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| AVAILABLE_MODELS = [ | ||
| { | ||
| 'details': { | ||
| 'description': 'Well rounded & customizable.', | ||
| 'dimensions': 384, | ||
| 'max_sequence': 512, | ||
| 'size_mb': 134 | ||
| }, | ||
| 'model': 'BAAI/bge-small-en-v1.5' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Well rounded & customizable.', | ||
| 'dimensions': 768, | ||
| 'max_sequence': 512, | ||
| 'size_mb': 438 | ||
| }, | ||
| 'model': 'BAAI/bge-base-en-v1.5' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Well rounded & slight RAG improvement.', | ||
| 'dimensions': 768, | ||
| 'max_sequence': 512, | ||
| 'size_mb': 438 | ||
| }, | ||
| 'model': 'BAAI/llm-embedder' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Well rounded & customizable.', | ||
| 'dimensions': 1024, | ||
| 'max_sequence': 512, | ||
| 'size_mb': 1340 | ||
| }, | ||
| 'model': 'BAAI/bge-large-en-v1.5' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Well rounded & customizable.', | ||
| 'dimensions': 768, | ||
| 'max_sequence': 512, | ||
| 'size_mb': 439 | ||
| }, | ||
| 'model': 'hkunlp/instructor-base' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Well rounded & customizable.', | ||
| 'dimensions': 1024, | ||
| 'max_sequence': 512, | ||
| 'size_mb': 1340 | ||
| }, | ||
| 'model': 'hkunlp/instructor-large' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Well rounded & customizable.', | ||
| 'dimensions': 1024, | ||
| 'max_sequence': 512, | ||
| 'size_mb': 4960 | ||
| }, | ||
| 'model': 'hkunlp/instructor-xl' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Well rounded', | ||
| 'dimensions': 312, | ||
| 'max_sequence': 512, | ||
| 'size_mb': 58 | ||
| }, | ||
| 'model': 'jinaai/jina-embedding-t-en-v1' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Well rounded', | ||
| 'dimensions': 512, | ||
| 'max_sequence': 512, | ||
| 'size_mb': 141 | ||
| }, | ||
| 'model': 'jinaai/jina-embedding-s-en-v1' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Well rounded', | ||
| 'dimensions': 768, | ||
| 'max_sequence': 512, | ||
| 'size_mb': 439 | ||
| }, | ||
| 'model': 'jinaai/jina-embedding-b-en-v1' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Well rounded', | ||
| 'dimensions': 1024, | ||
| 'max_sequence': 512, | ||
| 'size_mb': 1340 | ||
| }, | ||
| 'model': 'jinaai/jina-embedding-l-en-v1' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Clustering or semantic search', | ||
| 'dimensions': 768, | ||
| 'max_sequence': 512, | ||
| 'size_mb': 329 | ||
| }, | ||
| 'model': 'sentence-transformers/all-distilroberta-v1' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Clustering or semantic search', | ||
| 'dimensions': 384, | ||
| 'max_sequence': 256, | ||
| 'size_mb': 91 | ||
| }, | ||
| 'model': 'sentence-transformers/all-MiniLM-L6-v2' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Clustering or semantic search', | ||
| 'dimensions': 768, | ||
| 'max_sequence': 384, | ||
| 'size_mb': 438 | ||
| }, | ||
| 'model': 'sentence-transformers/all-mpnet-base-v2' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Semantic search.', | ||
| 'dimensions': 768, | ||
| 'max_sequence': 512, | ||
| 'size_mb': 219 | ||
| }, | ||
| 'model': 'sentence-transformers/gtr-t5-base' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Semantic search.', | ||
| 'dimensions': 768, | ||
| 'max_sequence': 512, | ||
| 'size_mb': 670 | ||
| }, | ||
| 'model': 'sentence-transformers/gtr-t5-large' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Semantic search.', | ||
| 'dimensions': 768, | ||
| 'max_sequence': 512, | ||
| 'size_mb': 2480 | ||
| }, | ||
| 'model': 'sentence-transformers/gtr-t5-xl' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Clustering or semantic search', | ||
| 'dimensions': 768, | ||
| 'max_sequence': 512, | ||
| 'size_mb': 265 | ||
| }, | ||
| 'model': 'sentence-transformers/msmarco-distilbert-base-v4' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Semantic search.', | ||
| 'dimensions': 768, | ||
| 'max_sequence': 384, | ||
| 'size_mb': 265 | ||
| }, | ||
| 'model': 'sentence-transformers/msmarco-distilbert-cos-v5' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Clustering or semantic search', | ||
| 'dimensions': 384, | ||
| 'max_sequence': 512, | ||
| 'size_mb': 91 | ||
| }, | ||
| 'model': 'sentence-transformers/msmarco-MiniLM-L-6-v3' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Semantic search.', | ||
| 'dimensions': 384, | ||
| 'max_sequence': 384, | ||
| 'size_mb': 91 | ||
| }, | ||
| 'model': 'sentence-transformers/msmarco-MiniLM-L6-cos-v5' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Clustering or semantic search', | ||
| 'dimensions': 768, | ||
| 'max_sequence': 510, | ||
| 'size_mb': 499 | ||
| }, | ||
| 'model': 'sentence-transformers/msmarco-roberta-base-v3' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Semantic search.', | ||
| 'dimensions': 768, | ||
| 'max_sequence': 512, | ||
| 'size_mb': 265 | ||
| }, | ||
| 'model': 'sentence-transformers/multi-qa-distilbert-cos-v1' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Semantic search.', | ||
| 'dimensions': 384, | ||
| 'max_sequence': 512, | ||
| 'size_mb': 91 | ||
| }, | ||
| 'model': 'sentence-transformers/multi-qa-MiniLM-L6-cos-v1' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Semantic search.', | ||
| 'dimensions': 768, | ||
| 'max_sequence': 512, | ||
| 'size_mb': 438 | ||
| }, | ||
| 'model': 'sentence-transformers/multi-qa-mpnet-base-cos-v1' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Sentence similarity', | ||
| 'dimensions': 768, | ||
| 'max_sequence': 256, | ||
| 'size_mb': 219 | ||
| }, | ||
| 'model': 'sentence-transformers/sentence-t5-base' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Sentence similarity', | ||
| 'dimensions': 768, | ||
| 'max_sequence': 256, | ||
| 'size_mb': 670 | ||
| }, | ||
| 'model': 'sentence-transformers/sentence-t5-large' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Sentence similarity', | ||
| 'dimensions': 768, | ||
| 'max_sequence': 256, | ||
| 'size_mb': 2480 | ||
| }, | ||
| 'model': 'sentence-transformers/sentence-t5-xl' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Well rounded', | ||
| 'dimensions': 384, | ||
| 'max_sequence': 512, | ||
| 'size_mb': 67 | ||
| }, | ||
| 'model': 'thenlper/gte-small' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Well rounded', | ||
| 'dimensions': 768, | ||
| 'max_sequence': 512, | ||
| 'size_mb': 219 | ||
| }, | ||
| 'model': 'thenlper/gte-base' | ||
| }, | ||
| { | ||
| 'details': { | ||
| 'description': 'Well rounded', | ||
| 'dimensions': 1024, | ||
| 'max_sequence': 512, | ||
| 'size_mb': 670 | ||
| }, | ||
| 'model': 'thenlper/gte-large' | ||
| } | ||
| ] | ||
|
|
||
| DOCUMENT_LOADERS = { | ||
| ".pdf": "PyMuPDFLoader", | ||
| ".docx": "Docx2txtLoader", | ||
| ".txt": "TextLoader", | ||
| ".enex": "EverNoteLoader", | ||
| ".epub": "UnstructuredEPubLoader", | ||
| ".eml": "UnstructuredEmailLoader", | ||
| ".msg": "UnstructuredEmailLoader", | ||
| ".csv": "UnstructuredCSVLoader", | ||
| ".xls": "UnstructuredExcelLoader", | ||
| ".xlsx": "UnstructuredExcelLoader", | ||
| ".xlsm": "UnstructuredExcelLoader", | ||
| ".rtf": "UnstructuredRTFLoader", | ||
| ".odt": "UnstructuredODTLoader", | ||
| ".md": "UnstructuredMarkdownLoader", | ||
| } | ||
|
|
||
| # Define model names | ||
| WHISPER_MODEL_NAMES = ["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large-v2"] | ||
|
|
||
| CHUNKS_ONLY_TOOLTIP = "Only return relevant chunks without connecting to the LLM. Extremely useful to test the chunk size/overlap settings." | ||
|
|
||
| SPEAK_RESPONSE_TOOLTIP = "Only click this after the LLM's entire response is received otherwise your computer might explode." | ||
|
|
||
| DOWNLOAD_EMBEDDING_MODEL_TOOLTIP = "Remember, wait until downloading is complete!" |