graphiti/graphiti_core/embedder
alan blount 432ff7577d
feat(gemini): simplify config for Gemini clients (#679)
The cross_encoder for Gemini already supported passing in a custom client.

I replicated the same input pattern to embedder and llm_client.

The value is, you can support custom API endpoints and other options like below:

        cross_encoder=GeminiRerankerClient(
            client=genai.Client(
                api_key=os.environ.get('GOOGLE_GENAI_API_KEY'),
                http_options=types.HttpOptions(api_version='v1alpha')),
            config=LLMConfig(
                model="gemini-2.5-flash-lite-preview-06-17"
            )
        ))
2025-07-05 21:14:55 -07:00
..
__init__.py Gemini support (#324) 2025-04-06 09:27:04 -07:00
azure_openai.py feat: add Azure client wrappers for embedding and LLM, integrate into server (#581) 2025-06-13 11:55:08 -04:00
client.py Bulk embed (#403) 2025-04-26 22:09:12 -04:00
gemini.py feat(gemini): simplify config for Gemini clients (#679) 2025-07-05 21:14:55 -07:00
openai.py Bulk embed (#403) 2025-04-26 22:09:12 -04:00
voyage.py Refactor imports (#675) 2025-07-05 08:57:07 -07:00