DevVault AI

DevVault AI is a source-cited RAG knowledge base for engineering teams. It lets users upload Markdown or plain text documents, stores document chunks with vector embeddings, retrieves semantically relevant source context, and answers questions with citations from the uploaded documents.

Features

• FastAPI backend with OpenAPI documentation.
• PostgreSQL database with pgvector support.
• Markdown and TXT document upload.
• Character-based document chunking with overlap.
• Local embedding generation through Ollama.
• Vector similarity search over uploaded document chunks.
• Source-cited chat responses generated from retrieved context.
• Document listing, detail, and delete endpoints.
• Alembic-managed database migrations.
• Docker Compose setup for PostgreSQL.

Tech Stack

Layer	Technology
API	FastAPI
Configuration	Pydantic Settings
Database	PostgreSQL
Vector storage/search	pgvector
ORM/database access	SQLAlchemy
Migrations	Alembic
Embeddings	Ollama embeddinggemma
Chat generation	Ollama llama3.2
Local infrastructure	Docker Compose

Architecture

User / API Client
-> FastAPI backend
-> Document upload and management routes
-> Chunking service
-> Ollama embedding model
-> PostgreSQL + pgvector
-> Retrieval service
-> Ollama chat model
-> Source-cited answer

RAG flow:

upload document
-> validate file
-> decode text
-> split into chunks
-> generate embeddings
-> store document, chunks, and vectors
ask question
-> embed question
-> retrieve nearest chunks
-> build grounded prompt context
-> generate answer
-> return answer with citations

Project Structure

devvault-ai/
backend/
app/
__init__.py
main.py
api/
__init__.py
routes/
__init__.py
chat.py
documents.py
health.py
search.py
core/
__init__.py
config.py
db/
__init__.py
base.py
session.py
models/
__init__.py
document.py
document_chunk.py
integrations/
__init__.py
ollama_client.py
schemas/
__init__.py
chat.py
document.py
search.py
services/
__init__.py
chat_service.py
chunking_service.py
document_service.py
embedding_service.py
retrieval_service.py
migrations/
scripts/
alembic.ini
requirements.txt
docker-compose.yml
README.md

Configuration

Create backend/.env with local settings:

APP_NAME=DevVault AIAPP_ENV=localDATABASE_URL=postgresql+psycopg://devvault:devvault@localhost:5433/devvaultOLLAMA_BASE_URL=http://localhost:11434OLLAMA_EMBED_MODEL=embeddinggemmaOLLAMA_CHAT_MODEL=llama3.2

Run Locally

1. Start PostgreSQL

Run Docker Compose from the project root:

cd D:\Github\devvault-ai
docker compose up -d

Check the container:

docker compose ps

2. Prepare Python Environment

Run from the backend folder:

cd D:\Github\devvault-ai\backend
python -m venv .venv
.venv\Scripts\Activate.ps1
pip install -r requirements.txt

3. Prepare Ollama Models

Make sure Ollama is running, then pull the local models:

ollama pull embeddinggemma
ollama pull llama3.2

4. Apply Migrations

Run Alembic from the backend folder:

cd D:\Github\devvault-ai\backend
.venv\Scripts\Activate.ps1
alembic upgrade head

This enables pgvector and creates the document/chunk tables.

5. Run the Backend

cd D:\Github\devvault-ai\backend
.venv\Scripts\Activate.ps1
fastapi dev app/main.py

The API runs at:

http://127.0.0.1:8000

OpenAPI docs:

http://127.0.0.1:8000/docs

API Endpoints

Method	Endpoint	Description
GET	/	Root welcome response.
GET	/health	Application health check.
GET	/health/db	Database connectivity check.
POST	/documents/upload	Upload a Markdown or TXT document.
GET	/documents	List uploaded documents with chunk counts.
GET	/documents/{document_id}	Get one document summary.
DELETE	/documents/{document_id}	Delete a document and its chunks.
POST	/search	Search uploaded chunks by semantic similarity.
POST	/chat	Ask a question and receive a source-cited answer.

Example Usage

Upload a Document

curl.exe-X POST `
http://127.0.0.1:8000/documents/upload `-F"file=@D:\Github\devvault-ai\sample.md"

Example response:

{
"document_id": 1,
"filename": "sample.md",
"chunk_count": 3
}

List Documents

curl.exe http://127.0.0.1:8000/documents

Search Documents

curl.exe-X POST `
http://127.0.0.1:8000/search `-H "Content-Type: application/json"`-d "{\"query\":\"What does this project use pgvector for?\",\"top_k\":3}"

Ask a Question

curl.exe-X POST `
http://127.0.0.1:8000/chat `-H "Content-Type: application/json"`-d "{\"question\":\"What does this project use pgvector for?\"}"

Example response shape:

{
"answer": "pgvector is used to store and compare document chunk embeddings for semantic retrieval.",
"citations": [
{
"chunk_id": 1,
"document_id": 1,
"filename": "sample.md",
"snippet": "..."
}
]
}

Database

Main tables:

Table	Purpose
documents	Stores uploaded document metadata.
document_chunks	Stores chunk text and VECTOR(768) embeddings.

Useful checks:

SELECT*FROM documents;
SELECT document_id, chunk_index, embedding IS NOT NULLAS has_embedding
FROM document_chunks
ORDER BY document_id, chunk_index;

Check embedding dimensions:

SELECT id, vector_dims(embedding) AS dimensions
FROM document_chunks
WHERE embedding IS NOT NULL;

Expected dimension:

768