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][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- param access_token: str | None = None#
Access token for GigaChat
- param auth_url: str | None = None#
Auth URL
- param base_url: str | None = None#
Base API URL
- param ca_bundle_file: str | None = None#
- param cert_file: str | None = None#
- param credentials: str | None = None#
Auth Token
- param key_file: str | None = None#
- param key_file_password: str | None = None#
- param model: str | None = None#
Model name to use.
- param password: str | None = None#
Password for authenticate
- param scope: str | None = None#
Permission scope for access token
- param timeout: float | None = 600#
Timeout for request. By default it works for long requests.
- param user: str | None = None#
Username for authenticate
- param verify_ssl_certs: bool | None = 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]
- embed_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]]
Examples using GigaChatEmbeddings