Scaling RAG building an efficient pipeline for 500k chunks with Gemini

Google's Gemini 2.5 Flash and text-embedding-004 models, combined with ChromaDB and PyMuPDF, form a RAG pipeline that processes approximately 2GB of data (500,000 text chunks) for technical documentation retrieval. The pipeline uses semantic chunking with 1000-character chunks and 200-character overlap, and leverages Gemini 2.5's context caching to reduce costs on static documents.

Volver al Portafolio / Scaling RAG: Building an Efficient Pipeline for 500k Chunks with Gemini 2.5 and Context Caching 1. The Architecture To process a volume of approximately 2GB roughly 500,000 text chunks , we need a high-performance pipeline: Extraction: PDF processing using PyMuPDF Fitz for speed. Intelligent Chunking: Semantic splitting to avoid breaking words, including strategic overlap. Vectorization: Utilizing text-embedding-004 or gemini-embedding-001 . Persistence: ChromaDB for vector storage and metadata management. Inference: Gemini 2.5 Flash , leveraging its native reasoning capabilities to filter noise. 2. Environment Setup We will use the latest Google GenAI SDK. Install the dependencies: pip install -q -U google-genai chromadb pymupdf 3. Smart Chunking: The Secret to Precision Correctly segmenting text represents 80% of RAG success. This function ensures we don't cut words mid-sentence while maintaining a meaningful overlap. python def chunk text text, size=1000, overlap=200 : chunks = start = 0 while start < len text : end = min start + size, len text if end < len text : last space = text.rfind ' ', start, end if last space = -1: end = last space segment = text start:end .strip if segment: chunks.append segment start = end - overlap if start = end: start = end + 1 if end = len text : break return chunks 4. Extraction and ChromaDB Ingestion We use PyMuPDF for its high performance when handling heavy technical manuals. python import fitz import chromadb db client = chromadb.PersistentClient path="./tech docs db" collection = db client.get or create collection name="technical documentation", metadata={"hnsw:space": "cosine"} def process and store pdf pdf path : doc = fitz.open pdf path for i, page in enumerate doc : text = page.get text chunks = chunk text text for j, chunk in enumerate chunks : chunk id = f"{pdf path} {i} {j}" collection.add documents= chunk , ids= chunk id , metadatas= {"source": pdf path, "page": i} 5. Embeddings with the New SDK Defining the vectorization logic using the latest embedding models: python from google import genai from google.genai import types client = genai.Client api key="YOUR API KEY" def get embedding text : result = client.models.embed content model="text-embedding-004", contents=text, config=types.EmbedContentConfig task type="RETRIEVAL QUERY" return result.embeddings 0 .values 6. Retrieval and Reasoning Gemini 2.5 Flash can "reason" over retrieved fragments. This is crucial for resolving contradictions often found in technical documentation. python def generate response query : 1. Vectorize query and search ChromaDB query vector = get embedding query results = collection.query query embeddings= query vector , n results=5 context = "\n\n".join results 'documents' 0 2. Generate response with reasoning capabilities prompt = f"""Act as a Senior Systems Engineer. Analyze the context and answer the question. Use your reasoning capabilities to validate data points before responding. Context: {context} Question: {query} If the information is not present in the context, state it clearly. Do not hallucinate technical data.""" response = client.models.generate content model="gemini-2.5-flash", contents=prompt return response.text 7. Cost Optimization: Context Caching If your 2GB documentation is static, sending the same tokens repeatedly is inefficient. Gemini 2.5 allows for Context Caching . How it works: Upload documents once to Google’s servers, creating a cache with a specific TTL Time-To-Live . Your RAG queries then target this cache. The Benefit: Reduces input token costs by up to 80% in long, multi-turn conversations. Conclusion Implementing RAG at scale requires precision in chunking and a model capable of distinguishing signal from noise. Gemini 2.5 Flash, combined with ChromaDB, provides an enterprise-grade solution with minimal maintenance overhead. References: