langchain_google_genai.embeddings
.GoogleGenerativeAIEmbeddings¶
- class langchain_google_genai.embeddings.GoogleGenerativeAIEmbeddings[source]¶
Bases:
BaseModel
,Embeddings
Google Generative AI Embeddings.
To use, you must have either:
The
GOOGLE_API_KEY`
environment variable set with your API key, orPass your API key using the google_api_key kwarg to the ChatGoogle constructor.
Example
from langchain_google_genai import GoogleGenerativeAIEmbeddings embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001") embeddings.embed_query("What's our Q1 revenue?")
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 client_options: Optional[Dict] = None¶
A dictionary of client options to pass to the Google API client, such as api_endpoint.
- param credentials: Any = None¶
The default custom credentials (google.auth.credentials.Credentials) to use when making API calls. If not provided, credentials will be ascertained from the GOOGLE_API_KEY envvar
- param google_api_key: Optional[SecretStr] = None¶
The Google API key to use. If not provided, the GOOGLE_API_KEY environment variable will be used.
- Constraints
type = string
writeOnly = True
format = password
- param model: str [Required]¶
The name of the embedding model to use. Example: models/embedding-001
- param request_options: Optional[Dict] = None¶
A dictionary of request options to pass to the Google API client.Example: {‘timeout’: 10}
- param task_type: Optional[str] = None¶
The task type. Valid options include: task_type_unspecified, retrieval_query, retrieval_document, semantic_similarity, classification, and clustering
- param transport: Optional[str] = None¶
A string, one of: [rest, grpc, grpc_asyncio].
- async aembed_documents(texts: List[str]) List[List[float]] ¶
Asynchronous Embed search docs.
- Parameters
texts (List[str]) –
- Return type
List[List[float]]
- async aembed_query(text: str) List[float] ¶
Asynchronous Embed query text.
- Parameters
text (str) –
- Return type
List[float]
- classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) Model ¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
- Parameters
_fields_set (Optional[SetStr]) –
values (Any) –
- Return type
Model
- copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) Model ¶
Duplicate a model, optionally choose which fields to include, exclude and change.
- Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) –
- Returns
new model instance
- Return type
Model
- dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) DictStrAny ¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
- Return type
DictStrAny
- embed_documents(texts: List[str], task_type: Optional[str] = None, titles: Optional[List[str]] = None, output_dimensionality: Optional[int] = None) List[List[float]] [source]¶
Embed a list of strings. Vertex AI currently sets a max batch size of 5 strings.
- Parameters
texts (List[str]) – List[str] The list of strings to embed.
batch_size – [int] The batch size of embeddings to send to the model
task_type (Optional[str]) – task_type (https://ai.google.dev/api/rest/v1/TaskType)
titles (Optional[List[str]]) – An optional list of titles for texts provided.
RETRIEVAL_DOCUMENT. (Only applicable when TaskType is) –
output_dimensionality (Optional[int]) – Optional reduced dimension for the output embedding.
https – //ai.google.dev/api/rest/v1/models/batchEmbedContents#EmbedContentRequest
- Returns
List of embeddings, one for each text.
- Return type
List[List[float]]
- embed_query(text: str, task_type: Optional[str] = None, title: Optional[str] = None, output_dimensionality: Optional[int] = None) List[float] [source]¶
Embed a text.
- Parameters
text (str) – The text to embed.
task_type (Optional[str]) – task_type (https://ai.google.dev/api/rest/v1/TaskType)
title (Optional[str]) – An optional title for the text.
RETRIEVAL_DOCUMENT. (Only applicable when TaskType is) –
output_dimensionality (Optional[int]) – Optional reduced dimension for the output embedding.
https – //ai.google.dev/api/rest/v1/models/batchEmbedContents#EmbedContentRequest
- Returns
Embedding for the text.
- Return type
List[float]
- classmethod from_orm(obj: Any) Model ¶
- Parameters
obj (Any) –
- Return type
Model
- json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) unicode ¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
- Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –
- Return type
unicode
- classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model ¶
- Parameters
path (Union[str, Path]) –
content_type (unicode) –
encoding (unicode) –
proto (Protocol) –
allow_pickle (bool) –
- Return type
Model
- classmethod parse_obj(obj: Any) Model ¶
- Parameters
obj (Any) –
- Return type
Model
- classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model ¶
- Parameters
b (Union[str, bytes]) –
content_type (unicode) –
encoding (unicode) –
proto (Protocol) –
allow_pickle (bool) –
- Return type
Model
- classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') DictStrAny ¶
- Parameters
by_alias (bool) –
ref_template (unicode) –
- Return type
DictStrAny
- classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) unicode ¶
- Parameters
by_alias (bool) –
ref_template (unicode) –
dumps_kwargs (Any) –
- Return type
unicode
- classmethod update_forward_refs(**localns: Any) None ¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
- Parameters
localns (Any) –
- Return type
None
- classmethod validate(value: Any) Model ¶
- Parameters
value (Any) –
- Return type
Model