LangChain is an innovative framework that simplifies the integration of LLMs with external data sources, enabling natural language processing (NLP) applications to access a wide array of information. One of its standout features is the Document Retrieval capability, which allows users to fetch relevant documents based on natural language queries.
This feature is particularly useful for applications that require real-time data access, such as chatbots or knowledge assistants. By seamlessly integrating document retrieval into your workflows, you can enhance the relevance and context of the information provided by your language model.
Here’s a simple example of how to implement document retrieval using LangChain:
from langchain.document_loaders import TextLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
# Load documents
loader = TextLoader("./data/my_documents.txt")
docs = loader.load()
# Create embeddings and vector store
embeddings = OpenAIEmbeddings()
vector_store = Chroma.from_documents(docs, embeddings)
# Set up the RetrievalQA chain
qa_chain = RetrievalQA.from_chain_type(
llm=OpenAIEmbeddings(),
chain_type="stuff",
retriever=vector_store.as_retriever()
)
# Query the chain
response = qa_chain.run("What is the significance of LangChain?")
print(response)
This code snippet demonstrates loading documents, creating embeddings, and setting up a RetrievalQA chain that you can use to ask questions about your documents. With just a few lines of code, you can tap into the power of LangChain to enhance your applications.
Stay tuned for more insights into how LangChain can transform your NLP workflows!