PolicyBot Intelligence — Advanced Scalable RAG Architecture Pack v2

Updated: 2026-06-18

This package is a complete documentation and architecture starter pack for building a scalable RAG product under the working name PolicyBot Intelligence.

What changed in v2

• Added a dedicated Vector Database Strategy. RAG is not complete without a proper vector store.
• Added recommended free/low-cost vector DB options: Qdrant, Pinecone, Chroma, and MongoDB Atlas Vector Search as an optional combined-store strategy.
• Added detailed setup, Docker, deployment, environment config, commands, DB schema, API contracts, scoring, logging, tracing, and operations docs.
• Added a highly scalable Multi-Model Fallback Architecture supporting 8 LLM providers (Gemini, Nvidia, Groq, OpenAI, Ollama, HuggingFace, Mistral, Deepseek) automatically switching to prevent downtime or rate limits.
• Upgraded ingestion pipeline for ultra-fast parallel vector embeddings via async batching (processing 100-page PDFs in seconds).
• Integrated an extensive LLM Analytics Dashboard tracking tokens, latency, and error rates via Recharts and MongoDB.
• Added Word .docx versions of the Markdown documents in docs/word/.
• Added Docker and config templates in templates/.

Recommended architecture choice

For your 10-day MVP, use:

Layer	Recommended choice	Reason
API backend	FastAPI	Fast async Python API, easy OpenAPI docs
Workflow engine	LangGraph	Step-by-step graph, traceable state, checkpoints
RAG utilities	LangChain	Loaders, retrievers, vector DB adapters
Metadata DB	MongoDB	Documents, users, traces, evaluations, file versions
Vector DB	Qdrant local/self-host or Pinecone serverless	Real semantic search layer
LLM	Gemini API	Start fast and low-cost
Embeddings	Gemini Embedding	Same provider path for first version
Background jobs	Redis + worker	Async ingestion, re-indexing, evaluation jobs
Frontend	React + Tailwind	Fast UI for chat, traces, scores, documents
Local deployment	Docker Compose	Repeatable dev setup

Folder structure

policybot_rag_architecture_pack_v2/
├── README.md
├── docs/
│   ├── 00_INDEX.md
│   ├── 01_PRODUCT_BRIEF_CLIENT.md
│   ├── 02_PRODUCT_REQUIREMENTS_PRD.md
│   ├── 03_DEVELOPER_TECHNICAL_SPEC.md
│   ├── 04_ARCHITECTURE_OVERVIEW.md
│   ├── 05_BACKEND_ARCHITECTURE.md
│   ├── 06_FRONTEND_ARCHITECTURE.md
│   ├── 07_RAG_PIPELINE_FLOW.md
│   ├── 08_VECTOR_DATABASE_STRATEGY.md
│   ├── 09_DATABASE_SCHEMA_MONGODB.md
│   ├── 10_API_CONTRACTS.md
│   ├── 11_SETUP_LOCAL.md
│   ├── 12_CONFIGURATION_ENV.md
│   ├── 13_DOCKER_GUIDE.md
│   ├── 14_DEPLOYMENT_GUIDE.md
│   ├── 15_OBSERVABILITY_TRACING_LOGGING.md
│   ├── 16_EVALUATION_SCORING.md
│   ├── 17_SECURITY_ACCESS_CONTROL.md
│   ├── 18_10_DAY_BUILD_PLAN.md
│   ├── 19_TESTING_QA.md
│   ├── 20_OPERATIONS_RUNBOOK.md
│   ├── 21_MODEL_PROVIDER_SWITCHING.md
│   ├── 22_COMMANDS_CHEATSHEET.md
│   ├── 23_REFERENCES.md
│   └── word/
│       └── .docx versions of each major document
├── diagrams/
│   ├── backend_architecture.png
│   ├── frontend_architecture.png
│   ├── full_stack_architecture.png
│   ├── backend_architecture.drawio
│   ├── frontend_architecture.drawio
│   └── full_stack_architecture.drawio
├── templates/
│   ├── .env.example
│   ├── docker-compose.yml
│   ├── Dockerfile.backend
│   ├── Dockerfile.frontend
│   ├── nginx.conf
│   ├── Makefile
│   └── policybot.config.yaml
└── examples/
    └── sample_query_trace.json

How to read this pack

Start with:

1. docs/01_PRODUCT_BRIEF_CLIENT.md for client-facing explanation.
2. docs/04_ARCHITECTURE_OVERVIEW.md for the complete architecture.
3. docs/08_VECTOR_DATABASE_STRATEGY.md for Pinecone/Qdrant/Chroma/MongoDB choices.
4. docs/11_SETUP_LOCAL.md and docs/13_DOCKER_GUIDE.md for setup.
5. docs/18_10_DAY_BUILD_PLAN.md for execution.

Important architecture decision

Use MongoDB for metadata and audit data. Use Qdrant/Pinecone/Chroma/MongoDB Atlas Vector Search for vectors. Do not store only raw documents in MongoDB and call it RAG. The vector DB layer is responsible for high-dimensional similarity search over chunks, metadata filters, document freshness, and semantic retrieval.

Default MVP recommendation

Use this for fastest 10-day build:

VECTOR_DB_PROVIDER=qdrant
VECTOR_DB_MODE=local_docker
LLM_PROVIDER=gemini
EMBEDDING_PROVIDER=gemini
METADATA_DB_PROVIDER=mongodb

Then later switch to Pinecone without rewriting business logic:

VECTOR_DB_PROVIDER=pinecone
PINECONE_API_KEY=your_key
PINECONE_INDEX_NAME=policybot-prod

Included architecture images

• diagrams/backend_architecture.png
• diagrams/frontend_architecture.png
• diagrams/full_stack_architecture.png

Each diagram also has a .drawio file that can be imported into draw.io / diagrams.net.