mirror of https://github.com/hwchase17/langchain
docs: lcel how to and cheatsheet (#21851)
Bagatur
2 weeks ago
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GitHub
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{
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"cells": [
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{
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"cell_type": "raw",
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"id": "77bf57fb-e990-45f2-8b5f-c76388b05966",
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"metadata": {},
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"source": [
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"---\n",
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"keywords: [LCEL]\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "50d57bf2-7104-4570-b3e5-90fd71e1bea1",
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"metadata": {},
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"source": [
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"# How to create a dynamic (self-constructing) chain\n",
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"\n",
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":::info Prerequisites\n",
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"\n",
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"This guide assumes familiarity with the following:\n",
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"- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
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"- [How to turn any function into a runnable](/docs/how_to/functions)\n",
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"\n",
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":::\n",
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"\n",
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"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",
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"\n",
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"```{=mdx}\n",
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"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
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"\n",
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"<ChatModelTabs\n",
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" customVarName=\"llm\"\n",
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"/>\n",
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"```"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "406bffc2-86d0-4cb9-9262-5c1e3442397a",
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"metadata": {},
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"outputs": [],
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"source": [
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"# | echo: false\n",
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"\n",
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"from langchain_anthropic import ChatAnthropic\n",
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"\n",
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"llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "0ae6692b-983e-40b8-aa2a-6c078d945b9e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"\"According to the context provided, Egypt's population in 2024 is estimated to be about 111 million.\""
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from langchain_core.output_parsers import StrOutputParser\n",
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"from langchain_core.prompts import ChatPromptTemplate\n",
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"from langchain_core.runnables import Runnable, RunnablePassthrough, chain\n",
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"\n",
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"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",
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"contextualize_prompt = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\"system\", contextualize_instructions),\n",
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" (\"placeholder\", \"{chat_history}\"),\n",
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" (\"human\", \"{question}\"),\n",
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" ]\n",
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")\n",
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"contextualize_question = contextualize_prompt | llm | StrOutputParser()\n",
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"\n",
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"qa_instructions = (\n",
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" \"\"\"Answer the user question given the following context:\\n\\n{context}.\"\"\"\n",
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")\n",
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"qa_prompt = ChatPromptTemplate.from_messages(\n",
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" [(\"system\", qa_instructions), (\"human\", \"{question}\")]\n",
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")\n",
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"\n",
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"\n",
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"@chain\n",
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"def contextualize_if_needed(input_: dict) -> Runnable:\n",
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" if input_.get(\"chat_history\"):\n",
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" # NOTE: This is returning another Runnable, not an actual output.\n",
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" return contextualize_question\n",
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" else:\n",
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" return RunnablePassthrough()\n",
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"\n",
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"\n",
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"@chain\n",
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"def fake_retriever(input_: dict) -> str:\n",
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" return \"egypt's population in 2024 is about 111 million\"\n",
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"\n",
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"\n",
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"full_chain = (\n",
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" RunnablePassthrough.assign(question=contextualize_if_needed).assign(\n",
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" context=fake_retriever\n",
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" )\n",
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" | qa_prompt\n",
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" | llm\n",
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" | StrOutputParser()\n",
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")\n",
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"\n",
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"full_chain.invoke(\n",
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" {\n",
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" \"question\": \"what about egypt\",\n",
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" \"chat_history\": [\n",
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" (\"human\", \"what's the population of indonesia\"),\n",
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" (\"ai\", \"about 276 million\"),\n",
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" ],\n",
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" }\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5076ddb4-4a99-47ad-b549-8ac27ca3e2c6",
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"metadata": {},
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"source": [
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"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",
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"\n",
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"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"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4fe6ca44-a643-4859-a290-be68403f51f0",
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"metadata": {},
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"source": [
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"Note that the streaming, batching, etc. capabilities of the returned Runnable are all preserved"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "6def37fa-5105-4090-9b07-77cb488ecd9c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"What\n",
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" is\n",
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" the\n",
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" population\n",
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" of\n",
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" Egypt\n",
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"?\n"
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]
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}
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],
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"source": [
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"for chunk in contextualize_if_needed.stream(\n",
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" {\n",
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" \"question\": \"what about egypt\",\n",
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" \"chat_history\": [\n",
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" (\"human\", \"what's the population of indonesia\"),\n",
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" (\"ai\", \"about 276 million\"),\n",
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" ],\n",
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" }\n",
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"):\n",
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" print(chunk)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "poetry-venv-2",
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"language": "python",
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"name": "poetry-venv-2"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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