LangChain is an innovative framework designed to simplify the development of applications powered by large language models. One of its standout features is Document Retrieval, which allows you to efficiently pull relevant documents from a data source based on user queries. This feature is particularly useful for chatbots, search engines, or any application requiring quick access to information.
To utilize the Document Retrieval feature, you will need to set up a vector store that can index and retrieve your documents. Below is a simple example demonstrating how to set up a vector store and perform a retrieval:
from langchain import VectorStore, Document
# Create sample documents
documents = [
Document(page_content="LangChain simplifies building applications with LLMs."),
Document(page_content="Document retrieval is a key feature in LangChain."),
]
# Initialize vector store (assuming an in-memory store for this example)
vector_store = VectorStore.from_documents(documents)
# Query the vector store for relevant documents
query = "What is the purpose of LangChain?"
results = vector_store.similarity_search(query)
# Print the results
for result in results:
print(result.page_content)
In this example, we create a vector store from a list of documents and perform a similarity search using a user query. The results will return the documents that best match the query, making it easier to provide accurate and relevant information to users.
With LangChain's Document Retrieval feature, you can enhance your applications' capabilities significantly. By integrating it into your projects, you can ensure users have quick and effective access to the information they need. Try it out and see how it improves your application!