Tutorial: Build a RAG pipeline with LangChain, OpenAI and Pinecone

Let's understand the complete system we'll be creating:

This is a Retrieval Augmented Generation (RAG) pipeline that allows users to chat with your content. Here's how it works:

1. When a user asks a question, their query is converted to a vector (embedding)
2. This vector is used to search your Pinecone database for similar content
3. The most relevant content is retrieved and injected into the LLM's prompt, the LLM generates a response based on your content, and the response is streamed back to the user along with citations

We'll build this system in the following order:

1. Data Processing: First, we'll process your content (blog posts, documentation, etc.) into a format suitable for vector search. We'll use a Jupyter Notebook for this phase.

2. Vector Database Creation: We'll convert your processed content into embeddings and store them in Pinecone, creating a searchable knowledge base.

3. Knowledge Base Testing: We'll verify our setup by running semantic search queries against the vector database to ensure we get relevant results.

4. Backend Development: We'll build the Next.js API that accepts user queries, converts queries to embeddings, retrieves relevant content from Pinecone, provides context to the LLM, and streams the response back to the user

5. Frontend Implementation: Finally, we'll create the chat interface that accepts user input, makes API calls to our backend, displays streaming responses, and shows related content and citations