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docs[patch]: Fix docs bugs in response to feedback (#23649)

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- Update Meta Llama 3 cookbook link
- Add prereq section and information on `messages_modifier` to LangGraph
migration guide
- Update `PydanticToolsParser` explanation and entrypoint in tool
calling guide
- Add more obvious warning to `OllamaFunctions`
- Fix Wikidata tool install flow
- Update Bedrock LLM initialization

@baskaryan can you add a bit of information on how to authenticate into
the `ChatBedrock` and `BedrockLLM` models? I wasn't able to figure it
out :(
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2
docs/docs/concepts.mdx
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@ -1055,7 +1055,7 @@ See several videos and cookbooks showcasing RAG with LangGraph:
- [Cookbooks for RAG using LangGraph](https://github.com/langchain-ai/langgraph/tree/main/examples/rag) - [Cookbooks for RAG using LangGraph](https://github.com/langchain-ai/langgraph/tree/main/examples/rag)
See our LangGraph RAG recipes with partners: See our LangGraph RAG recipes with partners:
- [Meta](https://github.com/meta-llama/llama-recipes/tree/main/recipes/use_cases/agents/langchain) - [Meta](https://github.com/meta-llama/llama-recipes/tree/main/recipes/3p_integrations/langchain)
- [Mistral](https://github.com/mistralai/cookbook/tree/main/third_party/langchain) - [Mistral](https://github.com/mistralai/cookbook/tree/main/third_party/langchain)
::: :::

36
docs/docs/how_to/migrate_agent.ipynb
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@ -21,7 +21,16 @@
"source": [ "source": [
"# How to migrate from legacy LangChain agents to LangGraph\n", "# How to migrate from legacy LangChain agents to LangGraph\n",
"\n", "\n",
"Here we focus on how to move from legacy LangChain agents to LangGraph agents.\n", ":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Agents](/docs/concepts/#agents)\n",
"- [LangGraph](https://langchain-ai.github.io/langgraph/)\n",
"- [Tool calling](/docs/how_to/tool_calling/)\n",
"\n",
":::\n",
"\n",
"Here we focus on how to move from legacy LangChain agents to more flexible [LangGraph](https://langchain-ai.github.io/langgraph/) agents.\n",
"LangChain agents (the [AgentExecutor](https://api.python.langchain.com/en/latest/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor) in particular) have multiple configuration parameters.\n", "LangChain agents (the [AgentExecutor](https://api.python.langchain.com/en/latest/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor) in particular) have multiple configuration parameters.\n",
"In this notebook we will show how those parameters map to the LangGraph react agent executor using the [create_react_agent](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent) prebuilt helper method.\n", "In this notebook we will show how those parameters map to the LangGraph react agent executor using the [create_react_agent](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent) prebuilt helper method.\n",
"\n", "\n",
@ -209,7 +218,7 @@
"\n", "\n",
"Let's take a look at all of these below. We will pass in custom instructions to get the agent to respond in Spanish.\n", "Let's take a look at all of these below. We will pass in custom instructions to get the agent to respond in Spanish.\n",
"\n", "\n",
"First up, using AgentExecutor:" "First up, using `AgentExecutor`:"
] ]
}, },
{ {
@ -252,7 +261,16 @@
"id": "bd5f5500-5ae4-4000-a9fd-8c5a2cc6404d", "id": "bd5f5500-5ae4-4000-a9fd-8c5a2cc6404d",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Now, let's pass a custom system message to [react agent executor](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent). This can either be a string or a LangChain SystemMessage." "Now, let's pass a custom system message to [react agent executor](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent).\n",
"\n",
"LangGraph's prebuilt `create_react_agent` does not take a prompt template directly as a parameter, but instead takes a [`messages_modifier`](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent) parameter. This modifies messages before they are passed into the model, and can be one of four values:\n",
"\n",
"- A `SystemMessage`, which is added to the beginning of the list of messages.\n",
"- A `string`, which is converted to a `SystemMessage` and added to the beginning of the list of messages.\n",
"- A `Callable`, which should take in a list of messages. The output is then passed to the language model.\n",
"- Or a [`Runnable`](/docs/concepts/#langchain-expression-language-lcel), which should should take in a list of messages. The output is then passed to the language model.\n",
"\n",
"Here's how it looks in action:"
] ]
}, },
{ {
@ -1226,6 +1244,18 @@
"except GraphRecursionError as e:\n", "except GraphRecursionError as e:\n",
" print(\"Stopping agent prematurely due to triggering stop condition\")" " print(\"Stopping agent prematurely due to triggering stop condition\")"
] ]
},
{
"cell_type": "markdown",
"id": "41377eb8",
"metadata": {},
"source": [
"## Next steps\n",
"\n",
"You've now learned how to migrate your LangChain agent executors to LangGraph.\n",
"\n",
"Next, check out other [LangGraph how-to guides](https://langchain-ai.github.io/langgraph/how-tos/)."
]
} }
], ],
"metadata": { "metadata": {

40
docs/docs/how_to/tool_calling.ipynb
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@ -24,6 +24,7 @@
"This guide assumes familiarity with the following concepts:\n", "This guide assumes familiarity with the following concepts:\n",
"- [Chat models](/docs/concepts/#chat-models)\n", "- [Chat models](/docs/concepts/#chat-models)\n",
"- [LangChain Tools](/docs/concepts/#tools)\n", "- [LangChain Tools](/docs/concepts/#tools)\n",
"- [Output parsers](/docs/concepts/#output-parsers)\n",
"\n", "\n",
":::\n", ":::\n",
"\n", "\n",
@ -51,6 +52,12 @@
"parameters matching the desired schema, then treat the generated output as your final \n", "parameters matching the desired schema, then treat the generated output as your final \n",
"result.\n", "result.\n",
"\n", "\n",
":::note\n",
"\n",
"If you only need formatted values, try the [.with_structured_output()](/docs/how_to/structured_output/#the-with_structured_output-method) chat model method as a simpler entrypoint.\n",
"\n",
":::\n",
"\n",
"However, tool calling goes beyond [structured output](/docs/how_to/structured_output/)\n", "However, tool calling goes beyond [structured output](/docs/how_to/structured_output/)\n",
"since you can pass responses from called tools back to the model to create longer interactions.\n", "since you can pass responses from called tools back to the model to create longer interactions.\n",
"For instance, given a search engine tool, an LLM might handle a \n", "For instance, given a search engine tool, an LLM might handle a \n",
@ -85,7 +92,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 1,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -116,7 +123,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -164,7 +171,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -185,16 +192,16 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_z9fCZJPE8AmtnIiD08Q6Najb', 'function': {'arguments': '{\"a\":3,\"b\":12}', 'name': 'Multiply'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 95, 'total_tokens': 113}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-7564c56a-3760-41f0-94fa-5c646bb11e60-0', tool_calls=[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_z9fCZJPE8AmtnIiD08Q6Najb'}], usage_metadata={'input_tokens': 95, 'output_tokens': 18, 'total_tokens': 113})" "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_g4RuAijtDcSeM96jXyCuiLSN', 'function': {'arguments': '{\"a\":3,\"b\":12}', 'name': 'Multiply'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 95, 'total_tokens': 113}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-5157d15a-7e0e-4ab1-af48-3d98010cd152-0', tool_calls=[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_g4RuAijtDcSeM96jXyCuiLSN'}], usage_metadata={'input_tokens': 95, 'output_tokens': 18, 'total_tokens': 113})"
] ]
}, },
"execution_count": 8, "execution_count": 4,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -235,7 +242,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -243,14 +250,15 @@
"text/plain": [ "text/plain": [
"[{'name': 'Multiply',\n", "[{'name': 'Multiply',\n",
" 'args': {'a': 3, 'b': 12},\n", " 'args': {'a': 3, 'b': 12},\n",
" 'id': 'call_KquHA7mSbgtAkpkmRPaFnJKa'},\n", " 'id': 'call_TnadLbWJu9HwDULRb51RNSMw'},\n",
" {'name': 'Add',\n", " {'name': 'Add',\n",
" 'args': {'a': 11, 'b': 49},\n", " 'args': {'a': 11, 'b': 49},\n",
" 'id': 'call_Fl0hQi4IBTzlpaJYlM5kPQhE'}]" " 'id': 'call_Q9vt1up05sOQScXvUYWzSpCg'}]"
] ]
}, },
"execution_count": 5,
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
@ -271,12 +279,13 @@
"a name, string arguments, identifier, and error message.\n", "a name, string arguments, identifier, and error message.\n",
"\n", "\n",
"If desired, [output parsers](/docs/how_to#output-parsers) can further \n", "If desired, [output parsers](/docs/how_to#output-parsers) can further \n",
"process the output. For example, we can convert back to the original Pydantic class:" "process the output. For example, we can convert existing values populated on the `.tool_calls` attribute back to the original Pydantic class using the\n",
"[PydanticToolsParser](https://api.python.langchain.com/en/latest/output_parsers/langchain_core.output_parsers.openai_tools.PydanticToolsParser.html):"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 6,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -285,12 +294,13 @@
"[Multiply(a=3, b=12), Add(a=11, b=49)]" "[Multiply(a=3, b=12), Add(a=11, b=49)]"
] ]
}, },
"execution_count": 6,
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"from langchain_core.output_parsers.openai_tools import PydanticToolsParser\n", "from langchain_core.output_parsers import PydanticToolsParser\n",
"\n", "\n",
"chain = llm_with_tools | PydanticToolsParser(tools=[Multiply, Add])\n", "chain = llm_with_tools | PydanticToolsParser(tools=[Multiply, Add])\n",
"chain.invoke(query)" "chain.invoke(query)"
@ -333,7 +343,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.11.9" "version": "3.10.5"
} }
}, },
"nbformat": 4, "nbformat": 4,

4
docs/docs/integrations/chat/bedrock.ipynb
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@ -29,7 +29,7 @@
"> your data using techniques such as fine-tuning and `Retrieval Augmented Generation` (`RAG`), and build \n", "> your data using techniques such as fine-tuning and `Retrieval Augmented Generation` (`RAG`), and build \n",
"> agents that execute tasks using your enterprise systems and data sources. Since `Amazon Bedrock` is \n", "> agents that execute tasks using your enterprise systems and data sources. Since `Amazon Bedrock` is \n",
"> serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy \n", "> serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy \n",
"> generative AI capabilities into your applications using the AWS services you are already familiar with.\n" "> generative AI capabilities into your applications using the AWS services you are already familiar with."
] ]
}, },
{ {
@ -47,7 +47,7 @@
} }
], ],
"source": [ "source": [
"%pip install --upgrade --quiet langchain-aws" "%pip install --upgrade --quiet langchain-aws"
] ]
}, },
{ {

6
docs/docs/integrations/chat/ollama_functions.ipynb
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@ -20,6 +20,12 @@
"Note that more powerful and capable models will perform better with complex schema and/or multiple functions. The examples below use llama3 and phi3 models.\n", "Note that more powerful and capable models will perform better with complex schema and/or multiple functions. The examples below use llama3 and phi3 models.\n",
"For a complete list of supported models and model variants, see the [Ollama model library](https://ollama.ai/library).\n", "For a complete list of supported models and model variants, see the [Ollama model library](https://ollama.ai/library).\n",
"\n", "\n",
":::warning\n",
"\n",
"This is an experimental wrapper that attempts to bolt-on tool calling support to models that do not natively support it. Use with caution.\n",
"\n",
":::\n",
"\n",
"## Setup\n", "## Setup\n",
"\n", "\n",
"Follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance.\n", "Follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance.\n",

12
docs/docs/integrations/llms/bedrock.ipynb
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@ -34,7 +34,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"%pip install --upgrade --quiet boto3" "%pip install --upgrade --quiet langchain_aws"
] ]
}, },
{ {
@ -45,9 +45,9 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"from langchain_community.llms import Bedrock\n", "from langchain_aws import BedrockLLM\n",
"\n", "\n",
"llm = Bedrock(\n", "llm = BedrockLLM(\n",
" credentials_profile_name=\"bedrock-admin\", model_id=\"amazon.titan-text-express-v1\"\n", " credentials_profile_name=\"bedrock-admin\", model_id=\"amazon.titan-text-express-v1\"\n",
")" ")"
] ]
@ -65,7 +65,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"custom_llm = Bedrock(\n", "custom_llm = BedrockLLM(\n",
" credentials_profile_name=\"bedrock-admin\",\n", " credentials_profile_name=\"bedrock-admin\",\n",
" provider=\"cohere\",\n", " provider=\"cohere\",\n",
" model_id=\"<Custom model ARN>\", # ARN like 'arn:aws:bedrock:...' obtained via provisioning the custom model\n", " model_id=\"<Custom model ARN>\", # ARN like 'arn:aws:bedrock:...' obtained via provisioning the custom model\n",
@ -108,7 +108,7 @@
"\n", "\n",
"\n", "\n",
"# Guardrails for Amazon Bedrock with trace\n", "# Guardrails for Amazon Bedrock with trace\n",
"llm = Bedrock(\n", "llm = BedrockLLM(\n",
" credentials_profile_name=\"bedrock-admin\",\n", " credentials_profile_name=\"bedrock-admin\",\n",
" model_id=\"<Model_ID>\",\n", " model_id=\"<Model_ID>\",\n",
" model_kwargs={},\n", " model_kwargs={},\n",
@ -134,7 +134,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.11.7" "version": "3.10.5"
} }
}, },
"nbformat": 4, "nbformat": 4,

19
docs/docs/integrations/platforms/aws.mdx
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@ -18,16 +18,6 @@ pip install langchain-community boto3
### Bedrock Chat ### Bedrock Chat
See a [usage example](/docs/integrations/chat/bedrock).
```python
from langchain_aws import ChatBedrock
```
## LLMs
### Bedrock
>[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that offers a choice of >[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that offers a choice of
> high-performing foundation models (FMs) from leading AI companies like `AI21 Labs`, `Anthropic`, `Cohere`, > 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 > `Meta`, `Stability AI`, and `Amazon` via a single API, along with a broad set of capabilities you need to
@ -38,6 +28,15 @@ from langchain_aws import ChatBedrock
> serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy > 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. > generative AI capabilities into your applications using the AWS services you are already familiar with.
See a [usage example](/docs/integrations/chat/bedrock).
```python
from langchain_aws import ChatBedrock
```
## LLMs
### Bedrock
See a [usage example](/docs/integrations/llms/bedrock). See a [usage example](/docs/integrations/llms/bedrock).

34
docs/docs/integrations/tools/wikidata.ipynb
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@ -23,12 +23,12 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"%pip install --upgrade --quiet wikibase-rest-api-client mediawikiapi" "%pip install --upgrade --quiet \"wikibase-rest-api-client<0.2\" mediawikiapi"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 2,
"id": "955988a1-ebc2-4c9a-9298-c493fe842de1", "id": "955988a1-ebc2-4c9a-9298-c493fe842de1",
"metadata": { "metadata": {
"execution": { "execution": {
@ -39,26 +39,6 @@
"shell.execute_reply.started": "2024-03-06T22:55:15.973114Z" "shell.execute_reply.started": "2024-03-06T22:55:15.973114Z"
} }
}, },
"outputs": [],
"source": [
"from langchain_community.tools.wikidata.tool import WikidataAPIWrapper, WikidataQueryRun\n",
"\n",
"wikidata = WikidataQueryRun(api_wrapper=WikidataAPIWrapper())"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "9926a8a7-3e4e-4a97-ba43-7e5a274b9561",
"metadata": {
"execution": {
"iopub.execute_input": "2024-03-06T22:54:38.551998Z",
"iopub.status.busy": "2024-03-06T22:54:38.551266Z",
"iopub.status.idle": "2024-03-06T22:54:51.913177Z",
"shell.execute_reply": "2024-03-06T22:54:51.911636Z",
"shell.execute_reply.started": "2024-03-06T22:54:38.551955Z"
}
},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
@ -77,7 +57,7 @@
"sport: athletics\n", "sport: athletics\n",
"place of birth: Maida Vale, Warrington Lodge\n", "place of birth: Maida Vale, Warrington Lodge\n",
"educated at: King's College, Princeton University, Sherborne School, Hazlehurst Community Primary School\n", "educated at: King's College, Princeton University, Sherborne School, Hazlehurst Community Primary School\n",
"employer: Victoria University of Manchester, Government Communications Headquarters, University of Cambridge, National Physical Laboratory\n", "employer: Victoria University of Manchester, Government Communications Headquarters, University of Cambridge, National Physical Laboratory (United Kingdom)\n",
"place of death: Wilmslow\n", "place of death: Wilmslow\n",
"field of work: cryptanalysis, computer science, mathematics, logic, cryptography\n", "field of work: cryptanalysis, computer science, mathematics, logic, cryptography\n",
"cause of death: cyanide poisoning\n", "cause of death: cyanide poisoning\n",
@ -98,13 +78,17 @@
} }
], ],
"source": [ "source": [
"from langchain_community.tools.wikidata.tool import WikidataAPIWrapper, WikidataQueryRun\n",
"\n",
"wikidata = WikidataQueryRun(api_wrapper=WikidataAPIWrapper())\n",
"\n",
"print(wikidata.run(\"Alan Turing\"))" "print(wikidata.run(\"Alan Turing\"))"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"id": "2762aa55-92bd-4e50-b433-8c5c37da465f", "id": "7188d62f",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [] "source": []
@ -126,7 +110,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.10.12" "version": "3.10.5"
} }
}, },
"nbformat": 4, "nbformat": 4,