Ml RAG Foundations

RAG Foundations

Retrieval-Augmented Generation (RAG) is the architecture of choice for building AI applications that need to answer questions based on specific, private, or up-to-date data.

This skill is a meta-guide that shows you how to combine other specialized skills in this library to build a production-grade RAG pipeline.

The Architecture

A standard RAG pipeline has five distinct stages:

1. Ingestion ("ETL"): Loading and cleaning your data.
2. Embedding: Converting text into vector representations.
3. Storage: Saving vectors in a database for fast similarity search.
4. Retrieval: Finding the most relevant context for a user query.
5. Generation: Using an LLM to synthesize an answer from the retrieved context.

Skill Stack

To build this, you will combine the following skills:

Implementation Steps

Phase 1: The Data Pipeline (Ingestion & Indexing)

Before you can answer questions, you must index your data.

1. Chunking Strategy:
   • Use ds-data-wrangling patterns to split your documents.
   • Tip: A 500-token chunk with 50-token overlap is a good starting point.
2. Vectorization:
   • Use ml-nlp-transformers to load an embedding model (e.g., all-MiniLM-L6-v2).
   • Generate vectors for each chunk.
3. Storage:
   • Use ops-docker-compose-orchestration to spin up a vector store.
   • Upsert your vectors + metadata (original text, source URL) into the DB.

Phase 2: The Inference Pipeline (Retrieval & Generation)

When a user asks a question:

1. Embed Query: Use the same model from Phase 1 to embed the user's question.
2. Retrieve: Query your Vector DB for the top $k$ (usually 3-5) most similar chunks.
3. Augment Context:
   • Format the retrieved chunks into a prompt.
   • Tip: Use meta-context-budgeting to ensure you don't exceed the token limit.
4. Generate:
   • Use gemini-api-dev to send the prompt + context to the model.
   • Stream the response back to the user.

Example: Minimal RAG Script (Python)

import google.genai as genai
from sentence_transformers import SentenceTransformer
import chromadb

# 1. Setup
client = genai.Client(api_key="GEMINI_API_KEY")
embedder = SentenceTransformer('all-MiniLM-L6-v2')
db = chromadb.PersistentClient(path="./rag_db")
collection = db.get_or_create_collection("docs")

# 2. Ingest (Simplified)
docs = ["RAG is great.", "Gemini has a 1M token context."]
ids = ["1", "2"]
embeddings = embedder.encode(docs).tolist()
collection.add(documents=docs, embeddings=embeddings, ids=ids)

# 3. Retrieve
query = "What is Gemini context size?"
query_vec = embedder.encode([query]).tolist()
results = collection.query(query_embeddings=query_vec, n_results=1)
context = results['documents'][0][0]

# 4. Generate
prompt = f"Context: {context}\n\nQuestion: {query}"
response = client.models.generate_content(
    model="gemini-2.0-flash", 
    contents=prompt
)
print(response.text)

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