| Name | llama-index-readers-faiss JSON |
| Version |
0.4.0
JSON |
| download |
| home_page | None |
| Summary | llama-index readers faiss integration |
| upload_time | 2025-07-30 20:51:31 |
| maintainer | jerryjliu |
| docs_url | None |
| author | None |
| requires_python | <4.0,>=3.9 |
| license | None |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# LlamaIndex Readers Integration: Faiss
## Overview
Faiss Reader retrieves documents through an existing in-memory Faiss index. These documents can then be used in a downstream LlamaIndex data structure. If you wish to use Faiss itself as an index to organize documents, insert documents, and perform queries on them, please use VectorStoreIndex with FaissVectorStore.
### Installation
You can install Faiss Reader via pip:
```bash
pip install llama-index-readers-faiss
```
## Usage
```python
from llama_index.readers.faiss import FaissReader
# Initialize FaissReader with an existing Faiss Index object
reader = FaissReader(index="<Faiss Index Object>")
# Load data from Faiss
documents = reader.load_data(
query="<Query Vector>", # 2D numpy array of query vectors
id_to_text_map={"<ID>": "<Text>"}, # A map from IDs to text
k=4, # Number of nearest neighbors to retrieve
separate_documents=True, # Whether to return separate documents
)
```
This loader is designed to be used as a way to load data into
[LlamaIndex](https://github.com/run-llama/llama_index/tree/main/llama_index).
Raw data
{
"_id": null,
"home_page": null,
"name": "llama-index-readers-faiss",
"maintainer": "jerryjliu",
"docs_url": null,
"requires_python": "<4.0,>=3.9",
"maintainer_email": null,
"keywords": null,
"author": null,
"author_email": "Your Name <you@example.com>",
"download_url": "https://files.pythonhosted.org/packages/9f/dc/0608cd74fd6c7c7b2a2aaad180aed3ced370b70af703d37c51c84e922719/llama_index_readers_faiss-0.4.0.tar.gz",
"platform": null,
"description": "# LlamaIndex Readers Integration: Faiss\n\n## Overview\n\nFaiss Reader retrieves documents through an existing in-memory Faiss index. These documents can then be used in a downstream LlamaIndex data structure. If you wish to use Faiss itself as an index to organize documents, insert documents, and perform queries on them, please use VectorStoreIndex with FaissVectorStore.\n\n### Installation\n\nYou can install Faiss Reader via pip:\n\n```bash\npip install llama-index-readers-faiss\n```\n\n## Usage\n\n```python\nfrom llama_index.readers.faiss import FaissReader\n\n# Initialize FaissReader with an existing Faiss Index object\nreader = FaissReader(index=\"<Faiss Index Object>\")\n\n# Load data from Faiss\ndocuments = reader.load_data(\n query=\"<Query Vector>\", # 2D numpy array of query vectors\n id_to_text_map={\"<ID>\": \"<Text>\"}, # A map from IDs to text\n k=4, # Number of nearest neighbors to retrieve\n separate_documents=True, # Whether to return separate documents\n)\n```\n\nThis loader is designed to be used as a way to load data into\n[LlamaIndex](https://github.com/run-llama/llama_index/tree/main/llama_index).\n",
"bugtrack_url": null,
"license": null,
"summary": "llama-index readers faiss integration",
"version": "0.4.0",
"project_urls": null,
"split_keywords": [],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "525e2f20a8fc360fcdf76758b25025972894426e022013d86eab4bd5ffb487f5",
"md5": "9f82bcb94c21a0441c44edd9dc5494a1",
"sha256": "6dfdbda79e8d3e3a1e9bb2a53a5df1ed4e040c4f7615c03ca2559d437fca32f2"
},
"downloads": -1,
"filename": "llama_index_readers_faiss-0.4.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "9f82bcb94c21a0441c44edd9dc5494a1",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": "<4.0,>=3.9",
"size": 3919,
"upload_time": "2025-07-30T20:51:30",
"upload_time_iso_8601": "2025-07-30T20:51:30.816703Z",
"url": "https://files.pythonhosted.org/packages/52/5e/2f20a8fc360fcdf76758b25025972894426e022013d86eab4bd5ffb487f5/llama_index_readers_faiss-0.4.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "9fdc0608cd74fd6c7c7b2a2aaad180aed3ced370b70af703d37c51c84e922719",
"md5": "6de2264f9f793a8253621bd82a5a920f",
"sha256": "07b8ce048432a1f8e6e0824311696a49de04171795eb76e94382e469b840e983"
},
"downloads": -1,
"filename": "llama_index_readers_faiss-0.4.0.tar.gz",
"has_sig": false,
"md5_digest": "6de2264f9f793a8253621bd82a5a920f",
"packagetype": "sdist",
"python_version": "source",
"requires_python": "<4.0,>=3.9",
"size": 4162,
"upload_time": "2025-07-30T20:51:31",
"upload_time_iso_8601": "2025-07-30T20:51:31.524590Z",
"url": "https://files.pythonhosted.org/packages/9f/dc/0608cd74fd6c7c7b2a2aaad180aed3ced370b70af703d37c51c84e922719/llama_index_readers_faiss-0.4.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-07-30 20:51:31",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "llama-index-readers-faiss"
}
