embeddings-and-search

Embeddings and Search

liter-llm exposes embeddings, web search (12 providers), OCR (4 providers), and reranking through the same provider/model routing convention.

Embeddings

import asyncio, os
from liter_llm import LlmClient

async def main() -> None:
    client = LlmClient(api_key=os.environ["OPENAI_API_KEY"])
    response = await client.embed(
        model="openai/text-embedding-3-small",
        input=["first document", "second document"],
    )
    for item in response.data:
        print(len(item.embedding))

asyncio.run(main())

Many embedding models support dimension selection and base64 output; pass dimensions / encoding_format where the provider allows it.

Web search (12 providers)

client = LlmClient(api_key=os.environ["BRAVE_API_KEY"])
response = await client.search(
    model="brave/web-search",
    query="What is the Rust programming language?",
    max_results=5,
)
for result in response.results:
    print(result.title, result.url)

OCR (4 providers)

client = LlmClient(api_key=os.environ["MISTRAL_API_KEY"])
response = await client.ocr(
    model="mistral/mistral-ocr-latest",
    document={"type": "document_url", "url": "https://example.com/invoice.pdf"},
)
for page in response.pages:
    print(page.index, page.markdown[:100])

Reranking

Use rerank(...) to score and order candidate documents against a query for retrieval pipelines — combine it with embed for hybrid retrieval. Routing follows the same provider/model convention.

Notes

• Search and OCR providers each need their own API key (e.g. BRAVE_API_KEY, MISTRAL_API_KEY); read them from env vars.
• See the upstream provider reference for the full list of the 12 search and 4 OCR backends and their model identifiers.