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----
-language: en
-license: apache-2.0
-library_name: faiss
-tags:
-- faiss
-- retrieval
-- vector-search
-- marine-images
-- bio-clip
-- oceangpt-x
----
-# Ocean-FAISS: Marine Image Retrieval Index
-
-High-speed FAISS vector index and metadata for marine image retrieval using BioCLIP embeddings. Core component of the [OceanGPT-X](https://huggingface.co/collections/zjunlp/oceangpt-x) pipeline.
-
-## Repository Contents
-| Path | Description |
-|------|-------------|
-| `faiss/index.faiss` | Pre-built FAISS index containing BioCLIP feature vectors |
-| `faiss/id_map.json` | Mapping between FAISS internal IDs and dataset image IDs |
-| `metadata/metadata.jsonl` | Rich metadata for each indexed image (species, location, capture info) |
-
-## Usage
-Requires `faiss-cpu` or `faiss-gpu`.
-```python
-import faiss
-import json
-import jsonlines
-
-index = faiss.read_index("faiss/index.faiss")
-with open("faiss/id_map.json", "r") as f:
-    id_map = json.load(f)
-
-# Query vector must match the embedding dimension of the index
-query_vector = ...  # Shape: (1, dim), dtype: float32
-D, I = index.search(query_vector, k=5)
-
-# Retrieve metadata
-with jsonlines.open("metadata/metadata.jsonl") as reader:
-    metadata = {obj["id"]: obj for obj in reader}
-
-for idx in I[0]:
-    img_id = id_map[str(idx)]
-    print(metadata.get(img_id, "Not found"))