Creating Retriever and Chain Objects with LLM for Responses

LangChain provides powerful tools for building applications that combine vector databases with large language models (LLMs). By using the RetrievalQAChain class, you can effectively retrieve relevant information from a vector database and generate responses using an LLM.

Prerequisites

• LangChain: Ensure you have LangChain installed on your system.
• Vector Database: Set up a vector database, such as Pinecone or FAISS.
• LLM: Choose an LLM, such as OpenAI’s GPT-3 or GPT-4.

Creating a Retriever

Import Necessary Libraries:

Python

from langchain.vectorstores import Pinecone
from langchain.llms import OpenAI
from langchain.chains import RetrievalQAChain

Initialize Pinecone:

Python

pinecone.init(api_key=”YOUR_API_KEY”, environment=”us-west1-gcp”)

Create a Pinecone Index:

Python

index_name = “my_index”
embedding_dimension = 512 # Adjust based on your embeddings
metric = “cosine” # Choose a suitable metric

collection = Pinecone(
index_name=index_name,
embedding_dimension=embedding_dimension,
metric=metric
)

Create a Retriever:

Python

retriever = collection.as_retriever()

Creating an LLMChain

Create an LLM:

Python

llm = OpenAI(temperature=0.7)

Create a Chain:

Python

chain = RetrievalQAChain(
llm=llm,
retriever=retriever
)

Using the Chain

Query the Chain:

Python

query = “What is the capital of France?”
response = chain.run(query)
print(response)

By using LangChain’s RetrievalQAChain, you can effectively combine a vector database with an LLM to create powerful question-answering applications. The chain automatically retrieves relevant documents from the vector database and passes them to the LLM for processing, providing a seamless integration between these two components.