https://github.com/morphik-org/morphik-core
Open source multi-modal RAG for building AI apps over private knowledge.
https://github.com/morphik-org/morphik-core
artificial-intelligence cache-augmented-generation colpali database litellm multimodal open-source rag rules-based-ingestion
Last synced: 13 days ago
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Open source multi-modal RAG for building AI apps over private knowledge.
- Host: GitHub
- URL: https://github.com/morphik-org/morphik-core
- Owner: morphik-org
- License: other
- Created: 2024-11-11T23:47:06.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-04-10T07:37:38.000Z (13 days ago)
- Last Synced: 2025-04-10T08:38:04.602Z (13 days ago)
- Topics: artificial-intelligence, cache-augmented-generation, colpali, database, litellm, multimodal, open-source, rag, rules-based-ingestion
- Language: Python
- Homepage: https://docs.morphik.ai
- Size: 114 MB
- Stars: 698
- Watchers: 8
- Forks: 37
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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- my-awesome - morphik-org/morphik-core - intelligence,cache-augmented-generation,colpali,database,litellm,multimodal,open-source,rag,rules-based-ingestion pushed_at:2025-04 star:1.0k fork:0.1k Open source multi-modal RAG for building AI apps over private knowledge. (Python)
README
# Morphik Core
**Note**: Morphik is launching a hosted service soon! Please sign up for the [waitlist](https://docs.google.com/forms/d/1gFoUKzECICugInLkRlAlgwrkRVorfNywAgkmcjmVGkE/edit).
[](https://github.com/morphik-org/morphik-core/tree/main?tab=License-1-ov-file#readme) [](https://pypi.org/project/morphik/) [](https://discord.gg/BwMtv3Zaju)
## What is Morphik?
Morphik is an open-source database designed for AI applications that simplifies working with unstructured data. It provides advanced RAG (Retrieval Augmented Generation) capabilities with multi-modal support, knowledge graphs, and intuitive APIs.
Built for scale and performance, Morphik can handle millions of documents while maintaining fast retrieval times. Whether you're prototyping a new AI application or deploying production-grade systems, Morphik provides the infrastructure you need.
## Features
- 📄 **First-class Support for Unstructured Data**
- Ingest ANY file format (PDFs, videos, text) with intelligent parsing
- Advanced retrieval with ColPali multi-modal embeddings
- Automatic document chunking and embedding
- 🧠 **Knowledge Graph Integration**
- Extract entities and relationships automatically
- Graph-enhanced retrieval for more relevant results
- Explore document connections visually
- 🔍 **Advanced RAG Capabilities**
- Multi-stage retrieval with vector search and reranking
- Fine-tuned similarity thresholds
- Detailed metadata filtering
- 📏 **Natural Language Rules Engine**
- Define schema-like rules for unstructured data
- Extract structured metadata during ingestion
- Transform documents with natural language instructions
- 💾 **Persistent KV-caching**
- Pre-process and "freeze" document states
- Reduce compute costs and response times
- Cache selective document subsets
- 🔌 **MCP Support**
- Model Context Protocol integration
- Easy knowledge sharing with AI systems
- 🧩 **Extensible Architecture**
- Support for custom parsers and embedding models
- Multiple storage backends (S3, local)
- Vector store integrations (PostgreSQL/pgvector, MongoDB)
## Quick Start
### Installation
```bash
# Clone the repository
git clone https://github.com/morphik-org/morphik-core.git
cd morphik-core
# Create a virtual environment
python3.12 -m venv .venv
source .venv/bin/activate # Linux/macOS
# Install dependencies
pip install -r requirements.txt
# Configure and start the server
python quick_setup.py
python start_server.py
```
### Using the Python SDK
```python
from morphik import Morphik
# Connect to Morphik server
db = Morphik("morphik://localhost:8000")
# Ingest a document
doc = db.ingest_text("This is a sample document about AI technology.",
metadata={"category": "tech", "author": "Morphik"})
# Ingest a file (PDF, DOCX, video, etc.)
doc = db.ingest_file("path/to/document.pdf",
metadata={"category": "research"})
# Use ColPali for multi-modal documents (PDFs with images, charts, etc.)
doc = db.ingest_file("path/to/report_with_charts.pdf", use_colpali=True)
# Apply natural language rules during ingestion
rules = [
{"type": "metadata_extraction", "schema": {"title": "string", "author": "string"}},
{"type": "natural_language", "prompt": "Remove all personally identifiable information"}
]
doc = db.ingest_file("path/to/document.pdf", rules=rules)
# Retrieve relevant document chunks
chunks = db.retrieve_chunks("What are the latest AI advancements?",
filters={"category": "tech"},
k=5)
# Generate a completion with context
response = db.query("Explain the benefits of knowledge graphs in AI applications",
filters={"category": "research"})
print(response.completion)
# Create and use a knowledge graph
db.create_graph("tech_graph", filters={"category": "tech"})
response = db.query("How does AI relate to cloud computing?",
graph_name="tech_graph",
hop_depth=2)
```
### Batch Operations
```python
# Ingest multiple files
docs = db.ingest_files(
["doc1.pdf", "doc2.pdf"],
metadata={"category": "research"},
parallel=True
)
# Ingest all PDFs in a directory
docs = db.ingest_directory(
"data/documents",
recursive=True,
pattern="*.pdf"
)
# Batch retrieve documents
docs = db.batch_get_documents(["doc_id1", "doc_id2"])
```
### Multi-modal Retrieval (ColPali)
```python
# Ingest a PDF with charts and images
db.ingest_file("report_with_charts.pdf", use_colpali=True)
# Retrieve relevant chunks, including images
chunks = db.retrieve_chunks(
"Show me the Q2 revenue chart",
use_colpali=True,
k=3
)
# Process retrieved images
for chunk in chunks:
if hasattr(chunk.content, 'show'): # If it's an image
chunk.content.show()
else:
print(chunk.content)
```
## Why Choose Morphik?
| Feature | Morphik | Traditional Vector DBs | Document DBs | LLM Frameworks |
|---------|-----------|---------------------|------------|---------------|
| **Multi-modal Support** | ✅ Advanced ColPali embedding for text + images | ❌ or Limited | ❌ | ❌ |
| **Knowledge Graphs** | ✅ Automated extraction & enhanced retrieval | ❌ | ❌ | ❌ |
| **Rules Engine** | ✅ Natural language rules & schema definition | ❌ | ❌ | Limited |
| **Caching** | ✅ Persistent KV-caching with selective updates | ❌ | ❌ | Limited |
| **Scalability** | ✅ Millions of documents with PostgreSQL/MongoDB | ✅ | ✅ | Limited |
| **Video Content** | ✅ Native video parsing & transcription | ❌ | ❌ | ❌ |
| **Deployment Options** | ✅ Self-hosted, cloud, or hybrid | Varies | Varies | Limited |
| **Open Source** | ✅ MIT License | Varies | Varies | Varies |
| **API & SDK** | ✅ Clean Python SDK & RESTful API | Varies | Varies | Varies |
### Key Advantages
- **ColPali Multi-modal Embeddings**: Process and retrieve from documents based on both textual and visual content, maintaining the visual context that other systems miss.
- **Cache Augmented Retrieval**: Pre-process and "freeze" document states to reduce compute costs by up to 80% and drastically improve response times.
- **Schema-like Rules for Unstructured Data**: Define rules to extract consistent metadata from unstructured content, bringing database-like queryability to any document format.
- **Enterprise-grade Scalability**: Built on proven database technologies (PostgreSQL/MongoDB) that can scale to millions of documents while maintaining sub-second retrieval times.
## Documentation
For comprehensive documentation:
- [Installation Guide](https://docs.morphik.ai/getting-started)
- [Core Concepts](https://docs.morphik.ai/concepts/naive-rag)
- [Python SDK](https://docs.morphik.ai/python-sdk/morphik)
- [API Reference](https://docs.morphik.ai/api-reference/health-check)
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Community
- [Discord](https://discord.gg/BwMtv3Zaju) - Join our community
- [GitHub](https://github.com/morphik-org/morphik-core) - Contribute to development
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Built with ❤️ by Morphik