Spaces:
Sleeping
Sleeping
File size: 2,707 Bytes
a8efc23 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
import time
import psutil
import glob
import PyPDF2
#import chromadb
from transformers import (
AutoTokenizer,
AutoModelForSeq2SeqLM,
AutoTokenizer, AutoModelForCausalLM,
pipeline
)
from transformers import LlamaTokenizer, LlamaForCausalLM,BitsAndBytesConfig
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import HuggingFaceEmbeddings
from llama_cpp import Llama
def RAG_Chain(pdf_file,question,llama_model):
model_path = "/home/mona/Downloads/Pubmed_model_GGUF"
pdf_reader = PyPDF2.PdfReader(pdf_file)
doc = ""
for page_num in range(len(pdf_reader.pages) ):
page = pdf_reader.pages[page_num]
doc += page.extract_text()
# Check if any documents were loaded
if not doc:
raise ValueError("No documents found. Please check the PDF directory path.")
# Split the loaded documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000, chunk_overlap=200)
splits = text_splitter.split_text(doc)
# Create HuggingFace embeddings and vector store
embedding_model_name = 'sentence-transformers/all-MiniLM-L6-v2' # Efficient model suitable for most tasks
embeddings = HuggingFaceEmbeddings(model_name=embedding_model_name)
__import__('pysqlite3')
import sys
sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
import chromadb
chromadb.api.client.SharedSystemClient.clear_system_cache()
vectorstore = Chroma.from_texts(texts=splits, embedding=embeddings)
# Define the retriever using Chroma
retriever = vectorstore.as_retriever(search_kwargs={"k": 4})
# Retrieve relevant documents
retrieved_docs = retriever.get_relevant_documents(question)
if not retrieved_docs:
return "No relevant information found in the documents."
# Format the context
formatted_context = "\n\n".join(doc.page_content for doc in retrieved_docs)
# Prepare the prompt for the LLM
formatted_prompt = (
f"Answer the question based on the context below.\n\n"
f"Context:\n{formatted_context}\n\nQuestion: {question}\n\nAnswer:"
)
answer = llama_model(formatted_prompt)
return answer["choices"][0]["text"]
# Instantiate the Llama model using the gguf file
'''
llama_model = Llama(
model_path,
n_ctx=2048, # Context length
#n_threads=8, # Number of CPU threads to use
temperature=0.7, # Sampling temperature
n_gpu_layers=2
)
'''
# Generate the answer
|