Delete embeddings.py

embeddings.py

@@ -1,112 +0,0 @@
-import os
-import json
-import logging
-from typing import List
-
-from txtai.embeddings import Embeddings
-
-# Set up logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-class EmbeddingsManager:
-    def __init__(self, base_path: str = "./indexes", model_path: str = "avsolatorio/GIST-all-MiniLM-L6-v2"):
-        """
-        Initializes the EmbeddingsManager.
-
-        Args:
-            base_path (str): Base directory to store indices.
-            model_path (str): Path or identifier for the embeddings model.
-        """
-        self.base_path = base_path
-        os.makedirs(self.base_path, exist_ok=True)
-        self.model_path = model_path
-        self.embeddings = Embeddings({"path": self.model_path})
-        logger.info(f"Embeddings model loaded from '{self.model_path}'. Base path set to '{self.base_path}'.")
-
-    def create_index(self, index_id: str, documents: List[str]) -> None:
-        """
-        Creates a new embeddings index with the provided documents.
-
-        Args:
-            index_id (str): Unique identifier for the index.
-            documents (List[str]): List of documents to be indexed.
-
-        Raises:
-            ValueError: If the index already exists.
-            Exception: For any other errors during indexing or saving.
-        """
-        index_path = os.path.join(self.base_path, index_id)
-        if os.path.exists(index_path):
-            logger.error(f"Index with index_id '{index_id}' already exists at '{index_path}'.")
-            raise ValueError(f"Index with index_id '{index_id}' already exists.")
-
-        try:
-            # Prepare documents for txtai indexing
-            document_tuples = [(i, text, None) for i, text in enumerate(documents)]
-            self.embeddings.index(document_tuples)
-            logger.info(f"Documents indexed for index_id '{index_id}'.")
-
-            # Create index directory
-            os.makedirs(index_path, exist_ok=True)
-
-            # Save embeddings
-            self.embeddings.save(os.path.join(index_path, "embeddings"))
-            logger.info(f"Embeddings saved to '{os.path.join(index_path, 'embeddings')}'.")
-
-            # Save document list
-            with open(os.path.join(index_path, "document_list.json"), "w", encoding='utf-8') as f:
-                json.dump(documents, f, ensure_ascii=False, indent=4)
-            logger.info(f"Document list saved to '{os.path.join(index_path, 'document_list.json')}'.")
-
-            logger.info(f"Index '{index_id}' created and saved successfully.")
-        except Exception as e:
-            logger.error(f"Failed to create index '{index_id}': {e}")
-            raise Exception(f"Failed to create index '{index_id}': {e}")
-
-    def query_index(self, index_id: str, query: str, num_results: int = 5) -> List[str]:
-        """
-        Queries an existing embeddings index.
-
-        Args:
-            index_id (str): Unique identifier for the index to query.
-            query (str): The search query.
-            num_results (int): Number of top results to return.
-
-        Returns:
-            List[str]: List of top matching documents.
-
-        Raises:
-            FileNotFoundError: If the index does not exist.
-            Exception: For any other errors during querying.
-        """
-        index_path = os.path.join(self.base_path, index_id)
-        if not os.path.exists(index_path):
-            logger.error(f"Index '{index_id}' not found at '{index_path}'.")
-            raise FileNotFoundError(f"Index '{index_id}' not found.")
-
-        try:
-            # Load embeddings from the index
-            self.embeddings.load(os.path.join(index_path, "embeddings"))
-            logger.info(f"Embeddings loaded from '{os.path.join(index_path, 'embeddings')}' for index '{index_id}'.")
-
-            # Load document list
-            document_list_path = os.path.join(index_path, "document_list.json")
-            if not os.path.exists(document_list_path):
-                logger.error(f"Document list not found at '{document_list_path}'.")
-                raise FileNotFoundError(f"Document list not found for index '{index_id}'.")
-
-            with open(document_list_path, "r", encoding='utf-8') as f:
-                document_list = json.load(f)
-            logger.info(f"Document list loaded from '{document_list_path}'.")
-
-            # Perform the search
-            results = self.embeddings.search(query, num_results)
-            queried_texts = [document_list[idx[0]] for idx in results]
-            logger.info(f"Query executed successfully on index '{index_id}'. Retrieved {len(queried_texts)} results.")
-
-            return queried_texts
-        except Exception as e:
-            logger.error(f"Failed to query index '{index_id}': {e}")
-            raise Exception(f"Failed to query index '{index_id}': {e}")