mirror of
https://github.com/hwchase17/langchain.git
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330 lines
8.4 KiB
Plaintext
330 lines
8.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "raw",
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"id": "df7d42b9-58a6-434c-a2d7-0b61142f6d3e",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_position: 4\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": "f2195672-0cab-4967-ba8a-c6544635547d",
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"metadata": {},
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"source": [
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"# How to handle multiple queries when doing query analysis\n",
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"\n",
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"Sometimes, a query analysis technique may allow for multiple queries to be generated. In these cases, we need to remember to run all queries and then to combine the results. We will show a simple example (using mock data) of how to do that."
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]
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},
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{
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"cell_type": "markdown",
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"id": "a4079b57-4369-49c9-b2ad-c809b5408d7e",
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"metadata": {},
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"source": [
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"## Setup\n",
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"#### Install dependencies"
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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": 1,
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"id": "e168ef5c-e54e-49a6-8552-5502854a6f01",
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"metadata": {},
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"outputs": [],
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"source": [
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"# %pip install -qU langchain langchain-community langchain-openai langchain-chroma"
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]
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},
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{
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"cell_type": "markdown",
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"id": "79d66a45-a05c-4d22-b011-b1cdbdfc8f9c",
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"metadata": {},
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"source": [
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"#### Set environment variables\n",
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"\n",
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"We'll use OpenAI in this example:"
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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": "40e2979e-a818-4b96-ac25-039336f94319",
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"metadata": {},
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"outputs": [],
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"source": [
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"import getpass\n",
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"import os\n",
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"\n",
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"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
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"\n",
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"# Optional, uncomment to trace runs with LangSmith. Sign up here: https://smith.langchain.com.\n",
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"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
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"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c20b48b8-16d7-4089-bc17-f2d240b3935a",
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"metadata": {},
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"source": [
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"### Create Index\n",
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"\n",
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"We will create a vectorstore over fake information."
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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": 1,
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"id": "1f621694",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_chroma import Chroma\n",
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"from langchain_openai import OpenAIEmbeddings\n",
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"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
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"\n",
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"texts = [\"Harrison worked at Kensho\", \"Ankush worked at Facebook\"]\n",
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"embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n",
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"vectorstore = Chroma.from_texts(\n",
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" texts,\n",
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" embeddings,\n",
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")\n",
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"retriever = vectorstore.as_retriever(search_kwargs={\"k\": 1})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "57396e23-c192-4d97-846b-5eacea4d6b8d",
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"metadata": {},
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"source": [
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"## Query analysis\n",
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"\n",
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"We will use function calling to structure the output. We will let it return multiple queries."
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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": "0b51dd76-820d-41a4-98c8-893f6fe0d1ea",
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import List, Optional\n",
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"\n",
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"from langchain_core.pydantic_v1 import BaseModel, Field\n",
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"\n",
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"\n",
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"class Search(BaseModel):\n",
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" \"\"\"Search over a database of job records.\"\"\"\n",
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"\n",
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" queries: List[str] = Field(\n",
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" ...,\n",
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" description=\"Distinct queries to search for\",\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": 3,
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"id": "783c03c3-8c72-4f88-9cf4-5829ce6745d6",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/harrisonchase/workplace/langchain/libs/core/langchain_core/_api/beta_decorator.py:86: LangChainBetaWarning: The function `with_structured_output` is in beta. It is actively being worked on, so the API may change.\n",
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" warn_beta(\n"
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]
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}
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],
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"source": [
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"from langchain_core.output_parsers.openai_tools import PydanticToolsParser\n",
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"from langchain_core.prompts import ChatPromptTemplate\n",
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"from langchain_core.runnables import RunnablePassthrough\n",
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"from langchain_openai import ChatOpenAI\n",
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"\n",
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"output_parser = PydanticToolsParser(tools=[Search])\n",
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"\n",
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"system = \"\"\"You have the ability to issue search queries to get information to help answer user information.\n",
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"\n",
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"If you need to look up two distinct pieces of information, you are allowed to do that!\"\"\"\n",
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"prompt = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\"system\", system),\n",
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" (\"human\", \"{question}\"),\n",
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" ]\n",
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")\n",
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"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
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"structured_llm = llm.with_structured_output(Search)\n",
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"query_analyzer = {\"question\": RunnablePassthrough()} | prompt | structured_llm"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b9564078",
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"metadata": {},
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"source": [
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"We can see that this allows for creating multiple queries"
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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": "bc1d3863",
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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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"Search(queries=['Harrison work location'])"
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]
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},
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"execution_count": 4,
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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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"query_analyzer.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": 5,
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"id": "af62af17-4f90-4dbd-a8b4-dfff51f1db95",
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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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"Search(queries=['Harrison work place', 'Ankush work place'])"
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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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"query_analyzer.invoke(\"where did Harrison and ankush Work\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c7c65b2f-7881-45fc-a47b-a4eaaf48245f",
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"metadata": {},
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"source": [
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"## Retrieval with query analysis\n",
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"\n",
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"So how would we include this in a chain? One thing that will make this a lot easier is if we call our retriever asyncronously - this will let us loop over the queries and not get blocked on the response time."
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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": "1e047d87",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.runnables import 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": 31,
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"id": "8dac7866",
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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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"async def custom_chain(question):\n",
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" response = await query_analyzer.ainvoke(question)\n",
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" docs = []\n",
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" for query in response.queries:\n",
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" new_docs = await retriever.ainvoke(query)\n",
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" docs.extend(new_docs)\n",
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" # You probably want to think about reranking or deduplicating documents here\n",
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" # But that is a separate topic\n",
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" return docs"
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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": 33,
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"id": "232ad8a7-7990-4066-9228-d35a555f7293",
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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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"[Document(page_content='Harrison worked at Kensho')]"
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]
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},
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"execution_count": 33,
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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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"await custom_chain.ainvoke(\"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": 34,
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"id": "28e14ba5",
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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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"[Document(page_content='Harrison worked at Kensho'),\n",
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" Document(page_content='Ankush worked at Facebook')]"
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]
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},
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"execution_count": 34,
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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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"await custom_chain.ainvoke(\"where did Harrison and ankush 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": null,
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"id": "88de5a36",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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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": "3.10.1"
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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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