build-rag

/build-rag

Build a complete RAG pipeline with best practices.

What I Need

Tell me:

• What documents/data will you be querying?
• What's your use case (Q&A, search, chatbot)?
• Any technology preferences (LangChain, LlamaIndex, custom)?
• Scale expectations (documents, queries/day)?

Workflow

Step 1: Document Processing

I'll help you set up:

• Document loading (PDF, web, database)
• Chunking strategy based on content type
• Metadata extraction

Step 2: Embedding & Indexing

We'll configure:

• Embedding model selection
• Vector store setup
• Index optimization

Step 3: Retrieval Pipeline

I'll implement:

• Basic vector search
• Hybrid search (if needed)
• Reranking layer

Step 4: Generation

We'll build:

• Prompt template
• Context formatting
• Citation handling

Step 5: Evaluation

I'll set up:

• Test dataset creation
• Retrieval metrics
• Quality monitoring

Architecture Options

Simple RAG:

Query → Embed → Search → Context → Generate → Answer

Production RAG:

Query → Cache Check → Hybrid Search → Rerank → Generate → Validate → Cache → Answer

Quick Start Templates

LangChain:

from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain.chains import RetrievalQA

vectorstore = Chroma.from_documents(docs, OpenAIEmbeddings())
qa = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(),
    retriever=vectorstore.as_retriever()
)

LlamaIndex:

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()