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docs: lcel how to and cheatsheet (langchain-ai#21851)
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| Original file line number | Diff line number | Diff line change |
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "raw", | ||
| "id": "77bf57fb-e990-45f2-8b5f-c76388b05966", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "---\n", | ||
| "keywords: [LCEL]\n", | ||
| "---" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "50d57bf2-7104-4570-b3e5-90fd71e1bea1", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# How to create a dynamic (self-constructing) chain\n", | ||
| "\n", | ||
| ":::info Prerequisites\n", | ||
| "\n", | ||
| "This guide assumes familiarity with the following:\n", | ||
| "- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n", | ||
| "- [How to turn any function into a runnable](/docs/how_to/functions)\n", | ||
| "\n", | ||
| ":::\n", | ||
| "\n", | ||
| "Sometimes we want to construct parts of a chain at runtime, depending on the chain inputs ([routing](/docs/how_to/routing/) is the most common example of this). We can create dynamic chains like this using a very useful property of RunnableLambda's, which is that if a RunnableLambda returns a Runnable, that Runnable is itself invoked. Let's see an example.\n", | ||
| "\n", | ||
| "```{=mdx}\n", | ||
| "import ChatModelTabs from \"@theme/ChatModelTabs\";\n", | ||
| "\n", | ||
| "<ChatModelTabs\n", | ||
| " customVarName=\"llm\"\n", | ||
| "/>\n", | ||
| "```" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "id": "406bffc2-86d0-4cb9-9262-5c1e3442397a", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# | echo: false\n", | ||
| "\n", | ||
| "from langchain_anthropic import ChatAnthropic\n", | ||
| "\n", | ||
| "llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 10, | ||
| "id": "0ae6692b-983e-40b8-aa2a-6c078d945b9e", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "\"According to the context provided, Egypt's population in 2024 is estimated to be about 111 million.\"" | ||
| ] | ||
| }, | ||
| "execution_count": 10, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "from langchain_core.output_parsers import StrOutputParser\n", | ||
| "from langchain_core.prompts import ChatPromptTemplate\n", | ||
| "from langchain_core.runnables import Runnable, RunnablePassthrough, chain\n", | ||
| "\n", | ||
| "contextualize_instructions = \"\"\"Convert the latest user question into a standalone question given the chat history. Don't answer the question, return the question and nothing else (no descriptive text).\"\"\"\n", | ||
| "contextualize_prompt = ChatPromptTemplate.from_messages(\n", | ||
| " [\n", | ||
| " (\"system\", contextualize_instructions),\n", | ||
| " (\"placeholder\", \"{chat_history}\"),\n", | ||
| " (\"human\", \"{question}\"),\n", | ||
| " ]\n", | ||
| ")\n", | ||
| "contextualize_question = contextualize_prompt | llm | StrOutputParser()\n", | ||
| "\n", | ||
| "qa_instructions = (\n", | ||
| " \"\"\"Answer the user question given the following context:\\n\\n{context}.\"\"\"\n", | ||
| ")\n", | ||
| "qa_prompt = ChatPromptTemplate.from_messages(\n", | ||
| " [(\"system\", qa_instructions), (\"human\", \"{question}\")]\n", | ||
| ")\n", | ||
| "\n", | ||
| "\n", | ||
| "@chain\n", | ||
| "def contextualize_if_needed(input_: dict) -> Runnable:\n", | ||
| " if input_.get(\"chat_history\"):\n", | ||
| " # NOTE: This is returning another Runnable, not an actual output.\n", | ||
| " return contextualize_question\n", | ||
| " else:\n", | ||
| " return RunnablePassthrough()\n", | ||
| "\n", | ||
| "\n", | ||
| "@chain\n", | ||
| "def fake_retriever(input_: dict) -> str:\n", | ||
| " return \"egypt's population in 2024 is about 111 million\"\n", | ||
| "\n", | ||
| "\n", | ||
| "full_chain = (\n", | ||
| " RunnablePassthrough.assign(question=contextualize_if_needed).assign(\n", | ||
| " context=fake_retriever\n", | ||
| " )\n", | ||
| " | qa_prompt\n", | ||
| " | llm\n", | ||
| " | StrOutputParser()\n", | ||
| ")\n", | ||
| "\n", | ||
| "full_chain.invoke(\n", | ||
| " {\n", | ||
| " \"question\": \"what about egypt\",\n", | ||
| " \"chat_history\": [\n", | ||
| " (\"human\", \"what's the population of indonesia\"),\n", | ||
| " (\"ai\", \"about 276 million\"),\n", | ||
| " ],\n", | ||
| " }\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "5076ddb4-4a99-47ad-b549-8ac27ca3e2c6", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "The key here is that `contextualize_if_needed` returns another Runnable and not an actual output. This returned Runnable is itself run when the full chain is executed.\n", | ||
| "\n", | ||
| "Looking at the trace we can see that, since we passed in chat_history, we executed the contextualize_question chain as part of the full chain: https://smith.langchain.com/public/9e0ae34c-4082-4f3f-beed-34a2a2f4c991/r" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "4fe6ca44-a643-4859-a290-be68403f51f0", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Note that the streaming, batching, etc. capabilities of the returned Runnable are all preserved" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 11, | ||
| "id": "6def37fa-5105-4090-9b07-77cb488ecd9c", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "What\n", | ||
| " is\n", | ||
| " the\n", | ||
| " population\n", | ||
| " of\n", | ||
| " Egypt\n", | ||
| "?\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "for chunk in contextualize_if_needed.stream(\n", | ||
| " {\n", | ||
| " \"question\": \"what about egypt\",\n", | ||
| " \"chat_history\": [\n", | ||
| " (\"human\", \"what's the population of indonesia\"),\n", | ||
| " (\"ai\", \"about 276 million\"),\n", | ||
| " ],\n", | ||
| " }\n", | ||
| "):\n", | ||
| " print(chunk)" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "poetry-venv-2", | ||
| "language": "python", | ||
| "name": "poetry-venv-2" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.9.1" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |
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