diff --git a/.github/scripts/check_diff.py b/.github/scripts/check_diff.py index 53a110c2129..70c377f27cc 100644 --- a/.github/scripts/check_diff.py +++ b/.github/scripts/check_diff.py @@ -37,7 +37,6 @@ IGNORED_PARTNERS = [ ] PY_312_MAX_PACKAGES = [ - "libs/partners/voyageai", "libs/partners/chroma", # https://github.com/chroma-core/chroma/issues/4382 ] diff --git a/.github/workflows/_integration_test.yml b/.github/workflows/_integration_test.yml index c2e750e40be..e64a4a62dff 100644 --- a/.github/workflows/_integration_test.yml +++ b/.github/workflows/_integration_test.yml @@ -41,6 +41,8 @@ jobs: FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }} GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }} ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }} + ANTHROPIC_FILES_API_IMAGE_ID: ${{ secrets.ANTHROPIC_FILES_API_IMAGE_ID }} + ANTHROPIC_FILES_API_PDF_ID: ${{ secrets.ANTHROPIC_FILES_API_PDF_ID }} AZURE_OPENAI_API_VERSION: ${{ secrets.AZURE_OPENAI_API_VERSION }} AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }} AZURE_OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY }} @@ -67,7 +69,6 @@ jobs: ES_CLOUD_ID: ${{ secrets.ES_CLOUD_ID }} ES_API_KEY: ${{ secrets.ES_API_KEY }} MONGODB_ATLAS_URI: ${{ secrets.MONGODB_ATLAS_URI }} - VOYAGE_API_KEY: ${{ secrets.VOYAGE_API_KEY }} COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }} UPSTAGE_API_KEY: ${{ secrets.UPSTAGE_API_KEY }} XAI_API_KEY: ${{ secrets.XAI_API_KEY }} diff --git a/.github/workflows/_release.yml b/.github/workflows/_release.yml index 4e8457c7214..2c0cd53716f 100644 --- a/.github/workflows/_release.yml +++ b/.github/workflows/_release.yml @@ -322,7 +322,6 @@ jobs: ES_CLOUD_ID: ${{ secrets.ES_CLOUD_ID }} ES_API_KEY: ${{ secrets.ES_API_KEY }} MONGODB_ATLAS_URI: ${{ secrets.MONGODB_ATLAS_URI }} - VOYAGE_API_KEY: ${{ secrets.VOYAGE_API_KEY }} UPSTAGE_API_KEY: ${{ secrets.UPSTAGE_API_KEY }} FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }} XAI_API_KEY: ${{ secrets.XAI_API_KEY }} @@ -345,6 +344,8 @@ jobs: fail-fast: false # Continue testing other partners if one fails env: ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }} + ANTHROPIC_FILES_API_IMAGE_ID: ${{ secrets.ANTHROPIC_FILES_API_IMAGE_ID }} + ANTHROPIC_FILES_API_PDF_ID: ${{ secrets.ANTHROPIC_FILES_API_PDF_ID }} OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} AZURE_OPENAI_API_VERSION: ${{ secrets.AZURE_OPENAI_API_VERSION }} AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }} diff --git a/.github/workflows/scheduled_test.yml b/.github/workflows/scheduled_test.yml index 0cc622f85a7..1b5b094818b 100644 --- a/.github/workflows/scheduled_test.yml +++ b/.github/workflows/scheduled_test.yml @@ -127,6 +127,8 @@ jobs: env: OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }} + ANTHROPIC_FILES_API_IMAGE_ID: ${{ secrets.ANTHROPIC_FILES_API_IMAGE_ID }} + ANTHROPIC_FILES_API_PDF_ID: ${{ secrets.ANTHROPIC_FILES_API_PDF_ID }} AZURE_OPENAI_API_VERSION: ${{ secrets.AZURE_OPENAI_API_VERSION }} AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }} AZURE_OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY }} diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 77042b47634..d27865859ee 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -103,12 +103,6 @@ repos: entry: make -C libs/partners/qdrant format files: ^libs/partners/qdrant/ pass_filenames: false - - id: voyageai - name: format partners/voyageai - language: system - entry: make -C libs/partners/voyageai format - files: ^libs/partners/voyageai/ - pass_filenames: false - id: root name: format docs, cookbook language: system diff --git a/README.md b/README.md index a645f23910b..6630f47a153 100644 --- a/README.md +++ b/README.md @@ -66,7 +66,7 @@ reliably handle complex tasks with LangGraph, our low-level agent orchestration framework. LangGraph offers customizable architecture, long-term memory, and human-in-the-loop workflows — and is trusted in production by companies like LinkedIn, Uber, Klarna, and GitLab. -- [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform) - Deploy +- [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/) - Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in diff --git a/docs/docs/concepts/agents.mdx b/docs/docs/concepts/agents.mdx index 960eb2a975d..3ca96125e1f 100644 --- a/docs/docs/concepts/agents.mdx +++ b/docs/docs/concepts/agents.mdx @@ -15,7 +15,7 @@ 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 +### Transitioning from AgentExecutor to LangGraph If you're currently using `AgentExecutor`, don't worry! We've prepared resources to help you: diff --git a/docs/docs/concepts/async.mdx b/docs/docs/concepts/async.mdx index d81c706db24..8e8a3de9a36 100644 --- a/docs/docs/concepts/async.mdx +++ b/docs/docs/concepts/async.mdx @@ -9,7 +9,7 @@ LLM based applications often involve a lot of I/O-bound operations, such as maki :::note You are expected to be familiar with asynchronous programming in Python before reading this guide. If you are not, please find appropriate resources online to learn how to program asynchronously in Python. -This guide specifically focuses on what you need to know to work with LangChain in an asynchronous context, assuming that you are already familiar with asynch +This guide specifically focuses on what you need to know to work with LangChain in an asynchronous context, assuming that you are already familiar with asynchronous programming. ::: ## Langchain asynchronous APIs diff --git a/docs/docs/concepts/callbacks.mdx b/docs/docs/concepts/callbacks.mdx index 5a6691baa5a..5899a4a98cd 100644 --- a/docs/docs/concepts/callbacks.mdx +++ b/docs/docs/concepts/callbacks.mdx @@ -6,7 +6,7 @@ LangChain provides a callback system that allows you to hook into the various stages of your LLM application. This is useful for logging, monitoring, streaming, and other tasks. -You can subscribe to these events by using the `callbacks` argument available throughout the API. This argument is list of handler objects, which are expected to implement one or more of the methods described below in more detail. +You can subscribe to these events by using the `callbacks` argument available throughout the API. This argument is a list of handler objects, which are expected to implement one or more of the methods described below in more detail. ## Callback events diff --git a/docs/docs/concepts/retrievers.mdx b/docs/docs/concepts/retrievers.mdx index baa3fc7c174..1305fb80406 100644 --- a/docs/docs/concepts/retrievers.mdx +++ b/docs/docs/concepts/retrievers.mdx @@ -32,7 +32,7 @@ The only requirement for a retriever is the ability to accepts a query and retur 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 is 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 diff --git a/docs/docs/concepts/text_splitters.mdx b/docs/docs/concepts/text_splitters.mdx index 335b854ec50..b5875dd33b9 100644 --- a/docs/docs/concepts/text_splitters.mdx +++ b/docs/docs/concepts/text_splitters.mdx @@ -3,8 +3,8 @@ :::info[Prerequisites] -* [Documents](/docs/concepts/retrievers/#interface) -* Tokenization(/docs/concepts/tokens) +* [Documents](./retrievers.mdx) +* [Tokenization](./tokens.mdx) ::: ## Overview diff --git a/docs/docs/how_to/custom_retriever.ipynb b/docs/docs/how_to/custom_retriever.ipynb index 31b6fb90a1c..85571a8bb48 100644 --- a/docs/docs/how_to/custom_retriever.ipynb +++ b/docs/docs/how_to/custom_retriever.ipynb @@ -98,7 +98,7 @@ " ) -> List[Document]:\n", " \"\"\"Sync implementations for retriever.\"\"\"\n", " matching_documents = []\n", - " for document in documents:\n", + " for document in self.documents:\n", " if len(matching_documents) > self.k:\n", " return matching_documents\n", "\n", diff --git a/docs/docs/how_to/document_loader_pdf.ipynb b/docs/docs/how_to/document_loader_pdf.ipynb index 766560c1db6..53f650313d8 100644 --- a/docs/docs/how_to/document_loader_pdf.ipynb +++ b/docs/docs/how_to/document_loader_pdf.ipynb @@ -20,7 +20,7 @@ "\n", "LangChain integrates with a host of PDF parsers. Some are simple and relatively low-level; others will support OCR and image-processing, or perform advanced document layout analysis. The right choice will depend on your needs. Below we enumerate the possibilities.\n", "\n", - "We will demonstrate these approaches on a [sample file](https://github.com/langchain-ai/langchain/blob/master/libs/community/tests/integration_tests/examples/layout-parser-paper.pdf):" + "We will demonstrate these approaches on a [sample file](https://github.com/langchain-ai/langchain-community/blob/main/libs/community/tests/examples/layout-parser-paper.pdf):" ] }, { diff --git a/docs/docs/integrations/chat/abso.ipynb b/docs/docs/integrations/chat/abso.ipynb index 8ed52bb4dc4..ed97843399e 100644 --- a/docs/docs/integrations/chat/abso.ipynb +++ b/docs/docs/integrations/chat/abso.ipynb @@ -17,7 +17,7 @@ "source": [ "# ChatAbso\n", "\n", - "This will help you getting started with ChatAbso [chat models](https://python.langchain.com/docs/concepts/chat_models/). For detailed documentation of all ChatAbso features and configurations head to the [API reference](https://python.langchain.com/api_reference/en/latest/chat_models/langchain_abso.chat_models.ChatAbso.html).\n", + "This will help you get started with ChatAbso [chat models](https://python.langchain.com/docs/concepts/chat_models/). For detailed documentation of all ChatAbso features and configurations, head to the [API reference](https://python.langchain.com/api_reference/en/latest/chat_models/langchain_abso.chat_models.ChatAbso.html).\n", "\n", "- You can find the full documentation for the Abso router [here] (https://abso.ai)\n", "\n", @@ -29,13 +29,13 @@ "| [ChatAbso](https://python.langchain.com/api_reference/en/latest/chat_models/langchain_abso.chat_models.ChatAbso.html) | [langchain-abso](https://python.langchain.com/api_reference/en/latest/abso_api_reference.html) | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-abso?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-abso?style=flat-square&label=%20) |\n", "\n", "## Setup\n", - "To access ChatAbso models you'll need to create an OpenAI account, get an API key, and install the `langchain-abso` integration package.\n", + "To access ChatAbso models, you'll need to create an OpenAI account, get an API key, and install the `langchain-abso` integration package.\n", "\n", "### Credentials\n", "\n", "- TODO: Update with relevant info.\n", "\n", - "Head to (TODO: link) to sign up to ChatAbso and generate an API key. Once you've done this set the ABSO_API_KEY environment variable:" + "Head to (TODO: link) to sign up for ChatAbso and generate an API key. Once you've done this, set the ABSO_API_KEY environment variable:" ] }, { @@ -198,7 +198,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.10" } }, "nbformat": 4, diff --git a/docs/docs/integrations/chat/ai21.ipynb b/docs/docs/integrations/chat/ai21.ipynb index b519548521d..b009662abc6 100644 --- a/docs/docs/integrations/chat/ai21.ipynb +++ b/docs/docs/integrations/chat/ai21.ipynb @@ -17,8 +17,6 @@ "source": [ "# ChatAI21\n", "\n", - "## Overview\n", - "\n", "This notebook covers how to get started with AI21 chat models.\n", "Note that different chat models support different parameters. See the [AI21 documentation](https://docs.ai21.com/reference) to learn more about the parameters in your chosen model.\n", "[See all AI21's LangChain components.](https://pypi.org/project/langchain-ai21/)\n", @@ -68,7 +66,9 @@ "cell_type": "markdown", "id": "f6844fff-3702-4489-ab74-732f69f3b9d7", "metadata": {}, - "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:" + "source": [ + "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:" + ] }, { "cell_type": "code", @@ -198,13 +198,17 @@ "cell_type": "markdown", "id": "39c0ccd229927eab", "metadata": {}, - "source": "# Tool Calls / Function Calling" + "source": [ + "# Tool Calls / Function Calling" + ] }, { "cell_type": "markdown", "id": "2bf6b40be07fe2d4", "metadata": {}, - "source": "This example shows how to use tool calling with AI21 models:" + "source": [ + "This example shows how to use tool calling with AI21 models:" + ] }, { "cell_type": "code", diff --git a/docs/docs/integrations/chat/anthropic.ipynb b/docs/docs/integrations/chat/anthropic.ipynb index b49379aee6a..cfe88b99ff1 100644 --- a/docs/docs/integrations/chat/anthropic.ipynb +++ b/docs/docs/integrations/chat/anthropic.ipynb @@ -325,6 +325,102 @@ "ai_msg.tool_calls" ] }, + { + "cell_type": "markdown", + "id": "535a16e4-cd5a-479f-b315-37c816ec4387", + "metadata": {}, + "source": [ + "## Multimodal\n", + "\n", + "Claude supports image and PDF inputs as content blocks, both in Anthropic's native format (see docs for [vision](https://docs.anthropic.com/en/docs/build-with-claude/vision#base64-encoded-image-example) and [PDF support](https://docs.anthropic.com/en/docs/build-with-claude/pdf-support)) as well as LangChain's [standard format](/docs/how_to/multimodal_inputs/).\n", + "\n", + "### Files API\n", + "\n", + "Claude also supports interactions with files through its managed [Files API](https://docs.anthropic.com/en/docs/build-with-claude/files). See examples below.\n", + "\n", + "The Files API can also be used to upload files to a container for use with Claude's built-in code-execution tools. See the [code execution](#code-execution) section below, for details.\n", + "\n", + "
\n", + "Images\n", + "\n", + "```python\n", + "# Upload image\n", + "\n", + "import anthropic\n", + "\n", + "client = anthropic.Anthropic()\n", + "file = client.beta.files.upload(\n", + " # Supports image/jpeg, image/png, image/gif, image/webp\n", + " file=(\"image.png\", open(\"/path/to/image.png\", \"rb\"), \"image/png\"),\n", + ")\n", + "image_file_id = file.id\n", + "\n", + "\n", + "# Run inference\n", + "from langchain_anthropic import ChatAnthropic\n", + "\n", + "llm = ChatAnthropic(\n", + " model=\"claude-sonnet-4-20250514\",\n", + " betas=[\"files-api-2025-04-14\"],\n", + ")\n", + "\n", + "input_message = {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\n", + " \"type\": \"text\",\n", + " \"text\": \"Describe this image.\",\n", + " },\n", + " {\n", + " \"type\": \"image\",\n", + " \"source\": {\n", + " \"type\": \"file\",\n", + " \"file_id\": image_file_id,\n", + " },\n", + " },\n", + " ],\n", + "}\n", + "llm.invoke([input_message])\n", + "```\n", + "\n", + "
\n", + "\n", + "
\n", + "PDFs\n", + "\n", + "```python\n", + "# Upload document\n", + "\n", + "import anthropic\n", + "\n", + "client = anthropic.Anthropic()\n", + "file = client.beta.files.upload(\n", + " file=(\"document.pdf\", open(\"/path/to/document.pdf\", \"rb\"), \"application/pdf\"),\n", + ")\n", + "pdf_file_id = file.id\n", + "\n", + "\n", + "# Run inference\n", + "from langchain_anthropic import ChatAnthropic\n", + "\n", + "llm = ChatAnthropic(\n", + " model=\"claude-sonnet-4-20250514\",\n", + " betas=[\"files-api-2025-04-14\"],\n", + ")\n", + "\n", + "input_message = {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"Describe this document.\"},\n", + " {\"type\": \"document\", \"source\": {\"type\": \"file\", \"file_id\": pdf_file_id}}\n", + " ],\n", + "}\n", + "llm.invoke([input_message])\n", + "```\n", + "\n", + "
" + ] + }, { "cell_type": "markdown", "id": "6e36d25c-f358-49e5-aefa-b99fbd3fec6b", @@ -454,6 +550,27 @@ "print(f\"\\nSecond:\\n{usage_2}\")" ] }, + { + "cell_type": "markdown", + "id": "9678656f-1ec4-4bf1-bf62-bbd49eb5c4e7", + "metadata": {}, + "source": [ + ":::tip Extended caching\n", + "\n", + " The cache lifetime is 5 minutes by default. If this is too short, you can apply one hour caching by enabling the `\"extended-cache-ttl-2025-04-11\"` beta header:\n", + "\n", + " ```python\n", + " llm = ChatAnthropic(\n", + " model=\"claude-3-7-sonnet-20250219\",\n", + " # highlight-next-line\n", + " betas=[\"extended-cache-ttl-2025-04-11\"],\n", + " )\n", + " ```\n", + " and specifying `\"cache_control\": {\"type\": \"ephemeral\", \"ttl\": \"1h\"}`.\n", + "\n", + ":::" + ] + }, { "cell_type": "markdown", "id": "141ce9c5-012d-4502-9d61-4a413b5d959a", @@ -953,6 +1070,159 @@ "response = llm_with_tools.invoke(\"How do I update a web app to TypeScript 5.5?\")" ] }, + { + "cell_type": "markdown", + "id": "1478cdc6-2e52-4870-80f9-b4ddf88f2db2", + "metadata": {}, + "source": [ + "### Code execution\n", + "\n", + "Claude can use a [code execution tool](https://docs.anthropic.com/en/docs/agents-and-tools/tool-use/code-execution-tool) to execute Python code in a sandboxed environment.\n", + "\n", + ":::info Code execution is supported since ``langchain-anthropic>=0.3.14``\n", + "\n", + ":::" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2ce13632-a2da-439f-a429-f66481501630", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_anthropic import ChatAnthropic\n", + "\n", + "llm = ChatAnthropic(\n", + " model=\"claude-sonnet-4-20250514\",\n", + " betas=[\"code-execution-2025-05-22\"],\n", + ")\n", + "\n", + "tool = {\"type\": \"code_execution_20250522\", \"name\": \"code_execution\"}\n", + "llm_with_tools = llm.bind_tools([tool])\n", + "\n", + "response = llm_with_tools.invoke(\n", + " \"Calculate the mean and standard deviation of \" \"[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "24076f91-3a3d-4e53-9618-429888197061", + "metadata": {}, + "source": [ + "
\n", + "Use with Files API\n", + "\n", + "Using the Files API, Claude can write code to access files for data analysis and other purposes. See example below:\n", + "\n", + "```python\n", + "# Upload file\n", + "\n", + "import anthropic\n", + "\n", + "client = anthropic.Anthropic()\n", + "file = client.beta.files.upload(\n", + " file=open(\"/path/to/sample_data.csv\", \"rb\")\n", + ")\n", + "file_id = file.id\n", + "\n", + "\n", + "# Run inference\n", + "from langchain_anthropic import ChatAnthropic\n", + "\n", + "llm = ChatAnthropic(\n", + " model=\"claude-sonnet-4-20250514\",\n", + " betas=[\"code-execution-2025-05-22\"],\n", + ")\n", + "\n", + "tool = {\"type\": \"code_execution_20250522\", \"name\": \"code_execution\"}\n", + "llm_with_tools = llm.bind_tools([tool])\n", + "\n", + "input_message = {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\n", + " \"type\": \"text\",\n", + " \"text\": \"Please plot these data and tell me what you see.\",\n", + " },\n", + " {\n", + " \"type\": \"container_upload\",\n", + " \"file_id\": file_id,\n", + " },\n", + " ]\n", + "}\n", + "llm_with_tools.invoke([input_message])\n", + "```\n", + "\n", + "Note that Claude may generate files as part of its code execution. You can access these files using the Files API:\n", + "```python\n", + "# Take all file outputs for demonstration purposes\n", + "file_ids = []\n", + "for block in response.content:\n", + " if block[\"type\"] == \"code_execution_tool_result\":\n", + " file_ids.extend(\n", + " content[\"file_id\"]\n", + " for content in block.get(\"content\", {}).get(\"content\", [])\n", + " if \"file_id\" in content\n", + " )\n", + "\n", + "for i, file_id in enumerate(file_ids):\n", + " file_content = client.beta.files.download(file_id)\n", + " file_content.write_to_file(f\"/path/to/file_{i}.png\")\n", + "```\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "040f381a-1768-479a-9a5e-aa2d7d77e0d5", + "metadata": {}, + "source": [ + "### Remote MCP\n", + "\n", + "Claude can use a [MCP connector tool](https://docs.anthropic.com/en/docs/agents-and-tools/mcp-connector) for model-generated calls to remote MCP servers.\n", + "\n", + ":::info Remote MCP is supported since ``langchain-anthropic>=0.3.14``\n", + "\n", + ":::" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "22fc4a89-e6d8-4615-96cb-2e117349aebf", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_anthropic import ChatAnthropic\n", + "\n", + "mcp_servers = [\n", + " {\n", + " \"type\": \"url\",\n", + " \"url\": \"https://mcp.deepwiki.com/mcp\",\n", + " \"name\": \"deepwiki\",\n", + " \"tool_configuration\": { # optional configuration\n", + " \"enabled\": True,\n", + " \"allowed_tools\": [\"ask_question\"],\n", + " },\n", + " \"authorization_token\": \"PLACEHOLDER\", # optional authorization\n", + " }\n", + "]\n", + "\n", + "llm = ChatAnthropic(\n", + " model=\"claude-sonnet-4-20250514\",\n", + " betas=[\"mcp-client-2025-04-04\"],\n", + " mcp_servers=mcp_servers,\n", + ")\n", + "\n", + "response = llm.invoke(\n", + " \"What transport protocols does the 2025-03-26 version of the MCP \"\n", + " \"spec (modelcontextprotocol/modelcontextprotocol) support?\"\n", + ")" + ] + }, { "cell_type": "markdown", "id": "2fd5d545-a40d-42b1-ad0c-0a79e2536c9b", diff --git a/docs/docs/integrations/chat/azure_ai.ipynb b/docs/docs/integrations/chat/azure_ai.ipynb index aeb97d149e6..3f877e42178 100644 --- a/docs/docs/integrations/chat/azure_ai.ipynb +++ b/docs/docs/integrations/chat/azure_ai.ipynb @@ -17,9 +17,9 @@ "source": [ "# AzureAIChatCompletionsModel\n", "\n", - "This will help you getting started with AzureAIChatCompletionsModel [chat models](/docs/concepts/chat_models). For detailed documentation of all AzureAIChatCompletionsModel features and configurations head to the [API reference](https://python.langchain.com/api_reference/azure_ai/chat_models/langchain_azure_ai.chat_models.AzureAIChatCompletionsModel.html)\n", + "This will help you get started with AzureAIChatCompletionsModel [chat models](/docs/concepts/chat_models). For detailed documentation of all AzureAIChatCompletionsModel features and configurations, head to the [API reference](https://python.langchain.com/api_reference/azure_ai/chat_models/langchain_azure_ai.chat_models.AzureAIChatCompletionsModel.html)\n", "\n", - "The AzureAIChatCompletionsModel class uses the Azure AI Foundry SDK. AI Foundry has several chat models including AzureOpenAI, Cohere, Llama, Phi-3/4, and DeepSeek-R1 to name a few. You can find information about their latest models and their costs, context windows, and supported input types in the [Azure docs](https://learn.microsoft.com/azure/ai-studio/how-to/model-catalog-overview).\n", + "The AzureAIChatCompletionsModel class uses the Azure AI Foundry SDK. AI Foundry has several chat models, including AzureOpenAI, Cohere, Llama, Phi-3/4, and DeepSeek-R1, among others. You can find information about their latest models and their costs, context windows, and supported input types in the [Azure docs](https://learn.microsoft.com/azure/ai-studio/how-to/model-catalog-overview).\n", "\n", "\n", "## Overview\n", @@ -37,12 +37,12 @@ "\n", "## Setup\n", "\n", - "To access AzureAIChatCompletionsModel models you'll need to create an [Azure account](https://azure.microsoft.com/pricing/purchase-options/azure-account), get an API key, and install the `langchain-azure-ai` integration package.\n", + "To access AzureAIChatCompletionsModel models, you'll need to create an [Azure account](https://azure.microsoft.com/pricing/purchase-options/azure-account), get an API key, and install the `langchain-azure-ai` integration package.\n", "\n", "### Credentials\n", "\n", "\n", - "Head to the [Azure docs](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/develop/sdk-overview?tabs=sync&pivots=programming-language-python) to see how to create your deployment and generate an API key. Once your model is deployed you click the 'get endpoint' button in AI Foundry. This will show you your endpoint and api key. Once you've done this set the AZURE_INFERENCE_CREDENTIAL and AZURE_INFERENCE_ENDPOINT environment variables:" + "Head to the [Azure docs](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/develop/sdk-overview?tabs=sync&pivots=programming-language-python) to see how to create your deployment and generate an API key. Once your model is deployed, you click the 'get endpoint' button in AI Foundry. This will show you your endpoint and api key. Once you've done this, set the AZURE_INFERENCE_CREDENTIAL and AZURE_INFERENCE_ENDPOINT environment variables:" ] }, { @@ -71,7 +71,7 @@ "id": "72ee0c4b-9764-423a-9dbf-95129e185210", "metadata": {}, "source": [ - "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:" + "If you want to get automated tracing of your model calls, you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:" ] }, { @@ -247,13 +247,13 @@ "source": [ "## API reference\n", "\n", - "For detailed documentation of all AzureAIChatCompletionsModel features and configurations head to the API reference: https://python.langchain.com/api_reference/azure_ai/chat_models/langchain_azure_ai.chat_models.AzureAIChatCompletionsModel.html" + "For detailed documentation of all AzureAIChatCompletionsModel features and configurations, head to the API reference: https://python.langchain.com/api_reference/azure_ai/chat_models/langchain_azure_ai.chat_models.AzureAIChatCompletionsModel.html" ] } ], "metadata": { "kernelspec": { - "display_name": "langchain-3-9", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -267,7 +267,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.12.10" } }, "nbformat": 4, diff --git a/docs/docs/integrations/chat/ernie.ipynb b/docs/docs/integrations/chat/ernie.ipynb index b109c65be3e..908e77fae27 100644 --- a/docs/docs/integrations/chat/ernie.ipynb +++ b/docs/docs/integrations/chat/ernie.ipynb @@ -25,17 +25,16 @@ "source": [ "**Deprecated Warning**\n", "\n", - "We recommend users using `langchain_community.chat_models.ErnieBotChat` \n", - "to use `langchain_community.chat_models.QianfanChatEndpoint` instead.\n", + "We recommend users switch from `langchain_community.chat_models.ErnieBotChat` to `langchain_community.chat_models.QianfanChatEndpoint`.\n", "\n", "documentation for `QianfanChatEndpoint` is [here](/docs/integrations/chat/baidu_qianfan_endpoint/).\n", "\n", - "they are 4 why we recommend users to use `QianfanChatEndpoint`:\n", + "There are 4 reasons why we recommend users to use `QianfanChatEndpoint`:\n", "\n", - "1. `QianfanChatEndpoint` support more LLM in the Qianfan platform.\n", - "2. `QianfanChatEndpoint` support streaming mode.\n", - "3. `QianfanChatEndpoint` support function calling usgage.\n", - "4. `ErnieBotChat` is lack of maintenance and deprecated." + "1. `QianfanChatEndpoint` supports more LLMs in the Qianfan platform.\n", + "2. `QianfanChatEndpoint` supports streaming mode.\n", + "3. `QianfanChatEndpoint` support function calling usage.\n", + "4. `ErnieBotChat` is no longer maintained and has been deprecated." ] }, { @@ -132,9 +131,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.12.10" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/docs/integrations/chat/llamacpp.ipynb b/docs/docs/integrations/chat/llamacpp.ipynb index dbd49b8d54d..16c44947f10 100644 --- a/docs/docs/integrations/chat/llamacpp.ipynb +++ b/docs/docs/integrations/chat/llamacpp.ipynb @@ -41,7 +41,7 @@ "\n", "## Setup\n", "\n", - "To get started and use **all** the features show below, we reccomend using a model that has been fine-tuned for tool-calling.\n", + "To get started and use **all** the features shown below, we recommend using a model that has been fine-tuned for tool-calling.\n", "\n", "We will use [\n", "Hermes-2-Pro-Llama-3-8B-GGUF](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF) from NousResearch. \n", @@ -204,7 +204,7 @@ "\n", "OpenAI has a [tool calling](https://platform.openai.com/docs/guides/function-calling) (we use \"tool calling\" and \"function calling\" interchangeably here) API that lets you describe tools and their arguments, and have the model return a JSON object with a tool to invoke and the inputs to that tool. tool-calling is extremely useful for building tool-using chains and agents, and for getting structured outputs from models more generally.\n", "\n", - "With `ChatLlamaCpp.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood these are converted to an OpenAI tool schemas, which looks like:\n", + "With `ChatLlamaCpp.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood, these are converted to an OpenAI tool schema, which looks like:\n", "```\n", "{\n", " \"name\": \"...\",\n", @@ -404,7 +404,7 @@ "source": [ "## API reference\n", "\n", - "For detailed documentation of all ChatLlamaCpp features and configurations head to the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.llamacpp.ChatLlamaCpp.html" + "For detailed documentation of all ChatLlamaCpp features and configurations, head to the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.llamacpp.ChatLlamaCpp.html" ] } ], @@ -424,7 +424,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.12.10" } }, "nbformat": 4, diff --git a/docs/docs/integrations/chat/maritalk.ipynb b/docs/docs/integrations/chat/maritalk.ipynb index ca93de3e327..998c8ec3a79 100644 --- a/docs/docs/integrations/chat/maritalk.ipynb +++ b/docs/docs/integrations/chat/maritalk.ipynb @@ -8,8 +8,6 @@ "\n", "# Maritalk\n", "\n", - "## Introduction\n", - "\n", "MariTalk is an assistant developed by the Brazilian company [Maritaca AI](https://www.maritaca.ai).\n", "MariTalk is based on language models that have been specially trained to understand Portuguese well.\n", "\n", diff --git a/docs/docs/integrations/chat/octoai.ipynb b/docs/docs/integrations/chat/octoai.ipynb index a0bbe98be8d..9d191362f81 100644 --- a/docs/docs/integrations/chat/octoai.ipynb +++ b/docs/docs/integrations/chat/octoai.ipynb @@ -16,7 +16,7 @@ "\n", "1. Get an API Token from [your OctoAI account page](https://octoai.cloud/settings).\n", " \n", - "2. Paste your API token in in the code cell below or use the `octoai_api_token` keyword argument.\n", + "2. Paste your API token in the code cell below or use the `octoai_api_token` keyword argument.\n", "\n", "Note: If you want to use a different model than the [available models](https://octoai.cloud/text?selectedTags=Chat), you can containerize the model and make a custom OctoAI endpoint yourself, by following [Build a Container from Python](https://octo.ai/docs/bring-your-own-model/advanced-build-a-container-from-scratch-in-python) and [Create a Custom Endpoint from a Container](https://octo.ai/docs/bring-your-own-model/create-custom-endpoints-from-a-container/create-custom-endpoints-from-a-container) and then updating your `OCTOAI_API_BASE` environment variable.\n" ] @@ -99,7 +99,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.12.10" }, "vscode": { "interpreter": { diff --git a/docs/docs/integrations/chat/openai.ipynb b/docs/docs/integrations/chat/openai.ipynb index a1b3adeb821..8df8e6d0c1b 100644 --- a/docs/docs/integrations/chat/openai.ipynb +++ b/docs/docs/integrations/chat/openai.ipynb @@ -915,6 +915,175 @@ "response_2.text()" ] }, + { + "cell_type": "markdown", + "id": "34ad0015-688c-4274-be55-93268b44f558", + "metadata": {}, + "source": [ + "#### Code interpreter\n", + "\n", + "OpenAI implements a [code interpreter](https://platform.openai.com/docs/guides/tools-code-interpreter) tool to support the sandboxed generation and execution of code.\n", + "\n", + "Example use:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "34826aae-6d48-4b84-bc00-89594a87d461", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_openai import ChatOpenAI\n", + "\n", + "llm = ChatOpenAI(model=\"o4-mini\", use_responses_api=True)\n", + "\n", + "llm_with_tools = llm.bind_tools(\n", + " [\n", + " {\n", + " \"type\": \"code_interpreter\",\n", + " # Create a new container\n", + " \"container\": {\"type\": \"auto\"},\n", + " }\n", + " ]\n", + ")\n", + "response = llm_with_tools.invoke(\n", + " \"Write and run code to answer the question: what is 3^3?\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "1b4d92b9-941f-4d54-93a5-b0c73afd66b2", + "metadata": {}, + "source": [ + "Note that the above command created a new container. We can also specify an existing container ID:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d8c82895-5011-4062-a1bb-278ec91321e9", + "metadata": {}, + "outputs": [], + "source": [ + "tool_outputs = response.additional_kwargs[\"tool_outputs\"]\n", + "assert len(tool_outputs) == 1\n", + "# highlight-next-line\n", + "container_id = tool_outputs[0][\"container_id\"]\n", + "\n", + "llm_with_tools = llm.bind_tools(\n", + " [\n", + " {\n", + " \"type\": \"code_interpreter\",\n", + " # Use an existing container\n", + " # highlight-next-line\n", + " \"container\": container_id,\n", + " }\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8db30501-522c-4915-963d-d60539b5c16e", + "metadata": {}, + "source": [ + "#### Remote MCP\n", + "\n", + "OpenAI implements a [remote MCP](https://platform.openai.com/docs/guides/tools-remote-mcp) tool that allows for model-generated calls to MCP servers.\n", + "\n", + "Example use:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "7044a87b-8b99-49e8-8ca4-e2a8ae49f65a", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_openai import ChatOpenAI\n", + "\n", + "llm = ChatOpenAI(model=\"o4-mini\", use_responses_api=True)\n", + "\n", + "llm_with_tools = llm.bind_tools(\n", + " [\n", + " {\n", + " \"type\": \"mcp\",\n", + " \"server_label\": \"deepwiki\",\n", + " \"server_url\": \"https://mcp.deepwiki.com/mcp\",\n", + " \"require_approval\": \"never\",\n", + " }\n", + " ]\n", + ")\n", + "response = llm_with_tools.invoke(\n", + " \"What transport protocols does the 2025-03-26 version of the MCP \"\n", + " \"spec (modelcontextprotocol/modelcontextprotocol) support?\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "0ed7494e-425d-4bdf-ab83-3164757031dd", + "metadata": {}, + "source": [ + "
\n", + "MCP Approvals\n", + "\n", + "OpenAI will at times request approval before sharing data with a remote MCP server.\n", + "\n", + "In the above command, we instructed the model to never require approval. We can also configure the model to always request approval, or to always request approval for specific tools:\n", + "\n", + "```python\n", + "llm_with_tools = llm.bind_tools(\n", + " [\n", + " {\n", + " \"type\": \"mcp\",\n", + " \"server_label\": \"deepwiki\",\n", + " \"server_url\": \"https://mcp.deepwiki.com/mcp\",\n", + " \"require_approval\": {\n", + " \"always\": {\n", + " \"tool_names\": [\"read_wiki_structure\"]\n", + " }\n", + " }\n", + " }\n", + " ]\n", + ")\n", + "response = llm_with_tools.invoke(\n", + " \"What transport protocols does the 2025-03-26 version of the MCP \"\n", + " \"spec (modelcontextprotocol/modelcontextprotocol) support?\"\n", + ")\n", + "```\n", + "\n", + "Responses may then include blocks with type `\"mcp_approval_request\"`.\n", + "\n", + "To submit approvals for an approval request, structure it into a content block in an input message:\n", + "\n", + "```python\n", + "approval_message = {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\n", + " \"type\": \"mcp_approval_response\",\n", + " \"approve\": True,\n", + " \"approval_request_id\": output[\"id\"],\n", + " }\n", + " for output in response.additional_kwargs[\"tool_outputs\"]\n", + " if output[\"type\"] == \"mcp_approval_request\"\n", + " ]\n", + "}\n", + "\n", + "next_response = llm_with_tools.invoke(\n", + " [approval_message],\n", + " # continue existing thread\n", + " previous_response_id=response.response_metadata[\"id\"]\n", + ")\n", + "```\n", + "\n", + "
" + ] + }, { "cell_type": "markdown", "id": "6fda05f0-4b81-4709-9407-f316d760ad50", diff --git a/docs/docs/integrations/chat/predictionguard.ipynb b/docs/docs/integrations/chat/predictionguard.ipynb index 6d06a7dd5ad..105fc56ceef 100644 --- a/docs/docs/integrations/chat/predictionguard.ipynb +++ b/docs/docs/integrations/chat/predictionguard.ipynb @@ -4,93 +4,99 @@ "cell_type": "markdown", "id": "3f0a201c", "metadata": {}, - "source": "# ChatPredictionGuard" + "source": [ + "# ChatPredictionGuard" + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": ">[Prediction Guard](https://predictionguard.com) is a secure, scalable GenAI platform that safeguards sensitive data, prevents common AI malfunctions, and runs on affordable hardware.\n", - "id": "c3adc2aac37985ac" + "id": "c3adc2aac37985ac", + "metadata": {}, + "source": [ + ">[Prediction Guard](https://predictionguard.com) is a secure, scalable GenAI platform that safeguards sensitive data, prevents common AI malfunctions, and runs on affordable hardware.\n" + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "## Overview", - "id": "4e1ec341481fb244" + "id": "4e1ec341481fb244", + "metadata": {}, + "source": [ + "## Overview" + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "b4090b7489e37a91", + "metadata": {}, "source": [ "### Integration details\n", "This integration utilizes the Prediction Guard API, which includes various safeguards and security features." - ], - "id": "b4090b7489e37a91" + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "e26e5b3240452162", + "metadata": {}, "source": [ "### Model features\n", "The models supported by this integration only feature text-generation currently, along with the input and output checks described here." - ], - "id": "e26e5b3240452162" + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "4fca548b61efb049", + "metadata": {}, "source": [ "## Setup\n", "To access Prediction Guard models, contact us [here](https://predictionguard.com/get-started) to get a Prediction Guard API key and get started. " - ], - "id": "4fca548b61efb049" + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "7cc34a9cd865690c", + "metadata": {}, "source": [ "### Credentials\n", "Once you have a key, you can set it with " - ], - "id": "7cc34a9cd865690c" + ] }, { + "cell_type": "code", + "execution_count": 2, + "id": "fa57fba89276da13", "metadata": { "ExecuteTime": { "end_time": "2025-04-21T18:23:30.746350Z", "start_time": "2025-04-21T18:23:30.744744Z" } }, - "cell_type": "code", + "outputs": [], "source": [ "import os\n", "\n", "if \"PREDICTIONGUARD_API_KEY\" not in os.environ:\n", " os.environ[\"PREDICTIONGUARD_API_KEY\"] = \"\"" - ], - "id": "fa57fba89276da13", - "outputs": [], - "execution_count": 2 + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "87dc1742af7b053", + "metadata": {}, "source": [ "### Installation\n", "Install the Prediction Guard Langchain integration with" - ], - "id": "87dc1742af7b053" + ] }, { + "cell_type": "code", + "execution_count": 3, + "id": "b816ae8553cba021", "metadata": { "ExecuteTime": { "end_time": "2025-04-21T18:23:33.359278Z", "start_time": "2025-04-21T18:23:32.853207Z" } }, - "cell_type": "code", - "source": "%pip install -qU langchain-predictionguard", - "id": "b816ae8553cba021", "outputs": [ { "name": "stdout", @@ -100,7 +106,9 @@ ] } ], - "execution_count": 3 + "source": [ + "%pip install -qU langchain-predictionguard" + ] }, { "cell_type": "markdown", @@ -108,63 +116,61 @@ "metadata": { "id": "mesCTyhnJkNS" }, - "source": "## Instantiation" + "source": [ + "## Instantiation" + ] }, { "cell_type": "code", + "execution_count": 4, "id": "7191a5ce", "metadata": { - "id": "2xe8JEUwA7_y", "ExecuteTime": { "end_time": "2025-04-21T18:23:39.812675Z", "start_time": "2025-04-21T18:23:39.666881Z" - } + }, + "id": "2xe8JEUwA7_y" }, - "source": "from langchain_predictionguard import ChatPredictionGuard", "outputs": [], - "execution_count": 4 + "source": [ + "from langchain_predictionguard import ChatPredictionGuard" + ] }, { "cell_type": "code", + "execution_count": 5, "id": "140717c9", "metadata": { - "id": "Ua7Mw1N4HcER", "ExecuteTime": { "end_time": "2025-04-21T18:23:41.590296Z", "start_time": "2025-04-21T18:23:41.253237Z" - } + }, + "id": "Ua7Mw1N4HcER" }, + "outputs": [], "source": [ "# If predictionguard_api_key is not passed, default behavior is to use the `PREDICTIONGUARD_API_KEY` environment variable.\n", "chat = ChatPredictionGuard(model=\"Hermes-3-Llama-3.1-8B\")" - ], - "outputs": [], - "execution_count": 5 + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "## Invocation", - "id": "8dbdfc55b638e4c2" + "id": "8dbdfc55b638e4c2", + "metadata": {}, + "source": [ + "## Invocation" + ] }, { + "cell_type": "code", + "execution_count": 4, + "id": "5a1635e7ae7134a3", "metadata": { "ExecuteTime": { "end_time": "2024-11-08T19:44:56.634939Z", "start_time": "2024-11-08T19:44:55.924534Z" } }, - "cell_type": "code", - "source": [ - "messages = [\n", - " (\"system\", \"You are a helpful assistant that tells jokes.\"),\n", - " (\"human\", \"Tell me a joke\"),\n", - "]\n", - "\n", - "ai_msg = chat.invoke(messages)\n", - "ai_msg" - ], - "id": "5a1635e7ae7134a3", "outputs": [ { "data": { @@ -177,18 +183,26 @@ "output_type": "execute_result" } ], - "execution_count": 4 + "source": [ + "messages = [\n", + " (\"system\", \"You are a helpful assistant that tells jokes.\"),\n", + " (\"human\", \"Tell me a joke\"),\n", + "]\n", + "\n", + "ai_msg = chat.invoke(messages)\n", + "ai_msg" + ] }, { + "cell_type": "code", + "execution_count": 5, + "id": "a6f8025726e5da3c", "metadata": { "ExecuteTime": { "end_time": "2024-11-08T19:44:57.501782Z", "start_time": "2024-11-08T19:44:57.498931Z" } }, - "cell_type": "code", - "source": "print(ai_msg.content)", - "id": "a6f8025726e5da3c", "outputs": [ { "name": "stdout", @@ -198,16 +212,21 @@ ] } ], - "execution_count": 5 + "source": [ + "print(ai_msg.content)" + ] }, { "cell_type": "markdown", "id": "e9e96106-8e44-4373-9c57-adc3d0062df3", "metadata": {}, - "source": "## Streaming" + "source": [ + "## Streaming" + ] }, { "cell_type": "code", + "execution_count": 6, "id": "ea62d2da-802c-4b8a-a63e-5d1d0a72540f", "metadata": { "ExecuteTime": { @@ -215,12 +234,6 @@ "start_time": "2024-11-08T19:44:59.095584Z" } }, - "source": [ - "chat = ChatPredictionGuard(model=\"Hermes-2-Pro-Llama-3-8B\")\n", - "\n", - "for chunk in chat.stream(\"Tell me a joke\"):\n", - " print(chunk.content, end=\"\", flush=True)" - ], "outputs": [ { "name": "stdout", @@ -232,33 +245,39 @@ ] } ], - "execution_count": 6 + "source": [ + "chat = ChatPredictionGuard(model=\"Hermes-2-Pro-Llama-3-8B\")\n", + "\n", + "for chunk in chat.stream(\"Tell me a joke\"):\n", + " print(chunk.content, end=\"\", flush=True)" + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "1227780d6e6728ba", + "metadata": {}, "source": [ "## Tool Calling\n", "\n", - "Prediction Guard has a tool calling API that lets you describe tools and their arguments, which enables the model return a JSON object with a tool to call and the inputs to that tool. Tool-calling is very useful for building tool-using chains and agents, and for getting structured outputs from models more generally.\n" - ], - "id": "1227780d6e6728ba" + "Prediction Guard has a tool calling API that lets you describe tools and their arguments, which enables the model to return a JSON object with a tool to call and the inputs to that tool. Tool-calling is very useful for building tool-using chains and agents, and for getting structured outputs from models more generally.\n" + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "23446aa52e01d1ba", + "metadata": {}, "source": [ "### ChatPredictionGuard.bind_tools()\n", "\n", "Using `ChatPredictionGuard.bind_tools()`, you can pass in Pydantic classes, dict schemas, and Langchain tools as tools to the model, which are then reformatted to allow for use by the model." - ], - "id": "23446aa52e01d1ba" + ] }, { - "metadata": {}, "cell_type": "code", - "outputs": [], "execution_count": null, + "id": "135efb0bfc5916c1", + "metadata": {}, + "outputs": [], "source": [ "from pydantic import BaseModel, Field\n", "\n", @@ -279,24 +298,18 @@ " [GetWeather, GetPopulation]\n", " # strict = True # enforce tool args schema is respected\n", ")" - ], - "id": "135efb0bfc5916c1" + ] }, { + "cell_type": "code", + "execution_count": 7, + "id": "8136f19a8836cd58", "metadata": { "ExecuteTime": { "end_time": "2025-04-21T18:42:41.834079Z", "start_time": "2025-04-21T18:42:40.289095Z" } }, - "cell_type": "code", - "source": [ - "ai_msg = llm_with_tools.invoke(\n", - " \"Which city is hotter today and which is bigger: LA or NY?\"\n", - ")\n", - "ai_msg" - ], - "id": "8136f19a8836cd58", "outputs": [ { "data": { @@ -309,28 +322,33 @@ "output_type": "execute_result" } ], - "execution_count": 7 + "source": [ + "ai_msg = llm_with_tools.invoke(\n", + " \"Which city is hotter today and which is bigger: LA or NY?\"\n", + ")\n", + "ai_msg" + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "84f405c45a35abe5", + "metadata": {}, "source": [ "### AIMessage.tool_calls\n", "\n", "Notice that the AIMessage has a tool_calls attribute. This contains in a standardized ToolCall format that is model-provider agnostic." - ], - "id": "84f405c45a35abe5" + ] }, { + "cell_type": "code", + "execution_count": 8, + "id": "bdcee85475019719", "metadata": { "ExecuteTime": { "end_time": "2025-04-21T18:43:00.429453Z", "start_time": "2025-04-21T18:43:00.426399Z" } }, - "cell_type": "code", - "source": "ai_msg.tool_calls", - "id": "bdcee85475019719", "outputs": [ { "data": { @@ -358,7 +376,9 @@ "output_type": "execute_result" } ], - "execution_count": 8 + "source": [ + "ai_msg.tool_calls" + ] }, { "cell_type": "markdown", @@ -386,6 +406,7 @@ }, { "cell_type": "code", + "execution_count": 7, "id": "9c5d7a87", "metadata": { "ExecuteTime": { @@ -393,16 +414,6 @@ "start_time": "2024-11-08T19:45:01.633319Z" } }, - "source": [ - "chat = ChatPredictionGuard(\n", - " model=\"Hermes-2-Pro-Llama-3-8B\", predictionguard_input={\"pii\": \"block\"}\n", - ")\n", - "\n", - "try:\n", - " chat.invoke(\"Hello, my name is John Doe and my SSN is 111-22-3333\")\n", - "except ValueError as e:\n", - " print(e)" - ], "outputs": [ { "name": "stdout", @@ -412,7 +423,16 @@ ] } ], - "execution_count": 7 + "source": [ + "chat = ChatPredictionGuard(\n", + " model=\"Hermes-2-Pro-Llama-3-8B\", predictionguard_input={\"pii\": \"block\"}\n", + ")\n", + "\n", + "try:\n", + " chat.invoke(\"Hello, my name is John Doe and my SSN is 111-22-3333\")\n", + "except ValueError as e:\n", + " print(e)" + ] }, { "cell_type": "markdown", @@ -424,6 +444,7 @@ }, { "cell_type": "code", + "execution_count": 8, "id": "a9f96fb4-00c3-4a39-b177-d1ccd5caecab", "metadata": { "ExecuteTime": { @@ -431,6 +452,15 @@ "start_time": "2024-11-08T19:45:03.275661Z" } }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Could not make prediction. prompt injection detected\n" + ] + } + ], "source": [ "chat = ChatPredictionGuard(\n", " model=\"Hermes-2-Pro-Llama-3-8B\",\n", @@ -443,17 +473,7 @@ " )\n", "except ValueError as e:\n", " print(e)" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Could not make prediction. prompt injection detected\n" - ] - } - ], - "execution_count": 8 + ] }, { "cell_type": "markdown", @@ -483,23 +503,15 @@ }, { "cell_type": "code", + "execution_count": 9, "id": "0cb3b91f", "metadata": { - "id": "PzxSbYwqTm2w", "ExecuteTime": { "end_time": "2024-11-08T19:45:10.044203Z", "start_time": "2024-11-08T19:45:05.692378Z" - } + }, + "id": "PzxSbYwqTm2w" }, - "source": [ - "chat = ChatPredictionGuard(\n", - " model=\"Hermes-2-Pro-Llama-3-8B\", predictionguard_output={\"toxicity\": True}\n", - ")\n", - "try:\n", - " chat.invoke(\"Please tell me something that would fail a toxicity check!\")\n", - "except ValueError as e:\n", - " print(e)" - ], "outputs": [ { "name": "stdout", @@ -509,7 +521,15 @@ ] } ], - "execution_count": 9 + "source": [ + "chat = ChatPredictionGuard(\n", + " model=\"Hermes-2-Pro-Llama-3-8B\", predictionguard_output={\"toxicity\": True}\n", + ")\n", + "try:\n", + " chat.invoke(\"Please tell me something that would fail a toxicity check!\")\n", + "except ValueError as e:\n", + " print(e)" + ] }, { "cell_type": "markdown", @@ -521,6 +541,7 @@ }, { "cell_type": "code", + "execution_count": 10, "id": "249da02a-d32d-4f91-82d0-10ec0505aec7", "metadata": { "ExecuteTime": { @@ -528,16 +549,6 @@ "start_time": "2024-11-08T19:45:10.109509Z" } }, - "source": [ - "chat = ChatPredictionGuard(\n", - " model=\"Hermes-2-Pro-Llama-3-8B\", predictionguard_output={\"factuality\": True}\n", - ")\n", - "\n", - "try:\n", - " chat.invoke(\"Make up something that would fail a factuality check!\")\n", - "except ValueError as e:\n", - " print(e)" - ], "outputs": [ { "name": "stdout", @@ -547,22 +558,47 @@ ] } ], - "execution_count": 10 + "source": [ + "chat = ChatPredictionGuard(\n", + " model=\"Hermes-2-Pro-Llama-3-8B\", predictionguard_output={\"factuality\": True}\n", + ")\n", + "\n", + "try:\n", + " chat.invoke(\"Make up something that would fail a factuality check!\")\n", + "except ValueError as e:\n", + " print(e)" + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "## Chaining", - "id": "3c81e5a85a765ece" + "id": "3c81e5a85a765ece", + "metadata": {}, + "source": [ + "## Chaining" + ] }, { + "cell_type": "code", + "execution_count": 11, + "id": "beb4e0666bb514a7", "metadata": { "ExecuteTime": { "end_time": "2024-11-08T19:45:17.525848Z", "start_time": "2024-11-08T19:45:15.197628Z" } }, - "cell_type": "code", + "outputs": [ + { + "data": { + "text/plain": [ + "AIMessage(content='Step 1: Determine the year Justin Bieber was born.\\nJustin Bieber was born on March 1, 1994.\\n\\nStep 2: Determine which NFL team won the Super Bowl in 1994.\\nThe 1994 Super Bowl was Super Bowl XXVIII, which took place on January 30, 1994. The winning team was the Dallas Cowboys, who defeated the Buffalo Bills with a score of 30-13.\\n\\nSo, the NFL team that won the Super Bowl in the year Justin Bieber was born is the Dallas Cowboys.', additional_kwargs={}, response_metadata={}, id='run-bbc94f8b-9ab0-4839-8580-a9e510bfc97a-0')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from langchain_core.prompts import PromptTemplate\n", "\n", @@ -577,30 +613,24 @@ "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n", "\n", "chat_chain.invoke({\"question\": question})" - ], - "id": "beb4e0666bb514a7", - "outputs": [ - { - "data": { - "text/plain": [ - "AIMessage(content='Step 1: Determine the year Justin Bieber was born.\\nJustin Bieber was born on March 1, 1994.\\n\\nStep 2: Determine which NFL team won the Super Bowl in 1994.\\nThe 1994 Super Bowl was Super Bowl XXVIII, which took place on January 30, 1994. The winning team was the Dallas Cowboys, who defeated the Buffalo Bills with a score of 30-13.\\n\\nSo, the NFL team that won the Super Bowl in the year Justin Bieber was born is the Dallas Cowboys.', additional_kwargs={}, response_metadata={}, id='run-bbc94f8b-9ab0-4839-8580-a9e510bfc97a-0')" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 11 + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "d87695d5ff1471c1", + "metadata": {}, "source": [ "## API reference\n", - "For detailed documentation of all ChatPredictionGuard features and configurations check out the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.predictionguard.ChatPredictionGuard.html" - ], - "id": "d87695d5ff1471c1" + "For detailed documentation of all ChatPredictionGuard features and configurations, check out the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.predictionguard.ChatPredictionGuard.html" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3664cc0e-841c-46f1-a158-4d5f5185bc94", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -622,7 +652,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.12.10" } }, "nbformat": 4, diff --git a/docs/docs/integrations/chat/solar.ipynb b/docs/docs/integrations/chat/solar.ipynb index f91c23e7832..40598334718 100644 --- a/docs/docs/integrations/chat/solar.ipynb +++ b/docs/docs/integrations/chat/solar.ipynb @@ -1,5 +1,13 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "516cad96-cfcb-4dd1-b70e-ecaef33e60ba", + "metadata": {}, + "source": [ + "Deprecated since version 0.0.34: Use langchain_upstage.ChatUpstage instead." + ] + }, { "cell_type": "code", "execution_count": 5, @@ -72,7 +80,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.0" + "version": "3.12.10" } }, "nbformat": 4, diff --git a/docs/docs/integrations/chat/symblai_nebula.ipynb b/docs/docs/integrations/chat/symblai_nebula.ipynb index a2a6c2d7444..6ad432b8b47 100644 --- a/docs/docs/integrations/chat/symblai_nebula.ipynb +++ b/docs/docs/integrations/chat/symblai_nebula.ipynb @@ -21,7 +21,6 @@ "source": [ "# Nebula (Symbl.ai)\n", "\n", - "## Overview\n", "This notebook covers how to get started with [Nebula](https://docs.symbl.ai/docs/nebula-llm) - Symbl.ai's chat model.\n", "\n", "### Integration details\n", diff --git a/docs/docs/integrations/chat/vectara.ipynb b/docs/docs/integrations/chat/vectara.ipynb index 920d1318775..912fd3245b0 100644 --- a/docs/docs/integrations/chat/vectara.ipynb +++ b/docs/docs/integrations/chat/vectara.ipynb @@ -5,8 +5,6 @@ "id": "134a0785", "metadata": {}, "source": [ - "## Overview\n", - "\n", "[Vectara](https://vectara.com/) is the trusted AI Assistant and Agent platform which focuses on enterprise readiness for mission-critical applications.\n", "Vectara serverless RAG-as-a-service provides all the components of RAG behind an easy-to-use API, including:\n", "1. A way to extract text from files (PDF, PPT, DOCX, etc)\n", diff --git a/docs/docs/integrations/chat/xai.ipynb b/docs/docs/integrations/chat/xai.ipynb index 01e06ccdb3a..6f13101000b 100644 --- a/docs/docs/integrations/chat/xai.ipynb +++ b/docs/docs/integrations/chat/xai.ipynb @@ -1,330 +1,373 @@ { - "cells": [ - { - "cell_type": "raw", - "id": "afaf8039", - "metadata": {}, - "source": [ - "---\n", - "sidebar_label: xAI\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "e49f1e0d", - "metadata": {}, - "source": [ - "# ChatXAI\n", - "\n", - "\n", - "This page will help you get started with xAI [chat models](../../concepts/chat_models.mdx). For detailed documentation of all `ChatXAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html).\n", - "\n", - "[xAI](https://console.x.ai/) offers an API to interact with Grok models.\n", - "\n", - "## Overview\n", - "### Integration details\n", - "\n", - "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/xai) | Package downloads | Package latest |\n", - "| :--- | :--- | :---: | :---: | :---: | :---: | :---: |\n", - "| [ChatXAI](https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html) | [langchain-xai](https://python.langchain.com/api_reference/xai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-xai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-xai?style=flat-square&label=%20) |\n", - "\n", - "### Model features\n", - "| [Tool calling](../../how_to/tool_calling.ipynb) | [Structured output](../../how_to/structured_output.ipynb) | JSON mode | [Image input](../../how_to/multimodal_inputs.ipynb) | Audio input | Video input | [Token-level streaming](../../how_to/chat_streaming.ipynb) | Native async | [Token usage](../../how_to/chat_token_usage_tracking.ipynb) | [Logprobs](../../how_to/logprobs.ipynb) |\n", - "| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n", - "| ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ |\n", - "\n", - "## Setup\n", - "\n", - "To access xAI models you'll need to create an xAI account, get an API key, and install the `langchain-xai` integration package.\n", - "\n", - "### Credentials\n", - "\n", - "Head to [this page](https://console.x.ai/) to sign up for xAI and generate an API key. Once you've done this set the `XAI_API_KEY` environment variable:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94", - "metadata": {}, - "outputs": [], - "source": [ - "import getpass\n", - "import os\n", - "\n", - "if \"XAI_API_KEY\" not in os.environ:\n", - " os.environ[\"XAI_API_KEY\"] = getpass.getpass(\"Enter your xAI API key: \")" - ] - }, - { - "cell_type": "markdown", - "id": "72ee0c4b-9764-423a-9dbf-95129e185210", - "metadata": {}, - "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:" - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de", - "metadata": {}, - "outputs": [], - "source": [ - "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n", - "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"" - ] - }, - { - "cell_type": "markdown", - "id": "0730d6a1-c893-4840-9817-5e5251676d5d", - "metadata": {}, - "source": [ - "### Installation\n", - "\n", - "The LangChain xAI integration lives in the `langchain-xai` package:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "652d6238-1f87-422a-b135-f5abbb8652fc", - "metadata": {}, - "outputs": [], - "source": [ - "%pip install -qU langchain-xai" - ] - }, - { - "cell_type": "markdown", - "id": "a38cde65-254d-4219-a441-068766c0d4b5", - "metadata": {}, - "source": [ - "## Instantiation\n", - "\n", - "Now we can instantiate our model object and generate chat completions:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain_xai import ChatXAI\n", - "\n", - "llm = ChatXAI(\n", - " model=\"grok-beta\",\n", - " temperature=0,\n", - " max_tokens=None,\n", - " timeout=None,\n", - " max_retries=2,\n", - " # other params...\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "2b4f3e15", - "metadata": {}, - "source": [ - "## Invocation" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "62e0dbc3", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "AIMessage(content=\"J'adore programmer.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 30, 'total_tokens': 36, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'stop', 'logprobs': None}, id='run-adffb7a3-e48a-4f52-b694-340d85abe5c3-0', usage_metadata={'input_tokens': 30, 'output_tokens': 6, 'total_tokens': 36, 'input_token_details': {}, 'output_token_details': {}})" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "messages = [\n", - " (\n", - " \"system\",\n", - " \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n", - " ),\n", - " (\"human\", \"I love programming.\"),\n", - "]\n", - "ai_msg = llm.invoke(messages)\n", - "ai_msg" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "J'adore programmer.\n" - ] - } - ], - "source": [ - "print(ai_msg.content)" - ] - }, - { - "cell_type": "markdown", - "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8", - "metadata": {}, - "source": [ - "## Chaining\n", - "\n", - "We can [chain](../../how_to/sequence.ipynb) our model with a prompt template like so:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "AIMessage(content='Ich liebe das Programmieren.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 7, 'prompt_tokens': 25, 'total_tokens': 32, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'stop', 'logprobs': None}, id='run-569fc8dc-101b-4e6d-864e-d4fa80df2b63-0', usage_metadata={'input_tokens': 25, 'output_tokens': 7, 'total_tokens': 32, 'input_token_details': {}, 'output_token_details': {}})" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from langchain_core.prompts import ChatPromptTemplate\n", - "\n", - "prompt = ChatPromptTemplate.from_messages(\n", - " [\n", - " (\n", - " \"system\",\n", - " \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n", - " ),\n", - " (\"human\", \"{input}\"),\n", - " ]\n", - ")\n", - "\n", - "chain = prompt | llm\n", - "chain.invoke(\n", - " {\n", - " \"input_language\": \"English\",\n", - " \"output_language\": \"German\",\n", - " \"input\": \"I love programming.\",\n", - " }\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "e074bce1-0994-4b83-b393-ae7aa7e21750", - "metadata": {}, - "source": [ - "## Tool calling\n", - "\n", - "ChatXAI has a [tool calling](https://docs.x.ai/docs#capabilities) (we use \"tool calling\" and \"function calling\" interchangeably here) API that lets you describe tools and their arguments, and have the model return a JSON object with a tool to invoke and the inputs to that tool. Tool-calling is extremely useful for building tool-using chains and agents, and for getting structured outputs from models more generally.\n", - "\n", - "### ChatXAI.bind_tools()\n", - "\n", - "With `ChatXAI.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood these are converted to an OpenAI tool schemas, which looks like:\n", - "```\n", - "{\n", - " \"name\": \"...\",\n", - " \"description\": \"...\",\n", - " \"parameters\": {...} # JSONSchema\n", - "}\n", - "```\n", - "and passed in every model invocation." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c6bfe929-ec02-46bd-9d54-76350edddabc", - "metadata": {}, - "outputs": [], - "source": [ - "from pydantic import BaseModel, Field\n", - "\n", - "\n", - "class GetWeather(BaseModel):\n", - " \"\"\"Get the current weather in a given location\"\"\"\n", - "\n", - " location: str = Field(..., description=\"The city and state, e.g. San Francisco, CA\")\n", - "\n", - "\n", - "llm_with_tools = llm.bind_tools([GetWeather])" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "5265c892-d8c2-48af-aef5-adbee1647ba6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "AIMessage(content='I am retrieving the current weather for San Francisco.', additional_kwargs={'tool_calls': [{'id': '0', 'function': {'arguments': '{\"location\":\"San Francisco, CA\"}', 'name': 'GetWeather'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 151, 'total_tokens': 162, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-73707da7-afec-4a52-bee1-a176b0ab8585-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': '0', 'type': 'tool_call'}], usage_metadata={'input_tokens': 151, 'output_tokens': 11, 'total_tokens': 162, 'input_token_details': {}, 'output_token_details': {}})" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ai_msg = llm_with_tools.invoke(\n", - " \"what is the weather like in San Francisco\",\n", - ")\n", - "ai_msg" - ] - }, - { - "cell_type": "markdown", - "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3", - "metadata": {}, - "source": [ - "## API reference\n", - "\n", - "For detailed documentation of all `ChatXAI` features and configurations head to the API reference: https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } + "cells": [ + { + "cell_type": "raw", + "id": "afaf8039", + "metadata": {}, + "source": [ + "---\n", + "sidebar_label: xAI\n", + "---" + ] }, - "nbformat": 4, - "nbformat_minor": 5 + { + "cell_type": "markdown", + "id": "e49f1e0d", + "metadata": {}, + "source": [ + "# ChatXAI\n", + "\n", + "\n", + "This page will help you get started with xAI [chat models](../../concepts/chat_models.mdx). For detailed documentation of all `ChatXAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html).\n", + "\n", + "[xAI](https://console.x.ai/) offers an API to interact with Grok models.\n", + "\n", + "## Overview\n", + "### Integration details\n", + "\n", + "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/xai) | Package downloads | Package latest |\n", + "| :--- | :--- | :---: | :---: | :---: | :---: | :---: |\n", + "| [ChatXAI](https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html) | [langchain-xai](https://python.langchain.com/api_reference/xai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-xai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-xai?style=flat-square&label=%20) |\n", + "\n", + "### Model features\n", + "| [Tool calling](../../how_to/tool_calling.ipynb) | [Structured output](../../how_to/structured_output.ipynb) | JSON mode | [Image input](../../how_to/multimodal_inputs.ipynb) | Audio input | Video input | [Token-level streaming](../../how_to/chat_streaming.ipynb) | Native async | [Token usage](../../how_to/chat_token_usage_tracking.ipynb) | [Logprobs](../../how_to/logprobs.ipynb) |\n", + "| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n", + "| ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ |\n", + "\n", + "## Setup\n", + "\n", + "To access xAI models you'll need to create an xAI account, get an API key, and install the `langchain-xai` integration package.\n", + "\n", + "### Credentials\n", + "\n", + "Head to [this page](https://console.x.ai/) to sign up for xAI and generate an API key. Once you've done this set the `XAI_API_KEY` environment variable:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94", + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "if \"XAI_API_KEY\" not in os.environ:\n", + " os.environ[\"XAI_API_KEY\"] = getpass.getpass(\"Enter your xAI API key: \")" + ] + }, + { + "cell_type": "markdown", + "id": "72ee0c4b-9764-423a-9dbf-95129e185210", + "metadata": {}, + "source": [ + "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de", + "metadata": {}, + "outputs": [], + "source": [ + "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n", + "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"" + ] + }, + { + "cell_type": "markdown", + "id": "0730d6a1-c893-4840-9817-5e5251676d5d", + "metadata": {}, + "source": [ + "### Installation\n", + "\n", + "The LangChain xAI integration lives in the `langchain-xai` package:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "652d6238-1f87-422a-b135-f5abbb8652fc", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -qU langchain-xai" + ] + }, + { + "cell_type": "markdown", + "id": "a38cde65-254d-4219-a441-068766c0d4b5", + "metadata": {}, + "source": [ + "## Instantiation\n", + "\n", + "Now we can instantiate our model object and generate chat completions:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_xai import ChatXAI\n", + "\n", + "llm = ChatXAI(\n", + " model=\"grok-beta\",\n", + " temperature=0,\n", + " max_tokens=None,\n", + " timeout=None,\n", + " max_retries=2,\n", + " # other params...\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "2b4f3e15", + "metadata": {}, + "source": [ + "## Invocation" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "62e0dbc3", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "AIMessage(content=\"J'adore programmer.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 30, 'total_tokens': 36, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'stop', 'logprobs': None}, id='run-adffb7a3-e48a-4f52-b694-340d85abe5c3-0', usage_metadata={'input_tokens': 30, 'output_tokens': 6, 'total_tokens': 36, 'input_token_details': {}, 'output_token_details': {}})" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "messages = [\n", + " (\n", + " \"system\",\n", + " \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n", + " ),\n", + " (\"human\", \"I love programming.\"),\n", + "]\n", + "ai_msg = llm.invoke(messages)\n", + "ai_msg" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "J'adore programmer.\n" + ] + } + ], + "source": [ + "print(ai_msg.content)" + ] + }, + { + "cell_type": "markdown", + "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8", + "metadata": {}, + "source": [ + "## Chaining\n", + "\n", + "We can [chain](../../how_to/sequence.ipynb) our model with a prompt template like so:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AIMessage(content='Ich liebe das Programmieren.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 7, 'prompt_tokens': 25, 'total_tokens': 32, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'stop', 'logprobs': None}, id='run-569fc8dc-101b-4e6d-864e-d4fa80df2b63-0', usage_metadata={'input_tokens': 25, 'output_tokens': 7, 'total_tokens': 32, 'input_token_details': {}, 'output_token_details': {}})" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from langchain_core.prompts import ChatPromptTemplate\n", + "\n", + "prompt = ChatPromptTemplate.from_messages(\n", + " [\n", + " (\n", + " \"system\",\n", + " \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n", + " ),\n", + " (\"human\", \"{input}\"),\n", + " ]\n", + ")\n", + "\n", + "chain = prompt | llm\n", + "chain.invoke(\n", + " {\n", + " \"input_language\": \"English\",\n", + " \"output_language\": \"German\",\n", + " \"input\": \"I love programming.\",\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "e074bce1-0994-4b83-b393-ae7aa7e21750", + "metadata": {}, + "source": [ + "## Tool calling\n", + "\n", + "ChatXAI has a [tool calling](https://docs.x.ai/docs#capabilities) (we use \"tool calling\" and \"function calling\" interchangeably here) API that lets you describe tools and their arguments, and have the model return a JSON object with a tool to invoke and the inputs to that tool. Tool-calling is extremely useful for building tool-using chains and agents, and for getting structured outputs from models more generally.\n", + "\n", + "### ChatXAI.bind_tools()\n", + "\n", + "With `ChatXAI.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood these are converted to an OpenAI tool schemas, which looks like:\n", + "```\n", + "{\n", + " \"name\": \"...\",\n", + " \"description\": \"...\",\n", + " \"parameters\": {...} # JSONSchema\n", + "}\n", + "```\n", + "and passed in every model invocation." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c6bfe929-ec02-46bd-9d54-76350edddabc", + "metadata": {}, + "outputs": [], + "source": [ + "from pydantic import BaseModel, Field\n", + "\n", + "\n", + "class GetWeather(BaseModel):\n", + " \"\"\"Get the current weather in a given location\"\"\"\n", + "\n", + " location: str = Field(..., description=\"The city and state, e.g. San Francisco, CA\")\n", + "\n", + "\n", + "llm_with_tools = llm.bind_tools([GetWeather])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5265c892-d8c2-48af-aef5-adbee1647ba6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AIMessage(content='I am retrieving the current weather for San Francisco.', additional_kwargs={'tool_calls': [{'id': '0', 'function': {'arguments': '{\"location\":\"San Francisco, CA\"}', 'name': 'GetWeather'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 151, 'total_tokens': 162, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-73707da7-afec-4a52-bee1-a176b0ab8585-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': '0', 'type': 'tool_call'}], usage_metadata={'input_tokens': 151, 'output_tokens': 11, 'total_tokens': 162, 'input_token_details': {}, 'output_token_details': {}})" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ai_msg = llm_with_tools.invoke(\n", + " \"what is the weather like in San Francisco\",\n", + ")\n", + "ai_msg" + ] + }, + { + "cell_type": "markdown", + "id": "00297c44-9bd6-4f1f-b364-2a7ff77090fd", + "metadata": {}, + "source": [ + "## Live Search\n", + "\n", + "xAI supports a [Live Search](https://docs.x.ai/docs/guides/live-search) feature that enables Grok to ground its answers using results from web searches:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d39cd1a4-80f2-48b2-8a74-3856d7706973", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_xai import ChatXAI\n", + "\n", + "llm = ChatXAI(\n", + " model=\"grok-3-latest\",\n", + " search_parameters={\n", + " \"mode\": \"auto\",\n", + " # Example optional parameters below:\n", + " \"max_search_results\": 3,\n", + " \"from_date\": \"2025-05-26\",\n", + " \"to_date\": \"2025-05-27\",\n", + " },\n", + ")\n", + "\n", + "llm.invoke(\"Provide me a digest of world news in the last 24 hours.\")" + ] + }, + { + "cell_type": "markdown", + "id": "cc62dc4d-e3ce-4b8b-8b94-d3e2e1a48bd1", + "metadata": {}, + "source": [ + "See [xAI docs](https://docs.x.ai/docs/guides/live-search) for the full set of web search options." + ] + }, + { + "cell_type": "markdown", + "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3", + "metadata": {}, + "source": [ + "## API reference\n", + "\n", + "For detailed documentation of all `ChatXAI` features and configurations head to the API reference: https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/docs/docs/integrations/document_loaders/blockchain.ipynb b/docs/docs/integrations/document_loaders/blockchain.ipynb index 438ad099bf5..dca505cb1be 100644 --- a/docs/docs/integrations/document_loaders/blockchain.ipynb +++ b/docs/docs/integrations/document_loaders/blockchain.ipynb @@ -9,15 +9,6 @@ "# Blockchain" ] }, - { - "cell_type": "markdown", - "metadata": { - "id": "5WjXERXzFEhg" - }, - "source": [ - "## Overview" - ] - }, { "attachments": {}, "cell_type": "markdown", diff --git a/docs/docs/integrations/document_loaders/google_firestore.ipynb b/docs/docs/integrations/document_loaders/google_firestore.ipynb index 139a848d813..e3d3a622ac1 100644 --- a/docs/docs/integrations/document_loaders/google_firestore.ipynb +++ b/docs/docs/integrations/document_loaders/google_firestore.ipynb @@ -57,7 +57,7 @@ }, "outputs": [], "source": [ - "%pip install -upgrade --quiet langchain-google-firestore" + "%pip install --upgrade --quiet langchain-google-firestore" ] }, { diff --git a/docs/docs/integrations/document_loaders/mintbase.ipynb b/docs/docs/integrations/document_loaders/mintbase.ipynb index 3b72cfbe49b..2f5aa3d195a 100644 --- a/docs/docs/integrations/document_loaders/mintbase.ipynb +++ b/docs/docs/integrations/document_loaders/mintbase.ipynb @@ -9,15 +9,6 @@ "# Near Blockchain" ] }, - { - "cell_type": "markdown", - "metadata": { - "id": "5WjXERXzFEhg" - }, - "source": [ - "## Overview" - ] - }, { "attachments": {}, "cell_type": "markdown", diff --git a/docs/docs/integrations/document_loaders/scrapfly.ipynb b/docs/docs/integrations/document_loaders/scrapfly.ipynb index 64ed86ffd4c..5676b16de0a 100644 --- a/docs/docs/integrations/document_loaders/scrapfly.ipynb +++ b/docs/docs/integrations/document_loaders/scrapfly.ipynb @@ -4,7 +4,6 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## ScrapFly\n", "[ScrapFly](https://scrapfly.io/) is a web scraping API with headless browser capabilities, proxies, and anti-bot bypass. It allows for extracting web page data into accessible LLM markdown or text." ] }, diff --git a/docs/docs/integrations/document_loaders/scrapingant.ipynb b/docs/docs/integrations/document_loaders/scrapingant.ipynb index 42e58ade57c..6f7b58af504 100644 --- a/docs/docs/integrations/document_loaders/scrapingant.ipynb +++ b/docs/docs/integrations/document_loaders/scrapingant.ipynb @@ -9,7 +9,6 @@ "---\n", "\n", "# ScrapingAnt\n", - "## Overview\n", "[ScrapingAnt](https://scrapingant.com/) is a web scraping API with headless browser capabilities, proxies, and anti-bot bypass. It allows for extracting web page data into accessible LLM markdown.\n", "\n", "This particular integration uses only Markdown extraction feature, but don't hesitate to [reach out to us](mailto:support@scrapingant.com) if you need more features provided by ScrapingAnt, but not yet implemented in this integration.\n", diff --git a/docs/docs/integrations/document_loaders/unstructured_pdfloader.ipynb b/docs/docs/integrations/document_loaders/unstructured_pdfloader.ipynb index 60cb109496b..d22fb7b25b5 100644 --- a/docs/docs/integrations/document_loaders/unstructured_pdfloader.ipynb +++ b/docs/docs/integrations/document_loaders/unstructured_pdfloader.ipynb @@ -6,8 +6,6 @@ "source": [ "# UnstructuredPDFLoader\n", "\n", - "## Overview\n", - "\n", "[Unstructured](https://unstructured-io.github.io/unstructured/) supports a common interface for working with unstructured or semi-structured file formats, such as Markdown or PDF. LangChain's [UnstructuredPDFLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.UnstructuredPDFLoader.html) integrates with Unstructured to parse PDF documents into LangChain [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) objects.\n", "\n", "Please see [this page](/docs/integrations/providers/unstructured/) for more information on installing system requirements.\n", @@ -34,7 +32,9 @@ { "cell_type": "markdown", "metadata": {}, - "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:" + "source": [ + "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:" + ] }, { "cell_type": "code", diff --git a/docs/docs/integrations/document_loaders/zeroxpdfloader.ipynb b/docs/docs/integrations/document_loaders/zeroxpdfloader.ipynb index ffaf82e6897..5be3611829c 100644 --- a/docs/docs/integrations/document_loaders/zeroxpdfloader.ipynb +++ b/docs/docs/integrations/document_loaders/zeroxpdfloader.ipynb @@ -6,7 +6,6 @@ "source": [ "# ZeroxPDFLoader\n", "\n", - "## Overview\n", "`ZeroxPDFLoader` is a document loader that leverages the [Zerox](https://github.com/getomni-ai/zerox) library. Zerox converts PDF documents into images, processes them using a vision-capable language model, and generates a structured Markdown representation. This loader allows for asynchronous operations and provides page-level document extraction.\n", "\n", "### Integration details\n", diff --git a/docs/docs/integrations/graphs/memgraph.ipynb b/docs/docs/integrations/graphs/memgraph.ipynb index 4ccfb6989b8..fd7f4336e77 100644 --- a/docs/docs/integrations/graphs/memgraph.ipynb +++ b/docs/docs/integrations/graphs/memgraph.ipynb @@ -65,14 +65,16 @@ "\n", "from langchain_core.prompts import PromptTemplate\n", "from langchain_memgraph.chains.graph_qa import MemgraphQAChain\n", - "from langchain_memgraph.graphs.memgraph import Memgraph\n", + "from langchain_memgraph.graphs.memgraph import MemgraphLangChain\n", "from langchain_openai import ChatOpenAI\n", "\n", "url = os.environ.get(\"MEMGRAPH_URI\", \"bolt://localhost:7687\")\n", "username = os.environ.get(\"MEMGRAPH_USERNAME\", \"\")\n", "password = os.environ.get(\"MEMGRAPH_PASSWORD\", \"\")\n", "\n", - "graph = Memgraph(url=url, username=username, password=password, refresh_schema=False)" + "graph = MemgraphLangChain(\n", + " url=url, username=username, password=password, refresh_schema=False\n", + ")" ] }, { diff --git a/docs/docs/integrations/providers/groq.mdx b/docs/docs/integrations/providers/groq.mdx index a7bc31364c2..53a8dab417b 100644 --- a/docs/docs/integrations/providers/groq.mdx +++ b/docs/docs/integrations/providers/groq.mdx @@ -1,6 +1,6 @@ # Groq ->[Groq](https://groq.com)developed the world's first Language Processing Unit™, or `LPU`. +>[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. > diff --git a/docs/docs/integrations/providers/memgraph.mdx b/docs/docs/integrations/providers/memgraph.mdx index a64203a85ba..fad5de9adbb 100644 --- a/docs/docs/integrations/providers/memgraph.mdx +++ b/docs/docs/integrations/providers/memgraph.mdx @@ -14,7 +14,7 @@ and use them to retrieve relevant information from the database. ```python from langchain_memgraph.chains.graph_qa import MemgraphQAChain -from langchain_memgraph.graphs.memgraph import Memgraph +from langchain_memgraph.graphs.memgraph import MemgraphLangChain ``` See a [usage example](/docs/integrations/graphs/memgraph) @@ -24,7 +24,7 @@ See a [usage example](/docs/integrations/graphs/memgraph) You can use the integration to construct a knowledge graph from unstructured data. ```python -from langchain_memgraph.graphs.memgraph import Memgraph +from langchain_memgraph.graphs.memgraph import MemgraphLangChain from langchain_experimental.graph_transformers import LLMGraphTransformer ``` diff --git a/docs/docs/integrations/providers/valyu.ipynb b/docs/docs/integrations/providers/valyu.ipynb index ca50d5018a1..f112b8b2805 100644 --- a/docs/docs/integrations/providers/valyu.ipynb +++ b/docs/docs/integrations/providers/valyu.ipynb @@ -4,13 +4,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# ValyuContext\n", - "\n", + "# Valyu Deep Search\n", ">[Valyu](https://www.valyu.network/) allows AI applications and agents to search the internet and proprietary data sources for relevant LLM ready information.\n", "\n", "This notebook goes over how to use Valyu in LangChain.\n", "\n", - "First, get an Valyu API key and add it as an environment variable. Get $10 free credit by [signing up here](https://exchange.valyu.network/).\n", + "First, get an Valyu API key and add it as an environment variable. Get $10 free credit by [signing up here](https://platform.valyu.network/).\n", "\n", "## Setup\n", "\n", @@ -43,12 +42,20 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain_valyu import ValyuContextRetriever\n", + "from langchain_valyu import ValyuRetriever\n", "\n", "valyu_api_key = \"YOUR API KEY\"\n", "\n", - "# Create a new instance of the ValyuContextRetriever\n", - "valyu_retriever = ValyuContextRetriever(valyu_api_key=valyu_api_key)\n", + "# Create a new instance of the ValyuRetriever\n", + "valyu_retriever = ValyuRetriever(\n", + " k=5,\n", + " search_type=\"all\",\n", + " relevance_threshold=0.5,\n", + " max_price=20.0,\n", + " start_date=\"2024-01-01\",\n", + " end_date=\"2024-12-31\",\n", + " valyu_api_key=valyu_api_key,\n", + ")\n", "\n", "# Search for a query and save the results\n", "docs = valyu_retriever.invoke(\"What are the benefits of renewable energy?\")\n", @@ -84,9 +91,10 @@ " query=\"What are agentic search-enhanced large reasoning models?\",\n", " search_type=\"all\",\n", " max_num_results=5,\n", - " similarity_threshold=0.4,\n", - " query_rewrite=False,\n", + " relevance_threshold=0.5,\n", " max_price=20.0,\n", + " start_date=\"2024-01-01\",\n", + " end_date=\"2024-12-31\",\n", ")\n", "\n", "print(\"Search Results:\", search_results)" diff --git a/docs/docs/integrations/providers/xai.ipynb b/docs/docs/integrations/providers/xai.ipynb index 5fa5b441b76..4840db40a53 100644 --- a/docs/docs/integrations/providers/xai.ipynb +++ b/docs/docs/integrations/providers/xai.ipynb @@ -45,7 +45,9 @@ "id": "8304b4d9", "metadata": {}, "source": [ - "## Example" + "## Example\n", + "\n", + "See [ChatXAI docs](/docs/integrations/chat/xai) for detail and supported features." ] }, { diff --git a/docs/docs/integrations/retrievers/index.mdx b/docs/docs/integrations/retrievers/index.mdx index 4bbc458c563..2da2337e6eb 100644 --- a/docs/docs/integrations/retrievers/index.mdx +++ b/docs/docs/integrations/retrievers/index.mdx @@ -34,4 +34,6 @@ The below retrievers will search over an external index (e.g., constructed from ## All retrievers +> **Note:** The descriptions in the table below are truncated for readability. + diff --git a/docs/docs/integrations/retrievers/valyu.ipynb b/docs/docs/integrations/retrievers/valyu.ipynb index eb663ec8fc8..b93bab51f4c 100644 --- a/docs/docs/integrations/retrievers/valyu.ipynb +++ b/docs/docs/integrations/retrievers/valyu.ipynb @@ -8,9 +8,9 @@ "\n", ">[Valyu](https://www.valyu.network/) allows AI applications and agents to search the internet and proprietary data sources for relevant LLM ready information.\n", "\n", - "This notebook goes over how to use Valyu context tool in LangChain.\n", + "This notebook goes over how to use Valyu deep search tool in LangChain.\n", "\n", - "First, get an Valyu API key and add it as an environment variable. Get $10 free credit by [signing up here](https://exchange.valyu.network/).\n", + "First, get an Valyu API key and add it as an environment variable. Get $10 free credit by [signing up here](https://platform.valyu.network/).\n", "\n", "## Setup\n", "\n", @@ -57,22 +57,25 @@ " The number of top results to return for each query.\n", "\n", "- `search_type: str = \"all\"` \n", - " The type of search to perform. Options may include \"all\", \"web\", \"proprietary\", etc., depending on your use case.\n", + " The type of search to perform: 'all', 'proprietary', or 'web'. Defaults to 'all'.\n", "\n", - "- `similarity_threshold: float = 0.4` \n", - " The minimum similarity score (between 0 and 1) required for a document to be considered relevant.\n", - "\n", - "- `query_rewrite: bool = False` \n", - " Whether to enable automatic rewriting of the query to improve search results.\n", + "- `relevance_threshold: float = 0.5` \n", + " The minimum relevance score (between 0 and 1) required for a document to be considered relevant. Defaults to 0.5.\n", " \n", "- `max_price: float = 20.0`\n", - " The maximum price (in USD) you are willing to spend per query.\n", + " The maximum price (in USD) you are willing to spend per query. Defaults to 20.0.\n", + "\n", + "- `start_date: Optional[str] = None`\n", + " Start date for time filtering in YYYY-MM-DD format (optional).\n", + "\n", + "- `end_date: Optional[str] = None`\n", + " End date for time filtering in YYYY-MM-DD format (optional).\n", "\n", "- `client: Optional[Valyu] = None` \n", " An optional custom Valyu client instance. If not provided, a new client will be created internally.\n", " \n", "- `valyu_api_key: Optional[str] = None` \n", - " Your Valyu API key. If not provided, the retriever will look for the `VALYU_API_KEY` environment variable.\n" + " Your Valyu API key. If not provided, the retriever will look for the `VALYU_API_KEY` environment variable." ] }, { @@ -81,14 +84,15 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain_valyu import ValyuContextRetriever\n", + "from langchain_valyu import ValyuRetriever\n", "\n", - "retriever = ValyuContextRetriever(\n", + "retriever = ValyuRetriever(\n", " k=5,\n", " search_type=\"all\",\n", - " similarity_threshold=0.4,\n", - " query_rewrite=False,\n", + " relevance_threshold=0.5,\n", " max_price=20.0,\n", + " start_date=\"2024-01-01\",\n", + " end_date=\"2024-12-31\",\n", " client=None,\n", " valyu_api_key=os.environ[\"VALYU_API_KEY\"],\n", ")" diff --git a/docs/docs/integrations/retrievers/wikipedia.ipynb b/docs/docs/integrations/retrievers/wikipedia.ipynb index bc36b7d947e..bb685fda181 100644 --- a/docs/docs/integrations/retrievers/wikipedia.ipynb +++ b/docs/docs/integrations/retrievers/wikipedia.ipynb @@ -17,7 +17,6 @@ "source": [ "# WikipediaRetriever\n", "\n", - "## Overview\n", ">[Wikipedia](https://wikipedia.org/) is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. `Wikipedia` is the largest and most-read reference work in history.\n", "\n", "This notebook shows how to retrieve wiki pages from `wikipedia.org` into the [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) format that is used downstream.\n", diff --git a/docs/docs/integrations/text_embedding/model2vec.ipynb b/docs/docs/integrations/text_embedding/model2vec.ipynb index d8ef5d35705..ee84545e800 100644 --- a/docs/docs/integrations/text_embedding/model2vec.ipynb +++ b/docs/docs/integrations/text_embedding/model2vec.ipynb @@ -5,8 +5,6 @@ "id": "e8712110", "metadata": {}, "source": [ - "## Overview\n", - "\n", "Model2Vec is a technique to turn any sentence transformer into a really small static model\n", "[model2vec](https://github.com/MinishLab/model2vec) can be used to generate embeddings." ] diff --git a/docs/docs/integrations/tools/google_books.ipynb b/docs/docs/integrations/tools/google_books.ipynb index 0954a1f1506..eeb60d6395a 100644 --- a/docs/docs/integrations/tools/google_books.ipynb +++ b/docs/docs/integrations/tools/google_books.ipynb @@ -11,15 +11,6 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Overview" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Integration details\n", - "\n", "The Google Books tool that supports the ReAct pattern and allows you to search the Google Books API. Google Books is the largest API in the world that keeps track of books in a curated manner. It has over 40 million entries, which can give users a significant amount of data." ] }, diff --git a/docs/docs/integrations/tools/google_jobs.ipynb b/docs/docs/integrations/tools/google_jobs.ipynb index 2b34022516e..539071fb5c8 100644 --- a/docs/docs/integrations/tools/google_jobs.ipynb +++ b/docs/docs/integrations/tools/google_jobs.ipynb @@ -47,46 +47,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: google-search-results in /opt/anaconda3/envs/langchain/lib/python3.10/site-packages (2.4.2)\n", - "Requirement already satisfied: requests in /opt/anaconda3/envs/langchain/lib/python3.10/site-packages (from google-search-results) (2.31.0)\n", - "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/anaconda3/envs/langchain/lib/python3.10/site-packages (from requests->google-search-results) (3.3.2)\n", - "Requirement already satisfied: idna<4,>=2.5 in /opt/anaconda3/envs/langchain/lib/python3.10/site-packages (from requests->google-search-results) (3.4)\n", - "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/anaconda3/envs/langchain/lib/python3.10/site-packages (from requests->google-search-results) (2.1.0)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /opt/anaconda3/envs/langchain/lib/python3.10/site-packages (from requests->google-search-results) (2023.11.17)\n" + "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ - "%pip install --upgrade --quiet google-search-results langchain-community" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "from langchain_community.tools.google_jobs import GoogleJobsQueryRun\n", - "from langchain_community.utilities.google_jobs import GoogleJobsAPIWrapper\n", - "\n", - "os.environ[\"SERPAPI_API_KEY\"] = \"[your serpapi key]\"\n", - "tool = GoogleJobsQueryRun(api_wrapper=GoogleJobsAPIWrapper())" + "%pip install --upgrade --quiet google-search-results langchain-community" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"SERPAPI_API_KEY\"] = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.tools.google_jobs import GoogleJobsQueryRun\n", + "from langchain_community.utilities.google_jobs import GoogleJobsAPIWrapper\n", + "\n", + "tool = GoogleJobsQueryRun(api_wrapper=GoogleJobsAPIWrapper())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'\\n_______________________________________________\\nJob Title: Entry Level Mechanical, Electrical, Physics Engineer for Controls PLC programing\\nCompany Name: Quest Global\\nLocation: Schenectady, NY\\nDescription: Job Requirements\\n\\nMechanical, Electrical, thermal and computer network engineers in developing control systems. The team works together closely in programing and testing control logic for industrial systems. The team will use simulations to test and represent real world operational conditions of electrical power plants.\\n\\nJob functions include:\\n• Desired Experience in programming controllers, like an Arduino, Allen Bradly, Siemens, GE Vie or any Industrial controllers with HMI interface.\\n• Working knowledge of instrumentation circuits, ex thermocouples, pressure transmitters,\\n• Reading logic diagrams and programing ladder logic from them\\n• Reviewing wiring diagrams for instrumentation installs\\n• Interpret P&ID/Electrical Schematics and experience with basic instrumentation.\\n• Collaborate with design engineers of various disciplines to develop control system requirements\\n• Support Field Engineering Services in technical resolution of field problems\\n• Providing input to quality assurance on projects\\n• Review and interpret customer specifications on mechanical systems\\n• Operational Knowledge of Steam or Gas Turbine Power Plants\\n• Ability to interpret functional requirements and translate into control logic.\\n• Ability to interpret P&ID/Electrical Schematics and experience with basic instrumentation\\n\\nRequired Skills and Experience\\n• Preferred Hands-on experience programming controllers, GE Mark V, VI, VIe series turbine control and Cimplicity HMI software or other industry related controllers like Audrino, LabView.\\n• Demonstrate experience working in the industrial controls or power generation industry\\n• Have a fundamental understanding of Steam Turbine and Power Plant construction\\n• Technical skills in engineering design and construction practices\\n• Proven team player with solid cooperation, flexibility and demonstrated capacity to learn complex information quickly\\n• Field experience in related equipment\\n• Strong organizational / project execution skills and prior success in working with or leading cross-functional teams\\n• Strong interpersonal skills with the ability to influence and lead others\\n• Passion for continuous learning and applying best practices to achieve practical solutions\\n\\nWork Experience\\n\\nSkills\\n• instrumentation\\n• ladder logic\\n• control systems\\n• electrical schematics\\n• control system\\n• programming controllers\\n• control\\n• collaborate\\n• quality assurance\\n• best practices\\n• wiring diagrams\\n• functional requirements\\n• industrial electrical controls\\n• project execution\\n• engineering design\\n• instrumentation circuits\\n• technical\\n• power plant construction\\n• electrical controls\\n• cross-functional teams\\n• continuous learning\\n• strong organizational\\n• mechanical systems\\n• technical skills\\n_______________________________________________\\n\\n'" + "\"\\n_______________________________________________\\nJob Title: Applied Physicist - Open Rank (Entry-Junior Level)-EOSL\\nCompany Name: Georgia Tech Research Institute\\nLocation: Atlanta, GA\\nDescription: Overview:\\n\\nThe Georgia Tech Research Institute (GTRI) is the nonprofit, applied research division of the Georgia Institute of Technology (Georgia Tech).\\u202fFounded in 1934 as the Engineering Experiment Station, GTRI has grown to more than 2,900 employees, supporting eight laboratories in over 20 locations around the country and performing more than $940 million of problem-solving research annually for government and industry.\\u202fGTRI's renowned researchers combine science, engineering, economics, policy, and technical expertise to solve complex problems for the U.S. federal government, state, and industry.\\n\\nGeorgia Tech's Mission and Values\\n\\nGeorgia Tech's mission is to develop leaders who advance technology and improve the human condition. The Institute has nine key values that are foundational to everything we do:\\n\\n1. Students are our top priority.\\n2. We strive for excellence.\\n3. We thrive on diversity.\\n4. We celebrate collaboration.\\n5. We champion innovation.\\n6. We safeguard freedom of inquiry and expression.\\n7. We nurture the wellbeing of our community.\\n8. We act ethically.\\n9. We are responsible stewards.\\n\\nOver the next decade, Georgia Tech will become an example of inclusive innovation, a leading technological research university of unmatched scale, relentlessly committed to serving the public good; breaking new ground in addressing the biggest local, national, and global challenges and opportunities of our time; making technology broadly accessible; and developing exceptional, principled leaders from all backgrounds ready to produce novel ideas and create solutions with real human impact.\\n\\nProject/Unit Description\\n\\nThe Electro-Optical and Infrared Systems Division’s (EISD’s) research is performed in all areas of the imaging chain. From basic geometric/radiometric modeling and analysis, through design and fabrication, to test and evaluation in the field, our team provides subject matter expertise to our customers in order to solve their most challenging problems in undersea, ground, airborne, and space domains. EISD is a collection of multi-disciplinary engineers and scientists encompassing optical, physics, mechanical, electrical, computer, and software domains, among others. Our portfolio consists of research programs in and related to the following application areas: Intelligence, Surveillance, and Reconnaissance (ISR); Undersea Warfare; Atmospheric Optics; Tactical Imaging, e.g. Degraded Visual Environments (DVE); Automatic Target Recognition; Intelligence Processing, Exploitation, and Dissemination (PED); Free-space Optical Communications; Space Situational Awareness (SSA); Tagging, Tracking, and Locating (TTL); LIDAR; Remote Sensing; etc.\\n\\nJob Purpose\\n\\nAn applied physicist leverages knowledge and skills from the fields of physics, chemistry, material science, and mathematics to contribute to the research, analysis, design, and development of innovative systems, sensors, materials, and devices. They design and conduct experiments and/or simulations to ascertain physical properties and behaviors. Experiments conducted may involve the measurement, characterization, and testing of equipment and environmental phenomenology using specialized tools. Fundamental physical principles are used to inform the design of systems and sensors, to evaluate their theoretical performance, and to develop algorithms. Areas of application may include: optical systems, energy systems and storage, quantum systems, EO/IR & RF M&S and analysis, and photonics, and new materials. An applied physicist might also contribute to the development and application of new materials related to energy production, harvesting, and storage or to areas as diverse as healthcare, entertainment, or national security.\\n\\nKey Responsibilities\\n• Perform basic tests, experiments, calculations, and/or simulations to determine physical parameters/properties and behaviors of materials and systems and conduct basic data analysis on the results of measurements and/or simulations\\n• Perform literature surveys on research topics\\n• Generate documentation of experiments and results\\n\\nRequired Minimum Qualifications\\n• Foundational understanding in at least one of the following research areas: remote sensing, optics, atmospheric phenomenology, imaging, optical communications, optical detection theory, etc.\\n• Experience developing algorithms and/or performing data analysis using one or more of the following programming languages: Python, MATLAB, IDL, etc.\\n• Hands-on experience performing benchtop optical system tests and/or familiarity with imaging systems and radiometric calculations\\n• Experience with generating documentation for detailing experiments and results\\n• Ability to obtain and maintain a DoD Secret Security Clearance\\n\\nPreferred Qualifications\\n• Active Secret Clearance\\n• Experience with at least one modeling & simulation and/or optical design tools (e.g. MODTRAN, DIRSIG, Zemax, Code V, etc.)\\n• Experience working with active and passive electro-optical systems including, but not limited to LIDARs, infrared imagers, hyperspectral imagers, polarimeters, lasers.\\n• Software development experience in Python, MATLAB, C/C++\\n• Experience specifying optical, opto-mechanical, laser, and/or sensor components to comply with system requirements\\n• Experience with image and signal processing\\n• Project management and task leadership experience\\n\\nTravel Requirements\\n\\n<10% travel\\n\\nEducation and Length of Experience\\n\\nThis position vacancy is an open-rank announcement. The final job offer will be dependent on candidate qualifications in alignment with Research Faculty Extension Professional ranks as outlined in section 3.2.1 of the Georgia Tech Faculty Handbook\\n• 0-5 years of related experience with a Bachelor’s degree in Physics, Applied Physics, Engineering Physics, Optical Engineering, Optical Science, Imaging Science, Aerospace Engineering, Electrical Engineering, Electrical and Computer Engineering, Atmospheric Science, Chemistry, Applied Mathematics, or other related discipline\\n• 0-3 years of related experience with a Masters’ degree in Physics, Applied Physics, Engineering Physics, Optical Engineering, Optical Science, Imaging Science, Aerospace Engineering, Electrical Engineering, Electrical and Computer Engineering, Atmospheric Science, Chemistry, Applied Mathematics, or other related discipline\\n• 0 years of related experience with a Ph.D. in Physics, Applied Physics, Engineering Physics, Optical Engineering, Optical Science, Imaging Science, Aerospace Engineering, Electrical Engineering, Electrical and Computer Engineering, Atmospheric Science, Chemistry, Applied Mathematics, or other related discipline\\n\\nPlease refer to our Research Faculty Technical Level Guidelines for minimum requirements at the higher levels.\\n\\nU.S. Citizenship Requirements\\n\\nDue to our research contracts with the U.S. federal government, candidates for this position must be U.S. Citizens.\\n\\nClearance Type Required\\n\\nCandidates must be able to obtain and maintain an active security clearance.\\n\\nBenefits at GTRI\\n\\nComprehensive information on currently offered GTRI benefits, including Health & Welfare, Retirement Plans, Tuition Reimbursement, Time Off, and Professional Development, can be found through this link: https://benefits.hr.gatech.edu/.\\n\\nEqual Employment Opportunity\\n\\nThe Georgia Institute of Technology (Georgia Tech) is an Equal Employment Opportunity Employer. The University is committed to maintaining a fair and respectful environment for all. To that end, and in accordance with federal and state law, Board of Regents policy, and University policy, Georgia Tech provides equal opportunity to all faculty, staff, students, and all other members of the Georgia Tech community, including applicants for admission and/or employment, contractors, volunteers, and participants in institutional programs, activities, or services. Georgia Tech complies with all applicable laws and regulations governing equal opportunity in the workplace and in educational activities.\\n\\nGeorgia Tech prohibits discrimination, including discriminatory harassment, on the basis of race, ethnicity, ancestry, color, religion, sex (including pregnancy), sexual orientation, gender identity, gender expression, national origin, age, disability, genetics, or veteran status in its programs, activities, employment, and admissions. This prohibition applies to faculty, staff, students, and all other members of the Georgia Tech community, including affiliates, invitees, and guests. Further, Georgia Tech prohibits citizenship status, immigration status, and national origin discrimination in hiring, firing, and recruitment, except where such restrictions are required in order to comply with law, regulation, executive order, or Attorney General directive, or where they are required by Federal, State, or local government contract.\\n\\nUSG Core Values Statement\\n\\nThe University System of Georgia is comprised of our 26 institutions of higher education and learning as well as the System Office. Our USG Statement of Core Values are Integrity, Excellence, Accountability, and Respect. These values serve as the foundation for all that we do as an organization, and each USG community member is responsible for demonstrating and upholding these standards. More details on the USG Statement of Core Values and Code of Conduct are available in USG Board Policy 8.2.18.1.2 and can be found on-line at https://www.usg.edu/policymanual/section8/C224/#p8.2.18_personnel_conduct.\\n\\nAdditionally, USG supports Freedom of Expression as stated in Board Policy 6.5 Freedom of Expression and Academic Freedom found on-line at https://www.usg.edu/policymanual/section6/C2653.\\n_______________________________________________\\n\\n\"" ] }, - "execution_count": 2, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -111,9 +114,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], "source": [ "%pip install --upgrade --quiet langgraph langchain-openai" ] @@ -127,7 +138,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = \"\"\n", + "os.environ[\"SERP_API_KEY\"] = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.agent_toolkits.load_tools import load_tools\n", + "\n", + "tools = load_tools([\"google-jobs\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from langgraph.prebuilt import create_react_agent\n", + "\n", + "agent = create_react_agent(\"openai:gpt-4.1-mini\", tools)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -139,91 +184,137 @@ "Give me an entry level job posting related to physics?\n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "Tool Calls:\n", - " google_jobs (call_PoViomaLqoCRXqNmxe7YDKTN)\n", - " Call ID: call_PoViomaLqoCRXqNmxe7YDKTN\n", + " google_jobs (call_Yz3sJ4l4FsN9ENsQBkdJUoLh)\n", + " Call ID: call_Yz3sJ4l4FsN9ENsQBkdJUoLh\n", " Args:\n", - " query: entry level physics job\n", + " query: entry level jobs related to physics\n", "=================================\u001b[1m Tool Message \u001b[0m=================================\n", "Name: google_jobs\n", "\n", "\n", "_______________________________________________\n", - "Job Title: New Grad - Math / Physics Background\n", - "Company Name: Bits to Atoms (YC S24)\n", - "Location: San Francisco, CA\n", - "Description: We are working with Canopy Labs to hire a new grad with a strong background in math or physics to join the team. You can learn more about Canopy Labs here.\n", + "Job Title: Applied Physicist - Open Rank (Entry-Junior Level)-EOSL\n", + "Company Name: Georgia Tech Research Institute\n", + "Location: Atlanta, GA\n", + "Description: Overview:\n", "\n", - "Company Overview\n", + "The Georgia Tech Research Institute (GTRI) is the nonprofit, applied research division of the Georgia Institute of Technology (Georgia Tech). Founded in 1934 as the Engineering Experiment Station, GTRI has grown to more than 2,900 employees, supporting eight laboratories in over 20 locations around the country and performing more than $940 million of problem-solving research annually for government and industry. GTRI's renowned researchers combine science, engineering, economics, policy, and technical expertise to solve complex problems for the U.S. federal government, state, and industry.\n", "\n", - "Canopy Labs is building digital humans that can:\n", - "• Hold real-time, multimodal video interactions that look and sound ultra-realistic\n", - "• Intelligently control their body: write, gesticulate, clap, etc.\n", - "• Exhibit spatial awareness: walk around and interact with their environment\n", + "Georgia Tech's Mission and Values\n", "\n", - "Their mission is to bring virtual humans to life through cutting-edge generative models, enabling natural, embodied AI interactions at scale.\n", + "Georgia Tech's mission is to develop leaders who advance technology and improve the human condition. The Institute has nine key values that are foundational to everything we do:\n", "\n", - "What We're Looking For\n", - "• Strong programming skills in Python\n", - "• Excited by ML research and solving really hard problems\n", - "• Can ship really, really fast\n", - "• Experienced with cloud platforms (AWS, GCP, Azure)\n", + "1. Students are our top priority.\n", + "2. We strive for excellence.\n", + "3. We thrive on diversity.\n", + "4. We celebrate collaboration.\n", + "5. We champion innovation.\n", + "6. We safeguard freedom of inquiry and expression.\n", + "7. We nurture the wellbeing of our community.\n", + "8. We act ethically.\n", + "9. We are responsible stewards.\n", "\n", - "Nice to Haves\n", - "• Experience with training and model deployment\n", - "• Contributions to open-source ML projects\n", + "Over the next decade, Georgia Tech will become an example of inclusive innovation, a leading technological research university of unmatched scale, relentlessly committed to serving the public good; breaking new ground in addressing the biggest local, national, and global challenges and opportunities of our time; making technology broadly accessible; and developing exceptional, principled leaders from all backgrounds ready to produce novel ideas and create solutions with real human impact.\n", "\n", - "It’s ok if you don’t have much practical experience yet – we want to find exceptional individuals, even if you have demonstrated your abilities in non-professional settings. For example, if you have built a bunch of side projects and just love making things work, or even competed in math or physics competitions, we want to speak with you.\n", + "Project/Unit Description\n", "\n", - "We're looking forward to hearing from you soon!\n", + "The Electro-Optical and Infrared Systems Division’s (EISD’s) research is performed in all areas of the imaging chain. From basic geometric/radiometric modeling and analysis, through design and fabrication, to test and evaluation in the field, our team provides subject matter expertise to our customers in order to solve their most challenging problems in undersea, ground, airborne, and space domains. EISD is a collection of multi-disciplinary engineers and scientists encompassing optical, physics, mechanical, electrical, computer, and software domains, among others. Our portfolio consists of research programs in and related to the following application areas: Intelligence, Surveillance, and Reconnaissance (ISR); Undersea Warfare; Atmospheric Optics; Tactical Imaging, e.g. Degraded Visual Environments (DVE); Automatic Target Recognition; Intelligence Processing, Exploitation, and Dissemination (PED); Free-space Optical Communications; Space Situational Awareness (SSA); Tagging, Tracking, and Locating (TTL); LIDAR; Remote Sensing; etc.\n", + "\n", + "Job Purpose\n", + "\n", + "An applied physicist leverages knowledge and skills from the fields of physics, chemistry, material science, and mathematics to contribute to the research, analysis, design, and development of innovative systems, sensors, materials, and devices. They design and conduct experiments and/or simulations to ascertain physical properties and behaviors. Experiments conducted may involve the measurement, characterization, and testing of equipment and environmental phenomenology using specialized tools. Fundamental physical principles are used to inform the design of systems and sensors, to evaluate their theoretical performance, and to develop algorithms. Areas of application may include: optical systems, energy systems and storage, quantum systems, EO/IR & RF M&S and analysis, and photonics, and new materials. An applied physicist might also contribute to the development and application of new materials related to energy production, harvesting, and storage or to areas as diverse as healthcare, entertainment, or national security.\n", + "\n", + "Key Responsibilities\n", + "• Perform basic tests, experiments, calculations, and/or simulations to determine physical parameters/properties and behaviors of materials and systems and conduct basic data analysis on the results of measurements and/or simulations\n", + "• Perform literature surveys on research topics\n", + "• Generate documentation of experiments and results\n", + "\n", + "Required Minimum Qualifications\n", + "• Foundational understanding in at least one of the following research areas: remote sensing, optics, atmospheric phenomenology, imaging, optical communications, optical detection theory, etc.\n", + "• Experience developing algorithms and/or performing data analysis using one or more of the following programming languages: Python, MATLAB, IDL, etc.\n", + "• Hands-on experience performing benchtop optical system tests and/or familiarity with imaging systems and radiometric calculations\n", + "• Experience with generating documentation for detailing experiments and results\n", + "• Ability to obtain and maintain a DoD Secret Security Clearance\n", + "\n", + "Preferred Qualifications\n", + "• Active Secret Clearance\n", + "• Experience with at least one modeling & simulation and/or optical design tools (e.g. MODTRAN, DIRSIG, Zemax, Code V, etc.)\n", + "• Experience working with active and passive electro-optical systems including, but not limited to LIDARs, infrared imagers, hyperspectral imagers, polarimeters, lasers.\n", + "• Software development experience in Python, MATLAB, C/C++\n", + "• Experience specifying optical, opto-mechanical, laser, and/or sensor components to comply with system requirements\n", + "• Experience with image and signal processing\n", + "• Project management and task leadership experience\n", + "\n", + "Travel Requirements\n", + "\n", + "<10% travel\n", + "\n", + "Education and Length of Experience\n", + "\n", + "This position vacancy is an open-rank announcement. The final job offer will be dependent on candidate qualifications in alignment with Research Faculty Extension Professional ranks as outlined in section 3.2.1 of the Georgia Tech Faculty Handbook\n", + "• 0-5 years of related experience with a Bachelor’s degree in Physics, Applied Physics, Engineering Physics, Optical Engineering, Optical Science, Imaging Science, Aerospace Engineering, Electrical Engineering, Electrical and Computer Engineering, Atmospheric Science, Chemistry, Applied Mathematics, or other related discipline\n", + "• 0-3 years of related experience with a Masters’ degree in Physics, Applied Physics, Engineering Physics, Optical Engineering, Optical Science, Imaging Science, Aerospace Engineering, Electrical Engineering, Electrical and Computer Engineering, Atmospheric Science, Chemistry, Applied Mathematics, or other related discipline\n", + "• 0 years of related experience with a Ph.D. in Physics, Applied Physics, Engineering Physics, Optical Engineering, Optical Science, Imaging Science, Aerospace Engineering, Electrical Engineering, Electrical and Computer Engineering, Atmospheric Science, Chemistry, Applied Mathematics, or other related discipline\n", + "\n", + "Please refer to our Research Faculty Technical Level Guidelines for minimum requirements at the higher levels.\n", + "\n", + "U.S. Citizenship Requirements\n", + "\n", + "Due to our research contracts with the U.S. federal government, candidates for this position must be U.S. Citizens.\n", + "\n", + "Clearance Type Required\n", + "\n", + "Candidates must be able to obtain and maintain an active security clearance.\n", + "\n", + "Benefits at GTRI\n", + "\n", + "Comprehensive information on currently offered GTRI benefits, including Health & Welfare, Retirement Plans, Tuition Reimbursement, Time Off, and Professional Development, can be found through this link: https://benefits.hr.gatech.edu/.\n", + "\n", + "Equal Employment Opportunity\n", + "\n", + "The Georgia Institute of Technology (Georgia Tech) is an Equal Employment Opportunity Employer. The University is committed to maintaining a fair and respectful environment for all. To that end, and in accordance with federal and state law, Board of Regents policy, and University policy, Georgia Tech provides equal opportunity to all faculty, staff, students, and all other members of the Georgia Tech community, including applicants for admission and/or employment, contractors, volunteers, and participants in institutional programs, activities, or services. Georgia Tech complies with all applicable laws and regulations governing equal opportunity in the workplace and in educational activities.\n", + "\n", + "Georgia Tech prohibits discrimination, including discriminatory harassment, on the basis of race, ethnicity, ancestry, color, religion, sex (including pregnancy), sexual orientation, gender identity, gender expression, national origin, age, disability, genetics, or veteran status in its programs, activities, employment, and admissions. This prohibition applies to faculty, staff, students, and all other members of the Georgia Tech community, including affiliates, invitees, and guests. Further, Georgia Tech prohibits citizenship status, immigration status, and national origin discrimination in hiring, firing, and recruitment, except where such restrictions are required in order to comply with law, regulation, executive order, or Attorney General directive, or where they are required by Federal, State, or local government contract.\n", + "\n", + "USG Core Values Statement\n", + "\n", + "The University System of Georgia is comprised of our 26 institutions of higher education and learning as well as the System Office. Our USG Statement of Core Values are Integrity, Excellence, Accountability, and Respect. These values serve as the foundation for all that we do as an organization, and each USG community member is responsible for demonstrating and upholding these standards. More details on the USG Statement of Core Values and Code of Conduct are available in USG Board Policy 8.2.18.1.2 and can be found on-line at https://www.usg.edu/policymanual/section8/C224/#p8.2.18_personnel_conduct.\n", + "\n", + "Additionally, USG supports Freedom of Expression as stated in Board Policy 6.5 Freedom of Expression and Academic Freedom found on-line at https://www.usg.edu/policymanual/section6/C2653.\n", "_______________________________________________\n", "\n", "\n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "\n", - "Here's an entry-level job posting related to physics:\n", + "Here is an entry-level job posting related to physics:\n", "\n", - "**Job Title:** New Grad - Math / Physics Background \n", - "**Company Name:** Bits to Atoms (YC S24) \n", - "**Location:** San Francisco, CA \n", + "Job Title: Applied Physicist - Open Rank (Entry-Junior Level)\n", + "Company: Georgia Tech Research Institute\n", + "Location: Atlanta, GA\n", "\n", - "**Description:** \n", - "We are working with Canopy Labs to hire a new grad with a strong background in math or physics to join the team. Canopy Labs is building digital humans that can:\n", - "- Hold real-time, multimodal video interactions that look and sound ultra-realistic.\n", - "- Intelligently control their body: write, gesticulate, clap, etc.\n", - "- Exhibit spatial awareness: walk around and interact with their environment.\n", + "Description:\n", + "The Applied Physicist will contribute to research, analysis, design, and development of innovative systems, sensors, materials, and devices in areas such as optical systems, energy systems, quantum systems, EO/IR & RF modeling and analysis, photonics, and new materials. Responsibilities include performing tests, experiments, calculations, and/or simulations to determine physical parameters and behaviors, conducting data analysis, and generating documentation. The role requires foundational understanding in areas like remote sensing, optics, atmospheric phenomenology, imaging, and optical detection theory and experience with programming languages such as Python, MATLAB, and IDL.\n", "\n", - "Their mission is to bring virtual humans to life through cutting-edge generative models, enabling natural, embodied AI interactions at scale.\n", + "Minimum Qualifications:\n", + "- Bachelor’s degree with 0-5 years experience or Master’s with 0-3 years or Ph.D. in Physics or related discipline.\n", + "- Hands-on experience with optical system tests or imaging systems and radiometric calculations.\n", + "- Ability to obtain and maintain a DoD Secret Security Clearance.\n", "\n", - "**What We're Looking For:**\n", - "- Strong programming skills in Python.\n", - "- Excited by ML research and solving really hard problems.\n", - "- Can ship really, really fast.\n", - "- Experienced with cloud platforms (AWS, GCP, Azure).\n", + "Preferred Qualifications:\n", + "- Active Secret Clearance.\n", + "- Experience with modeling & simulation or optical design tools.\n", + "- Experience with electro-optical systems, software development, image and signal processing.\n", "\n", - "**Nice to Haves:**\n", - "- Experience with training and model deployment.\n", - "- Contributions to open-source ML projects.\n", + "Travel: Less than 10%\n", + "U.S. Citizenship required.\n", "\n", - "It’s ok if you don’t have much practical experience yet – we want to find exceptional individuals, even if you have demonstrated your abilities in non-professional settings. For example, if you have built a bunch of side projects and just love making things work, or even competed in math or physics competitions, we want to speak with you.\n", + "Benefits include health & welfare, retirement plans, tuition reimbursement, time off, and professional development.\n", "\n", - "We're looking forward to hearing from you soon!\n" + "For detailed information and to apply, refer to Georgia Tech Research Institute career page.\n" ] } ], "source": [ - "import os\n", - "\n", - "from langchain.agents import load_tools\n", - "from langchain.chat_models import init_chat_model\n", - "from langgraph.prebuilt import create_react_agent\n", - "\n", - "os.environ[\"OPENAI_API_KEY\"] = \"[your openai key]\"\n", - "os.environ[\"SERP_API_KEY\"] = \"[your serpapi key]\"\n", - "\n", - "llm = init_chat_model(\"gpt-4o-mini\", model_provider=\"openai\")\n", - "tools = load_tools([\"google-jobs\"], llm=llm)\n", - "agent = create_react_agent(llm, tools)\n", - "\n", "events = agent.stream(\n", " {\"messages\": [(\"user\", \"Give me an entry level job posting related to physics?\")]},\n", " stream_mode=\"values\",\n", @@ -231,11 +322,18 @@ "for event in events:\n", " event[\"messages\"][-1].pretty_print()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -249,7 +347,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.12.4" } }, "nbformat": 4, diff --git a/docs/docs/integrations/tools/memgraph.ipynb b/docs/docs/integrations/tools/memgraph.ipynb index 0797e30309b..7dc1adbdd7e 100644 --- a/docs/docs/integrations/tools/memgraph.ipynb +++ b/docs/docs/integrations/tools/memgraph.ipynb @@ -17,8 +17,6 @@ "source": [ "# MemgraphToolkit\n", "\n", - "## Overview\n", - "\n", "This will help you getting started with the Memgraph [toolkit](/docs/concepts/tools/#toolkits). \n", "\n", "Tools within `MemgraphToolkit` are designed for the interaction with the `Memgraph` database.\n", @@ -87,9 +85,9 @@ "source": [ "from langchain.chat_models import init_chat_model\n", "from langchain_memgraph import MemgraphToolkit\n", - "from langchain_memgraph.graphs.memgraph import Memgraph\n", + "from langchain_memgraph.graphs.memgraph import MemgraphLangChain\n", "\n", - "db = Memgraph(url=url, username=username, password=password)\n", + "db = MemgraphLangChain(url=url, username=username, password=password)\n", "\n", "llm = init_chat_model(\"gpt-4o-mini\", model_provider=\"openai\")\n", "\n", diff --git a/docs/docs/integrations/tools/steam.ipynb b/docs/docs/integrations/tools/steam.ipynb index d6af64660c6..16390622c48 100644 --- a/docs/docs/integrations/tools/steam.ipynb +++ b/docs/docs/integrations/tools/steam.ipynb @@ -30,11 +30,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], "source": [ - "%pip install --upgrade --quiet python-steam-api python-decouple" + "%pip install --upgrade --quiet python-steam-api python-decouple steamspypi" ] }, { @@ -48,15 +56,15 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", - "os.environ[\"STEAM_KEY\"] = \"xyz\"\n", - "os.environ[\"STEAM_ID\"] = \"123\"\n", - "os.environ[\"OPENAI_API_KEY\"] = \"abc\"" + "os.environ[\"STEAM_KEY\"] = \"\"\n", + "os.environ[\"STEAM_ID\"] = \"\"\n", + "os.environ[\"OPENAI_API_KEY\"] = \"\"" ] }, { @@ -70,63 +78,83 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "from langchain.agents import AgentType, initialize_agent\n", "from langchain_community.agent_toolkits.steam.toolkit import SteamToolkit\n", "from langchain_community.utilities.steam import SteamWebAPIWrapper\n", - "from langchain_openai import OpenAI" + "\n", + "steam = SteamWebAPIWrapper()\n", + "tools = [steam.run]" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "llm = OpenAI(temperature=0)\n", - "Steam = SteamWebAPIWrapper()\n", - "toolkit = SteamToolkit.from_steam_api_wrapper(Steam)\n", - "agent = initialize_agent(\n", - " toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n", - ")" + "from langgraph.prebuilt import create_react_agent\n", + "\n", + "agent = create_react_agent(\"openai:gpt-4.1-mini\", tools)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "================================\u001b[1m Human Message \u001b[0m=================================\n", "\n", + "can you give the information about the game Terraria?\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " run (call_6vHAXSIL2MPugXxlv5uyf9Xk)\n", + " Call ID: call_6vHAXSIL2MPugXxlv5uyf9Xk\n", + " Args:\n", + " mode: get_games_details\n", + " game: Terraria\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "Name: run\n", "\n", - "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", - "\u001b[32;1m\u001b[1;3m I need to find the game details\n", - "Action: Get Games Details\n", - "Action Input: Terraria\u001b[0m\n", - "Observation: \u001b[36;1m\u001b[1;3mThe id is: 105600\n", + "The id is: [105600]\n", "The link is: https://store.steampowered.com/app/105600/Terraria/?snr=1_7_15__13\n", "The price is: $9.99\n", "The summary of the game is: Dig, Fight, Explore, Build: The very world is at your fingertips as you fight for survival, fortune, and glory. Will you delve deep into cavernous expanses in search of treasure and raw materials with which to craft ever-evolving gear, machinery, and aesthetics? Perhaps you will choose instead to seek out ever-greater foes to test your mettle in combat? Maybe you will decide to construct your own city to house the host of mysterious allies you may encounter along your travels? In the World of Terraria, the choice is yours!Blending elements of classic action games with the freedom of sandbox-style creativity, Terraria is a unique gaming experience where both the journey and the destination are completely in the player’s control. The Terraria adventure is truly as unique as the players themselves! Are you up for the monumental task of exploring, creating, and defending a world of your own? Key features: Sandbox Play Randomly generated worlds Free Content Updates \n", "The supported languages of the game are: English, French, Italian, German, Spanish - Spain, Polish, Portuguese - Brazil, Russian, Simplified Chinese\n", - "\u001b[0m\n", - "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n", - "Final Answer: Terraria is a game with an id of 105600, a link of https://store.steampowered.com/app/105600/Terraria/?snr=1_7_15__13, a price of $9.99, a summary of \"Dig, Fight, Explore, Build: The very world is at your fingertips as you fight for survival, fortune, and glory. Will you delve deep into cavernous expanses in search of treasure and raw materials with which to craft ever-evolving gear, machinery, and aesthetics? Perhaps you will choose instead to seek out ever-greater foes to test your mettle in combat? Maybe you will decide to construct your own city to house the host of mysterious allies you may encounter along your travels? In the World of Terraria, the choice is yours!Blending elements of classic action games with the freedom of sandbox-style creativity, Terraria is a unique gaming experience where both the journey and the destination are completely in the player’s control. The Terraria adventure is truly as unique as the players themselves! Are you up for the monumental task of exploring, creating, and defending a\u001b[0m\n", "\n", - "\u001b[1m> Finished chain.\u001b[0m\n", - "{'input': 'can you give the information about the game Terraria', 'output': 'Terraria is a game with an id of 105600, a link of https://store.steampowered.com/app/105600/Terraria/?snr=1_7_15__13, a price of $9.99, a summary of \"Dig, Fight, Explore, Build: The very world is at your fingertips as you fight for survival, fortune, and glory. Will you delve deep into cavernous expanses in search of treasure and raw materials with which to craft ever-evolving gear, machinery, and aesthetics? Perhaps you will choose instead to seek out ever-greater foes to test your mettle in combat? Maybe you will decide to construct your own city to house the host of mysterious allies you may encounter along your travels? In the World of Terraria, the choice is yours!Blending elements of classic action games with the freedom of sandbox-style creativity, Terraria is a unique gaming experience where both the journey and the destination are completely in the player’s control. The Terraria adventure is truly as unique as the players themselves! Are you up for the monumental task of exploring, creating, and defending a'}\n" + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "Terraria is a game where you can dig, fight, explore, and build in a world that is totally at your fingertips. The game gives you the freedom to survive, seek fortune, and achieve glory. You can explore cavernous expanses in search of treasure and materials to craft various gear, machinery, and aesthetic items. Alternatively, you can challenge powerful foes or construct your own city to house mysterious allies you may encounter. The game blends classic action elements with sandbox-style creativity, offering a unique experience where your journey and destination are controlled by you.\n", + "\n", + "Key features of Terraria include sandbox play, randomly generated worlds, and free content updates.\n", + "\n", + "The game is priced at $9.99 and supports multiple languages including English, French, Italian, German, Spanish (Spain), Polish, Portuguese (Brazil), Russian, and Simplified Chinese.\n", + "\n", + "You can find more information and purchase it here: [Terraria on Steam](https://store.steampowered.com/app/105600/Terraria/?snr=1_7_15__13).\n" ] } ], "source": [ - "out = agent(\"can you give the information about the game Terraria\")\n", - "print(out)" + "events = agent.stream(\n", + " {\"messages\": [(\"user\", \"can you give the information about the game Terraria?\")]},\n", + " stream_mode=\"values\",\n", + ")\n", + "for event in events:\n", + " event[\"messages\"][-1].pretty_print()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -145,7 +173,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.12.4" } }, "nbformat": 4, diff --git a/docs/docs/integrations/tools/valyu_context.ipynb b/docs/docs/integrations/tools/valyu_search.ipynb similarity index 98% rename from docs/docs/integrations/tools/valyu_context.ipynb rename to docs/docs/integrations/tools/valyu_search.ipynb index bfbccd6105c..f0b08e681e1 100644 --- a/docs/docs/integrations/tools/valyu_context.ipynb +++ b/docs/docs/integrations/tools/valyu_search.ipynb @@ -10,7 +10,7 @@ "\n", "This notebook goes over how to use Valyu context tool in LangChain.\n", "\n", - "First, get an Valyu API key and add it as an environment variable. Get $10 free credit by [signing up here](https://exchange.valyu.network/).\n", + "First, get an Valyu API key and add it as an environment variable. Get $10 free credit by [signing up here](https://platform.valyu.network/).\n", "\n", "## Overview\n", "\n", diff --git a/docs/docs/tutorials/extraction.ipynb b/docs/docs/tutorials/extraction.ipynb index 2c18e52ae95..c85bfdbdbb5 100644 --- a/docs/docs/tutorials/extraction.ipynb +++ b/docs/docs/tutorials/extraction.ipynb @@ -135,7 +135,7 @@ "2. Do not force the LLM to make up information! Above we used `Optional` for the attributes allowing the LLM to output `None` if it doesn't know the answer.\n", "\n", ":::important\n", - "For best performance, document the schema well and make sure the model isn't force to return results if there's no information to be extracted in the text.\n", + "For best performance, document the schema well and make sure the model isn't forced to return results if there's no information to be extracted in the text.\n", ":::\n", "\n", "## The Extractor\n", diff --git a/docs/docs/tutorials/rag.ipynb b/docs/docs/tutorials/rag.ipynb index a4601ce3a21..189c650346f 100644 --- a/docs/docs/tutorials/rag.ipynb +++ b/docs/docs/tutorials/rag.ipynb @@ -234,6 +234,8 @@ "_ = vector_store.add_documents(documents=all_splits)\n", "\n", "# Define prompt for question-answering\n", + "# N.B. for non-US LangSmith endpoints, you may need to specify\n", + "# api_url=\"https://api.smith.langchain.com\" in hub.pull.\n", "prompt = hub.pull(\"rlm/rag-prompt\")\n", "\n", "\n", @@ -535,7 +537,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "46f378c5-858c-488f-8aef-8b59a6280791", "metadata": {}, "outputs": [ @@ -553,6 +555,8 @@ "source": [ "from langchain import hub\n", "\n", + "# N.B. for non-US LangSmith endpoints, you may need to specify\n", + "# api_url=\"https://api.smith.langchain.com\" in hub.pull.\n", "prompt = hub.pull(\"rlm/rag-prompt\")\n", "\n", "example_messages = prompt.invoke(\n", diff --git a/libs/cli/langchain_cli/namespaces/migrate/main.py b/libs/cli/langchain_cli/namespaces/migrate/main.py index 0956f9d4f68..f875d344f08 100644 --- a/libs/cli/langchain_cli/namespaces/migrate/main.py +++ b/libs/cli/langchain_cli/namespaces/migrate/main.py @@ -50,7 +50,7 @@ def migrate( "reflect any imports from new packages. For example, if you see new " "imports from langchain_openai, langchain_anthropic or " "langchain_text_splitters you " - "should them to your dependencies! \n\n" + "should add them to your dependencies! \n\n" '⚠️ This script is a "best-effort", and is likely to make some ' "mistakes.\n\n" "🛡️ Backup your code prior to running the migration script -- it will " diff --git a/libs/core/langchain_core/language_models/chat_models.py b/libs/core/langchain_core/language_models/chat_models.py index a0e975271f6..1070a8877e4 100644 --- a/libs/core/langchain_core/language_models/chat_models.py +++ b/libs/core/langchain_core/language_models/chat_models.py @@ -129,6 +129,7 @@ def _format_for_tracing(messages: list[BaseMessage]) -> list[BaseMessage]: isinstance(block, dict) and block.get("type") == "image" and is_data_content_block(block) + and block.get("source_type") != "id" ): if message_to_trace is message: message_to_trace = message.model_copy() diff --git a/libs/core/langchain_core/utils/function_calling.py b/libs/core/langchain_core/utils/function_calling.py index aa288bedc53..61b705e4ace 100644 --- a/libs/core/langchain_core/utils/function_calling.py +++ b/libs/core/langchain_core/utils/function_calling.py @@ -554,9 +554,19 @@ def convert_to_openai_tool( Return OpenAI Responses API-style tools unchanged. This includes any dict with "type" in "file_search", "function", "computer_use_preview", "web_search_preview". + + .. versionchanged:: 0.3.61 + + Added support for OpenAI's built-in code interpreter and remote MCP tools. """ if isinstance(tool, dict): - if tool.get("type") in ("function", "file_search", "computer_use_preview"): + if tool.get("type") in ( + "function", + "file_search", + "computer_use_preview", + "code_interpreter", + "mcp", + ): return tool # As of 03.12.25 can be "web_search_preview" or "web_search_preview_2025_03_11" if (tool.get("type") or "").startswith("web_search_preview"): diff --git a/libs/core/langchain_core/version.py b/libs/core/langchain_core/version.py index ad48fd42b66..54116708403 100644 --- a/libs/core/langchain_core/version.py +++ b/libs/core/langchain_core/version.py @@ -1,3 +1,3 @@ """langchain-core version information and utilities.""" -VERSION = "0.3.60" +VERSION = "0.3.62" diff --git a/libs/core/pyproject.toml b/libs/core/pyproject.toml index 8f243bb1178..11dad3c054a 100644 --- a/libs/core/pyproject.toml +++ b/libs/core/pyproject.toml @@ -16,7 +16,7 @@ dependencies = [ "pydantic>=2.7.4", ] name = "langchain-core" -version = "0.3.60" +version = "0.3.62" description = "Building applications with LLMs through composability" readme = "README.md" diff --git a/libs/core/uv.lock b/libs/core/uv.lock index 11b89bb4b18..080f1f59d5d 100644 --- a/libs/core/uv.lock +++ b/libs/core/uv.lock @@ -943,7 +943,7 @@ wheels = [ [[package]] name = "langchain-core" -version = "0.3.60" +version = "0.3.62" source = { editable = "." } dependencies = [ { name = "jsonpatch" }, diff --git a/libs/langchain/langchain/agents/agent_types.py b/libs/langchain/langchain/agents/agent_types.py index e6a72a98aef..f1eafc0b1bb 100644 --- a/libs/langchain/langchain/agents/agent_types.py +++ b/libs/langchain/langchain/agents/agent_types.py @@ -15,7 +15,7 @@ from langchain._api.deprecation import AGENT_DEPRECATION_WARNING class AgentType(str, Enum): """An enum for agent types. - See documentation: https://python.langchain.com/docs/modules/agents/agent_types/ + See documentation: https://python.langchain.com/api_reference/langchain/agents/langchain.agents.agent_types.AgentType.html """ ZERO_SHOT_REACT_DESCRIPTION = "zero-shot-react-description" diff --git a/libs/langchain/langchain/chains/conversation/base.py b/libs/langchain/langchain/chains/conversation/base.py index 8f4e295cf05..e214158b03d 100644 --- a/libs/langchain/langchain/chains/conversation/base.py +++ b/libs/langchain/langchain/chains/conversation/base.py @@ -13,10 +13,7 @@ from langchain.memory.buffer import ConversationBufferMemory @deprecated( since="0.2.7", - alternative=( - "RunnableWithMessageHistory: " - "https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html" # noqa: E501 - ), + alternative="langchain_core.runnables.history.RunnableWithMessageHistory", removal="1.0", ) class ConversationChain(LLMChain): diff --git a/libs/langchain/tests/unit_tests/chat_models/test_base.py b/libs/langchain/tests/unit_tests/chat_models/test_base.py index 9c24390de48..e90b7d6dca0 100644 --- a/libs/langchain/tests/unit_tests/chat_models/test_base.py +++ b/libs/langchain/tests/unit_tests/chat_models/test_base.py @@ -193,6 +193,7 @@ def test_configurable_with_default() -> None: "name": None, "disable_streaming": False, "model": "claude-3-sonnet-20240229", + "mcp_servers": None, "max_tokens": 1024, "temperature": None, "thinking": None, @@ -203,6 +204,7 @@ def test_configurable_with_default() -> None: "stop_sequences": None, "anthropic_api_url": "https://api.anthropic.com", "anthropic_api_key": SecretStr("bar"), + "betas": None, "default_headers": None, "model_kwargs": {}, "streaming": False, diff --git a/libs/packages.yml b/libs/packages.yml index 30cd92799f5..9d10ddf5b56 100644 --- a/libs/packages.yml +++ b/libs/packages.yml @@ -152,8 +152,8 @@ packages: downloads: 20558 downloads_updated_at: '2025-05-08T20:26:05.985970+00:00' - name: langchain-voyageai - path: libs/partners/voyageai - repo: langchain-ai/langchain + path: libs/voyageai + repo: voyage-ai/langchain-voyageai downloads: 27698 downloads_updated_at: '2025-05-08T20:26:05.985970+00:00' - name: langchain-aws diff --git a/libs/partners/anthropic/langchain_anthropic/chat_models.py b/libs/partners/anthropic/langchain_anthropic/chat_models.py index a0313988604..3bc75804f08 100644 --- a/libs/partners/anthropic/langchain_anthropic/chat_models.py +++ b/libs/partners/anthropic/langchain_anthropic/chat_models.py @@ -1,4 +1,5 @@ import copy +import json import re import warnings from collections.abc import AsyncIterator, Iterator, Mapping, Sequence @@ -100,6 +101,7 @@ def _is_builtin_tool(tool: Any) -> bool: "computer_", "bash_", "web_search_", + "code_execution_", ] return any(tool_type.startswith(prefix) for prefix in _builtin_tool_prefixes) @@ -219,6 +221,14 @@ def _format_data_content_block(block: dict) -> dict: "data": block["data"], }, } + elif block["source_type"] == "id": + formatted_block = { + "type": "image", + "source": { + "type": "file", + "file_id": block["id"], + }, + } else: raise ValueError( "Anthropic only supports 'url' and 'base64' source_type for image " @@ -252,6 +262,14 @@ def _format_data_content_block(block: dict) -> dict: "data": block["text"], }, } + elif block["source_type"] == "id": + formatted_block = { + "type": "document", + "source": { + "type": "file", + "file_id": block["id"], + }, + } else: raise ValueError(f"Block of type {block['type']} is not supported.") @@ -340,6 +358,29 @@ def _format_messages( else: block.pop("text", None) content.append(block) + elif block["type"] in ("server_tool_use", "mcp_tool_use"): + formatted_block = { + k: v + for k, v in block.items() + if k + in ( + "type", + "id", + "input", + "name", + "server_name", # for mcp_tool_use + "cache_control", + ) + } + # Attempt to parse streamed output + if block.get("input") == {} and "partial_json" in block: + try: + input_ = json.loads(block["partial_json"]) + if input_: + formatted_block["input"] = input_ + except json.JSONDecodeError: + pass + content.append(formatted_block) elif block["type"] == "text": text = block.get("text", "") # Only add non-empty strings for now as empty ones are not @@ -375,6 +416,25 @@ def _format_messages( [HumanMessage(block["content"])] )[1][0]["content"] content.append({**block, **{"content": tool_content}}) + elif block["type"] in ( + "code_execution_tool_result", + "mcp_tool_result", + "web_search_tool_result", + ): + content.append( + { + k: v + for k, v in block.items() + if k + in ( + "type", + "content", + "tool_use_id", + "is_error", # for mcp_tool_result + "cache_control", + ) + } + ) else: content.append(block) else: @@ -472,20 +532,21 @@ class ChatAnthropic(BaseChatModel): **NOTE**: Any param which is not explicitly supported will be passed directly to the ``anthropic.Anthropic.messages.create(...)`` API every time to the model is invoked. For example: - .. code-block:: python - from langchain_anthropic import ChatAnthropic - import anthropic + .. code-block:: python - ChatAnthropic(..., extra_headers={}).invoke(...) + from langchain_anthropic import ChatAnthropic + import anthropic - # results in underlying API call of: + ChatAnthropic(..., extra_headers={}).invoke(...) - anthropic.Anthropic(..).messages.create(..., extra_headers={}) + # results in underlying API call of: - # which is also equivalent to: + anthropic.Anthropic(..).messages.create(..., extra_headers={}) - ChatAnthropic(...).invoke(..., extra_headers={}) + # which is also equivalent to: + + ChatAnthropic(...).invoke(..., extra_headers={}) Invoke: .. code-block:: python @@ -645,6 +706,35 @@ class ChatAnthropic(BaseChatModel): "After examining both images carefully, I can see that they are actually identical." + .. dropdown:: Files API + + You can also pass in files that are managed through Anthropic's + `Files API `_: + + .. code-block:: python + + from langchain_anthropic import ChatAnthropic + + llm = ChatAnthropic( + model="claude-sonnet-4-20250514", + betas=["files-api-2025-04-14"], + ) + input_message = { + "role": "user", + "content": [ + { + "type": "text", + "text": "Describe this document.", + }, + { + "type": "image", + "source_type": "id", + "id": "file_abc123...", + }, + ], + } + llm.invoke([input_message]) + PDF input: See `multimodal guides `_ for more detail. @@ -681,6 +771,35 @@ class ChatAnthropic(BaseChatModel): "This appears to be a simple document..." + .. dropdown:: Files API + + You can also pass in files that are managed through Anthropic's + `Files API `_: + + .. code-block:: python + + from langchain_anthropic import ChatAnthropic + + llm = ChatAnthropic( + model="claude-sonnet-4-20250514", + betas=["files-api-2025-04-14"], + ) + input_message = { + "role": "user", + "content": [ + { + "type": "text", + "text": "Describe this document.", + }, + { + "type": "file", + "source_type": "id", + "id": "file_abc123...", + }, + ], + } + llm.invoke([input_message]) + Extended thinking: Claude 3.7 Sonnet supports an `extended thinking `_ @@ -797,7 +916,7 @@ class ChatAnthropic(BaseChatModel): or by setting ``stream_usage=False`` when initializing ChatAnthropic. Prompt caching: - See LangChain `docs `_ + See LangChain `docs `_ for more detail. .. code-block:: python @@ -834,6 +953,24 @@ class ChatAnthropic(BaseChatModel): {'cache_read': 0, 'cache_creation': 1458} + .. dropdown:: Extended caching + + The cache lifetime is 5 minutes by default. If this is too short, you can + apply one hour caching by enabling the ``"extended-cache-ttl-2025-04-11"`` + beta header: + + .. code-block:: python + + llm = ChatAnthropic( + model="claude-3-7-sonnet-20250219", + betas=["extended-cache-ttl-2025-04-11"], + ) + + and specifying ``"cache_control": {"type": "ephemeral", "ttl": "1h"}``. + + See `Claude documentation `_ + for detail. + Token-efficient tool use (beta): See LangChain `docs `_ for more detail. @@ -875,7 +1012,7 @@ class ChatAnthropic(BaseChatModel): See LangChain `docs `_ for more detail. - Web search: + .. dropdown:: Web search .. code-block:: python @@ -890,7 +1027,53 @@ class ChatAnthropic(BaseChatModel): "How do I update a web app to TypeScript 5.5?" ) - Text editor: + .. dropdown:: Code execution + + .. code-block:: python + + llm = ChatAnthropic( + model="claude-sonnet-4-20250514", + betas=["code-execution-2025-05-22"], + ) + + tool = {"type": "code_execution_20250522", "name": "code_execution"} + llm_with_tools = llm.bind_tools([tool]) + + response = llm_with_tools.invoke( + "Calculate the mean and standard deviation of [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]" + ) + + .. dropdown:: Remote MCP + + .. code-block:: python + + from langchain_anthropic import ChatAnthropic + + mcp_servers = [ + { + "type": "url", + "url": "https://mcp.deepwiki.com/mcp", + "name": "deepwiki", + "tool_configuration": { # optional configuration + "enabled": True, + "allowed_tools": ["ask_question"], + }, + "authorization_token": "PLACEHOLDER", # optional authorization + } + ] + + llm = ChatAnthropic( + model="claude-sonnet-4-20250514", + betas=["mcp-client-2025-04-04"], + mcp_servers=mcp_servers, + ) + + response = llm.invoke( + "What transport protocols does the 2025-03-26 version of the MCP " + "spec (modelcontextprotocol/modelcontextprotocol) support?" + ) + + .. dropdown:: Text editor .. code-block:: python @@ -986,6 +1169,13 @@ class ChatAnthropic(BaseChatModel): default_headers: Optional[Mapping[str, str]] = None """Headers to pass to the Anthropic clients, will be used for every API call.""" + betas: Optional[list[str]] = None + """List of beta features to enable. If specified, invocations will be routed + through client.beta.messages.create. + + Example: ``betas=["mcp-client-2025-04-04"]`` + """ + model_kwargs: dict[str, Any] = Field(default_factory=dict) streaming: bool = False @@ -1000,6 +1190,13 @@ class ChatAnthropic(BaseChatModel): """Parameters for Claude reasoning, e.g., ``{"type": "enabled", "budget_tokens": 10_000}``""" + mcp_servers: Optional[list[dict[str, Any]]] = None + """List of MCP servers to use for the request. + + Example: ``mcp_servers=[{"type": "url", "url": "https://mcp.example.com/mcp", + "name": "example-mcp"}]`` + """ + @property def _llm_type(self) -> str: """Return type of chat model.""" @@ -1007,7 +1204,10 @@ class ChatAnthropic(BaseChatModel): @property def lc_secrets(self) -> dict[str, str]: - return {"anthropic_api_key": "ANTHROPIC_API_KEY"} + return { + "anthropic_api_key": "ANTHROPIC_API_KEY", + "mcp_servers": "ANTHROPIC_MCP_SERVERS", + } @classmethod def is_lc_serializable(cls) -> bool: @@ -1099,6 +1299,8 @@ class ChatAnthropic(BaseChatModel): "top_k": self.top_k, "top_p": self.top_p, "stop_sequences": stop or self.stop_sequences, + "betas": self.betas, + "mcp_servers": self.mcp_servers, "system": system, **self.model_kwargs, **kwargs, @@ -1107,6 +1309,18 @@ class ChatAnthropic(BaseChatModel): payload["thinking"] = self.thinking return {k: v for k, v in payload.items() if v is not None} + def _create(self, payload: dict) -> Any: + if "betas" in payload: + return self._client.beta.messages.create(**payload) + else: + return self._client.messages.create(**payload) + + async def _acreate(self, payload: dict) -> Any: + if "betas" in payload: + return await self._async_client.beta.messages.create(**payload) + else: + return await self._async_client.messages.create(**payload) + def _stream( self, messages: list[BaseMessage], @@ -1121,17 +1335,19 @@ class ChatAnthropic(BaseChatModel): kwargs["stream"] = True payload = self._get_request_payload(messages, stop=stop, **kwargs) try: - stream = self._client.messages.create(**payload) + stream = self._create(payload) coerce_content_to_string = ( not _tools_in_params(payload) and not _documents_in_params(payload) and not _thinking_in_params(payload) ) + block_start_event = None for event in stream: - msg = _make_message_chunk_from_anthropic_event( + msg, block_start_event = _make_message_chunk_from_anthropic_event( event, stream_usage=stream_usage, coerce_content_to_string=coerce_content_to_string, + block_start_event=block_start_event, ) if msg is not None: chunk = ChatGenerationChunk(message=msg) @@ -1155,17 +1371,19 @@ class ChatAnthropic(BaseChatModel): kwargs["stream"] = True payload = self._get_request_payload(messages, stop=stop, **kwargs) try: - stream = await self._async_client.messages.create(**payload) + stream = await self._acreate(payload) coerce_content_to_string = ( not _tools_in_params(payload) and not _documents_in_params(payload) and not _thinking_in_params(payload) ) + block_start_event = None async for event in stream: - msg = _make_message_chunk_from_anthropic_event( + msg, block_start_event = _make_message_chunk_from_anthropic_event( event, stream_usage=stream_usage, coerce_content_to_string=coerce_content_to_string, + block_start_event=block_start_event, ) if msg is not None: chunk = ChatGenerationChunk(message=msg) @@ -1234,7 +1452,7 @@ class ChatAnthropic(BaseChatModel): return generate_from_stream(stream_iter) payload = self._get_request_payload(messages, stop=stop, **kwargs) try: - data = self._client.messages.create(**payload) + data = self._create(payload) except anthropic.BadRequestError as e: _handle_anthropic_bad_request(e) return self._format_output(data, **kwargs) @@ -1253,7 +1471,7 @@ class ChatAnthropic(BaseChatModel): return await agenerate_from_stream(stream_iter) payload = self._get_request_payload(messages, stop=stop, **kwargs) try: - data = await self._async_client.messages.create(**payload) + data = await self._acreate(payload) except anthropic.BadRequestError as e: _handle_anthropic_bad_request(e) return self._format_output(data, **kwargs) @@ -1722,8 +1940,10 @@ def convert_to_anthropic_tool( def _tools_in_params(params: dict) -> bool: - return "tools" in params or ( - "extra_body" in params and params["extra_body"].get("tools") + return ( + "tools" in params + or ("extra_body" in params and params["extra_body"].get("tools")) + or "mcp_servers" in params ) @@ -1772,7 +1992,8 @@ def _make_message_chunk_from_anthropic_event( *, stream_usage: bool = True, coerce_content_to_string: bool, -) -> Optional[AIMessageChunk]: + block_start_event: Optional[anthropic.types.RawMessageStreamEvent] = None, +) -> tuple[Optional[AIMessageChunk], Optional[anthropic.types.RawMessageStreamEvent]]: """Convert Anthropic event to AIMessageChunk. Note that not all events will result in a message chunk. In these cases @@ -1800,7 +2021,17 @@ def _make_message_chunk_from_anthropic_event( elif ( event.type == "content_block_start" and event.content_block is not None - and event.content_block.type in ("tool_use", "document", "redacted_thinking") + and event.content_block.type + in ( + "tool_use", + "code_execution_tool_result", + "document", + "redacted_thinking", + "mcp_tool_use", + "mcp_tool_result", + "server_tool_use", + "web_search_tool_result", + ) ): if coerce_content_to_string: warnings.warn("Received unexpected tool content block.") @@ -1820,6 +2051,7 @@ def _make_message_chunk_from_anthropic_event( content=[content_block], tool_call_chunks=tool_call_chunks, # type: ignore ) + block_start_event = event elif event.type == "content_block_delta": if event.delta.type in ("text_delta", "citations_delta"): if coerce_content_to_string and hasattr(event.delta, "text"): @@ -1849,16 +2081,23 @@ def _make_message_chunk_from_anthropic_event( elif event.delta.type == "input_json_delta": content_block = event.delta.model_dump() content_block["index"] = event.index - content_block["type"] = "tool_use" - tool_call_chunk = create_tool_call_chunk( - index=event.index, - id=None, - name=None, - args=event.delta.partial_json, - ) + if ( + (block_start_event is not None) + and hasattr(block_start_event, "content_block") + and (block_start_event.content_block.type == "tool_use") + ): + tool_call_chunk = create_tool_call_chunk( + index=event.index, + id=None, + name=None, + args=event.delta.partial_json, + ) + tool_call_chunks = [tool_call_chunk] + else: + tool_call_chunks = [] message_chunk = AIMessageChunk( content=[content_block], - tool_call_chunks=[tool_call_chunk], # type: ignore + tool_call_chunks=tool_call_chunks, # type: ignore ) elif event.type == "message_delta" and stream_usage: usage_metadata = UsageMetadata( @@ -1877,7 +2116,7 @@ def _make_message_chunk_from_anthropic_event( else: pass - return message_chunk + return message_chunk, block_start_event @deprecated(since="0.1.0", removal="1.0.0", alternative="ChatAnthropic") diff --git a/libs/partners/anthropic/pyproject.toml b/libs/partners/anthropic/pyproject.toml index 6a9c54b70a7..ae3a0d3fe95 100644 --- a/libs/partners/anthropic/pyproject.toml +++ b/libs/partners/anthropic/pyproject.toml @@ -7,12 +7,12 @@ authors = [] license = { text = "MIT" } requires-python = ">=3.9" dependencies = [ - "anthropic<1,>=0.51.0", - "langchain-core<1.0.0,>=0.3.59", + "anthropic<1,>=0.52.0", + "langchain-core<1.0.0,>=0.3.62", "pydantic<3.0.0,>=2.7.4", ] name = "langchain-anthropic" -version = "0.3.13" +version = "0.3.14" description = "An integration package connecting AnthropicMessages and LangChain" readme = "README.md" diff --git a/libs/partners/anthropic/tests/integration_tests/test_chat_models.py b/libs/partners/anthropic/tests/integration_tests/test_chat_models.py index 9a4205172e6..de3e453af0b 100644 --- a/libs/partners/anthropic/tests/integration_tests/test_chat_models.py +++ b/libs/partners/anthropic/tests/integration_tests/test_chat_models.py @@ -1,6 +1,7 @@ """Test ChatAnthropic chat model.""" import json +import os from base64 import b64encode from typing import Optional @@ -863,3 +864,206 @@ def test_image_tool_calling() -> None: ] llm = ChatAnthropic(model="claude-3-5-sonnet-latest") llm.bind_tools([color_picker]).invoke(messages) + + +# TODO: set up VCR +def test_web_search() -> None: + pytest.skip() + llm = ChatAnthropic(model="claude-3-5-sonnet-latest") + + tool = {"type": "web_search_20250305", "name": "web_search", "max_uses": 1} + llm_with_tools = llm.bind_tools([tool]) + + input_message = { + "role": "user", + "content": [ + { + "type": "text", + "text": "How do I update a web app to TypeScript 5.5?", + } + ], + } + response = llm_with_tools.invoke([input_message]) + block_types = {block["type"] for block in response.content} + assert block_types == {"text", "server_tool_use", "web_search_tool_result"} + + # Test streaming + full: Optional[BaseMessageChunk] = None + for chunk in llm_with_tools.stream([input_message]): + assert isinstance(chunk, AIMessageChunk) + full = chunk if full is None else full + chunk + assert isinstance(full, AIMessageChunk) + assert isinstance(full.content, list) + block_types = {block["type"] for block in full.content} # type: ignore[index] + assert block_types == {"text", "server_tool_use", "web_search_tool_result"} + + # Test we can pass back in + next_message = { + "role": "user", + "content": "Please repeat the last search, but focus on sources from 2024.", + } + _ = llm_with_tools.invoke( + [input_message, full, next_message], + ) + + +def test_code_execution() -> None: + pytest.skip() + llm = ChatAnthropic( + model="claude-sonnet-4-20250514", + betas=["code-execution-2025-05-22"], + ) + + tool = {"type": "code_execution_20250522", "name": "code_execution"} + llm_with_tools = llm.bind_tools([tool]) + + input_message = { + "role": "user", + "content": [ + { + "type": "text", + "text": ( + "Calculate the mean and standard deviation of " + "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]" + ), + } + ], + } + response = llm_with_tools.invoke([input_message]) + block_types = {block["type"] for block in response.content} + assert block_types == {"text", "server_tool_use", "code_execution_tool_result"} + + # Test streaming + full: Optional[BaseMessageChunk] = None + for chunk in llm_with_tools.stream([input_message]): + assert isinstance(chunk, AIMessageChunk) + full = chunk if full is None else full + chunk + assert isinstance(full, AIMessageChunk) + assert isinstance(full.content, list) + block_types = {block["type"] for block in full.content} # type: ignore[index] + assert block_types == {"text", "server_tool_use", "code_execution_tool_result"} + + # Test we can pass back in + next_message = { + "role": "user", + "content": "Please add more comments to the code.", + } + _ = llm_with_tools.invoke( + [input_message, full, next_message], + ) + + +def test_remote_mcp() -> None: + pytest.skip() + mcp_servers = [ + { + "type": "url", + "url": "https://mcp.deepwiki.com/mcp", + "name": "deepwiki", + "tool_configuration": {"enabled": True, "allowed_tools": ["ask_question"]}, + "authorization_token": "PLACEHOLDER", + } + ] + + llm = ChatAnthropic( + model="claude-sonnet-4-20250514", + betas=["mcp-client-2025-04-04"], + mcp_servers=mcp_servers, + ) + + input_message = { + "role": "user", + "content": [ + { + "type": "text", + "text": ( + "What transport protocols does the 2025-03-26 version of the MCP " + "spec (modelcontextprotocol/modelcontextprotocol) support?" + ), + } + ], + } + response = llm.invoke([input_message]) + block_types = {block["type"] for block in response.content} + assert block_types == {"text", "mcp_tool_use", "mcp_tool_result"} + + # Test streaming + full: Optional[BaseMessageChunk] = None + for chunk in llm.stream([input_message]): + assert isinstance(chunk, AIMessageChunk) + full = chunk if full is None else full + chunk + assert isinstance(full, AIMessageChunk) + assert isinstance(full.content, list) + block_types = {block["type"] for block in full.content} + assert block_types == {"text", "mcp_tool_use", "mcp_tool_result"} + + # Test we can pass back in + next_message = { + "role": "user", + "content": "Please query the same tool again, but add 'please' to your query.", + } + _ = llm.invoke( + [input_message, full, next_message], + ) + + +@pytest.mark.parametrize("block_format", ["anthropic", "standard"]) +def test_files_api_image(block_format: str) -> None: + image_file_id = os.getenv("ANTHROPIC_FILES_API_IMAGE_ID") + if not image_file_id: + pytest.skip() + llm = ChatAnthropic( + model="claude-sonnet-4-20250514", + betas=["files-api-2025-04-14"], + ) + if block_format == "anthropic": + block = { + "type": "image", + "source": { + "type": "file", + "file_id": image_file_id, + }, + } + else: + # standard block format + block = { + "type": "image", + "source_type": "id", + "id": image_file_id, + } + input_message = { + "role": "user", + "content": [ + {"type": "text", "text": "Describe this image."}, + block, + ], + } + _ = llm.invoke([input_message]) + + +@pytest.mark.parametrize("block_format", ["anthropic", "standard"]) +def test_files_api_pdf(block_format: str) -> None: + pdf_file_id = os.getenv("ANTHROPIC_FILES_API_PDF_ID") + if not pdf_file_id: + pytest.skip() + llm = ChatAnthropic( + model="claude-sonnet-4-20250514", + betas=["files-api-2025-04-14"], + ) + if block_format == "anthropic": + block = {"type": "document", "source": {"type": "file", "file_id": pdf_file_id}} + else: + # standard block format + block = { + "type": "file", + "source_type": "id", + "id": pdf_file_id, + } + input_message = { + "role": "user", + "content": [ + {"type": "text", "text": "Describe this document."}, + block, + ], + } + _ = llm.invoke([input_message]) diff --git a/libs/partners/anthropic/tests/unit_tests/test_chat_models.py b/libs/partners/anthropic/tests/unit_tests/test_chat_models.py index 1c18fc08d94..2d418b6bddd 100644 --- a/libs/partners/anthropic/tests/unit_tests/test_chat_models.py +++ b/libs/partners/anthropic/tests/unit_tests/test_chat_models.py @@ -2,7 +2,7 @@ import os from typing import Any, Callable, Literal, Optional, cast -from unittest.mock import patch +from unittest.mock import MagicMock, patch import anthropic import pytest @@ -10,6 +10,8 @@ from anthropic.types import Message, TextBlock, Usage from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage from langchain_core.runnables import RunnableBinding from langchain_core.tools import BaseTool +from langchain_core.tracers.base import BaseTracer +from langchain_core.tracers.schemas import Run from pydantic import BaseModel, Field, SecretStr from pytest import CaptureFixture, MonkeyPatch @@ -994,3 +996,63 @@ def test_usage_metadata_standardization() -> None: assert result["input_tokens"] == 0 assert result["output_tokens"] == 0 assert result["total_tokens"] == 0 + + +class FakeTracer(BaseTracer): + def __init__(self) -> None: + super().__init__() + self.chat_model_start_inputs: list = [] + + def _persist_run(self, run: Run) -> None: + """Persist a run.""" + pass + + def on_chat_model_start(self, *args: Any, **kwargs: Any) -> Run: + self.chat_model_start_inputs.append({"args": args, "kwargs": kwargs}) + return super().on_chat_model_start(*args, **kwargs) + + +def test_mcp_tracing() -> None: + # Test we exclude sensitive information from traces + mcp_servers = [ + { + "type": "url", + "url": "https://mcp.deepwiki.com/mcp", + "name": "deepwiki", + "authorization_token": "PLACEHOLDER", + } + ] + + llm = ChatAnthropic( + model="claude-sonnet-4-20250514", + betas=["mcp-client-2025-04-04"], + mcp_servers=mcp_servers, + ) + + tracer = FakeTracer() + mock_client = MagicMock() + + def mock_create(*args: Any, **kwargs: Any) -> Message: + return Message( + id="foo", + content=[TextBlock(type="text", text="bar")], + model="baz", + role="assistant", + stop_reason=None, + stop_sequence=None, + usage=Usage(input_tokens=2, output_tokens=1), + type="message", + ) + + mock_client.messages.create = mock_create + input_message = HumanMessage("Test query") + with patch.object(llm, "_client", mock_client): + _ = llm.invoke([input_message], config={"callbacks": [tracer]}) + + # Test headers are not traced + assert len(tracer.chat_model_start_inputs) == 1 + assert "PLACEHOLDER" not in str(tracer.chat_model_start_inputs) + + # Test headers are correctly propagated to request + payload = llm._get_request_payload([input_message]) + assert payload["mcp_servers"][0]["authorization_token"] == "PLACEHOLDER" diff --git a/libs/partners/anthropic/uv.lock b/libs/partners/anthropic/uv.lock index 6e06e95131b..c439362fe36 100644 --- a/libs/partners/anthropic/uv.lock +++ b/libs/partners/anthropic/uv.lock @@ -22,7 +22,7 @@ wheels = [ [[package]] name = "anthropic" -version = "0.51.0" +version = "0.52.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "anyio" }, @@ -33,9 +33,9 @@ dependencies = [ { name = "sniffio" }, { name = "typing-extensions" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/63/4a/96f99a61ae299f9e5aa3e765d7342d95ab2e2ba5b69a3ffedb00ef779651/anthropic-0.51.0.tar.gz", hash = "sha256:6f824451277992af079554430d5b2c8ff5bc059cc2c968cdc3f06824437da201", size = 219063 } +sdist = { url = "https://files.pythonhosted.org/packages/57/fd/8a9332f5baf352c272494a9d359863a53385a208954c1a7251a524071930/anthropic-0.52.0.tar.gz", hash = "sha256:f06bc924d7eb85f8a43fe587b875ff58b410d60251b7dc5f1387b322a35bd67b", size = 229372 } wheels = [ - { url = "https://files.pythonhosted.org/packages/8c/6e/9637122c5f007103bd5a259f4250bd8f1533dd2473227670fd10a1457b62/anthropic-0.51.0-py3-none-any.whl", hash = "sha256:b8b47d482c9aa1f81b923555cebb687c2730309a20d01be554730c8302e0f62a", size = 263957 }, + { url = "https://files.pythonhosted.org/packages/a0/43/172c0031654908bbac2a87d356fff4de1b4947a9b14b9658540b69416417/anthropic-0.52.0-py3-none-any.whl", hash = "sha256:c026daa164f0e3bde36ce9cbdd27f5f1419fff03306be1e138726f42e6a7810f", size = 286076 }, ] [[package]] @@ -423,7 +423,7 @@ wheels = [ [[package]] name = "langchain-anthropic" -version = "0.3.13" +version = "0.3.14" source = { editable = "." } dependencies = [ { name = "anthropic" }, @@ -467,7 +467,7 @@ typing = [ [package.metadata] requires-dist = [ - { name = "anthropic", specifier = ">=0.51.0,<1" }, + { name = "anthropic", specifier = ">=0.52.0,<1" }, { name = "langchain-core", editable = "../../core" }, { name = "pydantic", specifier = ">=2.7.4,<3.0.0" }, ] @@ -502,7 +502,7 @@ typing = [ [[package]] name = "langchain-core" -version = "0.3.60" +version = "0.3.61" source = { editable = "../../core" } dependencies = [ { name = "jsonpatch" }, diff --git a/libs/partners/exa/README.md b/libs/partners/exa/README.md index ab06cd4f1b0..85dee325b75 100644 --- a/libs/partners/exa/README.md +++ b/libs/partners/exa/README.md @@ -27,6 +27,30 @@ results = exa.invoke("What is the capital of France?") print(results) ``` +### Advanced Features + +You can use advanced features like text limits, summaries, and live crawling: + +```python +from langchain_exa import ExaSearchRetriever, TextContentsOptions + +# Create a new instance with advanced options +exa = ExaSearchRetriever( + exa_api_key="YOUR API KEY", + k=20, # Number of results (1-100) + type="auto", # Can be "neural", "keyword", or "auto" + livecrawl="always", # Can be "always", "fallback", or "never" + summary=True, # Get an AI-generated summary of each result + text_contents_options={"max_characters": 3000} # Limit text length +) + +# Search for a query with custom summary prompt +exa_with_custom_summary = ExaSearchRetriever( + exa_api_key="YOUR API KEY", + summary={"query": "generate one line summary in simple words."} # Custom summary prompt +) +``` + ## Exa Search Results You can run the ExaSearchResults module as follows @@ -48,6 +72,33 @@ search_results = search_tool._run( print("Search Results:", search_results) ``` +### Advanced Features + +You can use advanced features like text limits, summaries, and live crawling: + +```python +from langchain_exa import ExaSearchResults + +# Initialize the ExaSearchResults tool +search_tool = ExaSearchResults(exa_api_key="YOUR API KEY") + +# Perform a search query with advanced options +search_results = search_tool._run( + query="Latest AI research papers", + num_results=10, # Number of results (1-100) + type="auto", # Can be "neural", "keyword", or "auto" + livecrawl="always", # Can be "always", "fallback", or "never" + summary=True, # Get an AI-generated summary of each result + text_contents_options={"max_characters": 2000} # Limit text length +) + +# With custom summary prompt +search_results_with_custom_summary = search_tool._run( + query="Latest AI research papers", + summary={"query": "generate one liner"} # Custom summary prompt +) +``` + ## Exa Find Similar Results You can run the ExaFindSimilarResults module as follows @@ -67,4 +118,22 @@ similar_results = find_similar_tool._run( ) print("Similar Results:", similar_results) +``` + +### Advanced Features + +```python +from langchain_exa import ExaFindSimilarResults + +# Initialize the ExaFindSimilarResults tool +find_similar_tool = ExaFindSimilarResults(exa_api_key="YOUR API KEY") + +# Find similar results with advanced options +similar_results = find_similar_tool._run( + url="http://espn.com", + num_results=10, # Number of results (1-100) + livecrawl="fallback", # Can be "always", "fallback", or "never" + summary=True, # Get an AI-generated summary of each result + text_contents_options={"max_characters": 1500} # Limit text length +) ``` \ No newline at end of file diff --git a/libs/partners/exa/langchain_exa/retrievers.py b/libs/partners/exa/langchain_exa/retrievers.py index 1585e6a5796..08b9b450fc8 100644 --- a/libs/partners/exa/langchain_exa/retrievers.py +++ b/libs/partners/exa/langchain_exa/retrievers.py @@ -27,6 +27,8 @@ def _get_metadata(result: Any) -> dict[str, Any]: metadata["highlights"] = result.highlights if getattr(result, "highlight_scores"): metadata["highlight_scores"] = result.highlight_scores + if getattr(result, "summary"): + metadata["summary"] = result.summary return metadata @@ -34,7 +36,7 @@ class ExaSearchRetriever(BaseRetriever): """Exa Search retriever.""" k: int = 10 # num_results - """The number of search results to return.""" + """The number of search results to return (1 to 100).""" include_domains: Optional[list[str]] = None """A list of domains to include in the search.""" exclude_domains: Optional[list[str]] = None @@ -50,11 +52,20 @@ class ExaSearchRetriever(BaseRetriever): use_autoprompt: Optional[bool] = None """Whether to use autoprompt for the search.""" type: str = "neural" - """The type of search, 'keyword' or 'neural'. Default: neural""" + """The type of search, 'keyword', 'neural', or 'auto'. Default: neural""" highlights: Optional[Union[HighlightsContentsOptions, bool]] = None """Whether to set the page content to the highlights of the results.""" - text_contents_options: Union[TextContentsOptions, Literal[True]] = True - """How to set the page content of the results""" + text_contents_options: Union[TextContentsOptions, dict[str, Any], Literal[True]] = ( + True + ) + """How to set the page content of the results. Can be True or a dict with options + like max_characters.""" + livecrawl: Optional[Literal["always", "fallback", "never"]] = None + """Option to crawl live webpages if content is not in the index. Options: "always", + "fallback", "never".""" + summary: Optional[Union[bool, dict[str, str]]] = None + """Whether to include a summary of the content. Can be a boolean or a dict with a + custom query.""" client: Exa = Field(default=None) exa_api_key: SecretStr = Field(default=None) @@ -82,6 +93,9 @@ class ExaSearchRetriever(BaseRetriever): start_published_date=self.start_published_date, end_published_date=self.end_published_date, use_autoprompt=self.use_autoprompt, + livecrawl=self.livecrawl, + summary=self.summary, + type=self.type, ) results = response.results diff --git a/libs/partners/exa/langchain_exa/tools.py b/libs/partners/exa/langchain_exa/tools.py index e22e4e709d8..381dade1c53 100644 --- a/libs/partners/exa/langchain_exa/tools.py +++ b/libs/partners/exa/langchain_exa/tools.py @@ -1,6 +1,6 @@ """Tool for the Exa Search API.""" -from typing import Any, Optional, Union +from typing import Any, Literal, Optional, Union from exa_py import Exa # type: ignore[untyped-import] from exa_py.api import ( @@ -74,8 +74,10 @@ class ExaSearchResults(BaseTool): # type: ignore[override] def _run( self, query: str, - num_results: int, - text_contents_options: Optional[Union[TextContentsOptions, bool]] = None, + num_results: int = 10, + text_contents_options: Optional[ + Union[TextContentsOptions, dict[str, Any], bool] + ] = None, highlights: Optional[Union[HighlightsContentsOptions, bool]] = None, include_domains: Optional[list[str]] = None, exclude_domains: Optional[list[str]] = None, @@ -84,9 +86,30 @@ class ExaSearchResults(BaseTool): # type: ignore[override] start_published_date: Optional[str] = None, end_published_date: Optional[str] = None, use_autoprompt: Optional[bool] = None, + livecrawl: Optional[Literal["always", "fallback", "never"]] = None, + summary: Optional[Union[bool, dict[str, str]]] = None, + type: Optional[Literal["neural", "keyword", "auto"]] = None, run_manager: Optional[CallbackManagerForToolRun] = None, ) -> Union[list[dict], str]: - """Use the tool.""" + """Use the tool. + + Args: + query: The search query. + num_results: The number of search results to return (1 to 100). Default: 10 + text_contents_options: How to set the page content of the results. Can be True or a dict with options like max_characters. + highlights: Whether to include highlights in the results. + include_domains: A list of domains to include in the search. + exclude_domains: A list of domains to exclude from the search. + start_crawl_date: The start date for the crawl (in YYYY-MM-DD format). + end_crawl_date: The end date for the crawl (in YYYY-MM-DD format). + start_published_date: The start date for when the document was published (in YYYY-MM-DD format). + end_published_date: The end date for when the document was published (in YYYY-MM-DD format). + use_autoprompt: Whether to use autoprompt for the search. + livecrawl: Option to crawl live webpages if content is not in the index. Options: "always", "fallback", "never" + summary: Whether to include a summary of the content. Can be a boolean or a dict with a custom query. + type: The type of search, 'keyword', 'neural', or 'auto'. + run_manager: The run manager for callbacks. + """ # noqa: E501 try: return self.client.search_and_contents( query, @@ -100,6 +123,9 @@ class ExaSearchResults(BaseTool): # type: ignore[override] start_published_date=start_published_date, end_published_date=end_published_date, use_autoprompt=use_autoprompt, + livecrawl=livecrawl, + summary=summary, + type=type, ) # type: ignore except Exception as e: return repr(e) @@ -128,8 +154,10 @@ class ExaFindSimilarResults(BaseTool): # type: ignore[override] def _run( self, url: str, - num_results: int, - text_contents_options: Optional[Union[TextContentsOptions, bool]] = None, + num_results: int = 10, + text_contents_options: Optional[ + Union[TextContentsOptions, dict[str, Any], bool] + ] = None, highlights: Optional[Union[HighlightsContentsOptions, bool]] = None, include_domains: Optional[list[str]] = None, exclude_domains: Optional[list[str]] = None, @@ -139,9 +167,29 @@ class ExaFindSimilarResults(BaseTool): # type: ignore[override] end_published_date: Optional[str] = None, exclude_source_domain: Optional[bool] = None, category: Optional[str] = None, + livecrawl: Optional[Literal["always", "fallback", "never"]] = None, + summary: Optional[Union[bool, dict[str, str]]] = None, run_manager: Optional[CallbackManagerForToolRun] = None, ) -> Union[list[dict], str]: - """Use the tool.""" + """Use the tool. + + Args: + url: The URL to find similar pages for. + num_results: The number of search results to return (1 to 100). Default: 10 + text_contents_options: How to set the page content of the results. Can be True or a dict with options like max_characters. + highlights: Whether to include highlights in the results. + include_domains: A list of domains to include in the search. + exclude_domains: A list of domains to exclude from the search. + start_crawl_date: The start date for the crawl (in YYYY-MM-DD format). + end_crawl_date: The end date for the crawl (in YYYY-MM-DD format). + start_published_date: The start date for when the document was published (in YYYY-MM-DD format). + end_published_date: The end date for when the document was published (in YYYY-MM-DD format). + exclude_source_domain: If True, exclude pages from the same domain as the source URL. + category: Filter for similar pages by category. + livecrawl: Option to crawl live webpages if content is not in the index. Options: "always", "fallback", "never" + summary: Whether to include a summary of the content. Can be a boolean or a dict with a custom query. + run_manager: The run manager for callbacks. + """ # noqa: E501 try: return self.client.find_similar_and_contents( url, @@ -156,6 +204,8 @@ class ExaFindSimilarResults(BaseTool): # type: ignore[override] end_published_date=end_published_date, exclude_source_domain=exclude_source_domain, category=category, + livecrawl=livecrawl, + summary=summary, ) # type: ignore except Exception as e: return repr(e) diff --git a/libs/partners/exa/pyproject.toml b/libs/partners/exa/pyproject.toml index 6fa6e07317e..64e73a160eb 100644 --- a/libs/partners/exa/pyproject.toml +++ b/libs/partners/exa/pyproject.toml @@ -6,9 +6,9 @@ build-backend = "pdm.backend" authors = [] license = { text = "MIT" } requires-python = ">=3.9" -dependencies = ["langchain-core<1.0.0,>=0.3.15", "exa-py<2.0.0,>=1.0.8"] +dependencies = ["langchain-core<1.0.0,>=0.3.60", "exa-py<2.0.0,>=1.0.8"] name = "langchain-exa" -version = "0.2.1" +version = "0.3.0" description = "An integration package connecting Exa and LangChain" readme = "README.md" diff --git a/libs/partners/exa/tests/integration_tests/test_retriever.py b/libs/partners/exa/tests/integration_tests/test_retriever.py index c1592b3c8c2..dc49e192dba 100644 --- a/libs/partners/exa/tests/integration_tests/test_retriever.py +++ b/libs/partners/exa/tests/integration_tests/test_retriever.py @@ -26,3 +26,19 @@ def test_exa_retriever_highlights() -> None: assert isinstance(highlight_scores, list) assert isinstance(highlights[0], str) assert isinstance(highlight_scores[0], float) + + +def test_exa_retriever_advanced_features() -> None: + retriever = ExaSearchRetriever( + k=3, text_contents_options={"max_characters": 1000}, summary=True, type="auto" + ) + res = retriever.invoke("best time to visit japan") + print(res) # noqa: T201 + assert len(res) == 3 # requested k=3 + assert isinstance(res, list) + assert isinstance(res[0], Document) + # Verify summary is in metadata + assert "summary" in res[0].metadata + assert isinstance(res[0].metadata["summary"], str) + # Verify text was limited + assert len(res[0].page_content) <= 1000 diff --git a/libs/partners/exa/tests/integration_tests/test_search_tool.py b/libs/partners/exa/tests/integration_tests/test_search_tool.py index 7fe49d0ddf2..46fed61dd5e 100644 --- a/libs/partners/exa/tests/integration_tests/test_search_tool.py +++ b/libs/partners/exa/tests/integration_tests/test_search_tool.py @@ -8,3 +8,23 @@ def test_search_tool() -> None: res = tool.invoke({"query": "best time to visit japan", "num_results": 5}) print(res) # noqa: T201 assert not isinstance(res, str) # str means error for this tool\ + + +def test_search_tool_advanced_features() -> None: + tool = ExaSearchResults() + res = tool.invoke( + { + "query": "best time to visit japan", + "num_results": 3, + "text_contents_options": {"max_characters": 1000}, + "summary": True, + "type": "auto", + } + ) + print(res) # noqa: T201 + assert not isinstance(res, str) # str means error for this tool + assert len(res.results) == 3 + # Verify summary exists + assert hasattr(res.results[0], "summary") + # Verify text was limited + assert len(res.results[0].text) <= 1000 diff --git a/libs/partners/exa/uv.lock b/libs/partners/exa/uv.lock index ec8dc0dc1fc..2378b3e4f10 100644 --- a/libs/partners/exa/uv.lock +++ b/libs/partners/exa/uv.lock @@ -1,5 +1,4 @@ version = 1 -revision = 1 requires-python = ">=3.9" resolution-markers = [ "python_full_version >= '3.13'", @@ -394,7 +393,7 @@ wheels = [ [[package]] name = "langchain-core" -version = "0.3.56" +version = "0.3.60" source = { editable = "../../core" } dependencies = [ { name = "jsonpatch" }, @@ -409,10 +408,9 @@ dependencies = [ [package.metadata] requires-dist = [ { name = "jsonpatch", specifier = ">=1.33,<2.0" }, - { name = "langsmith", specifier = ">=0.1.125,<0.4" }, + { name = "langsmith", specifier = ">=0.1.126,<0.4" }, { name = "packaging", specifier = ">=23.2,<25" }, - { name = "pydantic", marker = "python_full_version < '3.12.4'", specifier = ">=2.5.2,<3.0.0" }, - { name = "pydantic", marker = "python_full_version >= '3.12.4'", specifier = ">=2.7.4,<3.0.0" }, + { name = "pydantic", specifier = ">=2.7.4" }, { name = "pyyaml", specifier = ">=5.3" }, { name = "tenacity", specifier = ">=8.1.0,!=8.4.0,<10.0.0" }, { name = "typing-extensions", specifier = ">=4.7" }, @@ -453,7 +451,7 @@ typing = [ [[package]] name = "langchain-exa" -version = "0.2.1" +version = "0.3.0" source = { editable = "." } dependencies = [ { name = "exa-py" }, diff --git a/libs/partners/openai/langchain_openai/chat_models/base.py b/libs/partners/openai/langchain_openai/chat_models/base.py index 197af78fcbe..54a9cdd3031 100644 --- a/libs/partners/openai/langchain_openai/chat_models/base.py +++ b/libs/partners/openai/langchain_openai/chat_models/base.py @@ -775,16 +775,22 @@ class BaseChatOpenAI(BaseChatModel): with context_manager as response: is_first_chunk = True + has_reasoning = False for chunk in response: metadata = headers if is_first_chunk else {} if generation_chunk := _convert_responses_chunk_to_generation_chunk( - chunk, schema=original_schema_obj, metadata=metadata + chunk, + schema=original_schema_obj, + metadata=metadata, + has_reasoning=has_reasoning, ): if run_manager: run_manager.on_llm_new_token( generation_chunk.text, chunk=generation_chunk ) is_first_chunk = False + if "reasoning" in generation_chunk.message.additional_kwargs: + has_reasoning = True yield generation_chunk async def _astream_responses( @@ -811,16 +817,22 @@ class BaseChatOpenAI(BaseChatModel): async with context_manager as response: is_first_chunk = True + has_reasoning = False async for chunk in response: metadata = headers if is_first_chunk else {} if generation_chunk := _convert_responses_chunk_to_generation_chunk( - chunk, schema=original_schema_obj, metadata=metadata + chunk, + schema=original_schema_obj, + metadata=metadata, + has_reasoning=has_reasoning, ): if run_manager: await run_manager.on_llm_new_token( generation_chunk.text, chunk=generation_chunk ) is_first_chunk = False + if "reasoning" in generation_chunk.message.additional_kwargs: + has_reasoning = True yield generation_chunk def _should_stream_usage( @@ -1176,12 +1188,22 @@ class BaseChatOpenAI(BaseChatModel): self, stop: Optional[list[str]] = None, **kwargs: Any ) -> dict[str, Any]: """Get the parameters used to invoke the model.""" - return { + params = { "model": self.model_name, **super()._get_invocation_params(stop=stop), **self._default_params, **kwargs, } + # Redact headers from built-in remote MCP tool invocations + if (tools := params.get("tools")) and isinstance(tools, list): + params["tools"] = [ + ({**tool, "headers": "**REDACTED**"} if "headers" in tool else tool) + if isinstance(tool, dict) and tool.get("type") == "mcp" + else tool + for tool in tools + ] + + return params def _get_ls_params( self, stop: Optional[list[str]] = None, **kwargs: Any @@ -1456,6 +1478,8 @@ class BaseChatOpenAI(BaseChatModel): "file_search", "web_search_preview", "computer_use_preview", + "code_interpreter", + "mcp", ): tool_choice = {"type": tool_choice} # 'any' is not natively supported by OpenAI API. @@ -3150,12 +3174,22 @@ def _construct_responses_api_input(messages: Sequence[BaseMessage]) -> list: ): function_call["id"] = _id function_calls.append(function_call) - # Computer calls + # Built-in tool calls computer_calls = [] + code_interpreter_calls = [] + mcp_calls = [] tool_outputs = lc_msg.additional_kwargs.get("tool_outputs", []) for tool_output in tool_outputs: if tool_output.get("type") == "computer_call": computer_calls.append(tool_output) + elif tool_output.get("type") == "code_interpreter_call": + code_interpreter_calls.append(tool_output) + elif tool_output.get("type") == "mcp_call": + mcp_calls.append(tool_output) + else: + pass + input_.extend(code_interpreter_calls) + input_.extend(mcp_calls) msg["content"] = msg.get("content") or [] if lc_msg.additional_kwargs.get("refusal"): if isinstance(msg["content"], str): @@ -3196,6 +3230,7 @@ def _construct_responses_api_input(messages: Sequence[BaseMessage]) -> list: elif msg["role"] in ("user", "system", "developer"): if isinstance(msg["content"], list): new_blocks = [] + non_message_item_types = ("mcp_approval_response",) for block in msg["content"]: # chat api: {"type": "text", "text": "..."} # responses api: {"type": "input_text", "text": "..."} @@ -3216,10 +3251,15 @@ def _construct_responses_api_input(messages: Sequence[BaseMessage]) -> list: new_blocks.append(new_block) elif block["type"] in ("input_text", "input_image", "input_file"): new_blocks.append(block) + elif block["type"] in non_message_item_types: + input_.append(block) else: pass msg["content"] = new_blocks - input_.append(msg) + if msg["content"]: + input_.append(msg) + else: + input_.append(msg) else: input_.append(msg) @@ -3366,7 +3406,10 @@ def _construct_lc_result_from_responses_api( def _convert_responses_chunk_to_generation_chunk( - chunk: Any, schema: Optional[type[_BM]] = None, metadata: Optional[dict] = None + chunk: Any, + schema: Optional[type[_BM]] = None, + metadata: Optional[dict] = None, + has_reasoning: bool = False, ) -> Optional[ChatGenerationChunk]: content = [] tool_call_chunks: list = [] @@ -3429,6 +3472,10 @@ def _convert_responses_chunk_to_generation_chunk( "web_search_call", "file_search_call", "computer_call", + "code_interpreter_call", + "mcp_call", + "mcp_list_tools", + "mcp_approval_request", ): additional_kwargs["tool_outputs"] = [ chunk.item.model_dump(exclude_none=True, mode="json") @@ -3444,9 +3491,11 @@ def _convert_responses_chunk_to_generation_chunk( elif chunk.type == "response.refusal.done": additional_kwargs["refusal"] = chunk.refusal elif chunk.type == "response.output_item.added" and chunk.item.type == "reasoning": - additional_kwargs["reasoning"] = chunk.item.model_dump( - exclude_none=True, mode="json" - ) + if not has_reasoning: + # Hack until breaking release: store first reasoning item ID. + additional_kwargs["reasoning"] = chunk.item.model_dump( + exclude_none=True, mode="json" + ) elif chunk.type == "response.reasoning_summary_part.added": additional_kwargs["reasoning"] = { # langchain-core uses the `index` key to aggregate text blocks. diff --git a/libs/partners/openai/langchain_openai/embeddings/base.py b/libs/partners/openai/langchain_openai/embeddings/base.py index e9e688983fd..c23a466cff9 100644 --- a/libs/partners/openai/langchain_openai/embeddings/base.py +++ b/libs/partners/openai/langchain_openai/embeddings/base.py @@ -8,6 +8,7 @@ from typing import Any, Literal, Optional, Union, cast import openai import tiktoken from langchain_core.embeddings import Embeddings +from langchain_core.runnables.config import run_in_executor from langchain_core.utils import from_env, get_pydantic_field_names, secret_from_env from pydantic import BaseModel, ConfigDict, Field, SecretStr, model_validator from typing_extensions import Self @@ -525,7 +526,9 @@ class OpenAIEmbeddings(BaseModel, Embeddings): _chunk_size = chunk_size or self.chunk_size client_kwargs = {**self._invocation_params, **kwargs} - _iter, tokens, indices = self._tokenize(texts, _chunk_size) + _iter, tokens, indices = await run_in_executor( + None, self._tokenize, texts, _chunk_size + ) batched_embeddings: list[list[float]] = [] for i in range(0, len(tokens), _chunk_size): response = await self.async_client.create( diff --git a/libs/partners/openai/pyproject.toml b/libs/partners/openai/pyproject.toml index 9a31e868166..db44a90fa25 100644 --- a/libs/partners/openai/pyproject.toml +++ b/libs/partners/openai/pyproject.toml @@ -7,12 +7,12 @@ authors = [] license = { text = "MIT" } requires-python = ">=3.9" dependencies = [ - "langchain-core<1.0.0,>=0.3.59", + "langchain-core<1.0.0,>=0.3.61", "openai<2.0.0,>=1.68.2", "tiktoken<1,>=0.7", ] name = "langchain-openai" -version = "0.3.17" +version = "0.3.18" description = "An integration package connecting OpenAI and LangChain" readme = "README.md" diff --git a/libs/partners/openai/tests/integration_tests/chat_models/test_base.py b/libs/partners/openai/tests/integration_tests/chat_models/test_base.py index 005c5e8b8ff..4b02676e2a8 100644 --- a/libs/partners/openai/tests/integration_tests/chat_models/test_base.py +++ b/libs/partners/openai/tests/integration_tests/chat_models/test_base.py @@ -23,9 +23,9 @@ from langchain_core.messages import ( ) from langchain_core.outputs import ChatGeneration, ChatResult, LLMResult from langchain_core.prompts import ChatPromptTemplate -from langchain_tests.integration_tests.chat_models import _validate_tool_call_message from langchain_tests.integration_tests.chat_models import ( - magic_function as invalid_magic_function, + _validate_tool_call_message, + magic_function, ) from pydantic import BaseModel, Field @@ -682,21 +682,24 @@ def test_image_token_counting_png() -> None: @pytest.mark.parametrize("use_responses_api", [False, True]) def test_tool_calling_strict(use_responses_api: bool) -> None: - """Test tool calling with strict=True.""" + """Test tool calling with strict=True. - class magic_function(BaseModel): + Responses API appears to have fewer constraints on schema when strict=True. + """ + + class magic_function_notrequired_arg(BaseModel): """Applies a magic function to an input.""" - input: int + input: Optional[int] = Field(default=None) model = ChatOpenAI( - model="gpt-4o", temperature=0, use_responses_api=use_responses_api + model="gpt-4.1", temperature=0, use_responses_api=use_responses_api ) + # N.B. magic_function adds metadata to schema (min/max for number fields) model_with_tools = model.bind_tools([magic_function], strict=True) - - # invalid_magic_function adds metadata to schema that isn't supported by OpenAI. + # Having a not-required argument in the schema remains invalid. model_with_invalid_tool_schema = model.bind_tools( - [invalid_magic_function], strict=True + [magic_function_notrequired_arg], strict=True ) # Test invoke @@ -764,30 +767,6 @@ def test_structured_output_strict( assert isinstance(chunk, dict) # for mypy assert set(chunk.keys()) == {"setup", "punchline"} - # Invalid schema with optional fields: - class InvalidJoke(BaseModelProper): - """Joke to tell user.""" - - setup: str = FieldProper(description="question to set up a joke") - # Invalid field, can't have default value. - punchline: str = FieldProper( - default="foo", description="answer to resolve the joke" - ) - - chat = llm.with_structured_output(InvalidJoke, method=method, strict=True) - with pytest.raises(openai.BadRequestError): - chat.invoke("Tell me a joke about cats.") - with pytest.raises(openai.BadRequestError): - next(chat.stream("Tell me a joke about cats.")) - - chat = llm.with_structured_output( - InvalidJoke.model_json_schema(), method=method, strict=True - ) - with pytest.raises(openai.BadRequestError): - chat.invoke("Tell me a joke about cats.") - with pytest.raises(openai.BadRequestError): - next(chat.stream("Tell me a joke about cats.")) - @pytest.mark.parametrize("use_responses_api", [False, True]) @pytest.mark.parametrize(("model", "method"), [("gpt-4o-2024-08-06", "json_schema")]) diff --git a/libs/partners/openai/tests/integration_tests/chat_models/test_responses_api.py b/libs/partners/openai/tests/integration_tests/chat_models/test_responses_api.py index d05cfa82c27..6accecb9d88 100644 --- a/libs/partners/openai/tests/integration_tests/chat_models/test_responses_api.py +++ b/libs/partners/openai/tests/integration_tests/chat_models/test_responses_api.py @@ -11,6 +11,7 @@ from langchain_core.messages import ( AIMessageChunk, BaseMessage, BaseMessageChunk, + HumanMessage, ) from pydantic import BaseModel from typing_extensions import TypedDict @@ -377,3 +378,77 @@ def test_stream_reasoning_summary() -> None: message_2 = {"role": "user", "content": "Thank you."} response_2 = llm.invoke([message_1, response_1, message_2]) assert isinstance(response_2, AIMessage) + + +# TODO: VCR some of these +def test_code_interpreter() -> None: + llm = ChatOpenAI(model="o4-mini", use_responses_api=True) + llm_with_tools = llm.bind_tools( + [{"type": "code_interpreter", "container": {"type": "auto"}}] + ) + input_message = { + "role": "user", + "content": "Write and run code to answer the question: what is 3^3?", + } + response = llm_with_tools.invoke([input_message]) + _check_response(response) + tool_outputs = response.additional_kwargs["tool_outputs"] + assert tool_outputs + assert any(output["type"] == "code_interpreter_call" for output in tool_outputs) + + # Test streaming + # Use same container + tool_outputs = response.additional_kwargs["tool_outputs"] + assert len(tool_outputs) == 1 + container_id = tool_outputs[0]["container_id"] + llm_with_tools = llm.bind_tools( + [{"type": "code_interpreter", "container": container_id}] + ) + + full: Optional[BaseMessageChunk] = None + for chunk in llm_with_tools.stream([input_message]): + assert isinstance(chunk, AIMessageChunk) + full = chunk if full is None else full + chunk + assert isinstance(full, AIMessageChunk) + tool_outputs = full.additional_kwargs["tool_outputs"] + assert tool_outputs + assert any(output["type"] == "code_interpreter_call" for output in tool_outputs) + + # Test we can pass back in + next_message = {"role": "user", "content": "Please add more comments to the code."} + _ = llm_with_tools.invoke([input_message, full, next_message]) + + +def test_mcp_builtin() -> None: + pytest.skip() # TODO: set up VCR + llm = ChatOpenAI(model="o4-mini", use_responses_api=True) + + llm_with_tools = llm.bind_tools( + [ + { + "type": "mcp", + "server_label": "deepwiki", + "server_url": "https://mcp.deepwiki.com/mcp", + "require_approval": {"always": {"tool_names": ["read_wiki_structure"]}}, + } + ] + ) + response = llm_with_tools.invoke( + "What transport protocols does the 2025-03-26 version of the MCP spec " + "(modelcontextprotocol/modelcontextprotocol) support?" + ) + + approval_message = HumanMessage( + [ + { + "type": "mcp_approval_response", + "approve": True, + "approval_request_id": output["id"], + } + for output in response.additional_kwargs["tool_outputs"] + if output["type"] == "mcp_approval_request" + ] + ) + _ = llm_with_tools.invoke( + [approval_message], previous_response_id=response.response_metadata["id"] + ) diff --git a/libs/partners/openai/tests/integration_tests/llms/test_azure.py b/libs/partners/openai/tests/integration_tests/llms/test_azure.py index 3af71e31d55..4d601c0f2ae 100644 --- a/libs/partners/openai/tests/integration_tests/llms/test_azure.py +++ b/libs/partners/openai/tests/integration_tests/llms/test_azure.py @@ -149,7 +149,7 @@ def test_openai_streaming_callback() -> None: verbose=True, ) llm.invoke("Write me a sentence with 100 words.") - assert callback_handler.llm_streams == 12 + assert callback_handler.llm_streams < 15 @pytest.mark.scheduled @@ -172,5 +172,5 @@ async def test_openai_async_streaming_callback() -> None: verbose=True, ) result = await llm.agenerate(["Write me a sentence with 100 words."]) - assert callback_handler.llm_streams == 12 + assert callback_handler.llm_streams < 15 assert isinstance(result, LLMResult) diff --git a/libs/partners/openai/tests/unit_tests/chat_models/test_base.py b/libs/partners/openai/tests/unit_tests/chat_models/test_base.py index 8d1bc7726d6..49f9ed2b541 100644 --- a/libs/partners/openai/tests/unit_tests/chat_models/test_base.py +++ b/libs/partners/openai/tests/unit_tests/chat_models/test_base.py @@ -21,6 +21,8 @@ from langchain_core.messages import ( from langchain_core.messages.ai import UsageMetadata from langchain_core.outputs import ChatGeneration, ChatResult from langchain_core.runnables import RunnableLambda +from langchain_core.tracers.base import BaseTracer +from langchain_core.tracers.schemas import Run from openai.types.responses import ResponseOutputMessage from openai.types.responses.response import IncompleteDetails, Response, ResponseUsage from openai.types.responses.response_error import ResponseError @@ -1849,3 +1851,77 @@ def test_service_tier() -> None: llm = ChatOpenAI(model="o4-mini", service_tier="flex") payload = llm._get_request_payload([HumanMessage("Hello")]) assert payload["service_tier"] == "flex" + + +class FakeTracer(BaseTracer): + def __init__(self) -> None: + super().__init__() + self.chat_model_start_inputs: list = [] + + def _persist_run(self, run: Run) -> None: + """Persist a run.""" + pass + + def on_chat_model_start(self, *args: Any, **kwargs: Any) -> Run: + self.chat_model_start_inputs.append({"args": args, "kwargs": kwargs}) + return super().on_chat_model_start(*args, **kwargs) + + +def test_mcp_tracing() -> None: + # Test we exclude sensitive information from traces + llm = ChatOpenAI(model="o4-mini", use_responses_api=True) + + tracer = FakeTracer() + mock_client = MagicMock() + + def mock_create(*args: Any, **kwargs: Any) -> Response: + return Response( + id="resp_123", + created_at=1234567890, + model="o4-mini", + object="response", + parallel_tool_calls=True, + tools=[], + tool_choice="auto", + output=[ + ResponseOutputMessage( + type="message", + id="msg_123", + content=[ + ResponseOutputText( + type="output_text", text="Test response", annotations=[] + ) + ], + role="assistant", + status="completed", + ) + ], + ) + + mock_client.responses.create = mock_create + input_message = HumanMessage("Test query") + tools = [ + { + "type": "mcp", + "server_label": "deepwiki", + "server_url": "https://mcp.deepwiki.com/mcp", + "require_approval": "always", + "headers": {"Authorization": "Bearer PLACEHOLDER"}, + } + ] + with patch.object(llm, "root_client", mock_client): + llm_with_tools = llm.bind_tools(tools) + _ = llm_with_tools.invoke([input_message], config={"callbacks": [tracer]}) + + # Test headers are not traced + assert len(tracer.chat_model_start_inputs) == 1 + invocation_params = tracer.chat_model_start_inputs[0]["kwargs"]["invocation_params"] + for tool in invocation_params["tools"]: + if "headers" in tool: + assert tool["headers"] == "**REDACTED**" + for substring in ["Authorization", "Bearer", "PLACEHOLDER"]: + assert substring not in str(tracer.chat_model_start_inputs) + + # Test headers are correctly propagated to request + payload = llm_with_tools._get_request_payload([input_message], tools=tools) # type: ignore[attr-defined] + assert payload["tools"][0]["headers"]["Authorization"] == "Bearer PLACEHOLDER" diff --git a/libs/partners/openai/uv.lock b/libs/partners/openai/uv.lock index 65fd625bdb6..1b2e92c3086 100644 --- a/libs/partners/openai/uv.lock +++ b/libs/partners/openai/uv.lock @@ -479,7 +479,7 @@ wheels = [ [[package]] name = "langchain-core" -version = "0.3.60" +version = "0.3.61" source = { editable = "../../core" } dependencies = [ { name = "jsonpatch" }, @@ -537,7 +537,7 @@ typing = [ [[package]] name = "langchain-openai" -version = "0.3.17" +version = "0.3.18" source = { editable = "." } dependencies = [ { name = "langchain-core" }, @@ -992,7 +992,7 @@ wheels = [ [[package]] name = "openai" -version = "1.68.2" +version = "1.81.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "anyio" }, @@ -1004,9 +1004,9 @@ dependencies = [ { name = "tqdm" }, { name = "typing-extensions" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/3f/6b/6b002d5d38794645437ae3ddb42083059d556558493408d39a0fcea608bc/openai-1.68.2.tar.gz", hash = "sha256:b720f0a95a1dbe1429c0d9bb62096a0d98057bcda82516f6e8af10284bdd5b19", size = 413429 } +sdist = { url = "https://files.pythonhosted.org/packages/1c/89/a1e4f3fa7ca4f7fec90dbf47d93b7cd5ff65924926733af15044e302a192/openai-1.81.0.tar.gz", hash = "sha256:349567a8607e0bcffd28e02f96b5c2397d0d25d06732d90ab3ecbf97abf030f9", size = 456861 } wheels = [ - { url = "https://files.pythonhosted.org/packages/fd/34/cebce15f64eb4a3d609a83ac3568d43005cc9a1cba9d7fde5590fd415423/openai-1.68.2-py3-none-any.whl", hash = "sha256:24484cb5c9a33b58576fdc5acf0e5f92603024a4e39d0b99793dfa1eb14c2b36", size = 606073 }, + { url = "https://files.pythonhosted.org/packages/02/66/bcc7f9bf48e8610a33e3b5c96a5a644dad032d92404ea2a5e8b43ba067e8/openai-1.81.0-py3-none-any.whl", hash = "sha256:1c71572e22b43876c5d7d65ade0b7b516bb527c3d44ae94111267a09125f7bae", size = 717529 }, ] [[package]] diff --git a/libs/partners/voyageai/.gitignore b/libs/partners/voyageai/.gitignore deleted file mode 100644 index bee8a64b79a..00000000000 --- a/libs/partners/voyageai/.gitignore +++ /dev/null @@ -1 +0,0 @@ -__pycache__ diff --git a/libs/partners/voyageai/LICENSE b/libs/partners/voyageai/LICENSE deleted file mode 100644 index fc0602feecd..00000000000 --- a/libs/partners/voyageai/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2024 LangChain, Inc. - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/libs/partners/voyageai/Makefile b/libs/partners/voyageai/Makefile deleted file mode 100644 index c34c3e547e7..00000000000 --- a/libs/partners/voyageai/Makefile +++ /dev/null @@ -1,65 +0,0 @@ -.PHONY: all format lint test tests integration_tests docker_tests help extended_tests - -# Default target executed when no arguments are given to make. -all: help - -.EXPORT_ALL_VARIABLES: -UV_FROZEN = true - -# Define a variable for the test file path. -TEST_FILE ?= tests/unit_tests/ -integration_test integration_tests: TEST_FILE=tests/integration_tests/ - -test tests: - uv run --group test pytest --disable-socket --allow-unix-socket $(TEST_FILE) - -integration_test integration_tests: - uv run --group test --group test_integration pytest $(TEST_FILE) - -test_watch: - uv run --group test ptw --snapshot-update --now . -- -vv $(TEST_FILE) - - -###################### -# LINTING AND FORMATTING -###################### - -# Define a variable for Python and notebook files. -PYTHON_FILES=. -MYPY_CACHE=.mypy_cache -lint format: PYTHON_FILES=. -lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/voyageai --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') -lint_package: PYTHON_FILES=langchain_voyageai -lint_tests: PYTHON_FILES=tests -lint_tests: MYPY_CACHE=.mypy_cache_test - -lint lint_diff lint_package lint_tests: - [ "$(PYTHON_FILES)" = "" ] || uv run --all-groups ruff $(PYTHON_FILES) - [ "$(PYTHON_FILES)" = "" ] || uv run --all-groups ruff format $(PYTHON_FILES) --diff - [ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && uv run --all-groups mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) - -format format_diff: - [ "$(PYTHON_FILES)" = "" ] || uv run --group lint ruff format $(PYTHON_FILES) - [ "$(PYTHON_FILES)" = "" ] || uv run --group lint ruff --select I --fix $(PYTHON_FILES) - -spell_check: - uv run --all-groups codespell --toml pyproject.toml - -spell_fix: - uv run --all-groups codespell --toml pyproject.toml -w - -check_imports: $(shell find langchain_voyageai -name '*.py') - uv run --all-groups python ./scripts/check_imports.py $^ - -###################### -# HELP -###################### - -help: - @echo '----' - @echo 'check_imports - check imports' - @echo 'format - run code formatters' - @echo 'lint - run linters' - @echo 'test - run unit tests' - @echo 'tests - run unit tests' - @echo 'test TEST_FILE= - run all tests in file' diff --git a/libs/partners/voyageai/README.md b/libs/partners/voyageai/README.md index 9ee50e983e7..6c18d3a6ea9 100644 --- a/libs/partners/voyageai/README.md +++ b/libs/partners/voyageai/README.md @@ -1,21 +1,3 @@ -# langchain-voyageai +This package has moved! -This package contains the LangChain integrations for VoyageAI through their `voyageai` client package. - -## Installation and Setup - -- Install the LangChain partner package -```bash -pip install langchain-voyageai -``` -- Get an VoyageAI api key and set it as an environment variable (`VOYAGE_API_KEY`) or use the API key as a parameter in the Client. - - - -## Text Embedding Model - -See a [usage example](https://python.langchain.com/docs/integrations/text_embedding/voyageai) - -```python -from langchain_voyageai import VoyageAIEmbeddings -``` +https://github.com/voyage-ai/langchain-voyageai diff --git a/libs/partners/voyageai/langchain_voyageai/__init__.py b/libs/partners/voyageai/langchain_voyageai/__init__.py deleted file mode 100644 index 38dae75181c..00000000000 --- a/libs/partners/voyageai/langchain_voyageai/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from langchain_voyageai.embeddings import VoyageAIEmbeddings -from langchain_voyageai.rerank import VoyageAIRerank - -__all__ = ["VoyageAIEmbeddings", "VoyageAIRerank"] diff --git a/libs/partners/voyageai/langchain_voyageai/embeddings.py b/libs/partners/voyageai/langchain_voyageai/embeddings.py deleted file mode 100644 index 1accc645c1d..00000000000 --- a/libs/partners/voyageai/langchain_voyageai/embeddings.py +++ /dev/null @@ -1,156 +0,0 @@ -import logging -from collections.abc import Iterable -from typing import Any, Literal, Optional, cast - -import voyageai # type: ignore -from langchain_core.embeddings import Embeddings -from langchain_core.utils import secret_from_env -from pydantic import ( - BaseModel, - ConfigDict, - Field, - PrivateAttr, - SecretStr, - model_validator, -) -from typing_extensions import Self - -logger = logging.getLogger(__name__) - -DEFAULT_VOYAGE_2_BATCH_SIZE = 72 -DEFAULT_VOYAGE_3_LITE_BATCH_SIZE = 30 -DEFAULT_VOYAGE_3_BATCH_SIZE = 10 -DEFAULT_BATCH_SIZE = 7 - - -class VoyageAIEmbeddings(BaseModel, Embeddings): - """VoyageAIEmbeddings embedding model. - - Example: - .. code-block:: python - - from langchain_voyageai import VoyageAIEmbeddings - - model = VoyageAIEmbeddings() - """ - - _client: voyageai.Client = PrivateAttr() - _aclient: voyageai.client_async.AsyncClient = PrivateAttr() - model: str - batch_size: int - - output_dimension: Optional[Literal[256, 512, 1024, 2048]] = None - show_progress_bar: bool = False - truncation: bool = True - voyage_api_key: SecretStr = Field( - alias="api_key", - default_factory=secret_from_env( - "VOYAGE_API_KEY", - error_message="Must set `VOYAGE_API_KEY` environment variable or " - "pass `api_key` to VoyageAIEmbeddings constructor.", - ), - ) - - model_config = ConfigDict( - extra="forbid", - populate_by_name=True, - ) - - @model_validator(mode="before") - @classmethod - def default_values(cls, values: dict) -> Any: - """Set default batch size based on model""" - model = values.get("model") - batch_size = values.get("batch_size") - if batch_size is None: - values["batch_size"] = ( - DEFAULT_VOYAGE_2_BATCH_SIZE - if model in ["voyage-2", "voyage-02"] - else ( - DEFAULT_VOYAGE_3_LITE_BATCH_SIZE - if model == "voyage-3-lite" - else ( - DEFAULT_VOYAGE_3_BATCH_SIZE - if model == "voyage-3" - else DEFAULT_BATCH_SIZE - ) - ) - ) - return values - - @model_validator(mode="after") - def validate_environment(self) -> Self: - """Validate that VoyageAI credentials exist in environment.""" - api_key_str = self.voyage_api_key.get_secret_value() - self._client = voyageai.Client(api_key=api_key_str) - self._aclient = voyageai.client_async.AsyncClient(api_key=api_key_str) - return self - - def _get_batch_iterator(self, texts: list[str]) -> Iterable: - if self.show_progress_bar: - try: - from tqdm.auto import tqdm # type: ignore - except ImportError as e: - raise ImportError( - "Must have tqdm installed if `show_progress_bar` is set to True. " - "Please install with `pip install tqdm`." - ) from e - - _iter = tqdm(range(0, len(texts), self.batch_size)) - else: - _iter = range(0, len(texts), self.batch_size) # type: ignore - - return _iter - - def embed_documents(self, texts: list[str]) -> list[list[float]]: - """Embed search docs.""" - embeddings: list[list[float]] = [] - - _iter = self._get_batch_iterator(texts) - for i in _iter: - r = self._client.embed( - texts[i : i + self.batch_size], - model=self.model, - input_type="document", - truncation=self.truncation, - output_dimension=self.output_dimension, - ).embeddings - embeddings.extend(cast(Iterable[list[float]], r)) - return embeddings - - def embed_query(self, text: str) -> list[float]: - """Embed query text.""" - r = self._client.embed( - [text], - model=self.model, - input_type="query", - truncation=self.truncation, - output_dimension=self.output_dimension, - ).embeddings[0] - return cast(list[float], r) - - async def aembed_documents(self, texts: list[str]) -> list[list[float]]: - embeddings: list[list[float]] = [] - - _iter = self._get_batch_iterator(texts) - for i in _iter: - r = await self._aclient.embed( - texts[i : i + self.batch_size], - model=self.model, - input_type="document", - truncation=self.truncation, - output_dimension=self.output_dimension, - ) - embeddings.extend(cast(Iterable[list[float]], r.embeddings)) - - return embeddings - - async def aembed_query(self, text: str) -> list[float]: - r = await self._aclient.embed( - [text], - model=self.model, - input_type="query", - truncation=self.truncation, - output_dimension=self.output_dimension, - ) - return cast(list[float], r.embeddings[0]) diff --git a/libs/partners/voyageai/langchain_voyageai/py.typed b/libs/partners/voyageai/langchain_voyageai/py.typed deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/libs/partners/voyageai/langchain_voyageai/rerank.py b/libs/partners/voyageai/langchain_voyageai/rerank.py deleted file mode 100644 index 8a9e4f06973..00000000000 --- a/libs/partners/voyageai/langchain_voyageai/rerank.py +++ /dev/null @@ -1,156 +0,0 @@ -from __future__ import annotations - -import os -from collections.abc import Sequence -from copy import deepcopy -from typing import Any, Optional, Union - -import voyageai # type: ignore -from langchain_core.callbacks.manager import Callbacks -from langchain_core.documents import Document -from langchain_core.documents.compressor import BaseDocumentCompressor -from langchain_core.utils import convert_to_secret_str -from pydantic import ConfigDict, SecretStr, model_validator -from voyageai.object import RerankingObject # type: ignore - - -class VoyageAIRerank(BaseDocumentCompressor): - """Document compressor that uses `VoyageAI Rerank API`.""" - - client: voyageai.Client = None # type: ignore - aclient: voyageai.AsyncClient = None # type: ignore - """VoyageAI clients to use for compressing documents.""" - voyage_api_key: Optional[SecretStr] = None - """VoyageAI API key. Must be specified directly or via environment variable - VOYAGE_API_KEY.""" - model: str - """Model to use for reranking.""" - top_k: Optional[int] = None - """Number of documents to return.""" - truncation: bool = True - - model_config = ConfigDict( - arbitrary_types_allowed=True, - ) - - @model_validator(mode="before") - @classmethod - def validate_environment(cls, values: dict) -> Any: - """Validate that api key exists in environment.""" - voyage_api_key = values.get("voyage_api_key") or os.getenv( - "VOYAGE_API_KEY", None - ) - if voyage_api_key: - api_key_secretstr = convert_to_secret_str(voyage_api_key) - values["voyage_api_key"] = api_key_secretstr - - api_key_str = api_key_secretstr.get_secret_value() - else: - api_key_str = None - - values["client"] = voyageai.Client(api_key=api_key_str) - values["aclient"] = voyageai.AsyncClient(api_key=api_key_str) - - return values - - def _rerank( - self, - documents: Sequence[Union[str, Document]], - query: str, - ) -> RerankingObject: - """Returns an ordered list of documents ordered by their relevance - to the provided query. - - Args: - query: The query to use for reranking. - documents: A sequence of documents to rerank. - """ - docs = [ - doc.page_content if isinstance(doc, Document) else doc for doc in documents - ] - return self.client.rerank( - query=query, - documents=docs, - model=self.model, - top_k=self.top_k, - truncation=self.truncation, - ) - - async def _arerank( - self, - documents: Sequence[Union[str, Document]], - query: str, - ) -> RerankingObject: - """Returns an ordered list of documents ordered by their relevance - to the provided query. - - Args: - query: The query to use for reranking. - documents: A sequence of documents to rerank. - """ - docs = [ - doc.page_content if isinstance(doc, Document) else doc for doc in documents - ] - return await self.aclient.rerank( - query=query, - documents=docs, - model=self.model, - top_k=self.top_k, - truncation=self.truncation, - ) - - def compress_documents( - self, - documents: Sequence[Document], - query: str, - callbacks: Optional[Callbacks] = None, - ) -> Sequence[Document]: - """ - Compress documents using VoyageAI's rerank API. - - Args: - documents: A sequence of documents to compress. - query: The query to use for compressing the documents. - callbacks: Callbacks to run during the compression process. - - Returns: - A sequence of compressed documents in relevance_score order. - """ - if len(documents) == 0: - return [] - - compressed = [] - for res in self._rerank(documents, query).results: - doc = documents[res.index] - doc_copy = Document(doc.page_content, metadata=deepcopy(doc.metadata)) - doc_copy.metadata["relevance_score"] = res.relevance_score - compressed.append(doc_copy) - return compressed - - async def acompress_documents( - self, - documents: Sequence[Document], - query: str, - callbacks: Optional[Callbacks] = None, - ) -> Sequence[Document]: - """ - Compress documents using VoyageAI's rerank API. - - Args: - documents: A sequence of documents to compress. - query: The query to use for compressing the documents. - callbacks: Callbacks to run during the compression process. - - Returns: - A sequence of compressed documents in relevance_score order. - """ - if len(documents) == 0: - return [] - - compressed = [] - for res in (await self._arerank(documents, query)).results: - doc = documents[res.index] - doc_copy = Document(doc.page_content, metadata=deepcopy(doc.metadata)) - doc_copy.metadata["relevance_score"] = res.relevance_score - compressed.append(doc_copy) - return compressed diff --git a/libs/partners/voyageai/pyproject.toml b/libs/partners/voyageai/pyproject.toml deleted file mode 100644 index 76ffed4207f..00000000000 --- a/libs/partners/voyageai/pyproject.toml +++ /dev/null @@ -1,66 +0,0 @@ -[build-system] -requires = ["pdm-backend"] -build-backend = "pdm.backend" - -[tool.pdm.build] -includes = [] - -[project] -authors = [] -license = { text = "MIT" } -requires-python = "<3.13,>=3.9" -dependencies = [ - "langchain-core<1.0.0,>=0.3.29", - "voyageai<1,>=0.3.2", - "pydantic<3,>=2", -] -name = "langchain-voyageai" -version = "0.1.4" -description = "An integration package connecting VoyageAI and LangChain" -readme = "README.md" - -[project.urls] -"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/voyageai" -"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-voyageai%3D%3D0%22&expanded=true" -repository = "https://github.com/langchain-ai/langchain" - -[dependency-groups] -test = [ - "pytest<8.0.0,>=7.3.0", - "freezegun<2.0.0,>=1.2.2", - "pytest-mock<4.0.0,>=3.10.0", - "syrupy<5.0.0,>=4.0.2", - "pytest-watcher<1.0.0,>=0.3.4", - "pytest-asyncio<1.0.0,>=0.21.1", - "pytest-socket<1.0.0,>=0.7.0", - "langchain-core", -] -codespell = ["codespell<3.0.0,>=2.2.0"] -test_integration = [] -lint = ["ruff<1.0.0,>=0.1.5"] -dev = ["langchain-core"] -typing = ["mypy<2.0,>=1.10", "langchain-core"] - -[tool.uv.sources] -langchain-core = { path = "../../core", editable = true } - -[tool.mypy] -disallow_untyped_defs = "True" - -[tool.ruff] -target-version = "py39" - -[tool.ruff.lint] -select = ["E", "F", "I", "UP"] -ignore = [ "UP007", ] - -[tool.coverage.run] -omit = ["tests/*"] - -[tool.pytest.ini_options] -addopts = "--strict-markers --strict-config --durations=5" -markers = [ - "requires: mark tests as requiring a specific library", - "compile: mark placeholder test used to compile integration tests without running them", -] -asyncio_mode = "auto" diff --git a/libs/partners/voyageai/scripts/check_imports.py b/libs/partners/voyageai/scripts/check_imports.py deleted file mode 100644 index ba8de50118c..00000000000 --- a/libs/partners/voyageai/scripts/check_imports.py +++ /dev/null @@ -1,17 +0,0 @@ -import sys -import traceback -from importlib.machinery import SourceFileLoader - -if __name__ == "__main__": - files = sys.argv[1:] - has_failure = False - for file in files: - try: - SourceFileLoader("x", file).load_module() - except Exception: - has_failure = True - print(file) - traceback.print_exc() - print() - - sys.exit(1 if has_failure else 0) diff --git a/libs/partners/voyageai/scripts/lint_imports.sh b/libs/partners/voyageai/scripts/lint_imports.sh deleted file mode 100755 index 695613c7ba8..00000000000 --- a/libs/partners/voyageai/scripts/lint_imports.sh +++ /dev/null @@ -1,17 +0,0 @@ -#!/bin/bash - -set -eu - -# Initialize a variable to keep track of errors -errors=0 - -# make sure not importing from langchain or langchain_experimental -git --no-pager grep '^from langchain\.' . && errors=$((errors+1)) -git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1)) - -# Decide on an exit status based on the errors -if [ "$errors" -gt 0 ]; then - exit 1 -else - exit 0 -fi diff --git a/libs/partners/voyageai/tests/__init__.py b/libs/partners/voyageai/tests/__init__.py deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/libs/partners/voyageai/tests/integration_tests/__init__.py b/libs/partners/voyageai/tests/integration_tests/__init__.py deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/libs/partners/voyageai/tests/integration_tests/test_compile.py b/libs/partners/voyageai/tests/integration_tests/test_compile.py deleted file mode 100644 index 33ecccdfa0f..00000000000 --- a/libs/partners/voyageai/tests/integration_tests/test_compile.py +++ /dev/null @@ -1,7 +0,0 @@ -import pytest - - -@pytest.mark.compile -def test_placeholder() -> None: - """Used for compiling integration tests without running any real tests.""" - pass diff --git a/libs/partners/voyageai/tests/integration_tests/test_embeddings.py b/libs/partners/voyageai/tests/integration_tests/test_embeddings.py deleted file mode 100644 index 91ecc62a457..00000000000 --- a/libs/partners/voyageai/tests/integration_tests/test_embeddings.py +++ /dev/null @@ -1,62 +0,0 @@ -"""Test VoyageAI embeddings.""" - -from langchain_voyageai import VoyageAIEmbeddings - -# Please set VOYAGE_API_KEY in the environment variables -MODEL = "voyage-2" - - -def test_langchain_voyageai_embedding_documents() -> None: - """Test voyage embeddings.""" - documents = ["foo bar"] - embedding = VoyageAIEmbeddings(model=MODEL) # type: ignore[call-arg] - output = embedding.embed_documents(documents) - assert len(output) == 1 - assert len(output[0]) == 1024 - - -def test_langchain_voyageai_embedding_documents_multiple() -> None: - """Test voyage embeddings.""" - documents = ["foo bar", "bar foo", "foo"] - embedding = VoyageAIEmbeddings(model=MODEL, batch_size=2) - output = embedding.embed_documents(documents) - assert len(output) == 3 - assert len(output[0]) == 1024 - assert len(output[1]) == 1024 - assert len(output[2]) == 1024 - - -def test_langchain_voyageai_embedding_query() -> None: - """Test voyage embeddings.""" - document = "foo bar" - embedding = VoyageAIEmbeddings(model=MODEL) # type: ignore[call-arg] - output = embedding.embed_query(document) - assert len(output) == 1024 - - -async def test_langchain_voyageai_async_embedding_documents_multiple() -> None: - """Test voyage embeddings.""" - documents = ["foo bar", "bar foo", "foo"] - embedding = VoyageAIEmbeddings(model=MODEL, batch_size=2) - output = await embedding.aembed_documents(documents) - assert len(output) == 3 - assert len(output[0]) == 1024 - assert len(output[1]) == 1024 - assert len(output[2]) == 1024 - - -async def test_langchain_voyageai_async_embedding_query() -> None: - """Test voyage embeddings.""" - document = "foo bar" - embedding = VoyageAIEmbeddings(model=MODEL) # type: ignore[call-arg] - output = await embedding.aembed_query(document) - assert len(output) == 1024 - - -def test_langchain_voyageai_embedding_documents_with_output_dimension() -> None: - """Test voyage embeddings.""" - documents = ["foo bar"] - embedding = VoyageAIEmbeddings(model="voyage-3-large", output_dimension=256) # type: ignore[call-arg] - output = embedding.embed_documents(documents) - assert len(output) == 1 - assert len(output[0]) == 256 diff --git a/libs/partners/voyageai/tests/integration_tests/test_rerank.py b/libs/partners/voyageai/tests/integration_tests/test_rerank.py deleted file mode 100644 index 3e18ca0366e..00000000000 --- a/libs/partners/voyageai/tests/integration_tests/test_rerank.py +++ /dev/null @@ -1,68 +0,0 @@ -"""Test the voyageai reranker.""" - -import os - -from langchain_core.documents import Document - -from langchain_voyageai.rerank import VoyageAIRerank - - -def test_voyageai_reranker_init() -> None: - """Test the voyageai reranker initializes correctly.""" - VoyageAIRerank(voyage_api_key="foo", model="foo") # type: ignore[arg-type] - - -def test_sync() -> None: - rerank = VoyageAIRerank( - voyage_api_key=os.environ["VOYAGE_API_KEY"], # type: ignore[arg-type] - model="rerank-lite-1", - ) - doc_list = [ - "The Mediterranean diet emphasizes fish, olive oil, and vegetables" - ", believed to reduce chronic diseases.", - "Photosynthesis in plants converts light energy into glucose and " - "produces essential oxygen.", - "20th-century innovations, from radios to smartphones, centered " - "on electronic advancements.", - "Rivers provide water, irrigation, and habitat for aquatic species, " - "vital for ecosystems.", - "Apple’s conference call to discuss fourth fiscal quarter results and " - "business updates is scheduled for Thursday, November 2, 2023 at 2:00 " - "p.m. PT / 5:00 p.m. ET.", - "Shakespeare's works, like 'Hamlet' and 'A Midsummer Night's Dream,' " - "endure in literature.", - ] - documents = [Document(page_content=x) for x in doc_list] - - result = rerank.compress_documents( - query="When is the Apple's conference call scheduled?", documents=documents - ) - assert len(doc_list) == len(result) - - -async def test_async() -> None: - rerank = VoyageAIRerank( - voyage_api_key=os.environ["VOYAGE_API_KEY"], # type: ignore[arg-type] - model="rerank-lite-1", - ) - doc_list = [ - "The Mediterranean diet emphasizes fish, olive oil, and vegetables" - ", believed to reduce chronic diseases.", - "Photosynthesis in plants converts light energy into glucose and " - "produces essential oxygen.", - "20th-century innovations, from radios to smartphones, centered " - "on electronic advancements.", - "Rivers provide water, irrigation, and habitat for aquatic species, " - "vital for ecosystems.", - "Apple’s conference call to discuss fourth fiscal quarter results and " - "business updates is scheduled for Thursday, November 2, 2023 at 2:00 " - "p.m. PT / 5:00 p.m. ET.", - "Shakespeare's works, like 'Hamlet' and 'A Midsummer Night's Dream,' " - "endure in literature.", - ] - documents = [Document(page_content=x) for x in doc_list] - - result = await rerank.acompress_documents( - query="When is the Apple's conference call scheduled?", documents=documents - ) - assert len(doc_list) == len(result) diff --git a/libs/partners/voyageai/tests/unit_tests/__init__.py b/libs/partners/voyageai/tests/unit_tests/__init__.py deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/libs/partners/voyageai/tests/unit_tests/test_embeddings.py b/libs/partners/voyageai/tests/unit_tests/test_embeddings.py deleted file mode 100644 index 587376ab8b4..00000000000 --- a/libs/partners/voyageai/tests/unit_tests/test_embeddings.py +++ /dev/null @@ -1,61 +0,0 @@ -"""Test embedding model integration.""" - -from langchain_core.embeddings import Embeddings - -from langchain_voyageai import VoyageAIEmbeddings - -MODEL = "voyage-2" - - -def test_initialization_voyage_2() -> None: - """Test embedding model initialization.""" - emb = VoyageAIEmbeddings(api_key="NOT_A_VALID_KEY", model=MODEL) # type: ignore - assert isinstance(emb, Embeddings) - assert emb.batch_size == 72 - assert emb.model == MODEL - assert emb._client is not None - - -def test_initialization_voyage_2_with_full_api_key_name() -> None: - """Test embedding model initialization.""" - # Testing that we can initialize the model using `voyage_api_key` - # instead of `api_key` - emb = VoyageAIEmbeddings(voyage_api_key="NOT_A_VALID_KEY", model=MODEL) # type: ignore - assert isinstance(emb, Embeddings) - assert emb.batch_size == 72 - assert emb.model == MODEL - assert emb._client is not None - - -def test_initialization_voyage_1() -> None: - """Test embedding model initialization.""" - emb = VoyageAIEmbeddings(api_key="NOT_A_VALID_KEY", model="voyage-01") # type: ignore - assert isinstance(emb, Embeddings) - assert emb.batch_size == 7 - assert emb.model == "voyage-01" - assert emb._client is not None - - -def test_initialization_voyage_1_batch_size() -> None: - """Test embedding model initialization.""" - emb = VoyageAIEmbeddings( - api_key="NOT_A_VALID_KEY", # type: ignore - model="voyage-01", - batch_size=15, - ) - assert isinstance(emb, Embeddings) - assert emb.batch_size == 15 - assert emb.model == "voyage-01" - assert emb._client is not None - - -def test_initialization_with_output_dimension() -> None: - emb = VoyageAIEmbeddings( - api_key="NOT_A_VALID_KEY", # type: ignore - model="voyage-3-large", - output_dimension=256, - batch_size=10, - ) - assert isinstance(emb, Embeddings) - assert emb.model == "voyage-3-large" - assert emb.output_dimension == 256 diff --git a/libs/partners/voyageai/tests/unit_tests/test_imports.py b/libs/partners/voyageai/tests/unit_tests/test_imports.py deleted file mode 100644 index 9b8977126ad..00000000000 --- a/libs/partners/voyageai/tests/unit_tests/test_imports.py +++ /dev/null @@ -1,10 +0,0 @@ -from langchain_voyageai import __all__ - -EXPECTED_ALL = [ - "VoyageAIEmbeddings", - "VoyageAIRerank", -] - - -def test_all_imports() -> None: - assert sorted(EXPECTED_ALL) == sorted(__all__) diff --git a/libs/partners/voyageai/tests/unit_tests/test_rerank.py b/libs/partners/voyageai/tests/unit_tests/test_rerank.py deleted file mode 100644 index b12687dab85..00000000000 --- a/libs/partners/voyageai/tests/unit_tests/test_rerank.py +++ /dev/null @@ -1,83 +0,0 @@ -from collections import namedtuple -from typing import Any - -import pytest # type: ignore -from langchain_core.documents import Document -from voyageai.api_resources import VoyageResponse # type: ignore -from voyageai.object import RerankingObject # type: ignore - -from langchain_voyageai.rerank import VoyageAIRerank - -doc_list = [ - "The Mediterranean diet emphasizes fish, olive oil, and vegetables" - ", believed to reduce chronic diseases.", - "Photosynthesis in plants converts light energy into glucose and " - "produces essential oxygen.", - "20th-century innovations, from radios to smartphones, centered " - "on electronic advancements.", - "Rivers provide water, irrigation, and habitat for aquatic species, " - "vital for ecosystems.", - "Apple’s conference call to discuss fourth fiscal quarter results and " - "business updates is scheduled for Thursday, November 2, 2023 at 2:00 " - "p.m. PT / 5:00 p.m. ET.", - "Shakespeare's works, like 'Hamlet' and 'A Midsummer Night's Dream,' " - "endure in literature.", -] -documents = [Document(page_content=x) for x in doc_list] - - -@pytest.mark.requires("voyageai") -def test_init() -> None: - VoyageAIRerank( - voyage_api_key="foo", # type: ignore[arg-type] - model="rerank-lite-1", - ) - - -def get_mock_rerank_result() -> RerankingObject: - VoyageResultItem = namedtuple("VoyageResultItem", ["index", "relevance_score"]) - Usage = namedtuple("Usage", ["total_tokens"]) - voyage_response = VoyageResponse() - voyage_response.data = [ - VoyageResultItem(index=1, relevance_score=0.9), - VoyageResultItem(index=0, relevance_score=0.8), - ] - voyage_response.usage = Usage(total_tokens=255) - return RerankingObject(response=voyage_response, documents=doc_list) - - -@pytest.mark.requires("voyageai") -def test_rerank_unit_test(mocker: Any) -> None: - mocker.patch("voyageai.Client.rerank").return_value = get_mock_rerank_result() - expected_result = [ - Document( - page_content="Photosynthesis in plants converts light energy into " - "glucose and produces essential oxygen.", - metadata={"relevance_score": 0.9}, - ), - Document( - page_content="The Mediterranean diet emphasizes fish, olive oil, and " - "vegetables, believed to reduce chronic diseases.", - metadata={"relevance_score": 0.8}, - ), - ] - - rerank = VoyageAIRerank( - voyage_api_key="foo", # type: ignore[arg-type] - model="rerank-lite-1", - ) - result = rerank.compress_documents( - documents=documents, query="When is the Apple's conference call scheduled?" - ) - assert expected_result == result - - -def test_rerank_empty_input() -> None: - rerank = VoyageAIRerank( - voyage_api_key="foo", # type: ignore[arg-type] - model="rerank-lite-1", - ) - result = rerank.compress_documents( - documents=[], query="When is the Apple's conference call scheduled?" - ) - assert len(result) == 0 diff --git a/libs/partners/voyageai/uv.lock b/libs/partners/voyageai/uv.lock deleted file mode 100644 index 39e4d1b316e..00000000000 --- a/libs/partners/voyageai/uv.lock +++ /dev/null @@ -1,1625 +0,0 @@ -version = 1 -revision = 1 -requires-python = ">=3.9, <3.13" -resolution-markers = [ - 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Example: ``{"mode": "auto"}``.""" openai_api_key: Optional[SecretStr] = None openai_api_base: Optional[str] = None @@ -371,6 +394,18 @@ class ChatXAI(BaseChatOpenAI): # type: ignore[override] ) return self + @property + def _default_params(self) -> dict[str, Any]: + """Get default parameters.""" + params = super()._default_params + if self.search_parameters: + if "extra_body" in params: + params["extra_body"]["search_parameters"] = self.search_parameters + else: + params["extra_body"] = {"search_parameters": self.search_parameters} + + return params + def _create_chat_result( self, response: Union[dict, openai.BaseModel], @@ -386,6 +421,11 @@ class ChatXAI(BaseChatOpenAI): # type: ignore[override] response.choices[0].message.reasoning_content # type: ignore ) + if hasattr(response, "citations"): + rtn.generations[0].message.additional_kwargs["citations"] = ( + response.citations + ) + return rtn def _convert_chunk_to_generation_chunk( @@ -407,6 +447,10 @@ class ChatXAI(BaseChatOpenAI): # type: ignore[override] reasoning_content ) + if (citations := chunk.get("citations")) and generation_chunk: + if isinstance(generation_chunk.message, AIMessageChunk): + generation_chunk.message.additional_kwargs["citations"] = citations + return generation_chunk def with_structured_output( diff --git a/libs/partners/xai/pyproject.toml b/libs/partners/xai/pyproject.toml index 2db189d8d85..64d7dec054c 100644 --- a/libs/partners/xai/pyproject.toml +++ b/libs/partners/xai/pyproject.toml @@ -7,13 +7,13 @@ authors = [] license = { text = "MIT" } requires-python = ">=3.9" dependencies = [ - "langchain-openai<0.4,>=0.3.12", - "langchain-core<1.0.0,>=0.3.51", + "langchain-openai<0.4,>=0.3.18", + "langchain-core<1.0.0,>=0.3.62", "requests<3,>=2", "aiohttp<4,>=3.9.1", ] name = "langchain-xai" -version = "0.2.3" +version = "0.2.4" description = "An integration package connecting xAI and LangChain" readme = "README.md" diff --git a/libs/partners/xai/tests/integration_tests/test_chat_models_standard.py b/libs/partners/xai/tests/integration_tests/test_chat_models_standard.py index 1e14116c05d..67e5bd1707a 100644 --- a/libs/partners/xai/tests/integration_tests/test_chat_models_standard.py +++ b/libs/partners/xai/tests/integration_tests/test_chat_models_standard.py @@ -48,3 +48,24 @@ def test_reasoning_content() -> None: full = chunk if full is None else full + chunk assert isinstance(full, AIMessageChunk) assert full.additional_kwargs["reasoning_content"] + + +def test_web_search() -> None: + llm = ChatXAI( + model="grok-3-latest", + search_parameters={"mode": "auto", "max_search_results": 3}, + ) + + # Test invoke + response = llm.invoke("Provide me a digest of world news in the last 24 hours.") + assert response.content + assert response.additional_kwargs["citations"] + assert len(response.additional_kwargs["citations"]) <= 3 + + # Test streaming + full = None + for chunk in llm.stream("Provide me a digest of world news in the last 24 hours."): + full = chunk if full is None else full + chunk + assert isinstance(full, AIMessageChunk) + assert full.additional_kwargs["citations"] + assert len(full.additional_kwargs["citations"]) <= 3 diff --git a/libs/partners/xai/uv.lock b/libs/partners/xai/uv.lock index 37e6bf415a4..81cecd7d2de 100644 --- a/libs/partners/xai/uv.lock +++ b/libs/partners/xai/uv.lock @@ -631,7 +631,7 @@ wheels = [ [[package]] name = "langchain-core" -version = "0.3.60" +version = "0.3.62" source = { editable = "../../core" } dependencies = [ { name = "jsonpatch" }, @@ -689,7 +689,7 @@ typing = [ [[package]] name = "langchain-openai" -version = "0.3.17" +version = "0.3.18" source = { editable = "../openai" } dependencies = [ { name = "langchain-core" }, @@ -784,7 +784,7 @@ typing = [ [[package]] name = "langchain-xai" -version = "0.2.3" +version = "0.2.4" source = { editable = "." } dependencies = [ { name = "aiohttp" },