sightrag 0.1.6

SightRAG: Image and Video RAG. See. Search. Retrieve.

SightRAG

See. Search. Retrieve.

Search your images and videos with plain English. Three lines of code.

---

Quick Start

from sightrag import SightRAG

rag = SightRAG()
rag.index("./photos/")
results = rag.query("find empty shelf")

Install

pip install sightrag

For REST API:

pip install sightrag[api]

What SightRAG Does

Point SightRAG at any image folder, video file, or camera. Ask in plain English. Get back matched images with bounding boxes, timestamps, and confidence scores.

SightRAG is not a model. Not a wrapper. Not a framework plugin. It is a complete retrieval system that handles detection, embedding, indexing, and search — so you don't have to.

All models and indexes are stored in ~/.sightrag/ — your project folder stays clean.

Project Structure

sightrag/
├── sightrag/                    ← main library (pip install)
│   ├── core.py                  ← SightRAG class
│   ├── detector.py              ← YOLO detection
│   ├── embedder.py              ← CLIP embeddings
│   ├── indexer.py               ← image/video/camera indexing
│   ├── retriever.py             ← text + reference queries
│   ├── api.py                   ← REST API (FastAPI)
│   ├── store/
│   │   ├── base.py              ← abstract store interface
│   │   ├── sqlite_store.py      ← SQLite fallback
│   │   └── chroma_store.py      ← ChromaDB (default)
│   └── utils/
│       ├── image.py             ← image loading
│       ├── video.py             ← frame extraction
│       └── camera.py            ← webcam capture
│
├── demo_sightrag/               ← test locally — run the scripts
│   ├── sightrag_images.py       ← demo: image folder indexing
│   ├── sightrag_video.py        ← demo: video file indexing
│   ├── sightrag_livecam.py      ← demo: live webcam capture
│   ├── sightrag_restapi.py      ← demo: REST API usage
│   ├── input_images/            ← sample images to index
│   ├── reference_images/        ← sample reference query images
│   ├── camera_captures/         ← webcam frames stored here
│   └── video_samples/           ← put your videos here
│
├── notebooks/                   ← test on Google Colab
│   └── SightRAG_Colab_Demo.ipynb
│
├── examples/                    ← code examples
│   ├── basic_usage.py
│   ├── camera_example.py
│   ├── custom_domain_example.py
│   └── rest_api_example.py
│
├── tests/                       ← unit tests
│   └── test_core.py
│
├── docs/                        ← documentation
│   └── DOCKER.md
│
├── assets/                      ← banner image
├── setup.py                     ← PyPI packaging
├── pyproject.toml               ← build config
├── requirements.txt             ← dependencies
├── Dockerfile                   ← container
├── docker-compose.yml           ← one command deploy
├── LICENSE                      ← Apache 2.0
└── test_sightrag.py             ← quick test script

How To Test

Quick Test (terminal)

python test_sightrag.py

Demo Scripts (test each mode)

python demo_sightrag/sightrag_images.py     # image folder
python demo_sightrag/sightrag_video.py       # video files
python demo_sightrag/sightrag_livecam.py     # live webcam
python demo_sightrag/sightrag_restapi.py     # REST API

Google Colab

Upload notebooks/SightRAG_Colab_Demo.ipynb to Google Colab → Run All

Unit Tests

pip install pytest
python -m pytest tests/ -v

Usage

Image Folder

from sightrag import SightRAG

rag = SightRAG()
rag.index("./shelf_photos/")
results = rag.query("find empty shelf near dairy")

for r in results:
    print(f"{r['image_path']} — score: {r['score']} — {r['label']}")

Video File

rag = SightRAG()
rag.index("./cctv_footage.mp4")
results = rag.query("person near exit door")

for r in results:
    print(f"Timestamp: {r['timestamp']} — score: {r['score']}")

Mixed Folder (images + videos)

rag = SightRAG()
rag.index("./my_data/")  # automatically detects images AND videos

Live Camera

rag = SightRAG()
rag.index(source="camera")              # default webcam
rag.index(source="camera", camera_id=1) # specific camera
results = rag.query("find person")

Reference Image Query

results = rag.query(reference="sample_shelf.jpg")

Custom Domain (medical, industrial, satellite)

rag = SightRAG(domain_hint="pcb defect solder joint circuit board")
rag.index("./circuit_boards/")
results = rag.query("find defective solder joint")

SQLite Fallback (lightweight)

rag = SightRAG(store="sqlite")
rag.index("./small_dataset/")

REST API

pip install sightrag[api]

Start the server:

# Command line
sightrag-server

# Or from Python
from sightrag import serve
serve(port=8000)

# Or Docker (API starts automatically)
docker-compose up

API available at http://localhost:8000

Endpoints

Method	Endpoint	Description
GET	/	API info and available endpoints
GET	/status	Index count, store type, domain hint
POST	/index/folder	Index all images and videos in a folder
POST	/index/video	Index a video file
POST	/index/upload	Upload and index images directly
POST	/query/text	Search with plain English text
POST	/query/reference	Search with a reference image
DELETE	/index	Clear all indexed data

Examples

# Index a folder
curl -X POST http://localhost:8000/index/folder \
     -F "path=./data/"

# Search with text
curl -X POST http://localhost:8000/query/text \
     -F "text=find empty shelf" \
     -F "top_k=5"

# Upload and search with reference image
curl -X POST http://localhost:8000/query/reference \
     -F "file=@new_photo.jpg" \
     -F "top_k=5"

# Check status
curl http://localhost:8000/status

Interactive API docs available at http://localhost:8000/docs (Swagger UI).

Result Format

[
    {
        "image_path":  "./photos/shelf_042.jpg",
        "score":       0.9134,
        "label":       "bottle",
        "confidence":  0.8721,
        "bbox":        [120, 45, 380, 290],
        "timestamp":   "",
        "source_type": "image"
    }
]

Storage

SightRAG uses a built-in SQLite vector store by default — zero extra dependencies, works everywhere.

Store	Scale	Cost	Usage
SQLite (default)	Up to 100k images	Free	SightRAG()
ChromaDB (optional)	Large scale	Free	SightRAG(store="chroma")

Enterprise connectors (Qdrant, Pinecone, Azure) coming in v2.

Where SightRAG Stores Data

~/.sightrag/
├── models/      ← YOLO weights (auto-downloaded once)
└── index/       ← vector database (ChromaDB/SQLite)

Your project folder stays clean. No random .pt files or sightrag_index/ folders appearing.

Docker

docker-compose up

This starts the REST API server on port 8000. See Docker Guide for details.

Architecture

Input (images / video / camera / reference image)
        ↓
   Preprocessor (resize, validate, keyframe extract)
        ↓
   YOLO Detection (80 COCO classes + whole-image fallback)
        ↓
   CLIP Embedding (domain_hint enrichment for custom domains)
        ↓
   Vector Store (ChromaDB default / SQLite fallback)
        ↓
   Retrieval + Ranking (cosine similarity, confidence scoring)
        ↓
Output (matched images, timestamps, bounding boxes, scores)

Honest Limitations (v0.1.0)

• Indexing ~500 images takes ~2 minutes on CPU (one-time cost — search is instant after)
• Custom domains (medical, satellite) use whole-image CLIP without region grounding
• Single webcam only (multiple cameras in v2)
• SQLite vector store loads all vectors into memory for search

Roadmap

Version	Features
v0.1 (current)	Image + Video + Camera + REST API + ChromaDB
v0.2	C++ core, CLI, Grounding DINO, SAM
v0.3	Person Re-ID, scene graph, edge deployment
v1.0	Jetson Orin, compliance modes (GDPR/HIPAA/DPDP)

Three Library Ecosystem

SightRAG is part of the VK-Ant AI ecosystem:

Library	Purpose	Status
SightRAG	Image & Video RAG	v0.1
adaptive-intelligence	RL-based RAG orchestration	v4.0.7
llmevalkit	LLM evaluation (97+ tests)	Stable

License

Apache 2.0

Author

Built by Venkatkumar Rajan

• GitHub: https://github.com/VK-Ant
• LinkedIn: https://linkedin.com/in/vk-ant
• Portfolio: https://vk-ant.github.io/Venkatkumar