You can use LangChain with Upstash Vector to perform semantic search and manage vector embeddings. LangChain is a powerful framework that integrates with vector databases, including Upstash Vector, making it easy to build intelligent applications.
First, we need to create a Vector Index in the Upstash Console. To learn more about index creation, you can check out this page.
Install
pip install upstash-vector langchain langchain-community python-dotenv
Usage
from dotenv import load_dotenv
from langchain_community.vectorstores.upstash import UpstashVectorStore
from langchain.schema import Document
# Load environment variables
load_dotenv()
# Create a vector store instance
store = UpstashVectorStore(
embedding=True, # Embedding option enabled
)
# Sample documents to upload
documents = [
Document(page_content="Upstash Vector is a scalable vector database."),
Document(page_content="LangChain is a framework for building intelligent apps."),
Document(page_content="Semantic search enables advanced query matching."),
]
# Add documents to the Upstash Vector index
store.add_documents(documents)
# Perform a similarity search
query = "What is LangChain?"
results = store.similarity_search(query, k=3)
print("Similarity Search Results:")
for res in results:
print(res.page_content)
Query Results
Similarity Search Results:
LangChain is a framework for building intelligent apps.
Semantic search enables advanced query matching.
Upstash Vector is a scalable vector database.
Features
Semantic Search: Retrieve the most contextually relevant results using embeddings and vector similarity.
Namespace Support: Separate documents into different namespaces for better organization.
Metada Filtering: Metadata can be used to filter the results of a query.
Notes
- Upstash Vector supports custom embeddings; you can specify an embedding model when initializing
UpstashVectorStore.
- Use
.env files to manage your Upstash credentials for secure and reusable configuration.
To learn more, visit the LangChain documentation.
