LangChain provides powerful tools for building applications that leverage language models. One of its standout features is the integration of vector stores, which enable efficient storage and retrieval of embeddings.
Vector stores allow developers to conduct semantic searches and enhance their applications by using embeddings for various tasks, such as document retrieval and conversation context management.
Here’s a simple example demonstrating how to set up a vector store using LangChain:
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
# Initialize embeddings
embeddings = OpenAIEmbeddings()
# Define your vector store
vector_store = Chroma(embedding_function=embeddings)
# Example of adding documents
vector_store.add_texts(["This is a sample document.", "LangChain is amazing for vector searches!"])
In this code snippet, we initialize an OpenAI embeddings instance and a Chroma vector store. The add_texts method allows you to store your documents in the vector store, making them searchable using the embeddings.
With the power of vector stores, LangChain enables developers to create more intelligent and responsive applications that understand user intent better. Start exploring vector stores today!