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constants.py
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import os
# from dotenv import load_dotenv
from chromadb.config import Settings
# https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/excel.html?highlight=xlsx#microsoft-excel
from langchain.document_loaders import CSVLoader, PDFMinerLoader, TextLoader, UnstructuredExcelLoader, Docx2txtLoader
# load_dotenv()
ROOT_DIRECTORY = os.path.dirname(os.path.realpath(__file__))
# Define the folder for storing database
SOURCE_DIRECTORY = f"{ROOT_DIRECTORY}/SOURCE_DOCUMENTS"
PERSIST_DIRECTORY = f"{ROOT_DIRECTORY}/DB"
# Can be changed to a specific number
INGEST_THREADS = os.cpu_count() or 8
# Define the Chroma settings
CHROMA_SETTINGS = Settings(
anonymized_telemetry=False,
is_persistent=True,
)
# https://python.langchain.com/en/latest/_modules/langchain/document_loaders/excel.html#UnstructuredExcelLoader
DOCUMENT_MAP = {
".txt": TextLoader,
".md": TextLoader,
".py": TextLoader,
".pdf": PDFMinerLoader,
".csv": CSVLoader,
".xls": UnstructuredExcelLoader,
".xlsx": UnstructuredExcelLoader,
".docx": Docx2txtLoader,
".doc": Docx2txtLoader,
}
# Default Instructor Model
EMBEDDING_MODEL_NAME = "hkunlp/instructor-large" # Uses 1.5 GB of VRAM (High Accuracy with lower VRAM usage)
####
#### OTHER EMBEDDING MODEL OPTIONS
####
# EMBEDDING_MODEL_NAME = "hkunlp/instructor-xl" # Uses 5 GB of VRAM (Most Accurate of all models)
# EMBEDDING_MODEL_NAME = "intfloat/e5-large-v2" # Uses 1.5 GB of VRAM (A little less accurate than instructor-large)
# EMBEDDING_MODEL_NAME = "intfloat/e5-base-v2" # Uses 0.5 GB of VRAM (A good model for lower VRAM GPUs)
# EMBEDDING_MODEL_NAME = "all-MiniLM-L6-v2" # Uses 0.2 GB of VRAM (Less accurate but fastest - only requires 150mb of vram)
####
#### MULTILINGUAL EMBEDDING MODELS
####
# EMBEDDING_MODEL_NAME = "intfloat/multilingual-e5-large" # Uses 2.5 GB of VRAM
# EMBEDDING_MODEL_NAME = "intfloat/multilingual-e5-base" # Uses 1.2 GB of VRAM
#### SELECT AN OPEN SOURCE LLM (LARGE LANGUAGE MODEL)
# Select the Model ID and model_basename
# load the LLM for generating Natural Language responses
#### GPU VRAM Memory required for LLM Models (ONLY) by Billion Parameter value (B Model)
#### Does not include VRAM used by Embedding Models - which use an additional 2GB-7GB of VRAM depending on the model.
####
#### (B Model) (float32) (float16) (GPTQ 8bit) (GPTQ 4bit)
#### 7b 28 GB 14 GB 7 GB - 9 GB 3.5 GB - 5 GB
#### 13b 52 GB 26 GB 13 GB - 15 GB 6.5 GB - 8 GB
#### 32b 130 GB 65 GB 32.5 GB - 35 GB 16.25 GB - 19 GB
#### 65b 260.8 GB 130.4 GB 65.2 GB - 67 GB 32.6 GB - - 35 GB
MODEL_ID = "TheBloke/Llama-2-7B-Chat-GGML"
MODEL_BASENAME = "llama-2-7b-chat.ggmlv3.q4_0.bin"
####
#### (FOR HF MODELS)
####
# MODEL_ID = "TheBloke/vicuna-7B-1.1-HF"
# MODEL_BASENAME = None
# MODEL_ID = "TheBloke/Wizard-Vicuna-7B-Uncensored-HF"
# MODEL_ID = "TheBloke/guanaco-7B-HF"
# MODEL_ID = 'NousResearch/Nous-Hermes-13b' # Requires ~ 23GB VRAM. Using STransformers
# alongside will 100% create OOM on 24GB cards.
# llm = load_model(device_type, model_id=model_id)
####
#### (FOR GPTQ QUANTIZED) Select a llm model based on your GPU and VRAM GB. Does not include Embedding Models VRAM usage.
####
##### 48GB VRAM Graphics Cards (RTX 6000, RTX A6000 and other 48GB VRAM GPUs) #####
### 65b GPTQ LLM Models for 48GB GPUs (*** With best embedding model: hkunlp/instructor-xl ***)
# model_id = "TheBloke/guanaco-65B-GPTQ"
# model_basename = "model.safetensors"
# model_id = "TheBloke/Airoboros-65B-GPT4-2.0-GPTQ"
# model_basename = "model.safetensors"
# model_id = "TheBloke/gpt4-alpaca-lora_mlp-65B-GPTQ"
# model_basename = "model.safetensors"
# model_id = "TheBloke/Upstage-Llama1-65B-Instruct-GPTQ"
# model_basename = "model.safetensors"
##### 24GB VRAM Graphics Cards (RTX 3090 - RTX 4090 (35% Faster) - RTX A5000 - RTX A5500) #####
### 13b GPTQ Models for 24GB GPUs (*** With best embedding model: hkunlp/instructor-xl ***)
# model_id = "TheBloke/Wizard-Vicuna-13B-Uncensored-GPTQ"
# model_basename = "Wizard-Vicuna-13B-Uncensored-GPTQ-4bit-128g.compat.no-act-order.safetensors"
# model_id = "TheBloke/vicuna-13B-v1.5-GPTQ"
# model_basename = "model.safetensors"
# model_id = "TheBloke/Nous-Hermes-13B-GPTQ"
# model_basename = "nous-hermes-13b-GPTQ-4bit-128g.no-act.order"
# model_id = "TheBloke/WizardLM-13B-V1.2-GPTQ"
# model_basename = "gptq_model-4bit-128g.safetensors
### 30b GPTQ Models for 24GB GPUs (*** Requires using intfloat/e5-base-v2 instead of hkunlp/instructor-large as embedding model ***)
# model_id = "TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ"
# model_basename = "Wizard-Vicuna-30B-Uncensored-GPTQ-4bit--1g.act.order.safetensors"
# model_id = "TheBloke/WizardLM-30B-Uncensored-GPTQ"
# model_basename = "WizardLM-30B-Uncensored-GPTQ-4bit.act-order.safetensors"
##### 8-10GB VRAM Graphics Cards (RTX 3080 - RTX 3080 Ti - RTX 3070 Ti - 3060 Ti - RTX 2000 Series, Quadro RTX 4000, 5000, 6000) #####
### (*** Requires using intfloat/e5-small-v2 instead of hkunlp/instructor-large as embedding model ***)
### 7b GPTQ Models for 8GB GPUs
# model_id = "TheBloke/Wizard-Vicuna-7B-Uncensored-GPTQ"
# model_basename = "Wizard-Vicuna-7B-Uncensored-GPTQ-4bit-128g.no-act.order.safetensors"
# model_id = "TheBloke/WizardLM-7B-uncensored-GPTQ"
# model_basename = "WizardLM-7B-uncensored-GPTQ-4bit-128g.compat.no-act-order.safetensors"
# model_id = "TheBloke/wizardLM-7B-GPTQ"
# model_basename = "wizardLM-7B-GPTQ-4bit.compat.no-act-order.safetensors"
####
#### (FOR GGML) (Quantized cpu+gpu+mps) models - check if they support llama.cpp
####
# MODEL_ID = "TheBloke/wizard-vicuna-13B-GGML"
# MODEL_BASENAME = "wizard-vicuna-13B.ggmlv3.q4_0.bin"
# MODEL_BASENAME = "wizard-vicuna-13B.ggmlv3.q6_K.bin"
# MODEL_BASENAME = "wizard-vicuna-13B.ggmlv3.q2_K.bin"
# MODEL_ID = "TheBloke/orca_mini_3B-GGML"
# MODEL_BASENAME = "orca-mini-3b.ggmlv3.q4_0.bin"