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requirements.txt

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+langchain==0.0.225
+ctransformers==0.2.5
+sentence-transformers==2.2.2

test_llm.py

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+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']}")