修改重排bug，新增历史对话功能

src/config/consts.py

 # =============================
 # 资料存储数据库配置
 # =============================
-VEC_DB_HOST = '192.168.10.93'
+VEC_DB_HOST = '192.168.10.189'
 VEC_DB_DBNAME = 'lae'
 VEC_DB_USER = 'postgres'
 VEC_DB_PASSWORD = '111111'
@@ -10,7 +10,7 @@ VEC_DB_PORT = '5433'
 # =============================
 # 聊天相关数据库配置
 # =============================
-CHAT_DB_HOST = '192.168.10.93'
+CHAT_DB_HOST = '192.168.10.189'
 CHAT_DB_DBNAME = 'lae'
 CHAT_DB_USER = 'postgres'
 CHAT_DB_PASSWORD = '111111'
@@ -19,12 +19,12 @@ CHAT_DB_PORT = '5433'
 # =============================
 # 向量化模型路径配置
 # =============================
-EMBEEDING_MODEL_PATH = '/app/bge-large-zh-v1.5'
+EMBEEDING_MODEL_PATH = 'D:\\work\\py\\LAE\\bge-large-zh-v1.5'
 
 # =============================
 # 重排序模型路径配置
 # =============================
-RERANK_MODEL_PATH = '/app/bge-reranker-large'
+RERANK_MODEL_PATH = 'D:\\work\\py\\LAE\\bge-reranker-large'
 # RERANK_MODEL_PATH = 'BAAI/bge-reranker-large'
 
 # =============================
@@ -35,19 +35,19 @@ LLM_SERVER_URL = '192.168.10.102:8002'
 # =============================
 # FAISS相似性查找配置
 # =============================
-SIMILARITY_SHOW_NUMBER = 5
+SIMILARITY_SHOW_NUMBER = 10
 SIMILARITY_THRESHOLD = 0.8
 
 # =============================
 # FAISS向量库文件存储路径配置
 # =============================
-FAISS_STORE_PATH = '/app/faiss'
+FAISS_STORE_PATH = 'D:\\work\\py\\LAE\\faiss'
 INDEX_NAME = 'know'
 
 # =============================
 # 知识相关资料配置
 # =============================
-KNOWLEDGE_PATH = '/app/lae_data'
+KNOWLEDGE_PATH = 'D:\\work\\py\\LAE\\testdoc'
 
 # =============================
 # gradio服务相关配置
@@ -64,6 +64,12 @@ prompt1 = """'''
 请你根据上述已知资料回答下面的问题，问题如下：
 {question}"""
 
+prompt_enhancement_history_template = """{history}
+上面是之前的对话，下面是可参考的内容,参考内容中如果和问题不符合，可以不用参考。
+{context}
+请结合上述内容回答以下问题，不要提无关内容：
+{question}
+"""
 # =============================
 # NLP_BERT模型路径配置
 # =============================

src/controller/web.py

@@ -32,7 +32,7 @@ from src.config.consts import (
     VEC_DB_USER,
     VEC_DB_DBNAME,
     SIMILARITY_SHOW_NUMBER,
-    prompt1
+    prompt_enhancement_history_template
 )
 app = FastAPI()
 app.add_middleware(
@@ -62,8 +62,8 @@ base_llm = ChatOpenAI(
     model_name='Qwen2-7B',
     verbose=True
 )
-my_chat = QA(PromptTemplate(input_variables=["context", "question"], template=prompt1), base_llm,
-             {"temperature": 0.9}, ['context', 'question'], _db=c_db, _faiss_db=vecstore_faiss)
+my_chat = QA(PromptTemplate(input_variables=["history","context", "question"], template=prompt_enhancement_history_template), base_llm,
+             {"temperature": 0.9}, ['history','context', 'question'], _db=c_db, _faiss_db=vecstore_faiss,rerank=True)
 
 @app.post('/api/login')
 def login(phone_request: PhoneLoginRequest):
@@ -156,10 +156,13 @@ def question(chat_request: ChatRequest, token: str = Header(None)):
     question = chat_request.question
     crud = CRUD(_db=c_db)
     history = []
-    if session_id =="":
+    if session_id !="":
         history = crud.get_last_history(str(session_id))
     # answer = my_chat.chat(question)
-    answer, docs = my_chat.chat(question, with_similarity=True)
+    prompt = ""
+    for h in history:
+        prompt += "问：{}\n答：{}\n\n".format(h[0], h[1])
+    answer, docs = my_chat.chat_with_history(question,history=prompt, with_similarity=True)
     docs_json = []
     for d in docs:
         j ={}
@@ -198,7 +201,10 @@ def re_generate(chat_request: ReGenerateRequest, token: str = Header(None)):
     crud = CRUD(_db=c_db)
     last_turn_id = crud.get_last_turn_num(str(session_id))
     history = crud.get_last_history_before_turn_id(str(session_id),last_turn_id)
-    answer, docs = my_chat.chat(question, with_similarity=True)
+    prompt = ""
+    for h in history:
+        prompt += "问：{}\n答：{}\n\n".format(h[0], h[1])
+    answer, docs = my_chat.chat_with_history(question, history=prompt, with_similarity=True)
     docs_json = []
     for d in docs:
         j = {}

src/pgdb/knowledge/similarity.py

@@ -227,7 +227,6 @@ class VectorStore_FAISS(FAISS):
 
     @staticmethod
     def join_document(docs: List[Document]) -> str:
-        print(docs)
         return "".join([doc.page_content for doc in docs])
 
     @staticmethod

src/pgdb/knowledge/vec_txt_table.py

@@ -35,7 +35,7 @@ class TxtVector:
         query = f"SELECT paragraph_id,text FROM vec_txt WHERE vector_id = %s"
         self.db.execute_args(query, [search])
         answer = self.db.fetchall()
-        print(answer)
+        # print(answer)
         return answer[0]
 
     def create_table(self):

src/server/get_similarity.py

@@ -7,11 +7,15 @@ class GetSimilarity:
         self.faiss_db = _faiss_db
         self.similarity_docs = self.faiss_db.get_text_similarity(self.question)
         self.similarity_doc_txt = self.faiss_db.join_document(self.similarity_docs)
+        self.rerank_docs = ""
 
     def get_rerank(self,reranker:BgeRerank ,top_k = 5):
         rerank_docs = reranker.compress_documents(self.similarity_docs,self.question)
-        self.rerank_docs = rerank_docs
-        return self.faiss_db.join_document([d[1] for d in rerank_docs[:top_k]])
+        d_list = []
+        for d in rerank_docs[:top_k]:
+            d_list.append(d)
+        self.rerank_docs = rerank_docs[:top_k]
+        return self.faiss_db.join_document(d_list)
 
     def get_similarity_doc(self):
         return self.similarity_doc_txt

src/server/qa.py

@@ -37,8 +37,14 @@ prompt1 = """'''
 '''
 请你根据上述已知资料回答下面的问题，问题如下：
 {question}"""
+
+
+
+
 PROMPT1 = PromptTemplate(input_variables=["context", "question"], template=prompt1)
 
+
+
 # 预设的安全响应
 SAFE_RESPONSE = "您好，我不具备人类属性，因此没有名字。我可以协助您完成范围广泛的任务并提供有关各种主题的信息，比如回答问题，提是供定义和解释及建议。如果您有任何问题，请随时向我提问。"
 BLOCKED_KEYWORDS = ["文心一言", "百度", "模型"]
@@ -90,10 +96,6 @@ class QA:
                 self.cur_similarity = similarity.get_similarity_doc()
             similarity_docs = similarity.get_similarity_docs()
             rerank_docs = similarity.get_rerank_docs()
-            print("============== similarity ==============")
-            print(similarity_docs)
-            print("============== rerank ==============")
-            print(rerank_docs)
             self.cur_question = self.prompt.format(context=self.cur_similarity, question=self.cur_oquestion)
             if not _question:
                 return ""
@@ -103,9 +105,41 @@ class QA:
 
         self.update_history()
         if with_similarity:
-            return self.cur_answer, similarity_docs
+            if self.rerank:
+                return self.cur_answer, rerank_docs
+            else:
+                return self.cur_answer, similarity_docs
+        return self.cur_answer
+
+    def chat_with_history(self, _question,history,with_similarity=False):
+        self.cur_oquestion = _question
+        if self.contains_blocked_keywords(_question):
+            self.cur_answer = SAFE_RESPONSE
+        else:
+            # self.cur_similarity = self.get_similarity(_aquestion=self.cur_oquestion)
+            similarity = self.get_similarity_origin(_aquestion=self.cur_oquestion)
+            if self.rerank:
+                self.cur_similarity = similarity.get_rerank(self.rerank_model)
+            else:
+                self.cur_similarity = similarity.get_similarity_doc()
+            similarity_docs = similarity.get_similarity_docs()
+            rerank_docs = similarity.get_rerank_docs()
+            self.cur_question = self.prompt.format(history=history,context=self.cur_similarity, question=self.cur_oquestion)
+            if not _question:
+                return ""
+            self.cur_answer = self.llm.run(history=history,context=self.cur_similarity, question=self.cur_oquestion)
+            if self.contains_blocked_keywords(self.cur_answer):
+                self.cur_answer = SAFE_RESPONSE
+
+        self.update_history()
+        if with_similarity:
+            if self.rerank:
+                return self.cur_answer, rerank_docs
+            else:
+                return self.cur_answer, similarity_docs
         return self.cur_answer
 
+
     # 异步输出，逐渐输出答案
     async def async_chat(self, _question):
         self.cur_oquestion = _question