mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
462 lines
12 KiB
Plaintext
462 lines
12 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "raw",
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"id": "abe47592-909c-4844-bf44-9e55c2fb4bfa",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_position: 1\n",
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"title: RAG\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": "91c5ef3d",
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"metadata": {},
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"source": [
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"Let's look at adding in a retrieval step to a prompt and LLM, which adds up to a \"retrieval-augmented generation\" chain"
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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": null,
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"id": "7f25d9e9-d192-42e9-af50-5660a4bfb0d9",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install langchain openai faiss-cpu"
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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": 2,
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"id": "33be32af",
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"metadata": {},
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"outputs": [],
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"source": [
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"from operator import itemgetter\n",
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"\n",
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"from langchain.prompts import ChatPromptTemplate\n",
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.embeddings import OpenAIEmbeddings\n",
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"from langchain.schema.output_parser import StrOutputParser\n",
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"from langchain.schema.runnable import RunnablePassthrough\n",
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"from langchain.vectorstores import FAISS"
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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": 3,
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"id": "bfc47ec1",
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"metadata": {},
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"outputs": [],
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"source": [
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"vectorstore = FAISS.from_texts([\"harrison worked at kensho\"], embedding=OpenAIEmbeddings())\n",
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"retriever = vectorstore.as_retriever()\n",
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"\n",
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"template = \"\"\"Answer the question based only on the following context:\n",
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"{context}\n",
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"\n",
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"Question: {question}\n",
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"\"\"\"\n",
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"prompt = ChatPromptTemplate.from_template(template)\n",
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"\n",
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"model = ChatOpenAI()"
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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": "eae31755",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = (\n",
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" {\"context\": retriever, \"question\": RunnablePassthrough()} \n",
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" | prompt \n",
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" | model \n",
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" | StrOutputParser()\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": 5,
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"id": "f3040b0c",
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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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"'Harrison worked at Kensho.'"
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]
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},
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"execution_count": 5,
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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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"chain.invoke(\"where did harrison work?\")"
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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": 6,
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"id": "e1d20c7c",
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"metadata": {},
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"outputs": [],
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"source": [
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"template = \"\"\"Answer the question based only on the following context:\n",
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"{context}\n",
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"\n",
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"Question: {question}\n",
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"\n",
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"Answer in the following language: {language}\n",
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"\"\"\"\n",
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"prompt = ChatPromptTemplate.from_template(template)\n",
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"\n",
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"chain = {\n",
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" \"context\": itemgetter(\"question\") | retriever, \n",
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" \"question\": itemgetter(\"question\"), \n",
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" \"language\": itemgetter(\"language\")\n",
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"} | prompt | model | StrOutputParser()"
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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": 7,
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"id": "7ee8b2d4",
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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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"'Harrison ha lavorato a Kensho.'"
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]
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},
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"execution_count": 7,
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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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"chain.invoke({\"question\": \"where did harrison work\", \"language\": \"italian\"})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f007669c",
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"metadata": {},
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"source": [
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"## Conversational Retrieval Chain\n",
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"\n",
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"We can easily add in conversation history. This primarily means adding in chat_message_history"
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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": 8,
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"id": "3f30c348",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.schema.runnable import RunnableMap\n",
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"from langchain.schema import format_document"
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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": 9,
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"id": "64ab1dbf",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.prompts.prompt import PromptTemplate\n",
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"\n",
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"_template = \"\"\"Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.\n",
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"\n",
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"Chat History:\n",
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"{chat_history}\n",
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"Follow Up Input: {question}\n",
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"Standalone question:\"\"\"\n",
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"CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)"
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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": "7d628c97",
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"metadata": {},
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"outputs": [],
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"source": [
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"template = \"\"\"Answer the question based only on the following context:\n",
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"{context}\n",
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"\n",
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"Question: {question}\n",
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"\"\"\"\n",
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"ANSWER_PROMPT = ChatPromptTemplate.from_template(template)"
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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": "f60a5d0f",
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"metadata": {},
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"outputs": [],
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"source": [
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"DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template=\"{page_content}\")\n",
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"def _combine_documents(docs, document_prompt = DEFAULT_DOCUMENT_PROMPT, document_separator=\"\\n\\n\"):\n",
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" doc_strings = [format_document(doc, document_prompt) for doc in docs]\n",
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" return document_separator.join(doc_strings)"
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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": 12,
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"id": "7d007db6",
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import Tuple, List\n",
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"def _format_chat_history(chat_history: List[Tuple]) -> str:\n",
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" buffer = \"\"\n",
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" for dialogue_turn in chat_history:\n",
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" human = \"Human: \" + dialogue_turn[0]\n",
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" ai = \"Assistant: \" + dialogue_turn[1]\n",
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" buffer += \"\\n\" + \"\\n\".join([human, ai])\n",
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" return buffer"
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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": 13,
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"id": "5c32cc89",
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"metadata": {},
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"outputs": [],
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"source": [
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"_inputs = RunnableMap(\n",
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" {\n",
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" \"standalone_question\": {\n",
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" \"question\": lambda x: x[\"question\"],\n",
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" \"chat_history\": lambda x: _format_chat_history(x['chat_history'])\n",
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" } | CONDENSE_QUESTION_PROMPT | ChatOpenAI(temperature=0) | StrOutputParser(),\n",
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" }\n",
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")\n",
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"_context = {\n",
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" \"context\": itemgetter(\"standalone_question\") | retriever | _combine_documents,\n",
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" \"question\": lambda x: x[\"standalone_question\"]\n",
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"}\n",
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"conversational_qa_chain = _inputs | _context | ANSWER_PROMPT | ChatOpenAI()"
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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": 14,
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"id": "135c8205",
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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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"AIMessage(content='Harrison was employed at Kensho.', additional_kwargs={}, example=False)"
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]
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},
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"execution_count": 14,
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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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"conversational_qa_chain.invoke({\n",
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" \"question\": \"where did harrison work?\",\n",
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" \"chat_history\": [],\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": 15,
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"id": "424e7e7a",
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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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"AIMessage(content='Harrison worked at Kensho.', additional_kwargs={}, example=False)"
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]
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},
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"execution_count": 15,
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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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"conversational_qa_chain.invoke({\n",
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" \"question\": \"where did he work?\",\n",
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" \"chat_history\": [(\"Who wrote this notebook?\", \"Harrison\")],\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": "c5543183",
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"metadata": {},
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"source": [
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"### With Memory and returning source documents\n",
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"\n",
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"This shows how to use memory with the above. For memory, we need to manage that outside at the memory. For returning the retrieved documents, we just need to pass them through all the way."
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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": 16,
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"id": "e31dd17c",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.memory import ConversationBufferMemory"
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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": 17,
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"id": "d4bffe94",
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"metadata": {},
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"outputs": [],
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"source": [
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"memory = ConversationBufferMemory(return_messages=True, output_key=\"answer\", input_key=\"question\")"
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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": 18,
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"id": "733be985",
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"metadata": {},
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"outputs": [],
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"source": [
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"# First we add a step to load memory\n",
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"# This needs to be a RunnableMap because its the first input\n",
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"loaded_memory = RunnableMap(\n",
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" {\n",
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" \"question\": itemgetter(\"question\"),\n",
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" \"memory\": memory.load_memory_variables,\n",
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" }\n",
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")\n",
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"# Next we add a step to expand memory into the variables\n",
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"expanded_memory = {\n",
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" \"question\": itemgetter(\"question\"),\n",
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" \"chat_history\": lambda x: x[\"memory\"][\"history\"]\n",
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"}\n",
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"\n",
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"# Now we calculate the standalone question\n",
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"standalone_question = {\n",
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" \"standalone_question\": {\n",
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" \"question\": lambda x: x[\"question\"],\n",
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" \"chat_history\": lambda x: _format_chat_history(x['chat_history'])\n",
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" } | CONDENSE_QUESTION_PROMPT | ChatOpenAI(temperature=0) | StrOutputParser(),\n",
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"}\n",
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"# Now we retrieve the documents\n",
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"retrieved_documents = {\n",
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" \"docs\": itemgetter(\"standalone_question\") | retriever,\n",
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" \"question\": lambda x: x[\"standalone_question\"]\n",
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"}\n",
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"# Now we construct the inputs for the final prompt\n",
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"final_inputs = {\n",
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" \"context\": lambda x: _combine_documents(x[\"docs\"]),\n",
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" \"question\": itemgetter(\"question\")\n",
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"}\n",
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"# And finally, we do the part that returns the answers\n",
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"answer = {\n",
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" \"answer\": final_inputs | ANSWER_PROMPT | ChatOpenAI(),\n",
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" \"docs\": itemgetter(\"docs\"),\n",
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"}\n",
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"# And now we put it all together!\n",
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"final_chain = loaded_memory | expanded_memory | standalone_question | retrieved_documents | answer"
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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": 19,
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"id": "806e390c",
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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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"{'answer': AIMessage(content='Harrison was employed at Kensho.', additional_kwargs={}, example=False),\n",
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" 'docs': [Document(page_content='harrison worked at kensho', metadata={})]}"
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]
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},
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"execution_count": 19,
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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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"inputs = {\"question\": \"where did harrison work?\"}\n",
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"result = final_chain.invoke(inputs)\n",
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"result"
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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": 20,
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"id": "977399fd",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Note that the memory does not save automatically\n",
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"# This will be improved in the future\n",
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"# For now you need to save it yourself\n",
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"memory.save_context(inputs, {\"answer\": result[\"answer\"].content})"
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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": 21,
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"id": "f94f7de4",
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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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"{'history': [HumanMessage(content='where did harrison work?', additional_kwargs={}, example=False),\n",
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" AIMessage(content='Harrison was employed at Kensho.', additional_kwargs={}, example=False)]}"
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]
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},
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"execution_count": 21,
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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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"memory.load_memory_variables({})"
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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",
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"language": "python",
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"name": "poetry-venv"
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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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