feature:web server knowledge embedding

pilot/configs/model_config.py

@@ -41,3 +41,7 @@ DB_SETTINGS = {
     "host": "127.0.0.1",
     "port": 3306
 }
+
+VS_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "vs_store")
+KNOWLEDGE_UPLOAD_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "knowledge")
+MODEL_NAME_PATH = "model/all-MiniLM-L6-v2"

pilot/server/webserver.py

@@ -3,6 +3,7 @@
 
 import argparse
 import os
+import shutil
 import uuid
 import json
 import time
@@ -10,9 +11,10 @@ import gradio as gr
 import datetime
 import requests
 from urllib.parse import urljoin
-from pilot.configs.model_config import DB_SETTINGS
+from pilot.configs.model_config import DB_SETTINGS, KNOWLEDGE_UPLOAD_ROOT_PATH, MODEL_NAME_PATH, VS_ROOT_PATH
 from pilot.server.vectordb_qa import KnownLedgeBaseQA
 from pilot.connections.mysql import MySQLOperator
+from pilot.source_embedding.pdf_embedding import PDFEmbedding
 from pilot.vector_store.extract_tovec import get_vector_storelist, load_knownledge_from_doc, knownledge_tovec_st
 
 from pilot.configs.model_config import LOGDIR, VICUNA_MODEL_SERVER, LLM_MODEL, DATASETS_DIR
@@ -481,7 +483,15 @@ def build_single_model_ui():
         [state, mode, sql_mode, db_selector, temperature, max_output_tokens],
         [state, chatbot] + btn_list
     )
-
+    load_file_button.click(fn=knowledge_embedding_store,
+                           show_progress=True,
+                           inputs=[vs_name, files],
+                           outputs=[vs_name])
+    # load_folder_button.click(get_vector_store,
+    #                          show_progress=True,
+    #                          inputs=[vs_name, folder_files, 100 , chatbot, vs_add,
+    #                                  vs_add],
+    #                          outputs=["db-out", folder_files, chatbot])
     return state, chatbot, textbox, send_btn, button_row, parameter_row
 
 
@@ -520,6 +530,23 @@ def build_webdemo():
             raise ValueError(f"Unknown model list mode: {args.model_list_mode}")
     return demo
 
+
+def knowledge_embedding_store(vs_id, files):
+    # vs_path = os.path.join(VS_ROOT_PATH, vs_id)
+    if not os.path.exists(os.path.join(KNOWLEDGE_UPLOAD_ROOT_PATH, vs_id)):
+        os.makedirs(os.path.join(KNOWLEDGE_UPLOAD_ROOT_PATH, vs_id))
+    for file in files:
+        filename = os.path.split(file.name)[-1]
+        shutil.move(file.name, os.path.join(KNOWLEDGE_UPLOAD_ROOT_PATH, vs_id, filename))
+
+    knowledge_embedding = PDFEmbedding(file_path=os.path.join(KNOWLEDGE_UPLOAD_ROOT_PATH, vs_id, filename), model_name=MODEL_NAME_PATH,
+                                 vector_store_config={"vector_store_name": vs_id,
+                                                      "vector_store_path": KNOWLEDGE_UPLOAD_ROOT_PATH})
+    knowledge_embedding.source_embedding()
+    logger.info("knowledge embedding success")
+    return os.path.join(KNOWLEDGE_UPLOAD_ROOT_PATH, vs_id, filename + ".vectordb")
+
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--host", type=str, default="0.0.0.0")

pilot/source_embedding/source_embedding.py

@@ -54,8 +54,14 @@ class SourceEmbedding(ABC):
 
         persist_dir = os.path.join(self.vector_store_config["vector_store_path"],
                                    self.vector_store_config["vector_store_name"] + ".vectordb")
-        vector_store = Chroma.from_documents(docs, embeddings, persist_directory=persist_dir)
-        vector_store.persist()
+        self.vector_store = Chroma.from_documents(docs, embeddings, persist_directory=persist_dir)
+        self.vector_store.persist()
+
+    @register
+    def similar_search(self, doc, topk):
+        """vector store similarity_search"""
+
+        return self.vector_store.similarity_search(doc, topk)
 
     def source_embedding(self):
         if 'read' in registered_methods: