{"slug": "scaling-rag-building-an-efficient-pipeline-for-500k-chunks-with-gemini", "title": "Scaling RAG building an efficient pipeline for 500k chunks with Gemini", "summary": "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.", "body_md": "[Volver al Portafolio](/)\n\nScaling RAG: Building an Efficient Pipeline for 500k Chunks with Gemini 2.5 and Context Caching\n\n# 1. The Architecture\n\nTo process a volume of approximately 2GB (roughly 500,000 text chunks), we need a high-performance pipeline:\n\n**Extraction:** PDF processing using**PyMuPDF (Fitz)** for speed.**Intelligent Chunking:** Semantic splitting to avoid breaking words, including strategic overlap.**Vectorization:** Utilizing`text-embedding-004`\n\n(or`gemini-embedding-001`\n\n).**Persistence:****ChromaDB** for vector storage and metadata management.**Inference:****Gemini 2.5 Flash**, leveraging its native reasoning capabilities to filter noise.\n\n## 2. Environment Setup\n\nWe will use the latest Google GenAI SDK. Install the dependencies:\n\n```\npip install -q -U google-genai chromadb pymupdf\n```\n\n## 3. Smart Chunking: The Secret to Precision\n\nCorrectly segmenting text represents 80% of RAG success. This function ensures we don't cut words mid-sentence while maintaining a meaningful overlap.\n\n``` python\ndef chunk_text(text, size=1000, overlap=200):\n    chunks = []\n    start = 0\n    while start < len(text):\n        end = min(start + size, len(text))\n        \n        if end < len(text):\n            last_space = text.rfind(' ', start, end)\n            if last_space != -1:\n                end = last_space\n\n        segment = text[start:end].strip()\n        if segment:\n            chunks.append(segment)\n        \n        start = end - overlap\n        if start >= end: start = end + 1\n        if end >= len(text): break\n    return chunks\n```\n\n## 4. Extraction and ChromaDB Ingestion\n\nWe use PyMuPDF for its high performance when handling heavy technical manuals.\n\n``` python\nimport fitz\nimport chromadb\n\ndb_client = chromadb.PersistentClient(path=\"./tech_docs_db\")\ncollection = db_client.get_or_create_collection(\n    name=\"technical_documentation\",\n    metadata={\"hnsw:space\": \"cosine\"}\n)\n\ndef process_and_store_pdf(pdf_path):\n    doc = fitz.open(pdf_path)\n    for i, page in enumerate(doc):\n        text = page.get_text()\n        chunks = chunk_text(text)\n        \n        for j, chunk in enumerate(chunks):\n            chunk_id = f\"{pdf_path}_{i}_{j}\"\n            collection.add(\n                documents=[chunk],\n                ids=[chunk_id],\n                metadatas=[{\"source\": pdf_path, \"page\": i}]\n            )\n```\n\n## 5. Embeddings with the New SDK\n\nDefining the vectorization logic using the latest embedding models:\n\n``` python\nfrom google import genai\nfrom google.genai import types\n\nclient = genai.Client(api_key=\"YOUR_API_KEY\")\n\ndef get_embedding(text):\n    result = client.models.embed_content(\n        model=\"text-embedding-004\", \n        contents=text,\n        config=types.EmbedContentConfig(task_type=\"RETRIEVAL_QUERY\")\n    )\n    return result.embeddings[0].values\n```\n\n## 6. Retrieval and Reasoning\n\nGemini 2.5 Flash can \"reason\" over retrieved fragments. This is crucial for resolving contradictions often found in technical documentation.\n\n``` python\ndef generate_response(query):\n    # 1. Vectorize query and search ChromaDB\n    query_vector = get_embedding(query)\n    results = collection.query(\n        query_embeddings=[query_vector],\n        n_results=5\n    )\n    \n    context = \"\\n\\n\".join(results['documents'][0])\n\n    # 2. Generate response with reasoning capabilities\n    prompt = f\"\"\"Act as a Senior Systems Engineer. Analyze the context and answer the question.\n    Use your reasoning capabilities to validate data points before responding.\n    \n    Context:\n    {context}\n    \n    Question: {query}\n    \n    If the information is not present in the context, state it clearly. Do not hallucinate technical data.\"\"\"\n    \n    response = client.models.generate_content(\n        model=\"gemini-2.5-flash\",\n        contents=prompt\n    )\n    return response.text\n```\n\n## 7. Cost Optimization: Context Caching\n\nIf your 2GB documentation is static, sending the same tokens repeatedly is inefficient. Gemini 2.5 allows for **Context Caching**.\n\n**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.\n\n## Conclusion\n\nImplementing 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.\n\n**References:**", "url": "https://wpnews.pro/news/scaling-rag-building-an-efficient-pipeline-for-500k-chunks-with-gemini", "canonical_source": "https://www.rsolitario.com/scaling-rag-building-an-efficient-pipeline-for-500k-chunks-with-gemini-2-5-and-context-caching/", "published_at": "2026-08-20 03:58:22+00:00", "updated_at": "2026-08-20 04:17:20.713350+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-infrastructure", "ai-tools"], "entities": ["Google", "Gemini 2.5 Flash", "text-embedding-004", "ChromaDB", "PyMuPDF", "Gemini 2.5"], "alternates": {"html": "https://wpnews.pro/news/scaling-rag-building-an-efficient-pipeline-for-500k-chunks-with-gemini", "markdown": "https://wpnews.pro/news/scaling-rag-building-an-efficient-pipeline-for-500k-chunks-with-gemini.md", "text": "https://wpnews.pro/news/scaling-rag-building-an-efficient-pipeline-for-500k-chunks-with-gemini.txt", "jsonld": "https://wpnews.pro/news/scaling-rag-building-an-efficient-pipeline-for-500k-chunks-with-gemini.jsonld"}}