From 99d2421b6c9973f7abd81978a512a36f98b89835 Mon Sep 17 00:00:00 2001
From: Andrei Fajardo <92402603+nerdai@users.noreply.github.com>
Date: Mon, 12 Feb 2024 20:35:36 -0500
Subject: [PATCH] Delete txtai.py from chatgpt-plugin-reader package directory
(#10601)
---
.../llama_index/readers/txtai.py | 76 -------------------
1 file changed, 76 deletions(-)
delete mode 100644 llama-index-integrations/readers/llama-index-readers-chatgpt-plugin/llama_index/readers/txtai.py
diff --git a/llama-index-integrations/readers/llama-index-readers-chatgpt-plugin/llama_index/readers/txtai.py b/llama-index-integrations/readers/llama-index-readers-chatgpt-plugin/llama_index/readers/txtai.py
deleted file mode 100644
index 544430f39..000000000
--- a/llama-index-integrations/readers/llama-index-readers-chatgpt-plugin/llama_index/readers/txtai.py
+++ /dev/null
@@ -1,76 +0,0 @@
-"""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
--
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