test_llm.py

from langchain import PromptTemplate
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.document_loaders import PyPDFLoader, DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import DirectoryLoader,TextLoader
from langchain.llms import CTransformers
import sys
#**Step 1: Load the PDF File from Data Path****
# loader=DirectoryLoader('D:/Projects/Traf_LLM/data_traf/',
#                        glob= "LC.txt",
#                        loader_cls=PyPDFLoader)
pdf_file_path =r"D:\Projects\Traf_LLM\data_jsw\LC.pdf"
loader=PyPDFLoader(pdf_file_path)
documents=loader.load()


#print(documents)

#***Step 2: Split Text into Chunks***

text_splitter=RecursiveCharacterTextSplitter(
                                             chunk_size=500,
                                             chunk_overlap=50)


text_chunks=text_splitter.split_documents(documents)

print(len(text_chunks))
#**Step 3: Load the Embedding Model***


embeddings=HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2', model_kwargs={'device':'cpu'})


#**Step 4: Convert the Text Chunks into Embeddings and Create a FAISS Vector Store***
vector_store=FAISS.from_documents(text_chunks, embeddings)


##**Step 5: Find the Top 3 Answers for the Query***

query="Who is Drawee?"
docs = vector_store.similarity_search(query)

#print(docs)
llm=CTransformers(model="D:/Projects/Traf_LLM/models/llama-2-7b-chat.ggmlv3.q4_0.bin",
                  model_type="llama",
                  config={'max_new_tokens':128,
                          'temperature':0.01})


template="""Use the following pieces of information to answer the user's question.
If you dont know the answer just say you know, don't try to make up an answer.

Context:{context}
Question:{question}

Only return the helpful answer below and nothing else
Helpful answer
"""

qa_prompt=PromptTemplate(template=template, input_variables=['context', 'question'])

#start=timeit.default_timer()

chain = RetrievalQA.from_chain_type(llm=llm,
                                   chain_type='stuff',
                                   retriever=vector_store.as_retriever(search_kwargs={'k': 2}),
                                   return_source_documents=True,
                                   chain_type_kwargs={'prompt': qa_prompt})

#response=chain({'query': "YOLOv7 is trained on which dataset"})

#end=timeit.default_timer()
#print(f"Here is the complete Response: {response}")

#print(f"Here is the final answer: {response['result']}")

#print(f"Time to generate response: {end-start}")

while True:
    user_input=input(f"prompt:")
    if query=='exit':
        print('Exiting')
        sys.exit()
    if query=='':
        continue
    result=chain({'query':user_input})
    print(f"Answer:{result['result']}")