Handle Multiple Retrievers

Sometimes, a query analysis technique may allow for selection of which retriever to use. To use this, you will need to add some logic to select the retriever to do. We will show a simple example (using mock data) of how to do that.

Setup

Install dependencies

# %pip install -qU langchain langchain-community langchain-openai langchain-chroma

Set environment variables

We’ll use OpenAI in this example:

import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass()

# Optional, uncomment to trace runs with LangSmith. Sign up here: https://smith.langchain.com.
# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()

Create Index

We will create a vectorstore over fake information.

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings

texts = ["Harrison worked at Kensho"]
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
vectorstore = Chroma.from_texts(texts, embeddings, collection_name="harrison")
retriever_harrison = vectorstore.as_retriever(search_kwargs={"k": 1})

texts = ["Ankush worked at Facebook"]
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
vectorstore = Chroma.from_texts(texts, embeddings, collection_name="ankush")
retriever_ankush = vectorstore.as_retriever(search_kwargs={"k": 1})

API Reference:

• RecursiveCharacterTextSplitter
• OpenAIEmbeddings

Query analysis

We will use function calling to structure the output. We will let it return multiple queries.

from typing import List, Optional

from langchain_core.pydantic_v1 import BaseModel, Field


class Search(BaseModel):
    """Search for information about a person."""

    query: str = Field(
        ...,
        description="Query to look up",
    )
    person: str = Field(
        ...,
        description="Person to look things up for. Should be `HARRISON` or `ANKUSH`.",
    )

from langchain_core.output_parsers.openai_tools import PydanticToolsParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI

output_parser = PydanticToolsParser(tools=[Search])

system = """You have the ability to issue search queries to get information to help answer user information."""
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system),
        ("human", "{question}"),
    ]
)
llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
structured_llm = llm.with_structured_output(Search)
query_analyzer = {"question": RunnablePassthrough()} | prompt | structured_llm

API Reference:

• PydanticToolsParser
• ChatPromptTemplate
• RunnablePassthrough
• ChatOpenAI

We can see that this allows for routing between retrievers

query_analyzer.invoke("where did Harrison Work")

Search(query='workplace', person='HARRISON')

query_analyzer.invoke("where did ankush Work")

Search(query='workplace', person='ANKUSH')

Retrieval with query analysis

So how would we include this in a chain? We just need some simple logic to select the retriever and pass in the search query

from langchain_core.runnables import chain

API Reference:

• chain

retrievers = {
    "HARRISON": retriever_harrison,
    "ANKUSH": retriever_ankush,
}

@chain
def custom_chain(question):
    response = query_analyzer.invoke(question)
    retriever = retrievers[response.person]
    return retriever.invoke(response.query)

custom_chain.invoke("where did Harrison Work")

[Document(page_content='Harrison worked at Kensho')]

custom_chain.invoke("where did ankush Work")

[Document(page_content='Ankush worked at Facebook')]