embeddings-and-search
Use when generating embeddings, calling the 12 web-search providers, or running OCR over documents with the 4 OCR providers through liter-llm. Covers embed, search, and ocr methods plus reranking.
ソース情報
- リポジトリ
- xberg-io/liter-llm
- ソースの最終更新活動
- 2026年9月30日 16:23
- 検出された SKILL.md の言語
- 英語
- スター
- 260
- フォーク
- 23
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SKILL.md
ソースの指示 · 読み取り専用プレビュー- name
- embeddings-and-search
- description
- Use when generating embeddings, calling the 12 web-search providers, or running OCR over documents with the 4 OCR providers through liter-llm. Covers embed, search, and ocr methods plus reranking.
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# 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.
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