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26 lines
1008 B
Python
26 lines
1008 B
Python
import os
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# Important considerations when choosing models
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# Max tokens count needs to be high considering use case (at least 512)
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# Models used must be MIT or Apache license
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# Inference/Indexing speed
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# Bi/Cross-Encoder Model Configs
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# Use 'multi-qa-MiniLM-L6-cos-v1' if license is added because it is 3x faster (384 dimensional embedding)
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DOCUMENT_ENCODER_MODEL = "sentence-transformers/all-distilroberta-v1"
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CROSS_ENCODER_MODEL = "cross-encoder/ms-marco-MiniLM-L-6-v2"
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DOC_EMBEDDING_DIM = 768 # Depends on the document encoder model
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QUERY_EMBEDDING_CONTEXT_SIZE = 256
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DOC_EMBEDDING_CONTEXT_SIZE = 512
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CROSS_EMBED_CONTEXT_SIZE = 512
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# Purely an optimization, memory limitation consideration
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BATCH_SIZE_ENCODE_CHUNKS = 8
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# QA Model API Configs
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# https://platform.openai.com/docs/models/model-endpoint-compatibility
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INTERNAL_MODEL_VERSION = os.environ.get("INTERNAL_MODEL", "openai-chat-completion")
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OPENAPI_MODEL_VERSION = os.environ.get("OPENAI_MODEL_VERSION", "gpt-4")
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OPENAI_MAX_OUTPUT_TOKENS = 512
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