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The embedding model used for the FAISS vector store was previously hardcoded to sentence-transformers/all-MiniLM-L6-v2 in ai.py. This prevented users from selecting alternative embedding models suited to their hardware or language. This change introduces a configurable EMBEDDING_MODEL setting that allows the embedding model to be specified through configuration without modifying the source code. The default behavior remains unchanged.
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This PR makes the embedding model used by the FAISS vector store configurable.
Previously, the embedding model was hardcoded to
sentence-transformers/all-MiniLM-L6-v2 in ai.py. This prevented users
from selecting alternative embedding models based on their hardware,
language, or performance requirements.
This change introduces a new configuration parameter:
EMBEDDING_MODEL
The embedding model can now be specified through configuration
without modifying the source code, while keeping the default
behavior unchanged.