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.
def chunk_text(text, size=1000, overlap=200):<br>chunks = []<br>start = 0<br>while start = end: start = end + 1<br>if end >= len(text): break<br>return chunks
4. Extraction and ChromaDB Ingestion
We use PyMuPDF for its high performance when handling heavy technical manuals.
import fitz<br>import chromadb
db_client = chromadb.PersistentClient(path="./tech_docs_db")<br>collection = db_client.get_or_create_collection(<br>name="technical_documentation",<br>metadata={"hnsw:space": "cosine"}
def process_and_store_pdf(pdf_path):<br>doc = fitz.open(pdf_path)<br>for i, page in enumerate(doc):<br>text = page.get_text()<br>chunks = chunk_text(text)
for j, chunk in enumerate(chunks):<br>chunk_id = f"{pdf_path}_{i}_{j}"<br>collection.add(<br>documents=[chunk],<br>ids=[chunk_id],<br>metadatas=[{"source": pdf_path, "page": i}]
5. Embeddings with the New SDK
Defining the vectorization logic using the latest embedding models:
from google import genai<br>from google.genai import types
client = genai.Client(api_key="YOUR_API_KEY")
def get_embedding(text):<br>result = client.models.embed_content(<br>model="text-embedding-004",<br>contents=text,<br>config=types.EmbedContentConfig(task_type="RETRIEVAL_QUERY")<br>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.
def generate_response(query):<br># 1. Vectorize query and search ChromaDB<br>query_vector = get_embedding(query)<br>results = collection.query(<br>query_embeddings=[query_vector],<br>n_results=5
context = "\n\n".join(results['documents'][0])
# 2. Generate response with reasoning capabilities<br>prompt = f"""Act as a Senior Systems Engineer. Analyze the context and answer the question.<br>Use your reasoning capabilities to validate data points before responding.
Context:<br>{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(<br>model="gemini-2.5-flash",<br>contents=prompt<br>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:
[1] Google AI: Gemini Embeddings Documentation.
[2] ChromaDB: Core Concepts & Persistence.
[3] Paper: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.
[4] PyMuPDF (Fitz) Technical Docs.