HuggingFaceLLM

.gitignore

@@ -2,3 +2,4 @@
 __pycache__/
 appcompleta.py
 interface.py
+backend2.py

backend2.py

@@ -1,109 +0,0 @@
-import os
-import logging
-from concurrent.futures import ThreadPoolExecutor
-from pypdf import PdfReader
-from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import FAISS
-#from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain_huggingface import HuggingFaceEmbeddings
-import time
-import torch
-from dotenv import load_dotenv
-
-logging.basicConfig(
-    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
-    level=logging.DEBUG
-)
-logger = logging.getLogger(__name__)
-logging.getLogger('matplotlib').setLevel(logging.WARNING)  # Suppress Matplotlib debug messages
-
-load_dotenv()
-
-logger.debug("Environment variables loaded.")
-
-def load_single_document(filepath):
-    if filepath.endswith('.pdf'):
-        with open(filepath, 'rb') as file:
-            pdf_reader = PdfReader(file)
-            text = " ".join([page.extract_text() for page in pdf_reader.pages])
-    elif filepath.endswith('.txt'):
-        with open(filepath, 'r', encoding='utf-8') as file:
-            text = file.read()
-    else:
-        logger.warning("Unsupported file type: %s", filepath)
-        return {"content": "", "source": filepath}
-    
-    return {"content": text, "source": filepath}
-
-def load_documents(directory):
-    logger.debug("Loading documents from directory: %s", directory)
-    start_time = time.time()
-    filepaths = [os.path.join(directory, filename) for filename in os.listdir(directory) if filename.endswith('.pdf') or filename.endswith('.txt')]
-    
-    if not filepaths:
-        logger.error("No documents found in the directory.")
-    else:
-        logger.debug("Found %d documents", len(filepaths))
-    
-    documents = []
-    with ThreadPoolExecutor() as executor:
-        documents = list(executor.map(load_single_document, filepaths))
-    
-    end_time = time.time()
-    logger.debug("Loaded %d documents in %.2f seconds.", len(documents), end_time - start_time)
-    return documents
-
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-def prepare_documents(documents):
-    logger.debug("Preparing documents for embedding.")
-    start_time = time.time()
-    
-    if not documents:
-        logger.error("No documents to prepare.")
-        return None
-    
-    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-    texts = text_splitter.create_documents([doc["content"] for doc in documents], metadatas=[{"source": os.path.basename(doc["source"])} for doc in documents])  
-    
-    if not texts:
-        logger.error("No texts to embed after splitting.")
-        return None
-    
-    logger.debug(f"Created {len(texts)} text chunks.")
-    
-    modelPath = "sentence-transformers/all-MiniLM-l6-v2"
-    model_kwargs = {'device': device}
-    encode_kwargs = {'normalize_embeddings': False}
-    embeddings = HuggingFaceEmbeddings(model_name=modelPath, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs)
-
-    try:
-        db = FAISS.from_documents(texts, embeddings)
-        logger.debug("FAISS index created successfully.")
-    except Exception as e:
-        logger.error(f"Error creating FAISS index: {e}")
-        return None
-    
-    end_time = time.time()
-    logger.debug(f"Documents prepared in {end_time - start_time:.2f} seconds.")
-    return db
-
-def get_context_sources(question, db):
-    start_time = time.time()
-
-    if db is None:
-        logger.error("Database is None. Cannot perform similarity search.")
-        return "", ""
-
-    try:
-        docs = db.similarity_search(question, k=3)
-        context = " ".join([doc.page_content for doc in docs])
-        sources = ", ".join(set([doc.metadata['source'] for doc in docs]))
-    except Exception as e:
-        logger.error(f"Error during similarity search: {e}")
-        return "", ""
-    
-    end_time = time.time()
-    logger.debug(f"Similarity search done in {end_time - start_time:.2f} seconds.")
-
-    return context, sources

requirements.txt

@@ -1,4 +1,4 @@
-python-dotenv==0.21.0
+python-dotenv
 llama-index
 llama-index-embeddings-huggingface
 llama-index-llms-huggingface
@@ -7,19 +7,16 @@ sentence-transformers==2.2.2
 llama-index-readers-web
 llama-index-readers-file
 
-accelerate==0.33.0
-gradio==4.39.0
-spaces==0.29.2
-torch==2.2.0
-transformers==4.43.3
-llama-cpp-agent>=0.2.25
+accelerate
+gradio
+spaces
+torch
+transformers
+llama-cpp-agent
 setuptools
-faiss-cpu
 
 pydantic
 ipython
 #keras
 #keras-nlp
 #tensorflow
-langchain-community
-langchain-huggingface