Gemini api (#2630)

* Gemini-pro api

* Update gemini.py

* Update gemini.py

configs/model_config.py.example

@@ -56,6 +56,12 @@ ONLINE_LLM_MODEL = {
         "openai_proxy": "",
     },
 
+    # 获取api_key请前往https://makersuite.google.com/或者google cloud，使用前先确认网络正常，使用代理请在项目启动（python startup.py -a)环境内设置https_proxy环境变量
+    "gemini-api": {
+        "api_key": "", 
+        "provider": "GeminiWorker",
+    },
+
     # 具体注册及api key获取请前往 http://open.bigmodel.cn
     "zhipu-api": {
         "api_key": "",

configs/server_config.py.example

@@ -128,6 +128,9 @@ FSCHAT_MODEL_WORKERS = {
     "tiangong-api": {
         "port": 21009,
     },
+    "gemini-api": {
+        "port": 21012,
+    },
 }
 
 # fastchat multi model worker server

server/model_workers/__init__.py

@@ -8,3 +8,4 @@ from .qwen import QwenWorker
 from .baichuan import BaiChuanWorker
 from .azure import AzureWorker
 from .tiangong import TianGongWorker
+from .gemini import GeminiWorker

server/model_workers/gemini.py

@@ -0,0 +1,124 @@
+import sys
+from fastchat.conversation import Conversation
+from server.model_workers.base import *
+from server.utils import get_httpx_client
+from fastchat import conversation as conv
+import json,httpx
+from typing import List, Dict
+from configs import logger, log_verbose
+
+
+class GeminiWorker(ApiModelWorker):
+    def __init__(
+            self,
+            *,
+            controller_addr: str = None,
+            worker_addr: str = None,
+            model_names: List[str] = ["Gemini-api"],
+            **kwargs,
+    ):
+        kwargs.update(model_names=model_names, controller_addr=controller_addr, worker_addr=worker_addr)
+        kwargs.setdefault("context_len", 4096) #TODO 16K模型需要改成16384
+        super().__init__(**kwargs)
+
+    def create_gemini_messages(self,messages) -> json:
+        has_history = any(msg['role'] == 'assistant' for msg in messages)
+        gemini_msg = []
+
+        for msg in messages:
+            role = msg['role']
+            content = msg['content']
+            if role == 'system':
+                continue
+            if has_history:
+                if role == 'assistant':
+                    role = "model"
+                transformed_msg = {"role": role, "parts": [{"text": content}]}
+            else:
+                if role == 'user':
+                    transformed_msg = {"parts": [{"text": content}]}
+
+            gemini_msg.append(transformed_msg)
+
+        msg = dict(contents=gemini_msg)
+        return msg
+          
+    def do_chat(self, params: ApiChatParams) -> Dict:
+        params.load_config(self.model_names[0])
+        data = self.create_gemini_messages(messages=params.messages)
+        generationConfig=dict(
+            temperature = params.temperature,
+            topK = 1,
+            topP = 1,
+            maxOutputTokens = 4096,
+            stopSequences=[]
+        )
+
+        data['generationConfig'] = generationConfig         
+        url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent"+ '?key=' + params.api_key
+        headers = {
+            'Content-Type': 'application/json',
+        }
+        if log_verbose:
+            logger.info(f'{self.__class__.__name__}:url: {url}')
+            logger.info(f'{self.__class__.__name__}:headers: {headers}')
+            logger.info(f'{self.__class__.__name__}:data: {data}')
+
+        text = ""
+        json_string = ""
+        timeout = httpx.Timeout(60.0)
+        client=get_httpx_client(timeout=timeout)
+        with client.stream("POST", url, headers=headers, json=data) as response:
+            for line in response.iter_lines():
+                line = line.strip()
+                if not line or "[DONE]" in line:
+                    continue
+
+                json_string += line
+
+            try:
+                resp = json.loads(json_string)
+                if 'candidates' in resp:
+                    for candidate in resp['candidates']:
+                        content = candidate.get('content', {})
+                        parts = content.get('parts', [])
+                        for part in parts:
+                            if 'text' in part:
+                                text += part['text']
+                                yield {
+                                    "error_code": 0,
+                                    "text": text
+                                }
+                        print(text)       
+            except json.JSONDecodeError as e:
+                print("Failed to decode JSON:", e)
+                print("Invalid JSON string:", json_string)
+
+    def get_embeddings(self, params):
+        # TODO: 支持embeddings
+        print("embedding")
+        print(params)
+
+    def make_conv_template(self, conv_template: str = None, model_path: str = None) -> Conversation:
+        return conv.Conversation(
+            name=self.model_names[0],
+            system_message="You are a helpful, respectful and honest assistant.",
+            messages=[],
+            roles=["user", "assistant"],
+            sep="\n### ",
+            stop_str="###",
+        )
+
+
+if __name__ == "__main__":
+    import uvicorn
+    from server.utils import MakeFastAPIOffline
+    from fastchat.serve.base_model_worker import app
+
+    worker = GeminiWorker(
+        controller_addr="http://127.0.0.1:20001",
+        worker_addr="http://127.0.0.1:21012",
+    )
+    sys.modules["fastchat.serve.model_worker"].worker = worker
+    MakeFastAPIOffline(app)
+    uvicorn.run(app, port=21012)