feature(pu): add deepseek support ad set it as default llm

app_mqa_database.py

@@ -169,10 +169,10 @@ def rag_answer(question, k=5, user_id='user'):
             answer = best_answer
         else:
             retriever = get_retriever(vectorstore, k)
-            rag_chain = setup_rag_chain(model_name='kimi', temperature=temperature)
+            rag_chain = setup_rag_chain(model_name='deepseek', temperature=temperature)
             history_str = "\n".join([f"{role}: {text}" for role, text in conversation_history[user_id]])
             history_question = [history_str, question]
-            retrieved_documents, answer = execute_query(retriever, rag_chain, history_question, model_name='kimi',
+            retrieved_documents, answer = execute_query(retriever, rag_chain, history_question, model_name='deepseek',
                                                         temperature=temperature)
 
         # 获取总的对话记录数

rag_demo.py

@@ -18,7 +18,7 @@ from langchain.vectorstores import Weaviate
 from weaviate import Client
 from weaviate.embedded import EmbeddedOptions
 from zhipuai import ZhipuAI
-from openai import AzureOpenAI
+from openai import AzureOpenAI, OpenAI
 
 # 环境设置与文档下载
 load_dotenv()  # 加载环境变量
@@ -27,6 +27,7 @@ MIMIMAX_API_KEY = os.getenv("MIMIMAX_API_KEY")
 MIMIMAX_GROUP_ID = os.getenv("MIMIMAX_GROUP_ID")
 ZHIPUAI_API_KEY = os.getenv("ZHIPUAI_API_KEY")
 KIMI_OPENAI_API_KEY = os.getenv("KIMI_OPENAI_API_KEY")
+DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_OPENAI_API_KEY")
 
 AZURE_OPENAI_KEY = os.getenv("AZURE_OPENAI_KEY")
 AZURE_ENDPOINT = os.getenv("AZURE_ENDPOINT")
@@ -203,7 +204,6 @@ def execute_query_no_rag(model_name="gpt-4", temperature=0, query=""):
         return response.choices[0].message.content
     elif model_name == 'kimi':
         # 如果是'kimi'模型,使用专门的API调用方式
-        from openai import OpenAI
         client = OpenAI(
             api_key=KIMI_OPENAI_API_KEY,
             base_url="https://api.moonshot.cn/v1",
@@ -226,6 +226,29 @@ def execute_query_no_rag(model_name="gpt-4", temperature=0, query=""):
             stream=False  # 流式输出
         )
         return completion.choices[0].message.content
+    elif model_name == 'deepseek':
+        # 如果是'deepseek'模型,使用专门的API调用方式
+        client = OpenAI(
+            api_key="sk-c4a8fe52693a4aaab64e648c42f40be6",
+            base_url="https://api.deepseek.com"
+        )
+
+        response = client.chat.completions.create(
+            model="deepseek-chat",  # deepseek-coder
+            messages=[
+                {"role": "system", "content": "You are a helpful assistant"},
+                {"role": "user", "content": query},
+            ],
+            # max_tokens=4096,
+            # max_tokens=32000,
+            temperature=0.7,
+            stream=False,
+            frequency_penalty=0,
+            presence_penalty=0,
+            top_p=1,
+            logprobs=False,
+        )
+        return response.choices[0].message.content
     else:
         # 如果模型不支持,抛出异常
         raise ValueError(f"Unsupported model: {model_name}")
@@ -236,7 +259,9 @@ if __name__ == "__main__":
     file_path = './documents/LightZero_README_zh.md'
     # model_name = "glm-4"  # model_name=['abab6-chat', 'glm-4', 'gpt-3.5-turbo', 'gpt-4', 'gpt-4-turbo', 'azure_gpt-4', 'azure_gpt-35-turbo-16k', 'azure_gpt-35-turbo']
     # model_name = 'azure_gpt-4'
-    model_name = 'kimi'
+    # model_name = 'kimi'
+    model_name = 'deepseek'
+
     temperature = 0.01
     embedding_model = 'OpenAI'  # embedding_model=['HuggingFace', 'TensorflowHub', 'OpenAI']