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.

Was this page helpful?

Go SDKLlamaIndex