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Getting Started

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antonyrag edited this page Aug 4, 2026 · 1 revision

Getting Started

Install

pip install ragleap-rag
# or
uv add ragleap-rag

You'll need a PostgreSQL database with the pgvector extension enabled, and an API key for at least one supported provider (Gemini, Anthropic, OpenAI, or any OpenAI-compatible endpoint including local Ollama).

Your first ingest + ask

from ragleap import RagLeap, ProviderConfig, EmbeddingConfig

rag = RagLeap(
    database_url="postgresql://user:pass@localhost:5432/mydb",
    primary=ProviderConfig(provider="gemini", model="gemini-3.6-flash", api_key="..."),
    embedder=EmbeddingConfig(
        provider="gemini", model="models/gemini-embedding-001",
        dimensions=3072, api_key="...",
    ),
)
rag.init_schema()

result = rag.ingest_text(filename="handbook.pdf", text="...your document text...")
answer = rag.ask("What's our PTO policy?")

print(answer["answer"])
print(answer["sources"])

model= is always required for both primary and embedder — there are no hardcoded model defaults (see the breaking change in v0.9.0 and why: no provider's default model stays safe to hardcode forever).

Ingesting other content types

rag.ingest("report.pdf", raw_bytes)                          # any of 28 formats
rag.ingest_url("https://example.com/article")                 # clean text extraction
rag.ingest_image("scan.png", raw_bytes, mode="ocr")            # OCR or vision captioning
rag.ingest_audio("call.mp3", raw_bytes)                        # pluggable transcription
rag.ingest_video("webinar.mp4", raw_bytes)                     # audio extraction + transcription

Running locally with zero API cost (Ollama)

rag = RagLeap(
    database_url="postgresql://user:pass@localhost:5432/mydb",
    primary=ProviderConfig(provider="custom", model="qwen2.5:0.5b",
                            api_key="ollama", base_url="http://localhost:11434/v1"),
    embedder=EmbeddingConfig(provider="custom", model="nomic-embed-text",
                              dimensions=768, api_key="ollama",
                              base_url="http://localhost:11434/v1"),
)

Fully local, no API key cost — see Benchmarks for real measured latency on this setup (it's meaningfully slower than a hosted API on CPU-only hardware, which is the honest tradeoff of running free and local).

Next steps

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