add gradio tem

pilot/app.py

@@ -40,8 +40,8 @@ def get_answer(q):
     return response.response
 
 def get_similar(q):
-    from pilot.vector_store.extract_tovec import knownledge_tovec
+    from pilot.vector_store.extract_tovec import knownledge_tovec, knownledge_tovec_st
-    docsearch = knownledge_tovec("./datasets/plan.md")
+    docsearch = knownledge_tovec_st("./datasets/plan.md")
     docs = docsearch.similarity_search_with_score(q, k=1)
 
     for doc in docs:

pilot/model/vicuna_llm.py

@@ -8,7 +8,7 @@ from langchain.embeddings.base import Embeddings
 from pydantic import BaseModel
 from typing import Any, Mapping, Optional, List
 from langchain.llms.base import LLM
-from configs.model_config import *
+from pilot.configs.model_config import *
 
 class VicunaRequestLLM(LLM):
 

pilot/server/webserver.py

@@ -12,7 +12,7 @@ import requests
 from urllib.parse import urljoin
 from pilot.configs.model_config import DB_SETTINGS
 from pilot.connections.mysql_conn import MySQLOperator
-
+from pilot.vector_store.extract_tovec import get_vector_storelist
 
 from pilot.configs.model_config import LOGDIR, VICUNA_MODEL_SERVER, LLM_MODEL
 
@@ -42,6 +42,7 @@ disable_btn = gr.Button.update(interactive=True)
 enable_moderation = False
 models = []
 dbs = []
+vs_list = ["新建知识库"] + get_vector_storelist()
 
 priority = {
     "vicuna-13b": "aaa"
@@ -255,7 +256,7 @@ def build_single_model_ui():
         The service is a research preview intended for non-commercial use only. subject to the model [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA 
     """
 
-    vs_path, file_status, vs_list = gr.State(""), gr.State(""), gr.State(vs_list)
+    vs_path, file_status, vs_list = gr.State(""), gr.State(""), gr.State()
     state = gr.State()
     gr.Markdown(notice_markdown, elem_id="notice_markdown")
 
@@ -279,14 +280,6 @@ def build_single_model_ui():
         )
     tabs = gr.Tabs()
     with tabs:
-        with gr.TabItem("知识问答", elem_id="QA"):
-            doc2vec = gr.Column(visible=False)
-            with doc2vec:
-                mode = gr.Radio(["默认知识库对话", "新增知识库"])
-                vs_setting = gr.Accordion("配置知识库")
-                mode.change(fn=change_mode, inputs=mode, outputs=vs_setting)
-                with vs_setting:
-                    select_vs = gr.Dropdown()
         with gr.TabItem("SQL生成与诊断", elem_id="SQL"):
             # TODO A selector to choose database
             with gr.Row(elem_id="db_selector"):
@@ -296,6 +289,31 @@ def build_single_model_ui():
                     value=dbs[0] if len(models) > 0 else "",
                     interactive=True,
                     show_label=True).style(container=False) 
+                
+        with gr.TabItem("知识问答", elem_id="QA"):
+
+            mode = gr.Radio(["默认知识库对话", "新增知识库"])
+            vs_setting = gr.Accordion("配置知识库")
+            mode.change(fn=change_mode, inputs=mode, outputs=vs_setting)
+            with vs_setting:
+                vs_name = gr.Textbox(label="新知识库名称", lines=1, interactive=True)
+                vs_add = gr.Button("添加为新知识库")
+                with gr.Column() as doc2vec:
+                    gr.Markdown("向知识库中添加文件")
+                    with gr.Tab("上传文件"):
+                        files = gr.File(label="添加文件", 
+                                        file_types=[".txt", ".md", ".docx", ".pdf"],
+                                        file_count="multiple",
+                                        show_label=False
+                                        )
+
+                        load_file_button = gr.Button("上传并加载到知识库")
+                    with gr.Tab("上传文件夹"):
+                        folder_files = gr.File(label="添加文件",
+                                            file_count="directory",
+                                            show_label=False)
+                        load_folder_button = gr.Button("上传并加载到知识库")
+       
     
     with gr.Blocks():
         chatbot = grChatbot(elem_id="chatbot", visible=False).style(height=550)

pilot/vector_store/extract_tovec.py

@@ -26,13 +26,14 @@ def knownledge_tovec_st(filename):
     """ Use sentence transformers to embedding the document.
         https://github.com/UKPLab/sentence-transformers
     """
-    from pilot.configs.model_config import llm_model_config
+    from pilot.configs.model_config import LLM_MODEL_CONFIG
-    embeddings = HuggingFaceEmbeddings(model=llm_model_config["sentence-transforms"])
+    embeddings = HuggingFaceEmbeddings(model_name=LLM_MODEL_CONFIG["sentence-transforms"])
 
     with open(filename, "r") as f:
         knownledge = f.read()
-    
+  
     text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+
     texts = text_splitter(knownledge)
     docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": str(i)} for i in range(len(texts))])
     return docsearch