How to use VectorStoreRetrieverMemory in langchain with PGVector?

Question

I want to use VectorStoreRetrieverMemory in langchain with PGVector in python. I didn't get any documents related to it. can someone help me with it.

Answer 1

To use VectorStoreRetrieverMemory in langchain with PGVector, you need to first install the following dependencies:

pip install langchain
pip install pgvector

Once the dependencies are installed, you can create a VectorStoreRetrieverMemory object by passing in the connection string to your PostgreSQL database (or your preferred database) and the name of the collection that contains the vectorized documents.

from langchain.memory import VectorStoreRetrieverMemory

vector_store = VectorStoreRetrieverMemory(
    connection_string="postgresql://localhost:5432/my_database",
    collection="documents",
)

You can then use the retrieve() method to retrieve the documents that are most similar to a given query.

documents = vector_store.retrieve(query="What is the capital of France?", k=3)

for document in documents:
    print(document["text"])

Here is a complete example of how to use VectorStoreRetrieverMemory with PGVector:

import os

from langchain.llms import OpenAI
from langchain.chains import ConversationChain
from langchain.memory import VectorStoreRetrieverMemory

# Create a connection string to your PostgreSQL database.
connection_string = os.environ["PGVECTOR_CONNECTION_STRING"]

# Create a VectorStoreRetrieverMemory object.
vector_store = VectorStoreRetrieverMemory(connection_string, "documents")

# Create a ConversationChain object.
chain = ConversationChain(
    llm=OpenAI(),
    memory=vector_store,
)

# Start a conversation.
while True:
    query = input("")

    # Retrieve the most similar documents to the query.
    documents = vector_store.retrieve(query, k=3)

    # Print the most similar documents.
    for document in documents:
        print(document["text"])

    # Generate a response from the LLM.
    response = chain.generate(query)

    print(response)

This code will create a ConversationChain object that uses VectorStoreRetrieverMemory to retrieve the most similar documents to a user's query. The LLM will then be used to generate a response to the query.