refactor:rename knowledge embedding api

1.replace knowledge_embedding of embedding_engine

pilot/embedding_engine/embedding_engine.py

@@ -0,0 +1,66 @@
+from typing import Optional
+
+from chromadb.errors import NotEnoughElementsException
+from langchain.embeddings import HuggingFaceEmbeddings
+
+from pilot.configs.config import Config
+from pilot.embedding_engine.knowledge_type import get_knowledge_embedding, KnowledgeType
+from pilot.vector_store.connector import VectorStoreConnector
+
+CFG = Config()
+
+
+class EmbeddingEngine:
+    def __init__(
+        self,
+        model_name,
+        vector_store_config,
+        knowledge_type: Optional[str] = KnowledgeType.DOCUMENT.value,
+        knowledge_source: Optional[str] = None,
+    ):
+        """Initialize with knowledge embedding client, model_name, vector_store_config, knowledge_type, knowledge_source"""
+        self.knowledge_source = knowledge_source
+        self.model_name = model_name
+        self.vector_store_config = vector_store_config
+        self.knowledge_type = knowledge_type
+        self.embeddings = HuggingFaceEmbeddings(model_name=self.model_name)
+        self.vector_store_config["embeddings"] = self.embeddings
+
+    def knowledge_embedding(self):
+        self.knowledge_embedding_client = self.init_knowledge_embedding()
+        self.knowledge_embedding_client.source_embedding()
+
+    def knowledge_embedding_batch(self, docs):
+        # docs = self.knowledge_embedding_client.read_batch()
+        return self.knowledge_embedding_client.index_to_store(docs)
+
+    def read(self):
+        self.knowledge_embedding_client = self.init_knowledge_embedding()
+        return self.knowledge_embedding_client.read_batch()
+
+    def init_knowledge_embedding(self):
+        return get_knowledge_embedding(
+            self.knowledge_type, self.knowledge_source, self.vector_store_config
+        )
+
+    def similar_search(self, text, topk):
+        vector_client = VectorStoreConnector(
+            CFG.VECTOR_STORE_TYPE, self.vector_store_config
+        )
+        try:
+            ans = vector_client.similar_search(text, topk)
+        except NotEnoughElementsException:
+            ans = vector_client.similar_search(text, 1)
+        return ans
+
+    def vector_exist(self):
+        vector_client = VectorStoreConnector(
+            CFG.VECTOR_STORE_TYPE, self.vector_store_config
+        )
+        return vector_client.vector_name_exists()
+
+    def delete_by_ids(self, ids):
+        vector_client = VectorStoreConnector(
+            CFG.VECTOR_STORE_TYPE, self.vector_store_config
+        )
+        vector_client.delete_by_ids(ids=ids)