tools import tool -from pydantic import BaseModel, Field +from pydantic import BaseModel, Field # Define the schema for function arguments class OperationInput(BaseModel): diff --git a/docs/docs/integrations/providers/prolog.md b/docs/docs/integrations/providers/prolog.mdx similarity index 99% rename from docs/docs/integrations/providers/prolog.md rename to docs/docs/integrations/providers/prolog.mdx index 7d908a60679..02ec9374143 100644 --- a/docs/docs/integrations/providers/prolog.md +++ b/docs/docs/integrations/providers/prolog.mdx @@ -5,6 +5,7 @@ SWI-Prolog offers a comprehensive free Prolog environment. ## Installation and Setup Once SWI-Prolog has been installed, install lanchain-prolog using pip: + ```bash pip install langchain-prolog ``` diff --git a/docs/docs/integrations/providers/promptlayer.mdx b/docs/docs/integrations/providers/promptlayer.mdx index 550ff28f35b..fcbae958457 100644 --- a/docs/docs/integrations/providers/promptlayer.mdx +++ b/docs/docs/integrations/providers/promptlayer.mdx @@ -1,10 +1,10 @@ # PromptLayer ->[PromptLayer](https://docs.promptlayer.com/introduction) is a platform for prompt engineering. +>[PromptLayer](https://docs.promptlayer.com/introduction) is a platform for prompt engineering. > It also helps with the LLM observability to visualize requests, version prompts, and track usage. > ->While `PromptLayer` does have LLMs that integrate directly with LangChain (e.g. -> [`PromptLayerOpenAI`](https://docs.promptlayer.com/languages/langchain)), +>While `PromptLayer` does have LLMs that integrate directly with LangChain (e.g. +> [`PromptLayerOpenAI`](https://docs.promptlayer.com/languages/langchain)), > using a callback is the recommended way to integrate `PromptLayer` with LangChain. ## Installation and Setup diff --git a/docs/docs/integrations/providers/psychic.mdx b/docs/docs/integrations/providers/psychic.mdx index a415f8a5a48..ce786aa6d7b 100644 --- a/docs/docs/integrations/providers/psychic.mdx +++ b/docs/docs/integrations/providers/psychic.mdx @@ -8,9 +8,9 @@ sidebar_class_name: hidden This provider is no longer maintained, and may not work. Use with caution. ::: ->[Psychic](https://www.psychic.dev/) is a platform for integrating with SaaS tools like `Notion`, `Zendesk`, +>[Psychic](https://www.psychic.dev/) is a platform for integrating with SaaS tools like `Notion`, `Zendesk`, > `Confluence`, and `Google Drive` via OAuth and syncing documents from these applications to your SQL or vector -> database. You can think of it like Plaid for unstructured data. +> database. You can think of it like Plaid for unstructured data. ## Installation and Setup @@ -18,10 +18,10 @@ This provider is no longer maintained, and may not work. Use with caution. pip install psychicapi ``` -Psychic is easy to set up - you import the `react` library and configure it with your `Sidekick API` key, which you get -from the [Psychic dashboard](https://dashboard.psychic.dev/). When you connect the applications, you +Psychic is easy to set up - you import the `react` library and configure it with your `Sidekick API` key, which you get +from the [Psychic dashboard](https://dashboard.psychic.dev/). When you connect the applications, you view these connections from the dashboard and retrieve data using the server-side libraries. - + 1. Create an account in the [dashboard](https://dashboard.psychic.dev/). 2. Use the [react library](https://docs.psychic.dev/sidekick-link) to add the Psychic link modal to your frontend react app. You will use this to connect the SaaS apps. 3. Once you have created a connection, you can use the `PsychicLoader` by following the [example notebook](/docs/integrations/document_loaders/psychic) @@ -31,4 +31,4 @@ view these connections from the dashboard and retrieve data using the server-sid 1. **Universal API:** Instead of building OAuth flows and learning the APIs for every SaaS app, you integrate Psychic once and leverage our universal API to retrieve data. 2. **Data Syncs:** Data in your customers' SaaS apps can get stale fast. With Psychic you can configure webhooks to keep your documents up to date on a daily or realtime basis. -3. **Simplified OAuth:** Psychic handles OAuth end-to-end so that you don't have to spend time creating OAuth clients for each integration, keeping access tokens fresh, and handling OAuth redirect logic. \ No newline at end of file +3. **Simplified OAuth:** Psychic handles OAuth end-to-end so that you don't have to spend time creating OAuth clients for each integration, keeping access tokens fresh, and handling OAuth redirect logic. diff --git a/docs/docs/integrations/providers/pubmed.md b/docs/docs/integrations/providers/pubmed.mdx similarity index 91% rename from docs/docs/integrations/providers/pubmed.md rename to docs/docs/integrations/providers/pubmed.mdx index aa0a810cb65..64bf4be5385 100644 --- a/docs/docs/integrations/providers/pubmed.md +++ b/docs/docs/integrations/providers/pubmed.mdx @@ -2,8 +2,8 @@ # PubMed ->[PubMed®](https://pubmed.ncbi.nlm.nih.gov/) by `The National Center for Biotechnology Information, National Library of Medicine` -> comprises more than 35 million citations for biomedical literature from `MEDLINE`, life science journals, and online books. +>[PubMed®](https://pubmed.ncbi.nlm.nih.gov/) by `The National Center for Biotechnology Information, National Library of Medicine` +> comprises more than 35 million citations for biomedical literature from `MEDLINE`, life science journals, and online books. > Citations may include links to full text content from `PubMed Central` and publisher web sites. ## Setup diff --git a/docs/docs/integrations/providers/pygmalionai.mdx b/docs/docs/integrations/providers/pygmalionai.mdx index 2d98fdf38c0..f51983e9e10 100644 --- a/docs/docs/integrations/providers/pygmalionai.mdx +++ b/docs/docs/integrations/providers/pygmalionai.mdx @@ -1,7 +1,7 @@ # PygmalionAI ->[PygmalionAI](https://pygmalion.chat/) is a company supporting the -> open-source models by serving the inference endpoint +>[PygmalionAI](https://pygmalion.chat/) is a company supporting the +> open-source models by serving the inference endpoint > for the [Aphrodite Engine](https://github.com/PygmalionAI/aphrodite-engine). diff --git a/docs/docs/integrations/providers/qdrant.mdx b/docs/docs/integrations/providers/qdrant.mdx index ad047ce0721..a85f1669ec5 100644 --- a/docs/docs/integrations/providers/qdrant.mdx +++ b/docs/docs/integrations/providers/qdrant.mdx @@ -1,7 +1,7 @@ # Qdrant ->[Qdrant](https://qdrant.tech/documentation/) (read: quadrant) is a vector similarity search engine. -> It provides a production-ready service with a convenient API to store, search, and manage +>[Qdrant](https://qdrant.tech/documentation/) (read: quadrant) is a vector similarity search engine. +> It provides a production-ready service with a convenient API to store, search, and manage > points - vectors with an additional payload. `Qdrant` is tailored to extended filtering support. diff --git a/docs/docs/integrations/providers/redis.mdx b/docs/docs/integrations/providers/redis.mdx index 6a76e699f10..a6deafef1fb 100644 --- a/docs/docs/integrations/providers/redis.mdx +++ b/docs/docs/integrations/providers/redis.mdx @@ -1,9 +1,9 @@ # Redis ->[Redis (Remote Dictionary Server)](https://en.wikipedia.org/wiki/Redis) is an open-source in-memory storage, -> used as a distributed, in-memory key–value database, cache and message broker, with optional durability. -> Because it holds all data in memory and because of its design, `Redis` offers low-latency reads and writes, -> making it particularly suitable for use cases that require a cache. Redis is the most popular NoSQL database, +>[Redis (Remote Dictionary Server)](https://en.wikipedia.org/wiki/Redis) is an open-source in-memory storage, +> used as a distributed, in-memory key–value database, cache and message broker, with optional durability. +> Because it holds all data in memory and because of its design, `Redis` offers low-latency reads and writes, +> making it particularly suitable for use cases that require a cache. Redis is the most popular NoSQL database, > and one of the most popular databases overall. This page covers how to use the [Redis](https://redis.com) ecosystem within LangChain. @@ -48,7 +48,7 @@ Example: `redis_url = "redis://:secret-pass@localhost:6379/0"` #### Redis Sentinel connection url -For [Redis sentinel setups](https://redis.io/docs/management/sentinel/) the connection scheme is "redis+sentinel". +For [Redis sentinel setups](https://redis.io/docs/management/sentinel/) the connection scheme is "redis+sentinel". This is an unofficial extensions to the official IANA registered protocol schemes as long as there is no connection url for Sentinels available. @@ -56,7 +56,7 @@ Example: `redis_url = "redis+sentinel://:secret-pass@sentinel-host:26379/mymaste The format is `redis+sentinel://[[username]:[password]]@[host-or-ip]:[port]/[service-name]/[db-number]` with the default values of "service-name = mymaster" and "db-number = 0" if not set explicit. -The service-name is the redis server monitoring group name as configured within the Sentinel. +The service-name is the redis server monitoring group name as configured within the Sentinel. The current url format limits the connection string to one sentinel host only (no list can be given) and both Redis server and sentinel must have the same password set (if used). @@ -122,8 +122,8 @@ For a more detailed walkthrough of the Redis vectorstore wrapper, see [this note ## Retriever -The Redis vector store retriever wrapper generalizes the vectorstore class to perform -low-latency document retrieval. To create the retriever, simply +The Redis vector store retriever wrapper generalizes the vectorstore class to perform +low-latency document retrieval. To create the retriever, simply call `.as_retriever()` on the base vectorstore class. ## Memory diff --git a/docs/docs/integrations/providers/remembrall.mdx b/docs/docs/integrations/providers/remembrall.mdx index 822acab815a..a67b16b58a5 100644 --- a/docs/docs/integrations/providers/remembrall.mdx +++ b/docs/docs/integrations/providers/remembrall.mdx @@ -1,11 +1,11 @@ # Remembrall ->[Remembrall](https://remembrall.dev/) is a platform that gives a language model +>[Remembrall](https://remembrall.dev/) is a platform that gives a language model > long-term memory, retrieval augmented generation, and complete observability. - + ## Installation and Setup -To get started, [sign in with Github on the Remembrall platform](https://remembrall.dev/login) +To get started, [sign in with Github on the Remembrall platform](https://remembrall.dev/login) and copy your [API key from the settings page](https://remembrall.dev/dashboard/settings). diff --git a/docs/docs/integrations/providers/roam.mdx b/docs/docs/integrations/providers/roam.mdx index 322ade8d29a..fd7b1075bd5 100644 --- a/docs/docs/integrations/providers/roam.mdx +++ b/docs/docs/integrations/providers/roam.mdx @@ -1,7 +1,7 @@ # Roam >[ROAM](https://roamresearch.com/) is a note-taking tool for networked thought, designed to create a personal knowledge base. - + ## Installation and Setup There isn't any special setup for it. diff --git a/docs/docs/integrations/providers/robocorp.mdx b/docs/docs/integrations/providers/robocorp.mdx index edafdf75dd2..6679583702b 100644 --- a/docs/docs/integrations/providers/robocorp.mdx +++ b/docs/docs/integrations/providers/robocorp.mdx @@ -11,7 +11,7 @@ You need to install `langchain-robocorp` python package: pip install langchain-robocorp ``` -You will need a running instance of `Action Server` to communicate with from your agent application. +You will need a running instance of `Action Server` to communicate with from your agent application. See the [Robocorp Quickstart](https://github.com/robocorp/robocorp#quickstart) on how to setup Action Server and create your Actions. You can bootstrap a new project using Action Server `new` command. diff --git a/docs/docs/integrations/providers/rockset.mdx b/docs/docs/integrations/providers/rockset.mdx index 735c2181783..2d1565dd403 100644 --- a/docs/docs/integrations/providers/rockset.mdx +++ b/docs/docs/integrations/providers/rockset.mdx @@ -1,6 +1,6 @@ # Rockset ->[Rockset](https://rockset.com/product/) is a real-time analytics database service for serving low latency, high concurrency analytical queries at scale. It builds a Converged Index™ on structured and semi-structured data with an efficient store for vector embeddings. Its support for running SQL on schemaless data makes it a perfect choice for running vector search with metadata filters. +>[Rockset](https://rockset.com/product/) is a real-time analytics database service for serving low latency, high concurrency analytical queries at scale. It builds a Converged Index™ on structured and semi-structured data with an efficient store for vector embeddings. Its support for running SQL on schemaless data makes it a perfect choice for running vector search with metadata filters. ## Installation and Setup @@ -15,7 +15,7 @@ pip install rockset See a [usage example](/docs/integrations/vectorstores/rockset). ```python -from langchain_community.vectorstores import Rockset +from langchain_community.vectorstores import Rockset ``` ## Document Loader @@ -30,4 +30,4 @@ from langchain_community.document_loaders import RocksetLoader See a [usage example](/docs/integrations/memory/rockset_chat_message_history). ```python from langchain_community.chat_message_histories import RocksetChatMessageHistory -``` \ No newline at end of file +``` diff --git a/docs/docs/integrations/providers/runhouse.mdx b/docs/docs/integrations/providers/runhouse.mdx index d0b63ed4905..c96e359d71d 100644 --- a/docs/docs/integrations/providers/runhouse.mdx +++ b/docs/docs/integrations/providers/runhouse.mdx @@ -20,7 +20,7 @@ For a more detailed walkthrough of the Self-hosted LLMs, see [this notebook](/do ## Self-hosted Embeddings There are several ways to use self-hosted embeddings with LangChain via Runhouse. -For a basic self-hosted embedding from a Hugging Face Transformers model, you can use +For a basic self-hosted embedding from a Hugging Face Transformers model, you can use the `SelfHostedEmbedding` class. ```python from langchain_community.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM diff --git a/docs/docs/integrations/providers/sap.mdx b/docs/docs/integrations/providers/sap.mdx index d9fcd76f4a2..5fb45b30be5 100644 --- a/docs/docs/integrations/providers/sap.mdx +++ b/docs/docs/integrations/providers/sap.mdx @@ -1,8 +1,8 @@ # SAP ->[SAP SE(Wikipedia)](https://www.sap.com/about/company.html) is a German multinational -> software company. It develops enterprise software to manage business operation and -> customer relations. The company is the world's leading +>[SAP SE(Wikipedia)](https://www.sap.com/about/company.html) is a German multinational +> software company. It develops enterprise software to manage business operation and +> customer relations. The company is the world's leading > `enterprise resource planning (ERP)` software vendor. ## Installation and Setup @@ -15,7 +15,7 @@ pip install langchain-hana ## Vectorstore ->[SAP HANA Cloud Vector Engine](https://help.sap.com/docs/hana-cloud-database/sap-hana-cloud-sap-hana-database-vector-engine-guide/sap-hana-cloud-sap-hana-database-vector-engine-guide) is +>[SAP HANA Cloud Vector Engine](https://help.sap.com/docs/hana-cloud-database/sap-hana-cloud-sap-hana-database-vector-engine-guide/sap-hana-cloud-sap-hana-database-vector-engine-guide) is > a vector store fully integrated into the `SAP HANA Cloud` database. See a [usage example](/docs/integrations/vectorstores/sap_hanavector). diff --git a/docs/docs/integrations/providers/scrapegraph.mdx b/docs/docs/integrations/providers/scrapegraph.mdx index dffbc063d5f..109fcfc64d0 100644 --- a/docs/docs/integrations/providers/scrapegraph.mdx +++ b/docs/docs/integrations/providers/scrapegraph.mdx @@ -38,4 +38,4 @@ Each tool serves a specific purpose: - `SmartScraperTool`: Extract structured data from websites given a URL, prompt and optional output schema - `SmartCrawlerTool`: Extract data from multiple pages with advanced crawling options like depth control, page limits, and domain restrictions - `MarkdownifyTool`: Convert any webpage to clean markdown format -- `GetCreditsTool`: Check your remaining ScrapeGraph AI credits +- `GetCreditsTool`: Check your remaining ScrapeGraph AI credits diff --git a/docs/docs/integrations/providers/searchapi.mdx b/docs/docs/integrations/providers/searchapi.mdx index 569e7f7b49f..d8e8f09cc64 100644 --- a/docs/docs/integrations/providers/searchapi.mdx +++ b/docs/docs/integrations/providers/searchapi.mdx @@ -52,12 +52,12 @@ self_ask_with_search.run("Who lived longer: Plato, Socrates, or Aristotle?") Follow up: How old was Plato when he died? Intermediate answer: eighty Follow up: How old was Socrates when he died? -Intermediate answer: | Socrates | -| -------- | -| Born | c. 470 BC Deme Alopece, Athens | -| Died | 399 BC (aged approximately 71) Athens | -| Cause of death | Execution by forced suicide by poisoning | -| Spouse(s) | Xanthippe, Myrto | +Intermediate answer: | Socrates | +| -------- | +| Born | c. 470 BC Deme Alopece, Athens | +| Died | 399 BC (aged approximately 71) Athens | +| Cause of death | Execution by forced suicide by poisoning | +| Spouse(s) | Xanthippe, Myrto | Follow up: How old was Aristotle when he died? Intermediate answer: 62 years diff --git a/docs/docs/integrations/providers/searx.mdx b/docs/docs/integrations/providers/searx.mdx index 505304bab6c..be5ba474dcf 100644 --- a/docs/docs/integrations/providers/searx.mdx +++ b/docs/docs/integrations/providers/searx.mdx @@ -37,7 +37,7 @@ To use the wrapper we need to pass the host of the SearxNG instance to the wrapp 1. the named parameter `searx_host` when creating the instance. 2. exporting the environment variable `SEARXNG_HOST`. -You can use the wrapper to get results from a SearxNG instance. +You can use the wrapper to get results from a SearxNG instance. ```python from langchain_community.utilities import SearxSearchWrapper diff --git a/docs/docs/integrations/providers/semadb.mdx b/docs/docs/integrations/providers/semadb.mdx index 905ef966132..0ecd9cd545d 100644 --- a/docs/docs/integrations/providers/semadb.mdx +++ b/docs/docs/integrations/providers/semadb.mdx @@ -16,4 +16,4 @@ There is a basic wrapper around `SemaDB` collections allowing you to use it as a from langchain_community.vectorstores import SemaDB ``` -You can follow a tutorial on how to use the wrapper in [this notebook](/docs/integrations/vectorstores/semadb). \ No newline at end of file +You can follow a tutorial on how to use the wrapper in [this notebook](/docs/integrations/vectorstores/semadb). diff --git a/docs/docs/integrations/providers/shaleprotocol.md b/docs/docs/integrations/providers/shaleprotocol.mdx similarity index 92% rename from docs/docs/integrations/providers/shaleprotocol.md rename to docs/docs/integrations/providers/shaleprotocol.mdx index 553778cedea..bfb04a2ddfa 100644 --- a/docs/docs/integrations/providers/shaleprotocol.md +++ b/docs/docs/integrations/providers/shaleprotocol.mdx @@ -1,23 +1,23 @@ # Shale Protocol -[Shale Protocol](https://shaleprotocol.com) provides production-ready inference APIs for open LLMs. It's a Plug & Play API as it's hosted on a highly scalable GPU cloud infrastructure. +[Shale Protocol](https://shaleprotocol.com) provides production-ready inference APIs for open LLMs. It's a Plug & Play API as it's hosted on a highly scalable GPU cloud infrastructure. -Our free tier supports up to 1K daily requests per key as we want to eliminate the barrier for anyone to start building genAI apps with LLMs. +Our free tier supports up to 1K daily requests per key as we want to eliminate the barrier for anyone to start building genAI apps with LLMs. With Shale Protocol, developers/researchers can create apps and explore the capabilities of open LLMs at no cost. This page covers how Shale-Serve API can be incorporated with LangChain. -As of June 2023, the API supports Vicuna-13B by default. We are going to support more LLMs such as Falcon-40B in future releases. - +As of June 2023, the API supports Vicuna-13B by default. We are going to support more LLMs such as Falcon-40B in future releases. ## How to -### 1. Find the link to our Discord on https://shaleprotocol.com. Generate an API key through the "Shale Bot" on our Discord. No credit card is required and no free trials. It's a forever free tier with 1K limit per day per API key. +### 1. Find the link to our Discord on https://shaleprotocol.com. Generate an API key through the "Shale Bot" on our Discord. No credit card is required and no free trials. It's a forever free tier with 1K limit per day per API key -### 2. Use https://shale.live/v1 as OpenAI API drop-in replacement +### 2. Use https://shale.live/v1 as OpenAI API drop-in replacement For example + ```python from langchain_openai import OpenAI from langchain_core.prompts import PromptTemplate diff --git a/docs/docs/integrations/providers/sklearn.mdx b/docs/docs/integrations/providers/sklearn.mdx index a2d9e0554d7..71b3a4b6079 100644 --- a/docs/docs/integrations/providers/sklearn.mdx +++ b/docs/docs/integrations/providers/sklearn.mdx @@ -1,6 +1,6 @@ # scikit-learn ->[scikit-learn](https://scikit-learn.org/stable/) is an open-source collection of machine learning algorithms, +>[scikit-learn](https://scikit-learn.org/stable/) is an open-source collection of machine learning algorithms, > including some implementations of the [k nearest neighbors](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html). `SKLearnVectorStore` wraps this implementation and adds the possibility to persist the vector store in json, bson (binary json) or Apache Parquet format. ## Installation and Setup @@ -24,7 +24,7 @@ For a more detailed walkthrough of the SKLearnVectorStore wrapper, see [this not ## Retriever -`Support vector machines (SVMs)` are the supervised learning +`Support vector machines (SVMs)` are the supervised learning methods used for classification, regression and outliers detection. See a [usage example](/docs/integrations/retrievers/svm). diff --git a/docs/docs/integrations/providers/slack.mdx b/docs/docs/integrations/providers/slack.mdx index 9013e5b0cc2..edcbe0132dd 100644 --- a/docs/docs/integrations/providers/slack.mdx +++ b/docs/docs/integrations/providers/slack.mdx @@ -1,7 +1,7 @@ # Slack >[Slack](https://slack.com/) is an instant messaging program. - + ## Installation and Setup There isn't any special setup for it. diff --git a/docs/docs/integrations/providers/snowflake.mdx b/docs/docs/integrations/providers/snowflake.mdx index c42c7197588..c83e0ef4e55 100644 --- a/docs/docs/integrations/providers/snowflake.mdx +++ b/docs/docs/integrations/providers/snowflake.mdx @@ -1,6 +1,6 @@ # Snowflake -> [Snowflake](https://www.snowflake.com/) is a cloud-based data-warehousing platform +> [Snowflake](https://www.snowflake.com/) is a cloud-based data-warehousing platform > that allows you to store and query large amounts of data. This page covers how to use the `Snowflake` ecosystem within `LangChain`. @@ -8,8 +8,8 @@ This page covers how to use the `Snowflake` ecosystem within `LangChain`. ## Embedding models Snowflake offers their open-weight `arctic` line of embedding models for free -on [Hugging Face](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5). The most recent model, snowflake-arctic-embed-m-v1.5 feature [matryoshka embedding](https://arxiv.org/abs/2205.13147) which allows for effective vector truncation. -You can use these models via the +on [Hugging Face](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5). The most recent model, snowflake-arctic-embed-m-v1.5 feature [matryoshka embedding](https://arxiv.org/abs/2205.13147) which allows for effective vector truncation. +You can use these models via the [HuggingFaceEmbeddings](/docs/integrations/text_embedding/huggingfacehub) connector: ```shell @@ -24,7 +24,7 @@ model = HuggingFaceEmbeddings(model_name="snowflake/arctic-embed-m-v1.5") ## Document loader -You can use the [`SnowflakeLoader`](/docs/integrations/document_loaders/snowflake) +You can use the [`SnowflakeLoader`](/docs/integrations/document_loaders/snowflake) to load data from Snowflake: ```python diff --git a/docs/docs/integrations/providers/spacy.mdx b/docs/docs/integrations/providers/spacy.mdx index d893f12a3db..c8e40b44df3 100644 --- a/docs/docs/integrations/providers/spacy.mdx +++ b/docs/docs/integrations/providers/spacy.mdx @@ -1,7 +1,7 @@ # spaCy >[spaCy](https://spacy.io/) is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython. - + ## Installation and Setup diff --git a/docs/docs/integrations/providers/spark.mdx b/docs/docs/integrations/providers/spark.mdx index a7721415af8..0a3686f9cee 100644 --- a/docs/docs/integrations/providers/spark.mdx +++ b/docs/docs/integrations/providers/spark.mdx @@ -1,11 +1,11 @@ # Spark ->[Apache Spark](https://spark.apache.org/) is a unified analytics engine for -> large-scale data processing. It provides high-level APIs in Scala, Java, -> Python, and R, and an optimized engine that supports general computation -> graphs for data analysis. It also supports a rich set of higher-level -> tools including `Spark SQL` for SQL and DataFrames, `pandas API on Spark` -> for pandas workloads, `MLlib` for machine learning, +>[Apache Spark](https://spark.apache.org/) is a unified analytics engine for +> large-scale data processing. It provides high-level APIs in Scala, Java, +> Python, and R, and an optimized engine that supports general computation +> graphs for data analysis. It also supports a rich set of higher-level +> tools including `Spark SQL` for SQL and DataFrames, `pandas API on Spark` +> for pandas workloads, `MLlib` for machine learning, > `GraphX` for graph processing, and `Structured Streaming` for stream processing. ## Document loaders diff --git a/docs/docs/integrations/providers/spreedly.mdx b/docs/docs/integrations/providers/spreedly.mdx index 16930aa06e9..aa19f1936ea 100644 --- a/docs/docs/integrations/providers/spreedly.mdx +++ b/docs/docs/integrations/providers/spreedly.mdx @@ -1,7 +1,7 @@ # Spreedly >[Spreedly](https://docs.spreedly.com/) is a service that allows you to securely store credit cards and use them to transact against any number of payment gateways and third party APIs. It does this by simultaneously providing a card tokenization/vault service as well as a gateway and receiver integration service. Payment methods tokenized by Spreedly are stored at `Spreedly`, allowing you to independently store a card and then pass that card to different end points based on your business requirements. - + ## Installation and Setup See [setup instructions](/docs/integrations/document_loaders/spreedly). diff --git a/docs/docs/integrations/providers/sqlite.mdx b/docs/docs/integrations/providers/sqlite.mdx index 3bba662f0d8..8f3fbec7e80 100644 --- a/docs/docs/integrations/providers/sqlite.mdx +++ b/docs/docs/integrations/providers/sqlite.mdx @@ -1,9 +1,9 @@ # SQLite ->[SQLite](https://en.wikipedia.org/wiki/SQLite) is a database engine written in the -> C programming language. It is not a standalone app; rather, it is a library that -> software developers embed in their apps. As such, it belongs to the family of -> embedded databases. It is the most widely deployed database engine, as it is +>[SQLite](https://en.wikipedia.org/wiki/SQLite) is a database engine written in the +> C programming language. It is not a standalone app; rather, it is a library that +> software developers embed in their apps. As such, it belongs to the family of +> embedded databases. It is the most widely deployed database engine, as it is > used by several of the top web browsers, operating systems, mobile phones, and other embedded systems. ## Installation and Setup diff --git a/docs/docs/integrations/providers/stackexchange.mdx b/docs/docs/integrations/providers/stackexchange.mdx index 08167db3bac..6bf0e1f49fa 100644 --- a/docs/docs/integrations/providers/stackexchange.mdx +++ b/docs/docs/integrations/providers/stackexchange.mdx @@ -1,13 +1,13 @@ # Stack Exchange ->[Stack Exchange](https://en.wikipedia.org/wiki/Stack_Exchange) is a network of -question-and-answer (Q&A) websites on topics in diverse fields, each site covering +>[Stack Exchange](https://en.wikipedia.org/wiki/Stack_Exchange) is a network of +question-and-answer (Q&A) websites on topics in diverse fields, each site covering a specific topic, where questions, answers, and users are subject to a reputation award process. This page covers how to use the `Stack Exchange API` within LangChain. ## Installation and Setup -- Install requirements with +- Install requirements with ```bash pip install stackapi ``` diff --git a/docs/docs/integrations/providers/stochasticai.mdx b/docs/docs/integrations/providers/stochasticai.mdx index bd0b5484bb2..4092568dff1 100644 --- a/docs/docs/integrations/providers/stochasticai.mdx +++ b/docs/docs/integrations/providers/stochasticai.mdx @@ -11,7 +11,7 @@ It is broken into two parts: installation and setup, and then references to spec ### LLM -There exists an StochasticAI LLM wrapper, which you can access with +There exists an StochasticAI LLM wrapper, which you can access with ```python from langchain_community.llms import StochasticAI -``` \ No newline at end of file +``` diff --git a/docs/docs/integrations/providers/supabase.mdx b/docs/docs/integrations/providers/supabase.mdx index 7a574800d06..69202ddf091 100644 --- a/docs/docs/integrations/providers/supabase.mdx +++ b/docs/docs/integrations/providers/supabase.mdx @@ -1,11 +1,11 @@ # Supabase (Postgres) ->[Supabase](https://supabase.com/docs) is an open-source `Firebase` alternative. -> `Supabase` is built on top of `PostgreSQL`, which offers strong `SQL` +>[Supabase](https://supabase.com/docs) is an open-source `Firebase` alternative. +> `Supabase` is built on top of `PostgreSQL`, which offers strong `SQL` > querying capabilities and enables a simple interface with already-existing tools and frameworks. >[PostgreSQL](https://en.wikipedia.org/wiki/PostgreSQL) also known as `Postgres`, -> is a free and open-source relational database management system (RDBMS) +> is a free and open-source relational database management system (RDBMS) > emphasizing extensibility and `SQL` compliance. ## Installation and Setup diff --git a/docs/docs/integrations/providers/tableau.mdx b/docs/docs/integrations/providers/tableau.mdx index fc872a5fa12..727abad4392 100644 --- a/docs/docs/integrations/providers/tableau.mdx +++ b/docs/docs/integrations/providers/tableau.mdx @@ -1,7 +1,7 @@ # Tableau [Tableau](https://www.tableau.com/) is an analytics platform that enables anyone to -see and understand data. +see and understand data. ## Installation and Setup diff --git a/docs/docs/integrations/providers/taiga.mdx b/docs/docs/integrations/providers/taiga.mdx index 5d7c9876110..763519fe171 100644 --- a/docs/docs/integrations/providers/taiga.mdx +++ b/docs/docs/integrations/providers/taiga.mdx @@ -46,4 +46,4 @@ tools = toolkit.get_tools() ## Future Integrations -Check the [Taiga Developer Docs](https://docs.taiga.io/) for more information, and watch for updates or advanced usage examples in the [langchain_taiga GitHub repo](https://github.com/Shikenso-Analytics/langchain-taiga). \ No newline at end of file +Check the [Taiga Developer Docs](https://docs.taiga.io/) for more information, and watch for updates or advanced usage examples in the [langchain_taiga GitHub repo](https://github.com/Shikenso-Analytics/langchain-taiga). diff --git a/docs/docs/integrations/providers/tair.mdx b/docs/docs/integrations/providers/tair.mdx index d84d7378033..d29861fcc4b 100644 --- a/docs/docs/integrations/providers/tair.mdx +++ b/docs/docs/integrations/providers/tair.mdx @@ -1,9 +1,9 @@ # Tair ->[Alibaba Cloud Tair](https://www.alibabacloud.com/help/en/tair/latest/what-is-tair) is a cloud native in-memory database service -> developed by `Alibaba Cloud`. It provides rich data models and enterprise-grade capabilities to -> support your real-time online scenarios while maintaining full compatibility with open-source `Redis`. -> `Tair` also introduces persistent memory-optimized instances that are based on +>[Alibaba Cloud Tair](https://www.alibabacloud.com/help/en/tair/latest/what-is-tair) is a cloud native in-memory database service +> developed by `Alibaba Cloud`. It provides rich data models and enterprise-grade capabilities to +> support your real-time online scenarios while maintaining full compatibility with open-source `Redis`. +> `Tair` also introduces persistent memory-optimized instances that are based on > new non-volatile memory (NVM) storage medium. ## Installation and Setup diff --git a/docs/docs/integrations/providers/tavily.mdx b/docs/docs/integrations/providers/tavily.mdx index 1e6785efdf3..93514800f5d 100644 --- a/docs/docs/integrations/providers/tavily.mdx +++ b/docs/docs/integrations/providers/tavily.mdx @@ -1,9 +1,9 @@ # Tavily [Tavily](https://tavily.com) is a search engine, specifically designed for AI agents. -Tavily provides both a search and extract API, AI developers can effortlessly integrate their -applications with realtime online information. Tavily’s primary mission is to provide factual -and reliable information from trusted sources, enhancing the accuracy and reliability of AI +Tavily provides both a search and extract API, AI developers can effortlessly integrate their +applications with realtime online information. Tavily’s primary mission is to provide factual +and reliable information from trusted sources, enhancing the accuracy and reliability of AI generated content and reasoning. ## Installation and Setup diff --git a/docs/docs/integrations/providers/tencent.mdx b/docs/docs/integrations/providers/tencent.mdx index 9efe6deed7d..b550580eaad 100644 --- a/docs/docs/integrations/providers/tencent.mdx +++ b/docs/docs/integrations/providers/tencent.mdx @@ -1,17 +1,17 @@ # Tencent ->[Tencent Holdings Ltd. (Wikipedia)](https://en.wikipedia.org/wiki/Tencent) (Chinese: 腾讯; pinyin: Téngxùn) -> is a Chinese multinational technology conglomerate and holding company headquartered -> in Shenzhen. `Tencent` is one of the highest grossing multimedia companies in the +>[Tencent Holdings Ltd. (Wikipedia)](https://en.wikipedia.org/wiki/Tencent) (Chinese: 腾讯; pinyin: Téngxùn) +> is a Chinese multinational technology conglomerate and holding company headquartered +> in Shenzhen. `Tencent` is one of the highest grossing multimedia companies in the > world based on revenue. It is also the world's largest company in the video game industry > based on its equity investments. - -## Chat model ->[Tencent's hybrid model API](https://cloud.tencent.com/document/product/1729) (`Hunyuan API`) -> implements dialogue communication, content generation, -> analysis and understanding, and can be widely used in various scenarios such as intelligent +## Chat model + +>[Tencent's hybrid model API](https://cloud.tencent.com/document/product/1729) (`Hunyuan API`) +> implements dialogue communication, content generation, +> analysis and understanding, and can be widely used in various scenarios such as intelligent > customer service, intelligent marketing, role playing, advertising, copyrighting, product description, > script creation, resume generation, article writing, code generation, data analysis, and content > analysis. @@ -28,12 +28,12 @@ from langchain_community.chat_models import ChatHunyuan ### Tencent COS ->[Tencent Cloud Object Storage (COS)](https://www.tencentcloud.com/products/cos) is a distributed -> storage service that enables you to store any amount of data from anywhere via HTTP/HTTPS protocols. -> `COS` has no restrictions on data structure or format. It also has no bucket size limit and -> partition management, making it suitable for virtually any use case, such as data delivery, -> data processing, and data lakes. COS provides a web-based console, multi-language SDKs and APIs, -> command line tool, and graphical tools. It works well with Amazon S3 APIs, allowing you to quickly +>[Tencent Cloud Object Storage (COS)](https://www.tencentcloud.com/products/cos) is a distributed +> storage service that enables you to store any amount of data from anywhere via HTTP/HTTPS protocols. +> `COS` has no restrictions on data structure or format. It also has no bucket size limit and +> partition management, making it suitable for virtually any use case, such as data delivery, +> data processing, and data lakes. COS provides a web-based console, multi-language SDKs and APIs, +> command line tool, and graphical tools. It works well with Amazon S3 APIs, allowing you to quickly > access community tools and plugins. Install the Python SDK: @@ -64,7 +64,7 @@ from qcloud_cos import CosConfig ### Tencent VectorDB ->[Tencent Cloud VectorDB](https://www.tencentcloud.com/products/vdb) is a fully managed, +>[Tencent Cloud VectorDB](https://www.tencentcloud.com/products/vdb) is a fully managed, > self-developed enterprise-level distributed database service >dedicated to storing, retrieving, and analyzing multidimensional vector data. The database supports a variety of index >types and similarity calculation methods, and a single index supports 1 billion vectors, millions of QPS, and @@ -88,7 +88,7 @@ from langchain_community.vectorstores import TencentVectorDB ### WeChat ->[WeChat](https://www.wechat.com/) or `Weixin` in Chinese is a Chinese +>[WeChat](https://www.wechat.com/) or `Weixin` in Chinese is a Chinese > instant messaging, social media, and mobile payment app developed by `Tencent`. See a [usage example](/docs/integrations/chat_loaders/wechat). diff --git a/docs/docs/integrations/providers/tensorflow_datasets.mdx b/docs/docs/integrations/providers/tensorflow_datasets.mdx index b3cc150977b..be21c77bfc2 100644 --- a/docs/docs/integrations/providers/tensorflow_datasets.mdx +++ b/docs/docs/integrations/providers/tensorflow_datasets.mdx @@ -1,10 +1,10 @@ # TensorFlow Datasets ->[TensorFlow Datasets](https://www.tensorflow.org/datasets) is a collection of datasets ready to use, -> with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed -> as [tf.data.Datasets](https://www.tensorflow.org/api_docs/python/tf/data/Dataset), -> enabling easy-to-use and high-performance input pipelines. To get started see -> the [guide](https://www.tensorflow.org/datasets/overview) and +>[TensorFlow Datasets](https://www.tensorflow.org/datasets) is a collection of datasets ready to use, +> with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed +> as [tf.data.Datasets](https://www.tensorflow.org/api_docs/python/tf/data/Dataset), +> enabling easy-to-use and high-performance input pipelines. To get started see +> the [guide](https://www.tensorflow.org/datasets/overview) and > the [list of datasets](https://www.tensorflow.org/datasets/catalog/overview#all_datasets). diff --git a/docs/docs/integrations/providers/tensorlake.mdx b/docs/docs/integrations/providers/tensorlake.mdx index 9690dddaa7e..1a8661cb1c1 100644 --- a/docs/docs/integrations/providers/tensorlake.mdx +++ b/docs/docs/integrations/providers/tensorlake.mdx @@ -1,15 +1,15 @@ # Tensorlake -Tensorlake is the AI Data Cloud that reliably transforms data from unstructured sources into ingestion-ready formats for AI Applications. +Tensorlake is the AI Data Cloud that reliably transforms data from unstructured sources into ingestion-ready formats for AI Applications. -The `langchain-tensorlake` package provides seamless integration between [Tensorlake](https://tensorlake.ai) and [LangChain](https://langchain.com), +The `langchain-tensorlake` package provides seamless integration between [Tensorlake](https://tensorlake.ai) and [LangChain](https://langchain.com), enabling you to build sophisticated document processing agents with enhanced parsing features, like signature detection. ## Tensorlake feature overview Tensorlake gives you tools to: - Extract: Schema-driven structured data extraction to pull out specific fields from documents. -- Parse: Convert documents to markdown to build RAG/Knowledge Graph systems. +- Parse: Convert documents to markdown to build RAG/Knowledge Graph systems. - Orchestrate: Build programmable workflows for large-scale ingestion and enrichment of Documents, Text, Audio, Video and more. Learn more at [docs.tensorlake.ai](https://docs.tensorlake.ai/introduction) @@ -24,13 +24,13 @@ pip install -U langchain-tensorlake --- -## Examples +## Examples Follow a [full tutorial](https://docs.tensorlake.ai/examples/tutorials/real-estate-agent-with-langgraph-cli) on how to detect signatures in unstructured documents using the `langchain-tensorlake` tool. Or check out this [colab notebook](https://colab.research.google.com/drive/1VRWIPCWYnjcRtQL864Bqm9CJ6g4EpRqs?usp=sharing) for a quick start. ---- +--- ## Quick Start ### 1. Set up your environment @@ -67,7 +67,7 @@ async def main(question): ), name="real-estate-agent", ) - + # Run the agent result = await agent.ainvoke({"messages": [{"role": "user", "content": question}]}) @@ -92,5 +92,5 @@ if __name__ == "__main__": ## Need help? -Reach out to us on [Slack](https://join.slack.com/t/tensorlakecloud/shared_invite/zt-32fq4nmib-gO0OM5RIar3zLOBm~ZGqKg) or on the -[package repository on GitHub](https://github.com/tensorlakeai/langchain-tensorlake) directly. \ No newline at end of file +Reach out to us on [Slack](https://join.slack.com/t/tensorlakecloud/shared_invite/zt-32fq4nmib-gO0OM5RIar3zLOBm~ZGqKg) or on the +[package repository on GitHub](https://github.com/tensorlakeai/langchain-tensorlake) directly. diff --git a/docs/docs/integrations/providers/tidb.mdx b/docs/docs/integrations/providers/tidb.mdx index 401b4300c48..787b66f4a4a 100644 --- a/docs/docs/integrations/providers/tidb.mdx +++ b/docs/docs/integrations/providers/tidb.mdx @@ -1,15 +1,15 @@ # TiDB > [TiDB Cloud](https://www.pingcap.com/tidb-serverless), is a comprehensive Database-as-a-Service (DBaaS) solution, -> that provides dedicated and serverless options. `TiDB Serverless` is now integrating -> a built-in vector search into the MySQL landscape. With this enhancement, you can seamlessly -> develop AI applications using `TiDB Serverless` without the need for a new database or additional +> that provides dedicated and serverless options. `TiDB Serverless` is now integrating +> a built-in vector search into the MySQL landscape. With this enhancement, you can seamlessly +> develop AI applications using `TiDB Serverless` without the need for a new database or additional > technical stacks. Create a free TiDB Serverless cluster and start using the vector search feature at https://pingcap.com/ai. ## Installation and Setup -You have to get the connection details for the TiDB database. +You have to get the connection details for the TiDB database. Visit the [TiDB Cloud](https://tidbcloud.com/) to get the connection details. ```bash diff --git a/docs/docs/integrations/providers/tigergraph.mdx b/docs/docs/integrations/providers/tigergraph.mdx index 95a62635c83..d974bcb87fc 100644 --- a/docs/docs/integrations/providers/tigergraph.mdx +++ b/docs/docs/integrations/providers/tigergraph.mdx @@ -1,7 +1,7 @@ # TigerGraph >[TigerGraph](https://www.tigergraph.com/tigergraph-db/) is a natively distributed and high-performance graph database. -> The storage of data in a graph format of vertices and edges leads to rich relationships, +> The storage of data in a graph format of vertices and edges leads to rich relationships, > ideal for grouding LLM responses. ## Installation and Setup diff --git a/docs/docs/integrations/providers/transwarp.mdx b/docs/docs/integrations/providers/transwarp.mdx index 4406f885bab..99a16970ea8 100644 --- a/docs/docs/integrations/providers/transwarp.mdx +++ b/docs/docs/integrations/providers/transwarp.mdx @@ -1,16 +1,16 @@ # Transwarp ->[Transwarp](https://www.transwarp.cn/en/introduction) aims to build -> enterprise-level big data and AI infrastructure software, -> to shape the future of data world. It provides enterprises with -> infrastructure software and services around the whole data lifecycle, -> including integration, storage, governance, modeling, analysis, -> mining and circulation. -> -> `Transwarp` focuses on technology research and -> development and has accumulated core technologies in these aspects: -> distributed computing, SQL compilations, database technology, -> unification for multi-model data management, container-based cloud computing, +>[Transwarp](https://www.transwarp.cn/en/introduction) aims to build +> enterprise-level big data and AI infrastructure software, +> to shape the future of data world. It provides enterprises with +> infrastructure software and services around the whole data lifecycle, +> including integration, storage, governance, modeling, analysis, +> mining and circulation. +> +> `Transwarp` focuses on technology research and +> development and has accumulated core technologies in these aspects: +> distributed computing, SQL compilations, database technology, +> unification for multi-model data management, container-based cloud computing, > and big data analytics and intelligence. ## Installation diff --git a/docs/docs/integrations/providers/trulens.mdx b/docs/docs/integrations/providers/trulens.mdx index 327a6de3720..f735e22a7a8 100644 --- a/docs/docs/integrations/providers/trulens.mdx +++ b/docs/docs/integrations/providers/trulens.mdx @@ -19,14 +19,14 @@ See the integration details in the [TruLens documentation](https://www.trulens.o ### Tracking -Once you've created your LLM chain, you can use TruLens for evaluation and tracking. -TruLens has a number of [out-of-the-box Feedback Functions](https://www.trulens.org/trulens_eval/evaluation/feedback_functions/), +Once you've created your LLM chain, you can use TruLens for evaluation and tracking. +TruLens has a number of [out-of-the-box Feedback Functions](https://www.trulens.org/trulens_eval/evaluation/feedback_functions/), and is also an extensible framework for LLM evaluation. Create the feedback functions: ```python -from trulens_eval.feedback import Feedback, Huggingface, +from trulens_eval.feedback import Feedback, Huggingface, # Initialize HuggingFace-based feedback function collection class: hugs = Huggingface() @@ -47,10 +47,10 @@ toxicity = Feedback(openai.toxicity).on_input() ### Chains -After you've set up Feedback Function(s) for evaluating your LLM, you can wrap your application with +After you've set up Feedback Function(s) for evaluating your LLM, you can wrap your application with TruChain to get detailed tracing, logging and evaluation of your LLM app. -Note: See code for the `chain` creation is in +Note: See code for the `chain` creation is in the [TruLens documentation](https://www.trulens.org/trulens_eval/getting_started/quickstarts/langchain_quickstart/). ```python @@ -79,4 +79,4 @@ tru = Tru() tru.run_dashboard() # open a Streamlit app to explore ``` -For more information on TruLens, visit [trulens.org](https://www.trulens.org/) \ No newline at end of file +For more information on TruLens, visit [trulens.org](https://www.trulens.org/) diff --git a/docs/docs/integrations/providers/typesense.mdx b/docs/docs/integrations/providers/typesense.mdx index 5bb2b3ca0e4..db7fb968aa9 100644 --- a/docs/docs/integrations/providers/typesense.mdx +++ b/docs/docs/integrations/providers/typesense.mdx @@ -1,9 +1,9 @@ # Typesense -> [Typesense](https://typesense.org) is an open-source, in-memory search engine, that you can either -> [self-host](https://typesense.org/docs/guide/install-typesense.html#option-2-local-machine-self-hosting) or run +> [Typesense](https://typesense.org) is an open-source, in-memory search engine, that you can either +> [self-host](https://typesense.org/docs/guide/install-typesense.html#option-2-local-machine-self-hosting) or run > on [Typesense Cloud](https://cloud.typesense.org/). -> `Typesense` focuses on performance by storing the entire index in RAM (with a backup on disk) and also +> `Typesense` focuses on performance by storing the entire index in RAM (with a backup on disk) and also > focuses on providing an out-of-the-box developer experience by simplifying available options and setting good defaults. ## Installation and Setup diff --git a/docs/docs/integrations/providers/unstructured.mdx b/docs/docs/integrations/providers/unstructured.mdx index 8425ff1faa2..763d8200684 100644 --- a/docs/docs/integrations/providers/unstructured.mdx +++ b/docs/docs/integrations/providers/unstructured.mdx @@ -68,8 +68,8 @@ from langchain_community.document_loaders import UnstructuredCHMLoader ### UnstructuredCSVLoader -A `comma-separated values` (`CSV`) file is a delimited text file that uses -a comma to separate values. Each line of the file is a data record. +A `comma-separated values` (`CSV`) file is a delimited text file that uses +a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. See a [usage example](/docs/integrations/document_loaders/csv#unstructuredcsvloader). @@ -88,9 +88,9 @@ from langchain_community.document_loaders import UnstructuredEmailLoader ### UnstructuredEPubLoader -[EPUB](https://en.wikipedia.org/wiki/EPUB) is an `e-book file format` that uses -the “.epub” file extension. The term is short for electronic publication and -is sometimes styled `ePub`. `EPUB` is supported by many e-readers, and compatible +[EPUB](https://en.wikipedia.org/wiki/EPUB) is an `e-book file format` that uses +the “.epub” file extension. The term is short for electronic publication and +is sometimes styled `ePub`. `EPUB` is supported by many e-readers, and compatible software is available for most smartphones, tablets, and computers. See a [usage example](/docs/integrations/document_loaders/epub). @@ -141,9 +141,9 @@ from langchain_community.document_loaders import UnstructuredMarkdownLoader ### UnstructuredODTLoader -The `Open Document Format for Office Applications (ODF)`, also known as `OpenDocument`, -is an open file format for word processing documents, spreadsheets, presentations -and graphics and using ZIP-compressed XML files. It was developed with the aim of +The `Open Document Format for Office Applications (ODF)`, also known as `OpenDocument`, +is an open file format for word processing documents, spreadsheets, presentations +and graphics and using ZIP-compressed XML files. It was developed with the aim of providing an open, XML-based file format specification for office applications. See a [usage example](/docs/integrations/document_loaders/odt). @@ -180,7 +180,7 @@ from langchain_community.document_loaders import UnstructuredPowerPointLoader ### UnstructuredRSTLoader -A `reStructured Text` (`RST`) file is a file format for textual data +A `reStructured Text` (`RST`) file is a file format for textual data used primarily in the Python programming language community for technical documentation. See a [usage example](/docs/integrations/document_loaders/rst). diff --git a/docs/docs/integrations/providers/upstash.mdx b/docs/docs/integrations/providers/upstash.mdx index d1bfa783c23..74bba17008e 100644 --- a/docs/docs/integrations/providers/upstash.mdx +++ b/docs/docs/integrations/providers/upstash.mdx @@ -1,11 +1,11 @@ Upstash offers developers serverless databases and messaging -platforms to build powerful applications without having to worry +platforms to build powerful applications without having to worry about the operational complexity of running databases at scale. One significant advantage of Upstash is that their databases support HTTP and all of their SDKs use HTTP. This means that you can run this in serverless platforms, edge or any platform that does not support TCP connections. -Currently, there are two Upstash integrations available for LangChain: +Currently, there are two Upstash integrations available for LangChain: Upstash Vector as a vector embedding database and Upstash Redis as a cache and memory store. # Upstash Vector @@ -166,7 +166,7 @@ store.delete(["id1", "id2"]) You can get information about your database like the distance metric dimension using the info function. -When an insert happens, the database an indexing takes place. While this is happening new vectors can not be queried. `pendingVectorCount` represents the number of vector that are currently being indexed. +When an insert happens, the database an indexing takes place. While this is happening new vectors can not be queried. `pendingVectorCount` represents the number of vector that are currently being indexed. ```python info = store.info() diff --git a/docs/docs/integrations/providers/uptrain.md b/docs/docs/integrations/providers/uptrain.mdx similarity index 90% rename from docs/docs/integrations/providers/uptrain.md rename to docs/docs/integrations/providers/uptrain.mdx index e371f27870d..937cb7d8ed3 100644 --- a/docs/docs/integrations/providers/uptrain.md +++ b/docs/docs/integrations/providers/uptrain.mdx @@ -1,8 +1,8 @@ # UpTrain >[UpTrain](https://uptrain.ai/) is an open-source unified platform to evaluate and ->improve Generative AI applications. It provides grades for 20+ preconfigured evaluations ->(covering language, code, embedding use cases), performs root cause analysis on failure +>improve Generative AI applications. It provides grades for 20+ preconfigured evaluations +>(covering language, code, embedding use cases), performs root cause analysis on failure >cases and gives insights on how to resolve them. ## Installation and Setup diff --git a/docs/docs/integrations/providers/usearch.mdx b/docs/docs/integrations/providers/usearch.mdx index cdbc99ecc90..bf6ff9c44f5 100644 --- a/docs/docs/integrations/providers/usearch.mdx +++ b/docs/docs/integrations/providers/usearch.mdx @@ -1,12 +1,12 @@ # USearch >[USearch](https://unum-cloud.github.io/usearch/) is a Smaller & Faster Single-File Vector Search Engine. ->`USearch's` base functionality is identical to `FAISS`, and the interface should look -> familiar if you have ever investigated Approximate Nearest Neighbors search. -> `USearch` and `FAISS` both employ `HNSW` algorithm, but they differ significantly -> in their design principles. `USearch` is compact and broadly compatible with FAISS without +>`USearch's` base functionality is identical to `FAISS`, and the interface should look +> familiar if you have ever investigated Approximate Nearest Neighbors search. +> `USearch` and `FAISS` both employ `HNSW` algorithm, but they differ significantly +> in their design principles. `USearch` is compact and broadly compatible with FAISS without > sacrificing performance, with a primary focus on user-defined metrics and fewer dependencies. -> +> ## Installation and Setup We need to install `usearch` python package. diff --git a/docs/docs/integrations/providers/valthera.mdx b/docs/docs/integrations/providers/valthera.mdx index 7a7f56963a0..7822bd6736a 100644 --- a/docs/docs/integrations/providers/valthera.mdx +++ b/docs/docs/integrations/providers/valthera.mdx @@ -1,7 +1,7 @@ # Valthera > [Valthera](https://github.com/valthera/valthera) is an open-source framework that empowers LLM Agents to drive meaningful, context-aware user engagement. It evaluates user motivation and ability in real time, ensuring that notifications and actions are triggered only when users are most receptive. -> +> > **langchain-valthera** integrates Valthera with LangChain, enabling developers to build smarter, behavior-driven engagement systems that deliver personalized interactions. ## Installation and Setup @@ -60,4 +60,4 @@ print("✅ ValtheraTool successfully initialized for LangChain integration!") ``` -The langchain-valthera integration allows you to assess user behavior and decide on the best course of action for engagement, ensuring that interactions are both timely and relevant within your LangChain applications. \ No newline at end of file +The langchain-valthera integration allows you to assess user behavior and decide on the best course of action for engagement, ensuring that interactions are both timely and relevant within your LangChain applications. diff --git a/docs/docs/integrations/providers/vearch.md b/docs/docs/integrations/providers/vearch.mdx similarity index 100% rename from docs/docs/integrations/providers/vearch.md rename to docs/docs/integrations/providers/vearch.mdx diff --git a/docs/docs/integrations/providers/vespa.mdx b/docs/docs/integrations/providers/vespa.mdx index 7796fde96d7..933f86c1c3d 100644 --- a/docs/docs/integrations/providers/vespa.mdx +++ b/docs/docs/integrations/providers/vespa.mdx @@ -1,8 +1,8 @@ # Vespa ->[Vespa](https://vespa.ai/) is a fully featured search engine and vector database. +>[Vespa](https://vespa.ai/) is a fully featured search engine and vector database. > It supports vector search (ANN), lexical search, and search in structured data, all in the same query. - + ## Installation and Setup diff --git a/docs/docs/integrations/providers/vlite.mdx b/docs/docs/integrations/providers/vlite.mdx index 6599dec7201..f286f193df8 100644 --- a/docs/docs/integrations/providers/vlite.mdx +++ b/docs/docs/integrations/providers/vlite.mdx @@ -28,4 +28,4 @@ from langchain_community.vectorstores import vlite ### Usage -For a more detailed walkthrough of the vlite wrapper, see [this notebook](/docs/integrations/vectorstores/vlite). \ No newline at end of file +For a more detailed walkthrough of the vlite wrapper, see [this notebook](/docs/integrations/vectorstores/vlite). diff --git a/docs/docs/integrations/providers/wandb.mdx b/docs/docs/integrations/providers/wandb.mdx index 8eb85116ff0..8ed3f45db37 100644 --- a/docs/docs/integrations/providers/wandb.mdx +++ b/docs/docs/integrations/providers/wandb.mdx @@ -1,9 +1,9 @@ # Weights & Biases ->[Weights & Biases](https://wandb.ai/) is provider of the AI developer platform to train and +>[Weights & Biases](https://wandb.ai/) is provider of the AI developer platform to train and > fine-tune AI models and develop AI applications. - -`Weights & Biase` products can be used to log metrics and artifacts during training, + +`Weights & Biase` products can be used to log metrics and artifacts during training, and to trace the execution of your code. There are several main ways to use `Weights & Biases` products within LangChain: diff --git a/docs/docs/integrations/providers/wolfram_alpha.mdx b/docs/docs/integrations/providers/wolfram_alpha.mdx index 84b017be255..c44ad6f5d28 100644 --- a/docs/docs/integrations/providers/wolfram_alpha.mdx +++ b/docs/docs/integrations/providers/wolfram_alpha.mdx @@ -1,12 +1,12 @@ # Wolfram Alpha ->[WolframAlpha](https://en.wikipedia.org/wiki/WolframAlpha) is an answer engine developed by `Wolfram Research`. +>[WolframAlpha](https://en.wikipedia.org/wiki/WolframAlpha) is an answer engine developed by `Wolfram Research`. > It answers factual queries by computing answers from externally sourced data. This page covers how to use the `Wolfram Alpha API` within LangChain. ## Installation and Setup -- Install requirements with +- Install requirements with ```bash pip install wolframalpha ``` diff --git a/docs/docs/integrations/providers/writer.mdx b/docs/docs/integrations/providers/writer.mdx index 10680e49587..d7330086d3a 100644 --- a/docs/docs/integrations/providers/writer.mdx +++ b/docs/docs/integrations/providers/writer.mdx @@ -56,4 +56,4 @@ from langchain_writer.tools import GraphTool ``` See [details](/docs/integrations/tools/writer). -Writer-specific remotely invoking tool \ No newline at end of file +Writer-specific remotely invoking tool diff --git a/docs/docs/integrations/providers/xata.mdx b/docs/docs/integrations/providers/xata.mdx index 986468d63c7..f2cf6cc52fa 100644 --- a/docs/docs/integrations/providers/xata.mdx +++ b/docs/docs/integrations/providers/xata.mdx @@ -1,10 +1,10 @@ # Xata -> [Xata](https://xata.io) is a serverless data platform, based on `PostgreSQL`. -> It provides a Python SDK for interacting with your database, and a UI +> [Xata](https://xata.io) is a serverless data platform, based on `PostgreSQL`. +> It provides a Python SDK for interacting with your database, and a UI > for managing your data. -> `Xata` has a native vector type, which can be added to any table, and -> supports similarity search. LangChain inserts vectors directly to `Xata`, +> `Xata` has a native vector type, which can be added to any table, and +> supports similarity search. LangChain inserts vectors directly to `Xata`, > and queries it for the nearest neighbors of a given vector, so that you can > use all the LangChain Embeddings integrations with `Xata`. @@ -15,7 +15,7 @@ We need to install `xata` python package. ```bash -pip install xata==1.0.0a7 +pip install xata==1.0.0a7 ``` ## Vector Store diff --git a/docs/docs/integrations/providers/xinference.mdx b/docs/docs/integrations/providers/xinference.mdx index 88a471f3b44..fd904228fb1 100644 --- a/docs/docs/integrations/providers/xinference.mdx +++ b/docs/docs/integrations/providers/xinference.mdx @@ -3,14 +3,14 @@ This page demonstrates how to use [Xinference](https://github.com/xorbitsai/inference) with LangChain. -`Xinference` is a powerful and versatile library designed to serve LLMs, -speech recognition models, and multimodal models, even on your laptop. -With Xorbits Inference, you can effortlessly deploy and serve your or +`Xinference` is a powerful and versatile library designed to serve LLMs, +speech recognition models, and multimodal models, even on your laptop. +With Xorbits Inference, you can effortlessly deploy and serve your or state-of-the-art built-in models using just a single command. ## Installation and Setup -Xinference can be installed via pip from PyPI: +Xinference can be installed via pip from PyPI: ```bash pip install "xinference[all]" @@ -18,7 +18,7 @@ pip install "xinference[all]" ## LLM -Xinference supports various models compatible with GGML, including chatglm, baichuan, whisper, +Xinference supports various models compatible with GGML, including chatglm, baichuan, whisper, vicuna, and orca. To view the builtin models, run the command: ```bash @@ -54,18 +54,18 @@ You can also start a local instance of Xinference by running: xinference ``` -Once Xinference is running, an endpoint will be accessible for model management via CLI or -Xinference client. +Once Xinference is running, an endpoint will be accessible for model management via CLI or +Xinference client. -For local deployment, the endpoint will be http://localhost:9997. +For local deployment, the endpoint will be http://localhost:9997. For cluster deployment, the endpoint will be http://$\{supervisor_host\}:9997. -Then, you need to launch a model. You can specify the model names and other attributes -including model_size_in_billions and quantization. You can use command line interface (CLI) to -do it. For example, +Then, you need to launch a model. You can specify the model names and other attributes +including model_size_in_billions and quantization. You can use command line interface (CLI) to +do it. For example, ```bash xinference launch -n orca -s 3 -q q4_0 @@ -98,7 +98,7 @@ For more information and detailed examples, refer to the ### Embeddings Xinference also supports embedding queries and documents. See -[example for xinference embeddings](/docs/integrations/text_embedding/xinference) +[example for xinference embeddings](/docs/integrations/text_embedding/xinference) for a more detailed demo. diff --git a/docs/docs/integrations/providers/yahoo.mdx b/docs/docs/integrations/providers/yahoo.mdx index 70023477f55..28993cd2cb7 100644 --- a/docs/docs/integrations/providers/yahoo.mdx +++ b/docs/docs/integrations/providers/yahoo.mdx @@ -2,8 +2,8 @@ >[Yahoo (Wikipedia)](https://en.wikipedia.org/wiki/Yahoo) is an American web services provider. > -> It provides a web portal, search engine Yahoo Search, and related -> services, including `My Yahoo`, `Yahoo Mail`, `Yahoo News`, +> It provides a web portal, search engine Yahoo Search, and related +> services, including `My Yahoo`, `Yahoo Mail`, `Yahoo News`, > `Yahoo Finance`, `Yahoo Sports` and its advertising platform, `Yahoo Native`. diff --git a/docs/docs/integrations/providers/yandex.mdx b/docs/docs/integrations/providers/yandex.mdx index 40600d89f30..cc9cd25145b 100644 --- a/docs/docs/integrations/providers/yandex.mdx +++ b/docs/docs/integrations/providers/yandex.mdx @@ -2,11 +2,11 @@ All functionality related to Yandex Cloud ->[Yandex Cloud](https://cloud.yandex.com/en/) is a public cloud platform. +>[Yandex Cloud](https://cloud.yandex.com/en/) is a public cloud platform. ## Installation and Setup -Yandex Cloud SDK can be installed via pip from PyPI: +Yandex Cloud SDK can be installed via pip from PyPI: ```bash pip install yandexcloud @@ -46,7 +46,7 @@ from langchain_community.embeddings import YandexGPTEmbeddings ### YandexSTTParser -It transcribes and parses audio files. +It transcribes and parses audio files. `YandexSTTParser` is similar to the `OpenAIWhisperParser`. See a [usage example with OpenAIWhisperParser](/docs/integrations/document_loaders/youtube_audio). diff --git a/docs/docs/integrations/providers/yeagerai.mdx b/docs/docs/integrations/providers/yeagerai.mdx index 6483cce9001..7553162991d 100644 --- a/docs/docs/integrations/providers/yeagerai.mdx +++ b/docs/docs/integrations/providers/yeagerai.mdx @@ -3,12 +3,12 @@ This page covers how to use [Yeager.ai](https://yeager.ai) to generate LangChain tools and agents. ## What is Yeager.ai? -Yeager.ai is an ecosystem designed to simplify the process of creating AI agents and tools. +Yeager.ai is an ecosystem designed to simplify the process of creating AI agents and tools. It features yAgents, a No-code LangChain Agent Builder, which enables users to build, test, and deploy AI solutions with ease. Leveraging the LangChain framework, yAgents allows seamless integration with various language models and resources, making it suitable for developers, researchers, and AI enthusiasts across diverse applications. ## yAgents -Low code generative agent designed to help you build, prototype, and deploy Langchain tools with ease. +Low code generative agent designed to help you build, prototype, and deploy Langchain tools with ease. ### How to use? ``` @@ -38,6 +38,6 @@ You can see a video of how it works [here](https://www.youtube.com/watch?v=KA5hC As you become more familiar with yAgents, you can create more advanced tools and agents to automate your work and enhance your productivity. -For more information, see [yAgents' Github](https://github.com/yeagerai/yeagerai-agent) or our [docs](https://yeagerai.gitbook.io/docs/general/welcome-to-yeager.ai) +For more information, see [yAgents' Github](https://github.com/yeagerai/yeagerai-agent) or our [docs](https://yeagerai.gitbook.io/docs/general/welcome-to-yeager.ai) diff --git a/docs/docs/integrations/providers/yellowbrick.mdx b/docs/docs/integrations/providers/yellowbrick.mdx index 911f1ab5250..27b99aac26a 100644 --- a/docs/docs/integrations/providers/yellowbrick.mdx +++ b/docs/docs/integrations/providers/yellowbrick.mdx @@ -1,8 +1,8 @@ # Yellowbrick ->[Yellowbrick](https://yellowbrick.com/) is a provider of -> Enterprise Data Warehousing, Ad-hoc and Streaming Analytics, -> BI and AI workloads. +>[Yellowbrick](https://yellowbrick.com/) is a provider of +> Enterprise Data Warehousing, Ad-hoc and Streaming Analytics, +> BI and AI workloads. ## Vector store diff --git a/docs/docs/integrations/providers/zhipuai.mdx b/docs/docs/integrations/providers/zhipuai.mdx index 0bcad6c4f48..c4425e9c927 100644 --- a/docs/docs/integrations/providers/zhipuai.mdx +++ b/docs/docs/integrations/providers/zhipuai.mdx @@ -1,11 +1,11 @@ # Zhipu AI ->[Zhipu AI](https://www.zhipuai.cn/en/aboutus), originating from the technological -> advancements of `Tsinghua University's Computer Science Department`, -> is an artificial intelligence company with the mission of teaching machines -> to think like humans. Its world-leading AI team has developed the cutting-edge -> large language and multimodal models and built the high-precision billion-scale -> knowledge graphs, the combination of which uniquely empowers us to create a powerful +>[Zhipu AI](https://www.zhipuai.cn/en/aboutus), originating from the technological +> advancements of `Tsinghua University's Computer Science Department`, +> is an artificial intelligence company with the mission of teaching machines +> to think like humans. Its world-leading AI team has developed the cutting-edge +> large language and multimodal models and built the high-precision billion-scale +> knowledge graphs, the combination of which uniquely empowers us to create a powerful > data- and knowledge-driven cognitive engine towards artificial general intelligence. diff --git a/docs/docs/integrations/text_embedding/google_generative_ai.ipynb b/docs/docs/integrations/text_embedding/google_generative_ai.ipynb index 26a022c8f77..7afe3c0578c 100644 --- a/docs/docs/integrations/text_embedding/google_generative_ai.ipynb +++ b/docs/docs/integrations/text_embedding/google_generative_ai.ipynb @@ -101,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "eedc551e-a1f3-4fd8-8d65-4e0784c4441b", "metadata": {}, "outputs": [ @@ -123,7 +123,7 @@ "source": [ "from langchain_google_genai import GoogleGenerativeAIEmbeddings\n", "\n", - "embeddings = GoogleGenerativeAIEmbeddings(model=\"models/gemini-embedding-exp-03-07\")\n", + "embeddings = GoogleGenerativeAIEmbeddings(model=\"models/gemini-embedding-001\")\n", "vector = embeddings.embed_query(\"hello, world!\")\n", "vector[:5]" ] @@ -245,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "f1f077db-8eb4-49f7-8866-471a8528dcdb", "metadata": {}, "outputs": [ @@ -267,10 +267,10 @@ "from sklearn.metrics.pairwise import cosine_similarity\n", "\n", "query_embeddings = GoogleGenerativeAIEmbeddings(\n", - " model=\"models/gemini-embedding-exp-03-07\", task_type=\"RETRIEVAL_QUERY\"\n", + " model=\"models/gemini-embedding-001\", task_type=\"RETRIEVAL_QUERY\"\n", ")\n", "doc_embeddings = GoogleGenerativeAIEmbeddings(\n", - " model=\"models/gemini-embedding-exp-03-07\", task_type=\"RETRIEVAL_DOCUMENT\"\n", + " model=\"models/gemini-embedding-001\", task_type=\"RETRIEVAL_DOCUMENT\"\n", ")\n", "\n", "q_embed = query_embeddings.embed_query(\"What is the capital of France?\")\n", diff --git a/docs/docs/integrations/tools/brightdata-webscraperapi.ipynb b/docs/docs/integrations/tools/brightdata-webscraperapi.ipynb index d0ece37ac7e..b3cf9830621 100644 --- a/docs/docs/integrations/tools/brightdata-webscraperapi.ipynb +++ b/docs/docs/integrations/tools/brightdata-webscraperapi.ipynb @@ -253,7 +253,7 @@ "from langgraph.prebuilt import create_react_agent\n", "\n", "# Initialize the LLM\n", - "llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash\", google_api_key=\"your-api-key\")\n", + "llm = ChatGoogleGenerativeAI(model=\"gemini-2.5-flash\", google_api_key=\"your-api-key\")\n", "\n", "# Initialize the Bright Data Web Scraper API tool\n", "scraper_tool = BrightDataWebScraperAPI(bright_data_api_key=\"your-api-key\")\n", diff --git a/docs/docs/integrations/tools/brightdata_serp.ipynb b/docs/docs/integrations/tools/brightdata_serp.ipynb index 6b2d3ea8f89..0cbab0f5f21 100644 --- a/docs/docs/integrations/tools/brightdata_serp.ipynb +++ b/docs/docs/integrations/tools/brightdata_serp.ipynb @@ -233,7 +233,7 @@ "from langgraph.prebuilt import create_react_agent\n", "\n", "# Initialize the LLM\n", - "llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash\", google_api_key=\"your-api-key\")\n", + "llm = ChatGoogleGenerativeAI(model=\"gemini-2.5-flash\", google_api_key=\"your-api-key\")\n", "\n", "# Initialize the Bright Data SERP tool\n", "serp_tool = BrightDataSERP(\n", diff --git a/docs/docs/integrations/tools/brightdata_unlocker.ipynb b/docs/docs/integrations/tools/brightdata_unlocker.ipynb index 90dd00abf03..019e28a1174 100644 --- a/docs/docs/integrations/tools/brightdata_unlocker.ipynb +++ b/docs/docs/integrations/tools/brightdata_unlocker.ipynb @@ -275,7 +275,7 @@ "from langgraph.prebuilt import create_react_agent\n", "\n", "# Initialize the LLM\n", - "llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash\", google_api_key=\"your-api-key\")\n", + "llm = ChatGoogleGenerativeAI(model=\"gemini-2.5-flash\", google_api_key=\"your-api-key\")\n", "\n", "# Initialize the tool\n", "bright_data_tool = BrightDataUnlocker(bright_data_api_key=\"your-api-key\")\n", diff --git a/docs/docs/integrations/vectorstores/astradb.ipynb b/docs/docs/integrations/vectorstores/astradb.ipynb index 0e1e7b02743..b2ed0408d57 100644 --- a/docs/docs/integrations/vectorstores/astradb.ipynb +++ b/docs/docs/integrations/vectorstores/astradb.ipynb @@ -606,7 +606,7 @@ "\n", "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n", "\n", - "- [Tutorials](/docs/tutorials/)\n", + "- [Tutorials](/docs/tutorials/rag)\n", "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n", "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)" ] diff --git a/docs/docs/integrations/vectorstores/clickhouse.ipynb b/docs/docs/integrations/vectorstores/clickhouse.ipynb index 8c47cbb64d6..2498a16a5c8 100644 --- a/docs/docs/integrations/vectorstores/clickhouse.ipynb +++ b/docs/docs/integrations/vectorstores/clickhouse.ipynb @@ -358,7 +358,7 @@ "\n", "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n", "\n", - "- [Tutorials](/docs/tutorials/)\n", + "- [Tutorials](/docs/tutorials/rag)\n", "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n", "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)" ] diff --git a/docs/docs/integrations/vectorstores/couchbase.ipynb b/docs/docs/integrations/vectorstores/couchbase.ipynb index 01922fae4e3..c9e44a50bd4 100644 --- a/docs/docs/integrations/vectorstores/couchbase.ipynb +++ b/docs/docs/integrations/vectorstores/couchbase.ipynb @@ -918,7 +918,7 @@ "\n", "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n", "\n", - "- [Tutorials](/docs/tutorials/)\n", + "- [Tutorials](/docs/tutorials/rag)\n", "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n", "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)" ] diff --git a/docs/docs/integrations/vectorstores/databricks_vector_search.ipynb b/docs/docs/integrations/vectorstores/databricks_vector_search.ipynb index 77a046d9c91..b8bb0efe68c 100644 --- a/docs/docs/integrations/vectorstores/databricks_vector_search.ipynb +++ b/docs/docs/integrations/vectorstores/databricks_vector_search.ipynb @@ -494,7 +494,7 @@ "\n", "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n", "\n", - "- [Tutorials](/docs/tutorials/)\n", + "- [Tutorials](/docs/tutorials/rag)\n", "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n", "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)" ] diff --git a/docs/docs/integrations/vectorstores/elasticsearch.ipynb b/docs/docs/integrations/vectorstores/elasticsearch.ipynb index 6bc0c0c6ce1..180c17aa22b 100644 --- a/docs/docs/integrations/vectorstores/elasticsearch.ipynb +++ b/docs/docs/integrations/vectorstores/elasticsearch.ipynb @@ -940,7 +940,7 @@ "\n", "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n", "\n", - "- [Tutorials](/docs/tutorials/)\n", + "- [Tutorials](/docs/tutorials/rag)\n", "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n", "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)" ] diff --git a/docs/docs/integrations/vectorstores/faiss.ipynb b/docs/docs/integrations/vectorstores/faiss.ipynb index f931db9ba80..5d64e514786 100644 --- a/docs/docs/integrations/vectorstores/faiss.ipynb +++ b/docs/docs/integrations/vectorstores/faiss.ipynb @@ -410,7 +410,7 @@ "\n", "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n", "\n", - "- [Tutorials](/docs/tutorials/)\n", + "- [Tutorials](/docs/tutorials/rag)\n", "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n", "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)" ] diff --git a/docs/docs/integrations/vectorstores/milvus.ipynb b/docs/docs/integrations/vectorstores/milvus.ipynb index 8745e7d0f60..94b1d2c3f35 100644 --- a/docs/docs/integrations/vectorstores/milvus.ipynb +++ b/docs/docs/integrations/vectorstores/milvus.ipynb @@ -671,7 +671,7 @@ "\n", "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n", "\n", - "- [Tutorials](/docs/tutorials/)\n", + "- [Tutorials](/docs/tutorials/rag)\n", "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n", "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)" ] diff --git a/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb b/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb index d44367f4c07..dfee5a5178e 100644 --- a/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb +++ b/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb @@ -488,7 +488,7 @@ "\n", "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n", "\n", - "- [Tutorials](/docs/tutorials/)\n", + "- [Tutorials](/docs/tutorials/rag)\n", "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n", "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)" ] diff --git a/docs/docs/integrations/vectorstores/opengauss.ipynb b/docs/docs/integrations/vectorstores/opengauss.ipynb index 28e9b1183d3..a0540c2ba45 100644 --- a/docs/docs/integrations/vectorstores/opengauss.ipynb +++ b/docs/docs/integrations/vectorstores/opengauss.ipynb @@ -264,7 +264,7 @@ "\n", "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n", "\n", - "- [Tutorials](/docs/tutorials/)\n", + "- [Tutorials](/docs/tutorials/rag)\n", "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n", "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval/)" ] diff --git a/docs/docs/integrations/vectorstores/pgvector.ipynb b/docs/docs/integrations/vectorstores/pgvector.ipynb index f02700abb06..a31c300dddf 100644 --- a/docs/docs/integrations/vectorstores/pgvector.ipynb +++ b/docs/docs/integrations/vectorstores/pgvector.ipynb @@ -435,7 +435,7 @@ "\n", "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n", "\n", - "- [Tutorials](/docs/tutorials/)\n", + "- [Tutorials](/docs/tutorials/rag)\n", "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n", "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)" ] diff --git a/docs/docs/integrations/vectorstores/pgvectorstore.ipynb b/docs/docs/integrations/vectorstores/pgvectorstore.ipynb index f770b4fe5ea..2b0edced75a 100644 --- a/docs/docs/integrations/vectorstores/pgvectorstore.ipynb +++ b/docs/docs/integrations/vectorstores/pgvectorstore.ipynb @@ -741,6 +741,7 @@ "\n", "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n", "\n", + "- [Tutorials](/docs/tutorials/rag)\n", "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n", "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)" ] diff --git a/docs/docs/integrations/vectorstores/pinecone.ipynb b/docs/docs/integrations/vectorstores/pinecone.ipynb index 7f8b4dad393..4bb17e72b75 100644 --- a/docs/docs/integrations/vectorstores/pinecone.ipynb +++ b/docs/docs/integrations/vectorstores/pinecone.ipynb @@ -342,7 +342,7 @@ "\n", "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n", "\n", - "- [Tutorials](/docs/tutorials/)\n", + "- [Tutorials](/docs/tutorials/rag)\n", "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n", "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)" ] diff --git a/docs/docs/integrations/vectorstores/qdrant.ipynb b/docs/docs/integrations/vectorstores/qdrant.ipynb index d4f8acaaedf..c863e4ccf4e 100644 --- a/docs/docs/integrations/vectorstores/qdrant.ipynb +++ b/docs/docs/integrations/vectorstores/qdrant.ipynb @@ -749,7 +749,7 @@ "\n", "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n", "\n", - "- [Tutorials](/docs/tutorials/)\n", + "- [Tutorials](/docs/tutorials/rag)\n", "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n", "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)" ] diff --git a/docs/docs/integrations/vectorstores/redis.ipynb b/docs/docs/integrations/vectorstores/redis.ipynb index ae92648b38b..7b63459f3f3 100644 --- a/docs/docs/integrations/vectorstores/redis.ipynb +++ b/docs/docs/integrations/vectorstores/redis.ipynb @@ -762,7 +762,7 @@ "\n", "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n", "\n", - "- [Tutorials](/docs/tutorials/)\n", + "- [Tutorials](/docs/tutorials/rag)\n", "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n", "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)" ] diff --git a/docs/docs/integrations/vectorstores/sqlserver.ipynb b/docs/docs/integrations/vectorstores/sqlserver.ipynb index 2e6ee2a33c9..768708e9fa4 100644 --- a/docs/docs/integrations/vectorstores/sqlserver.ipynb +++ b/docs/docs/integrations/vectorstores/sqlserver.ipynb @@ -756,7 +756,7 @@ "\n", "The LangChain Vector store simplifies building sophisticated Q&A systems by enabling efficient similarity searches to find the top 10 relevant documents based on the user's query. The **retriever** is created from the **vector\\_store,** and the question-answer chain is built using the **create\\_stuff\\_documents\\_chain** function. A prompt template is crafted using the **ChatPromptTemplate** class, ensuring structured and context-rich responses. Often in Q&A applications it's important to show users the sources that were used to generate the answer. LangChain's built-in **create\\_retrieval\\_chain** will propagate retrieved source documents to the output under the \"context\" key:\n", "\n", - "Read more about Langchain RAG tutorials & the terminologies mentioned above [here](https:/python.langchain.com/docs/tutorials/rag)" + "Read more about Langchain RAG tutorials & the terminologies mentioned above [here](/docs/tutorials/rag)" ] }, { diff --git a/docs/docs/versions/v0_2/deprecations.mdx b/docs/docs/versions/v0_2/deprecations.mdx index d08d885c2d3..e0a34d15e51 100644 --- a/docs/docs/versions/v0_2/deprecations.mdx +++ b/docs/docs/versions/v0_2/deprecations.mdx @@ -899,4 +899,4 @@ Deprecated: 0.1.14 Removal: 0.3.0 -Alternative: [with_structured_output](/docs/how_to/structured_output/#the-with_structured_output-method) method on chat models that support tool calling. \ No newline at end of file +Alternative: [with_structured_output](/docs/how_to/structured_output/#the-with_structured_output-method) method on chat models that support tool calling. diff --git a/docs/docs/versions/v0_2/index.mdx b/docs/docs/versions/v0_2/index.mdx index 07bb76dfdee..ea538450c79 100644 --- a/docs/docs/versions/v0_2/index.mdx +++ b/docs/docs/versions/v0_2/index.mdx @@ -34,8 +34,8 @@ we hope that it will help you migrate your code more quickly. The migration script has the following limitations: -1. It’s limited to helping users move from old imports to new imports. It does not help address other deprecations. -2. It can’t handle imports that involve `as` . +1. It's limited to helping users move from old imports to new imports. It does not help address other deprecations. +2. It can't handle imports that involve `as` . 3. New imports are always placed in global scope, even if the old import that was replaced was located inside some local scope (e..g, function body). 4. It will likely miss some deprecated imports. @@ -66,8 +66,8 @@ You will need to run the migration script **twice** as it only applies one impor For example, say your code still uses `from langchain.chat_models import ChatOpenAI`: -After the first run, you’ll get: `from langchain_community.chat_models import ChatOpenAI` -After the second run, you’ll get: `from langchain_openai import ChatOpenAI` +After the first run, you'll get: `from langchain_community.chat_models import ChatOpenAI` +After the second run, you'll get: `from langchain_openai import ChatOpenAI` ```bash # Run a first time diff --git a/docs/docs/versions/v0_2/overview.mdx b/docs/docs/versions/v0_2/overview.mdx index 7fdb3fa78a7..3a72d760599 100644 --- a/docs/docs/versions/v0_2/overview.mdx +++ b/docs/docs/versions/v0_2/overview.mdx @@ -99,4 +99,4 @@ There is also a third less tangible benefit which is that being integration-agno All of which is to say that there’s no large benefits to `langchain` depending on `langchain-community` and some obvious downsides: the functionality in `langchain` should be integration agnostic anyways, `langchain-community` can’t be properly versioned, and depending on `langchain-community` increases the [vulnerability surface](https://github.com/langchain-ai/langchain/discussions/19083) of `langchain`. -For more context about the reason for the organization please see our blog: https://blog.langchain.dev/langchain-v0-1-0/ \ No newline at end of file +For more context about the reason for the organization please see our blog: https://blog.langchain.dev/langchain-v0-1-0/ diff --git a/docs/docs/versions/v0_3/index.mdx b/docs/docs/versions/v0_3/index.mdx index f553ab19866..cbd70ea58f0 100644 --- a/docs/docs/versions/v0_3/index.mdx +++ b/docs/docs/versions/v0_3/index.mdx @@ -10,7 +10,7 @@ **These are the only breaking changes.** -## What’s new +## What's new The following features have been added during the development of 0.2.x: @@ -95,7 +95,7 @@ well as updating the `langchain_core.pydantic_v1` and `langchain.pydantic_v1` im Once you've updated to recent versions of the packages, you may need to address the following issues stemming from the internal switch from Pydantic v1 to Pydantic v2: -- If your code depends on Pydantic aside from LangChain, you will need to upgrade your pydantic version constraints to be `pydantic>=2,<3`. See [Pydantic’s migration guide](https://docs.pydantic.dev/latest/migration/) for help migrating your non-LangChain code to Pydantic v2 if you use pydantic v1. +- If your code depends on Pydantic aside from LangChain, you will need to upgrade your pydantic version constraints to be `pydantic>=2,<3`. See [Pydantic's migration guide](https://docs.pydantic.dev/latest/migration/) for help migrating your non-LangChain code to Pydantic v2 if you use pydantic v1. - There are a number of side effects to LangChain components caused by the internal switch from Pydantic v1 to v2. We have listed some of the common cases below together with the recommended solutions. ## Common issues when transitioning to Pydantic 2 @@ -246,8 +246,8 @@ You will need to run the migration script **twice** as it only applies one impor For example, say that your code is still using the old import `from langchain.chat_models import ChatOpenAI`: -After the first run, you’ll get: `from langchain_community.chat_models import ChatOpenAI` -After the second run, you’ll get: `from langchain_openai import ChatOpenAI` +After the first run, you'll get: `from langchain_community.chat_models import ChatOpenAI` +After the second run, you'll get: `from langchain_openai import ChatOpenAI` ```bash # Run a first time diff --git a/docs/src/theme/ChatModelTabs.js b/docs/src/theme/ChatModelTabs.js index bbac33385d1..c7c3cff3cf3 100644 --- a/docs/src/theme/ChatModelTabs.js +++ b/docs/src/theme/ChatModelTabs.js @@ -36,17 +36,17 @@ export const CustomDropdown = ({ selectedOption, options, onSelect, modelType }) return (
- Select {text}:
-