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https://github.com/hwchase17/langchain.git
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Retriever that can re-phase user inputs (#8026)
Simple retriever that applies an LLM between the user input and the
query pass the to retriever.
It can be used to pre-process the user input in any way.
The default prompt:
```
DEFAULT_QUERY_PROMPT = PromptTemplate(
input_variables=["question"],
template="""You are an assistant tasked with taking a natural languge query from a user
and converting it into a query for a vectorstore. In this process, you strip out
information that is not relevant for the retrieval task. Here is the user query: {question} """
)
```
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This commit is contained in:
Lance Martin
GitHub
222
docs/extras/integrations/retrievers/re_phrase.ipynb
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222
docs/extras/integrations/retrievers/re_phrase.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "e8624be2",
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"metadata": {},
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"source": [
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"# RePhraseQueryRetriever\n",
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"\n",
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"Simple retriever that applies an LLM between the user input and the query pass the to retriever.\n",
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"\n",
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"It can be used to pre-process the user input in any way.\n",
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"\n",
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"The default prompt used in the `from_llm` classmethod:\n",
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"\n",
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"```\n",
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"DEFAULT_TEMPLATE = \"\"\"You are an assistant tasked with taking a natural language \\\n",
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"query from a user and converting it into a query for a vectorstore. \\\n",
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"In this process, you strip out information that is not relevant for \\\n",
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"the retrieval task. Here is the user query: {question}\"\"\"\n",
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"```\n",
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"\n",
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"Create a vectorstore."
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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": "1bfa6834",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.document_loaders import WebBaseLoader\n",
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"\n",
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"loader = WebBaseLoader(\"https://lilianweng.github.io/posts/2023-06-23-agent/\")\n",
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"data = loader.load()\n",
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"\n",
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"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
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"\n",
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"text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n",
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"all_splits = text_splitter.split_documents(data)\n",
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"\n",
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"from langchain.vectorstores import Chroma\n",
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"from langchain.embeddings import OpenAIEmbeddings\n",
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"\n",
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"vectorstore = Chroma.from_documents(documents=all_splits, embedding=OpenAIEmbeddings())"
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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": "d0b51556",
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"metadata": {},
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"outputs": [],
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"source": [
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"import logging\n",
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"\n",
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"logging.basicConfig()\n",
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"logging.getLogger(\"langchain.retrievers.re_phraser\").setLevel(logging.INFO)"
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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": "20e1e787",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.retrievers import RePhraseQueryRetriever"
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]
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},
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{
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"cell_type": "markdown",
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"id": "88c0a972",
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"metadata": {},
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"source": [
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"## Using the default prompt"
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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": "503994bd",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = ChatOpenAI(temperature=0)\n",
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"retriever_from_llm = RePhraseQueryRetriever.from_llm(\n",
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" retriever=vectorstore.as_retriever(), llm=llm\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": "8d17ecc9",
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"metadata": {
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"scrolled": false
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},
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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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"INFO:langchain.retrievers.re_phraser:Re-phrased question: The user query can be converted into a query for a vectorstore as follows:\n",
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"\n",
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"\"approaches to Task Decomposition\"\n"
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]
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}
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],
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"source": [
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"docs = retriever_from_llm.get_relevant_documents(\n",
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" \"Hi I'm Lance. What are the approaches to Task Decomposition?\"\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": 6,
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"id": "76d54f1a",
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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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"INFO:langchain.retrievers.re_phraser:Re-phrased question: Query for vectorstore: \"Types of Memory\"\n"
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]
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}
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],
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"source": [
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"docs = retriever_from_llm.get_relevant_documents(\n",
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" \"I live in San Francisco. What are the Types of Memory?\"\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": "0513a6e2",
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"metadata": {},
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"source": [
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"## Supply a prompt"
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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": "410d6a64",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain import LLMChain\n",
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"from langchain.prompts import PromptTemplate\n",
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"\n",
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"QUERY_PROMPT = PromptTemplate(\n",
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" input_variables=[\"question\"],\n",
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" template=\"\"\"You are an assistant tasked with taking a natural languge query from a user\n",
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" and converting it into a query for a vectorstore. In the process, strip out all \n",
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" information that is not relevant for the retrieval task and return a new, simplified\n",
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" question for vectorstore retrieval. The new user query should be in pirate speech.\n",
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" Here is the user query: {question} \"\"\",\n",
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")\n",
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"llm = ChatOpenAI(temperature=0)\n",
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"llm_chain = LLMChain(llm=llm, prompt=QUERY_PROMPT)"
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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": "2dbffdd3",
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"metadata": {},
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"outputs": [],
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"source": [
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"retriever_from_llm_chain = RePhraseQueryRetriever(\n",
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" retriever=vectorstore.as_retriever(), llm_chain=llm_chain\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": 9,
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"id": "103b4be3",
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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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"INFO:langchain.retrievers.re_phraser:Re-phrased question: Ahoy matey! What be Maximum Inner Product Search, ye scurvy dog?\n"
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]
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}
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],
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"source": [
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"docs = retriever_from_llm_chain.get_relevant_documents(\n",
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" \"Hi I'm Lance. What is Maximum Inner Product Search?\"\n",
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")"
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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": "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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