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zk$X(?MG2>%xgN66E6(fok&)`6gtT_^ z?(2#JzW=ny7R5%^THuDGXg0O`4|1VPk+oX*TDiA^R$4&)Cv0f3Deq*-kkoMB=U07i zO$2@Gn{0+ur~$fJ7jFE#DU;CnS$FE*jkWce2AOr;ZKZucoLS}2=Xe(-)6(nH92bNm;8V@E^Z@S@ao-E8#Jvn8uRX;H?F;L>!4`leXR-BP1HI! zDGwn{*1JIumiWruP*GTAD(-F7uYMu6?oP`yXA1 Date: Sat, 18 May 2024 17:50:39 +0300 Subject: [PATCH 02/17] Edit langchain_community --- localGPT_UI.py | 2 +- run_localGPT.py | 2 +- run_localGPT_API.py | 4 ++-- utils.py | 6 +++--- 4 files changed, 7 insertions(+), 7 deletions(-) diff --git a/localGPT_UI.py b/localGPT_UI.py index 6a357e9c..b746465a 100644 --- a/localGPT_UI.py +++ b/localGPT_UI.py @@ -4,7 +4,7 @@ from run_localGPT import load_model from langchain.vectorstores import Chroma from constants import CHROMA_SETTINGS, EMBEDDING_MODEL_NAME, PERSIST_DIRECTORY, MODEL_ID, MODEL_BASENAME -from langchain.embeddings import HuggingFaceInstructEmbeddings +from langchain_community.embeddings import HuggingFaceInstructEmbeddings from langchain.chains import RetrievalQA from streamlit_extras.add_vertical_space import add_vertical_space from langchain.prompts import PromptTemplate diff --git a/run_localGPT.py b/run_localGPT.py index 185c983c..50701c56 100644 --- a/run_localGPT.py +++ b/run_localGPT.py @@ -4,7 +4,7 @@ import torch import utils from langchain.chains import RetrievalQA -from langchain.embeddings import HuggingFaceInstructEmbeddings +from langchain_community.embeddings import HuggingFaceInstructEmbeddings from langchain.llms import HuggingFacePipeline from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler # for streaming response from langchain.callbacks.manager import CallbackManager diff --git a/run_localGPT_API.py b/run_localGPT_API.py index b345612f..037e7132 100644 --- a/run_localGPT_API.py +++ b/run_localGPT_API.py @@ -7,9 +7,9 @@ import torch from flask import Flask, jsonify, request from langchain.chains import RetrievalQA -from langchain.embeddings import HuggingFaceInstructEmbeddings +from langchain_community.embeddings import HuggingFaceInstructEmbeddings -# from langchain.embeddings import HuggingFaceEmbeddings +# from langchain_community.embeddings import HuggingFaceEmbeddings from run_localGPT import load_model from prompt_template_utils import get_prompt_template diff --git a/utils.py b/utils.py index 4e9fd820..b0e7fd91 100644 --- a/utils.py +++ b/utils.py @@ -2,9 +2,9 @@ import csv from datetime import datetime from constants import EMBEDDING_MODEL_NAME -from langchain.embeddings import HuggingFaceInstructEmbeddings -from langchain.embeddings import HuggingFaceBgeEmbeddings -from langchain.embeddings import HuggingFaceEmbeddings +from langchain_community.embeddings import HuggingFaceInstructEmbeddings +from langchain_community.embeddings import HuggingFaceBgeEmbeddings +from langchain_community.embeddings import HuggingFaceEmbeddings def log_to_csv(question, answer): From 1680bb232b56d9918711bc820be766d6c6ffaf38 Mon Sep 17 00:00:00 2001 From: Mahmoudkhorshed-Queens Date: Sat, 18 May 2024 17:54:10 +0300 Subject: [PATCH 03/17] Add langchain_community.vectorstores --- localGPT_UI.py | 2 +- run_localGPT.py | 2 +- run_localGPT_API.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/localGPT_UI.py b/localGPT_UI.py index b746465a..f2b09340 100644 --- a/localGPT_UI.py +++ b/localGPT_UI.py @@ -2,7 +2,7 @@ import subprocess import streamlit as st from run_localGPT import load_model -from langchain.vectorstores import Chroma +from langchain_community.vectorstores import Chroma from constants import CHROMA_SETTINGS, EMBEDDING_MODEL_NAME, PERSIST_DIRECTORY, MODEL_ID, MODEL_BASENAME from langchain_community.embeddings import HuggingFaceInstructEmbeddings from langchain.chains import RetrievalQA diff --git a/run_localGPT.py b/run_localGPT.py index 50701c56..8bbad87e 100644 --- a/run_localGPT.py +++ b/run_localGPT.py @@ -15,7 +15,7 @@ from utils import get_embeddings # from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler -from langchain.vectorstores import Chroma +from langchain_community.vectorstores import Chroma from transformers import ( GenerationConfig, pipeline, diff --git a/run_localGPT_API.py b/run_localGPT_API.py index 037e7132..fff8d16d 100644 --- a/run_localGPT_API.py +++ b/run_localGPT_API.py @@ -14,7 +14,7 @@ from prompt_template_utils import get_prompt_template # from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler -from langchain.vectorstores import Chroma +from langchain_community.vectorstores import Chroma from werkzeug.utils import secure_filename from constants import CHROMA_SETTINGS, EMBEDDING_MODEL_NAME, PERSIST_DIRECTORY, MODEL_ID, MODEL_BASENAME From 7fdb5aac9c3e210eb3f1777d6b1cc11dc97fab2b Mon Sep 17 00:00:00 2001 From: Mahmoudkhorshed-Queens Date: Sat, 18 May 2024 19:05:20 +0300 Subject: [PATCH 04/17] Edit Streamlit --- localGPT_UI.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/localGPT_UI.py b/localGPT_UI.py index f2b09340..09209b62 100644 --- a/localGPT_UI.py +++ b/localGPT_UI.py @@ -6,10 +6,12 @@ from constants import CHROMA_SETTINGS, EMBEDDING_MODEL_NAME, PERSIST_DIRECTORY, MODEL_ID, MODEL_BASENAME from langchain_community.embeddings import HuggingFaceInstructEmbeddings from langchain.chains import RetrievalQA -from streamlit_extras.add_vertical_space import add_vertical_space +from streamlit_extras import add_vertical_space from langchain.prompts import PromptTemplate from langchain.memory import ConversationBufferMemory +# Use the function in your Streamlit app +add_vertical_space(10) def model_memory(): # Adding history to the model. 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z@s#5#2I>tpMwA59Y^$;@`m-6t#Kr?FJq+v_zVZZM(12ID=U|l|3s!G=i}{5Hx9(MpntZB1R#>iSy^(CplaO9`nTI!c#cN=#vs%E?OQheI&LC77wZmvk z0!)rY#Ie?Igm&V&FKZs+3&p#%<)SeU5oCWHXSMbfV>V`KUyY~Qt@^u3Ij*g=iNfpq z5{ri|NgI-47-z;ZSG|S@Dq7c@$9vD{e*JR#^RK5bFCHFVzJCAH%RgQ|+@7vJ+&tbt zDgag`b~@dh-kd&uxO%#}c)EPz8GZNS;pXw~)6?w#raevUa`~T$NTG#SEq}+ z$9MBF$oGGL`aIt%Gf Date: Sat, 18 May 2024 20:18:46 +0300 Subject: [PATCH 06/17] EDit Model TheBloke/Llama-2-7B-32K-Instruct-GPTQ --- constants.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/constants.py b/constants.py index 14bc5192..4d0fce5a 100644 --- a/constants.py +++ b/constants.py @@ -107,7 +107,8 @@ # MODEL_BASENAME = "Meta-Llama-3-8B-Instruct.Q4_K_M.gguf" # LLAMA 3 # use for Apple Silicon -MODEL_ID = "meta-llama/Meta-Llama-3-8B-Instruct" +# MODEL_ID = "meta-llama/Meta-Llama-3-8B-Instruct" +MODEL_ID = "TheBloke/Llama-2-7B-32K-Instruct-GPTQ" MODEL_BASENAME = None # LLAMA 3 # use for NVIDIA GPUs From 9c0c7b8507396ebf9bc072f22b778c2ef46e00fc Mon Sep 17 00:00:00 2001 From: Mahmoudkhorshed-Queens Date: Sat, 18 May 2024 20:49:14 +0300 Subject: [PATCH 07/17] Edit Base name --- constants.py | 2 +- ingest.py | 9 +++++++-- 2 files changed, 8 insertions(+), 3 deletions(-) diff --git a/constants.py b/constants.py index 4d0fce5a..c868654c 100644 --- a/constants.py +++ b/constants.py @@ -109,7 +109,7 @@ # LLAMA 3 # use for Apple Silicon # MODEL_ID = "meta-llama/Meta-Llama-3-8B-Instruct" MODEL_ID = "TheBloke/Llama-2-7B-32K-Instruct-GPTQ" -MODEL_BASENAME = None +MODEL_BASENAME = "Llama-2-7B-32K-Instruct-GPTQ" # LLAMA 3 # use for NVIDIA GPUs # MODEL_ID = "unsloth/llama-3-8b-bnb-4bit" diff --git a/ingest.py b/ingest.py index 8586b659..45ff1175 100644 --- a/ingest.py +++ b/ingest.py @@ -6,7 +6,7 @@ import torch from langchain.docstore.document import Document from langchain.text_splitter import Language, RecursiveCharacterTextSplitter -from langchain_community.vectorstores import Chroma +from langchain_community.vectorstores import Chroma , FAISS from utils import get_embeddings from constants import ( @@ -173,7 +173,12 @@ def main(device_type): persist_directory=PERSIST_DIRECTORY, client_settings=CHROMA_SETTINGS, ) - + # db = FAISS.from_documents( + # texts, + # embeddings, + # persist_directory=PERSIST_DIRECTORY, + # client_settings=FAISS_SETTINGS, + # ) if __name__ == "__main__": logging.basicConfig( From cc1e1efc8aac210b6ff5f26cee1ee0971ba69eff Mon Sep 17 00:00:00 2001 From: Mahmoudkhorshed-Queens Date: Sat, 18 May 2024 22:06:03 +0300 Subject: [PATCH 08/17] Download mistral --- constants.py | 11 +++++++---- run_localGPT.py | 12 ++++++++++-- 2 files changed, 17 insertions(+), 6 deletions(-) diff --git a/constants.py b/constants.py index c868654c..244b3dae 100644 --- a/constants.py +++ b/constants.py @@ -2,6 +2,7 @@ # from dotenv import load_dotenv from chromadb.config import Settings +# from faiss.config import Settings # https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/excel.html?highlight=xlsx#microsoft-excel from langchain_community.document_loaders import CSVLoader, PDFMinerLoader, TextLoader, UnstructuredExcelLoader, Docx2txtLoader @@ -108,15 +109,17 @@ # LLAMA 3 # use for Apple Silicon # MODEL_ID = "meta-llama/Meta-Llama-3-8B-Instruct" -MODEL_ID = "TheBloke/Llama-2-7B-32K-Instruct-GPTQ" -MODEL_BASENAME = "Llama-2-7B-32K-Instruct-GPTQ" +# MODEL_ID = "TheBloke/Llama-2-7B-32K-Instruct-GPTQ" +# MODEL_BASENAME = None#"Llama-2-7B-32K-Instruct-GPTQ" +# MODEL_BASENAME = "model.safetensors.awq" + # LLAMA 3 # use for NVIDIA GPUs # MODEL_ID = "unsloth/llama-3-8b-bnb-4bit" # MODEL_BASENAME = None -# MODEL_ID = "TheBloke/Mistral-7B-Instruct-v0.1-GGUF" -# MODEL_BASENAME = "mistral-7b-instruct-v0.1.Q8_0.gguf" +MODEL_ID = "TheBloke/Mistral-7B-Instruct-v0.1-GGUF" +MODEL_BASENAME = "mistral-7b-instruct-v0.1.Q8_0.gguf" # MODEL_ID = "TheBloke/Llama-2-70b-Chat-GGUF" # MODEL_BASENAME = "llama-2-70b-chat.Q4_K_M.gguf" diff --git a/run_localGPT.py b/run_localGPT.py index 8bbad87e..51b0c520 100644 --- a/run_localGPT.py +++ b/run_localGPT.py @@ -15,7 +15,7 @@ from utils import get_embeddings # from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler -from langchain_community.vectorstores import Chroma +from langchain_community.vectorstores import Chroma ,faiss from transformers import ( GenerationConfig, pipeline, @@ -132,7 +132,15 @@ def retrieval_qa_pipline(device_type, use_history, promptTemplate_type="llama"): logging.info(f"Loaded embeddings from {EMBEDDING_MODEL_NAME}") # load the vectorstore - db = Chroma(persist_directory=PERSIST_DIRECTORY, embedding_function=embeddings, client_settings=CHROMA_SETTINGS) + db = Chroma(persist_directory=PERSIST_DIRECTORY, + embedding_function=embeddings, + client_settings=CHROMA_SETTINGS) + + # db = faiss(persist_directory=PERSIST_DIRECTORY, + # embedding_function=embeddings, + # client_settings=CHROMA_SETTINGS) + + retriever = db.as_retriever() # get the prompt template and memory if set by the user. From 032ec5c202ac759caf1bd2ab70f39e69d934fb8d Mon Sep 17 00:00:00 2001 From: Mahmoudkhorshed-Queens Date: Sat, 18 May 2024 22:14:31 +0300 Subject: [PATCH 09/17] Run this mode using invoke --- Results/New Microsoft Word Document.docx | 0 Results/~$w Microsoft Word Document.docx | Bin 0 -> 162 bytes run_localGPT.py | 4 +++- 3 files changed, 3 insertions(+), 1 deletion(-) create mode 100644 Results/New Microsoft Word Document.docx create mode 100644 Results/~$w Microsoft Word Document.docx diff --git a/Results/New Microsoft Word Document.docx b/Results/New Microsoft Word Document.docx new file mode 100644 index 00000000..e69de29b diff --git a/Results/~$w Microsoft Word Document.docx b/Results/~$w Microsoft Word Document.docx new file mode 100644 index 0000000000000000000000000000000000000000..c0983e4cb89535aca49200d16eead24d327cd629 GIT binary patch literal 162 zcmd<^&d4t+&PYvRAO&zRcr#=$VZ6{Mkyc@06a?*ng9R* literal 0 HcmV?d00001 diff --git a/run_localGPT.py b/run_localGPT.py index 51b0c520..db276af5 100644 --- a/run_localGPT.py +++ b/run_localGPT.py @@ -264,7 +264,9 @@ def main(device_type, show_sources, use_history, model_type, save_qa): if query == "exit": break # Get the answer from the chain - res = qa(query) + # res = qa(query) + res = qa.invoke(query) + answer, docs = res["result"], res["source_documents"] # Print the result From db6da4731e955901514c4fdbee8c97b5d00363d0 Mon Sep 17 00:00:00 2001 From: Mahmoudkhorshed-Queens Date: Sun, 19 May 2024 00:26:39 +0300 Subject: [PATCH 10/17] Edit --- chroma.sqlite3 | Bin 0 -> 2846720 bytes constants.py | 2 +- ingest.py | 16 ++++++++-------- run_localGPT.py | 30 +++++++++++++++++++++++------- 4 files changed, 32 insertions(+), 16 deletions(-) create mode 100644 chroma.sqlite3 diff --git a/chroma.sqlite3 b/chroma.sqlite3 new file mode 100644 index 0000000000000000000000000000000000000000..7b78ee17c174249900e793c036f90af49cecab16 GIT binary patch literal 2846720 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(Default is cuda)", ) +def save_faiss_index(db, index_path, metadata_path): + faiss.write_index(db.index, index_path) + metadata = { + "index_to_docstore_id": db.index_to_docstore_id, + "docstore": db.docstore, + } + with open(metadata_path, "wb") as f: + pickle.dump(metadata, f) + +def load_faiss_index(index_path, metadata_path): + index = faiss.read_index(index_path) + with open(metadata_path, "rb") as f: + metadata = pickle.load(f) + docstore = metadata["docstore"] + index_to_docstore_id = metadata["index_to_docstore_id"] + db = FAISS(index=index, docstore=docstore, index_to_docstore_id=index_to_docstore_id) + return db + + + def main(device_type): # Load documents and split in chunks logging.info(f"Loading documents from {SOURCE_DIRECTORY}") @@ -173,12 +200,30 @@ def main(device_type): # persist_directory=PERSIST_DIRECTORY, # client_settings=CHROMA_SETTINGS, # ) + # if os.path.exists(INDEX_PATH) and os.path.exists(METADATA_PATH): + # db = load_faiss_index(INDEX_PATH, METADATA_PATH) + # logging.info("Loaded FAISS index and metadata from disk.") + # else: + + # d = embeddings.shape[1] + # index = faiss.IndexFlatL2(d) + # index.add(embeddings) + + # docstore = InMemoryDocstore() + # index_to_docstore_id = {i: doc["id"] for i, doc in enumerate(texts)} + + # db = FAISS(index=index, docstore=docstore, index_to_docstore_id=index_to_docstore_id) + + # save_faiss_index(db, INDEX_PATH, METADATA_PATH) + # logging.info("Saved FAISS index and metadata to disk.") + db = FAISS.from_documents( texts, embeddings, - # persist_directory=PERSIST_DIRECTORY, - # client_settings=CHROMA_SETTINGS, - ) + persist_directory=PERSIST_DIRECTORY, + client_settings=CHROMA_SETTINGS, + ) + if __name__ == "__main__": logging.basicConfig( diff --git a/run_localGPT.py b/run_localGPT.py index 03f04808..bf157731 100644 --- a/run_localGPT.py +++ b/run_localGPT.py @@ -12,6 +12,9 @@ from langchain_community.embeddings import OpenAIEmbeddings from langchain_community.docstore.in_memory import InMemoryDocstore import faiss +from langchain_community.vectorstores.faiss import FAISS +from langchain_community.embeddings import HuggingFaceEmbeddings +from langchain_community.docstore.in_memory import InMemoryDocstore callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]) @@ -141,18 +144,18 @@ def retrieval_qa_pipline(device_type, use_history, promptTemplate_type="llama"): # client_settings=CHROMA_SETTINGS) # Initialize the FAISS index - faiss_index = faiss.IndexFlatL2(embeddings) - # Initialize the docstore - docstore = InMemoryDocstore() - # Initialize the index_to_docstore_id - index_to_docstore_id = {} + # faiss_index = faiss.IndexFlatL2(embeddings) + # # Initialize the docstore + # docstore = InMemoryDocstore() + # # Initialize the index_to_docstore_id + # index_to_docstore_id = {} - db = FAISS( - embedding_function=embeddings, - index=faiss_index, - docstore=docstore, - index_to_docstore_id=index_to_docstore_id - ) + # db = FAISS( + # embedding_function=embeddings, + # index=faiss_index, + # docstore=docstore, + # index_to_docstore_id=index_to_docstore_id + # ) # # Add documents and their embeddings to the FAISS index and the docstore # for i, (text, embedding) in enumerate(zip(df['Text'].tolist(), embeddings)): @@ -160,7 +163,8 @@ def retrieval_qa_pipline(device_type, use_history, promptTemplate_type="llama"): retriever = db.as_retriever() # get the prompt template and memory if set by the user. - prompt, memory = get_prompt_template(promptTemplate_type=promptTemplate_type, history=use_history) + prompt, memory = get_prompt_template(promptTemplate_type=promptTemplate_type, + history=use_history) # load the llm pipeline llm = load_model(device_type, model_id=MODEL_ID, model_basename=MODEL_BASENAME, LOGGING=logging) From 3c6c222fa9b10da386798e4d334be10e9da20a9a Mon Sep 17 00:00:00 2001 From: Mahmoudkhorshed-Queens Date: Mon, 20 May 2024 16:09:53 +0300 Subject: [PATCH 12/17] Edit main device _type --- ingest.py | 7 ++++++- run_localGPT.py | 23 ++++++++++++++--------- 2 files changed, 20 insertions(+), 10 deletions(-) diff --git a/ingest.py b/ingest.py index a15ee5c8..834d71c5 100644 --- a/ingest.py +++ b/ingest.py @@ -170,6 +170,7 @@ def load_faiss_index(index_path, metadata_path): def main(device_type): + print(f"Running on device: {device_type}") # Load documents and split in chunks logging.info(f"Loading documents from {SOURCE_DIRECTORY}") documents = load_documents(SOURCE_DIRECTORY) @@ -224,9 +225,13 @@ def main(device_type): client_settings=CHROMA_SETTINGS, ) +import argparse if __name__ == "__main__": logging.basicConfig( format="%(asctime)s - %(levelname)s - %(filename)s:%(lineno)s - %(message)s", level=logging.INFO ) - main() + parser = argparse.ArgumentParser(description="Ingest script for localGPT") + parser.add_argument("--device_type", type=str, required=True, help="Device type (cpu or gpu)") + args = parser.parse_args() + main(args.device_type) diff --git a/run_localGPT.py b/run_localGPT.py index bf157731..4c2a848a 100644 --- a/run_localGPT.py +++ b/run_localGPT.py @@ -143,19 +143,24 @@ def retrieval_qa_pipline(device_type, use_history, promptTemplate_type="llama"): # embedding_function=embeddings, # client_settings=CHROMA_SETTINGS) + # print(embeddings) + # Initialize the FAISS index - # faiss_index = faiss.IndexFlatL2(embeddings) + faiss_index = faiss.IndexFlatL2(embeddings.embed()) + # # Initialize the docstore - # docstore = InMemoryDocstore() + docstore = InMemoryDocstore() # # Initialize the index_to_docstore_id - # index_to_docstore_id = {} + index_to_docstore_id = {} + # Add the embeddings to the index + faiss_index.add(embeddings) - # db = FAISS( - # embedding_function=embeddings, - # index=faiss_index, - # docstore=docstore, - # index_to_docstore_id=index_to_docstore_id - # ) + db = FAISS( + embedding_function=embeddings, + index=faiss_index, + docstore=docstore, + index_to_docstore_id=index_to_docstore_id + ) # # Add documents and their embeddings to the FAISS index and the docstore # for i, (text, embedding) in enumerate(zip(df['Text'].tolist(), embeddings)): From 3f95ae39d5ba9e2a87cdae0e92002099eeb1a530 Mon Sep 17 00:00:00 2001 From: Mahmoudkhorshed-Queens Date: Mon, 20 May 2024 18:23:56 +0300 Subject: [PATCH 13/17] Edits --- constants.py | 4 ++-- ingest.py | 50 ++++++++++++++++++++++++++++++++++++++++++++++--- run_localGPT.py | 16 +++++++++++----- 3 files changed, 60 insertions(+), 10 deletions(-) diff --git a/constants.py b/constants.py index dc36bc80..ddadd26c 100644 --- a/constants.py +++ b/constants.py @@ -62,8 +62,8 @@ } # Default Instructor Model -EMBEDDING_MODEL_NAME = "hkunlp/instructor-large" # Uses 1.5 GB of VRAM (High Accuracy with lower VRAM usage) - +# EMBEDDING_MODEL_NAME = "hkunlp/instructor-large" # Uses 1.5 GB of VRAM (High Accuracy with lower VRAM usage) +EMBEDDING_MODEL_NAME = 'TheBloke/Mistral-7B-Instruct-v0.1-GGUF' #### #### OTHER EMBEDDING MODEL OPTIONS #### diff --git a/ingest.py b/ingest.py index 834d71c5..c88de80c 100644 --- a/ingest.py +++ b/ingest.py @@ -3,6 +3,7 @@ from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor, as_completed import faiss import pickle +from transformers import AutoModel, AutoTokenizer import click import torch @@ -164,7 +165,9 @@ def load_faiss_index(index_path, metadata_path): metadata = pickle.load(f) docstore = metadata["docstore"] index_to_docstore_id = metadata["index_to_docstore_id"] - db = FAISS(index=index, docstore=docstore, index_to_docstore_id=index_to_docstore_id) + db = FAISS(index=index, + docstore=docstore, + index_to_docstore_id=index_to_docstore_id) return db @@ -189,6 +192,7 @@ def main(device_type): (2) Provides additional arguments for instructor and BGE models to improve results, pursuant to the instructions contained on their respective huggingface repository, project page or github repository. + """ embeddings = get_embeddings(device_type) @@ -217,12 +221,52 @@ def main(device_type): # save_faiss_index(db, INDEX_PATH, METADATA_PATH) # logging.info("Saved FAISS index and metadata to disk.") + + # Load the model and tokenizer + model_name = EMBEDDING_MODEL_NAME + tokenizer = AutoTokenizer.from_pretrained(model_name) + model = AutoModel.from_pretrained(model_name) + # Tokenize the input texts + inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt") + # Get the embeddings from the model + with torch.no_grad(): + outputs = model(**inputs) + # Extract the last hidden states (embeddings) + embeddings = outputs.last_hidden_state + # Pool the embeddings (e.g., mean pooling) + pooled_embeddings = embeddings.mean(dim=1) + # Convert the embeddings to a NumPy array + numpy_embeddings = pooled_embeddings.cpu().numpy() + + # Get the dimension of the vectors + vector_dimension = numpy_embeddings.shape[1] + + # Create the FAISS index + faiss_index = faiss.IndexFlatL2(vector_dimension) + print(faiss_index.is_trained) + # Add the embeddings to the index + faiss_index.add(numpy_embeddings) + # Save the index + faiss.write_index(faiss_index, index_file_path) + print(f"Index saved to {index_file_path}") + print(faiss_index.ntotal) + + # Define the directory and file name to save the index + persist_dir = PERSIST_DIRECTORY + index_file_path = os.path.join(persist_dir, 'faiss_index.index') + + # Load the index to verify + faiss_index_loaded = faiss.read_index(index_file_path) + print(f"Index loaded from {index_file_path}") + + # Verify the loaded index + print(f"Number of vectors in the loaded index: {faiss_index_loaded.ntotal}") db = FAISS.from_documents( texts, embeddings, - persist_directory=PERSIST_DIRECTORY, - client_settings=CHROMA_SETTINGS, + # persist_directory=PERSIST_DIRECTORY, + # client_settings=CHROMA_SETTINGS, ) import argparse diff --git a/run_localGPT.py b/run_localGPT.py index 4c2a848a..f5b530d0 100644 --- a/run_localGPT.py +++ b/run_localGPT.py @@ -8,6 +8,7 @@ from langchain.llms import HuggingFacePipeline from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler # for streaming response from langchain.callbacks.manager import CallbackManager +from transformers import AutoModel, AutoTokenizer from langchain_community.embeddings import OpenAIEmbeddings from langchain_community.docstore.in_memory import InMemoryDocstore @@ -142,11 +143,16 @@ def retrieval_qa_pipline(device_type, use_history, promptTemplate_type="llama"): # db = chroma(persist_directory=PERSIST_DIRECTORY, # embedding_function=embeddings, # client_settings=CHROMA_SETTINGS) - - # print(embeddings) + + + print(embeddings) + + + + # Initialize the FAISS index - faiss_index = faiss.IndexFlatL2(embeddings.embed()) + faiss_index = faiss.IndexFlatL2(768) # # Initialize the docstore docstore = InMemoryDocstore() @@ -158,8 +164,8 @@ def retrieval_qa_pipline(device_type, use_history, promptTemplate_type="llama"): db = FAISS( embedding_function=embeddings, index=faiss_index, - docstore=docstore, - index_to_docstore_id=index_to_docstore_id + # docstore=docstore, + # index_to_docstore_id=index_to_docstore_id ) # # Add documents and their embeddings to the FAISS index and the docstore From 9d5e4a5ce6800de0b9f1794544dac9d9f86a10f3 Mon Sep 17 00:00:00 2001 From: Mahmoudkhorshed-Queens Date: Mon, 20 May 2024 22:22:44 +0300 Subject: [PATCH 14/17] Edit --- Results/New Microsoft Word Document.docx | Bin 18028 -> 28343 bytes constants.py | 4 +- ingest.py | 61 ++++++++++++----------- run_localGPT.py | 38 ++++++++------ 4 files changed, 56 insertions(+), 47 deletions(-) diff --git a/Results/New Microsoft Word Document.docx b/Results/New Microsoft Word Document.docx index 5bbe7a10426eb58437623710aebd12f58eb70520..16480725b20e7acb7bd97f3710bb95be6e87bde7 100644 GIT binary patch delta 23263 zcmV)UK(N2;i~+aZ0kFXae~M^$wg)@_03hxG01*HH0C#V4WG`fIV|8t1ZgehqZEWm) z+j8SZlIZ)1IRAj?h~49HsL`kbs!-@%_XMtgCX+j?ag}j-K%9? zuiw6TQ_YsiDz5ZZIxCB+xT^J8v3gTnU8S?+%}r6x-%x`X^0#$a%#x~t`TRJ)j;mMQ 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