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

- 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 :(

docs/docs/concepts.mdx

@@ -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)
 
 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)
 
 :::

docs/docs/how_to/migrate_agent.ipynb

@@ -21,7 +21,16 @@
    "source": [
     "# How to migrate from legacy LangChain agents to LangGraph\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",
     "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",
@@ -209,7 +218,7 @@
     "\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",
-    "First up, using AgentExecutor:"
+    "First up, using `AgentExecutor`:"
    ]
   },
   {
@@ -252,7 +261,16 @@
    "id": "bd5f5500-5ae4-4000-a9fd-8c5a2cc6404d",
    "metadata": {},
    "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",
     "    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": {

docs/docs/how_to/tool_calling.ipynb

@@ -24,6 +24,7 @@
     "This guide assumes familiarity with the following concepts:\n",
     "- [Chat models](/docs/concepts/#chat-models)\n",
     "- [LangChain Tools](/docs/concepts/#tools)\n",
+    "- [Output parsers](/docs/concepts/#output-parsers)\n",
     "\n",
     ":::\n",
     "\n",
@@ -51,6 +52,12 @@
     "parameters matching the desired schema, then treat the generated output as your final \n",
     "result.\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",
     "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",
@@ -85,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -116,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -164,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -185,16 +192,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "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": {},
      "output_type": "execute_result"
     }
@@ -235,22 +242,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "[{'name': 'Multiply',\n",
        "  'args': {'a': 3, 'b': 12},\n",
-       "  'id': 'call_KquHA7mSbgtAkpkmRPaFnJKa'},\n",
+       "  'id': 'call_TnadLbWJu9HwDULRb51RNSMw'},\n",
        " {'name': 'Add',\n",
        "  'args': {'a': 11, 'b': 49},\n",
-       "  'id': 'call_Fl0hQi4IBTzlpaJYlM5kPQhE'}]"
+       "  'id': 'call_Q9vt1up05sOQScXvUYWzSpCg'}]"
       ]
      },
+     "execution_count": 5,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -271,12 +279,13 @@
     "a name, string arguments, identifier, and error message.\n",
     "\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",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -285,12 +294,13 @@
        "[Multiply(a=3, b=12), Add(a=11, b=49)]"
       ]
      },
+     "execution_count": 6,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "from langchain_core.output_parsers.openai_tools import PydanticToolsParser\n",
+    "from langchain_core.output_parsers import PydanticToolsParser\n",
     "\n",
     "chain = llm_with_tools | PydanticToolsParser(tools=[Multiply, Add])\n",
     "chain.invoke(query)"
@@ -333,7 +343,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/bedrock.ipynb

@@ -29,7 +29,7 @@
     "> 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",
     "> 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": [
-    "%pip install --upgrade --quiet  langchain-aws"
+    "%pip install --upgrade --quiet langchain-aws"
    ]
   },
   {

docs/docs/integrations/chat/ollama_functions.ipynb

@@ -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",
     "For a complete list of supported models and model variants, see the [Ollama model library](https://ollama.ai/library).\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",
     "\n",
     "Follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance.\n",

docs/docs/integrations/llms/bedrock.ipynb

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

docs/docs/integrations/platforms/aws.mdx

@@ -18,16 +18,6 @@ pip install langchain-community boto3
 
 ### 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 
 > 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 
@@ -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 
 > 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).