initial commit
This commit is contained in:
716
venv/Lib/site-packages/langchain_community/embeddings/openai.py
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716
venv/Lib/site-packages/langchain_community/embeddings/openai.py
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@@ -0,0 +1,716 @@
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from __future__ import annotations
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import logging
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import os
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import warnings
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from typing import (
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Any,
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Callable,
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Dict,
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List,
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Literal,
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Mapping,
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Optional,
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Sequence,
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Set,
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Tuple,
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Union,
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cast,
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)
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import numpy as np
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from langchain_core._api.deprecation import deprecated
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from langchain_core.embeddings import Embeddings
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from langchain_core.utils import (
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get_from_dict_or_env,
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get_pydantic_field_names,
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pre_init,
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)
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from pydantic import BaseModel, ConfigDict, Field, model_validator
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from tenacity import (
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AsyncRetrying,
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before_sleep_log,
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retry,
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retry_if_exception_type,
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stop_after_attempt,
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wait_exponential,
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)
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from langchain_community.utils.openai import is_openai_v1
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logger = logging.getLogger(__name__)
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def _create_retry_decorator(embeddings: OpenAIEmbeddings) -> Callable[[Any], Any]:
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import openai
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# Wait 2^x * 1 second between each retry starting with
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# retry_min_seconds seconds, then up to retry_max_seconds seconds,
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# then retry_max_seconds seconds afterwards
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# retry_min_seconds and retry_max_seconds are optional arguments of
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# OpenAIEmbeddings
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return retry(
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reraise=True,
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stop=stop_after_attempt(embeddings.max_retries),
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wait=wait_exponential(
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multiplier=1,
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min=embeddings.retry_min_seconds,
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max=embeddings.retry_max_seconds,
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),
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retry=(
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retry_if_exception_type(openai.error.Timeout)
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| retry_if_exception_type(openai.error.APIError)
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| retry_if_exception_type(openai.error.APIConnectionError)
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| retry_if_exception_type(openai.error.RateLimitError)
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| retry_if_exception_type(openai.error.ServiceUnavailableError)
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),
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before_sleep=before_sleep_log(logger, logging.WARNING),
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)
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def _async_retry_decorator(embeddings: OpenAIEmbeddings) -> Any:
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import openai
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# Wait 2^x * 1 second between each retry starting with
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# retry_min_seconds seconds, then up to retry_max_seconds seconds,
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# then retry_max_seconds seconds afterwards
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# retry_min_seconds and retry_max_seconds are optional arguments of
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# OpenAIEmbeddings
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async_retrying = AsyncRetrying(
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reraise=True,
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stop=stop_after_attempt(embeddings.max_retries),
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wait=wait_exponential(
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multiplier=1,
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min=embeddings.retry_min_seconds,
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max=embeddings.retry_max_seconds,
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),
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retry=(
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retry_if_exception_type(openai.error.Timeout)
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| retry_if_exception_type(openai.error.APIError)
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| retry_if_exception_type(openai.error.APIConnectionError)
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| retry_if_exception_type(openai.error.RateLimitError)
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| retry_if_exception_type(openai.error.ServiceUnavailableError)
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),
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before_sleep=before_sleep_log(logger, logging.WARNING),
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)
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def wrap(func: Callable) -> Callable:
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async def wrapped_f(*args: Any, **kwargs: Any) -> Callable:
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async for _ in async_retrying:
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return await func(*args, **kwargs)
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raise AssertionError("this is unreachable")
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return wrapped_f
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return wrap
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# https://stackoverflow.com/questions/76469415/getting-embeddings-of-length-1-from-langchain-openaiembeddings
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def _check_response(response: dict, skip_empty: bool = False) -> dict:
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if any(len(d["embedding"]) == 1 for d in response["data"]) and not skip_empty:
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import openai
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raise openai.error.APIError("OpenAI API returned an empty embedding")
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return response
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def embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any:
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"""Use tenacity to retry the embedding call."""
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if is_openai_v1():
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return embeddings.client.create(**kwargs)
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retry_decorator = _create_retry_decorator(embeddings)
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@retry_decorator
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def _embed_with_retry(**kwargs: Any) -> Any:
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response = embeddings.client.create(**kwargs)
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return _check_response(response, skip_empty=embeddings.skip_empty)
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return _embed_with_retry(**kwargs)
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async def async_embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any:
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"""Use tenacity to retry the embedding call."""
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if is_openai_v1():
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return await embeddings.async_client.create(**kwargs)
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@_async_retry_decorator(embeddings)
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async def _async_embed_with_retry(**kwargs: Any) -> Any:
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response = await embeddings.client.acreate(**kwargs)
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return _check_response(response, skip_empty=embeddings.skip_empty)
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return await _async_embed_with_retry(**kwargs)
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@deprecated(
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since="0.0.9",
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removal="1.0",
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alternative_import="langchain_openai.OpenAIEmbeddings",
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)
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class OpenAIEmbeddings(BaseModel, Embeddings):
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"""OpenAI embedding models.
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To use, you should have the ``openai`` python package installed, and the
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environment variable ``OPENAI_API_KEY`` set with your API key or pass it
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as a named parameter to the constructor.
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Example:
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.. code-block:: python
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from langchain_community.embeddings import OpenAIEmbeddings
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openai = OpenAIEmbeddings(openai_api_key="my-api-key")
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In order to use the library with Microsoft Azure endpoints, you need to set
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the OPENAI_API_TYPE, OPENAI_API_BASE, OPENAI_API_KEY and OPENAI_API_VERSION.
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The OPENAI_API_TYPE must be set to 'azure' and the others correspond to
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the properties of your endpoint.
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In addition, the deployment name must be passed as the model parameter.
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Example:
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.. code-block:: python
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import os
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os.environ["OPENAI_API_TYPE"] = "azure"
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os.environ["OPENAI_API_BASE"] = "https://<your-endpoint.openai.azure.com/"
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os.environ["OPENAI_API_KEY"] = "your AzureOpenAI key"
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os.environ["OPENAI_API_VERSION"] = "2023-05-15"
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os.environ["OPENAI_PROXY"] = "http://your-corporate-proxy:8080"
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from langchain_community.embeddings.openai import OpenAIEmbeddings
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embeddings = OpenAIEmbeddings(
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deployment="your-embeddings-deployment-name",
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model="your-embeddings-model-name",
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openai_api_base="https://your-endpoint.openai.azure.com/",
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openai_api_type="azure",
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)
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text = "This is a test query."
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query_result = embeddings.embed_query(text)
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"""
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client: Any = Field(default=None, exclude=True) #: :meta private:
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async_client: Any = Field(default=None, exclude=True) #: :meta private:
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model: str = "text-embedding-ada-002"
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# to support Azure OpenAI Service custom deployment names
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deployment: Optional[str] = model
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# TODO: Move to AzureOpenAIEmbeddings.
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openai_api_version: Optional[str] = Field(default=None, alias="api_version")
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"""Automatically inferred from env var `OPENAI_API_VERSION` if not provided."""
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# to support Azure OpenAI Service custom endpoints
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openai_api_base: Optional[str] = Field(default=None, alias="base_url")
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"""Base URL path for API requests, leave blank if not using a proxy or service
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emulator."""
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# to support Azure OpenAI Service custom endpoints
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openai_api_type: Optional[str] = None
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# to support explicit proxy for OpenAI
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openai_proxy: Optional[str] = None
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embedding_ctx_length: int = 8191
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"""The maximum number of tokens to embed at once."""
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openai_api_key: Optional[str] = Field(default=None, alias="api_key")
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"""Automatically inferred from env var `OPENAI_API_KEY` if not provided."""
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openai_organization: Optional[str] = Field(default=None, alias="organization")
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"""Automatically inferred from env var `OPENAI_ORG_ID` if not provided."""
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allowed_special: Union[Literal["all"], Set[str]] = set()
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disallowed_special: Union[Literal["all"], Set[str], Sequence[str]] = "all"
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chunk_size: int = 1000
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"""Maximum number of texts to embed in each batch"""
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max_retries: int = 2
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"""Maximum number of retries to make when generating."""
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request_timeout: Optional[Union[float, Tuple[float, float], Any]] = Field(
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default=None, alias="timeout"
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)
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"""Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or
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None."""
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headers: Any = None
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tiktoken_enabled: bool = True
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"""Set this to False for non-OpenAI implementations of the embeddings API, e.g.
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the `--extensions openai` extension for `text-generation-webui`"""
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tiktoken_model_name: Optional[str] = None
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"""The model name to pass to tiktoken when using this class.
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Tiktoken is used to count the number of tokens in documents to constrain
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them to be under a certain limit. By default, when set to None, this will
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be the same as the embedding model name. However, there are some cases
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where you may want to use this Embedding class with a model name not
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supported by tiktoken. This can include when using Azure embeddings or
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when using one of the many model providers that expose an OpenAI-like
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API but with different models. In those cases, in order to avoid erroring
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when tiktoken is called, you can specify a model name to use here."""
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show_progress_bar: bool = False
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"""Whether to show a progress bar when embedding."""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Holds any model parameters valid for `create` call not explicitly specified."""
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skip_empty: bool = False
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"""Whether to skip empty strings when embedding or raise an error.
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Defaults to not skipping."""
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default_headers: Union[Mapping[str, str], None] = None
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default_query: Union[Mapping[str, object], None] = None
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# Configure a custom httpx client. See the
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# [httpx documentation](https://www.python-httpx.org/api/#client) for more details.
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retry_min_seconds: int = 4
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"""Min number of seconds to wait between retries"""
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retry_max_seconds: int = 20
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"""Max number of seconds to wait between retries"""
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http_client: Union[Any, None] = None
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"""Optional httpx.Client."""
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||||
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model_config = ConfigDict(
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populate_by_name=True, extra="forbid", protected_namespaces=()
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)
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|
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@model_validator(mode="before")
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@classmethod
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||||
def build_extra(cls, values: Dict[str, Any]) -> Any:
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||||
"""Build extra kwargs from additional params that were passed in."""
|
||||
all_required_field_names = get_pydantic_field_names(cls)
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||||
extra = values.get("model_kwargs", {})
|
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for field_name in list(values):
|
||||
if field_name in extra:
|
||||
raise ValueError(f"Found {field_name} supplied twice.")
|
||||
if field_name not in all_required_field_names:
|
||||
warnings.warn(
|
||||
f"""WARNING! {field_name} is not default parameter.
|
||||
{field_name} was transferred to model_kwargs.
|
||||
Please confirm that {field_name} is what you intended."""
|
||||
)
|
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extra[field_name] = values.pop(field_name)
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||||
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||||
invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
|
||||
if invalid_model_kwargs:
|
||||
raise ValueError(
|
||||
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
|
||||
f"Instead they were passed in as part of `model_kwargs` parameter."
|
||||
)
|
||||
|
||||
values["model_kwargs"] = extra
|
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return values
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|
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@pre_init
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validate that api key and python package exists in environment."""
|
||||
values["openai_api_key"] = get_from_dict_or_env(
|
||||
values, "openai_api_key", "OPENAI_API_KEY"
|
||||
)
|
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values["openai_api_base"] = values["openai_api_base"] or os.getenv(
|
||||
"OPENAI_API_BASE"
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||||
)
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||||
values["openai_api_type"] = get_from_dict_or_env(
|
||||
values,
|
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"openai_api_type",
|
||||
"OPENAI_API_TYPE",
|
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default="",
|
||||
)
|
||||
values["openai_proxy"] = get_from_dict_or_env(
|
||||
values,
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"openai_proxy",
|
||||
"OPENAI_PROXY",
|
||||
default="",
|
||||
)
|
||||
if values["openai_api_type"] in ("azure", "azure_ad", "azuread"):
|
||||
default_api_version = "2023-05-15"
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||||
# Azure OpenAI embedding models allow a maximum of 2048
|
||||
# texts at a time in each batch
|
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# See: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#embeddings
|
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values["chunk_size"] = min(values["chunk_size"], 2048)
|
||||
else:
|
||||
default_api_version = ""
|
||||
values["openai_api_version"] = get_from_dict_or_env(
|
||||
values,
|
||||
"openai_api_version",
|
||||
"OPENAI_API_VERSION",
|
||||
default=default_api_version,
|
||||
)
|
||||
# Check OPENAI_ORGANIZATION for backwards compatibility.
|
||||
values["openai_organization"] = (
|
||||
values["openai_organization"]
|
||||
or os.getenv("OPENAI_ORG_ID")
|
||||
or os.getenv("OPENAI_ORGANIZATION")
|
||||
)
|
||||
try:
|
||||
import openai
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"Could not import openai python package. "
|
||||
"Please install it with `pip install openai`."
|
||||
)
|
||||
else:
|
||||
if is_openai_v1():
|
||||
if values["openai_api_type"] in ("azure", "azure_ad", "azuread"):
|
||||
warnings.warn(
|
||||
"If you have openai>=1.0.0 installed and are using Azure, "
|
||||
"please use the `AzureOpenAIEmbeddings` class."
|
||||
)
|
||||
client_params = {
|
||||
"api_key": values["openai_api_key"],
|
||||
"organization": values["openai_organization"],
|
||||
"base_url": values["openai_api_base"],
|
||||
"timeout": values["request_timeout"],
|
||||
"max_retries": values["max_retries"],
|
||||
"default_headers": values["default_headers"],
|
||||
"default_query": values["default_query"],
|
||||
"http_client": values["http_client"],
|
||||
}
|
||||
if not values.get("client"):
|
||||
values["client"] = openai.OpenAI(**client_params).embeddings
|
||||
if not values.get("async_client"):
|
||||
values["async_client"] = openai.AsyncOpenAI(
|
||||
**client_params
|
||||
).embeddings
|
||||
elif not values.get("client"):
|
||||
values["client"] = openai.Embedding
|
||||
else:
|
||||
pass
|
||||
return values
|
||||
|
||||
@property
|
||||
def _invocation_params(self) -> Dict[str, Any]:
|
||||
if is_openai_v1():
|
||||
openai_args: Dict = {"model": self.model, **self.model_kwargs}
|
||||
else:
|
||||
openai_args = {
|
||||
"model": self.model,
|
||||
"request_timeout": self.request_timeout,
|
||||
"headers": self.headers,
|
||||
"api_key": self.openai_api_key,
|
||||
"organization": self.openai_organization,
|
||||
"api_base": self.openai_api_base,
|
||||
"api_type": self.openai_api_type,
|
||||
"api_version": self.openai_api_version,
|
||||
**self.model_kwargs,
|
||||
}
|
||||
if self.openai_api_type in ("azure", "azure_ad", "azuread"):
|
||||
openai_args["engine"] = self.deployment
|
||||
# TODO: Look into proxy with openai v1.
|
||||
if self.openai_proxy:
|
||||
try:
|
||||
import openai
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"Could not import openai python package. "
|
||||
"Please install it with `pip install openai`."
|
||||
)
|
||||
|
||||
openai.proxy = {
|
||||
"http": self.openai_proxy,
|
||||
"https": self.openai_proxy,
|
||||
}
|
||||
return openai_args
|
||||
|
||||
# please refer to
|
||||
# https://github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb
|
||||
def _get_len_safe_embeddings(
|
||||
self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None
|
||||
) -> List[List[float]]:
|
||||
"""
|
||||
Generate length-safe embeddings for a list of texts.
|
||||
|
||||
This method handles tokenization and embedding generation, respecting the
|
||||
set embedding context length and chunk size. It supports both tiktoken
|
||||
and HuggingFace tokenizer based on the tiktoken_enabled flag.
|
||||
|
||||
Args:
|
||||
texts (List[str]): A list of texts to embed.
|
||||
engine (str): The engine or model to use for embeddings.
|
||||
chunk_size (Optional[int]): The size of chunks for processing embeddings.
|
||||
|
||||
Returns:
|
||||
List[List[float]]: A list of embeddings for each input text.
|
||||
"""
|
||||
|
||||
tokens = []
|
||||
indices = []
|
||||
model_name = self.tiktoken_model_name or self.model
|
||||
_chunk_size = chunk_size or self.chunk_size
|
||||
|
||||
# If tiktoken flag set to False
|
||||
if not self.tiktoken_enabled:
|
||||
try:
|
||||
from transformers import AutoTokenizer
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"Could not import transformers python package. "
|
||||
"This is needed in order to for OpenAIEmbeddings without "
|
||||
"`tiktoken`. Please install it with `pip install transformers`. "
|
||||
)
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
pretrained_model_name_or_path=model_name
|
||||
)
|
||||
for i, text in enumerate(texts):
|
||||
# Tokenize the text using HuggingFace transformers
|
||||
tokenized = tokenizer.encode(text, add_special_tokens=False)
|
||||
|
||||
# Split tokens into chunks respecting the embedding_ctx_length
|
||||
for j in range(0, len(tokenized), self.embedding_ctx_length):
|
||||
token_chunk = tokenized[j : j + self.embedding_ctx_length]
|
||||
|
||||
# Convert token IDs back to a string
|
||||
chunk_text = tokenizer.decode(token_chunk)
|
||||
tokens.append(chunk_text)
|
||||
indices.append(i)
|
||||
else:
|
||||
try:
|
||||
import tiktoken
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"Could not import tiktoken python package. "
|
||||
"This is needed in order to for OpenAIEmbeddings. "
|
||||
"Please install it with `pip install tiktoken`."
|
||||
)
|
||||
|
||||
try:
|
||||
encoding = tiktoken.encoding_for_model(model_name)
|
||||
except KeyError:
|
||||
logger.warning("Warning: model not found. Using cl100k_base encoding.")
|
||||
model = "cl100k_base"
|
||||
encoding = tiktoken.get_encoding(model)
|
||||
for i, text in enumerate(texts):
|
||||
if self.model.endswith("001"):
|
||||
# See: https://github.com/openai/openai-python/
|
||||
# issues/418#issuecomment-1525939500
|
||||
# replace newlines, which can negatively affect performance.
|
||||
text = text.replace("\n", " ")
|
||||
|
||||
token = encoding.encode(
|
||||
text=text,
|
||||
allowed_special=self.allowed_special,
|
||||
disallowed_special=self.disallowed_special,
|
||||
)
|
||||
|
||||
# Split tokens into chunks respecting the embedding_ctx_length
|
||||
for j in range(0, len(token), self.embedding_ctx_length):
|
||||
tokens.append(token[j : j + self.embedding_ctx_length])
|
||||
indices.append(i)
|
||||
|
||||
if self.show_progress_bar:
|
||||
try:
|
||||
from tqdm.auto import tqdm
|
||||
|
||||
_iter = tqdm(range(0, len(tokens), _chunk_size))
|
||||
except ImportError:
|
||||
_iter = range(0, len(tokens), _chunk_size)
|
||||
else:
|
||||
_iter = range(0, len(tokens), _chunk_size)
|
||||
|
||||
batched_embeddings: List[List[float]] = []
|
||||
for i in _iter:
|
||||
response = embed_with_retry(
|
||||
self,
|
||||
input=tokens[i : i + _chunk_size],
|
||||
**self._invocation_params,
|
||||
)
|
||||
if not isinstance(response, dict):
|
||||
response = response.dict()
|
||||
batched_embeddings.extend(r["embedding"] for r in response["data"])
|
||||
|
||||
results: List[List[List[float]]] = [[] for _ in range(len(texts))]
|
||||
num_tokens_in_batch: List[List[int]] = [[] for _ in range(len(texts))]
|
||||
for i in range(len(indices)):
|
||||
if self.skip_empty and len(batched_embeddings[i]) == 1:
|
||||
continue
|
||||
results[indices[i]].append(batched_embeddings[i])
|
||||
num_tokens_in_batch[indices[i]].append(len(tokens[i]))
|
||||
|
||||
embeddings: List[List[float]] = [[] for _ in range(len(texts))]
|
||||
for i in range(len(texts)):
|
||||
_result = results[i]
|
||||
if len(_result) == 0:
|
||||
average_embedded = embed_with_retry(
|
||||
self,
|
||||
input="",
|
||||
**self._invocation_params,
|
||||
)
|
||||
if not isinstance(average_embedded, dict):
|
||||
average_embedded = average_embedded.dict()
|
||||
average = average_embedded["data"][0]["embedding"]
|
||||
else:
|
||||
average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
|
||||
embeddings[i] = (average / np.linalg.norm(average)).tolist()
|
||||
|
||||
return embeddings
|
||||
|
||||
# please refer to
|
||||
# https://github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb
|
||||
async def _aget_len_safe_embeddings(
|
||||
self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None
|
||||
) -> List[List[float]]:
|
||||
"""
|
||||
Asynchronously generate length-safe embeddings for a list of texts.
|
||||
|
||||
This method handles tokenization and asynchronous embedding generation,
|
||||
respecting the set embedding context length and chunk size. It supports both
|
||||
`tiktoken` and HuggingFace `tokenizer` based on the tiktoken_enabled flag.
|
||||
|
||||
Args:
|
||||
texts (List[str]): A list of texts to embed.
|
||||
engine (str): The engine or model to use for embeddings.
|
||||
chunk_size (Optional[int]): The size of chunks for processing embeddings.
|
||||
|
||||
Returns:
|
||||
List[List[float]]: A list of embeddings for each input text.
|
||||
"""
|
||||
|
||||
tokens = []
|
||||
indices = []
|
||||
model_name = self.tiktoken_model_name or self.model
|
||||
_chunk_size = chunk_size or self.chunk_size
|
||||
|
||||
# If tiktoken flag set to False
|
||||
if not self.tiktoken_enabled:
|
||||
try:
|
||||
from transformers import AutoTokenizer
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"Could not import transformers python package. "
|
||||
"This is needed in order to for OpenAIEmbeddings without "
|
||||
" `tiktoken`. Please install it with `pip install transformers`."
|
||||
)
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
pretrained_model_name_or_path=model_name
|
||||
)
|
||||
for i, text in enumerate(texts):
|
||||
# Tokenize the text using HuggingFace transformers
|
||||
tokenized = tokenizer.encode(text, add_special_tokens=False)
|
||||
|
||||
# Split tokens into chunks respecting the embedding_ctx_length
|
||||
for j in range(0, len(tokenized), self.embedding_ctx_length):
|
||||
token_chunk = tokenized[j : j + self.embedding_ctx_length]
|
||||
|
||||
# Convert token IDs back to a string
|
||||
chunk_text = tokenizer.decode(token_chunk)
|
||||
tokens.append(chunk_text)
|
||||
indices.append(i)
|
||||
else:
|
||||
try:
|
||||
import tiktoken
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"Could not import tiktoken python package. "
|
||||
"This is needed in order to for OpenAIEmbeddings. "
|
||||
"Please install it with `pip install tiktoken`."
|
||||
)
|
||||
|
||||
try:
|
||||
encoding = tiktoken.encoding_for_model(model_name)
|
||||
except KeyError:
|
||||
logger.warning("Warning: model not found. Using cl100k_base encoding.")
|
||||
model = "cl100k_base"
|
||||
encoding = tiktoken.get_encoding(model)
|
||||
for i, text in enumerate(texts):
|
||||
if self.model.endswith("001"):
|
||||
# See: https://github.com/openai/openai-python/
|
||||
# issues/418#issuecomment-1525939500
|
||||
# replace newlines, which can negatively affect performance.
|
||||
text = text.replace("\n", " ")
|
||||
|
||||
token = encoding.encode(
|
||||
text=text,
|
||||
allowed_special=self.allowed_special,
|
||||
disallowed_special=self.disallowed_special,
|
||||
)
|
||||
|
||||
# Split tokens into chunks respecting the embedding_ctx_length
|
||||
for j in range(0, len(token), self.embedding_ctx_length):
|
||||
tokens.append(token[j : j + self.embedding_ctx_length])
|
||||
indices.append(i)
|
||||
|
||||
batched_embeddings: List[List[float]] = []
|
||||
_chunk_size = chunk_size or self.chunk_size
|
||||
for i in range(0, len(tokens), _chunk_size):
|
||||
response = await async_embed_with_retry(
|
||||
self,
|
||||
input=tokens[i : i + _chunk_size],
|
||||
**self._invocation_params,
|
||||
)
|
||||
|
||||
if not isinstance(response, dict):
|
||||
response = response.dict()
|
||||
batched_embeddings.extend(r["embedding"] for r in response["data"])
|
||||
|
||||
results: List[List[List[float]]] = [[] for _ in range(len(texts))]
|
||||
num_tokens_in_batch: List[List[int]] = [[] for _ in range(len(texts))]
|
||||
for i in range(len(indices)):
|
||||
results[indices[i]].append(batched_embeddings[i])
|
||||
num_tokens_in_batch[indices[i]].append(len(tokens[i]))
|
||||
|
||||
embeddings: List[List[float]] = [[] for _ in range(len(texts))]
|
||||
for i in range(len(texts)):
|
||||
_result = results[i]
|
||||
if len(_result) == 0:
|
||||
average_embedded = await async_embed_with_retry(
|
||||
self,
|
||||
input="",
|
||||
**self._invocation_params,
|
||||
)
|
||||
if not isinstance(average_embedded, dict):
|
||||
average_embedded = average_embedded.dict()
|
||||
average = average_embedded["data"][0]["embedding"]
|
||||
else:
|
||||
average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
|
||||
embeddings[i] = (average / np.linalg.norm(average)).tolist()
|
||||
|
||||
return embeddings
|
||||
|
||||
def embed_documents(
|
||||
self, texts: List[str], chunk_size: Optional[int] = 0
|
||||
) -> List[List[float]]:
|
||||
"""Call out to OpenAI's embedding endpoint for embedding search docs.
|
||||
|
||||
Args:
|
||||
texts: The list of texts to embed.
|
||||
chunk_size: The chunk size of embeddings. If None, will use the chunk size
|
||||
specified by the class.
|
||||
|
||||
Returns:
|
||||
List of embeddings, one for each text.
|
||||
"""
|
||||
# NOTE: to keep things simple, we assume the list may contain texts longer
|
||||
# than the maximum context and use length-safe embedding function.
|
||||
engine = cast(str, self.deployment)
|
||||
return self._get_len_safe_embeddings(
|
||||
texts, engine=engine, chunk_size=chunk_size
|
||||
)
|
||||
|
||||
async def aembed_documents(
|
||||
self, texts: List[str], chunk_size: Optional[int] = 0
|
||||
) -> List[List[float]]:
|
||||
"""Call out to OpenAI's embedding endpoint async for embedding search docs.
|
||||
|
||||
Args:
|
||||
texts: The list of texts to embed.
|
||||
chunk_size: The chunk size of embeddings. If None, will use the chunk size
|
||||
specified by the class.
|
||||
|
||||
Returns:
|
||||
List of embeddings, one for each text.
|
||||
"""
|
||||
# NOTE: to keep things simple, we assume the list may contain texts longer
|
||||
# than the maximum context and use length-safe embedding function.
|
||||
engine = cast(str, self.deployment)
|
||||
return self._get_len_safe_embeddings(
|
||||
texts, engine=engine, chunk_size=chunk_size
|
||||
)
|
||||
|
||||
def embed_query(self, text: str) -> List[float]:
|
||||
"""Call out to OpenAI's embedding endpoint for embedding query text.
|
||||
|
||||
Args:
|
||||
text: The text to embed.
|
||||
|
||||
Returns:
|
||||
Embedding for the text.
|
||||
"""
|
||||
return self.embed_documents([text])[0]
|
||||
|
||||
async def aembed_query(self, text: str) -> List[float]:
|
||||
"""Call out to OpenAI's embedding endpoint async for embedding query text.
|
||||
|
||||
Args:
|
||||
text: The text to embed.
|
||||
|
||||
Returns:
|
||||
Embedding for the text.
|
||||
"""
|
||||
embeddings = await self.aembed_documents([text])
|
||||
return embeddings[0]
|
||||
Reference in New Issue
Block a user