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273 lines
10 KiB
Python
273 lines
10 KiB
Python
"""Wrapper around OpenAI embedding models."""
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from __future__ import annotations
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import logging
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from typing import Any, Callable, Dict, List, Optional
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import numpy as np
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from pydantic import BaseModel, Extra, root_validator
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from tenacity import (
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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.embeddings.base import Embeddings
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from langchain.utils import get_from_dict_or_env
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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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min_seconds = 4
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max_seconds = 10
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# Wait 2^x * 1 second between each retry starting with
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# 4 seconds, then up to 10 seconds, then 10 seconds afterwards
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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(multiplier=1, min=min_seconds, max=max_seconds),
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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 embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any:
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"""Use tenacity to retry the completion call."""
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retry_decorator = _create_retry_decorator(embeddings)
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@retry_decorator
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def _completion_with_retry(**kwargs: Any) -> Any:
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return embeddings.client.create(**kwargs)
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return _completion_with_retry(**kwargs)
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class OpenAIEmbeddings(BaseModel, Embeddings):
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"""Wrapper around 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.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 optionally and
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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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from langchain.embeddings.openai import OpenAIEmbeddings
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embeddings = OpenAIEmbeddings(model="your-embeddings-deployment-name")
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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 #: :meta private:
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model: str = "text-embedding-ada-002"
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# TODO: deprecate these two in favor of model
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# https://community.openai.com/t/api-update-engines-models/18597
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# https://github.com/openai/openai-python/issues/132
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document_model_name: str = "text-embedding-ada-002"
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query_model_name: str = "text-embedding-ada-002"
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embedding_ctx_length: int = -1
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openai_api_key: Optional[str] = None
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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 = 6
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"""Maximum number of retries to make when generating."""
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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# TODO: deprecate this
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@root_validator(pre=True)
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def get_model_names(cls, values: Dict) -> Dict:
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# model_name is for first generation, and model is for second generation.
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# Both are not allowed together.
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if "model_name" in values and "model" in values:
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raise ValueError(
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"Both `model_name` and `model` were provided, "
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"but only one should be."
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)
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"""Get model names from just old model name."""
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if "model_name" in values:
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if "document_model_name" in values:
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raise ValueError(
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"Both `model_name` and `document_model_name` were provided, "
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"but only one should be."
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)
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if "query_model_name" in values:
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raise ValueError(
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"Both `model_name` and `query_model_name` were provided, "
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"but only one should be."
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)
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model_name = values.pop("model_name")
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values["document_model_name"] = f"text-search-{model_name}-doc-001"
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values["query_model_name"] = f"text-search-{model_name}-query-001"
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# Set document/query model names from model parameter.
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if "model" in values:
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if "document_model_name" in values:
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raise ValueError(
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"Both `model` and `document_model_name` were provided, "
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"but only one should be."
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)
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if "query_model_name" in values:
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raise ValueError(
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"Both `model` and `query_model_name` were provided, "
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"but only one should be."
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)
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model = values.get("model")
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values["document_model_name"] = model
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values["query_model_name"] = model
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return values
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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openai_api_key = get_from_dict_or_env(
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values, "openai_api_key", "OPENAI_API_KEY"
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)
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try:
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import openai
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openai.api_key = openai_api_key
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values["client"] = openai.Embedding
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except ImportError:
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raise ValueError(
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"Could not import openai python package. "
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"Please it install it with `pip install openai`."
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)
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return values
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# please refer to
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# https://github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb
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def _get_len_safe_embeddings(
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self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None
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) -> List[List[float]]:
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embeddings: List[List[float]] = [[] for i in range(len(texts))]
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try:
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import tiktoken
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tokens = []
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indices = []
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encoding = tiktoken.model.encoding_for_model(self.document_model_name)
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for i, text in enumerate(texts):
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# replace newlines, which can negatively affect performance.
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text = text.replace("\n", " ")
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token = encoding.encode(text)
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for j in range(0, len(token), self.embedding_ctx_length):
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tokens += [token[j : j + self.embedding_ctx_length]]
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indices += [i]
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batched_embeddings = []
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_chunk_size = chunk_size or self.chunk_size
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for i in range(0, len(tokens), _chunk_size):
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response = embed_with_retry(
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self,
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input=tokens[i : i + _chunk_size],
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engine=self.document_model_name,
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)
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batched_embeddings += [r["embedding"] for r in response["data"]]
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results: List[List[List[float]]] = [[] for i in range(len(texts))]
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lens: List[List[int]] = [[] for i in range(len(texts))]
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for i in range(len(indices)):
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results[indices[i]].append(batched_embeddings[i])
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lens[indices[i]].append(len(batched_embeddings[i]))
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for i in range(len(texts)):
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average = np.average(results[i], axis=0, weights=lens[i])
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embeddings[i] = (average / np.linalg.norm(average)).tolist()
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return embeddings
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except ImportError:
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raise ValueError(
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"Could not import tiktoken python package. "
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"This is needed in order to for OpenAIEmbeddings. "
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"Please it install it with `pip install tiktoken`."
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)
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def _embedding_func(self, text: str, *, engine: str) -> List[float]:
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"""Call out to OpenAI's embedding endpoint."""
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# replace newlines, which can negatively affect performance.
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if self.embedding_ctx_length > 0:
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return self._get_len_safe_embeddings([text], engine=engine)[0]
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else:
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text = text.replace("\n", " ")
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return embed_with_retry(self, input=[text], engine=engine)["data"][0][
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"embedding"
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]
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def embed_documents(
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self, texts: List[str], chunk_size: Optional[int] = 0
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) -> List[List[float]]:
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"""Call out to OpenAI's embedding endpoint for embedding search docs.
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Args:
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texts: The list of texts to embed.
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chunk_size: The chunk size of embeddings. If None, will use the chunk size
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specified by the class.
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Returns:
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List of embeddings, one for each text.
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"""
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# handle large batches of texts
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if self.embedding_ctx_length > 0:
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return self._get_len_safe_embeddings(texts, engine=self.document_model_name)
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else:
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results = []
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_chunk_size = chunk_size or self.chunk_size
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for i in range(0, len(texts), _chunk_size):
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response = embed_with_retry(
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self,
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input=texts[i : i + _chunk_size],
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engine=self.document_model_name,
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)
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results += [r["embedding"] for r in response["data"]]
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return results
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def embed_query(self, text: str) -> List[float]:
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"""Call out to OpenAI's embedding endpoint for embedding query text.
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Args:
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text: The text to embed.
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Returns:
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Embeddings for the text.
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"""
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embedding = self._embedding_func(text, engine=self.query_model_name)
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return embedding
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