embeddings-and-search

<!-- AI-RULEZ :: GENERATED FILE — DO NOT EDIT Content-Hash: blake3:2486d6f9ae493f698ce3c5349658dacd142fb7dd3cd27a707c4acf3aa8877a55 Source-Hash: blake3:3cb955aedbbbd51d9b5f60a411d90b9386534c5f14bf20a056bef60f7dace49e Schema-Version: v1 --> # Embeddings and Search liter-llm exposes embeddings, web search (12 providers), OCR (4 providers), and reranking through the same `provider/model` routing convention. ## Embeddings ```python 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) ```python 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) ```python 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 - 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.