marcocosta10000
6b3b6541a7
Initial project setup including the main agent script, configuration files, profile JSON state, systemd service unit, startup script, deployment documentation, and Python dependencies. Includes .env, venv/, and __pycache__/ which should likely be excluded via .gitignore to avoid committing secrets and artifacts.
262 lines
10 KiB
Python
262 lines
10 KiB
Python
# File generated from our OpenAPI spec by Stainless.
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from __future__ import annotations
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import base64
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from typing import List, Union, Iterable, cast
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from typing_extensions import Literal
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import httpx
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from .. import _legacy_response
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from ..types import CreateEmbeddingResponse, embedding_create_params
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from .._types import NOT_GIVEN, Body, Query, Headers, NotGiven
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from .._utils import is_given, maybe_transform
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from .._compat import cached_property
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from .._extras import numpy as np, has_numpy
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from .._resource import SyncAPIResource, AsyncAPIResource
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from .._response import to_streamed_response_wrapper, async_to_streamed_response_wrapper
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from .._base_client import (
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make_request_options,
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)
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__all__ = ["Embeddings", "AsyncEmbeddings"]
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class Embeddings(SyncAPIResource):
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@cached_property
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def with_raw_response(self) -> EmbeddingsWithRawResponse:
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return EmbeddingsWithRawResponse(self)
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@cached_property
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def with_streaming_response(self) -> EmbeddingsWithStreamingResponse:
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return EmbeddingsWithStreamingResponse(self)
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def create(
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self,
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*,
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input: Union[str, List[str], Iterable[int], Iterable[Iterable[int]]],
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model: Union[str, Literal["text-embedding-ada-002", "text-embedding-3-small", "text-embedding-3-large"]],
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dimensions: int | NotGiven = NOT_GIVEN,
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encoding_format: Literal["float", "base64"] | NotGiven = NOT_GIVEN,
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user: str | NotGiven = NOT_GIVEN,
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# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
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# The extra values given here take precedence over values defined on the client or passed to this method.
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extra_headers: Headers | None = None,
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extra_query: Query | None = None,
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extra_body: Body | None = None,
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timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
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) -> CreateEmbeddingResponse:
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"""
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Creates an embedding vector representing the input text.
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Args:
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input: Input text to embed, encoded as a string or array of tokens. To embed multiple
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inputs in a single request, pass an array of strings or array of token arrays.
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The input must not exceed the max input tokens for the model (8192 tokens for
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`text-embedding-ada-002`), cannot be an empty string, and any array must be 2048
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dimensions or less.
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[Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
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for counting tokens.
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model: ID of the model to use. You can use the
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[List models](https://platform.openai.com/docs/api-reference/models/list) API to
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see all of your available models, or see our
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[Model overview](https://platform.openai.com/docs/models/overview) for
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descriptions of them.
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dimensions: The number of dimensions the resulting output embeddings should have. Only
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supported in `text-embedding-3` and later models.
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encoding_format: The format to return the embeddings in. Can be either `float` or
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[`base64`](https://pypi.org/project/pybase64/).
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user: A unique identifier representing your end-user, which can help OpenAI to monitor
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and detect abuse.
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[Learn more](https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids).
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extra_headers: Send extra headers
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extra_query: Add additional query parameters to the request
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extra_body: Add additional JSON properties to the request
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timeout: Override the client-level default timeout for this request, in seconds
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"""
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params = {
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"input": input,
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"model": model,
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"user": user,
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"dimensions": dimensions,
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"encoding_format": encoding_format,
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}
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if not is_given(encoding_format) and has_numpy():
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params["encoding_format"] = "base64"
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def parser(obj: CreateEmbeddingResponse) -> CreateEmbeddingResponse:
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if is_given(encoding_format):
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# don't modify the response object if a user explicitly asked for a format
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return obj
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for embedding in obj.data:
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data = cast(object, embedding.embedding)
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if not isinstance(data, str):
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# numpy is not installed / base64 optimisation isn't enabled for this model yet
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continue
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embedding.embedding = np.frombuffer( # type: ignore[no-untyped-call]
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base64.b64decode(data), dtype="float32"
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).tolist()
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return obj
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return self._post(
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"/embeddings",
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body=maybe_transform(params, embedding_create_params.EmbeddingCreateParams),
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options=make_request_options(
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extra_headers=extra_headers,
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extra_query=extra_query,
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extra_body=extra_body,
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timeout=timeout,
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post_parser=parser,
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),
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cast_to=CreateEmbeddingResponse,
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)
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class AsyncEmbeddings(AsyncAPIResource):
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@cached_property
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def with_raw_response(self) -> AsyncEmbeddingsWithRawResponse:
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return AsyncEmbeddingsWithRawResponse(self)
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@cached_property
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def with_streaming_response(self) -> AsyncEmbeddingsWithStreamingResponse:
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return AsyncEmbeddingsWithStreamingResponse(self)
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async def create(
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self,
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*,
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input: Union[str, List[str], Iterable[int], Iterable[Iterable[int]]],
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model: Union[str, Literal["text-embedding-ada-002", "text-embedding-3-small", "text-embedding-3-large"]],
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dimensions: int | NotGiven = NOT_GIVEN,
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encoding_format: Literal["float", "base64"] | NotGiven = NOT_GIVEN,
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user: str | NotGiven = NOT_GIVEN,
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# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
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# The extra values given here take precedence over values defined on the client or passed to this method.
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extra_headers: Headers | None = None,
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extra_query: Query | None = None,
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extra_body: Body | None = None,
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timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
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) -> CreateEmbeddingResponse:
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"""
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Creates an embedding vector representing the input text.
|
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Args:
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input: Input text to embed, encoded as a string or array of tokens. To embed multiple
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|
inputs in a single request, pass an array of strings or array of token arrays.
|
|
The input must not exceed the max input tokens for the model (8192 tokens for
|
|
`text-embedding-ada-002`), cannot be an empty string, and any array must be 2048
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|
dimensions or less.
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[Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
|
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for counting tokens.
|
|
|
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model: ID of the model to use. You can use the
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|
[List models](https://platform.openai.com/docs/api-reference/models/list) API to
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see all of your available models, or see our
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[Model overview](https://platform.openai.com/docs/models/overview) for
|
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descriptions of them.
|
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dimensions: The number of dimensions the resulting output embeddings should have. Only
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supported in `text-embedding-3` and later models.
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|
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encoding_format: The format to return the embeddings in. Can be either `float` or
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[`base64`](https://pypi.org/project/pybase64/).
|
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user: A unique identifier representing your end-user, which can help OpenAI to monitor
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|
and detect abuse.
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[Learn more](https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids).
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extra_headers: Send extra headers
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|
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extra_query: Add additional query parameters to the request
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extra_body: Add additional JSON properties to the request
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timeout: Override the client-level default timeout for this request, in seconds
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"""
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params = {
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"input": input,
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"model": model,
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"user": user,
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"dimensions": dimensions,
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"encoding_format": encoding_format,
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}
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if not is_given(encoding_format) and has_numpy():
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params["encoding_format"] = "base64"
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def parser(obj: CreateEmbeddingResponse) -> CreateEmbeddingResponse:
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if is_given(encoding_format):
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# don't modify the response object if a user explicitly asked for a format
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return obj
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for embedding in obj.data:
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data = cast(object, embedding.embedding)
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if not isinstance(data, str):
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# numpy is not installed / base64 optimisation isn't enabled for this model yet
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continue
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embedding.embedding = np.frombuffer( # type: ignore[no-untyped-call]
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base64.b64decode(data), dtype="float32"
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).tolist()
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return obj
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return await self._post(
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"/embeddings",
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body=maybe_transform(params, embedding_create_params.EmbeddingCreateParams),
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options=make_request_options(
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extra_headers=extra_headers,
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extra_query=extra_query,
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extra_body=extra_body,
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timeout=timeout,
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post_parser=parser,
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),
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cast_to=CreateEmbeddingResponse,
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)
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class EmbeddingsWithRawResponse:
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def __init__(self, embeddings: Embeddings) -> None:
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self._embeddings = embeddings
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self.create = _legacy_response.to_raw_response_wrapper(
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embeddings.create,
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)
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class AsyncEmbeddingsWithRawResponse:
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def __init__(self, embeddings: AsyncEmbeddings) -> None:
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self._embeddings = embeddings
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self.create = _legacy_response.async_to_raw_response_wrapper(
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embeddings.create,
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)
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class EmbeddingsWithStreamingResponse:
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def __init__(self, embeddings: Embeddings) -> None:
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self._embeddings = embeddings
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self.create = to_streamed_response_wrapper(
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embeddings.create,
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)
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class AsyncEmbeddingsWithStreamingResponse:
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def __init__(self, embeddings: AsyncEmbeddings) -> None:
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self._embeddings = embeddings
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self.create = async_to_streamed_response_wrapper(
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embeddings.create,
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)
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