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Refa: migrate CV model chat to Async (#11828)
### What problem does this PR solve? Migrate CV model chat to Async. #11750 ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) - [x] Refactoring
This commit is contained in:
Yongteng Lei
@ -14,6 +14,7 @@
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# limitations under the License.
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#
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import asyncio
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import io
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import re
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@ -50,7 +51,7 @@ def chunk(filename, binary, tenant_id, lang, callback=None, **kwargs):
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}
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)
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cv_mdl = LLMBundle(tenant_id, llm_type=LLMType.IMAGE2TEXT, lang=lang)
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ans = cv_mdl.chat(system="", history=[], gen_conf={}, video_bytes=binary, filename=filename)
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ans = asyncio.run(cv_mdl.async_chat(system="", history=[], gen_conf={}, video_bytes=binary, filename=filename))
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callback(0.8, "CV LLM respond: %s ..." % ans[:32])
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ans += "\n" + ans
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tokenize(doc, ans, eng)
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@ -12,6 +12,7 @@
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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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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import asyncio
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import io
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import json
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import os
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@ -634,7 +635,7 @@ class Parser(ProcessBase):
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self.set_output("output_format", conf["output_format"])
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cv_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.IMAGE2TEXT, llm_name=conf["llm_id"])
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txt = cv_mdl.chat(system="", history=[], gen_conf={}, video_bytes=blob, filename=name)
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txt = asyncio.run(cv_mdl.async_chat(system="", history=[], gen_conf={}, video_bytes=blob, filename=name))
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self.set_output("text", txt)
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@ -28,7 +28,7 @@ import json_repair
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import litellm
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import openai
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from openai import AsyncOpenAI, OpenAI
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from openai.lib.azure import AzureOpenAI
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from openai.lib.azure import AzureOpenAI, AsyncAzureOpenAI
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from strenum import StrEnum
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from common.token_utils import num_tokens_from_string, total_token_count_from_response
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@ -535,6 +535,7 @@ class AzureChat(Base):
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api_version = json.loads(key).get("api_version", "2024-02-01")
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super().__init__(key, model_name, base_url, **kwargs)
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self.client = AzureOpenAI(api_key=api_key, azure_endpoint=base_url, api_version=api_version)
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self.async_client = AsyncAzureOpenAI(api_key=key, base_url=base_url, api_version=api_version)
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self.model_name = model_name
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@property
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@ -14,6 +14,7 @@
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# limitations under the License.
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#
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import asyncio
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import base64
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import json
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import logging
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@ -27,9 +28,8 @@ from pathlib import Path
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from urllib.parse import urljoin
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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 zhipuai import ZhipuAI
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from openai import OpenAI, AsyncOpenAI
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from openai.lib.azure import AzureOpenAI, AsyncAzureOpenAI
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from common.token_utils import num_tokens_from_string, total_token_count_from_response
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from rag.nlp import is_english
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@ -76,9 +76,9 @@ class Base(ABC):
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pmpt.append({"type": "image_url", "image_url": {"url": img if isinstance(img, str) and img.startswith("data:") else f"data:image/png;base64,{img}"}})
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return pmpt
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def chat(self, system, history, gen_conf, images=None, **kwargs):
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async def async_chat(self, system, history, gen_conf, images=None, **kwargs):
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try:
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response = self.client.chat.completions.create(
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response = await self.async_client.chat.completions.create(
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model=self.model_name,
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messages=self._form_history(system, history, images),
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extra_body=self.extra_body,
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@ -87,17 +87,17 @@ class Base(ABC):
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except Exception as e:
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return "**ERROR**: " + str(e), 0
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def chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
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async def async_chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
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ans = ""
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tk_count = 0
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try:
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response = self.client.chat.completions.create(
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response = await self.async_client.chat.completions.create(
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model=self.model_name,
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messages=self._form_history(system, history, images),
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stream=True,
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extra_body=self.extra_body,
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)
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for resp in response:
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async for resp in response:
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if not resp.choices[0].delta.content:
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continue
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delta = resp.choices[0].delta.content
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@ -191,6 +191,7 @@ class GptV4(Base):
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base_url = "https://api.openai.com/v1"
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self.api_key = key
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self.client = OpenAI(api_key=key, base_url=base_url)
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self.async_client = AsyncOpenAI(api_key=key, base_url=base_url)
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self.model_name = model_name
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self.lang = lang
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super().__init__(**kwargs)
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@ -221,6 +222,7 @@ class AzureGptV4(GptV4):
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api_key = json.loads(key).get("api_key", "")
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api_version = json.loads(key).get("api_version", "2024-02-01")
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self.client = AzureOpenAI(api_key=api_key, azure_endpoint=kwargs["base_url"], api_version=api_version)
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self.async_client = AsyncAzureOpenAI(api_key=api_key, azure_endpoint=kwargs["base_url"], api_version=api_version)
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self.model_name = model_name
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self.lang = lang
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Base.__init__(self, **kwargs)
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@ -243,7 +245,7 @@ class QWenCV(GptV4):
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base_url = "https://dashscope.aliyuncs.com/compatible-mode/v1"
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super().__init__(key, model_name, lang=lang, base_url=base_url, **kwargs)
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def chat(self, system, history, gen_conf, images=None, video_bytes=None, filename="", **kwargs):
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async def async_chat(self, system, history, gen_conf, images=None, video_bytes=None, filename="", **kwargs):
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if video_bytes:
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try:
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summary, summary_num_tokens = self._process_video(video_bytes, filename)
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@ -313,7 +315,8 @@ class Zhipu4V(GptV4):
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_FACTORY_NAME = "ZHIPU-AI"
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def __init__(self, key, model_name="glm-4v", lang="Chinese", **kwargs):
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self.client = ZhipuAI(api_key=key)
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self.client = OpenAI(api_key=key, base_url="https://open.bigmodel.cn/api/paas/v4/")
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self.async_client = AsyncOpenAI(api_key=key, base_url="https://open.bigmodel.cn/api/paas/v4/")
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self.model_name = model_name
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self.lang = lang
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Base.__init__(self, **kwargs)
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@ -342,20 +345,20 @@ class Zhipu4V(GptV4):
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)
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return response.json()
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def chat(self, system, history, gen_conf, images=None, stream=False, **kwargs):
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async def async_chat(self, system, history, gen_conf, images=None, **kwargs):
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if system and history and history[0].get("role") != "system":
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history.insert(0, {"role": "system", "content": system})
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gen_conf = self._clean_conf(gen_conf)
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logging.info(json.dumps(history, ensure_ascii=False, indent=2))
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response = self.client.chat.completions.create(model=self.model_name, messages=self._form_history(system, history, images), stream=False, **gen_conf)
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response = await self.async_client.chat.completions.create(model=self.model_name, messages=self._form_history(system, history, images), stream=False, **gen_conf)
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content = response.choices[0].message.content.strip()
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cleaned = re.sub(r"<\|(begin_of_box|end_of_box)\|>", "", content).strip()
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return cleaned, total_token_count_from_response(response)
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def chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
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async def async_chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
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from rag.llm.chat_model import LENGTH_NOTIFICATION_CN, LENGTH_NOTIFICATION_EN
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from rag.nlp import is_chinese
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@ -366,8 +369,8 @@ class Zhipu4V(GptV4):
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tk_count = 0
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try:
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logging.info(json.dumps(history, ensure_ascii=False, indent=2))
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response = self.client.chat.completions.create(model=self.model_name, messages=self._form_history(system, history, images), stream=True, **gen_conf)
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for resp in response:
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response = await self.async_client.chat.completions.create(model=self.model_name, messages=self._form_history(system, history, images), stream=True, **gen_conf)
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async for resp in response:
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if not resp.choices[0].delta.content:
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continue
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delta = resp.choices[0].delta.content
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@ -412,6 +415,7 @@ class StepFunCV(GptV4):
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if not base_url:
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base_url = "https://api.stepfun.com/v1"
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self.client = OpenAI(api_key=key, base_url=base_url)
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self.async_client = AsyncOpenAI(api_key=key, base_url=base_url)
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self.model_name = model_name
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self.lang = lang
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Base.__init__(self, **kwargs)
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@ -425,6 +429,7 @@ class VolcEngineCV(GptV4):
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base_url = "https://ark.cn-beijing.volces.com/api/v3"
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ark_api_key = json.loads(key).get("ark_api_key", "")
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self.client = OpenAI(api_key=ark_api_key, base_url=base_url)
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self.async_client = AsyncOpenAI(api_key=ark_api_key, base_url=base_url)
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self.model_name = json.loads(key).get("ep_id", "") + json.loads(key).get("endpoint_id", "")
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self.lang = lang
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Base.__init__(self, **kwargs)
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@ -438,6 +443,7 @@ class LmStudioCV(GptV4):
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raise ValueError("Local llm url cannot be None")
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base_url = urljoin(base_url, "v1")
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self.client = OpenAI(api_key="lm-studio", base_url=base_url)
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self.async_client = AsyncOpenAI(api_key="lm-studio", base_url=base_url)
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self.model_name = model_name
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self.lang = lang
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Base.__init__(self, **kwargs)
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@ -451,6 +457,7 @@ class OpenAI_APICV(GptV4):
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raise ValueError("url cannot be None")
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base_url = urljoin(base_url, "v1")
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self.client = OpenAI(api_key=key, base_url=base_url)
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self.async_client = AsyncOpenAI(api_key=key, base_url=base_url)
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self.model_name = model_name.split("___")[0]
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self.lang = lang
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Base.__init__(self, **kwargs)
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@ -491,6 +498,7 @@ class OpenRouterCV(GptV4):
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base_url = "https://openrouter.ai/api/v1"
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api_key = json.loads(key).get("api_key", "")
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self.client = OpenAI(api_key=api_key, base_url=base_url)
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self.async_client = AsyncOpenAI(api_key=api_key, base_url=base_url)
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self.model_name = model_name
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self.lang = lang
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Base.__init__(self, **kwargs)
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@ -522,6 +530,7 @@ class LocalAICV(GptV4):
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raise ValueError("Local cv model url cannot be None")
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base_url = urljoin(base_url, "v1")
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self.client = OpenAI(api_key="empty", base_url=base_url)
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self.async_client = AsyncOpenAI(api_key="empty", base_url=base_url)
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self.model_name = model_name.split("___")[0]
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self.lang = lang
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Base.__init__(self, **kwargs)
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@ -533,6 +542,7 @@ class XinferenceCV(GptV4):
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def __init__(self, key, model_name="", lang="Chinese", base_url="", **kwargs):
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base_url = urljoin(base_url, "v1")
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self.client = OpenAI(api_key=key, base_url=base_url)
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self.async_client = AsyncOpenAI(api_key=key, base_url=base_url)
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self.model_name = model_name
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self.lang = lang
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Base.__init__(self, **kwargs)
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@ -546,6 +556,7 @@ class GPUStackCV(GptV4):
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raise ValueError("Local llm url cannot be None")
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base_url = urljoin(base_url, "v1")
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self.client = OpenAI(api_key=key, base_url=base_url)
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self.async_client = AsyncOpenAI(api_key=key, base_url=base_url)
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self.model_name = model_name
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self.lang = lang
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Base.__init__(self, **kwargs)
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@ -635,19 +646,19 @@ class OllamaCV(Base):
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except Exception as e:
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return "**ERROR**: " + str(e), 0
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def chat(self, system, history, gen_conf, images=None, **kwargs):
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async def async_chat(self, system, history, gen_conf, images=None, **kwargs):
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try:
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response = self.client.chat(model=self.model_name, messages=self._form_history(system, history, images), options=self._clean_conf(gen_conf), keep_alive=self.keep_alive)
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response = await asyncio.to_thread(self.client.chat, model=self.model_name, messages=self._form_history(system, history, images), options=self._clean_conf(gen_conf), keep_alive=self.keep_alive)
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ans = response["message"]["content"].strip()
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return ans, response["eval_count"] + response.get("prompt_eval_count", 0)
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except Exception as e:
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return "**ERROR**: " + str(e), 0
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def chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
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async def async_chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
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ans = ""
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try:
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response = self.client.chat(model=self.model_name, messages=self._form_history(system, history, images), stream=True, options=self._clean_conf(gen_conf), keep_alive=self.keep_alive)
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response = await asyncio.to_thread(self.client.chat, model=self.model_name, messages=self._form_history(system, history, images), stream=True, options=self._clean_conf(gen_conf), keep_alive=self.keep_alive)
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for resp in response:
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if resp["done"]:
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yield resp.get("prompt_eval_count", 0) + resp.get("eval_count", 0)
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@ -780,41 +791,41 @@ class GeminiCV(Base):
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)
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return res.text, total_token_count_from_response(res)
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def chat(self, system, history, gen_conf, images=None, video_bytes=None, filename="", **kwargs):
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async def async_chat(self, system, history, gen_conf, images=None, video_bytes=None, filename="", **kwargs):
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if video_bytes:
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try:
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size = len(video_bytes) if video_bytes else 0
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logging.info(f"[GeminiCV] chat called with video: filename={filename} size={size}")
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summary, summary_num_tokens = self._process_video(video_bytes, filename)
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logging.info(f"[GeminiCV] async_chat called with video: filename={filename} size={size}")
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summary, summary_num_tokens = await asyncio.to_thread(self._process_video, video_bytes, filename)
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return summary, summary_num_tokens
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except Exception as e:
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logging.info(f"[GeminiCV] chat video error: {e}")
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logging.info(f"[GeminiCV] async_chat video error: {e}")
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return "**ERROR**: " + str(e), 0
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from google.genai import types
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history_len = len(history) if history else 0
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images_len = len(images) if images else 0
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logging.info(f"[GeminiCV] chat called: history_len={history_len} images_len={images_len} gen_conf={gen_conf}")
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logging.info(f"[GeminiCV] async_chat called: history_len={history_len} images_len={images_len} gen_conf={gen_conf}")
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generation_config = types.GenerateContentConfig(
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temperature=gen_conf.get("temperature", 0.3),
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top_p=gen_conf.get("top_p", 0.7),
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)
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try:
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response = self.client.models.generate_content(
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response = await self.client.aio.models.generate_content(
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model=self.model_name,
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contents=self._form_history(system, history, images),
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config=generation_config,
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)
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ans = response.text
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logging.info("[GeminiCV] chat completed")
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logging.info("[GeminiCV] async_chat completed")
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return ans, total_token_count_from_response(response)
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except Exception as e:
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logging.warning(f"[GeminiCV] chat error: {e}")
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logging.warning(f"[GeminiCV] async_chat error: {e}")
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return "**ERROR**: " + str(e), 0
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def chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
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async def async_chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
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ans = ""
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response = None
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try:
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@ -826,15 +837,15 @@ class GeminiCV(Base):
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)
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history_len = len(history) if history else 0
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images_len = len(images) if images else 0
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logging.info(f"[GeminiCV] chat_streamly called: history_len={history_len} images_len={images_len} gen_conf={gen_conf}")
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logging.info(f"[GeminiCV] async_chat_streamly called: history_len={history_len} images_len={images_len} gen_conf={gen_conf}")
|
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|
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response_stream = self.client.models.generate_content_stream(
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response_stream = await self.client.aio.models.generate_content_stream(
|
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model=self.model_name,
|
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contents=self._form_history(system, history, images),
|
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config=generation_config,
|
||||
)
|
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|
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for chunk in response_stream:
|
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async for chunk in response_stream:
|
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if chunk.text:
|
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ans += chunk.text
|
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yield chunk.text
|
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@ -939,17 +950,17 @@ class NvidiaCV(Base):
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response = self._request(vision_prompt)
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return (response["choices"][0]["message"]["content"].strip(), total_token_count_from_response(response))
|
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|
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def chat(self, system, history, gen_conf, images=None, **kwargs):
|
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async def async_chat(self, system, history, gen_conf, images=None, **kwargs):
|
||||
try:
|
||||
response = self._request(self._form_history(system, history, images), gen_conf)
|
||||
response = await asyncio.to_thread(self._request, self._form_history(system, history, images), gen_conf)
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return (response["choices"][0]["message"]["content"].strip(), total_token_count_from_response(response))
|
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except Exception as e:
|
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return "**ERROR**: " + str(e), 0
|
||||
|
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def chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
|
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async def async_chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
|
||||
total_tokens = 0
|
||||
try:
|
||||
response = self._request(self._form_history(system, history, images), gen_conf)
|
||||
response = await asyncio.to_thread(self._request, self._form_history(system, history, images), gen_conf)
|
||||
cnt = response["choices"][0]["message"]["content"]
|
||||
total_tokens += total_token_count_from_response(response)
|
||||
for resp in cnt:
|
||||
@ -967,6 +978,7 @@ class AnthropicCV(Base):
|
||||
import anthropic
|
||||
|
||||
self.client = anthropic.Anthropic(api_key=key)
|
||||
self.async_client = anthropic.AsyncAnthropic(api_key=key)
|
||||
self.model_name = model_name
|
||||
self.system = ""
|
||||
self.max_tokens = 8192
|
||||
@ -1012,17 +1024,18 @@ class AnthropicCV(Base):
|
||||
gen_conf["max_tokens"] = self.max_tokens
|
||||
return gen_conf
|
||||
|
||||
def chat(self, system, history, gen_conf, images=None, **kwargs):
|
||||
async def async_chat(self, system, history, gen_conf, images=None, **kwargs):
|
||||
gen_conf = self._clean_conf(gen_conf)
|
||||
ans = ""
|
||||
try:
|
||||
response = self.client.messages.create(
|
||||
response = await self.async_client.messages.create(
|
||||
model=self.model_name,
|
||||
messages=self._form_history(system, history, images),
|
||||
system=system,
|
||||
stream=False,
|
||||
**gen_conf,
|
||||
).to_dict()
|
||||
)
|
||||
response = response.to_dict()
|
||||
ans = response["content"][0]["text"]
|
||||
if response["stop_reason"] == "max_tokens":
|
||||
ans += "...\nFor the content length reason, it stopped, continue?" if is_english([ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？"
|
||||
@ -1033,11 +1046,11 @@ class AnthropicCV(Base):
|
||||
except Exception as e:
|
||||
return ans + "\n**ERROR**: " + str(e), 0
|
||||
|
||||
def chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
|
||||
async def async_chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
|
||||
gen_conf = self._clean_conf(gen_conf)
|
||||
total_tokens = 0
|
||||
try:
|
||||
response = self.client.messages.create(
|
||||
response = self.async_client.messages.create(
|
||||
model=self.model_name,
|
||||
messages=self._form_history(system, history, images),
|
||||
system=system,
|
||||
@ -1045,7 +1058,7 @@ class AnthropicCV(Base):
|
||||
**gen_conf,
|
||||
)
|
||||
think = False
|
||||
for res in response:
|
||||
async for res in response:
|
||||
if res.type == "content_block_delta":
|
||||
if res.delta.type == "thinking_delta" and res.delta.thinking:
|
||||
if not think:
|
||||
@ -1117,18 +1130,18 @@ class GoogleCV(AnthropicCV, GeminiCV):
|
||||
else:
|
||||
return GeminiCV.describe_with_prompt(self, image, prompt)
|
||||
|
||||
def chat(self, system, history, gen_conf, images=None, **kwargs):
|
||||
async def async_chat(self, system, history, gen_conf, images=None, **kwargs):
|
||||
if "claude" in self.model_name:
|
||||
return AnthropicCV.chat(self, system, history, gen_conf, images)
|
||||
return await AnthropicCV.async_chat(self, system, history, gen_conf, images)
|
||||
else:
|
||||
return GeminiCV.chat(self, system, history, gen_conf, images)
|
||||
return await GeminiCV.async_chat(self, system, history, gen_conf, images)
|
||||
|
||||
def chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
|
||||
async def async_chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
|
||||
if "claude" in self.model_name:
|
||||
for ans in AnthropicCV.chat_streamly(self, system, history, gen_conf, images):
|
||||
async for ans in AnthropicCV.async_chat_streamly(self, system, history, gen_conf, images):
|
||||
yield ans
|
||||
else:
|
||||
for ans in GeminiCV.chat_streamly(self, system, history, gen_conf, images):
|
||||
async for ans in GeminiCV.async_chat_streamly(self, system, history, gen_conf, images):
|
||||
yield ans
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user