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 create_client
from liter_llm._internal_bindings import EmbeddingRequest

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

asyncio.run(main())

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

Web search (12 providers)

from liter_llm._internal_bindings import SearchRequest

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

OCR (4 providers)

from liter_llm._internal_bindings import OcrRequest

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

Reranking

Build a RerankRequest (model, query, documents) and call client.rerank(request) to score and order candidate documents against a query for retrieval pipelines — combine it with embed for hybrid retrieval. Each result carries index and relevance_score. Routing follows the same provider/model convention.

Notes