42 lines
1.8 KiB
Plaintext
42 lines
1.8 KiB
Plaintext
ASSESSOR_POSTGRES_DB=assessor
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ASSESSOR_POSTGRES_USER=assessor
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ASSESSOR_POSTGRES_PASSWORD=replace-with-a-long-random-local-password
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ASSESSOR_POSTGRES_PORT=5432
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ASSESSOR_ARTIFACT_ROOT=C:\\assessor-data\\artifacts
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ASSESSOR_GPO_TIMEOUT_MS=30000
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ASSESSOR_GPO_MAX_CONCURRENT=1
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ASSESSOR_GPO_MEMORY_MB=512
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ASSESSOR_GPO_JOB_LEASE_MS=60000
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ASSESSOR_GPO_JOB_RETRY_MS=5000
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ASSESSOR_GPO_JOB_POLL_MS=1000
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# Qdrant is a rebuildable regulatory retrieval index. This key is local/deployment secret material.
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ASSESSOR_QDRANT_URL=http://127.0.0.1:6333
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ASSESSOR_QDRANT_PORT=6333
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ASSESSOR_QDRANT_API_KEY=replace-with-a-long-random-local-qdrant-key
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# Required only for regulatory ingestion/retrieval. Use an approved multilingual embedding model.
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# ASSESSOR_RAG_EMBEDDING_BASE_URL=https://work-litellm.example/v1
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# ASSESSOR_RAG_EMBEDDING_MODEL=approved-multilingual-embedding-model
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# ASSESSOR_RAG_EMBEDDING_API_KEY=replace-with-work-litellm-key
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# ASSESSOR_RAG_EMBEDDING_DIMENSIONS=1024
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# Set true only when the provider supports OpenAI's dimensions request (for example text-embedding-3-large).
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# Leave false/unset for llama.cpp, which returns the model's native dimension.
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# ASSESSOR_RAG_EMBEDDING_REQUEST_DIMENSIONS=false
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# ASSESSOR_RAG_EMBEDDING_TIMEOUT_MS=60000
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# ASSESSOR_RAG_CHUNK_CHARACTERS=1600
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# ASSESSOR_RAG_EMBEDDING_BATCH_SIZE=32
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# Optional work LiteLLM AI Overview. Leave ASSESSOR_AI_MODE unset to keep outbound AI disabled.
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# Store real values only in the ignored .env.postgres or deployment environment secret.
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# ASSESSOR_AI_MODE=litellm
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# ASSESSOR_AI_BASE_URL=https://work-litellm.example/v1
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# ASSESSOR_AI_MODEL=approved-model-alias
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# ASSESSOR_AI_API_KEY=replace-with-work-litellm-key
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# ASSESSOR_AI_TIMEOUT_MS=60000
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# ASSESSOR_AI_CHUNK_CHARS=80000
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# ASSESSOR_AI_MAX_OUTPUT_BYTES=262144
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# ASSESSOR_AI_MAX_CALLS=64
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# ASSESSOR_AI_JOB_LEASE_MS=300000
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