final main updates

rag/llm/cv_model.py

@@ -33,7 +33,8 @@ from rag.nlp import is_english
 from rag.prompts.generator import vision_llm_describe_prompt
 from common.token_utils import num_tokens_from_string, total_token_count_from_response
 
-
+from rag.llm.chat_model import LENGTH_NOTIFICATION_CN, LENGTH_NOTIFICATION_EN
+from rag.nlp import is_chinese
 class Base(ABC):
     def __init__(self, **kwargs):
         # Configure retry parameters
@@ -209,6 +210,7 @@ class GptV4(Base):
             model=self.model_name,
             messages=self.prompt(b64),
             extra_body=self.extra_body,
+            unused = None,
         )
         return res.choices[0].message.content.strip(), total_token_count_from_response(res)
 
@@ -320,30 +322,106 @@ class Zhipu4V(GptV4):
 
     def __init__(self, key, model_name="glm-4v", lang="Chinese", **kwargs):
         self.client = ZhipuAI(api_key=key)
-        self.api_key = key
         self.model_name = model_name
         self.lang = lang
         Base.__init__(self, **kwargs)
 
+
+    def _clean_conf(self, gen_conf):
+        if "max_tokens" in gen_conf:
+            del gen_conf["max_tokens"]
+        gen_conf = self._clean_conf_plealty(gen_conf)
+        return gen_conf
+
+
+    def _clean_conf_plealty(self, gen_conf):
+        if "presence_penalty" in gen_conf:
+            del gen_conf["presence_penalty"]
+        if "frequency_penalty" in gen_conf:
+            del gen_conf["frequency_penalty"]
+        return gen_conf
+
+
+    def _request(self, msg, stream, gen_conf={}):
+        response = requests.post(
+            self.base_url,
+            json={
+                "model": self.model_name,
+                "messages": msg,
+                "stream": stream,
+                **gen_conf
+            },
+            headers= {
+            "Authorization": f"Bearer {self.api_key}",  
+            "Content-Type": "application/json",
+            }
+        )
+        return response.json()
+
+
+    def chat(self, system, history, gen_conf, images=None, stream=False, **kwargs):
+        if system and history and history[0].get("role") != "system":
+            history.insert(0, {"role": "system", "content": system})
+
+        gen_conf = self._clean_conf(gen_conf)
+
+        logging.info(json.dumps(history, ensure_ascii=False, indent=2))
+        response = self.client.chat.completions.create(model=self.model_name, messages=self._form_history(system, history, images), stream=False, **gen_conf)
+        content = response.choices[0].message.content.strip()
+
+        cleaned = re.sub(r"<\|(begin_of_box|end_of_box)\|>", "", content).strip()
+        return cleaned, total_token_count_from_response(response)
+    
+
+    def chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
+        from rag.llm.chat_model import LENGTH_NOTIFICATION_CN, LENGTH_NOTIFICATION_EN
+        from rag.nlp import is_chinese
+
+        if system and history and history[0].get("role") != "system":
+            history.insert(0, {"role": "system", "content": system})
+        gen_conf = self._clean_conf(gen_conf)
+        ans = ""
+        tk_count = 0
+        try:
+            logging.info(json.dumps(history, ensure_ascii=False, indent=2))
+            response = self.client.chat.completions.create(model=self.model_name, messages=self._form_history(system, history, images), stream=True, **gen_conf)
+            for resp in response:
+                if not resp.choices[0].delta.content:
+                    continue
+                delta = resp.choices[0].delta.content
+                ans = delta
+                if resp.choices[0].finish_reason == "length":
+                    if is_chinese(ans):
+                        ans += LENGTH_NOTIFICATION_CN
+                    else:
+                        ans += LENGTH_NOTIFICATION_EN
+                    tk_count = total_token_count_from_response(resp)
+                if resp.choices[0].finish_reason == "stop":
+                    tk_count = total_token_count_from_response(resp)
+                yield ans
+        except Exception as e:
+            yield ans + "\n**ERROR**: " + str(e)
+
+        yield tk_count
+
+
     def describe(self, image):
         return self.describe_with_prompt(image)
 
+
     def describe_with_prompt(self, image, prompt=None):
         b64 = self.image2base64(image)
         if prompt is None:
             prompt = "Describe this image."
 
-        payload = {
+        # Chat messages
-            "model": self.model_name,
+        messages = [
-            "messages": [
             {
                 "role": "user",
                 "content": [
                     {
                         "type": "image_url",
-                            "image_url": {
+                        "image_url": { "url": b64 }
-                                "url": b64
-                            }
                     },
                     {
                         "type": "text",
@@ -352,21 +430,16 @@ class Zhipu4V(GptV4):
                 ]
             }
         ]
-        }
 
-        headers = {
+        resp = self.client.chat.completions.create(
-            "Authorization": f"Bearer {self.api_key}",
+            model=self.model_name,
-            "Content-Type": "application/json"
+            messages=messages,
-        }
+            stream=False
-
-        resp = requests.post(
-            "https://open.bigmodel.cn/api/paas/v4/chat/completions",
-            json=payload,
-            headers=headers
         )
 
-        content = resp.json()["choices"][0]["message"]["content"].strip()
+        content = resp.choices[0].message.content.strip()
         cleaned = re.sub(r"<\|(begin_of_box|end_of_box)\|>", "", content).strip()
+
         return cleaned, num_tokens_from_string(cleaned)