mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
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
parent
6c3573e7f6
commit
d1b95db874
222
docs/extras/integrations/retrievers/re_phrase.ipynb
Normal file
222
docs/extras/integrations/retrievers/re_phrase.ipynb
Normal file
@ -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
|
||||
}
|
||||
@ -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
Normal file
87
libs/langchain/langchain/retrievers/re_phraser.py
Normal file
@ -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
|
||||
Loading…
Reference in New Issue
Block a user