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Retriever that can re-phase user inputs (#8026)

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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>
pull/8738/head
Lance Martin 1 year ago committed by GitHub
parent 6c3573e7f6
commit d1b95db874
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222
docs/extras/integrations/retrievers/re_phrase.ipynb
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@ -0,0 +1,222 @@
{
"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 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": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

2
libs/langchain/langchain/retrievers/__init__.py
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@ -42,6 +42,7 @@ from langchain.retrievers.milvus import MilvusRetriever
from langchain.retrievers.multi_query import MultiQueryRetriever
from langchain.retrievers.pinecone_hybrid_search import PineconeHybridSearchRetriever
from langchain.retrievers.pubmed import PubMedRetriever
from langchain.retrievers.re_phraser import RePhraseQueryRetriever
from langchain.retrievers.remote_retriever import RemoteLangChainRetriever
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain.retrievers.svm import SVMRetriever
@ -86,6 +87,7 @@ __all__ = [
"ZepRetriever",
"ZillizRetriever",
"DocArrayRetriever",
"RePhraseQueryRetriever",
"WebResearchRetriever",
"EnsembleRetriever",
]

87
libs/langchain/langchain/retrievers/re_phraser.py
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@ -0,0 +1,87 @@
import logging
from typing import List
from langchain.callbacks.manager import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain.chains.llm import LLMChain
from langchain.llms.base import BaseLLM
from langchain.prompts.prompt import PromptTemplate
from langchain.schema import BaseRetriever, Document
logger = logging.getLogger(__name__)
# Default template
DEFAULT_TEMPLATE = """You are an assistant tasked with taking a natural language \
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}"""
# Default prompt
DEFAULT_QUERY_PROMPT = PromptTemplate.from_template(DEFAULT_TEMPLATE)
class RePhraseQueryRetriever(BaseRetriever):
"""Given a user query, use an LLM to re-phrase it.
Then, retrieve docs for re-phrased query."""
retriever: BaseRetriever
llm_chain: LLMChain
@classmethod
def from_llm(
cls,
retriever: BaseRetriever,
llm: BaseLLM,
prompt: PromptTemplate = DEFAULT_QUERY_PROMPT,
) -> "RePhraseQueryRetriever":
"""Initialize from llm using default template.
The prompt used here expects a single input: `question`
Args:
retriever: retriever to query documents from
llm: llm for query generation using DEFAULT_QUERY_PROMPT
prompt: prompt template for query generation
Returns:
RePhraseQueryRetriever
"""
llm_chain = LLMChain(llm=llm, prompt=prompt)
return cls(
retriever=retriever,
llm_chain=llm_chain,
)
def _get_relevant_documents(
self,
query: str,
*,
run_manager: CallbackManagerForRetrieverRun,
) -> List[Document]:
"""Get relevated documents given a user question.
Args:
query: user question
Returns:
Relevant documents for re-phrased question
"""
response = self.llm_chain(query, callbacks=run_manager.get_child())
re_phrased_question = response["text"]
logger.info(f"Re-phrased question: {re_phrased_question}")
docs = self.retriever.get_relevant_documents(
re_phrased_question, callbacks=run_manager.get_child()
)
return docs
async def _aget_relevant_documents(
self,
query: str,
*,
run_manager: AsyncCallbackManagerForRetrieverRun,
) -> List[Document]:
raise NotImplementedError
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