liviate-rag

Client SDK for Liviate's RAG stack — ingest, embed, retrieve, rerank, and (optionally) generate — in a few lines of Python.

pip install liviate-rag

Quickstart

from liviate_rag import RAGClient

client = RAGClient(api_key="...")  # or set LIVIATE_API_KEY

client.ingest("handbook.pdf", collection="hotel-kirstine")

result = client.query("Har I parkering?", collection="hotel-kirstine", model="anthropic/claude-sonnet-5")
print(result.answer)
print(result.sources)
print(result.usage)
print(result.timing)

Want ranked context only, and to call your own LLM?

context = client.retrieve("Har I parkering?", collection="hotel-kirstine", top_k=5)

An AsyncRAGClient with the same method surface is available for async codebases:

from liviate_rag import AsyncRAGClient

async with AsyncRAGClient() as client:
    result = await client.query("...", collection="...", model="...")

Ingest

ingest() handles a single file, URL, text string, or file-like stream — detecting which one automatically from content, never a filename extension. See _ingest/detect.py for the exact detection order.

Supported file types

Plus raw text (source_type="text") and URLs (a single page is scraped or downloaded automatically depending on its content type).

OCR / scanned images are explicitly out of scope for v1 — ingest() raises UnsupportedFileType rather than failing silently or half-parsing.

Whole-site crawling (ingest_site()) is not yet implemented — it raises NotImplementedError. Use ingest() with a list of individual page URLs in the meantime; it already accepts a batch of sources in one call.

If you ingest into a collection with a non-default embed_model=, pass the same embed_model= to retrieve()/query() when querying it — a query embedded with a different model than the collection's vectors either returns garbage or fails outright on a dimension mismatch.

Removing content

result = client.ingest("handbook.pdf", collection="hotel-kirstine")
client.delete("hotel-kirstine", ids=result.point_ids)          # by id
client.delete("hotel-kirstine", filter={"must": [...]})        # by metadata filter

Errors

Every error from the Liviate API — whatever the underlying transport, including calls routed through the openai client for embed()/query()'s generation step — surfaces as this package's own exception hierarchy (liviate_rag.LiviateError and subclasses: APIError, RateLimitError, UnsupportedFileType, IngestTimeout), never a raw httpx/openai exception. The one deliberate exception: ingest() raises the builtin ValueError when it can't classify a source.

Development

pip install -e ".[dev]"
pytest tests/unit tests/integration   # no network/credentials required
LIVIATE_E2E=1 pytest tests/e2e         # real environment, run manually