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LambdaDB is a serverless AI database for RAG and agents. Unify full-text, multi-vector, and hybrid search on a flexible document model. Handle infinite persistent memory and massive concurrency instantly— at 1/10th the cost.

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Installation and setup

Install the Python SDK: 
pip install langchain-lambdadb
You’ll also need a LambdaDB account with:
  • Project URL
  • Project API key
Get your credentials at lambdadb.ai.

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Vector store

There exists a wrapper around LambdaDB collections, allowing you to use it as a vectorstore for semantic search, RAG, and other AI applications. To import this vectorstore: 
from langchain_lambdadb import LambdaDBVectorStore

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Quick start

from langchain_lambdadb import LambdaDBVectorStore
from langchain_openai import OpenAIEmbeddings
from lambdadb import LambdaDB
import os

# Initialize client
client = LambdaDB(
    server_url=os.environ["LAMBDADB_SERVER_URL"],
    project_api_key=os.environ["LAMBDADB_API_KEY"]
)

# Create vector store with existing collection
vector_store = LambdaDBVectorStore(
    client=client,
    collection_name="my_collection",  # Must exist beforehand
    embedding=OpenAIEmbeddings()
)
For a more detailed walkthrough of the LambdaDB wrapper, see the full documentation.
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