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remove chroma from docs

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This commit is contained in:
Josh Hawkins 2024-10-04 19:17:47 -05:00
parent e28767b107
commit a4d43a97c4
2 changed files with 3 additions and 3 deletions
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2
docker/main/Dockerfile
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@ -163,7 +163,7 @@ RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
COPY docker/main/requirements.txt /requirements.txt
RUN pip3 install -r /requirements.txt
# Build pysqlite3 from source to support ChromaDB
# Build pysqlite3 from source
COPY docker/main/build_pysqlite3.sh /build_pysqlite3.sh
RUN /build_pysqlite3.sh

4
docs/docs/configuration/semantic_search.md
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@ -5,7 +5,7 @@ title: Using Semantic Search
Semantic Search in Frigate allows you to find tracked objects within your review items using either the image itself, a user-defined text description, or an automatically generated one. This feature works by creating _embeddings_ — numerical vector representations — for both the images and text descriptions of your tracked objects. By comparing these embeddings, Frigate assesses their similarities to deliver relevant search results.
Frigate has support for two models to create embeddings, both of which run locally: [OpenAI CLIP](https://openai.com/research/clip) and [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). Embeddings are then saved to a local instance of [ChromaDB](https://trychroma.com).
Frigate has support for two models to create embeddings, both of which run locally: [OpenAI CLIP](https://openai.com/research/clip) and [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). Embeddings are then saved to Frigate's database.
Semantic Search is accessed via the _Explore_ view in the Frigate UI.
@ -29,7 +29,7 @@ If you are enabling the Search feature for the first time, be advised that Friga
### OpenAI CLIP
This model is able to embed both images and text into the same vector space, which allows `image -> image` and `text -> image` similarity searches. Frigate uses this model on tracked objects to encode the thumbnail image and store it in Chroma. When searching for tracked objects via text in the search box, Frigate will perform a `text -> image` similarity search against this embedding. When clicking "Find Similar" in the tracked object detail pane, Frigate will perform an `image -> image` similarity search to retrieve the closest matching thumbnails.
This model is able to embed both images and text into the same vector space, which allows `image -> image` and `text -> image` similarity searches. Frigate uses this model on tracked objects to encode the thumbnail image and store it in the database. When searching for tracked objects via text in the search box, Frigate will perform a `text -> image` similarity search against this embedding. When clicking "Find Similar" in the tracked object detail pane, Frigate will perform an `image -> image` similarity search to retrieve the closest matching thumbnails.
### all-MiniLM-L6-v2