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https://github.com/hwchase17/langchain
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33 lines
817 B
Plaintext
33 lines
817 B
Plaintext
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# Facebook Faiss
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>[Facebook AI Similarity Search (Faiss)](https://engineering.fb.com/2017/03/29/data-infrastructure/faiss-a-library-for-efficient-similarity-search/)
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> is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that
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> search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting
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> code for evaluation and parameter tuning.
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[Faiss documentation](https://faiss.ai/).
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## Installation and Setup
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We need to install `faiss` python package.
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```bash
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pip install faiss-gpu # For CUDA 7.5+ supported GPU's.
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```
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OR
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```bash
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pip install faiss-cpu # For CPU Installation
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```
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## Vector Store
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See a [usage example](/docs/integrations/vectorstores/faiss).
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```python
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from langchain.vectorstores import FAISS
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```
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