update:default knowledge init

pilot/server/vectordb_qa.py

@@ -5,6 +5,7 @@ from langchain.prompts import PromptTemplate
 
 from pilot.configs.model_config import VECTOR_SEARCH_TOP_K
 from pilot.conversation import conv_qa_prompt_template
+from pilot.logs import logger
 from pilot.model.vicuna_llm import VicunaLLM
 from pilot.vector_store.file_loader import KnownLedge2Vector
 
@@ -28,3 +29,27 @@ class KnownLedgeBaseQA:
         context = [d.page_content for d in docs]
         result = prompt.format(context="\n".join(context), question=query)
         return result
+
+    @staticmethod
+    def build_knowledge_prompt(query, docs, state):
+        prompt_template = PromptTemplate(
+            template=conv_qa_prompt_template, input_variables=["context", "question"]
+        )
+        context = [d.page_content for d in docs]
+        result = prompt_template.format(context="\n".join(context), question=query)
+        state.messages[-2][1] = result
+        prompt = state.get_prompt()
+
+        if len(prompt) > 4000:
+            logger.info("prompt length greater than 4000, rebuild")
+            context = context[:2000]
+            prompt_template = PromptTemplate(
+                template=conv_qa_prompt_template,
+                input_variables=["context", "question"],
+            )
+            result = prompt_template.format(context="\n".join(context), question=query)
+            state.messages[-2][1] = result
+            prompt = state.get_prompt()
+            print("new prompt length:" + str(len(prompt)))
+
+        return prompt

pilot/server/webserver.py

@@ -13,7 +13,8 @@ from urllib.parse import urljoin
 
 import gradio as gr
 import requests
-from langchain import PromptTemplate
+
+from pilot.server.vectordb_qa import KnownLedgeBaseQA
 
 ROOT_PATH = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 sys.path.append(ROOT_PATH)
@@ -40,16 +41,13 @@ from pilot.conversation import (
 )
 from pilot.plugins import scan_plugins
 from pilot.prompts.auto_mode_prompt import AutoModePrompt
-from pilot.prompts.generator import PromptGenerator
 from pilot.server.gradio_css import code_highlight_css
 from pilot.server.gradio_patch import Chatbot as grChatbot
-from pilot.server.vectordb_qa import KnownLedgeBaseQA
 from pilot.source_embedding.knowledge_embedding import KnowledgeEmbedding
 from pilot.utils import build_logger, server_error_msg
 from pilot.vector_store.extract_tovec import (
     get_vector_storelist,
     knownledge_tovec_st,
-    load_knownledge_from_doc,
 )
 
 logger = build_logger("webserver", LOGDIR + "webserver.log")
@@ -263,10 +261,19 @@ def http_bot(
     prompt = state.get_prompt()
     skip_echo_len = len(prompt.replace("</s>", " ")) + 1
     if mode == conversation_types["default_knownledge"] and not db_selector:
+        vector_store_config = {
+            "vector_store_name": "default",
+            "vector_store_path": KNOWLEDGE_UPLOAD_ROOT_PATH,
+        }
+        knowledge_embedding_client = KnowledgeEmbedding(
+            file_path="",
+            model_name=LLM_MODEL_CONFIG["text2vec"],
+            local_persist=False,
+            vector_store_config=vector_store_config,
+        )
         query = state.messages[-2][1]
-        knqa = KnownLedgeBaseQA()
+        docs = knowledge_embedding_client.similar_search(query, VECTOR_SEARCH_TOP_K)
-        state.messages[-2][1] = knqa.get_similar_answer(query)
+        prompt = KnownLedgeBaseQA.build_knowledge_prompt(query, docs, state)
-        prompt = state.get_prompt()
         state.messages[-2][1] = query
         skip_echo_len = len(prompt.replace("</s>", " ")) + 1
 
@@ -285,26 +292,7 @@ def http_bot(
         )
         query = state.messages[-2][1]
         docs = knowledge_embedding_client.similar_search(query, VECTOR_SEARCH_TOP_K)
-        context = [d.page_content for d in docs]
+        prompt = KnownLedgeBaseQA.build_knowledge_prompt(query, docs, state)
-        prompt_template = PromptTemplate(
-            template=conv_qa_prompt_template, input_variables=["context", "question"]
-        )
-        result = prompt_template.format(context="\n".join(context), question=query)
-        state.messages[-2][1] = result
-        prompt = state.get_prompt()
-        print("prompt length:" + str(len(prompt)))
-
-        if len(prompt) > 4000:
-            logger.info("prompt length greater than 4000, rebuild")
-            context = context[:2000]
-            prompt_template = PromptTemplate(
-                template=conv_qa_prompt_template,
-                input_variables=["context", "question"],
-            )
-            result = prompt_template.format(context="\n".join(context), question=query)
-            state.messages[-2][1] = result
-            prompt = state.get_prompt()
-            print("new prompt length:" + str(len(prompt)))
 
         state.messages[-2][1] = query
         skip_echo_len = len(prompt.replace("</s>", " ")) + 1
@@ -697,7 +685,7 @@ if __name__ == "__main__":
     # 配置初始化
     cfg = Config()
 
-    dbs = get_database_list()
+    # dbs = get_database_list()
     cfg.set_plugins(scan_plugins(cfg, cfg.debug_mode))
 
     # 加载插件可执行命令

pilot/source_embedding/knowledge_embedding.py

@@ -2,7 +2,7 @@ import os
 
 import markdown
 from bs4 import BeautifulSoup
-from langchain.document_loaders import PyPDFLoader, TextLoader, markdown
+from langchain.document_loaders import PyPDFLoader, TextLoader
 from langchain.embeddings import HuggingFaceEmbeddings
 
 from pilot.configs.config import Config