langchain/libs/partners/openai/langchain_openai/embeddings/base.py

from __future__ import annotations

import logging
import os
import warnings
from typing import (
    Any,
    Dict,
    Iterable,
    List,
    Literal,
    Mapping,
    Optional,
    Sequence,
    Set,
    Tuple,
    Union,
    cast,
)

import openai
import tiktoken
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import (
    BaseModel,
    Extra,
    Field,
    SecretStr,
    root_validator,
)
from langchain_core.utils import (
    convert_to_secret_str,
    get_from_dict_or_env,
    get_pydantic_field_names,
)

logger = logging.getLogger(__name__)


class OpenAIEmbeddings(BaseModel, Embeddings):
    """OpenAI embedding models.

    To use, you should have the
    environment variable ``OPENAI_API_KEY`` set with your API key or pass it
    as a named parameter to the constructor.

    Example:
        .. code-block:: python

            from langchain_openai import OpenAIEmbeddings

            openai = OpenAIEmbeddings(model="text-embedding-3-large")

    In order to use the library with Microsoft Azure endpoints, use
        AzureOpenAIEmbeddings.

    """

    client: Any = Field(default=None, exclude=True)  #: :meta private:
    async_client: Any = Field(default=None, exclude=True)  #: :meta private:
    model: str = "text-embedding-ada-002"
    dimensions: Optional[int] = None
    """The number of dimensions the resulting output embeddings should have.

    Only supported in `text-embedding-3` and later models.
    """
    # to support Azure OpenAI Service custom deployment names
    deployment: Optional[str] = model
    # TODO: Move to AzureOpenAIEmbeddings.
    openai_api_version: Optional[str] = Field(default=None, alias="api_version")
    """Automatically inferred from env var `OPENAI_API_VERSION` if not provided."""
    # to support Azure OpenAI Service custom endpoints
    openai_api_base: Optional[str] = Field(default=None, alias="base_url")
    """Base URL path for API requests, leave blank if not using a proxy or service
        emulator."""
    # to support Azure OpenAI Service custom endpoints
    openai_api_type: Optional[str] = None
    # to support explicit proxy for OpenAI
    openai_proxy: Optional[str] = None
    embedding_ctx_length: int = 8191
    """The maximum number of tokens to embed at once."""
    openai_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
    """Automatically inferred from env var `OPENAI_API_KEY` if not provided."""
    openai_organization: Optional[str] = Field(default=None, alias="organization")
    """Automatically inferred from env var `OPENAI_ORG_ID` if not provided."""
    allowed_special: Union[Literal["all"], Set[str]] = set()
    disallowed_special: Union[Literal["all"], Set[str], Sequence[str]] = "all"
    chunk_size: int = 1000
    """Maximum number of texts to embed in each batch"""
    max_retries: int = 2
    """Maximum number of retries to make when generating."""
    request_timeout: Optional[Union[float, Tuple[float, float], Any]] = Field(
        default=None, alias="timeout"
    )
    """Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or
        None."""
    headers: Any = None
    tiktoken_enabled: bool = True
    """Set this to False for non-OpenAI implementations of the embeddings API, e.g.
    the `--extensions openai` extension for `text-generation-webui`"""
    tiktoken_model_name: Optional[str] = None
    """The model name to pass to tiktoken when using this class.
    Tiktoken is used to count the number of tokens in documents to constrain
    them to be under a certain limit. By default, when set to None, this will
    be the same as the embedding model name. However, there are some cases
    where you may want to use this Embedding class with a model name not
    supported by tiktoken. This can include when using Azure embeddings or
    when using one of the many model providers that expose an OpenAI-like
    API but with different models. In those cases, in order to avoid erroring
    when tiktoken is called, you can specify a model name to use here."""
    show_progress_bar: bool = False
    """Whether to show a progress bar when embedding."""
    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
    """Holds any model parameters valid for `create` call not explicitly specified."""
    skip_empty: bool = False
    """Whether to skip empty strings when embedding or raise an error.
    Defaults to not skipping."""
    default_headers: Union[Mapping[str, str], None] = None
    default_query: Union[Mapping[str, object], None] = None
    # Configure a custom httpx client. See the
    # [httpx documentation](https://www.python-httpx.org/api/#client) for more details.
    retry_min_seconds: int = 4
    """Min number of seconds to wait between retries"""
    retry_max_seconds: int = 20
    """Max number of seconds to wait between retries"""
    http_client: Union[Any, None] = None
    """Optional httpx.Client."""

    class Config:
        """Configuration for this pydantic object."""

        extra = Extra.forbid
        allow_population_by_field_name = True

    @root_validator(pre=True)
    def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
        """Build extra kwargs from additional params that were passed in."""
        all_required_field_names = get_pydantic_field_names(cls)
        extra = values.get("model_kwargs", {})
        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."""
                )
                extra[field_name] = values.pop(field_name)

        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
        return values

    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key and python package exists in environment."""
        openai_api_key = get_from_dict_or_env(
            values, "openai_api_key", "OPENAI_API_KEY"
        )
        values["openai_api_key"] = (
            convert_to_secret_str(openai_api_key) if openai_api_key else None
        )
        values["openai_api_base"] = values["openai_api_base"] or os.getenv(
            "OPENAI_API_BASE"
        )
        values["openai_api_type"] = get_from_dict_or_env(
            values,
            "openai_api_type",
            "OPENAI_API_TYPE",
            default="",
        )
        values["openai_proxy"] = get_from_dict_or_env(
            values,
            "openai_proxy",
            "OPENAI_PROXY",
            default="",
        )
        if values["openai_api_type"] in ("azure", "azure_ad", "azuread"):
            default_api_version = "2023-05-15"
            # Azure OpenAI embedding models allow a maximum of 16 texts
            # at a time in each batch
            # See: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#embeddings
            values["chunk_size"] = min(values["chunk_size"], 16)
        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")
        )
        if values["openai_api_type"] in ("azure", "azure_ad", "azuread"):
            raise ValueError(
                "If you are using Azure, "
                "please use the `AzureOpenAIEmbeddings` class."
            )
        client_params = {
            "api_key": (
                values["openai_api_key"].get_secret_value()
                if values["openai_api_key"]
                else None
            ),
            "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
        return values

    @property
    def _invocation_params(self) -> Dict[str, Any]:
        params: Dict = {"model": self.model, **self.model_kwargs}
        if self.dimensions is not None:
            params["dimensions"] = self.dimensions
        return params

    # 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  # noqa: F401
            except ImportError:
                raise ValueError(
                    "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:
                encoding = tiktoken.encoding_for_model(model_name)
            except KeyError:
                encoding = tiktoken.get_encoding("cl100k_base")
            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: Iterable = 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 = self.client.create(
                input=tokens[i : i + _chunk_size], **self._invocation_params
            )
            if not isinstance(response, dict):
                response = response.model_dump()
            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 = self.client.create(
                    input="", **self._invocation_params
                )
                if not isinstance(average_embedded, dict):
                    average_embedded = average_embedded.model_dump()
                average = average_embedded["data"][0]["embedding"]
            else:
                # should be same as
                # average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
                total_weight = sum(num_tokens_in_batch[i])
                average = [
                    sum(
                        val * weight
                        for val, weight in zip(embedding, num_tokens_in_batch[i])
                    )
                    / total_weight
                    for embedding in zip(*_result)
                ]

            # should be same as
            #  embeddings[i] = (average / np.linalg.norm(average)).tolist()
            magnitude = sum(val**2 for val in average) ** 0.5
            embeddings[i] = [val / magnitude for val in average]

        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 ValueError(
                    "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:
                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 self.async_client.create(
                input=tokens[i : i + _chunk_size], **self._invocation_params
            )

            if not isinstance(response, dict):
                response = response.model_dump()
            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 self.async_client.create(
                    input="", **self._invocation_params
                )
                if not isinstance(average_embedded, dict):
                    average_embedded = average_embedded.model_dump()
                average = average_embedded["data"][0]["embedding"]
            else:
                # should be same as
                # average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
                total_weight = sum(num_tokens_in_batch[i])
                average = [
                    sum(
                        val * weight
                        for val, weight in zip(embedding, num_tokens_in_batch[i])
                    )
                    / total_weight
                    for embedding in zip(*_result)
                ]
            # should be same as
            # embeddings[i] = (average / np.linalg.norm(average)).tolist()
            magnitude = sum(val**2 for val in average) ** 0.5
            embeddings[i] = [val / magnitude for val in average]

        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)

    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 await self._aget_len_safe_embeddings(texts, engine=engine)

    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]