langchain/docs/extras/integrations/retrievers/re_phrase.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"id": "e8624be2",
"metadata": {},
"source": [
"# RePhraseQueryRetriever\n",
"\n",
"Simple retriever that applies an LLM between the user input and the query pass the to retriever.\n",
"\n",
"It can be used to pre-process the user input in any way.\n",
"\n",
"The default prompt used in the `from_llm` classmethod:\n",
"\n",
"```\n",
"DEFAULT_TEMPLATE = \"\"\"You are an assistant tasked with taking a natural language \\\n",
"query from a user and converting it into a query for a vectorstore. \\\n",
"In this process, you strip out information that is not relevant for \\\n",
"the retrieval task. Here is the user query: {question}\"\"\"\n",
"```\n",
"\n",
"Create a vectorstore."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "1bfa6834",
"metadata": {},
"outputs": [],
"source": [
"from langchain.document_loaders import WebBaseLoader\n",
"\n",
"loader = WebBaseLoader(\"https://lilianweng.github.io/posts/2023-06-23-agent/\")\n",
"data = loader.load()\n",
"\n",
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"\n",
"text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n",
"all_splits = text_splitter.split_documents(data)\n",
"\n",
"from langchain.vectorstores import Chroma\n",
"from langchain.embeddings import OpenAIEmbeddings\n",
"\n",
"vectorstore = Chroma.from_documents(documents=all_splits, embedding=OpenAIEmbeddings())"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d0b51556",
"metadata": {},
"outputs": [],
"source": [
"import logging\n",
"\n",
"logging.basicConfig()\n",
"logging.getLogger(\"langchain.retrievers.re_phraser\").setLevel(logging.INFO)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "20e1e787",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.retrievers import RePhraseQueryRetriever"
]
},
{
"cell_type": "markdown",
"id": "88c0a972",
"metadata": {},
"source": [
"## Using the default prompt"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "503994bd",
"metadata": {},
"outputs": [],
"source": [
"llm = ChatOpenAI(temperature=0)\n",
"retriever_from_llm = RePhraseQueryRetriever.from_llm(\n",
" retriever=vectorstore.as_retriever(), llm=llm\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "8d17ecc9",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:langchain.retrievers.re_phraser:Re-phrased question: The user query can be converted into a query for a vectorstore as follows:\n",
"\n",
"\"approaches to Task Decomposition\"\n"
]
}
],
"source": [
"docs = retriever_from_llm.get_relevant_documents(\n",
" \"Hi I'm Lance. What are the approaches to Task Decomposition?\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "76d54f1a",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:langchain.retrievers.re_phraser:Re-phrased question: Query for vectorstore: \"Types of Memory\"\n"
]
}
],
"source": [
"docs = retriever_from_llm.get_relevant_documents(\n",
" \"I live in San Francisco. What are the Types of Memory?\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "0513a6e2",
"metadata": {},
"source": [
"## Supply a prompt"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "410d6a64",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains import LLMChain\n",
"from langchain.prompts import PromptTemplate\n",
"\n",
"QUERY_PROMPT = PromptTemplate(\n",
" input_variables=[\"question\"],\n",
" template=\"\"\"You are an assistant tasked with taking a natural languge query from a user\n",
" and converting it into a query for a vectorstore. In the process, strip out all \n",
" information that is not relevant for the retrieval task and return a new, simplified\n",
" question for vectorstore retrieval. The new user query should be in pirate speech.\n",
" Here is the user query: {question} \"\"\",\n",
")\n",
"llm = ChatOpenAI(temperature=0)\n",
"llm_chain = LLMChain(llm=llm, prompt=QUERY_PROMPT)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2dbffdd3",
"metadata": {},
"outputs": [],
"source": [
"retriever_from_llm_chain = RePhraseQueryRetriever(\n",
" retriever=vectorstore.as_retriever(), llm_chain=llm_chain\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "103b4be3",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:langchain.retrievers.re_phraser:Re-phrased question: Ahoy matey! What be Maximum Inner Product Search, ye scurvy dog?\n"
]
}
],
"source": [
"docs = retriever_from_llm_chain.get_relevant_documents(\n",
" \"Hi I'm Lance. What is Maximum Inner Product Search?\"\n",
")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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"nbformat": 4,
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}