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"