RagLeap
No CUDA, no 2GB torch. 23MB reranker. 6 vector DBs.
10 followers
No CUDA, no 2GB torch. 23MB reranker. 6 vector DBs.
10 followers
The open-source core of RagLeap — a self-hosted RAG chat engine (document ingestion, retrieval, citations) powering RagLeap's full AI business manager: Voice AI, WhatsApp/Telegram/Discord bots, database & CRM integrations, workflow automation, and a private executive assistant. Foundation layer — hosted platform at ragleap.com - antonyrag/ragleap-core
Quickstart for anyone who wants to try it:
pip install ragleap-rag
from ragleap import RagLeap, ProviderConfig, EmbeddingConfig
rag = RagLeap(
database_url="postgresql://user:pass@localhost/mydb",
embedder=EmbeddingConfig(provider="gemini", api_key="YOUR_KEY"),
primary=ProviderConfig(provider="gemini", api_key="YOUR_KEY"),
)
rag.init_schema()
rag.ingest("doc.pdf", open("doc.pdf","rb").read())
answer = rag.ask("your question")
print(answer["answer"])
Works on CPU with 23MB ONNX. No GPU needed.
I also added WhatsApp/Telegram/Discord examples in /examples folder on GitHub.
Which one should I demo next?
🚀 24 Hour Update from the maker
Thanks to everyone who tried RagLeap today!
Quick stats:
- GitHub: 4+ stars, 143 commits
- pip install: `pip install ragleap-rag` is live
- Runs on: CPU only. Tested on 8GB RAM laptop
Most common question: "Is this really faster than LangChain?"
Answer: Yes. No 2GB torch download. 23MB ONNX loads in 2 seconds.
I just pushed:
1. Neo4j support → ragleap-graph is WIP
2. 3 new vector DBs → Qdrant, Weaviate, Chroma coming
3. Full roadmap in pinned comment above
If you get it running, drop your use-case below 👇
I'll personally help you debug in comments.
Last 12 hours to upvote. Appreciate the support!
🗺️ 2026 Roadmap - The 8 Package Vision
Thanks for the support today! Here's where RagLeap is headed:
v0.11.2 DONE: ragleap-rag - CPU-first RAG engine
Q3 2026 NEXT:
- ragleap-graph: Neo4j knowledge-graph RAG
- ragleap-vectorstores: Qdrant, Weaviate, Chroma support
- ragleap-tools: search, code exec, calculators
- ragleap-integrations: MCP-native + WhatsApp/Telegram/Discord
Q4 2026 AFTER:
- ragleap-agents: Role-based crews + tool-calling + HITL
- ragleap-flows: Low-code orchestration
NEW: ragleap-observability: Tracing + hallucination detection + LLM-as-judge evals
ONGOING: ragleap-ops: Docker/K8s/Helm templates
LATER: ragleap-studio: Visual builder UI
All MIT licensed. All CPU-first.
Which package do you want first? 👇
🚀 2 Hour Update
We just hit:
- 4 GitHub stars
- 1 fork
- 141 commits on ragleap-core
pip install ragleap-rag is live if you want to try the CPU-first RAG engine.
Biggest question I'm getting: "How is this different from LangChain?"
Answer: No abstraction layers. 23MB ONNX reranker instead of 2GB torch. Built-in WhatsApp/Telegram/Discord. You own the DB.
What feature should I build next?
A) More vector DBs
B) Better WhatsApp bot templates
C) One-click deploy script
Comment below 👇 I'll build the top voted one this week.
⏰ Last 3 hours to support RagLeap on PH
Building this solo for 4 months. 143 commits.
Goal: Make RAG accessible on CPU for every dev.
If you believe in "no GPU needed AI", please upvote 🙏
Roadmap is pinned above. `ragleap-graph` with Neo4j starts next week.
Thanks to everyone who commented and starred. This keeps me going.