CODEWITHSUNDEEP
solropenai-apilangchainpy-langchainpysolr
Blue Cheese
Blue Cheese

Reputation: 385

Can I use Apache Solr as a vectorstore in LangChain?

Is there a way to connect Solr to use it as a vectorstore for a chatbot being developed using LangChain and OpenAI API? Solr is currently not supported as a vectorstore in LangChain, but LangChain's chatbot claims that it is possible to connect to external unsupported vector databases.

This was the chatbot's response:

If you want to use a vector store that is not directly supported by Langchain, you can still integrate it into your Langchain workflow by implementing a custom retriever. Here's a general outline of how you can do this:

• Understand the vector store: Familiarize yourself with the API and functionality of the vector store you want to use. Understand how to query the vector store and retrieve relevant documents based on a given query.

• Implement a custom retriever: Create a custom retriever class that interacts with the vector store. This class should have methods to connect to the vector store, query the store with a given query, and retrieve the relevant documents.

• Integrate the custom retriever into Langchain: In your Langchain code, create an instance of your custom retriever class and configure it with the necessary parameters, such as the connection details for the vector store.

• Use the custom retriever in the Langchain chain: Incorporate the custom retriever into your Langchain chain by specifying it as part of the context in the chain definition. This allows the chain to utilize the custom retriever for retrieving relevant documents based on the input query.

By implementing a custom retriever, you can leverage the functionality of the vector store that is not directly supported by Langchain. This approach allows you to integrate any vector store into your Langchain workflow, as long as you can interact with it programmatically. Please note that implementing a custom retriever requires a good understanding of the vector store's API and functionality. You may need to adapt the implementation based on the specific requirements and capabilities of the vector store you are using.

from langchain.retrievers import BaseRetriever
from your_vector_store_client import YourVectorStoreClient

class CustomVectorStoreRetriever(BaseRetriever):
    def __init__(self, vector_store_client):
        self.vector_store_client = vector_store_client

    def connect(self):
        # Implement the connection logic to your vector store
        self.vector_store_client.connect()

    def query(self, query):
        # Implement the query logic to retrieve relevant documents from your vector store
        results = self.vector_store_client.query(query)
        return results

# Create an instance of your vector store client
vector_store_client = YourVectorStoreClient()

# Create an instance of your custom retriever
retriever = CustomVectorStoreRetriever(vector_store_client)

# Connect to the vector store
retriever.connect()

However, I am not sure how this can be integrated with Solr using pysolr.

Upvotes: 3

Views: 1498

Answers (1)

Alex Byrth
Alex Byrth

Reputation: 1499

There is new library on Pypi, Eurelis LangChain Solr vectorstore , which allows LangChain to use Apache Solr as vector store. Of course, you must have a Solr core schema supporting vector (knn_vector) field, as explained in Solr's Dense Vector Search guide:

<fieldType name="knn_vector" class="solr.DenseVectorField" vectorDimension="4" similarityFunction="cosine"/>
<field name="vector" type="knn_vector" indexed="true" stored="true"/>

Basic example:

from langchain.embeddings import HuggingFaceEmbeddings
from eurelis_langchain_solr_vectorstore import Solr

embeddings = HuggingFaceEmbeddings()  # you are free to use any embeddings method allowed by langchain
vector_store = Solr(embeddings, core_kwargs={
        'page_content_field': 'text_t',  # field containing the text content
        'vector_field': 'vector',        # field containing the embeddings of the text content
        'core_name': 'langchain',        # core name
        'url_base': 'http://localhost:8983/solr' # base url to access solr
    })

Upvotes: 0

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