## 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[​](#setup "Direct link to Setup")

#### Install dependencies[​](#install-dependencies "Direct link to Install dependencies")

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

#### Set environment variables[​](#set-environment-variables "Direct link to Set environment variables")

We’ll use OpenAI in this example:

```
import getpassimport osos.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[​](#create-index "Direct link to Create Index")

We will create a vectorstore over fake information.

```
from langchain.text_splitter import RecursiveCharacterTextSplitterfrom langchain_chroma import Chromafrom langchain_openai import OpenAIEmbeddingstexts = ["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})
```

## Query analysis[​](#query-analysis "Direct link to Query analysis")

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

```
from typing import List, Optionalfrom langchain_core.pydantic_v1 import BaseModel, Fieldclass 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 PydanticToolsParserfrom langchain_core.prompts import ChatPromptTemplatefrom langchain_core.runnables import RunnablePassthroughfrom langchain_openai import ChatOpenAIoutput_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
```

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[​](#retrieval-with-query-analysis "Direct link to 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
```

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

```
@chaindef 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')]
```