mirror of
https://github.com/infiniflow/ragflow.git
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Refa: automatic LLMs registration (#8651)
### What problem does this PR solve? Support automatic LLMs registration. ### Type of change - [x] Refactoring
This commit is contained in:
Yongteng Lei
@ -13,16 +13,18 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import os
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import requests
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from openai.lib.azure import AzureOpenAI
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import io
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from abc import ABC
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from openai import OpenAI
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import json
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from rag.utils import num_tokens_from_string
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import base64
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import io
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import json
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import os
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import re
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from abc import ABC
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import requests
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from openai import OpenAI
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from openai.lib.azure import AzureOpenAI
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from rag.utils import num_tokens_from_string
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class Base(ABC):
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@ -30,11 +32,7 @@ class Base(ABC):
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pass
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def transcription(self, audio, **kwargs):
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transcription = self.client.audio.transcriptions.create(
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model=self.model_name,
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file=audio,
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response_format="text"
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)
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transcription = self.client.audio.transcriptions.create(model=self.model_name, file=audio, response_format="text")
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return transcription.text.strip(), num_tokens_from_string(transcription.text.strip())
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def audio2base64(self, audio):
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@ -46,6 +44,8 @@ class Base(ABC):
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class GPTSeq2txt(Base):
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_FACTORY_NAME = "OpenAI"
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def __init__(self, key, model_name="whisper-1", base_url="https://api.openai.com/v1"):
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if not base_url:
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base_url = "https://api.openai.com/v1"
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@ -54,31 +54,34 @@ class GPTSeq2txt(Base):
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class QWenSeq2txt(Base):
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_FACTORY_NAME = "Tongyi-Qianwen"
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def __init__(self, key, model_name="paraformer-realtime-8k-v1", **kwargs):
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import dashscope
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dashscope.api_key = key
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self.model_name = model_name
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def transcription(self, audio, format):
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from http import HTTPStatus
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from dashscope.audio.asr import Recognition
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recognition = Recognition(model=self.model_name,
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format=format,
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sample_rate=16000,
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callback=None)
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recognition = Recognition(model=self.model_name, format=format, sample_rate=16000, callback=None)
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result = recognition.call(audio)
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ans = ""
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if result.status_code == HTTPStatus.OK:
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for sentence in result.get_sentence():
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ans += sentence.text.decode('utf-8') + '\n'
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ans += sentence.text.decode("utf-8") + "\n"
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return ans, num_tokens_from_string(ans)
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return "**ERROR**: " + result.message, 0
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class AzureSeq2txt(Base):
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_FACTORY_NAME = "Azure-OpenAI"
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def __init__(self, key, model_name, lang="Chinese", **kwargs):
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self.client = AzureOpenAI(api_key=key, azure_endpoint=kwargs["base_url"], api_version="2024-02-01")
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self.model_name = model_name
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@ -86,43 +89,33 @@ class AzureSeq2txt(Base):
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class XinferenceSeq2txt(Base):
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_FACTORY_NAME = "Xinference"
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def __init__(self, key, model_name="whisper-small", **kwargs):
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self.base_url = kwargs.get('base_url', None)
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self.base_url = kwargs.get("base_url", None)
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self.model_name = model_name
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self.key = key
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def transcription(self, audio, language="zh", prompt=None, response_format="json", temperature=0.7):
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if isinstance(audio, str):
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audio_file = open(audio, 'rb')
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audio_file = open(audio, "rb")
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audio_data = audio_file.read()
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audio_file_name = audio.split("/")[-1]
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else:
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audio_data = audio
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audio_file_name = "audio.wav"
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payload = {
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"model": self.model_name,
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"language": language,
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"prompt": prompt,
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"response_format": response_format,
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"temperature": temperature
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}
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payload = {"model": self.model_name, "language": language, "prompt": prompt, "response_format": response_format, "temperature": temperature}
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files = {
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"file": (audio_file_name, audio_data, 'audio/wav')
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}
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files = {"file": (audio_file_name, audio_data, "audio/wav")}
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try:
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response = requests.post(
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f"{self.base_url}/v1/audio/transcriptions",
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files=files,
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data=payload
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)
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response = requests.post(f"{self.base_url}/v1/audio/transcriptions", files=files, data=payload)
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response.raise_for_status()
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result = response.json()
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if 'text' in result:
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transcription_text = result['text'].strip()
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if "text" in result:
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transcription_text = result["text"].strip()
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return transcription_text, num_tokens_from_string(transcription_text)
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else:
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return "**ERROR**: Failed to retrieve transcription.", 0
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@ -132,11 +125,11 @@ class XinferenceSeq2txt(Base):
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class TencentCloudSeq2txt(Base):
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def __init__(
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self, key, model_name="16k_zh", base_url="https://asr.tencentcloudapi.com"
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):
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from tencentcloud.common import credential
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_FACTORY_NAME = "Tencent Cloud"
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def __init__(self, key, model_name="16k_zh", base_url="https://asr.tencentcloudapi.com"):
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from tencentcloud.asr.v20190614 import asr_client
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from tencentcloud.common import credential
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key = json.loads(key)
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sid = key.get("tencent_cloud_sid", "")
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@ -146,11 +139,12 @@ class TencentCloudSeq2txt(Base):
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self.model_name = model_name
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def transcription(self, audio, max_retries=60, retry_interval=5):
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import time
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from tencentcloud.asr.v20190614 import models
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from tencentcloud.common.exception.tencent_cloud_sdk_exception import (
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TencentCloudSDKException,
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)
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from tencentcloud.asr.v20190614 import models
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import time
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b64 = self.audio2base64(audio)
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try:
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@ -174,9 +168,7 @@ class TencentCloudSeq2txt(Base):
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while retries < max_retries:
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resp = self.client.DescribeTaskStatus(req)
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if resp.Data.StatusStr == "success":
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text = re.sub(
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r"\[\d+:\d+\.\d+,\d+:\d+\.\d+\]\s*", "", resp.Data.Result
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).strip()
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text = re.sub(r"\[\d+:\d+\.\d+,\d+:\d+\.\d+\]\s*", "", resp.Data.Result).strip()
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return text, num_tokens_from_string(text)
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elif resp.Data.StatusStr == "failed":
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return (
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@ -195,6 +187,8 @@ class TencentCloudSeq2txt(Base):
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class GPUStackSeq2txt(Base):
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_FACTORY_NAME = "GPUStack"
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def __init__(self, key, model_name, base_url):
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if not base_url:
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raise ValueError("url cannot be None")
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@ -206,8 +200,11 @@ class GPUStackSeq2txt(Base):
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class GiteeSeq2txt(Base):
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_FACTORY_NAME = "GiteeAI"
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def __init__(self, key, model_name="whisper-1", base_url="https://ai.gitee.com/v1/"):
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if not base_url:
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base_url = "https://ai.gitee.com/v1/"
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self.client = OpenAI(api_key=key, base_url=base_url)
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self.model_name = model_name
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self.model_name = model_name
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