ragflow-main/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 logging
import re
import threading
import requests
from huggingface_hub import snapshot_download
from zhipuai import ZhipuAI
import os
from abc import ABC
from ollama import Client
import dashscope
from openai import OpenAI
import numpy as np
import asyncio

from api import settings
from api.utils.file_utils import get_home_cache_dir
from rag.utils import num_tokens_from_string, truncate
import google.generativeai as genai
import json


class Base(ABC):
    def __init__(self, key, model_name):
        pass

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

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

    def total_token_count(self, resp):
        try:
            return resp.usage.total_tokens
        except Exception:
            pass
        try:
            return resp["usage"]["total_tokens"]
        except Exception:
            pass
        return 0


class DefaultEmbedding(Base):
    _model = None
    _model_name = ""
    _model_lock = threading.Lock()

    def __init__(self, key, model_name, **kwargs):
        """
        If you have trouble downloading HuggingFace models, -_^ this might help!!

        For Linux:
        export HF_ENDPOINT=https://hf-mirror.com

        For Windows:
        Good luck
        ^_-

        """
        if not settings.LIGHTEN:
            with DefaultEmbedding._model_lock:
                from FlagEmbedding import FlagModel
                import torch
                if not DefaultEmbedding._model or model_name != DefaultEmbedding._model_name:
                    try:
                        DefaultEmbedding._model = FlagModel(os.path.join(get_home_cache_dir(), re.sub(r"^[a-zA-Z0-9]+/", "", model_name)),
                                                            query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
                                                            use_fp16=torch.cuda.is_available())
                        DefaultEmbedding._model_name = model_name
                    except Exception:
                        model_dir = snapshot_download(repo_id="BAAI/bge-large-zh-v1.5",
                                                      local_dir=os.path.join(get_home_cache_dir(), re.sub(r"^[a-zA-Z0-9]+/", "", model_name)),
                                                      local_dir_use_symlinks=False)
                        DefaultEmbedding._model = FlagModel(model_dir,
                                                            query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
                                                            use_fp16=torch.cuda.is_available())
        self._model = DefaultEmbedding._model
        self._model_name = DefaultEmbedding._model_name

    def encode(self, texts: list):
        batch_size = 16
        texts = [truncate(t, 2048) for t in texts]
        token_count = 0
        for t in texts:
            token_count += num_tokens_from_string(t)
        ress = []
        for i in range(0, len(texts), batch_size):
            ress.extend(self._model.encode(texts[i:i + batch_size]).tolist())
        return np.array(ress), token_count

    def encode_queries(self, text: str):
        token_count = num_tokens_from_string(text)
        return self._model.encode_queries([text]).tolist()[0], token_count


class OpenAIEmbed(Base):
    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):
            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 += self.total_token_count(res)
        return np.array(ress), total_tokens

    def encode_queries(self, text):
        res = self.client.embeddings.create(input=[truncate(text, 8191)],
                                            model=self.model_name)
        return np.array(res.data[0].embedding), self.total_token_count(res)


class LocalAIEmbed(Base):
    def __init__(self, key, model_name, base_url):
        if not base_url:
            raise ValueError("Local embedding model url cannot be None")
        if base_url.split("/")[-1] != "v1":
            base_url = os.path.join(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)
            ress.extend([d.embedding for d in res.data])
        # 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):
    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 BaiChuanEmbed(OpenAIEmbed):
    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):
    def __init__(self, key, model_name="text_embedding_v2", **kwargs):
        self.key = key
        self.model_name = model_name

    def encode(self, texts: list):
        import dashscope
        batch_size = 4
        try:
            res = []
            token_count = 0
            texts = [truncate(t, 2048) for t in texts]
            for i in range(0, len(texts), batch_size):
                resp = dashscope.TextEmbedding.call(
                    model=self.model_name,
                    input=texts[i:i + batch_size],
                    api_key=self.key,
                    text_type="document"
                )
                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 += self.total_token_count(resp)
            return np.array(res), token_count
        except Exception as e:
            raise Exception("Account abnormal. Please ensure it's on good standing to use QWen's "+self.model_name)
        return np.array([]), 0

    def encode_queries(self, text):
        try:
            resp = dashscope.TextEmbedding.call(
                model=self.model_name,
                input=text[:2048],
                api_key=self.key,
                text_type="query"
            )
            return np.array(resp["output"]["embeddings"][0]
                            ["embedding"]), self.total_token_count(resp)
        except Exception:
            raise Exception("Account abnormal. Please ensure it's on good standing to use QWen's "+self.model_name)
        return np.array([]), 0


class ZhipuEmbed(Base):
    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)
            arr.append(res.data[0].embedding)
            tks_num += self.total_token_count(res)
        return np.array(arr), tks_num

    def encode_queries(self, text):
        res = self.client.embeddings.create(input=text,
                                            model=self.model_name)
        return np.array(res.data[0].embedding), self.total_token_count(res)


class OllamaEmbed(Base):
    def __init__(self, key, model_name, **kwargs):
        self.client = Client(host=kwargs["base_url"])
        self.model_name = model_name

    def encode(self, texts: list):
        arr = []
        tks_num = 0
        for txt in texts:
            res = self.client.embeddings(prompt=txt,
                                         model=self.model_name)
            arr.append(res["embedding"])
            tks_num += 128
        return np.array(arr), tks_num

    def encode_queries(self, text):
        res = self.client.embeddings(prompt=text,
                                     model=self.model_name)
        return np.array(res["embedding"]), 128


class FastEmbed(DefaultEmbedding):
    
    def __init__(
            self,
            key: str | None = None,
            model_name: str = "BAAI/bge-small-en-v1.5",
            cache_dir: str | None = None,
            threads: int | None = None,
            **kwargs,
    ):
        if not settings.LIGHTEN:
            with FastEmbed._model_lock:
                from fastembed import TextEmbedding
                if not DefaultEmbedding._model or model_name != DefaultEmbedding._model_name:
                    try:
                        DefaultEmbedding._model = TextEmbedding(model_name, cache_dir, threads, **kwargs)
                        DefaultEmbedding._model_name = model_name
                    except Exception:
                        cache_dir = snapshot_download(repo_id="BAAI/bge-small-en-v1.5",
                                                      local_dir=os.path.join(get_home_cache_dir(),
                                                                             re.sub(r"^[a-zA-Z0-9]+/", "", model_name)),
                                                      local_dir_use_symlinks=False)
                        DefaultEmbedding._model = TextEmbedding(model_name, cache_dir, threads, **kwargs)
        self._model = DefaultEmbedding._model
        self._model_name = model_name

    def encode(self, texts: list):
        # Using the internal tokenizer to encode the texts and get the total
        # number of tokens
        encodings = self._model.model.tokenizer.encode_batch(texts)
        total_tokens = sum(len(e) for e in encodings)

        embeddings = [e.tolist() for e in self._model.embed(texts, batch_size=16)]

        return np.array(embeddings), total_tokens

    def encode_queries(self, text: str):
        # Using the internal tokenizer to encode the texts and get the total
        # number of tokens
        encoding = self._model.model.tokenizer.encode(text)
        embedding = next(self._model.query_embed(text)).tolist()

        return np.array(embedding), len(encoding.ids)


class XinferenceEmbed(Base):
    def __init__(self, key, model_name="", base_url=""):
        if base_url.split("/")[-1] != "v1":
            base_url = os.path.join(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 = 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 += self.total_token_count(res)
        return np.array(ress), total_tokens

    def encode_queries(self, text):
        res = self.client.embeddings.create(input=[text],
                                            model=self.model_name)
        return np.array(res.data[0].embedding), self.total_token_count(res)


class YoudaoEmbed(Base):
    _client = None

    def __init__(self, key=None, model_name="maidalun1020/bce-embedding-base_v1", **kwargs):
        if not settings.LIGHTEN and not YoudaoEmbed._client:
            from BCEmbedding import EmbeddingModel as qanthing
            try:
                logging.info("LOADING BCE...")
                YoudaoEmbed._client = qanthing(model_name_or_path=os.path.join(
                    get_home_cache_dir(),
                    "bce-embedding-base_v1"))
            except Exception:
                YoudaoEmbed._client = qanthing(
                    model_name_or_path=model_name.replace(
                        "maidalun1020", "InfiniFlow"))

    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 JinaEmbed(Base):
    def __init__(self, key, model_name="jina-embeddings-v3",
                 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):
        texts = [truncate(t, 8196) for t in texts]
        batch_size = 16
        ress = []
        token_count = 0
        for i in range(0, len(texts), batch_size):
            data = {
                "model": self.model_name,
                "input": texts[i:i + batch_size],
                'encoding_type': 'float'
            }
            res = requests.post(self.base_url, headers=self.headers, json=data).json()
            ress.extend([d["embedding"] for d in res["data"]])
            token_count += self.total_token_count(res)
        return np.array(ress), token_count

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


class InfinityEmbed(Base):
    _model = None

    def __init__(
            self,
            model_names: list[str] = ("BAAI/bge-small-en-v1.5",),
            engine_kwargs: dict = {},
            key = None,
    ):

        from infinity_emb import EngineArgs
        from infinity_emb.engine import AsyncEngineArray

        self._default_model = model_names[0]
        self.engine_array = AsyncEngineArray.from_args([EngineArgs(model_name_or_path = model_name, **engine_kwargs) for model_name in model_names])

    async def _embed(self, sentences: list[str], model_name: str = ""):
        if not model_name:
            model_name = self._default_model
        engine = self.engine_array[model_name]
        was_already_running = engine.is_running
        if not was_already_running:
            await engine.astart()
        embeddings, usage = await engine.embed(sentences=sentences)
        if not was_already_running:
            await engine.astop()
        return embeddings, usage

    def encode(self, texts: list[str], model_name: str = "") -> tuple[np.ndarray, int]:
        # Using the internal tokenizer to encode the texts and get the total
        # number of tokens
        embeddings, usage = asyncio.run(self._embed(texts, model_name))
        return np.array(embeddings), usage

    def encode_queries(self, text: str) -> tuple[np.ndarray, int]:
        # Using the internal tokenizer to encode the texts and get the total
        # number of tokens
        return self.encode([text])


class MistralEmbed(Base):
    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):
        texts = [truncate(t, 8196) for t in texts]
        batch_size = 16
        ress = []
        token_count = 0
        for i in range(0, len(texts), batch_size):
            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 += self.total_token_count(res)
        return np.array(ress), token_count

    def encode_queries(self, text):
        res = self.client.embeddings(input=[truncate(text, 8196)],
                                            model=self.model_name)
        return np.array(res.data[0].embedding), self.total_token_count(res)


class BedrockEmbed(Base):
    def __init__(self, key, model_name,
                 **kwargs):
        import boto3
        self.bedrock_ak = json.loads(key).get('bedrock_ak', '')
        self.bedrock_sk = json.loads(key).get('bedrock_sk', '')
        self.bedrock_region = json.loads(key).get('bedrock_region', '')
        self.model_name = model_name
        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)

    def encode(self, texts: list):
        texts = [truncate(t, 8196) for t in texts]
        embeddings = []
        token_count = 0
        for text in texts:
            if self.model_name.split('.')[0] == 'amazon':
                body = {"inputText": text}
            elif self.model_name.split('.')[0] == 'cohere':
                body = {"texts": [text], "input_type": 'search_document'}

            response = self.client.invoke_model(modelId=self.model_name, body=json.dumps(body))
            model_response = json.loads(response["body"].read())
            embeddings.extend([model_response["embedding"]])
            token_count += num_tokens_from_string(text)

        return np.array(embeddings), token_count

    def encode_queries(self, text):
        embeddings = []
        token_count = num_tokens_from_string(text)
        if self.model_name.split('.')[0] == 'amazon':
            body = {"inputText": truncate(text, 8196)}
        elif self.model_name.split('.')[0] == 'cohere':
            body = {"texts": [truncate(text, 8196)], "input_type": 'search_query'}

        response = self.client.invoke_model(modelId=self.model_name, body=json.dumps(body))
        model_response = json.loads(response["body"].read())
        embeddings.extend(model_response["embedding"])

        return np.array(embeddings), token_count


class GeminiEmbed(Base):
    def __init__(self, key, model_name='models/text-embedding-004',
                 **kwargs):
        self.key = key
        self.model_name = 'models/' + model_name
        
    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)
        genai.configure(api_key=self.key)
        batch_size = 16
        ress = []
        for i in range(0, len(texts), batch_size):
            result = genai.embed_content(
                model=self.model_name,
                content=texts[i: i + batch_size],
                task_type="retrieval_document",
                title="Embedding of single string")
            ress.extend(result['embedding'])
        return np.array(ress),token_count
    
    def encode_queries(self, text):
        genai.configure(api_key=self.key)
        result = genai.embed_content(
            model=self.model_name,
            content=truncate(text,2048),
            task_type="retrieval_document",
            title="Embedding of single string")
        token_count = num_tokens_from_string(text)
        return np.array(result['embedding']), token_count


class NvidiaEmbed(Base):
    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",
            }
            res = requests.post(self.base_url, headers=self.headers, json=payload).json()
            ress.extend([d["embedding"] for d in res["data"]])
            token_count += self.total_token_count(res)
        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):
    def __init__(self, key, model_name, base_url):
        if not base_url:
            raise ValueError("Local llm url cannot be None")
        if base_url.split("/")[-1] != "v1":
            base_url = os.path.join(base_url, "v1")
        self.client = OpenAI(api_key="lm-studio", base_url=base_url)
        self.model_name = model_name


class OpenAI_APIEmbed(OpenAIEmbed):
    def __init__(self, key, model_name, base_url):
        if not base_url:
            raise ValueError("url cannot be None")
        if base_url.split("/")[-1] != "v1":
            base_url = os.path.join(base_url, "v1")
        self.client = OpenAI(api_key=key, base_url=base_url)
        self.model_name = model_name.split("___")[0]


class CoHereEmbed(Base):
    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"],
            )
            ress.extend([d for d in res.embeddings.float])
            token_count += res.meta.billed_units.input_tokens
        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"],
        )
        return np.array(res.embeddings.float[0]), int(
            res.meta.billed_units.input_tokens
        )


class TogetherAIEmbed(OpenAIEmbed):
    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):
    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):
    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):
    def __init__(
        self, key, model_name, base_url="https://api.siliconflow.cn/v1/embeddings"
    ):
        if not base_url:
            base_url = "https://api.siliconflow.cn/v1/embeddings"
        self.headers = {
            "accept": "application/json",
            "content-type": "application/json",
            "authorization": f"Bearer {key}",
        }
        self.base_url = 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]
            payload = {
                "model": self.model_name,
                "input": texts_batch,
                "encoding_format": "float",
            }
            res = requests.post(self.base_url, json=payload, headers=self.headers).json()
            if "data" not in res or not isinstance(res["data"], list) or len(res["data"]) != len(texts_batch):
                raise ValueError(f"SILICONFLOWEmbed.encode got invalid response from {self.base_url}")
            ress.extend([d["embedding"] for d in res["data"]])
            token_count += self.total_token_count(res)
        return np.array(ress), token_count

    def encode_queries(self, text):
        payload = {
            "model": self.model_name,
            "input": text,
            "encoding_format": "float",
        }
        res = requests.post(self.base_url, json=payload, headers=self.headers).json()
        if "data" not in res or not isinstance(res["data"], list) or len(res["data"])!= 1:
            raise ValueError(f"SILICONFLOWEmbed.encode_queries got invalid response from {self.base_url}")
        return np.array(res["data"][0]["embedding"]), self.total_token_count(res)


class ReplicateEmbed(Base):
    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):
    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
        return (
            np.array([r["embedding"] for r in res["data"]]),
            self.total_token_count(res),
        )

    def encode_queries(self, text):
        res = self.client.do(model=self.model_name, texts=[text]).body
        return (
            np.array([r["embedding"] for r in res["data"]]),
            self.total_token_count(res),
        )


class VoyageEmbed(Base):
    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"
            )
            ress.extend(res.embeddings)
            token_count += res.total_tokens
        return np.array(ress), token_count

    def encode_queries(self, text):
        res = self.client.embed(
            texts=text, model=self.model_name, input_type="query"
            )
        return np.array(res.embeddings)[0], res.total_tokens


class HuggingFaceEmbed(Base):
    def __init__(self, key, model_name, base_url=None):
        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):
        embeddings = []
        for text in texts:
            response = requests.post(
                f"{self.base_url}/embed",
                json={"inputs": text},
                headers={'Content-Type': 'application/json'}
            )
            if response.status_code == 200:
                embedding = response.json()
                embeddings.append(embedding[0])
            else:
                raise Exception(f"Error: {response.status_code} - {response.text}")
        return np.array(embeddings), sum([num_tokens_from_string(text) for text in texts])

    def encode_queries(self, text):
        response = requests.post(
            f"{self.base_url}/embed",
            json={"inputs": text},
            headers={'Content-Type': 'application/json'}
        )
        if response.status_code == 200:
            embedding = response.json()
            return np.array(embedding[0]), num_tokens_from_string(text)
        else:
            raise Exception(f"Error: {response.status_code} - {response.text}")


class VolcEngineEmbed(OpenAIEmbed):
    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"
        ark_api_key = json.loads(key).get('ark_api_key', '')
        model_name = json.loads(key).get('ep_id', '') + json.loads(key).get('endpoint_id', '')
        super().__init__(ark_api_key,model_name,base_url)

class GPUStackEmbed(OpenAIEmbed):
    def __init__(self, key, model_name, base_url):
        if not base_url:
            raise ValueError("url cannot be None")
        if base_url.split("/")[-1] != "v1-openai":
            base_url = os.path.join(base_url, "v1-openai")

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