Acadify Solution — Enterprise RAG Boilerplate 🛠️

Reference retrieval-augmented generation backend with metadata filtering, zero-trust PII masking, hierarchical parsing, semantic chunking, multi-stage retrieval, parallel tokenized vector updates, and response verification.

Built by Acadify Solution to demonstrate production-grade software engineering baselines for venture-backed startups and enterprises.

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🛡️ Software Quality & Compliance Standards

To maintain the highest tier of engineering discipline, all Acadify Solution projects implement strict quality baselines:

• Security & Compliance by Design:
  • PII Masking: Custom interceptors automatically sanitize personally identifiable information (PII) before routing to model gateways or storing in databases.
  • Zero-Trust Proxies: Structured logs and tracing conform to HIPAA & SOC2 alignment.
• Rigorous Test Coverage:
  • Unit testing benchmarks for APIs, data parsing, chunking, and retrieval pipelines using pytest and mock database structures.
• Disciplined SDLC:
  • Mandatory code quality gating, static analysis linting via ruff, and formatting via black.

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🛠️ Core Technology Stack

• Framework: FastAPI (Python 3.11)
• Vector Databases: Pinecone • Qdrant • pgvector (PostgreSQL)
• LLM Orchestration: LangChain • langchain-core • langchain-anthropic (Claude 3.5 Sonnet)
• Tooling: SQLAlchemy (async) • tiktoken • numpy • pytest-asyncio

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🏗️ System Architecture & Ingestion Pipeline

The document ingestion pipeline is structured to flow sequentially through the following stages:

1. Ingestion Endpoint: Accepts document text or uploads from the client.
2. PII Masking Interceptor: Redacts sensitive data, replacing personal details with secure tokens.
3. Hierarchical Parser: Segments text by Markdown heading levels, maintaining relationships.
4. Semantic Chunker: Partitions sections using a local cosine similarity algorithm to identify thematic shifts.
5. Parallel Ingestor: Batches chunks and embeds them, utilizing semaphores to load vectors concurrently into the vector store.
6. Vector Database: Connects to the active database (Qdrant, Pinecone, or pgvector) to index the chunks.

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🚀 Getting Started

1. Requirements

• Python 3.11+
• Docker & Docker Compose (for local database services)

2. Configure Environment

Copy .env.example to .env and fill out your keys:

cp .env.example .env

To swap between vector stores, change the VECTOR_DB_TYPE variable inside .env:

• qdrant (uses AsyncQdrantClient)
• pinecone (uses Pinecone Python client)
• pgvector (uses PostgreSQL SQLAlchemy async sessions)

3. Spin Up Local Databases

Deploy Qdrant and pgvector locally via Docker:

docker-compose up -d

4. Install Dependencies & Start Server

Set up a virtual environment and run the Uvicorn application:

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
uvicorn app.main:app --reload --port 8000

The API documentation will be available at http://localhost:8000/docs.

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🚦 Verification & Test Suite

Run the comprehensive unit and integration test suite (runs in-memory using deterministic numpy vector mocks, ensuring it executes offline without external APIs):

pytest -v

To run lint checks:

ruff check .

To run code formatter checks:

black --check .

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🤝 Build With Us

Whether you want to build and scale a custom AI system, launch an enterprise-grade MVP, or audit your existing LLM deployment for compliance and latency, Acadify Solution brings Silicon Valley engineering standards directly to your team.

Website: acadifysolution.com | Email: contact@acadifysolution.com

© 2026 Acadify Solution. All rights reserved. Globally distributed team.