From 6de01dc601daeea1c81d0cb267d3a559c1a6ce80 Mon Sep 17 00:00:00 2001 From: yongtenglei Date: Wed, 19 Nov 2025 11:32:03 +0800 Subject: [PATCH] add gemini-3-pro-preview --- conf/llm_factories.json | 9 +- rag/llm/cv_model.py | 210 ++++++++++++++++++++++++++++------------ 2 files changed, 155 insertions(+), 64 deletions(-) diff --git a/conf/llm_factories.json b/conf/llm_factories.json index 3e70e0243..0eefa7a14 100644 --- a/conf/llm_factories.json +++ b/conf/llm_factories.json @@ -1429,6 +1429,13 @@ "status": "1", "rank": "980", "llm": [ + { + "llm_name": "gemini-3-pro-preview", + "tags": "LLM,CHAT,1M,IMAGE2TEXT", + "max_tokens": 1048576, + "model_type": "image2text", + "is_tools": true + }, { "llm_name": "gemini-2.5-flash", "tags": "LLM,CHAT,1024K,IMAGE2TEXT", @@ -5474,4 +5481,4 @@ ] } ] -} \ No newline at end of file +} diff --git a/rag/llm/cv_model.py b/rag/llm/cv_model.py index 15c8943d8..e3a0465ba 100644 --- a/rag/llm/cv_model.py +++ b/rag/llm/cv_model.py @@ -350,7 +350,7 @@ class Zhipu4V(GptV4): **gen_conf }, headers= { - "Authorization": f"Bearer {self.api_key}", + "Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json", } ) @@ -369,10 +369,10 @@ class Zhipu4V(GptV4): 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.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": @@ -439,7 +439,7 @@ class Zhipu4V(GptV4): cleaned = re.sub(r"<\|(begin_of_box|end_of_box)\|>", "", content).strip() return cleaned, num_tokens_from_string(cleaned) - + class StepFunCV(GptV4): _FACTORY_NAME = "StepFun" @@ -723,29 +723,80 @@ class GeminiCV(Base): _FACTORY_NAME = "Gemini" def __init__(self, key, model_name="gemini-1.0-pro-vision-latest", lang="Chinese", **kwargs): - from google.generativeai import GenerativeModel, client + from google import genai - client.configure(api_key=key) - _client = client.get_default_generative_client() - self.api_key=key + self.api_key = key self.model_name = model_name - self.model = GenerativeModel(model_name=self.model_name) - self.model._client = _client + self.client = genai.Client(api_key=key) self.lang = lang Base.__init__(self, **kwargs) + logging.info(f"[GeminiCV] Initialized with model={self.model_name} lang={self.lang}") + + def _image_to_part(self, image): + from google.genai import types + + if isinstance(image, str) and image.startswith("data:") and ";base64," in image: + header, b64data = image.split(",", 1) + mime = header.split(":", 1)[1].split(";", 1)[0] + data = base64.b64decode(b64data) + else: + data_url = self.image2base64(image) + header, b64data = data_url.split(",", 1) + mime = header.split(":", 1)[1].split(";", 1)[0] + data = base64.b64decode(b64data) + + return types.Part( + inline_data=types.Blob( + mime_type=mime, + data=data, + ) + ) def _form_history(self, system, history, images=None): - hist = [] - if system: - hist.append({"role": "user", "parts": [system, history[0]["content"]]}) + from google.genai import types + + contents = [] + images = images or [] + system_len = len(system) if isinstance(system, str) else 0 + history_len = len(history) if history else 0 + images_len = len(images) + logging.info(f"[GeminiCV] _form_history called: system_len={system_len} history_len={history_len} images_len={images_len}") + + image_parts = [] for img in images: - hist[0]["parts"].append(("data:image/jpeg;base64," + img) if img[:4]!="data" else img) - for h in history[1:]: - hist.append({"role": "user" if h["role"]=="user" else "model", "parts": [h["content"]]}) - return hist + try: + image_parts.append(self._image_to_part(img)) + except Exception: + continue + + remaining_history = history or [] + if system or remaining_history: + parts = [] + if system: + parts.append(types.Part(text=system)) + if remaining_history: + first = remaining_history[0] + parts.append(types.Part(text=first.get("content", ""))) + remaining_history = remaining_history[1:] + parts.extend(image_parts) + contents.append(types.Content(role="user", parts=parts)) + elif image_parts: + contents.append(types.Content(role="user", parts=image_parts)) + + role_map = {"user": "user", "assistant": "model", "system": "user"} + for h in remaining_history: + role = role_map.get(h.get("role"), "user") + contents.append( + types.Content( + role=role, + parts=[types.Part(text=h.get("content", ""))], + ) + ) + + return contents def describe(self, image): - from PIL.Image import open + from google.genai import types prompt = ( "请用中文详细描述一下图中的内容，比如时间，地点，人物，事情，人物心情等，如果有数据请提取出数据。" @@ -753,74 +804,106 @@ class GeminiCV(Base): else "Please describe the content of this picture, like where, when, who, what happen. If it has number data, please extract them out." ) - if image is bytes: - with BytesIO(image) as bio: - with open(bio) as img: - input = [prompt, img] - res = self.model.generate_content(input) - return res.text, total_token_count_from_response(res) - else: - b64 = self.image2base64_rawvalue(image) - with BytesIO(base64.b64decode(b64)) as bio: - with open(bio) as img: - input = [prompt, img] - res = self.model.generate_content(input) - return res.text, total_token_count_from_response(res) + contents = [ + types.Content( + role="user", + parts=[ + types.Part(text=prompt), + self._image_to_part(image), + ], + ) + ] + + res = self.client.models.generate_content( + model=self.model_name, + contents=contents, + ) + return res.text, total_token_count_from_response(res) def describe_with_prompt(self, image, prompt=None): - from PIL.Image import open + from google.genai import types + + vision_prompt = prompt if prompt else vision_llm_describe_prompt() - if image is bytes: - with BytesIO(image) as bio: - with open(bio) as img: - input = [vision_prompt, img] - res = self.model.generate_content(input) - return res.text, total_token_count_from_response(res) - else: - b64 = self.image2base64_rawvalue(image) - with BytesIO(base64.b64decode(b64)) as bio: - with open(bio) as img: - input = [vision_prompt, img] - res = self.model.generate_content(input) - return res.text, total_token_count_from_response(res) + contents = [ + types.Content( + role="user", + parts=[ + types.Part(text=vision_prompt), + self._image_to_part(image), + ], + ) + ] + + res = self.client.models.generate_content( + model=self.model_name, + contents=contents, + ) + return res.text, total_token_count_from_response(res) def chat(self, system, history, gen_conf, images=None, video_bytes=None, filename="", **kwargs): if video_bytes: try: + size = len(video_bytes) if video_bytes else 0 + logging.info(f"[GeminiCV] chat called with video: filename={filename} size={size}") summary, summary_num_tokens = self._process_video(video_bytes, filename) return summary, summary_num_tokens except Exception as e: + logging.info(f"[GeminiCV] chat video error: {e}") return "**ERROR**: " + str(e), 0 - generation_config = dict(temperature=gen_conf.get("temperature", 0.3), top_p=gen_conf.get("top_p", 0.7)) + from google.genai import types + + history_len = len(history) if history else 0 + images_len = len(images) if images else 0 + logging.info(f"[GeminiCV] chat called: history_len={history_len} images_len={images_len} gen_conf={gen_conf}") + + generation_config = types.GenerateContentConfig( + temperature=gen_conf.get("temperature", 0.3), + top_p=gen_conf.get("top_p", 0.7), + ) try: - response = self.model.generate_content( - self._form_history(system, history, images), - generation_config=generation_config) + response = self.client.models.generate_content( + model=self.model_name, + contents=self._form_history(system, history, images), + config=generation_config, + ) ans = response.text - return ans, total_token_count_from_response(ans) + logging.info("[GeminiCV] chat completed") + return ans, total_token_count_from_response(response) except Exception as e: + logging.warning(f"[GeminiCV] chat error: {e}") return "**ERROR**: " + str(e), 0 def chat_streamly(self, system, history, gen_conf, images=None, **kwargs): ans = "" response = None try: - generation_config = dict(temperature=gen_conf.get("temperature", 0.3), top_p=gen_conf.get("top_p", 0.7)) - response = self.model.generate_content( - self._form_history(system, history, images), - generation_config=generation_config, - stream=True, + from google.genai import types + + generation_config = types.GenerateContentConfig( + temperature=gen_conf.get("temperature", 0.3), + top_p=gen_conf.get("top_p", 0.7), + ) + history_len = len(history) if history else 0 + images_len = len(images) if images else 0 + logging.info(f"[GeminiCV] chat_streamly called: history_len={history_len} images_len={images_len} gen_conf={gen_conf}") + + response_stream = self.client.models.generate_content_stream( + model=self.model_name, + contents=self._form_history(system, history, images), + config=generation_config, ) - for resp in response: - if not resp.text: - continue - ans = resp.text - yield ans + for chunk in response_stream: + if chunk.text: + ans += chunk.text + yield chunk.text + logging.info("[GeminiCV] chat_streamly completed") except Exception as e: + logging.warning(f"[GeminiCV] chat_streamly error: {e}") yield ans + "\n**ERROR**: " + str(e) yield total_token_count_from_response(response) @@ -830,7 +913,8 @@ class GeminiCV(Base): from google.genai import types video_size_mb = len(video_bytes) / (1024 * 1024) - client = genai.Client(api_key=self.api_key) + client = self.client if hasattr(self, "client") else genai.Client(api_key=self.api_key) + logging.info(f"[GeminiCV] _process_video called: filename={filename} size_mb={video_size_mb:.2f}") tmp_path = None try: @@ -856,10 +940,10 @@ class GeminiCV(Base): ) summary = response.text or "" - logging.info(f"Video summarized: {summary[:32]}...") + logging.info(f"[GeminiCV] Video summarized: {summary[:32]}...") return summary, num_tokens_from_string(summary) except Exception as e: - logging.error(f"Video processing failed: {e}") + logging.warning(f"[GeminiCV] Video processing failed: {e}") raise finally: if tmp_path and tmp_path.exists():