mirror of
https://github.com/hwchase17/langchain.git
synced 2025-08-17 08:29:28 +00:00
Merge branch 'master' into pprados/pdf-router
This commit is contained in:
Philippe PRADOS
Philippe Prados
commit
dfda5a0355
@ -275,3 +275,7 @@ if os.environ.get("READTHEDOCS", "") == "True":
|
||||
html_context["READTHEDOCS"] = True
|
||||
|
||||
master_doc = "index"
|
||||
|
||||
# If a signature’s length in characters exceeds 60,
|
||||
# each parameter within the signature will be displayed on an individual logical line
|
||||
maximum_signature_line_length = 60
|
||||
|
||||
@ -19,9 +19,15 @@
|
||||
"\n",
|
||||
"This doc will help you get started with AWS Bedrock [chat models](/docs/concepts/chat_models). Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon via a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI. Using Amazon Bedrock, you can easily experiment with and evaluate top FMs for your use case, privately customize them with your data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG), and build agents that execute tasks using your enterprise systems and data sources. Since Amazon Bedrock is serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with.\n",
|
||||
"\n",
|
||||
"For more information on which models are accessible via Bedrock, head to the [AWS docs](https://docs.aws.amazon.com/bedrock/latest/userguide/models-features.html).\n",
|
||||
"AWS Bedrock maintains a [Converse API](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_Converse.html) which provides a unified conversational interface for Bedrock models. This API does not yet support custom models. You can see a list of all [models that are supported here](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html).\n",
|
||||
"\n",
|
||||
"For detailed documentation of all ChatBedrock features and configurations head to the [API reference](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html).\n",
|
||||
":::info\n",
|
||||
"\n",
|
||||
"We recommend the Converse API for users who do not need to use custom models. It can be accessed using [ChatBedrockConverse](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html).\n",
|
||||
"\n",
|
||||
":::\n",
|
||||
"\n",
|
||||
"For detailed documentation of all Bedrock features and configurations head to the [API reference](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html).\n",
|
||||
"\n",
|
||||
"## Overview\n",
|
||||
"### Integration details\n",
|
||||
@ -29,8 +35,12 @@
|
||||
"| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/bedrock) | Package downloads | Package latest |\n",
|
||||
"| :--- | :--- | :---: | :---: | :---: | :---: | :---: |\n",
|
||||
"| [ChatBedrock](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html) | [langchain-aws](https://python.langchain.com/api_reference/aws/index.html) | ❌ | beta | ✅ |  |  |\n",
|
||||
"| [ChatBedrockConverse](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html) | [langchain-aws](https://python.langchain.com/api_reference/aws/index.html) | ❌ | beta | ✅ |  |  |\n",
|
||||
"\n",
|
||||
"### Model features\n",
|
||||
"\n",
|
||||
"The below apply to both `ChatBedrock` and `ChatBedrockConverse`.\n",
|
||||
"\n",
|
||||
"| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
|
||||
"| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n",
|
||||
"| ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ |\n",
|
||||
@ -48,7 +58,9 @@
|
||||
"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:"
|
||||
"source": [
|
||||
"To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@ -98,11 +110,12 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_aws import ChatBedrock\n",
|
||||
"from langchain_aws import ChatBedrockConverse\n",
|
||||
"\n",
|
||||
"llm = ChatBedrock(\n",
|
||||
" model_id=\"anthropic.claude-3-sonnet-20240229-v1:0\",\n",
|
||||
" model_kwargs=dict(temperature=0),\n",
|
||||
"llm = ChatBedrockConverse(\n",
|
||||
" model_id=\"anthropic.claude-3-5-sonnet-20240620-v1:0\",\n",
|
||||
" # temperature=...,\n",
|
||||
" # max_tokens=...,\n",
|
||||
" # other params...\n",
|
||||
")"
|
||||
]
|
||||
@ -117,19 +130,17 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "62e0dbc3",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"execution_count": 2,
|
||||
"id": "fcd8de52-4a1b-4875-b463-d41b031e06a1",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"AIMessage(content=\"Voici la traduction en français :\\n\\nJ'aime la programmation.\", additional_kwargs={'usage': {'prompt_tokens': 29, 'completion_tokens': 21, 'total_tokens': 50}, 'stop_reason': 'end_turn', 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0'}, response_metadata={'usage': {'prompt_tokens': 29, 'completion_tokens': 21, 'total_tokens': 50}, 'stop_reason': 'end_turn', 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0'}, id='run-fdb07dc3-ff72-430d-b22b-e7824b15c766-0', usage_metadata={'input_tokens': 29, 'output_tokens': 21, 'total_tokens': 50})"
|
||||
"AIMessage(content=\"J'adore la programmation.\", additional_kwargs={}, response_metadata={'ResponseMetadata': {'RequestId': 'b07d1630-06f2-44b1-82bf-e82538dd2215', 'HTTPStatusCode': 200, 'HTTPHeaders': {'date': 'Wed, 16 Apr 2025 19:35:34 GMT', 'content-type': 'application/json', 'content-length': '206', 'connection': 'keep-alive', 'x-amzn-requestid': 'b07d1630-06f2-44b1-82bf-e82538dd2215'}, 'RetryAttempts': 0}, 'stopReason': 'end_turn', 'metrics': {'latencyMs': [488]}, 'model_name': 'anthropic.claude-3-5-sonnet-20240620-v1:0'}, id='run-d09ed928-146a-4336-b1fd-b63c9e623494-0', usage_metadata={'input_tokens': 29, 'output_tokens': 11, 'total_tokens': 40, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -148,7 +159,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": 3,
|
||||
"id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -156,9 +167,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Voici la traduction en français :\n",
|
||||
"\n",
|
||||
"J'aime la programmation.\n"
|
||||
"J'adore la programmation.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@ -168,7 +177,146 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
|
||||
"id": "4da16f3e-e80b-48c0-8036-c1cc5f7c8c05",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Streaming\n",
|
||||
"\n",
|
||||
"Note that `ChatBedrockConverse` emits content blocks while streaming:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "605e04fa-1a76-47ac-8c92-fe128659663e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"content=[] additional_kwargs={} response_metadata={} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd'\n",
|
||||
"content=[{'type': 'text', 'text': 'J', 'index': 0}] additional_kwargs={} response_metadata={} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd'\n",
|
||||
"content=[{'type': 'text', 'text': \"'adore la\", 'index': 0}] additional_kwargs={} response_metadata={} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd'\n",
|
||||
"content=[{'type': 'text', 'text': ' programmation.', 'index': 0}] additional_kwargs={} response_metadata={} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd'\n",
|
||||
"content=[{'index': 0}] additional_kwargs={} response_metadata={} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd'\n",
|
||||
"content=[] additional_kwargs={} response_metadata={'stopReason': 'end_turn'} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd'\n",
|
||||
"content=[] additional_kwargs={} response_metadata={'metrics': {'latencyMs': 600}, 'model_name': 'anthropic.claude-3-5-sonnet-20240620-v1:0'} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd' usage_metadata={'input_tokens': 29, 'output_tokens': 11, 'total_tokens': 40, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for chunk in llm.stream(messages):\n",
|
||||
" print(chunk)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "0ef05abb-9c04-4dc3-995e-f857779644d5",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can filter to text using the [.text()](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html#langchain_core.messages.ai.AIMessage.text) method on the output:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "2a4e743f-ea7d-4e5a-9b12-f9992362de8b",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"|J|'adore la| programmation.||||"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for chunk in llm.stream(messages):\n",
|
||||
" print(chunk.text(), end=\"|\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a77519e5-897d-41a0-a9bb-55300fa79efc",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Prompt caching\n",
|
||||
"\n",
|
||||
"Bedrock supports [caching](https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html) of elements of your prompts, including messages and tools. This allows you to re-use large documents, instructions, [few-shot documents](/docs/concepts/few_shot_prompting/), and other data to reduce latency and costs.\n",
|
||||
"\n",
|
||||
":::note\n",
|
||||
"\n",
|
||||
"Not all models support prompt caching. See supported models [here](https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html#prompt-caching-models).\n",
|
||||
"\n",
|
||||
":::\n",
|
||||
"\n",
|
||||
"To enable caching on an element of a prompt, mark its associated content block using the `cachePoint` key. See example below:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "d5f63d01-85e8-4797-a2be-0fea747a6049",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"First invocation:\n",
|
||||
"{'cache_creation': 1528, 'cache_read': 0}\n",
|
||||
"\n",
|
||||
"Second:\n",
|
||||
"{'cache_creation': 0, 'cache_read': 1528}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import requests\n",
|
||||
"from langchain_aws import ChatBedrockConverse\n",
|
||||
"\n",
|
||||
"llm = ChatBedrockConverse(model=\"us.anthropic.claude-3-7-sonnet-20250219-v1:0\")\n",
|
||||
"\n",
|
||||
"# Pull LangChain readme\n",
|
||||
"get_response = requests.get(\n",
|
||||
" \"https://raw.githubusercontent.com/langchain-ai/langchain/master/README.md\"\n",
|
||||
")\n",
|
||||
"readme = get_response.text\n",
|
||||
"\n",
|
||||
"messages = [\n",
|
||||
" {\n",
|
||||
" \"role\": \"user\",\n",
|
||||
" \"content\": [\n",
|
||||
" {\n",
|
||||
" \"type\": \"text\",\n",
|
||||
" \"text\": \"What's LangChain, according to its README?\",\n",
|
||||
" },\n",
|
||||
" {\n",
|
||||
" \"type\": \"text\",\n",
|
||||
" \"text\": f\"{readme}\",\n",
|
||||
" },\n",
|
||||
" {\n",
|
||||
" \"cachePoint\": {\"type\": \"default\"},\n",
|
||||
" },\n",
|
||||
" ],\n",
|
||||
" },\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"response_1 = llm.invoke(messages)\n",
|
||||
"response_2 = llm.invoke(messages)\n",
|
||||
"\n",
|
||||
"usage_1 = response_1.usage_metadata[\"input_token_details\"]\n",
|
||||
"usage_2 = response_2.usage_metadata[\"input_token_details\"]\n",
|
||||
"\n",
|
||||
"print(f\"First invocation:\\n{usage_1}\")\n",
|
||||
"print(f\"\\nSecond:\\n{usage_2}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "1b550667-af5b-4557-b84f-c8f865dad6cb",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Chaining\n",
|
||||
@ -179,13 +327,13 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
|
||||
"id": "6033f3fa-0e96-46e3-abb3-1530928fea88",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"AIMessage(content='Ich liebe Programmieren.', additional_kwargs={'usage': {'prompt_tokens': 23, 'completion_tokens': 11, 'total_tokens': 34}, 'stop_reason': 'end_turn', 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0'}, response_metadata={'usage': {'prompt_tokens': 23, 'completion_tokens': 11, 'total_tokens': 34}, 'stop_reason': 'end_turn', 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0'}, id='run-5ad005ce-9f31-4670-baa0-9373d418698a-0', usage_metadata={'input_tokens': 23, 'output_tokens': 11, 'total_tokens': 34})"
|
||||
"AIMessage(content=\"Here's the German translation:\\n\\nIch liebe das Programmieren.\", additional_kwargs={}, response_metadata={'ResponseMetadata': {'RequestId': '1de3d7c0-8062-4f7e-bb8a-8f725b97a8b0', 'HTTPStatusCode': 200, 'HTTPHeaders': {'date': 'Wed, 16 Apr 2025 19:32:51 GMT', 'content-type': 'application/json', 'content-length': '243', 'connection': 'keep-alive', 'x-amzn-requestid': '1de3d7c0-8062-4f7e-bb8a-8f725b97a8b0'}, 'RetryAttempts': 0}, 'stopReason': 'end_turn', 'metrics': {'latencyMs': [719]}, 'model_name': 'anthropic.claude-3-5-sonnet-20240620-v1:0'}, id='run-7021fcd7-704e-496b-a92e-210139614402-0', usage_metadata={'input_tokens': 23, 'output_tokens': 19, 'total_tokens': 42, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
@ -216,131 +364,6 @@
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Bedrock Converse API\n",
|
||||
"\n",
|
||||
"AWS has recently released the Bedrock Converse API which provides a unified conversational interface for Bedrock models. This API does not yet support custom models. You can see a list of all [models that are supported here](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html). To improve reliability the ChatBedrock integration will switch to using the Bedrock Converse API as soon as it has feature parity with the existing Bedrock API. Until then a separate [ChatBedrockConverse](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html) integration has been released.\n",
|
||||
"\n",
|
||||
"We recommend using `ChatBedrockConverse` for users who do not need to use custom models.\n",
|
||||
"\n",
|
||||
"You can use it like so:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "ae728e59-94d4-40cf-9d24-25ad8723fc59",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"AIMessage(content=\"Voici la traduction en français :\\n\\nJ'aime la programmation.\", response_metadata={'ResponseMetadata': {'RequestId': '4fcbfbe9-f916-4df2-b0bd-ea1147b550aa', 'HTTPStatusCode': 200, 'HTTPHeaders': {'date': 'Wed, 21 Aug 2024 17:23:49 GMT', 'content-type': 'application/json', 'content-length': '243', 'connection': 'keep-alive', 'x-amzn-requestid': '4fcbfbe9-f916-4df2-b0bd-ea1147b550aa'}, 'RetryAttempts': 0}, 'stopReason': 'end_turn', 'metrics': {'latencyMs': 672}}, id='run-77ee9810-e32b-45dc-9ccb-6692253b1f45-0', usage_metadata={'input_tokens': 29, 'output_tokens': 21, 'total_tokens': 50})"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from langchain_aws import ChatBedrockConverse\n",
|
||||
"\n",
|
||||
"llm = ChatBedrockConverse(\n",
|
||||
" model=\"anthropic.claude-3-sonnet-20240229-v1:0\",\n",
|
||||
" temperature=0,\n",
|
||||
" max_tokens=None,\n",
|
||||
" # other params...\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"llm.invoke(messages)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "4da16f3e-e80b-48c0-8036-c1cc5f7c8c05",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Streaming\n",
|
||||
"\n",
|
||||
"Note that `ChatBedrockConverse` emits content blocks while streaming:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "7794b32e-d8de-4973-bf0f-39807dc745f0",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"content=[] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
|
||||
"content=[{'type': 'text', 'text': 'Vo', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
|
||||
"content=[{'type': 'text', 'text': 'ici', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
|
||||
"content=[{'type': 'text', 'text': ' la', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
|
||||
"content=[{'type': 'text', 'text': ' tra', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
|
||||
"content=[{'type': 'text', 'text': 'duction', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
|
||||
"content=[{'type': 'text', 'text': ' en', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
|
||||
"content=[{'type': 'text', 'text': ' français', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
|
||||
"content=[{'type': 'text', 'text': ' :', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
|
||||
"content=[{'type': 'text', 'text': '\\n\\nJ', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
|
||||
"content=[{'type': 'text', 'text': \"'\", 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
|
||||
"content=[{'type': 'text', 'text': 'a', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
|
||||
"content=[{'type': 'text', 'text': 'ime', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
|
||||
"content=[{'type': 'text', 'text': ' la', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
|
||||
"content=[{'type': 'text', 'text': ' programm', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
|
||||
"content=[{'type': 'text', 'text': 'ation', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
|
||||
"content=[{'type': 'text', 'text': '.', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
|
||||
"content=[{'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
|
||||
"content=[] response_metadata={'stopReason': 'end_turn'} id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
|
||||
"content=[] response_metadata={'metrics': {'latencyMs': 713}} id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8' usage_metadata={'input_tokens': 29, 'output_tokens': 21, 'total_tokens': 50}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for chunk in llm.stream(messages):\n",
|
||||
" print(chunk)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "0ef05abb-9c04-4dc3-995e-f857779644d5",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"An output parser can be used to filter to text, if desired:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "2a4e743f-ea7d-4e5a-9b12-f9992362de8b",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"|Vo|ici| la| tra|duction| en| français| :|\n",
|
||||
"\n",
|
||||
"J|'|a|ime| la| programm|ation|.||||"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from langchain_core.output_parsers import StrOutputParser\n",
|
||||
"\n",
|
||||
"chain = llm | StrOutputParser()\n",
|
||||
"\n",
|
||||
"for chunk in chain.stream(messages):\n",
|
||||
" print(chunk, end=\"|\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
|
||||
|
||||
@ -408,7 +408,7 @@
|
||||
"\n",
|
||||
":::\n",
|
||||
"\n",
|
||||
"OpenAI supports a [Responses](https://platform.openai.com/docs/guides/responses-vs-chat-completions) API that is oriented toward building [agentic](/docs/concepts/agents/) applications. It includes a suite of [built-in tools](https://platform.openai.com/docs/guides/tools?api-mode=responses), including web and file search. It also supports management of [conversation state](https://platform.openai.com/docs/guides/conversation-state?api-mode=responses), allowing you to continue a conversational thread without explicitly passing in previous messages.\n",
|
||||
"OpenAI supports a [Responses](https://platform.openai.com/docs/guides/responses-vs-chat-completions) API that is oriented toward building [agentic](/docs/concepts/agents/) applications. It includes a suite of [built-in tools](https://platform.openai.com/docs/guides/tools?api-mode=responses), including web and file search. It also supports management of [conversation state](https://platform.openai.com/docs/guides/conversation-state?api-mode=responses), allowing you to continue a conversational thread without explicitly passing in previous messages, as well as the output from [reasoning processes](https://platform.openai.com/docs/guides/reasoning?api-mode=responses).\n",
|
||||
"\n",
|
||||
"`ChatOpenAI` will route to the Responses API if one of these features is used. You can also specify `use_responses_api=True` when instantiating `ChatOpenAI`.\n",
|
||||
"\n",
|
||||
@ -1056,6 +1056,77 @@
|
||||
"print(second_response.text())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "67bf5bd2-0935-40a0-b1cd-c6662b681d4b",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Reasoning output\n",
|
||||
"\n",
|
||||
"Some OpenAI models will generate separate text content illustrating their reasoning process. See OpenAI's [reasoning documentation](https://platform.openai.com/docs/guides/reasoning?api-mode=responses) for details.\n",
|
||||
"\n",
|
||||
"OpenAI can return a summary of the model's reasoning (although it doesn't expose the raw reasoning tokens). To configure `ChatOpenAI` to return this summary, specify the `reasoning` parameter:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "8d322f3a-0732-45ab-ac95-dfd4596e0d85",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'3^3 = 3 × 3 × 3 = 27.'"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from langchain_openai import ChatOpenAI\n",
|
||||
"\n",
|
||||
"reasoning = {\n",
|
||||
" \"effort\": \"medium\", # 'low', 'medium', or 'high'\n",
|
||||
" \"summary\": \"auto\", # 'detailed', 'auto', or None\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"llm = ChatOpenAI(\n",
|
||||
" model=\"o4-mini\",\n",
|
||||
" use_responses_api=True,\n",
|
||||
" model_kwargs={\"reasoning\": reasoning},\n",
|
||||
")\n",
|
||||
"response = llm.invoke(\"What is 3^3?\")\n",
|
||||
"\n",
|
||||
"# Output\n",
|
||||
"response.text()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "d7dcc082-b7c8-41b7-a5e2-441b9679e41b",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"**Calculating power of three**\n",
|
||||
"\n",
|
||||
"The user is asking for the result of 3 to the power of 3, which I know is 27. It's a straightforward question, so I’ll keep my answer concise: 27. I could explain that this is the same as multiplying 3 by itself twice: 3 × 3 × 3 equals 27. However, since the user likely just needs the answer, I’ll simply respond with 27.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Reasoning\n",
|
||||
"reasoning = response.additional_kwargs[\"reasoning\"]\n",
|
||||
"for block in reasoning[\"summary\"]:\n",
|
||||
" print(block[\"text\"])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "57e27714",
|
||||
|
||||
@ -35,9 +35,18 @@ from langchain_aws import ChatBedrock
|
||||
```
|
||||
|
||||
### Bedrock Converse
|
||||
AWS has recently released the Bedrock Converse API which provides a unified conversational interface for Bedrock models. This API does not yet support custom models. You can see a list of all [models that are supported here](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html). To improve reliability the ChatBedrock integration will switch to using the Bedrock Converse API as soon as it has feature parity with the existing Bedrock API. Until then a separate [ChatBedrockConverse](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html) integration has been released.
|
||||
AWS Bedrock maintains a [Converse API](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_Converse.html)
|
||||
that provides a unified conversational interface for Bedrock models. This API does not
|
||||
yet support custom models. You can see a list of all
|
||||
[models that are supported here](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html).
|
||||
|
||||
We recommend using `ChatBedrockConverse` for users who do not need to use custom models. See the [docs](/docs/integrations/chat/bedrock/#bedrock-converse-api) and [API reference](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html) for more detail.
|
||||
:::info
|
||||
|
||||
We recommend the Converse API for users who do not need to use custom models. It can be accessed using [ChatBedrockConverse](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html).
|
||||
|
||||
:::
|
||||
|
||||
See a [usage example](/docs/integrations/chat/bedrock).
|
||||
|
||||
```python
|
||||
from langchain_aws import ChatBedrockConverse
|
||||
|
||||
32
docs/docs/integrations/providers/galaxia.mdx
Normal file
32
docs/docs/integrations/providers/galaxia.mdx
Normal file
@ -0,0 +1,32 @@
|
||||
# Smabbler
|
||||
> Smabbler’s graph-powered platform boosts AI development by transforming data into a structured knowledge foundation.
|
||||
|
||||
# Galaxia
|
||||
|
||||
> Galaxia Knowledge Base is an integrated knowledge base and retrieval mechanism for RAG. In contrast to standard solution, it is based on Knowledge Graphs built using symbolic NLP and Knowledge Representation solutions. Provided texts are analysed and transformed into Graphs containing text, language and semantic information. This rich structure allows for retrieval that is based on semantic information, not on vector similarity/distance.
|
||||
|
||||
Implementing RAG using Galaxia involves first uploading your files to [Galaxia](https://beta.cloud.smabbler.com/home), analyzing them there and then building a model (knowledge graph). When the model is built, you can use `GalaxiaRetriever` to connect to the API and start retrieving.
|
||||
|
||||
More information: [docs](https://smabbler.gitbook.io/smabbler)
|
||||
|
||||
## Installation
|
||||
```
|
||||
pip install langchain-galaxia-retriever
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
```
|
||||
from langchain_galaxia_retriever.retriever import GalaxiaRetriever
|
||||
|
||||
gr = GalaxiaRetriever(
|
||||
api_url="beta.api.smabbler.com",
|
||||
api_key="<key>",
|
||||
knowledge_base_id="<knowledge_base_id>",
|
||||
n_retries=10,
|
||||
wait_time=5,
|
||||
)
|
||||
|
||||
result = gr.invoke('<test question>')
|
||||
print(result)
|
||||
|
||||
213
docs/docs/integrations/retrievers/galaxia-retriever.ipynb
Normal file
213
docs/docs/integrations/retrievers/galaxia-retriever.ipynb
Normal file
@ -0,0 +1,213 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "raw",
|
||||
"id": "2af1fec5-4ca6-4167-8ee1-13314aac3258",
|
||||
"metadata": {
|
||||
"vscode": {
|
||||
"languageId": "raw"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"---\n",
|
||||
"sidebar_label: Galaxia\n",
|
||||
"---"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "1d7d6cbc-4373-4fb5-94dd-acd610165452",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Galaxia Retriever\n",
|
||||
"\n",
|
||||
"Galaxia is GraphRAG solution, which automates document processing, knowledge base (Graph Language Model) creation and retrieval:\n",
|
||||
"[galaxia-rag](https://smabbler.gitbook.io/smabbler/api-rag/smabblers-api-rag)\n",
|
||||
"\n",
|
||||
"To use Galaxia first upload your texts and create a Graph Language Model here: [smabbler-cloud](https://beta.cloud.smabbler.com)\n",
|
||||
"\n",
|
||||
"After the model is built and activated, you will be able to use this integration to retrieve what you need.\n",
|
||||
"\n",
|
||||
"The module repository is located here: [github](https://github.com/rrozanski-smabbler/galaxia-langchain)\n",
|
||||
"\n",
|
||||
"### Integration details\n",
|
||||
"| Retriever | Self-host | Cloud offering | Package |\n",
|
||||
"| :--- | :--- | :---: | :---: |\n",
|
||||
"[Galaxia Retriever](https://github.com/rrozanski-smabbler/galaxia-langchain) | ❌ | ✅ | __langchain-galaxia-retriever__ |"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "82fa1c05-c205-4429-a74c-e6c81c4e8611",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup\n",
|
||||
"Before you can retrieve anything you need to create your Graph Language Model here: [smabbler-cloud](https://beta.cloud.smabbler.com)\n",
|
||||
"\n",
|
||||
"following these 3 simple steps: [rag-instruction](https://smabbler.gitbook.io/smabbler/api-rag/build-rag-model-in-3-steps)\n",
|
||||
"\n",
|
||||
"Don't forget to activate the model after building it!"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "91897867-eb39-4c3b-8df8-5427043ecdcd",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Installation\n",
|
||||
"The retriever is implemented in the following package: [pypi](https://pypi.org/project/langchain-galaxia-retriever/)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "ceca36f2-013c-4b28-81fe-8808d0cf6419",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%pip install -qU langchain-galaxia-retriever"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "019e0e50-5e66-440b-9cf1-d21b4009bf13",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Instantiation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c7188217-4b26-4201-b15a-b7a5f263f815",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_galaxia_retriever.retriever import GalaxiaRetriever\n",
|
||||
"\n",
|
||||
"gr = GalaxiaRetriever(\n",
|
||||
" api_url=\"beta.api.smabbler.com\",\n",
|
||||
" api_key=\"<key>\", # you can find it here: https://beta.cloud.smabbler.com/user/account\n",
|
||||
" knowledge_base_id=\"<knowledge_base_id>\", # you can find it in https://beta.cloud.smabbler.com , in the model table\n",
|
||||
" n_retries=10,\n",
|
||||
" wait_time=5,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "02d288a5-4f76-472e-9a60-eea8e6b8dc7a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Usage"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "5f79e03f-77a6-4eb6-b41d-f3da2f897654",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"result = gr.invoke(\"<test question>\")\n",
|
||||
"print(result)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "ffb2a595-a901-477a-a374-efd091bc1c9a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Use within a chain"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "9c2e2394-ca33-47be-a851-551b4216daea",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# | output: false\n",
|
||||
"# | echo: false\n",
|
||||
"\n",
|
||||
"from langchain_openai import ChatOpenAI\n",
|
||||
"\n",
|
||||
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "ed8699d6-d65d-40ea-8c58-8d809cc512cf",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_core.output_parsers import StrOutputParser\n",
|
||||
"from langchain_core.prompts import ChatPromptTemplate\n",
|
||||
"from langchain_core.runnables import RunnablePassthrough\n",
|
||||
"\n",
|
||||
"prompt = ChatPromptTemplate.from_template(\n",
|
||||
" \"\"\"Answer the question based only on the context provided.\n",
|
||||
"\n",
|
||||
"Context: {context}\n",
|
||||
"\n",
|
||||
"Question: {question}\"\"\"\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def format_docs(docs):\n",
|
||||
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"chain = (\n",
|
||||
" {\"context\": gr | format_docs, \"question\": RunnablePassthrough()}\n",
|
||||
" | prompt\n",
|
||||
" | llm\n",
|
||||
" | StrOutputParser()\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "f9b944d7-8800-4926-b1ce-fcdc52ecda1c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"chain.invoke(\"<test question>\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "11b5c9a5-0a66-415f-98f8-f12080cad30a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## API reference\n",
|
||||
"\n",
|
||||
"For more information about Galaxia Retriever check its implementation on github [github](https://github.com/rrozanski-smabbler/galaxia-langchain)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.11.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -1696,7 +1696,7 @@ class PDFRouterParser(BaseBlobParser):
|
||||
("Xdvipdfmx", {"producer": "xdvipdfmx.*", "page1":"Hello"}, PDFPlumberParser()),
|
||||
("defautl", {}, PyPDFium2Parser())
|
||||
]
|
||||
loader = PDFRouterLoader(filename, routes)
|
||||
loader = PDFRouterLoader(filename, routes=routes)
|
||||
loader.load()
|
||||
```
|
||||
"""
|
||||
|
||||
@ -276,9 +276,6 @@ class AIMessage(BaseMessage):
|
||||
return (base.strip() + "\n" + "\n".join(lines)).strip()
|
||||
|
||||
|
||||
AIMessage.model_rebuild()
|
||||
|
||||
|
||||
class AIMessageChunk(AIMessage, BaseMessageChunk):
|
||||
"""Message chunk from an AI."""
|
||||
|
||||
|
||||
@ -22,9 +22,6 @@ class ChatMessage(BaseMessage):
|
||||
"""The type of the message (used during serialization). Defaults to "chat"."""
|
||||
|
||||
|
||||
ChatMessage.model_rebuild()
|
||||
|
||||
|
||||
class ChatMessageChunk(ChatMessage, BaseMessageChunk):
|
||||
"""Chat Message chunk."""
|
||||
|
||||
|
||||
@ -30,9 +30,6 @@ class FunctionMessage(BaseMessage):
|
||||
"""The type of the message (used for serialization). Defaults to "function"."""
|
||||
|
||||
|
||||
FunctionMessage.model_rebuild()
|
||||
|
||||
|
||||
class FunctionMessageChunk(FunctionMessage, BaseMessageChunk):
|
||||
"""Function Message chunk."""
|
||||
|
||||
|
||||
@ -52,9 +52,6 @@ class HumanMessage(BaseMessage):
|
||||
super().__init__(content=content, **kwargs)
|
||||
|
||||
|
||||
HumanMessage.model_rebuild()
|
||||
|
||||
|
||||
class HumanMessageChunk(HumanMessage, BaseMessageChunk):
|
||||
"""Human Message chunk."""
|
||||
|
||||
|
||||
@ -26,6 +26,3 @@ class RemoveMessage(BaseMessage):
|
||||
raise ValueError(msg)
|
||||
|
||||
super().__init__("", id=id, **kwargs)
|
||||
|
||||
|
||||
RemoveMessage.model_rebuild()
|
||||
|
||||
@ -46,9 +46,6 @@ class SystemMessage(BaseMessage):
|
||||
super().__init__(content=content, **kwargs)
|
||||
|
||||
|
||||
SystemMessage.model_rebuild()
|
||||
|
||||
|
||||
class SystemMessageChunk(SystemMessage, BaseMessageChunk):
|
||||
"""System Message chunk."""
|
||||
|
||||
|
||||
@ -146,9 +146,6 @@ class ToolMessage(BaseMessage, ToolOutputMixin):
|
||||
super().__init__(content=content, **kwargs)
|
||||
|
||||
|
||||
ToolMessage.model_rebuild()
|
||||
|
||||
|
||||
class ToolMessageChunk(ToolMessage, BaseMessageChunk):
|
||||
"""Tool Message chunk."""
|
||||
|
||||
|
||||
@ -133,9 +133,6 @@ class ListOutputParser(BaseTransformOutputParser[list[str]]):
|
||||
yield [part]
|
||||
|
||||
|
||||
ListOutputParser.model_rebuild()
|
||||
|
||||
|
||||
class CommaSeparatedListOutputParser(ListOutputParser):
|
||||
"""Parse the output of an LLM call to a comma-separated list."""
|
||||
|
||||
|
||||
@ -114,9 +114,6 @@ class PydanticOutputParser(JsonOutputParser, Generic[TBaseModel]):
|
||||
return self.pydantic_object
|
||||
|
||||
|
||||
PydanticOutputParser.model_rebuild()
|
||||
|
||||
|
||||
_PYDANTIC_FORMAT_INSTRUCTIONS = """The output should be formatted as a JSON instance that conforms to the JSON schema below.
|
||||
|
||||
As an example, for the schema {{"properties": {{"foo": {{"title": "Foo", "description": "a list of strings", "type": "array", "items": {{"type": "string"}}}}}}, "required": ["foo"]}}
|
||||
|
||||
@ -31,6 +31,3 @@ class StrOutputParser(BaseTransformOutputParser[str]):
|
||||
def parse(self, text: str) -> str:
|
||||
"""Returns the input text with no changes."""
|
||||
return text
|
||||
|
||||
|
||||
StrOutputParser.model_rebuild()
|
||||
|
||||
@ -132,6 +132,3 @@ class PipelinePromptTemplate(BasePromptTemplate):
|
||||
@property
|
||||
def _prompt_type(self) -> str:
|
||||
raise ValueError
|
||||
|
||||
|
||||
PipelinePromptTemplate.model_rebuild()
|
||||
|
||||
@ -5650,9 +5650,6 @@ class RunnableBindingBase(RunnableSerializable[Input, Output]):
|
||||
yield item
|
||||
|
||||
|
||||
RunnableBindingBase.model_rebuild()
|
||||
|
||||
|
||||
class RunnableBinding(RunnableBindingBase[Input, Output]):
|
||||
"""Wrap a Runnable with additional functionality.
|
||||
|
||||
|
||||
@ -8,7 +8,6 @@ from abc import abstractmethod
|
||||
from collections.abc import (
|
||||
AsyncIterator,
|
||||
Iterator,
|
||||
Mapping, # noqa: F401 Needed by pydantic
|
||||
Sequence,
|
||||
)
|
||||
from functools import wraps
|
||||
@ -464,9 +463,6 @@ class RunnableConfigurableFields(DynamicRunnable[Input, Output]):
|
||||
return (self.default, config)
|
||||
|
||||
|
||||
RunnableConfigurableFields.model_rebuild()
|
||||
|
||||
|
||||
# Before Python 3.11 native StrEnum is not available
|
||||
class StrEnum(str, enum.Enum):
|
||||
"""String enum."""
|
||||
|
||||
@ -6,6 +6,7 @@ import ast
|
||||
import asyncio
|
||||
import inspect
|
||||
import textwrap
|
||||
from collections.abc import Mapping, Sequence
|
||||
from contextvars import Context
|
||||
from functools import lru_cache
|
||||
from inspect import signature
|
||||
@ -33,8 +34,6 @@ if TYPE_CHECKING:
|
||||
Awaitable,
|
||||
Coroutine,
|
||||
Iterable,
|
||||
Mapping,
|
||||
Sequence,
|
||||
)
|
||||
|
||||
from langchain_core.runnables.schema import StreamEvent
|
||||
|
||||
@ -176,6 +176,3 @@ class Tool(BaseTool):
|
||||
args_schema=args_schema,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
Tool.model_rebuild()
|
||||
|
||||
@ -227,9 +227,6 @@ class SerializableModel(GenericFakeChatModel):
|
||||
return True
|
||||
|
||||
|
||||
SerializableModel.model_rebuild()
|
||||
|
||||
|
||||
def test_serialization_with_rate_limiter() -> None:
|
||||
"""Test model serialization with rate limiter."""
|
||||
from langchain_core.load import dumps
|
||||
|
||||
@ -45,8 +45,6 @@ def test_base_generation_parser() -> None:
|
||||
assert isinstance(content, str)
|
||||
return content.swapcase()
|
||||
|
||||
StrInvertCase.model_rebuild()
|
||||
|
||||
model = GenericFakeChatModel(messages=iter([AIMessage(content="hEllo")]))
|
||||
chain = model | StrInvertCase()
|
||||
assert chain.invoke("") == "HeLLO"
|
||||
|
||||
@ -35,9 +35,6 @@ class FakeStructuredChatModel(FakeListChatModel):
|
||||
return "fake-messages-list-chat-model"
|
||||
|
||||
|
||||
FakeStructuredChatModel.model_rebuild()
|
||||
|
||||
|
||||
def test_structured_prompt_pydantic() -> None:
|
||||
class OutputSchema(BaseModel):
|
||||
name: str
|
||||
|
||||
@ -1188,9 +1188,6 @@ class HardCodedRetriever(BaseRetriever):
|
||||
return self.documents
|
||||
|
||||
|
||||
HardCodedRetriever.model_rebuild()
|
||||
|
||||
|
||||
async def test_event_stream_with_retriever() -> None:
|
||||
"""Test the event stream with a retriever."""
|
||||
retriever = HardCodedRetriever(
|
||||
|
||||
20
libs/core/tests/unit_tests/test_pydantic_imports.py
Normal file
20
libs/core/tests/unit_tests/test_pydantic_imports.py
Normal file
@ -0,0 +1,20 @@
|
||||
import importlib
|
||||
from pathlib import Path
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
|
||||
def test_all_models_built() -> None:
|
||||
for path in Path("../core/langchain_core/").glob("*"):
|
||||
module_name = path.stem
|
||||
if not module_name.startswith(".") and path.suffix != ".typed":
|
||||
module = importlib.import_module("langchain_core." + module_name)
|
||||
all_ = getattr(module, "__all__", [])
|
||||
for attr_name in all_:
|
||||
attr = getattr(module, attr_name)
|
||||
try:
|
||||
if issubclass(attr, BaseModel):
|
||||
assert attr.__pydantic_complete__ is True
|
||||
except TypeError:
|
||||
# This is expected for non-class attributes
|
||||
pass
|
||||
@ -1091,9 +1091,6 @@ class FooBase(BaseTool):
|
||||
return assert_bar(bar, bar_config)
|
||||
|
||||
|
||||
FooBase.model_rebuild()
|
||||
|
||||
|
||||
class AFooBase(FooBase):
|
||||
async def _arun(self, bar: Any, bar_config: RunnableConfig, **kwargs: Any) -> Any:
|
||||
return assert_bar(bar, bar_config)
|
||||
|
||||
@ -606,3 +606,7 @@ packages:
|
||||
- name: langchain-ydb
|
||||
path: .
|
||||
repo: ydb-platform/langchain-ydb
|
||||
- name: langchain-galaxia-retriever
|
||||
provider_page: galaxia
|
||||
path: .
|
||||
repo: rrozanski-smabbler/galaxia-langchain
|
||||
|
||||
@ -6,9 +6,9 @@ build-backend = "pdm.backend"
|
||||
authors = []
|
||||
license = { text = "MIT" }
|
||||
requires-python = "<4.0,>=3.9"
|
||||
dependencies = ["ollama<1,>=0.4.4", "langchain-core<1.0.0,>=0.3.51"]
|
||||
dependencies = ["ollama<1,>=0.4.4", "langchain-core<1.0.0,>=0.3.52"]
|
||||
name = "langchain-ollama"
|
||||
version = "0.3.1"
|
||||
version = "0.3.2"
|
||||
description = "An integration package connecting Ollama and LangChain"
|
||||
readme = "README.md"
|
||||
|
||||
|
||||
@ -1,7 +1,8 @@
|
||||
version = 1
|
||||
requires-python = ">=3.9, <4.0"
|
||||
resolution-markers = [
|
||||
"python_full_version >= '3.12.4'",
|
||||
"python_full_version >= '3.13'",
|
||||
"python_full_version >= '3.12.4' and python_full_version < '3.13'",
|
||||
"python_full_version >= '3.12' and python_full_version < '3.12.4'",
|
||||
"python_full_version < '3.12'",
|
||||
]
|
||||
@ -287,7 +288,7 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "langchain-core"
|
||||
version = "0.3.51"
|
||||
version = "0.3.52"
|
||||
source = { editable = "../../core" }
|
||||
dependencies = [
|
||||
{ name = "jsonpatch" },
|
||||
@ -323,10 +324,12 @@ test = [
|
||||
{ name = "freezegun", specifier = ">=1.2.2,<2.0.0" },
|
||||
{ name = "grandalf", specifier = ">=0.8,<1.0" },
|
||||
{ name = "langchain-tests", directory = "../../standard-tests" },
|
||||
{ name = "numpy", marker = "python_full_version < '3.12'", specifier = ">=1.24.0,<2.0.0" },
|
||||
{ name = "numpy", marker = "python_full_version >= '3.12'", specifier = ">=1.26.0,<3" },
|
||||
{ name = "numpy", marker = "python_full_version < '3.13'", specifier = ">=1.26.4" },
|
||||
{ name = "numpy", marker = "python_full_version >= '3.13'", specifier = ">=2.1.0" },
|
||||
{ name = "pytest", specifier = ">=8,<9" },
|
||||
{ name = "pytest-asyncio", specifier = ">=0.21.1,<1.0.0" },
|
||||
{ name = "pytest-benchmark" },
|
||||
{ name = "pytest-codspeed" },
|
||||
{ name = "pytest-mock", specifier = ">=3.10.0,<4.0.0" },
|
||||
{ name = "pytest-socket", specifier = ">=0.7.0,<1.0.0" },
|
||||
{ name = "pytest-watcher", specifier = ">=0.3.4,<1.0.0" },
|
||||
@ -337,15 +340,14 @@ test = [
|
||||
test-integration = []
|
||||
typing = [
|
||||
{ name = "langchain-text-splitters", directory = "../../text-splitters" },
|
||||
{ name = "mypy", specifier = ">=1.10,<1.11" },
|
||||
{ name = "types-jinja2", specifier = ">=2.11.9,<3.0.0" },
|
||||
{ name = "mypy", specifier = ">=1.15,<1.16" },
|
||||
{ name = "types-pyyaml", specifier = ">=6.0.12.2,<7.0.0.0" },
|
||||
{ name = "types-requests", specifier = ">=2.28.11.5,<3.0.0.0" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "langchain-ollama"
|
||||
version = "0.3.1"
|
||||
version = "0.3.2"
|
||||
source = { editable = "." }
|
||||
dependencies = [
|
||||
{ name = "langchain-core" },
|
||||
@ -403,7 +405,7 @@ typing = [
|
||||
|
||||
[[package]]
|
||||
name = "langchain-tests"
|
||||
version = "0.3.17"
|
||||
version = "0.3.18"
|
||||
source = { editable = "../../standard-tests" }
|
||||
dependencies = [
|
||||
{ name = "httpx" },
|
||||
@ -420,7 +422,8 @@ dependencies = [
|
||||
requires-dist = [
|
||||
{ name = "httpx", specifier = ">=0.25.0,<1" },
|
||||
{ name = "langchain-core", editable = "../../core" },
|
||||
{ name = "numpy", specifier = ">=1.26.2,<3" },
|
||||
{ name = "numpy", marker = "python_full_version < '3.13'", specifier = ">=1.26.2" },
|
||||
{ name = "numpy", marker = "python_full_version >= '3.13'", specifier = ">=2.1.0" },
|
||||
{ name = "pytest", specifier = ">=7,<9" },
|
||||
{ name = "pytest-asyncio", specifier = ">=0.20,<1" },
|
||||
{ name = "pytest-socket", specifier = ">=0.6.0,<1" },
|
||||
@ -558,7 +561,8 @@ name = "numpy"
|
||||
version = "2.2.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
resolution-markers = [
|
||||
"python_full_version >= '3.12.4'",
|
||||
"python_full_version >= '3.13'",
|
||||
"python_full_version >= '3.12.4' and python_full_version < '3.13'",
|
||||
"python_full_version >= '3.12' and python_full_version < '3.12.4'",
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/ec/d0/c12ddfd3a02274be06ffc71f3efc6d0e457b0409c4481596881e748cb264/numpy-2.2.2.tar.gz", hash = "sha256:ed6906f61834d687738d25988ae117683705636936cc605be0bb208b23df4d8f", size = 20233295 }
|
||||
|
||||
@ -2132,6 +2132,40 @@ class ChatOpenAI(BaseChatOpenAI): # type: ignore[override]
|
||||
|
||||
"Your name is Bob. How can I help you today, Bob?"
|
||||
|
||||
.. dropdown:: Reasoning output
|
||||
|
||||
OpenAI's Responses API supports `reasoning models <https://platform.openai.com/docs/guides/reasoning?api-mode=responses>`_
|
||||
that expose a summary of internal reasoning processes.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_openai import ChatOpenAI
|
||||
|
||||
reasoning = {
|
||||
"effort": "medium", # 'low', 'medium', or 'high'
|
||||
"summary": "auto", # 'detailed', 'auto', or None
|
||||
}
|
||||
|
||||
llm = ChatOpenAI(
|
||||
model="o4-mini", use_responses_api=True, model_kwargs={"reasoning": reasoning}
|
||||
)
|
||||
response = llm.invoke("What is 3^3?")
|
||||
|
||||
print(f"Output: {response.text()}")
|
||||
print(f"Reasoning: {response.additional_kwargs['reasoning']}")
|
||||
|
||||
.. code-block:: none
|
||||
|
||||
Output: 3^3 = 27.
|
||||
|
||||
Reasoning: {
|
||||
'id': 'rs_67fffc44b1c08191b6ca9bead6d832590433145b1786f809',
|
||||
'summary': [
|
||||
{'text': 'The user wants to know...', 'type': 'summary_text'}
|
||||
],
|
||||
'type': 'reasoning'
|
||||
}
|
||||
|
||||
.. dropdown:: Structured output
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
9
uv.lock
9
uv.lock
@ -2338,7 +2338,7 @@ dependencies = [
|
||||
requires-dist = [
|
||||
{ name = "chromadb", specifier = ">=0.4.0,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,!=0.5.10,!=0.5.11,!=0.5.12,<0.7.0" },
|
||||
{ name = "langchain-core", editable = "libs/core" },
|
||||
{ name = "numpy", marker = "python_full_version < '3.13'", specifier = ">=1.22.4" },
|
||||
{ name = "numpy", marker = "python_full_version < '3.13'", specifier = ">=1.26.0" },
|
||||
{ name = "numpy", marker = "python_full_version >= '3.13'", specifier = ">=2.1.0" },
|
||||
]
|
||||
|
||||
@ -2451,7 +2451,7 @@ typing = [
|
||||
{ name = "langchain", editable = "libs/langchain" },
|
||||
{ name = "langchain-core", editable = "libs/core" },
|
||||
{ name = "langchain-text-splitters", editable = "libs/text-splitters" },
|
||||
{ name = "mypy", specifier = ">=1.12,<2.0" },
|
||||
{ name = "mypy", specifier = ">=1.15,<2.0" },
|
||||
{ name = "mypy-protobuf", specifier = ">=3.0.0,<4.0.0" },
|
||||
{ name = "types-chardet", specifier = ">=5.0.4.6,<6.0.0.0" },
|
||||
{ name = "types-pytz", specifier = ">=2023.3.0.0,<2024.0.0.0" },
|
||||
@ -2503,6 +2503,8 @@ test = [
|
||||
{ name = "numpy", marker = "python_full_version >= '3.13'", specifier = ">=2.1.0" },
|
||||
{ name = "pytest", specifier = ">=8,<9" },
|
||||
{ name = "pytest-asyncio", specifier = ">=0.21.1,<1.0.0" },
|
||||
{ name = "pytest-benchmark" },
|
||||
{ name = "pytest-codspeed" },
|
||||
{ name = "pytest-mock", specifier = ">=3.10.0,<4.0.0" },
|
||||
{ name = "pytest-socket", specifier = ">=0.7.0,<1.0.0" },
|
||||
{ name = "pytest-watcher", specifier = ">=0.3.4,<1.0.0" },
|
||||
@ -2513,8 +2515,7 @@ test = [
|
||||
test-integration = []
|
||||
typing = [
|
||||
{ name = "langchain-text-splitters", directory = "libs/text-splitters" },
|
||||
{ name = "mypy", specifier = ">=1.10,<1.11" },
|
||||
{ name = "types-jinja2", specifier = ">=2.11.9,<3.0.0" },
|
||||
{ name = "mypy", specifier = ">=1.15,<1.16" },
|
||||
{ name = "types-pyyaml", specifier = ">=6.0.12.2,<7.0.0.0" },
|
||||
{ name = "types-requests", specifier = ">=2.28.11.5,<3.0.0.0" },
|
||||
]
|
||||
|
||||
Loading…
Reference in New Issue
Block a user