ragleap-rag 0.1.0

A fast, honest, self-hosted RAG engine — hybrid dense+sparse retrieval, streaming, provider fallback, and real token usage reporting. BYOK, no vendor lock-in.

ragleap-rag

A fast, honest, self-hosted RAG engine. Hybrid dense+sparse retrieval, streaming, provider fallback, and real token usage reporting — no vendor lock-in, bring your own API keys.

pip install ragleap-rag[gemini]

from ragleap import RagLeap, ProviderConfig

rag = RagLeap(
    database_url="postgresql://user:pass@localhost/mydb",
    embedder_api_key="your-gemini-key",
    primary=ProviderConfig(provider="gemini", api_key="your-gemini-key"),
)
rag.init_schema()  # one-time, idempotent

with open("document.pdf", "rb") as f:
    result = rag.ingest("document.pdf", f.read())
print(f"Ingested {result.chunks_stored} chunks")

answer = rag.ask("What does this document say?")
print(answer["answer"])
print("Sources:", answer["sources"])
print("Tokens used:", answer["usage"])

Why ragleap-rag

• Hybrid search by default — combines pgvector dense similarity with Postgres full-text sparse search via Reciprocal Rank Fusion, catching both semantic matches and exact keyword/identifier matches.
• Real provider fallback — configure backup LLM providers; if your primary fails (rate limit, outage, bad key), it retries automatically.
• Streaming — real per-provider streaming for Gemini, Anthropic, and any OpenAI-compatible endpoint.
• Real token usage — every answer reports actual prompt_tokens, completion_tokens, total_tokens from the provider's own response, not an estimate.
• Context budget trimming — automatically drops lowest-ranked chunks to stay within a character budget, so you're not paying for more context than necessary.
• BYOK, always — no system-provided keys, ever. You own your data, your database, and your API costs.

Requirements

A PostgreSQL database with the pgvector extension available (CREATE EXTENSION vector — rag.init_schema() handles the rest). Embeddings currently use Google Gemini (gemini-embedding-001); generation supports Gemini, Anthropic, and any OpenAI-compatible provider (OpenAI, Groq, Mistral, Together, OpenRouter, Ollama, DeepSeek, xAI, Cohere, Perplexity, or a custom endpoint).

Status

This is a young, actively-developed extraction from ragleap-core, the open-source self-hosted RAG platform. Full documentation, more embedding providers, and companion packages (ragleap-graph for knowledge-graph-boosted retrieval, ragleap-integrations for CRM/database connectors) are in progress.

License

MIT