增加openai服务支持，销售分析可以使用openai或zhipu

apitest/api_test_local.sh

@@ -49,3 +49,40 @@ curl -N -X POST http://127.0.0.1:5002/api/v2/zhipu/retrive/stream \
 	"message":"视睿电子教学课件系统续费项目更新日志：**项目进展描述**：了解到客户需求和降本要求后，与项目经理杨建线下沟通。客户同意在剩余2套系统上增加模块，但要求降价，具体数 量待内部讨论。",
 	"knowledge_id":"1843318172036575232"
 }'
+
+
+curl -N -X POST http://127.0.0.1:5002/api/v2/zhipu/analysis/stream \
+-H "Content-Type: application/json" \
+-d '{
+"message":"分析商机广汽汇理汽车金融有限公司的商机建议",
+"knowledge_id":"1843318172036575232"
+}'
+
+curl -N -X POST http://127.0.0.1:5002/api/v2/zhipu/analysis/stream \
+-H "Content-Type: application/json" \
+-d '{
+"message":"2. 更新我负责的广汽汇理汽车金融有限公司项目的最新动作:1，今日和客户有做了一次技术交流，他们最近和Ocean Base和Gold DB也做了交流，以及内部也做了沟通，接下来他们希望能够拿出一个业务场景做测试，已确定哪个产品更适合他们。",
+"knowledge_id":"1843318172036575232"
+}'
+
+
+curl -N -X POST http://127.0.0.1:5002/api/v2/zhipu/analysis/stream \
+-H "Content-Type: application/json" \
+-d '{
+"message":"更新我负责的广汽汇理汽车金融有限公司项目的最新动作:已经完成了POC，客户对POC效果表示满意",
+"knowledge_id":"1843318172036575232"
+}'
+
+curl -N -X POST http://127.0.0.1:5002/api/v2/zhipu/analysis/stream \
+-H "Content-Type: application/json" \
+-d '{
+"message":"openai",
+"knowledge_id":"1843318172036575232"
+}'
+
+curl -N -X POST http://127.0.0.1:5002/api/v2/zhipu/analysis/stream \
+-H "Content-Type: application/json" \
+-d '{
+"message":"zhipu",
+"knowledge_id":"1843318172036575232"
+}'

app/api/v1/zhipu_controller.py

@@ -19,7 +19,7 @@ def stream_sse():
     message = data.get('message', '')
 
     def event_stream():
-        for chunk in zhipu_service.talk_to_zhipu_sse(message):
+        for chunk in zhipu_service.generate_response_sse(message):
             if chunk:
                 yield chunk
 
@@ -30,7 +30,7 @@ def non_stream():
     data = request.json
     message = data.get('message', '')
 
-    response = zhipu_service.talk_to_zhipu(message)
+    response = zhipu_service.generate_response(message)
     print(f'response: {response}')
     return response
 
@@ -188,7 +188,7 @@ def analysis_stream():
 """
 
     def event_stream():
-        for chunk in zhipu_service.talk_to_zhipu_sse(prompt_analysis):
+        for chunk in zhipu_service.generate_response_sse(prompt_analysis):
             if chunk:
                 yield chunk
 

app/api/v2/zhipu_controller_v2.py

@@ -4,12 +4,18 @@ from flask import Blueprint, request, Response,session
 from app.services.zhipu_service import ZhipuService
 from app.services.zhipu_alltool_service import ZhipuAlltoolService
 from app.services.zhipu_file_service import ZhipuFileService
+from app.services.zhipu_kb_service import ZhipuKbService
+from app.services.openai_service import OpenaiService
 from app.utils.prompt_repository import PromptRepository  # Add this import
+from app.utils.sessions import init_session
 
 zhipu_controller_v2 = Blueprint('zhipu_controller_v2', __name__)
+
 zhipu_service = ZhipuService()
+openai_service = OpenaiService()
 zhipu_alltool_service = ZhipuAlltoolService()
 zhipu_file_service = ZhipuFileService()
+zhipu_kb_service = ZhipuKbService()
 
 response_headers = {'Content-Type': 'text/event-stream',
             'Cache-Control': 'no-cache',
@@ -66,7 +72,7 @@ def retrive_stream():
 
         def event_stream_retrive():
             accumulated_result = ""
-            for chunk in zhipu_service.retrive_sse(message, knowledge_id, system_prompt="你是一个销售助理，语言对话请以第一人称你我进行"):
+            for chunk in zhipu_kb_service.retrive_sse(message, knowledge_id, system_prompt="你是一个销售助理，语言对话请以第一人称你我进行"):
                 if chunk:
                     accumulated_result += chunk
                     chunk_out = format_chunk(chunk, None, None)
@@ -92,7 +98,7 @@ def retrive_stream():
             prompt_report_template = PromptRepository().get_prompt("report_template")
             prompt_report_title = f"根据用户提问中\"\"\" {message} \"\"\" 中提到的项目信息 在知识库中查找该项目的销售日志。如果销售日志中缺乏模板中的要点（时间，参与人，事件，获得信息，信息来源，项目进展描述）信息，则该要点内容留空，不要填充信息 日报模板： \"\"\" {prompt_report_template} \"\"\"。输出: 日志报告"
             generated_report = ""
-            for chunk in zhipu_service.retrive_sse(prompt_report_title + message, knowledge_id, None):
+            for chunk in zhipu_kb_service.retrive_sse(prompt_report_title + message, knowledge_id, None):
                 if chunk:
                     print(chunk)
                     generated_report += chunk
@@ -123,7 +129,7 @@ def retrive_stream():
             prompt_report_template = PromptRepository().get_prompt("report_template")
             prompt_report_title = f"根据用户提问中\"\"\" {message} \"\"\" 中提到的项目信息 在知识库中查找该项目的销售日志并结合用户提供的新的日志信息 \"\"\"{message} \"\"\"生成日报。如果销售日志中缺乏模板中的要点（时间，参与人，事件，获得信息，信息来源，项目进展描述）信息，则该要点内容留空，不要填充信息 日报模板： \"\"\" {prompt_report_template} \"\"\"。输出: 日志报告"
             generated_report = ""
-            for chunk in zhipu_service.retrive_sse(prompt_report_title + message, knowledge_id, None):
+            for chunk in zhipu_kb_service.retrive_sse(prompt_report_title + message, knowledge_id, None):
                 if chunk:
                     print(chunk)
                     generated_report += chunk
@@ -142,11 +148,32 @@ def retrive_stream():
 
 @zhipu_controller_v2.route('/zhipu/analysis/stream', methods=['POST'])
 def analysis_stream():
+    init_session()
     data = request.json
     message = data.get('message', '')
     knowledge_id = data.get('knowledge_id', '')
     message = message.replace("我", "我（徐春峰）")
 
+    if 'zhipu' in message.lower() or '智谱' in message:
+        logger.info(f'switch to zhipu service, save to session')
+        session['llm_service'] = 'zhipu'
+        return format_chunk("切换到智谱AI服务", None, None)
+    if 'openai' in message.lower() or 'openai' in message:
+        logger.info(f'switch to openai service, save to session')
+        session['llm_service'] = 'openai'
+        return format_chunk("切换到openai服务", None, None)
+    # 默认使用智谱AI服务
+    llm_service = zhipu_service
+    logger.info(f'llm_service: {session["llm_service"]}')
+    current_service = session.get('llm_service', 'zhipu')  # Default to 'zhipu' if not set
+    
+    if current_service == 'openai':
+        logger.info('Using OpenAI service')
+        llm_service = openai_service
+    else:
+        logger.info('Using Zhipu service')
+        llm_service = zhipu_service
+
     logger.info(f'/zhipu/analysis/stream v2: {message}')
 
     intent_categories =["analyze_sales","provide_sales_update_info"]
@@ -158,6 +185,11 @@ def analysis_stream():
     if '更新' in message and '我负责' in message and '项目' in message and '最新' in message:
         classification_result = {"category":"provide_sales_update_info"}
 
+    # if 'openai' in message.lower():
+    #     logger.info(f'switch to openai service')
+    #     llm_service = openai_service
+    #     message = message.replace("openai", "")
+        
 
     additional_business_info = ""
     if classification_result.get('category') == 'analyze_sales':
@@ -182,7 +214,7 @@ def analysis_stream():
 请根据用户提供的如下信息，查找相关的 '当前详细状态及已完成工作','Sales stage' 信息，并返回给用户:
 {message}
 """
-    business_info = zhipu_service.retrive(prompt_get_business_info, knowledge_id, None)
+    business_info = zhipu_kb_service.retrive(prompt_get_business_info, knowledge_id, None)
     logger.info(f'business_info: {business_info}')
 
     analysis_rule = PromptRepository().get_prompt('sales_analysis')
@@ -220,7 +252,7 @@ def analysis_stream():
 
     def event_stream():
         accumulated_result = ""
-        for chunk in zhipu_service.talk_to_zhipu_sse(prompt_analysis):
+        for chunk in llm_service.generate_response_sse(prompt_analysis):
             if chunk:
                 accumulated_result += chunk
                 chunk_out = format_chunk(chunk, None, None) 

app/services/ai_service_interface.py

@@ -0,0 +1,11 @@
+from abc import ABC, abstractmethod
+
+class AIServiceInterface(ABC):
+    @abstractmethod
+    def generate_response(self, prompt):
+        pass
+
+    @abstractmethod
+    def generate_response_sse(self, prompt):
+        pass
+

app/services/openai_service.py

@@ -0,0 +1,61 @@
+import logging
+import time
+from openai import OpenAI
+from app.services.ai_service_interface import AIServiceInterface
+# from app.utils.prompt_repository import PromptRepository
+
+logger = logging.getLogger(__name__)
+
+class OpenaiService(AIServiceInterface):
+    def __init__(self):
+        self.client = OpenAI(base_url="https://ai.xorbit.link:8443/e5b2a5e5-b41d-4715-9d50-d4a3b0c1a85f/v1", api_key="sk-proj-e5b2a5e5b41d47159d50d4a3b0c1a85f")
+    
+    def generate_response(self, prompt):
+        try:
+            response = self.client.chat.completions.create(
+                model="gpt-4o-mini",
+                messages=[{"role": "system", "content": prompt}],
+                temperature=0.7,
+            )
+            return response.choices[0].message.content
+        except Exception as e:
+            logger.error(f"Error generating response: {e}")
+            return "An error occurred while generating the response."
+
+    def generate_response_sse(self, prompt):
+        try:
+            response = self.client.chat.completions.create(
+                model="gpt-4o-mini",
+                messages=[{"role": "system", "content": prompt}],
+                temperature=0.7,
+                stream=True
+            )
+            for chunk in response:
+                if chunk.choices[0].delta.content is not None:
+                    yield chunk.choices[0].delta.content
+            yield "answer provided by openai"
+        except Exception as e:
+            logger.error(f"Error generating SSE response: {e}")
+            yield "An error occurred while generating the SSE response."
+
+
+if __name__ == "__main__":
+    # Set up logging
+    logging.basicConfig(level=logging.INFO)
+
+    # Create an instance of OpenaiService
+    openai_service = OpenaiService()
+
+    # Test the generate_response method
+    test_prompt = "What is the capital of France?"
+    response = openai_service.generate_response(test_prompt)
+    print(f"Response to '{test_prompt}': {response}")
+
+    # Test the generate_response_sse method
+    print("\nTesting generate_response_sse:")
+    sse_prompt = "Count from 1 to 5 slowly."
+    for chunk in openai_service.generate_response_sse(sse_prompt):
+        print(chunk, end='', flush=True)
+        time.sleep(0.1)  # Add a small delay to simulate streaming
+    print("\nSSE response complete.")
+

app/services/zhipu_kb_service.py

@@ -0,0 +1,83 @@
+import logging
+import time
+from zhipuai import ZhipuAI
+from app.utils.prompt_repository import PromptRepository
+
+logger = logging.getLogger(__name__)
+
+class ZhipuKbService:
+    def __init__(self):
+        self.model_name = "glm-4"
+        self.app_secret_key = "d54f764a1d67c17d857bd3983b772016.GRjowY0fyiMNurLc"
+        logger.info("ZhipuKbService initialized with model: %s", self.model_name)
+
+
+    def retrive(self, message, knowledge_id, prompt_template):
+        logger.info("Starting retrive call with knowledge_id: %s", knowledge_id)
+        start_time = time.time()
+        client = ZhipuAI(api_key=self.app_secret_key)
+        default_prompt = "从文档\n\"\"\"\n{{knowledge}}\n\"\"\"\n中找问题\n\"\"\"\n{{question}}\n\"\"\"\n的答案，找到答案就仅使用文档语句回答问题，找不到答案就用自身知识回答并且告诉用户该信息不是来自文档。\n不要复述问题，直接开始回答。"
+
+        if prompt_template is None or prompt_template == "":
+            prompt_template = default_prompt
+        try:
+            response = client.chat.completions.create(
+                model="glm-4",
+                messages=[
+                    {"role": "user", "content": message},
+                ],
+                tools=[
+                    {
+                        "type": "retrieval",
+                        "retrieval": {
+                            "knowledge_id": knowledge_id,
+                            "prompt_template": prompt_template
+                        }
+                    }
+                ],
+                stream=False,
+                temperature=0.01,
+                top_p=0.1,
+            )
+            result = response.choices[0].message.content
+            end_time = time.time()
+            logger.info("retrive call completed in %.2f seconds", end_time - start_time)
+            return result
+        except Exception as e:
+            logger.error("Error in retrive: %s", str(e))
+            raise
+
+    def retrive_sse(self, message, knowledge_id, prompt_template=None,system_prompt=None):
+        logger.info("Starting retrive_sse call with knowledge_id: %s, message:%s", knowledge_id, message)
+        start_time = time.time()
+        client = ZhipuAI(api_key=self.app_secret_key)
+        default_prompt = "从文档\n\"\"\"\n{{knowledge}}\n\"\"\"\n中找问题\n\"\"\"\n{{question}}\n\"\"\"\n的答案，找到答案就仅使用文档语句回答问题，找不到答案就告诉用户知识库中没有该信息。\n不要复述问题，直接开始回答。"
+        messages = [{"role": "user", "content": message}]
+        # if system_prompt != None:
+        #     messages.append({"role": "system", "content": system_prompt})
+        if prompt_template is None or prompt_template == "":
+            prompt_template = default_prompt
+        try:
+            response = client.chat.completions.create(
+                model="glm-4",
+                messages=messages,
+                tools=[
+                    {
+                        "type": "retrieval",
+                        "retrieval": {
+                            "knowledge_id": knowledge_id,
+                            "prompt_template": prompt_template
+                        }
+                    }
+                ],
+                stream=True,
+                temperature=0.01,
+                top_p=0.1,
+            )
+            for chunk in response:
+                yield chunk.choices[0].delta.content
+            end_time = time.time()
+            logger.info("retrive_sse call completed in %.2f seconds", end_time - start_time)
+        except Exception as e:
+            logger.error("Error in retrive_sse: %s", str(e))
+            raise

app/services/zhipu_service.py

@@ -1,47 +1,47 @@
 import logging
 import time
 from zhipuai import ZhipuAI
+from app.services.ai_service_interface import AIServiceInterface
 from app.utils.prompt_repository import PromptRepository
 
 logger = logging.getLogger(__name__)
 
-class ZhipuService:
+class ZhipuService(AIServiceInterface):
     def __init__(self):
         self.model_name = "glm-4"
         self.app_secret_key = "d54f764a1d67c17d857bd3983b772016.GRjowY0fyiMNurLc"
+        self.client = ZhipuAI(api_key=self.app_secret_key)
         logger.info("ZhipuService initialized with model: %s", self.model_name)
 
-    def talk_to_zhipu(self, message):
+    def generate_response(self, prompt):
-        logger.info("Starting talk_to_zhipu call")
+        logger.info("Starting generate_response call")
         start_time = time.time()
-        client = ZhipuAI(api_key=self.app_secret_key)
         try:
-            response = client.chat.completions.create(
+            response = self.client.chat.completions.create(
                 model=self.model_name,
                 messages=[
-                    {"role": "user", "content": message},
+                    {"role": "user", "content": prompt},
                 ],
                 stream=False,
                 temperature=0.01,
                 top_p=0.1,
             )
-            accum_resp = response.choices[0].message.content
+            result = response.choices[0].message.content
             end_time = time.time()
-            logger.info("talk_to_zhipu call completed in %.2f seconds", end_time - start_time)
+            logger.info("generate_response call completed in %.2f seconds", end_time - start_time)
-            return accum_resp
+            return result
         except Exception as e:
-            logger.error("Error in talk_to_zhipu: %s", str(e))
+            logger.error("Error in generate_response: %s", str(e))
             raise
 
-    def talk_to_zhipu_sse(self, message):
+    def generate_response_sse(self, prompt):
-        logger.info("Starting talk_to_zhipu_sse call")
+        logger.info("Starting generate_response_sse call")
         start_time = time.time()
-        client = ZhipuAI(api_key=self.app_secret_key)
         try:
-            response = client.chat.completions.create(
+            response = self.client.chat.completions.create(
                 model=self.model_name,
                 messages=[
-                    {"role": "user", "content": message},
+                    {"role": "user", "content": prompt},
                 ],
                 stream=True,
                 temperature=0.01,
@@ -50,21 +50,20 @@ class ZhipuService:
             for chunk in response:
                 yield chunk.choices[0].delta.content
             end_time = time.time()
-            logger.info("talk_to_zhipu_sse call completed in %.2f seconds", end_time - start_time)
+            logger.info("generate_response_sse call completed in %.2f seconds", end_time - start_time)
         except Exception as e:
-            logger.error("Error in talk_to_zhipu_sse: %s", str(e))
+            logger.error("Error in generate_response_sse: %s", str(e))
             raise
 
     def retrive(self, message, knowledge_id, prompt_template):
         logger.info("Starting retrive call with knowledge_id: %s", knowledge_id)
         start_time = time.time()
-        client = ZhipuAI(api_key=self.app_secret_key)
         default_prompt = "从文档\n\"\"\"\n{{knowledge}}\n\"\"\"\n中找问题\n\"\"\"\n{{question}}\n\"\"\"\n的答案，找到答案就仅使用文档语句回答问题，找不到答案就用自身知识回答并且告诉用户该信息不是来自文档。\n不要复述问题，直接开始回答。"
 
         if prompt_template is None or prompt_template == "":
             prompt_template = default_prompt
         try:
-            response = client.chat.completions.create(
+            response = self.client.chat.completions.create(
                 model="glm-4",
                 messages=[
                     {"role": "user", "content": message},
@@ -93,7 +92,6 @@ class ZhipuService:
     def retrive_sse(self, message, knowledge_id, prompt_template=None,system_prompt=None):
         logger.info("Starting retrive_sse call with knowledge_id: %s, message:%s", knowledge_id, message)
         start_time = time.time()
-        client = ZhipuAI(api_key=self.app_secret_key)
         default_prompt = "从文档\n\"\"\"\n{{knowledge}}\n\"\"\"\n中找问题\n\"\"\"\n{{question}}\n\"\"\"\n的答案，找到答案就仅使用文档语句回答问题，找不到答案就告诉用户知识库中没有该信息。\n不要复述问题，直接开始回答。"
         messages = [{"role": "user", "content": message}]
         # if system_prompt != None:
@@ -101,7 +99,7 @@ class ZhipuService:
         if prompt_template is None or prompt_template == "":
             prompt_template = default_prompt
         try:
-            response = client.chat.completions.create(
+            response = self.client.chat.completions.create(
                 model="glm-4",
                 messages=messages,
                 tools=[
@@ -132,11 +130,10 @@ class ZhipuService:
         prompt_report_template = PromptRepository().get_prompt("report_template")
 
         prompt_report_missing_check = f"""{prompt_report_template}
-        请检查以下日志信息是否完整，如果信息缺失则提示要求用户需要补充的信息要点，如果信息完整请直接返回“上述日志信息为全部信息”。日志信息如下：\n\"\"\"\n{message}\n\"\"\"\n"""
+        请检查以下日志信息是否完整，如果信息缺失则提示要求用户需要补充的信息要点，如果信息完整请直接返回"上述日志信息为全部信息"。日志信息如下：\n\"\"\"\n{message}\n\"\"\"\n"""
 
-        client = ZhipuAI(api_key=self.app_secret_key)
         try:
-            response = client.chat.completions.create(
+            response = self.client.chat.completions.create(
                 model="glm-4-flash",
                 messages=[
                     {"role": "user", "content": prompt_report_missing_check},

app/utils/sessions.py

@@ -9,3 +9,5 @@ def init_session():
     if 'history' not in session:
         session['history'] = []  # 初始化会话历史
         session['session_info'] = {}
+    if 'llm_service' not in session:
+        session['llm_service'] = 'zhipu'

requirements.txt

@@ -18,3 +18,4 @@ zipp==3.20.2
 zhipuai==2.1.5.20230904
 pytz==2024.2
 flask-debug==0.4.3
+openai==1.52.2