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https://github.com/blakeblackshear/frigate.git
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36cbffcc5e
* Initial re-implementation of semantic search * put docker-compose back and make reindex match docs * remove debug code and fix import * fix docs * manually build pysqlite3 as binaries are only available for x86-64 * update comment in build_pysqlite3.sh * only embed objects * better error handling when genai fails * ask ollama to pull requested model at startup * update ollama docs * address some PR review comments * fix lint * use IPC to write description, update docs for reindex * remove gemini-pro-vision from docs as it will be unavailable soon * fix OpenAI doc available models * fix api error in gemini and metadata for embeddings
64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
"""CLIP Embeddings for Frigate."""
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import os
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from typing import Tuple, Union
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import onnxruntime as ort
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from chromadb import EmbeddingFunction, Embeddings
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from chromadb.api.types import (
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Documents,
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Images,
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is_document,
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is_image,
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)
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from onnx_clip import OnnxClip
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from frigate.const import MODEL_CACHE_DIR
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class Clip(OnnxClip):
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"""Override load models to download to cache directory."""
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@staticmethod
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def _load_models(
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model: str,
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silent: bool,
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) -> Tuple[ort.InferenceSession, ort.InferenceSession]:
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"""
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These models are a part of the container. Treat as as such.
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"""
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if model == "ViT-B/32":
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IMAGE_MODEL_FILE = "clip_image_model_vitb32.onnx"
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TEXT_MODEL_FILE = "clip_text_model_vitb32.onnx"
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elif model == "RN50":
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IMAGE_MODEL_FILE = "clip_image_model_rn50.onnx"
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TEXT_MODEL_FILE = "clip_text_model_rn50.onnx"
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else:
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raise ValueError(f"Unexpected model {model}. No `.onnx` file found.")
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models = []
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for model_file in [IMAGE_MODEL_FILE, TEXT_MODEL_FILE]:
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path = os.path.join(MODEL_CACHE_DIR, "clip", model_file)
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models.append(OnnxClip._load_model(path, silent))
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return models[0], models[1]
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class ClipEmbedding(EmbeddingFunction):
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"""Embedding function for CLIP model used in Chroma."""
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def __init__(self, model: str = "ViT-B/32"):
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"""Initialize CLIP Embedding function."""
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self.model = Clip(model)
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def __call__(self, input: Union[Documents, Images]) -> Embeddings:
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embeddings: Embeddings = []
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for item in input:
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if is_image(item):
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result = self.model.get_image_embeddings([item])
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embeddings.append(result[0, :].tolist())
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elif is_document(item):
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result = self.model.get_text_embeddings([item])
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embeddings.append(result[0, :].tolist())
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return embeddings
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