update:knowledge load script

tools/knowlege_init.py

@@ -0,0 +1,45 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+import argparse
+
+from pilot.configs.model_config import DATASETS_DIR, LLM_MODEL_CONFIG, VECTOR_SEARCH_TOP_K, \
+    KNOWLEDGE_UPLOAD_ROOT_PATH
+from pilot.source_embedding.knowledge_embedding import KnowledgeEmbedding
+
+
+class LocalKnowledgeInit:
+    embeddings: object = None
+    model_name = LLM_MODEL_CONFIG["text2vec"]
+    top_k: int = VECTOR_SEARCH_TOP_K
+
+    def __init__(self) -> None:
+        pass
+
+    def knowledge_persist(self, file_path, vector_name, append_mode):
+        """ knowledge persist """
+        kv = KnowledgeEmbedding(
+            file_path=file_path,
+            model_name=LLM_MODEL_CONFIG["text2vec"],
+            vector_store_config= {"vector_store_name":vector_name, "vector_store_path": KNOWLEDGE_UPLOAD_ROOT_PATH})
+        vector_store = kv.knowledge_persist_initialization(append_mode)
+        return vector_store
+
+    def query(self, q):
+        """Query similar doc from Vector """
+        vector_store = self.init_vector_store()
+        docs = vector_store.similarity_search_with_score(q, k=self.top_k)
+        for doc in docs:
+            dc, s = doc
+            yield s, dc
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--vector_name", type=str, default="default")
+    parser.add_argument("--append", type=bool, default=False)
+    args = parser.parse_args()
+    vector_name = args.vector_name
+    append_mode = args.append
+    kv  = LocalKnowledgeInit()
+    vector_store = kv.knowledge_persist(file_path=DATASETS_DIR, vector_name=vector_name, append_mode=append_mode)
+    docs = vector_store.similarity_search("小明",1)
+    print("your knowledge embedding success...")