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.
معلومات المصدر
- المستودع
- xberg-io/liter-llm
- آخر نشاط في المصدر
- ١٠ أكتوبر ٢٠٢٦ في ٠٧:٤٧
- لغة SKILL.md المكتشفة
- الإنجليزية
- النجوم
- ٢٦٢
- التفرعات
- ٢٣
خيارات التثبيت
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
مراجعة ملفات المصدر
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
عرض SKILL.md
SKILL.md
تعليمات المصدر · معاينة للقراءة فقط- 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.
<!--
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.
عرض على GitHub