LANGCHAIN — Pinecone Serverless

The Web does not just connect machines, it connects people. — Tim Berners-Lee

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In this tutorial, we’ll demonstrate how to build and deploy a retrieval augmented generation (RAG) application with Pinecone Serverless. Pinecone Serverless addresses the challenges of hosted vectorstore management, rapid RAG application deployment, and RAG observability.

# Step 1: Connect to Pinecone Serverless
import pinecone

# Set up Pinecone client
api_key = 'your_api_key'
pinecone.init(api_key=api_key)

# Connect to Pinecone index
index_name = 'your_index_name'
index = pinecone.Index(name=index_name)

Pinecone Serverless provides usage-based-pricing and unlimited scalability for hosted vectorstore management. To demonstrate Pinecone Serverless, we’ll show how to connect a Pinecone Serverless index to a RAG chain in LangChain. This includes leveraging Cohere embeddings for similarity search on the index and GPT-4 for answer synthesis based on the retrieved chunks.

# Step 2: Connect RAG chain to Pinecone Serverless index
from langchain import RAGChain

# Initialize RAGChain
rag_chain = RAGChain()

# Connect to Pinecone Serverless index
index_connection = rag_chain.connect_to_pinecone(index=index)

Next, we’ll show how to convert the RAG chain into a web service with LangServe. LangServe allows the chain to be deployed using hosted LangServe.

# Step 3: Convert RAG chain into a web service with LangServe
from langchain import LangServe

# Initialize LangServe
lang_serve = LangServe()

# Convert RAG chain into a web service
web_service = lang_serve.convert_to_web_service(rag_chain)

Lastly, we’ll use LangSmith to monitor the input and outputs of the RAG application, providing seamless observability.

# Step 4: Monitor the RAG application with LangSmith
from langchain import LangSmith

# Initialize LangSmith
lang_smith = LangSmith()

# Monitor the RAG application
lang_smith.monitor_rag_application(web_service)

By following this tutorial, you’ll learn how to bridge the gap between prototyping and production for RAG applications. Pinecone Serverless, paired with LCEL, Hosted LangServe, and LangSmith, provides a powerful toolset for deploying RAG applications with ease.

To get started with building and deploying a RAG app with Pinecone Serverless, you can access the template repository for this tutorial here. This repository demonstrates the integration of Pinecone Serverless with a RAG chain in LangChain, showcasing the seamless deployment of a production-ready RAG application.

By leveraging Pinecone Serverless, LangServe, and LangSmith, you can create robust RAG applications with usage-based pricing and support for unlimited scaling, addressing the pain points often encountered in vectorstore productionization. This tutorial provides a comprehensive guide for developers looking to deploy RAG applications in production environments.

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In conclusion, Pinecone Serverless, in combination with LangChain’s tools, offers a compelling solution for building and deploying RAG applications with ease and efficiency.