ragflow/rag/llm/embedding_model.py

#
#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
#  limitations under the License.
#
import json
import os
import threading
from abc import ABC
from contextlib import contextmanager
from urllib.parse import urljoin
from json.decoder import JSONDecodeError

import dashscope
import numpy as np
import requests
from ollama import Client
from openai import OpenAI
from zhipuai import ZhipuAI

from common.exceptions import ModelException
from common.log_utils import log_exception
from common.token_utils import num_tokens_from_string, truncate, total_token_count_from_response
from common import settings
import logging
import base64

logger = logging.getLogger(__name__)


def _raise_model_exception_if_failed(resp):
    status_code = resp.status_code
    if status_code >= 400:
        if status_code < 500 and status_code not in [408, 429]:
            raise ModelException(f"status: {resp.status_code}, response: {resp.text}", retryable=False)
        raise ModelException(f"status: {resp.status_code}, response: {resp.text}", retryable=True)


def _dashscope_base_url_for_log(base_url: str) -> str:
    """Log host/path only (no query string) so secrets in URLs are not printed."""
    return base_url.split("?", 1)[0].strip()[:256]


def _dashscope_native_http_api_url(base_url: str | None) -> str | None:
    """
    Resolve the DashScope *native* HTTP API root for Tongyi-Qianwen (Qwen) text embeddings.

    RAGFlow often stores an OpenAI-compatible base URL (e.g. ``.../compatible-mode/v1``) for
    the same provider. The ``dashscope`` Python SDK used by ``TextEmbedding.call`` does *not*
    use that path; it expects ``https://<host>/api/v1`` instead.

    Users outside mainland China are directed to the international endpoint
    (``dashscope-intl.aliyuncs.com``); domestic traffic uses ``dashscope.aliyuncs.com``.
    When ``base_url`` already points at the native API root (ends with ``/api/v1``), it is
    returned unchanged so custom or regional deployments keep working.
    """
    if not base_url:
        return None
    u = base_url.strip().rstrip("/")
    safe = _dashscope_base_url_for_log(u)
    if u.endswith("/api/v1"):
        logger.debug("DashScope Tongyi-Qianwen embedding: using native API base as configured (%s)", safe)
        return u
    # International (Singapore) DashScope — required for overseas Tongyi-Qianwen accounts.
    if "dashscope-intl.aliyuncs.com" in u:
        resolved = "https://dashscope-intl.aliyuncs.com/api/v1"
        logger.info(
            "DashScope Tongyi-Qianwen embedding: mapped configured base_url to intl native API (%s -> %s)",
            safe,
            resolved,
        )
        return resolved
    # China mainland DashScope default host.
    if "dashscope.aliyuncs.com" in u:
        resolved = "https://dashscope.aliyuncs.com/api/v1"
        logger.info(
            "DashScope Tongyi-Qianwen embedding: mapped configured base_url to CN native API (%s -> %s)",
            safe,
            resolved,
        )
        return resolved
    logger.warning(
        "DashScope Tongyi-Qianwen embedding: base_url is set but not recognized as a DashScope host; "
        "using SDK default endpoint (%s)",
        safe,
    )
    return None


@contextmanager
def _dashscope_native_api_url_scope(url: str | None):
    """
    Temporarily set ``dashscope.base_http_api_url`` for the duration of a single SDK call,
    then restore the previous value. Narrows the window where concurrent threads see a mismatch.
    """
    if not url:
        yield
        return
    prev = getattr(dashscope, "base_http_api_url", None)
    dashscope.base_http_api_url = url
    try:
        yield
    finally:
        dashscope.base_http_api_url = prev


class Base(ABC):
    def __init__(self, key, model_name, **kwargs):
        """
        Constructor for abstract base class.
        Parameters are accepted for interface consistency but are not stored.
        Subclasses should implement their own initialization as needed.
        """
        pass

    def encode(self, texts: list):
        raise NotImplementedError("Please implement encode method!")

    def encode_queries(self, text: str):
        raise NotImplementedError("Please implement encode method!")


class BuiltinEmbed(Base):
    _FACTORY_NAME = "Builtin"
    MAX_TOKENS = {"Qwen/Qwen3-Embedding-0.6B": 30000, "BAAI/bge-m3": 8000, "BAAI/bge-small-en-v1.5": 500}
    _model = None
    _model_name = ""
    _max_tokens = 500
    _model_lock = threading.Lock()

    def __init__(self, key, model_name, **kwargs):
        logging.info(f"Initialize BuiltinEmbed according to settings.EMBEDDING_CFG: {settings.EMBEDDING_CFG}")
        embedding_cfg = settings.EMBEDDING_CFG
        if not BuiltinEmbed._model and "tei-" in os.getenv("COMPOSE_PROFILES", ""):
            with BuiltinEmbed._model_lock:
                BuiltinEmbed._model_name = settings.EMBEDDING_MDL
                BuiltinEmbed._max_tokens = BuiltinEmbed.MAX_TOKENS.get(settings.EMBEDDING_MDL, 500)
                BuiltinEmbed._model = HuggingFaceEmbed(embedding_cfg["api_key"], settings.EMBEDDING_MDL, base_url=embedding_cfg["base_url"])
        self._model = BuiltinEmbed._model
        self._model_name = BuiltinEmbed._model_name
        self._max_tokens = BuiltinEmbed._max_tokens

    def encode(self, texts: list):
        batch_size = 16
        # TEI is able to auto truncate inputs according to https://github.com/huggingface/text-embeddings-inference.
        token_count = 0
        batches = []
        for i in range(0, len(texts), batch_size):
            embeddings, token_count_delta = self._model.encode(texts[i : i + batch_size])
            token_count += token_count_delta
            batches.append(embeddings)
        ress = np.vstack(batches) if batches else np.array([])
        return ress, token_count

    def encode_queries(self, text: str):
        return self._model.encode_queries(text)


class OpenAIEmbed(Base):
    _FACTORY_NAME = "OpenAI"

    def __init__(self, key, model_name="text-embedding-ada-002", base_url="https://api.openai.com/v1"):
        if not base_url:
            base_url = "https://api.openai.com/v1"
        self.client = OpenAI(api_key=key, base_url=base_url)
        self.model_name = model_name

    def encode(self, texts: list):
        # OpenAI requires batch size <=16
        batch_size = 16
        texts = [truncate(t, 8191) for t in texts]
        ress = []
        total_tokens = 0
        for i in range(0, len(texts), batch_size):
            try:
                res = self.client.embeddings.create(input=texts[i : i + batch_size], model=self.model_name, encoding_format="float", extra_body={"drop_params": True})
            except Exception as _e:
                raise ModelException(f"Error: {_e}")
            try:
                ress.extend([d.embedding for d in res.data])
                total_tokens += total_token_count_from_response(res)
            except Exception as _e:
                log_exception(_e, res)
                raise Exception(f"Error: {res}")
        return np.array(ress), total_tokens

    def encode_queries(self, text):
        try:
            res = self.client.embeddings.create(input=[truncate(text, 8191)], model=self.model_name, encoding_format="float", extra_body={"drop_params": True})
        except Exception as _e:
            raise ModelException(f"Error: {_e}")
        try:
            return np.array(res.data[0].embedding), total_token_count_from_response(res)
        except Exception as _e:
            log_exception(_e, res)
            raise Exception(f"Error: {res}")


class LocalAIEmbed(Base):
    _FACTORY_NAME = "LocalAI"

    def __init__(self, key, model_name, base_url):
        if not base_url:
            raise ValueError("Local embedding model url cannot be None")
        base_url = urljoin(base_url, "v1")
        self.client = OpenAI(api_key="empty", base_url=base_url)
        self.model_name = model_name.split("___")[0]

    def encode(self, texts: list):
        batch_size = 16
        ress = []
        for i in range(0, len(texts), batch_size):
            res = self.client.embeddings.create(input=texts[i : i + batch_size], model=self.model_name)
            try:
                ress.extend([d.embedding for d in res.data])
            except Exception as _e:
                log_exception(_e, res)
                raise Exception(f"Error: {res}")
        # local embedding for LmStudio donot count tokens
        return np.array(ress), 1024

    def encode_queries(self, text):
        embds, cnt = self.encode([text])
        return np.array(embds[0]), cnt


class AzureEmbed(OpenAIEmbed):
    _FACTORY_NAME = "Azure-OpenAI"

    def __init__(self, key, model_name, **kwargs):
        from openai.lib.azure import AzureOpenAI

        api_key = json.loads(key).get("api_key", "")
        api_version = json.loads(key).get("api_version", "2024-02-01")
        self.client = AzureOpenAI(api_key=api_key, azure_endpoint=kwargs["base_url"], api_version=api_version)
        self.model_name = model_name


class AstraflowEmbed(OpenAIEmbed):
    _FACTORY_NAME = "Astraflow"

    def __init__(self, key, model_name, base_url="https://api-us-ca.umodelverse.ai/v1"):
        if not base_url:
            base_url = "https://api-us-ca.umodelverse.ai/v1"
        super().__init__(key, model_name, base_url)


class AstraflowCNEmbed(OpenAIEmbed):
    _FACTORY_NAME = "Astraflow-CN"

    def __init__(self, key, model_name, base_url="https://api.modelverse.cn/v1"):
        if not base_url:
            base_url = "https://api.modelverse.cn/v1"
        super().__init__(key, model_name, base_url)


class FuturMixEmbed(OpenAIEmbed):
    _FACTORY_NAME = "FuturMix"

    def __init__(self, key, model_name="text-embedding-3-small", base_url="https://futurmix.ai/v1"):
        if not base_url:
            base_url = "https://futurmix.ai/v1"
        super().__init__(key, model_name, base_url)
        logging.info("[FuturMix] Embedding initialized with model %s", model_name)


class BaiChuanEmbed(OpenAIEmbed):
    _FACTORY_NAME = "BaiChuan"

    def __init__(self, key, model_name="Baichuan-Text-Embedding", base_url="https://api.baichuan-ai.com/v1"):
        if not base_url:
            base_url = "https://api.baichuan-ai.com/v1"
        super().__init__(key, model_name, base_url)


class QWenEmbed(Base):
    """
    Embeddings for Alibaba Tongyi-Qianwen via the DashScope ``TextEmbedding`` API.

    ``base_url`` comes from the user's embedding-model configuration (often the same host
    as the OpenAI-compatible chat endpoint). This class maps known DashScope hosts to the
    native ``/api/v1`` base URL so international and China endpoints both work.
    """

    _FACTORY_NAME = "Tongyi-Qianwen"

    def __init__(self, key, model_name="text_embedding_v2", base_url=None, **kwargs):
        self.key = key
        self.model_name = model_name
        # Native API root for the SDK; None if base_url is absent or not a known DashScope host.
        self._dashscope_http_api_url = _dashscope_native_http_api_url(base_url)

    def encode(self, texts: list):
        import time

        import dashscope

        batch_size = 4
        res = []
        token_count = 0
        texts = [truncate(t, 2048) for t in texts]
        for i in range(0, len(texts), batch_size):

            retry_max, retry_wait_secs = 5, 10
            for retry in range(retry_max):
                with _dashscope_native_api_url_scope(self._dashscope_http_api_url):
                    resp = dashscope.TextEmbedding.call(model=self.model_name, input=texts[i : i + batch_size], api_key=self.key, text_type="document")
                status_code = resp.status_code
                if status_code >= 400 and status_code < 500 and status_code not in [408, 429]:
                    raise ModelException(f"Error, status: {status_code}, response: {resp}")
                    # No need to retry for 4XX error
                if status_code == 200:
                    break
                if retry < retry_max - 1:
                    logging.warning(f"Got error response from DashScope API (status: {status_code}, response: {resp}). Wait {retry_wait_secs} seconds. Retrying...")
                    time.sleep(retry_wait_secs)
                else:
                    raise ModelException(f"Error after {retry_max} retries., status: {status_code}, response: {resp}")

            try:
                embds = [[] for _ in range(len(resp["output"]["embeddings"]))]
                for e in resp["output"]["embeddings"]:
                    embds[e["text_index"]] = e["embedding"]
                res.extend(embds)
                token_count += total_token_count_from_response(resp)
            except Exception as _e:
                log_exception(_e, resp)
                raise ModelException(f"Error: {status_code}: {resp}")
        return np.array(res), token_count

    def encode_queries(self, text):
        with _dashscope_native_api_url_scope(self._dashscope_http_api_url):
            resp = dashscope.TextEmbedding.call(model=self.model_name, input=text[:2048], api_key=self.key, text_type="query")
        status_code = resp.status_code
        if status_code != 200:
            raise ModelException(f"Error: status: {status_code}: code: {resp.get('code')}, message: {resp.get('message')}")
            # No need to retry for 4XX error
        try:
            return np.array(resp["output"]["embeddings"][0]["embedding"]), total_token_count_from_response(resp)
        except Exception as _e:
            log_exception(_e, resp)
            raise ModelException(f"Error: {status_code}: {resp}")


class ZhipuEmbed(Base):
    _FACTORY_NAME = "ZHIPU-AI"

    def __init__(self, key, model_name="embedding-2", **kwargs):
        self.client = ZhipuAI(api_key=key)
        self.model_name = model_name

    def encode(self, texts: list):
        arr = []
        tks_num = 0
        MAX_LEN = -1
        if self.model_name.lower() == "embedding-2":
            MAX_LEN = 512
        if self.model_name.lower() == "embedding-3":
            MAX_LEN = 3072
        if MAX_LEN > 0:
            texts = [truncate(t, MAX_LEN) for t in texts]

        for txt in texts:
            res = self.client.embeddings.create(input=txt, model=self.model_name)
            try:
                arr.append(res.data[0].embedding)
                tks_num += total_token_count_from_response(res)
            except Exception as _e:
                log_exception(_e, res)
                raise Exception(f"Error: {res}")
        return np.array(arr), tks_num

    def encode_queries(self, text):
        res = self.client.embeddings.create(input=text, model=self.model_name)
        try:
            return np.array(res.data[0].embedding), total_token_count_from_response(res)
        except Exception as _e:
            log_exception(_e, res)
            raise Exception(f"Error: {res}")


class OllamaEmbed(Base):
    _FACTORY_NAME = "Ollama"

    _special_tokens = ["<|endoftext|>"]

    def __init__(self, key, model_name, **kwargs):
        self.client = Client(host=kwargs["base_url"]) if not key or key == "x" else Client(host=kwargs["base_url"], headers={"Authorization": f"Bearer {key}"})
        self.model_name = model_name
        self.keep_alive = kwargs.get("ollama_keep_alive", int(os.environ.get("OLLAMA_KEEP_ALIVE", -1)))

    def encode(self, texts: list):
        arr = []
        tks_num = 0
        for txt in texts:
            # remove special tokens if they exist base on regex in one request
            for token in OllamaEmbed._special_tokens:
                txt = txt.replace(token, "")
            res = self.client.embeddings(prompt=txt, model=self.model_name, options={"use_mmap": True}, keep_alive=self.keep_alive)
            try:
                arr.append(res["embedding"])
            except Exception as _e:
                log_exception(_e, res)
                raise Exception(f"Error: {res}")
            tks_num += 128
        return np.array(arr), tks_num

    def encode_queries(self, text):
        # remove special tokens if they exist
        for token in OllamaEmbed._special_tokens:
            text = text.replace(token, "")
        res = self.client.embeddings(prompt=text, model=self.model_name, options={"use_mmap": True}, keep_alive=self.keep_alive)
        try:
            return np.array(res["embedding"]), 128
        except Exception as _e:
            log_exception(_e, res)
            raise Exception(f"Error: {res}")


class XinferenceEmbed(Base):
    _FACTORY_NAME = "Xinference"

    def __init__(self, key, model_name="", base_url=""):
        base_url = urljoin(base_url, "v1")
        self.client = OpenAI(api_key=key, base_url=base_url)
        self.model_name = model_name

    def encode(self, texts: list):
        batch_size = 16
        ress = []
        total_tokens = 0
        for i in range(0, len(texts), batch_size):
            res = None
            try:
                res = self.client.embeddings.create(input=texts[i : i + batch_size], model=self.model_name)
                ress.extend([d.embedding for d in res.data])
                total_tokens += total_token_count_from_response(res)
            except Exception as _e:
                log_exception(_e, res)
                raise Exception(f"Error: {res}")
        return np.array(ress), total_tokens

    def encode_queries(self, text):
        res = None
        try:
            res = self.client.embeddings.create(input=[text], model=self.model_name)
            return np.array(res.data[0].embedding), total_token_count_from_response(res)
        except Exception as _e:
            log_exception(_e, res)
            raise Exception(f"Error: {res}")


class YoudaoEmbed(Base):
    _FACTORY_NAME = "Youdao"
    _client = None

    def __init__(self, key=None, model_name="maidalun1020/bce-embedding-base_v1", **kwargs):
        pass

    def encode(self, texts: list):
        batch_size = 10
        res = []
        token_count = 0
        for t in texts:
            token_count += num_tokens_from_string(t)
        for i in range(0, len(texts), batch_size):
            embds = YoudaoEmbed._client.encode(texts[i : i + batch_size])
            res.extend(embds)
        return np.array(res), token_count

    def encode_queries(self, text):
        embds = YoudaoEmbed._client.encode([text])
        return np.array(embds[0]), num_tokens_from_string(text)


class JinaMultiVecEmbed(Base):
    _FACTORY_NAME = "Jina"

    def __init__(self, key, model_name="jina-embeddings-v4", base_url="https://api.jina.ai/v1/embeddings"):
        self.base_url = "https://api.jina.ai/v1/embeddings"
        self.headers = {"Content-Type": "application/json", "Authorization": f"Bearer {key}"}
        self.model_name = model_name

    def encode(self, texts: list[str | bytes], task="retrieval.passage"):
        batch_size = 16
        ress = []
        token_count = 0
        input = []
        for text in texts:
            if isinstance(text, str):
                input.append({"text": text})
            elif isinstance(text, bytes):
                img_b64s = None
                try:
                    base64.b64decode(text, validate=True)
                    img_b64s = text.decode("utf8")
                except Exception:
                    img_b64s = base64.b64encode(text).decode("utf8")
                input.append({"image": img_b64s})  # base64 encoded image
        for i in range(0, len(texts), batch_size):
            data = {"model": self.model_name, "input": input[i : i + batch_size]}
            if "v4" in self.model_name:
                data["return_multivector"] = True

            if "v3" in self.model_name or "v4" in self.model_name:
                data["task"] = task
                data["truncate"] = True

            response = requests.post(self.base_url, headers=self.headers, json=data, timeout=30)
            _raise_model_exception_if_failed(response)
            try:
                res = response.json()
                for d in res["data"]:
                    if data.get("return_multivector", False):  # v4
                        token_embs = np.asarray(d["embeddings"], dtype=np.float32)
                        chunk_emb = token_embs.mean(axis=0)

                    else:
                        # v2/v3
                        chunk_emb = np.asarray(d["embedding"], dtype=np.float32)

                    ress.append(chunk_emb)

                token_count += total_token_count_from_response(res)
            except Exception as _e:
                log_exception(_e, response)
                raise Exception(f"Error: {response}")
        return np.array(ress), token_count

    def encode_queries(self, text):
        embds, cnt = self.encode([text], task="retrieval.query")
        return np.array(embds[0]), cnt


class MistralEmbed(Base):
    _FACTORY_NAME = "Mistral"

    def __init__(self, key, model_name="mistral-embed", base_url=None):
        from mistralai.client import MistralClient

        self.client = MistralClient(api_key=key)
        self.model_name = model_name

    def encode(self, texts: list):
        import time
        import random

        texts = [truncate(t, 8196) for t in texts]
        batch_size = 16
        ress = []
        token_count = 0
        for i in range(0, len(texts), batch_size):
            retry_max = 5
            while retry_max > 0:
                try:
                    res = self.client.embeddings(input=texts[i : i + batch_size], model=self.model_name)
                    ress.extend([d.embedding for d in res.data])
                    token_count += total_token_count_from_response(res)
                    break
                except Exception as _e:
                    if retry_max == 1:
                        log_exception(_e)
                    delay = random.uniform(20, 60)
                    time.sleep(delay)
                    retry_max -= 1
        return np.array(ress), token_count

    def encode_queries(self, text):
        import time
        import random

        retry_max = 5
        while retry_max > 0:
            try:
                res = self.client.embeddings(input=[truncate(text, 8196)], model=self.model_name)
                return np.array(res.data[0].embedding), total_token_count_from_response(res)
            except Exception as _e:
                if retry_max == 1:
                    log_exception(_e)
                delay = random.randint(20, 60)
                time.sleep(delay)
                retry_max -= 1


class BedrockEmbed(Base):
    _FACTORY_NAME = "Bedrock"

    def __init__(self, key, model_name, **kwargs):
        import boto3

        # `key` protocol (backend stores as JSON string in `api_key`):
        # - Must decode into a dict.
        # - Required: `auth_mode`, `bedrock_region`.
        # - Supported auth modes:
        #   - "access_key_secret": requires `bedrock_ak` + `bedrock_sk`.
        #   - "iam_role": requires `aws_role_arn` and assumes role via STS.
        #   - else: treated as "assume_role" (default AWS credential chain).
        key = json.loads(key)
        mode = key.get("auth_mode")
        if not mode:
            logging.error("Bedrock auth_mode is not provided in the key")
            raise ValueError("Bedrock auth_mode must be provided in the key")

        self.bedrock_region = key.get("bedrock_region")

        self.model_name = model_name
        self.is_amazon = self.model_name.split(".")[0] == "amazon"
        self.is_cohere = self.model_name.split(".")[0] == "cohere"

        if mode == "access_key_secret":
            self.bedrock_ak = key.get("bedrock_ak")
            self.bedrock_sk = key.get("bedrock_sk")
            self.client = boto3.client(service_name="bedrock-runtime", region_name=self.bedrock_region, aws_access_key_id=self.bedrock_ak, aws_secret_access_key=self.bedrock_sk)
        elif mode == "iam_role":
            self.aws_role_arn = key.get("aws_role_arn")
            sts_client = boto3.client("sts", region_name=self.bedrock_region)
            resp = sts_client.assume_role(RoleArn=self.aws_role_arn, RoleSessionName="BedrockSession")
            creds = resp["Credentials"]

            self.client = boto3.client(
                service_name="bedrock-runtime",
                aws_access_key_id=creds["AccessKeyId"],
                aws_secret_access_key=creds["SecretAccessKey"],
                aws_session_token=creds["SessionToken"],
            )
        else:  # assume_role
            self.client = boto3.client("bedrock-runtime", region_name=self.bedrock_region)

    def encode(self, texts: list):
        texts = [truncate(t, 8196) for t in texts]
        embeddings = []
        token_count = 0
        for text in texts:
            if self.is_amazon:
                body = {"inputText": text}
            elif self.is_cohere:
                body = {"texts": [text], "input_type": "search_document"}

            response = self.client.invoke_model(modelId=self.model_name, body=json.dumps(body))
            try:
                model_response = json.loads(response["body"].read())
                embeddings.extend([model_response["embedding"]])
                token_count += num_tokens_from_string(text)
            except Exception as _e:
                log_exception(_e, response)

        return np.array(embeddings), token_count

    def encode_queries(self, text):
        embeddings = []
        token_count = num_tokens_from_string(text)
        if self.is_amazon:
            body = {"inputText": truncate(text, 8196)}
        elif self.is_cohere:
            body = {"texts": [truncate(text, 8196)], "input_type": "search_query"}

        response = self.client.invoke_model(modelId=self.model_name, body=json.dumps(body))
        try:
            model_response = json.loads(response["body"].read())
            embeddings.extend(model_response["embedding"])
        except Exception as _e:
            log_exception(_e, response)

        return np.array(embeddings), token_count


class GeminiEmbed(Base):
    _FACTORY_NAME = "Gemini"

    def __init__(self, key, model_name="gemini-embedding-001", **kwargs):
        from google import genai
        from google.genai import types

        self.key = key
        self.model_name = model_name[7:] if model_name.startswith("models/") else model_name
        self.client = genai.Client(api_key=self.key)
        self.types = types

    @staticmethod
    def _parse_embedding_vector(embedding):
        if isinstance(embedding, dict):
            values = embedding.get("values")
            if values is None:
                values = embedding.get("embedding")
            if values is not None:
                return values

        values = getattr(embedding, "values", None)
        if values is None:
            values = getattr(embedding, "embedding", None)
        if values is not None:
            return values

        raise TypeError(f"Unsupported embedding payload: {type(embedding)}")

    @classmethod
    def _parse_embedding_response(cls, response):
        if response is None:
            raise ValueError("Embedding response is empty")

        embeddings = getattr(response, "embeddings", None)
        if embeddings is None and isinstance(response, dict):
            embeddings = response.get("embeddings")

        if embeddings is None:
            return [cls._parse_embedding_vector(response)]

        return [cls._parse_embedding_vector(item) for item in embeddings]

    def _build_embedding_config(self):
        task_type = "RETRIEVAL_DOCUMENT"
        if hasattr(self.types, "TaskType"):
            task_type = getattr(self.types.TaskType, "RETRIEVAL_DOCUMENT", task_type)
        try:
            return self.types.EmbedContentConfig(task_type=task_type, title="Embedding of single string")
        except TypeError:
            # Compatible with SDK versions that do not accept title in embed config.
            return self.types.EmbedContentConfig(task_type=task_type)

    def encode(self, texts: list):
        texts = [truncate(t, 2048) for t in texts]
        token_count = sum(num_tokens_from_string(text) for text in texts)
        config = self._build_embedding_config()
        batch_size = 16
        ress = []
        for i in range(0, len(texts), batch_size):
            result = None
            try:
                result = self.client.models.embed_content(
                    model=self.model_name,
                    contents=texts[i : i + batch_size],
                    config=config,
                )
                ress.extend(self._parse_embedding_response(result))
            except Exception as _e:
                log_exception(_e, result)
                raise Exception(f"Error: {result}")
        return np.array(ress), token_count

    def encode_queries(self, text):
        config = self._build_embedding_config()
        result = None
        token_count = num_tokens_from_string(text)
        try:
            result = self.client.models.embed_content(
                model=self.model_name,
                contents=[truncate(text, 2048)],
                config=config,
            )
            return np.array(self._parse_embedding_response(result)[0]), token_count
        except Exception as _e:
            log_exception(_e, result)
            raise Exception(f"Error: {result}")


class NvidiaEmbed(Base):
    _FACTORY_NAME = "NVIDIA"

    def __init__(self, key, model_name, base_url="https://integrate.api.nvidia.com/v1/embeddings"):
        if not base_url:
            base_url = "https://integrate.api.nvidia.com/v1/embeddings"
        self.api_key = key
        self.base_url = base_url
        self.headers = {
            "accept": "application/json",
            "Content-Type": "application/json",
            "authorization": f"Bearer {self.api_key}",
        }
        self.model_name = model_name
        if model_name == "nvidia/embed-qa-4":
            self.base_url = "https://ai.api.nvidia.com/v1/retrieval/nvidia/embeddings"
            self.model_name = "NV-Embed-QA"
        if model_name == "snowflake/arctic-embed-l":
            self.base_url = "https://ai.api.nvidia.com/v1/retrieval/snowflake/arctic-embed-l/embeddings"

    def encode(self, texts: list):
        batch_size = 16
        ress = []
        token_count = 0
        for i in range(0, len(texts), batch_size):
            payload = {
                "input": texts[i : i + batch_size],
                "input_type": "query",
                "model": self.model_name,
                "encoding_format": "float",
                "truncate": "END",
            }
            response = requests.post(self.base_url, headers=self.headers, json=payload, timeout=30)
            _raise_model_exception_if_failed(response)
            try:
                res = response.json()
                ress.extend([d["embedding"] for d in res["data"]])
                token_count += total_token_count_from_response(res)
            except Exception as _e:
                log_exception(_e, response)
                raise Exception(f"Error: {response}")
        return np.array(ress), token_count

    def encode_queries(self, text):
        embds, cnt = self.encode([text])
        return np.array(embds[0]), cnt


class LmStudioEmbed(LocalAIEmbed):
    _FACTORY_NAME = "LM-Studio"

    def __init__(self, key, model_name, base_url):
        if not base_url:
            raise ValueError("Local llm url cannot be None")
        base_url = urljoin(base_url, "v1")
        self.client = OpenAI(api_key="lm-studio", base_url=base_url)
        self.model_name = model_name


class OpenAI_APIEmbed(OpenAIEmbed):
    _FACTORY_NAME = ["VLLM", "OpenAI-API-Compatible"]

    def __init__(self, key, model_name, base_url):
        if not base_url:
            raise ValueError("url cannot be None")
        base_url = urljoin(base_url, "v1")
        self.client = OpenAI(api_key=key, base_url=base_url)
        self.model_name = model_name.split("___")[0]


class CoHereEmbed(Base):
    _FACTORY_NAME = "Cohere"

    def __init__(self, key, model_name, base_url=None):
        from cohere import Client

        self.client = Client(api_key=key)
        self.model_name = model_name

    def encode(self, texts: list):
        batch_size = 16
        ress = []
        token_count = 0
        for i in range(0, len(texts), batch_size):
            res = self.client.embed(
                texts=texts[i : i + batch_size],
                model=self.model_name,
                input_type="search_document",
                embedding_types=["float"],
            )
            try:
                ress.extend([d for d in res.embeddings.float])
                token_count += total_token_count_from_response(res)
            except Exception as _e:
                log_exception(_e, res)
                raise Exception(f"Error: {res}")
        return np.array(ress), token_count

    def encode_queries(self, text):
        res = self.client.embed(
            texts=[text],
            model=self.model_name,
            input_type="search_query",
            embedding_types=["float"],
        )
        try:
            return np.array(res.embeddings.float[0]), int(total_token_count_from_response(res))
        except Exception as _e:
            log_exception(_e, res)
            raise Exception(f"Error: {res}")


class TogetherAIEmbed(OpenAIEmbed):
    _FACTORY_NAME = "TogetherAI"

    def __init__(self, key, model_name, base_url="https://api.together.xyz/v1"):
        if not base_url:
            base_url = "https://api.together.xyz/v1"
        super().__init__(key, model_name, base_url=base_url)


class PerfXCloudEmbed(OpenAIEmbed):
    _FACTORY_NAME = "PerfXCloud"

    def __init__(self, key, model_name, base_url="https://cloud.perfxlab.cn/v1"):
        if not base_url:
            base_url = "https://cloud.perfxlab.cn/v1"
        super().__init__(key, model_name, base_url)


class UpstageEmbed(OpenAIEmbed):
    _FACTORY_NAME = "Upstage"

    def __init__(self, key, model_name, base_url="https://api.upstage.ai/v1/solar"):
        if not base_url:
            base_url = "https://api.upstage.ai/v1/solar"
        super().__init__(key, model_name, base_url)


class SILICONFLOWEmbed(Base):
    _FACTORY_NAME = "SILICONFLOW"

    def __init__(self, key, model_name, base_url="https://api.siliconflow.cn/v1/embeddings"):
        normalized_base_url = (base_url or "").strip()
        if not normalized_base_url:
            normalized_base_url = "https://api.siliconflow.cn/v1/embeddings"
        if "/embeddings" not in normalized_base_url:
            normalized_base_url = urljoin(f"{normalized_base_url.rstrip('/')}/", "embeddings").rstrip("/")
        self.headers = {
            "accept": "application/json",
            "content-type": "application/json",
            "authorization": f"Bearer {key}",
        }
        self.base_url = normalized_base_url
        self.model_name = model_name

    def encode(self, texts: list):
        batch_size = 16
        ress = []
        token_count = 0
        for i in range(0, len(texts), batch_size):
            texts_batch = texts[i : i + batch_size]
            if self.model_name in ["BAAI/bge-large-zh-v1.5", "BAAI/bge-large-en-v1.5"]:
                # limit 512, 340 is almost safe
                texts_batch = [" " if not text.strip() else truncate(text, 256) for text in texts_batch]
            else:
                texts_batch = [" " if not text.strip() else text for text in texts_batch]

            payload = {
                "model": self.model_name,
                "input": texts_batch,
                "encoding_format": "float",
            }
            response = requests.post(self.base_url, json=payload, headers=self.headers, timeout=30)
            _raise_model_exception_if_failed(response)
            try:
                res = response.json()
                ress.extend([d["embedding"] for d in res["data"]])
                token_count += total_token_count_from_response(res)
            except Exception as _e:
                log_exception(_e, response)
                raise Exception(f"Error: {response}")

        return np.array(ress), token_count

    def encode_queries(self, text):
        payload = {
            "model": self.model_name,
            "input": text,
            "encoding_format": "float",
        }
        response = requests.post(self.base_url, json=payload, headers=self.headers, timeout=30)
        _raise_model_exception_if_failed(response)
        try:
            res = response.json()
            return np.array(res["data"][0]["embedding"]), total_token_count_from_response(res)
        except Exception as _e:
            log_exception(_e, response)
            raise Exception(f"Error: {response}")


class ReplicateEmbed(Base):
    _FACTORY_NAME = "Replicate"

    def __init__(self, key, model_name, base_url=None):
        from replicate.client import Client

        self.model_name = model_name
        self.client = Client(api_token=key)

    def encode(self, texts: list):
        batch_size = 16
        token_count = sum([num_tokens_from_string(text) for text in texts])
        ress = []
        for i in range(0, len(texts), batch_size):
            res = self.client.run(self.model_name, input={"texts": texts[i : i + batch_size]})
            ress.extend(res)
        return np.array(ress), token_count

    def encode_queries(self, text):
        res = self.client.embed(self.model_name, input={"texts": [text]})
        return np.array(res), num_tokens_from_string(text)


class BaiduYiyanEmbed(Base):
    _FACTORY_NAME = "BaiduYiyan"

    def __init__(self, key, model_name, base_url=None):
        import qianfan

        key = json.loads(key)
        ak = key.get("yiyan_ak", "")
        sk = key.get("yiyan_sk", "")
        self.client = qianfan.Embedding(ak=ak, sk=sk)
        self.model_name = model_name

    def encode(self, texts: list, batch_size=16):
        res = self.client.do(model=self.model_name, texts=texts).body
        try:
            return (
                np.array([r["embedding"] for r in res["data"]]),
                total_token_count_from_response(res),
            )
        except Exception as _e:
            log_exception(_e, res)
            raise Exception(f"Error: {res}")

    def encode_queries(self, text):
        res = self.client.do(model=self.model_name, texts=[text]).body
        try:
            return (
                np.array([r["embedding"] for r in res["data"]]),
                total_token_count_from_response(res),
            )
        except Exception as _e:
            log_exception(_e, res)
            raise Exception(f"Error: {res}")


class VoyageEmbed(Base):
    _FACTORY_NAME = "Voyage AI"

    def __init__(self, key, model_name, base_url=None):
        import voyageai

        self.client = voyageai.Client(api_key=key)
        self.model_name = model_name

    def encode(self, texts: list):
        batch_size = 16
        ress = []
        token_count = 0
        for i in range(0, len(texts), batch_size):
            res = self.client.embed(texts=texts[i : i + batch_size], model=self.model_name, input_type="document")
            try:
                ress.extend(res.embeddings)
                token_count += res.total_tokens
            except Exception as _e:
                log_exception(_e, res)
                raise Exception(f"Error: {res}")
        return np.array(ress), token_count

    def encode_queries(self, text):
        res = self.client.embed(texts=text, model=self.model_name, input_type="query")
        try:
            return np.array(res.embeddings)[0], res.total_tokens
        except Exception as _e:
            log_exception(_e, res)
            raise Exception(f"Error: {res}")


class HuggingFaceEmbed(Base):
    _FACTORY_NAME = "HuggingFace"

    def __init__(self, key, model_name, base_url=None, **kwargs):
        if not model_name:
            raise ValueError("Model name cannot be None")
        self.key = key
        self.model_name = model_name.split("___")[0]
        self.base_url = base_url or "http://127.0.0.1:8080"

    def encode(self, texts: list):
        response = requests.post(f"{self.base_url}/embed", json={"inputs": texts}, headers={"Content-Type": "application/json"}, timeout=30)
        _raise_model_exception_if_failed(response)
        embeddings = response.json()
        return np.array(embeddings), sum([num_tokens_from_string(text) for text in texts])

    def encode_queries(self, text: str):
        response = requests.post(f"{self.base_url}/embed", json={"inputs": text}, headers={"Content-Type": "application/json"}, timeout=30)
        _raise_model_exception_if_failed(response)
        embedding = response.json()[0]
        return np.array(embedding), num_tokens_from_string(text)


class VolcEngineEmbed(Base):
    _FACTORY_NAME = "VolcEngine"

    def __init__(self, key, model_name, base_url="https://ark.cn-beijing.volces.com/api/v3"):
        if not base_url:
            base_url = "https://ark.cn-beijing.volces.com/api/v3"
        self.base_url = base_url

        try:
            cfg = json.loads(key)
            self.ark_api_key = cfg.get("ark_api_key", "")
        except JSONDecodeError:
            self.ark_api_key = key
        self.model_name = model_name

    @staticmethod
    def _extract_embedding(result: dict) -> list[float]:
        if not isinstance(result, dict):
            raise TypeError(f"Unexpected response type: {type(result)}")

        data = result.get("data")
        if data is None:
            raise KeyError("Missing 'data' in response")

        if isinstance(data, list):
            if not data:
                raise ValueError("Empty 'data' in response")
            item = data[0]
        elif isinstance(data, dict):
            item = data
        else:
            raise TypeError(f"Unexpected 'data' type: {type(data)}")

        if not isinstance(item, dict):
            raise TypeError("Unexpected item shape in 'data'")
        if "embedding" not in item:
            raise KeyError("Missing 'embedding' in response item")
        return item["embedding"]

    def _encode_texts(self, texts: list[str]):
        from common.http_client import sync_request

        url = f"{self.base_url}/embeddings/multimodal"
        headers = {"Content-Type": "application/json", "Authorization": f"Bearer {self.ark_api_key}"}

        ress: list[list[float]] = []
        total_tokens = 0
        for text in texts:
            request_body = {"model": self.model_name, "input": [{"type": "text", "text": text}]}
            response = sync_request(method="POST", url=url, headers=headers, json=request_body, timeout=60)
            if response.status_code != 200:
                raise Exception(f"Error: {response.status_code} - {response.text}")
            result = response.json()
            try:
                ress.append(self._extract_embedding(result))
                total_tokens += total_token_count_from_response(result)
            except Exception as _e:
                log_exception(_e)

        return np.array(ress), total_tokens

    def encode(self, texts: list):
        return self._encode_texts(texts)

    def encode_queries(self, text: str):
        embeddings, tokens = self._encode_texts([text])
        return embeddings[0], tokens


class GPUStackEmbed(OpenAIEmbed):
    _FACTORY_NAME = "GPUStack"

    def __init__(self, key, model_name, base_url):
        if not base_url:
            raise ValueError("url cannot be None")
        base_url = urljoin(base_url, "v1")

        self.client = OpenAI(api_key=key, base_url=base_url)
        self.model_name = model_name


class NovitaEmbed(SILICONFLOWEmbed):
    _FACTORY_NAME = "NovitaAI"

    def __init__(self, key, model_name, base_url="https://api.novita.ai/v3/openai/embeddings"):
        if not base_url:
            base_url = "https://api.novita.ai/v3/openai/embeddings"
        super().__init__(key, model_name, base_url)


class GiteeEmbed(SILICONFLOWEmbed):
    _FACTORY_NAME = "GiteeAI"

    def __init__(self, key, model_name, base_url="https://ai.gitee.com/v1/embeddings"):
        if not base_url:
            base_url = "https://ai.gitee.com/v1/embeddings"
        super().__init__(key, model_name, base_url)


class DeepInfraEmbed(OpenAIEmbed):
    _FACTORY_NAME = "DeepInfra"

    def __init__(self, key, model_name, base_url="https://api.deepinfra.com/v1/openai"):
        if not base_url:
            base_url = "https://api.deepinfra.com/v1/openai"
        super().__init__(key, model_name, base_url)


class Ai302Embed(Base):
    _FACTORY_NAME = "302.AI"

    def __init__(self, key, model_name, base_url="https://api.302.ai/v1/embeddings"):
        if not base_url:
            base_url = "https://api.302.ai/v1/embeddings"
        super().__init__(key, model_name, base_url)


class CometAPIEmbed(OpenAIEmbed):
    _FACTORY_NAME = "CometAPI"

    def __init__(self, key, model_name, base_url="https://api.cometapi.com/v1"):
        if not base_url:
            base_url = "https://api.cometapi.com/v1"
        super().__init__(key, model_name, base_url)


class DeerAPIEmbed(OpenAIEmbed):
    _FACTORY_NAME = "DeerAPI"

    def __init__(self, key, model_name, base_url="https://api.deerapi.com/v1"):
        if not base_url:
            base_url = "https://api.deerapi.com/v1"
        super().__init__(key, model_name, base_url)


class JiekouAIEmbed(OpenAIEmbed):
    _FACTORY_NAME = "Jiekou.AI"

    def __init__(self, key, model_name, base_url="https://api.jiekou.ai/openai/v1/embeddings"):
        if not base_url:
            base_url = "https://api.jiekou.ai/openai/v1/embeddings"
        super().__init__(key, model_name, base_url)


class RAGconEmbed(OpenAIEmbed):
    """
    RAGcon Embedding Provider - routes through LiteLLM proxy

    Default Base URL: https://connect.ragcon.ai/v1
    """

    _FACTORY_NAME = "RAGcon"

    def __init__(self, key, model_name="text-embedding-3-small", base_url=None):
        if not base_url:
            base_url = "https://connect.ragcon.com/v1"

        super().__init__(key, model_name, base_url)


class PerplexityEmbed(Base):
    _FACTORY_NAME = "Perplexity"

    def __init__(self, key, model_name="pplx-embed-v1-0.6b", base_url="https://api.perplexity.ai"):
        if not base_url:
            base_url = "https://api.perplexity.ai"
        self.base_url = base_url.rstrip("/")
        self.api_key = key
        self.model_name = model_name
        self.headers = {
            "Content-Type": "application/json",
            "Authorization": f"Bearer {self.api_key}",
        }

    @staticmethod
    def _decode_base64_int8(b64_str):
        raw = base64.b64decode(b64_str)
        return np.frombuffer(raw, dtype=np.int8).astype(np.float32)

    def _is_contextualized(self):
        return "context" in self.model_name

    def encode(self, texts: list):
        batch_size = 512
        ress = []
        token_count = 0

        if self._is_contextualized():
            url = f"{self.base_url}/v1/contextualizedembeddings"
            for i in range(0, len(texts), batch_size):
                batch = texts[i : i + batch_size]
                payload = {
                    "model": self.model_name,
                    "input": [[chunk] for chunk in batch],
                    "encoding_format": "base64_int8",
                }
                response = requests.post(url, headers=self.headers, json=payload, timeout=30)
                _raise_model_exception_if_failed(response)
                try:
                    res = response.json()
                    for doc in res["data"]:
                        for chunk_emb in doc["data"]:
                            ress.append(self._decode_base64_int8(chunk_emb["embedding"]))
                    token_count += res.get("usage", {}).get("total_tokens", 0)
                except Exception as _e:
                    log_exception(_e, response)
                    raise Exception(f"Error: {response.text}")
        else:
            url = f"{self.base_url}/v1/embeddings"
            for i in range(0, len(texts), batch_size):
                batch = texts[i : i + batch_size]
                payload = {
                    "model": self.model_name,
                    "input": batch,
                    "encoding_format": "base64_int8",
                }
                response = requests.post(url, headers=self.headers, json=payload, timeout=30)
                _raise_model_exception_if_failed(response)
                try:
                    res = response.json()
                    for d in res["data"]:
                        ress.append(self._decode_base64_int8(d["embedding"]))
                    token_count += res.get("usage", {}).get("total_tokens", 0)
                except Exception as _e:
                    log_exception(_e, response)
                    raise Exception(f"Error: {response.text}")

        return np.array(ress), token_count

    def encode_queries(self, text):
        embds, cnt = self.encode([text])
        return np.array(embds[0]), cnt