langchain_community.embeddings.gigachat.GigaChatEmbeddings¶
- class langchain_community.embeddings.gigachat.GigaChatEmbeddings[source]¶
Bases:
BaseModel,EmbeddingsGigaChat Embeddings models.
Example
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
- param access_token: Optional[str] = None¶
Access token for GigaChat
- param auth_url: Optional[str] = None¶
Auth URL
- param base_url: Optional[str] = None¶
Base API URL
- param ca_bundle_file: Optional[str] = None¶
- param cert_file: Optional[str] = None¶
- param credentials: Optional[str] = None¶
Auth Token
- param key_file: Optional[str] = None¶
- param key_file_password: Optional[str] = None¶
- param model: Optional[str] = None¶
Model name to use.
- param password: Optional[str] = None¶
Password for authenticate
- param scope: Optional[str] = None¶
Permission scope for access token
- param timeout: Optional[float] = 600¶
Timeout for request. By default it works for long requests.
- param user: Optional[str] = None¶
Username for authenticate
- param verify_ssl_certs: Optional[bool] = None¶
Check certificates for all requests
- async aembed_documents(texts: List[str]) List[List[float]][source]¶
Embed documents using a GigaChat embeddings models.
- Parameters
texts (List[str]) – The list of texts to embed.
- Returns
List of embeddings, one for each text.
- Return type
List[List[float]]
- async aembed_query(text: str) List[float][source]¶
Embed a query using a GigaChat embeddings models.
- Parameters
text (str) – The text to embed.
- Returns
Embeddings for the text.
- Return type
List[float]