LangChain is a powerful framework that simplifies the integration of language models into various applications. One of its standout features is the Document Retrieval functionality, which allows developers to efficiently search and retrieve relevant documents from a large corpus based on user queries.
This feature enhances the capability of language models by providing them with contextual information, ensuring that responses are informed and relevant. Below is a simple example of how to use LangChain's Document Retrieval feature:
from langchain.chains import RetrievalChain
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
# Assume documents is a list of text documents
documents = ["Document 1 content...", "Document 2 content...", "Document 3 content..."]
# Create embeddings and a vector store
embeddings = OpenAIEmbeddings()
vector_store = FAISS.from_texts(documents, embeddings)
# Create a retrieval chain
retrieval_chain = RetrievalChain(
vector_store=vector_store,
model="openai-gpt-3.5-turbo"
)
# Retrieve relevant documents based on a query
query = "What insights can I gain from Document 2?"
results = retrieval_chain.run(query)
print(results)
This code snippet demonstrates how to set up document retrieval using LangChain. By querying the chain, you can quickly access relevant documents that provide the insights you need. This feature not only streamlines the search process but also enhances the overall user experience.