From a5eaa1101a4855ce6960ad05fbf16882d97745d6 Mon Sep 17 00:00:00 2001
From: BBC-Esq <bbc@chintellalaw.com>
Date: Sat, 23 Dec 2023 19:59:03 -0500
Subject: [PATCH] v2.7.4

---
 src/constants.py | 307 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 307 insertions(+)
 create mode 100644 src/constants.py

diff --git a/src/constants.py b/src/constants.py
new file mode 100644
index 00000000..8c6936ab
--- /dev/null
+++ b/src/constants.py
@@ -0,0 +1,307 @@
+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!"
\ No newline at end of file