| Name | llama-index-readers-faiss JSON |
| Version |
0.1.4
JSON |
| download |
| home_page | None |
| Summary | llama-index readers faiss integration |
| upload_time | 2024-05-02 17:11:22 |
| maintainer | jerryjliu |
| docs_url | None |
| author | Your Name |
| requires_python | <4.0,>=3.8.1 |
| license | MIT |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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# 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).
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"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",
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