Delete txtai.py from chatgpt-plugin-reader package directory (#10601)

llama-index-integrations/readers/llama-index-readers-chatgpt-plugin/llama_index/readers/txtai.py

-"""txtai reader."""
-
-from typing import Any, Dict, List
-
-import numpy as np
-from llama_index.readers.base import BaseReader
-from llama_index.schema import Document
-
-
-class TxtaiReader(BaseReader):
-    """txtai reader.
-
-    Retrieves documents through an existing in-memory txtai index.
-    These documents can then be used in a downstream LlamaIndex data structure.
-    If you wish use txtai itself as an index to to organize documents,
-    insert documents, and perform queries on them, please use VectorStoreIndex
-    with TxtaiVectorStore.
-
-    Args:
-        txtai_index (txtai.ann.ANN): A txtai Index object (required)
-
-    """
-
-    def __init__(self, index: Any):
-        """Initialize with parameters."""
-        import_err_msg = """
-            `txtai` package not found. For instructions on
-            how to install `txtai` please visit
-            https://neuml.github.io/txtai/install/
-        """
-        try:
-            import txtai  # noqa
-        except ImportError:
-            raise ImportError(import_err_msg)
-
-        self._index = index
-
-    def load_data(
-        self,
-        query: np.ndarray,
-        id_to_text_map: Dict[str, str],
-        k: int = 4,
-        separate_documents: bool = True,
-    ) -> List[Document]:
-        """Load data from txtai index.
-
-        Args:
-            query (np.ndarray): A 2D numpy array of query vectors.
-            id_to_text_map (Dict[str, str]): A map from ID's to text.
-            k (int): Number of nearest neighbors to retrieve. Defaults to 4.
-            separate_documents (Optional[bool]): Whether to return separate
-                documents. Defaults to True.
-
-        Returns:
-            List[Document]: A list of documents.
-
-        """
-        search_result = self._index.search(query, k)
-        documents = []
-        for query_result in search_result:
-            for doc_id, _ in query_result:
-                doc_id = str(doc_id)
-                if doc_id not in id_to_text_map:
-                    raise ValueError(
-                        f"Document ID {doc_id} not found in id_to_text_map."
-                    )
-                text = id_to_text_map[doc_id]
-                documents.append(Document(text=text))
-
-        if not separate_documents:
-            # join all documents into one
-            text_list = [doc.get_content() for doc in documents]
-            text = "\n\n".join(text_list)
-            documents = [Document(text=text)]
-
-        return documents