"""Embedding implementations."""

from typing import Any, Dict, List, Optional

import aiohttp
import requests

from dbgpt._private.pydantic import EXTRA_FORBID, BaseModel, ConfigDict, Field
from dbgpt.core import Embeddings
from dbgpt.core.awel.flow import Parameter, ResourceCategory, register_resource
from dbgpt.util.i18n_utils import _
from dbgpt.util.tracer import DBGPT_TRACER_SPAN_ID, root_tracer

DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
DEFAULT_INSTRUCT_MODEL = "hkunlp/instructor-large"
DEFAULT_BGE_MODEL = "BAAI/bge-large-en"
DEFAULT_EMBED_INSTRUCTION = "Represent the document for retrieval: "
DEFAULT_QUERY_INSTRUCTION = (
    "Represent the question for retrieving supporting documents: "
)
DEFAULT_QUERY_BGE_INSTRUCTION_EN = (
    "Represent this question for searching relevant passages: "
)
DEFAULT_QUERY_BGE_INSTRUCTION_ZH = "为这个句子生成表示以用于检索相关文章："


@register_resource(
    _("HuggingFace Embeddings"),
    "huggingface_embeddings",
    category=ResourceCategory.EMBEDDINGS,
    description=_("HuggingFace sentence_transformers embedding models."),
    parameters=[
        Parameter.build_from(
            _("Model Name"),
            "model_name",
            str,
            optional=True,
            default=DEFAULT_MODEL_NAME,
            description=_("Model name to use."),
        ),
        # TODO, support more parameters
    ],
)
class HuggingFaceEmbeddings(BaseModel, Embeddings):
    """HuggingFace sentence_transformers embedding models.

    To use, you should have the ``sentence_transformers`` python package installed.

    Refer to `Langchain Embeddings <https://github.com/langchain-ai/langchain/tree/
    master/libs/langchain/langchain/embeddings>`_.

    Example:
        .. code-block:: python

            from dbgpt.rag.embedding import HuggingFaceEmbeddings

            model_name = "sentence-transformers/all-mpnet-base-v2"
            model_kwargs = {"device": "cpu"}
            encode_kwargs = {"normalize_embeddings": False}
            hf = HuggingFaceEmbeddings(
                model_name=model_name,
                model_kwargs=model_kwargs,
                encode_kwargs=encode_kwargs,
            )
    """

    model_config = ConfigDict(extra=EXTRA_FORBID, protected_namespaces=())

    client: Any  #: :meta private:
    model_name: str = DEFAULT_MODEL_NAME
    """Model name to use."""
    cache_folder: Optional[str] = Field(None, description="Path of the cache folder.")
    """Path to store models. Can be also set by SENTENCE_TRANSFORMERS_HOME
    environment variable."""
    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
    """Keyword arguments to pass to the model."""
    encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
    """Keyword arguments to pass when calling the `encode` method of the model."""
    multi_process: bool = False
    """Run encode() on multiple GPUs."""

    def __init__(self, **kwargs: Any):
        """Initialize the sentence_transformer."""
        try:
            import sentence_transformers

        except ImportError as exc:
            raise ImportError(
                "Could not import sentence_transformers python package. "
                "Please install it with `pip install sentence-transformers`."
            ) from exc

        kwargs["client"] = sentence_transformers.SentenceTransformer(
            kwargs.get("model_name") or DEFAULT_MODEL_NAME,
            cache_folder=kwargs.get("cache_folder"),
            **(kwargs.get("model_kwargs") or {}),
        )
        super().__init__(**kwargs)

    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Compute doc embeddings using a HuggingFace transformer model.

        Args:
            texts: The list of texts to embed.

        Returns:
            List of embeddings, one for each text.
        """
        import sentence_transformers

        texts = list(map(lambda x: x.replace("\n", " "), texts))
        if self.multi_process:
            pool = self.client.start_multi_process_pool()
            embeddings = self.client.encode_multi_process(texts, pool)
            sentence_transformers.SentenceTransformer.stop_multi_process_pool(pool)
        else:
            embeddings = self.client.encode(texts, **self.encode_kwargs)

        return embeddings.tolist()

    def embed_query(self, text: str) -> List[float]:
        """Compute query embeddings using a HuggingFace transformer model.

        Args:
            text: The text to embed.

        Returns:
            Embeddings for the text.
        """
        return self.embed_documents([text])[0]


@register_resource(
    _("HuggingFace Instructor Embeddings"),
    "huggingface_instructor_embeddings",
    category=ResourceCategory.EMBEDDINGS,
    description=_("HuggingFace Instructor embeddings."),
    parameters=[
        Parameter.build_from(
            _("Model Name"),
            "model_name",
            str,
            optional=True,
            default=DEFAULT_INSTRUCT_MODEL,
            description=_("Model name to use."),
        ),
        Parameter.build_from(
            _("Embed Instruction"),
            "embed_instruction",
            str,
            optional=True,
            default=DEFAULT_EMBED_INSTRUCTION,
            description=_("Instruction to use for embedding documents."),
        ),
        Parameter.build_from(
            _("Query Instruction"),
            "query_instruction",
            str,
            optional=True,
            default=DEFAULT_QUERY_INSTRUCTION,
            description=_("Instruction to use for embedding query."),
        ),
    ],
)
class HuggingFaceInstructEmbeddings(BaseModel, Embeddings):
    """Wrapper around sentence_transformers embedding models.

    To use, you should have the ``sentence_transformers``
    and ``InstructorEmbedding`` python packages installed.

    Example:
        .. code-block:: python

            from dbgpt.rag.embeddings import HuggingFaceInstructEmbeddings

            model_name = "hkunlp/instructor-large"
            model_kwargs = {"device": "cpu"}
            encode_kwargs = {"normalize_embeddings": True}
            hf = HuggingFaceInstructEmbeddings(
                model_name=model_name,
                model_kwargs=model_kwargs,
                encode_kwargs=encode_kwargs,
            )
    """

    model_config = ConfigDict(extra=EXTRA_FORBID, protected_namespaces=())

    client: Any  #: :meta private:
    model_name: str = DEFAULT_INSTRUCT_MODEL
    """Model name to use."""
    cache_folder: Optional[str] = None
    """Path to store models. Can be also set by SENTENCE_TRANSFORMERS_HOME
    environment variable."""
    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
    """Keyword arguments to pass to the model."""
    encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
    """Keyword arguments to pass when calling the `encode` method of the model."""
    embed_instruction: str = DEFAULT_EMBED_INSTRUCTION
    """Instruction to use for embedding documents."""
    query_instruction: str = DEFAULT_QUERY_INSTRUCTION
    """Instruction to use for embedding query."""

    def __init__(self, **kwargs: Any):
        """Initialize the sentence_transformer."""
        try:
            from InstructorEmbedding import INSTRUCTOR

            kwargs["client"] = INSTRUCTOR(
                kwargs.get("model_name"),
                cache_folder=kwargs.get("cache_folder"),
                **kwargs.get("model_kwargs"),
            )
        except ImportError as e:
            raise ImportError("Dependencies for InstructorEmbedding not found.") from e

        super().__init__(**kwargs)

    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Compute doc embeddings using a HuggingFace instruct model.

        Args:
            texts: The list of texts to embed.

        Returns:
            List of embeddings, one for each text.
        """
        instruction_pairs = [[self.embed_instruction, text] for text in texts]
        embeddings = self.client.encode(instruction_pairs, **self.encode_kwargs)
        return embeddings.tolist()

    def embed_query(self, text: str) -> List[float]:
        """Compute query embeddings using a HuggingFace instruct model.

        Args:
            text: The text to embed.

        Returns:
            Embeddings for the text.
        """
        instruction_pair = [self.query_instruction, text]
        embedding = self.client.encode([instruction_pair], **self.encode_kwargs)[0]
        return embedding.tolist()


# TODO: Support AWEL flow
class HuggingFaceBgeEmbeddings(BaseModel, Embeddings):
    """HuggingFace BGE sentence_transformers embedding models.

    To use, you should have the ``sentence_transformers`` python package installed.

    refer to `Langchain Embeddings <https://github.com/langchain-ai/langchain/tree/
    master/libs/langchain/langchain/embeddings>`_.

    Example:
        .. code-block:: python

            from dbgpt.rag.embeddings import HuggingFaceBgeEmbeddings

            model_name = "BAAI/bge-large-en"
            model_kwargs = {"device": "cpu"}
            encode_kwargs = {"normalize_embeddings": True}
            hf = HuggingFaceBgeEmbeddings(
                model_name=model_name,
                model_kwargs=model_kwargs,
                encode_kwargs=encode_kwargs,
            )
    """

    model_config = ConfigDict(extra=EXTRA_FORBID, protected_namespaces=())

    client: Any  #: :meta private:
    model_name: str = DEFAULT_BGE_MODEL
    """Model name to use."""
    cache_folder: Optional[str] = None
    """Path to store models.
    Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
    """Keyword arguments to pass to the model."""
    encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
    """Keyword arguments to pass when calling the `encode` method of the model."""
    query_instruction: str = DEFAULT_QUERY_BGE_INSTRUCTION_EN
    """Instruction to use for embedding query."""

    def __init__(self, **kwargs: Any):
        """Initialize the sentence_transformer."""
        try:
            import sentence_transformers

        except ImportError as exc:
            raise ImportError(
                "Could not import sentence_transformers python package. "
                "Please install it with `pip install sentence_transformers`."
            ) from exc

        kwargs["client"] = sentence_transformers.SentenceTransformer(
            kwargs.get("model_name"),
            cache_folder=kwargs.get("cache_folder"),
            **(kwargs.get("model_kwargs") or {}),
        )

        super().__init__(**kwargs)
        if "-zh" in self.model_name:
            self.query_instruction = DEFAULT_QUERY_BGE_INSTRUCTION_ZH

    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Compute doc embeddings using a HuggingFace transformer model.

        Args:
            texts: The list of texts to embed.

        Returns:
            List of embeddings, one for each text.
        """
        texts = [t.replace("\n", " ") for t in texts]
        embeddings = self.client.encode(texts, **self.encode_kwargs)
        return embeddings.tolist()

    def embed_query(self, text: str) -> List[float]:
        """Compute query embeddings using a HuggingFace transformer model.

        Args:
            text: The text to embed.

        Returns:
            Embeddings for the text.
        """
        text = text.replace("\n", " ")
        embedding = self.client.encode(
            self.query_instruction + text, **self.encode_kwargs
        )
        return embedding.tolist()


@register_resource(
    _("HuggingFace Inference API Embeddings"),
    "huggingface_inference_api_embeddings",
    category=ResourceCategory.EMBEDDINGS,
    description=_("HuggingFace Inference API embeddings."),
    parameters=[
        Parameter.build_from(
            _("API Key"),
            "api_key",
            str,
            description=_("Your API key for the HuggingFace Inference API."),
        ),
        Parameter.build_from(
            _("Model Name"),
            "model_name",
            str,
            optional=True,
            default="sentence-transformers/all-MiniLM-L6-v2",
            description=_("The name of the model to use for text embeddings."),
        ),
    ],
)
class HuggingFaceInferenceAPIEmbeddings(BaseModel, Embeddings):
    """Embed texts using the HuggingFace API.

    Requires a HuggingFace Inference API key and a model name.
    """

    model_config = ConfigDict(arbitrary_types_allowed=True, protected_namespaces=())

    api_key: str
    """Your API key for the HuggingFace Inference API."""
    model_name: str = "sentence-transformers/all-MiniLM-L6-v2"
    """The name of the model to use for text embeddings."""

    @property
    def _api_url(self) -> str:
        return (
            "https://api-inference.huggingface.co"
            "/pipeline"
            "/feature-extraction"
            f"/{self.model_name}"
        )

    @property
    def _headers(self) -> dict:
        return {"Authorization": f"Bearer {self.api_key}"}

    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Get the embeddings for a list of texts.

        Args:
            texts (Documents): A list of texts to get embeddings for.

        Returns:
            Embedded texts as List[List[float]], where each inner List[float]
                corresponds to a single input text.

        Example:
            .. code-block:: python

                from langchain.embeddings import HuggingFaceInferenceAPIEmbeddings

                hf_embeddings = HuggingFaceInferenceAPIEmbeddings(
                    api_key="your_api_key",
                    model_name="sentence-transformers/all-MiniLM-l6-v2",
                )
                texts = ["Hello, world!", "How are you?"]
                hf_embeddings.embed_documents(texts)
        """
        response = requests.post(
            self._api_url,
            headers=self._headers,
            json={
                "inputs": texts,
                "options": {"wait_for_model": True, "use_cache": True},
            },
        )
        return response.json()

    def embed_query(self, text: str) -> List[float]:
        """Compute query embeddings using a HuggingFace transformer model.

        Args:
            text: The text to embed.

        Returns:
            Embeddings for the text.
        """
        return self.embed_documents([text])[0]


def _handle_request_result(res: requests.Response) -> List[List[float]]:
    """Parse the result from a request.

    Args:
        res: The response from the request.

    Returns:
        List[List[float]]: The embeddings.

    Raises:
        RuntimeError: If the response is not successful.
    """
    res.raise_for_status()
    resp = res.json()
    if "data" not in resp:
        raise RuntimeError(resp["detail"])
    embeddings = resp["data"]
    # Sort resulting embeddings by index
    sorted_embeddings = sorted(embeddings, key=lambda e: e["index"])  # type: ignore
    # Return just the embeddings
    return [result["embedding"] for result in sorted_embeddings]


@register_resource(
    _("Jina AI Embeddings"),
    "jina_embeddings",
    category=ResourceCategory.EMBEDDINGS,
    description=_("Jina AI embeddings."),
    parameters=[
        Parameter.build_from(
            _("API Key"),
            "api_key",
            str,
            description=_("Your API key for the Jina AI API."),
        ),
        Parameter.build_from(
            _("Model Name"),
            "model_name",
            str,
            optional=True,
            default="jina-embeddings-v2-base-en",
            description=_("The name of the model to use for text embeddings."),
        ),
    ],
)
class JinaEmbeddings(BaseModel, Embeddings):
    """Jina AI embeddings.

    This class is used to get embeddings for a list of texts using the Jina AI API.
    It requires an API key and a model name. The default model name is
    "jina-embeddings-v2-base-en".
    """

    model_config = ConfigDict(arbitrary_types_allowed=True, protected_namespaces=())

    api_url: Any  #: :meta private:
    session: Any  #: :meta private:
    api_key: str
    """API key for the Jina AI API.."""
    model_name: str = "jina-embeddings-v2-base-en"
    """The name of the model to use for text embeddings. Defaults to
    "jina-embeddings-v2-base-en"."""

    def __init__(self, **kwargs):
        """Create a new JinaEmbeddings instance."""
        try:
            import requests
        except ImportError:
            raise ValueError(
                "The requests python package is not installed. Please install it with "
                "`pip install requests`"
            )
        if "api_url" not in kwargs:
            kwargs["api_url"] = "https://api.jina.ai/v1/embeddings"
        if "session" not in kwargs:  # noqa: SIM401
            session = requests.Session()
        else:
            session = kwargs["session"]
        api_key = kwargs.get("api_key")
        if api_key:
            session.headers.update(
                {
                    "Authorization": f"Bearer {api_key}",
                    "Accept-Encoding": "identity",
                }
            )
        kwargs["session"] = session

        super().__init__(**kwargs)

    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Get the embeddings for a list of texts.

        Args:
            texts (Documents): A list of texts to get embeddings for.

        Returns:
            Embedded texts as List[List[float]], where each inner List[float]
                corresponds to a single input text.
        """
        # Call Jina AI Embedding API
        resp = self.session.post(  # type: ignore
            self.api_url, json={"input": texts, "model": self.model_name}
        )
        return _handle_request_result(resp)

    def embed_query(self, text: str) -> List[float]:
        """Compute query embeddings using a Jina AI embedding model.

        Args:
            text: The text to embed.

        Returns:
            Embeddings for the text.
        """
        return self.embed_documents([text])[0]


@register_resource(
    _("OpenAPI Embeddings"),
    "openapi_embeddings",
    category=ResourceCategory.EMBEDDINGS,
    description=_("OpenAPI embeddings."),
    parameters=[
        Parameter.build_from(
            _("API URL"),
            "api_url",
            str,
            optional=True,
            default="http://localhost:8100/api/v1/embeddings",
            description=_("The URL of the embeddings API."),
        ),
        Parameter.build_from(
            _("API Key"),
            "api_key",
            str,
            optional=True,
            default=None,
            description=_("Your API key for the Open API."),
        ),
        Parameter.build_from(
            _("Model Name"),
            "model_name",
            str,
            optional=True,
            default="text2vec",
            description=_("The name of the model to use for text embeddings."),
        ),
        Parameter.build_from(
            _("Timeout"),
            "timeout",
            int,
            optional=True,
            default=60,
            description=_("The timeout for the request in seconds."),
        ),
    ],
)
class OpenAPIEmbeddings(BaseModel, Embeddings):
    """The OpenAPI embeddings.

    This class is used to get embeddings for a list of texts using the API.
    This API is compatible with the OpenAI Embedding API.

    Examples:
        Using OpenAI's API:
        .. code-block:: python

            from dbgpt.rag.embedding import OpenAPIEmbeddings

            openai_embeddings = OpenAPIEmbeddings(
                api_url="https://api.openai.com/v1/embeddings",
                api_key="your_api_key",
                model_name="text-embedding-3-small",
            )
            texts = ["Hello, world!", "How are you?"]
            openai_embeddings.embed_documents(texts)

        Using DB-GPT APIServer's embedding API:
        To use the DB-GPT APIServer's embedding API, you should deploy DB-GPT according
        to the `Cluster Deploy
        <https://docs.dbgpt.site/docs/installation/model_service/cluster>`_.

        A simple example:
        1. Deploy Model Cluster with following command:
        .. code-block:: bash

            dbgpt start controller --port 8000

        2. Deploy Embedding Model Worker with following command:
        .. code-block:: bash

            dbgpt start worker --model_name text2vec \
            --model_path /app/models/text2vec-large-chinese \
            --worker_type text2vec \
            --port 8003 \
            --controller_addr http://127.0.0.1:8000

        3. Deploy API Server with following command:
        .. code-block:: bash

            dbgpt start apiserver --controller_addr http://127.0.0.1:8000 \
            --api_keys my_api_token --port 8100

        Now, you can use the API server to get embeddings:
        .. code-block:: python

            from dbgpt.rag.embedding import OpenAPIEmbeddings

            openai_embeddings = OpenAPIEmbeddings(
                api_url="http://localhost:8100/api/v1/embeddings",
                api_key="my_api_token",
                model_name="text2vec",
            )
            texts = ["Hello, world!", "How are you?"]
            openai_embeddings.embed_documents(texts)
    """

    model_config = ConfigDict(arbitrary_types_allowed=True, protected_namespaces=())

    api_url: str = Field(
        default="http://localhost:8100/api/v1/embeddings",
        description="The URL of the embeddings API.",
    )
    api_key: Optional[str] = Field(
        default=None, description="The API key for the embeddings API."
    )
    model_name: str = Field(
        default="text2vec", description="The name of the model to use."
    )
    timeout: int = Field(
        default=60, description="The timeout for the request in seconds."
    )
    pass_trace_id: bool = Field(
        default=True, description="Whether to pass the trace ID to the API."
    )

    session: Optional[requests.Session] = None

    def __init__(self, **kwargs):
        """Initialize the OpenAPIEmbeddings."""
        try:
            import requests
        except ImportError:
            raise ValueError(
                "The requests python package is not installed. "
                "Please install it with `pip install requests`"
            )
        if "session" not in kwargs:  # noqa: SIM401
            session = requests.Session()
        else:
            session = kwargs["session"]
        api_key = kwargs.get("api_key")
        if api_key:
            session.headers.update({"Authorization": f"Bearer {api_key}"})
        kwargs["session"] = session
        super().__init__(**kwargs)

    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Get the embeddings for a list of texts.

        Args:
            texts (Documents): A list of texts to get embeddings for.

        Returns:
            Embedded texts as List[List[float]], where each inner List[float]
                corresponds to a single input text.
        """
        # Call OpenAI Embedding API
        headers = {}
        current_span_id = root_tracer.get_current_span_id()
        if self.pass_trace_id and current_span_id:
            # Set the trace ID if available
            headers[DBGPT_TRACER_SPAN_ID] = current_span_id
        res = self.session.post(  # type: ignore
            self.api_url,
            json={"input": texts, "model": self.model_name},
            timeout=self.timeout,
            headers=headers,
        )
        return _handle_request_result(res)

    def embed_query(self, text: str) -> List[float]:
        """Compute query embeddings using a OpenAPI embedding model.

        Args:
            text: The text to embed.

        Returns:
            Embeddings for the text.
        """
        return self.embed_documents([text])[0]

    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
        """Asynchronous Embed search docs.

        Args:
            texts: A list of texts to get embeddings for.

        Returns:
            List[List[float]]: Embedded texts as List[List[float]], where each inner
                List[float] corresponds to a single input text.
        """
        headers = {"Authorization": f"Bearer {self.api_key}"}
        current_span_id = root_tracer.get_current_span_id()
        if self.pass_trace_id and current_span_id:
            # Set the trace ID if available
            headers[DBGPT_TRACER_SPAN_ID] = current_span_id
        async with aiohttp.ClientSession(
            headers=headers, timeout=aiohttp.ClientTimeout(total=self.timeout)
        ) as session:
            async with session.post(
                self.api_url, json={"input": texts, "model": self.model_name}
            ) as resp:
                resp.raise_for_status()
                data = await resp.json()
                if "data" not in data:
                    raise RuntimeError(data["detail"])
                embeddings = data["data"]
                sorted_embeddings = sorted(embeddings, key=lambda e: e["index"])
                return [result["embedding"] for result in sorted_embeddings]

    async def aembed_query(self, text: str) -> List[float]:
        """Asynchronous Embed query text."""
        embeddings = await self.aembed_documents([text])
        return embeddings[0]


class OllamaEmbeddings(BaseModel, Embeddings):
    """Ollama proxy embeddings.

    This class is used to get embeddings for a list of texts using the Ollama API.
    It requires a proxy server url `api_url` and a model name `model_name`.
    The default model name is "llama2".
    """

    model_config = ConfigDict(arbitrary_types_allowed=True, protected_namespaces=())

    api_url: str = Field(
        default="http://localhost:11434",
        description="The URL of the embeddings API.",
    )
    model_name: str = Field(
        default="llama2", description="The name of the model to use."
    )

    def __init__(self, **kwargs):
        """Initialize the OllamaEmbeddings."""
        super().__init__(**kwargs)

    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Get the embeddings for a list of texts.

        Args:
            texts (Documents): A list of texts to get embeddings for.

        Returns:
            Embedded texts as List[List[float]], where each inner List[float]
                corresponds to a single input text.
        """
        return [self.embed_query(text) for text in texts]

    def embed_query(self, text: str) -> List[float]:
        """Compute query embeddings using a OpenAPI embedding model.

        Args:
            text: The text to embed.

        Returns:
            Embeddings for the text.
        """
        try:
            import ollama
            from ollama import Client
        except ImportError as e:
            raise ValueError(
                "Could not import python package: ollama "
                "Please install ollama by command `pip install ollama"
            ) from e
        try:
            return (
                Client(self.api_url).embeddings(model=self.model_name, prompt=text)
            )["embedding"]
        except ollama.ResponseError as e:
            raise ValueError(f"**Ollama Response Error, Please CheckErrorInfo.**: {e}")

    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
        """Asynchronous Embed search docs.

        Args:
            texts: A list of texts to get embeddings for.

        Returns:
            List[List[float]]: Embedded texts as List[List[float]], where each inner
                List[float] corresponds to a single input text.
        """
        embeddings = []
        for text in texts:
            embedding = await self.aembed_query(text)
            embeddings.append(embedding)
        return embeddings

    async def aembed_query(self, text: str) -> List[float]:
        """Asynchronous Embed query text."""
        try:
            import ollama
            from ollama import AsyncClient
        except ImportError:
            raise ValueError(
                "The ollama python package is not installed. "
                "Please install it with `pip install ollama`"
            )
        try:
            embedding = await AsyncClient(host=self.api_url).embeddings(
                model=self.model_name, prompt=text
            )
            return embedding["embedding"]
        except ollama.ResponseError as e:
            raise ValueError(f"**Ollama Response Error, Please CheckErrorInfo.**: {e}")


class TongYiEmbeddings(BaseModel, Embeddings):
    """The tongyi embeddings.

    import dashscope
    from http import HTTPStatus
    from dashscope import TextEmbedding

    dashscope.api_key = ''
    def embed_with_list_of_str():
        resp = TextEmbedding.call(
            model=TextEmbedding.Models.text_embedding_v1,
            # 最多支持10条，每条最长支持2048tokens
            input=['风急天高猿啸哀', '渚清沙白鸟飞回', '无边落木萧萧下', '不尽长江滚滚来']
        )
        if resp.status_code == HTTPStatus.OK:
            print(resp)
        else:
            print(resp)

    if __name__ == '__main__':
        embed_with_list_of_str()
    """

    model_config = ConfigDict(arbitrary_types_allowed=True, protected_namespaces=())
    api_key: Optional[str] = Field(
        default=None, description="The API key for the embeddings API."
    )
    model_name: str = Field(
        default="text-embedding-v1", description="The name of the model to use."
    )

    def __init__(self, **kwargs):
        """Initialize the OpenAPIEmbeddings."""
        try:
            import dashscope  # type: ignore
        except ImportError as exc:
            raise ValueError(
                "Could not import python package: dashscope "
                "Please install dashscope by command `pip install dashscope"
            ) from exc
        dashscope.TextEmbedding.api_key = kwargs.get("api_key")
        super().__init__(**kwargs)
        self._api_key = kwargs.get("api_key")

    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Get the embeddings for a list of texts.

        Args:
            texts (Documents): A list of texts to get embeddings for.

        Returns:
            Embedded texts as List[List[float]], where each inner List[float]
                corresponds to a single input text.
        """
        from dashscope import TextEmbedding

        # 最多支持10条，每条最长支持2048tokens
        resp = TextEmbedding.call(
            model=self.model_name, input=texts, api_key=self._api_key
        )
        if "output" not in resp:
            raise RuntimeError(resp["message"])

        embeddings = resp["output"]["embeddings"]
        sorted_embeddings = sorted(embeddings, key=lambda e: e["text_index"])

        return [result["embedding"] for result in sorted_embeddings]

    def embed_query(self, text: str) -> List[float]:
        """Compute query embeddings using a OpenAPI embedding model.

        Args:
            text: The text to embed.

        Returns:
            Embeddings for the text.
        """
        return self.embed_documents([text])[0]


class QianFanEmbeddings(BaseModel, Embeddings):
    """Baidu Qianfan Embeddings embedding models.

    Embed:
       .. code-block:: python

           # embed the documents
           vectors = embeddings.embed_documents([text1, text2, ...])

           # embed the query
           vectors = embeddings.embed_query(text)

    """  # noqa: E501

    client: Any
    chunk_size: int = 16
    endpoint: str = ""
    """Endpoint of the Qianfan Embedding, required if custom model used."""
    model_config = ConfigDict(arbitrary_types_allowed=True, protected_namespaces=())
    api_key: Optional[str] = Field(
        default=None, description="The API key for the embeddings API."
    )
    api_secret: Optional[str] = Field(
        default=None, description="The Secret key for the embeddings API."
    )
    """Model name
    you could get from https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu

    for now, we support Embedding-V1 and
    - Embedding-V1 （默认模型）
    - bge-large-en
    - bge-large-zh

    preset models are mapping to an endpoint.
    `model` will be ignored if `endpoint` is set
    """
    model_name: str = Field(
        default="text-embedding-v1", description="The name of the model to use."
    )
    init_kwargs: Dict[str, Any] = Field(default_factory=dict)
    """init kwargs for qianfan client init, such as `query_per_second` which is
        associated with qianfan resource object to limit QPS"""

    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
    """extra params for model invoke using with `do`."""

    def __init__(self, **kwargs):
        """Initialize the QianFanEmbeddings."""
        try:
            import qianfan
        except ImportError as exc:
            raise ValueError(
                "Could not import python package: qianfan. "
                "Please install qianfan by running `pip install qianfan`."
            ) from exc

        qianfan_ak = kwargs.get("api_key")
        qianfan_sk = kwargs.get("api_secret")
        model_name = kwargs.get("model_name")

        if not qianfan_ak or not qianfan_sk or not model_name:
            raise ValueError(
                "API key, API secret, and model name are required to initialize "
                "QianFanEmbeddings."
            )

        params = {
            "model": model_name,
            "ak": qianfan_ak,
            "sk": qianfan_sk,
        }

        # Initialize the qianfan.Embedding client
        kwargs["client"] = qianfan.Embedding(**params)
        super().__init__(**kwargs)

    def embed_query(self, text: str) -> List[float]:
        """Compute query embeddings using a QianFan embedding model."""
        resp = self.embed_documents([text])
        return resp[0]

    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """
        Embeds a list of text documents using the AutoVOT algorithm.

        Args:
            texts (List[str]): A list of text documents to embed.

        Returns:
            List[List[float]]: A list of embeddings for each document in the input list.
                            Each embedding is represented as a list of float values.
        """
        text_in_chunks = [
            texts[i : i + self.chunk_size]
            for i in range(0, len(texts), self.chunk_size)
        ]
        lst = []
        for chunk in text_in_chunks:
            resp = self.client.do(texts=chunk, **self.model_kwargs)
            lst.extend([res["embedding"] for res in resp["data"]])
        return lst