add recreate_vector_store to kb_doc_api, and the router in api.py.

server/api.py

@@ -11,7 +11,8 @@ from starlette.responses import RedirectResponse
 from server.chat import (chat, knowledge_base_chat, openai_chat,
                          search_engine_chat)
 from server.knowledge_base import (list_kbs, create_kb, delete_kb,
-                                   list_docs, upload_doc, delete_doc, update_doc)
+                                   list_docs, upload_doc, delete_doc,
+                                   update_doc, recreate_vector_store)
 from server.utils import BaseResponse, ListResponse
 
 nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
@@ -97,6 +98,9 @@ def create_app():
              response_model=BaseResponse,
              summary="上传文件到知识库，并删除另一个文件"
              )(update_doc)
+    app.post("/knowledge_base/recreate_vector_store",
+             summary="根据content中文档重建向量库，流式输出处理进度。"
+             )(recreate_vector_store)
     return app
 
 app = create_app()

server/knowledge_base/kb_doc_api.py

@@ -5,7 +5,10 @@ from fastapi import File, Form, UploadFile
 from server.utils import BaseResponse, ListResponse
 from server.knowledge_base.utils import (validate_kb_name, get_kb_path, get_doc_path,
                                          get_file_path, file2text, docs2vs,
-                                         refresh_vs_cache, )
+                                         refresh_vs_cache, get_vs_path, )
+from fastapi.responses import StreamingResponse
+import json
+import shutil
 
 
 async def list_docs(knowledge_base_name: str):
@@ -98,3 +101,30 @@ async def update_doc():
 async def download_doc():
     # TODO: 下载文件
     pass
+
+
+async def recreate_vector_store(knowledge_base_name: str):
+    '''
+    recreate vector store from the content.
+    this is usefull when user can copy files to content folder directly instead of upload through network.
+    '''
+    async def output(kb):
+        vs_path = get_vs_path(kb)
+        if os.path.isdir(vs_path):
+            shutil.rmtree(vs_path)
+        os.mkdir(vs_path)
+        print(f"start to recreate vectore in {vs_path}")
+
+        docs = (await list_docs(kb)).data
+        for i, filename in enumerate(docs):
+            filepath = get_file_path(kb, filename)
+            print(f"processing {filepath} to vector store.")
+            docs = file2text(filepath)
+            docs2vs(docs, kb)
+            yield json.dumps({
+                "total": len(docs),
+                "finished": i + 1,
+                "doc": filename,
+            })
+    
+    return StreamingResponse(output(knowledge_base_name), media_type="text/event-stream")

webui_pages/utils.py

@@ -444,9 +444,31 @@ class ApiRequest:
             )
             return response.json()
 
+    def recreate_vector_store(
+        self,
+        knowledge_base_name: str,
+        no_remote_api: bool = None,
+    ):
+        '''
+        对应api.py/knowledge_base/recreate_vector_store接口
+        '''
+        if no_remote_api is None:
+            no_remote_api = self.no_remote_api
+
+        if no_remote_api:
+            from server.knowledge_base.kb_doc_api import recreate_vector_store
+            response = run_async(recreate_vector_store(knowledge_base_name))
+            return self._fastapi_stream2generator(response, as_json=True)
+        else:
+            response = self.post(
+                "/knowledge_base/recreate_vector_store",
+                json={"knowledge_base_name": knowledge_base_name},
+            )
+            return self._httpx_stream2generator(response, as_json=True)
+
 
 if __name__ == "__main__":
-    api = ApiRequest()
+    api = ApiRequest(no_remote_api=True)
 
     # print(api.chat_fastchat(
     #     messages=[{"role": "user", "content": "hello"}]
@@ -464,4 +486,7 @@ if __name__ == "__main__":
     # for t in r:
     #     print(t)
 
-    print(api.list_knowledge_bases())
+    # print(api.list_knowledge_bases())
+
+    for t in api.recreate_vector_store('kblog'):
+        print(t)