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docs: add docs on StrOutputParser (#28089)
Think it's worth adding a quick guide and including in the table in the concepts page. `StrOutputParser` can make it easier to deal with the union type for message content. For example, ChatAnthropic with bound tools will generate string content if there are no tool calls and `list[dict]` content otherwise. I'm also considering removing the output parser section from the ["quickstart" tutorial](https://python.langchain.com/docs/tutorials/llm_chain/); we can link to this guide instead.
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docs/cassettes/output_parser_string_28eeace3-3896-497f-93ad-544cbfb7f15c.msgpack.zlib
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docs/cassettes/output_parser_string_8ac74999-0740-4178-8efd-32a855592f71.msgpack.zlib
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docs/cassettes/output_parser_string_8c87553e-4f85-46c4-8f1e-666f6a261a50.msgpack.zlib
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docs/cassettes/output_parser_string_9cbb8848-9101-465e-b230-0f7af6fb4105.msgpack.zlib
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,202 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "1d6024e0-3847-4418-b8a8-6b8f83adf4c2", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# How to parse text from message objects\n", | ||
| "\n", | ||
| ":::info Prerequisites\n", | ||
| "\n", | ||
| "This guide assumes familiarity with the following concepts:\n", | ||
| "- [Chat models](/docs/concepts/chat_models/)\n", | ||
| "- [Messages](/docs/concepts/messages/)\n", | ||
| "- [Output parsers](/docs/concepts/output_parsers/)\n", | ||
| "- [LangChain Expression Language (LCEL)](/docs/concepts/lcel/)\n", | ||
| "\n", | ||
| ":::\n", | ||
| "\n", | ||
| "LangChain [message](/docs/concepts/messages/) objects support content in a [variety of formats](/docs/concepts/messages/#content), including text, [multimodal data](/docs/concepts/multimodality/), and a list of [content block](/docs/concepts/messages/#aimessage) dicts.\n", | ||
| "\n", | ||
| "The format of [Chat model](/docs/concepts/chat_models/) response content may depend on the provider. For example, the chat model for [Anthropic](/docs/integrations/chat/anthropic/) will return string content for typical string input:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "id": "8ac74999-0740-4178-8efd-32a855592f71", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "'Hi there! How are you doing today? Is there anything I can help you with?'" | ||
| ] | ||
| }, | ||
| "execution_count": 1, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "from langchain_anthropic import ChatAnthropic\n", | ||
| "\n", | ||
| "llm = ChatAnthropic(model=\"claude-3-5-haiku-latest\")\n", | ||
| "\n", | ||
| "response = llm.invoke(\"Hello\")\n", | ||
| "response.content" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "69b7c3ae-0022-4737-9db7-f44db3402de2", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "But when tool calls are generated, the response content is structured into content blocks that convey the model's reasoning process:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "id": "8c87553e-4f85-46c4-8f1e-666f6a261a50", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "[{'text': \"I'll help you get the current weather for San Francisco, California. Let me check that for you right away.\",\n", | ||
| " 'type': 'text'},\n", | ||
| " {'id': 'toolu_015PwwcKxWYctKfY3pruHFyy',\n", | ||
| " 'input': {'location': 'San Francisco, CA'},\n", | ||
| " 'name': 'get_weather',\n", | ||
| " 'type': 'tool_use'}]" | ||
| ] | ||
| }, | ||
| "execution_count": 2, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "from langchain_core.tools import tool\n", | ||
| "\n", | ||
| "\n", | ||
| "@tool\n", | ||
| "def get_weather(location: str) -> str:\n", | ||
| " \"\"\"Get the weather from a location.\"\"\"\n", | ||
| "\n", | ||
| " return \"Sunny.\"\n", | ||
| "\n", | ||
| "\n", | ||
| "llm_with_tools = llm.bind_tools([get_weather])\n", | ||
| "\n", | ||
| "response = llm_with_tools.invoke(\"What's the weather in San Francisco, CA?\")\n", | ||
| "response.content" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "039f6d62-098f-42c9-8b07-43cb1f2a831b", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "To automatically parse text from message objects irrespective of the format of the underlying content, we can use [StrOutputParser](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.string.StrOutputParser.html). We can compose it with a chat model as follows:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "id": "0bb9b4dd-64a9-463d-9c71-df147630f3c3", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from langchain_core.output_parsers import StrOutputParser\n", | ||
| "\n", | ||
| "chain = llm_with_tools | StrOutputParser()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "4929c724-471f-4f77-a231-36e9af9418a3", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "[StrOutputParser](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.string.StrOutputParser.html) simplifies the extraction of text from message objects:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "id": "9cbb8848-9101-465e-b230-0f7af6fb4105", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "I'll help you check the weather in San Francisco, CA right away.\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "response = chain.invoke(\"What's the weather in San Francisco, CA?\")\n", | ||
| "print(response)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "13642ad5-325d-4d9b-b97e-cac40345bfbc", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "This is particularly useful in streaming contexts:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 5, | ||
| "id": "28eeace3-3896-497f-93ad-544cbfb7f15c", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "|I'll| help| you get| the current| weather for| San Francisco, California|. Let| me retrieve| that| information for you.||||||||||" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "for chunk in chain.stream(\"What's the weather in San Francisco, CA?\"):\n", | ||
| " print(chunk, end=\"|\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "858e2071-a483-404e-9eca-c73a4466fd83", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "See the [API Reference](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.string.StrOutputParser.html) for more information." | ||
| ] | ||
| } | ||
| ], | ||
| "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 | ||
| } |