Rename app.py to app_RAG.py

app.py

@@ -1,422 +0,0 @@
-# import torch
-# import transformers
-# from transformers import RagRetriever, RagSequenceForGeneration, AutoTokenizer, AutoModelForCausalLM
-# import gradio as gr
-
-# device = 'cuda' if torch.cuda.is_available() else 'cpu'
-
-
-# dataset_path = "./5k_index_data/my_knowledge_dataset"
-# index_path = "./5k_index_data/my_knowledge_dataset_hnsw_index.faiss"
-
-# tokenizer = AutoTokenizer.from_pretrained("facebook/rag-sequence-nq")
-# retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq", index_name="custom",
-#                                             passages_path = dataset_path,
-#                                             index_path = index_path,
-#                                             n_docs = 5)
-# rag_model = RagSequenceForGeneration.from_pretrained('facebook/rag-sequence-nq', retriever=retriever)
-# rag_model.retriever.init_retrieval()
-# rag_model.to(device)
-# model = AutoModelForCausalLM.from_pretrained('HuggingFaceH4/zephyr-7b-beta',
-#                               device_map = 'auto',
-#                               torch_dtype = torch.bfloat16,
-#                              )
-
-
-
-# def strip_title(title):
-#     if title.startswith('"'):
-#         title = title[1:]
-#     if title.endswith('"'):
-#         title = title[:-1]
-    
-#     return title
-
-# # getting the correct format to input in gemma model
-# def input_format(query, context):
-#     sys_instruction = f'Context:\n {context} \n Given the following information, generate answer to the question. Provide links in the answer from the information to increase credebility.'
-#     message = f'Question: {query}'
-
-#     return f'<bos><start_of_turn>\n{sys_instruction}' + f' {message}<end_of_turn>\n'
-
-# # retrieving and generating  answer in one call
-# def retrieved_info(query, rag_model = rag_model, generating_model = model):
-#     # Tokenize Query
-#     retriever_input_ids = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
-#         [query],
-#         return_tensors = 'pt',
-#         padding = True,
-#         truncation = True,
-#     )['input_ids'].to(device)
-
-#     # Retrieve Documents
-#     question_encoder_output = rag_model.rag.question_encoder(retriever_input_ids)
-#     question_encoder_pool_output = question_encoder_output[0]
-
-#     result = rag_model.retriever(
-#         retriever_input_ids,
-#         question_encoder_pool_output.cpu().detach().to(torch.float32).numpy(),
-#         prefix = rag_model.rag.generator.config.prefix,
-#         n_docs = rag_model.config.n_docs,
-#         return_tensors = 'pt',
-#     )
-
-#     # Preparing query and retrieved docs for model
-#     all_docs = rag_model.retriever.index.get_doc_dicts(result.doc_ids)
-#     retrieved_context = []
-#     for docs in all_docs:
-#         titles = [strip_title(title) for title in docs['title']]
-#         texts = docs['text']
-#         for title, text in zip(titles, texts):
-#             retrieved_context.append(f'{title}: {text}')
-
-#     generation_model_input = input_format(query, retrieved_context)
-
-#     # Generating answer using gemma model
-#     tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
-#     input_ids = tokenizer(generation_model_input, return_tensors='pt').to(device)
-#     output = generating_model.generate(input_ids, max_new_tokens = 256)
-    
-#     return tokenizer.decode(output[0])
-
-
-
-
-
-
-# def respond(
-#     message,
-#     history: list[tuple[str, str]],
-#     system_message,
-#     max_tokens ,
-#     temperature,
-#     top_p,
-# ):
-#     if message:  # If there's a user query
-#         response = retrieved_info(message)  # Get the answer from your local FAISS and Q&A model
-#         return response
-
-#     # In case no message, return an empty string
-#     return ""
-
-
-
-# """
-# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-# """
-# # Custom title and description
-# title = "🧠 Welcome to Your AI Knowledge Assistant"
-# description = """
-# Hi!! I am your loyal assistant. My functionality is based on the RAG model. I retrieve relevant information and provide answers based on that. Ask me any questions, and let me assist you.
-# My capabilities are limited because I am still in the development phase. I will do my best to assist you. SOOO LET'S BEGGINNNN......
-# """
-
-# demo = gr.ChatInterface(
-#     respond,
-#     type = 'messages',
-#     additional_inputs=[
-#         gr.Textbox(value="You are a helpful and friendly assistant.", label="System message"),
-#         gr.Slider(minimum=1, maximum=2048, value=256, step=1, label="Max new tokens"),
-#         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-#         gr.Slider(
-#             minimum=0.1,
-#             maximum=1.0,
-#             value=0.95,
-#             step=0.05,
-#             label="Top-p (nucleus sampling)",
-#         ),
-#     ],
-#     title=title,
-#     description=description,
-#     textbox=gr.Textbox(placeholder=["'What is the future of AI?' or 'App Development'"]),
-#     examples=[["✨Future of AI"], ["📱App Development"]],
-#     example_icons=["🤖", "📱"],
-#     theme="compact",
-#     submit_btn = True,
-# )
-
-
-# if __name__ == "__main__":
-#     demo.launch(share = True )
-
-# import torch
-# import transformers
-# from transformers import RagRetriever, RagSequenceForGeneration, AutoTokenizer, AutoModelForCausalLM
-# import gradio as gr
-
-# device = 'cuda' if torch.cuda.is_available() else 'cpu'
-
-
-# dataset_path = "./5k_index_data/my_knowledge_dataset"
-# index_path = "./5k_index_data/my_knowledge_dataset_hnsw_index.faiss"
-
-# tokenizer = AutoTokenizer.from_pretrained("facebook/rag-sequence-nq")
-# retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq", index_name="custom",
-#                                             passages_path = dataset_path,
-#                                             index_path = index_path,
-#                                             n_docs = 5)
-# rag_model = RagSequenceForGeneration.from_pretrained('facebook/rag-sequence-nq', retriever=retriever)
-# rag_model.retriever.init_retrieval()
-# rag_model.to(device)
-# model = AutoModelForCausalLM.from_pretrained('HuggingFaceH4/zephyr-7b-beta',
-#                               device_map = 'auto',
-#                               torch_dtype = torch.bfloat16,
-#                              )
-
-
-
-# def strip_title(title):
-#     if title.startswith('"'):
-#         title = title[1:]
-#     if title.endswith('"'):
-#         title = title[:-1]
-    
-#     return title
-
-# # getting the correct format to input in gemma model
-# def input_format(query, context):
-# #     sys_instruction = f'Context:\n {context} \n Given the following information, generate answer to the question. Provide links in the answer from the information to increase credebility.'
-# #     message = f'Question: {query}'
-
-# #     return f'<bos><start_of_turn>\n{sys_instruction}' + f' {message}<end_of_turn>\n'
-#     return [
-#         {
-#             "role": "system", "content": f'Context:\n {context} \n Given the following information, generate answer to the question. Provide links in the answer from the information to increase credebility.' },
-        
-#         {
-#             "role": "user", "content": f"{query}"},
-#     ]
-
-# # retrieving and generating  answer in one call
-# def retrieved_info(query, rag_model = rag_model, generating_model = model):
-#     # Tokenize Query
-#     retriever_input_ids = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
-#         [query],
-#         return_tensors = 'pt',
-#         padding = True,
-#         truncation = True,
-#     )['input_ids'].to(device)
-
-#     # Retrieve Documents
-#     question_encoder_output = rag_model.rag.question_encoder(retriever_input_ids)
-#     question_encoder_pool_output = question_encoder_output[0]
-
-#     result = rag_model.retriever(
-#         retriever_input_ids,
-#         question_encoder_pool_output.cpu().detach().to(torch.float32).numpy(),
-#         prefix = rag_model.rag.generator.config.prefix,
-#         n_docs = rag_model.config.n_docs,
-#         return_tensors = 'pt',
-#     )
-
-#     # Preparing query and retrieved docs for model
-#     all_docs = rag_model.retriever.index.get_doc_dicts(result.doc_ids)
-#     retrieved_context = []
-#     for docs in all_docs:
-#         titles = [strip_title(title) for title in docs['title']]
-#         texts = docs['text']
-#         for title, text in zip(titles, texts):
-#             retrieved_context.append(f'{title}: {text}')
-#         print(retrieved_context)
-
-#     generation_model_input = input_format(query, retrieved_context[0])
-
-#     # Generating answer using gemma model
-#     tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
-#     input_ids = tokenizer(generation_model_input, return_tensors='pt')['input_ids'].to(device)
-#     output = generating_model.generate(input_ids, max_new_tokens = 256)
-    
-#     return tokenizer.decode(output[0])
-
-
-# def respond(
-#     message,
-#     history: list[tuple[str, str]],
-#     system_message,
-#     max_tokens ,
-#     temperature,
-#     top_p,
-# ):
-#     if message:  # If there's a user query
-#         response = retrieved_info(message)  # Get the answer from your local FAISS and Q&A model
-#         return response
-
-#     # In case no message, return an empty string
-#     return ""
-
-
-
-# """
-# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-# """
-# # Custom title and description
-# title = "🧠 Welcome to Your AI Knowledge Assistant"
-# description = """
-# Hi!! I am your loyal assistant. My functionality is based on the RAG model. I retrieve relevant information and provide answers based on that. Ask me any questions, and let me assist you.
-# My capabilities are limited because I am still in the development phase. I will do my best to assist you. SOOO LET'S BEGGINNNN......
-# """
-
-# demo = gr.ChatInterface(
-#     respond,
-#     type = 'messages',
-#     additional_inputs=[
-#         gr.Textbox(value="You are a helpful and friendly assistant.", label="System message"),
-#         gr.Slider(minimum=1, maximum=2048, value=256, step=1, label="Max new tokens"),
-#         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-#         gr.Slider(
-#             minimum=0.1,
-#             maximum=1.0,
-#             value=0.95,
-#             step=0.05,
-#             label="Top-p (nucleus sampling)",
-#         ),
-#     ],
-#     title=title,
-#     description=description,
-#     textbox=gr.Textbox(placeholder=["'What is the future of AI?' or 'App Development'"]),
-#     examples=[["✨Future of AI"], ["📱App Development"]],
-#     #example_icons=["🤖", "📱"],
-#     theme="compact",
-#     submit_btn = True,
-# )
-
-
-# if __name__ == "__main__":
-#     demo.launch(share = True,
-#                 show_error = True)
-
-
-import torch
-import transformers
-from transformers import RagRetriever, RagSequenceForGeneration, AutoTokenizer, AutoModelForCausalLM, pipeline
-import gradio as gr
-
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-
-
-dataset_path = "./5k_index_data/my_knowledge_dataset"
-index_path = "./5k_index_data/my_knowledge_dataset_hnsw_index.faiss"
-
-retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq", index_name="custom",
-                                            passages_path = dataset_path,
-                                            index_path = index_path,
-                                            n_docs = 5)
-rag_model = RagSequenceForGeneration.from_pretrained('facebook/rag-sequence-nq', retriever=retriever)
-rag_model.retriever.init_retrieval()
-rag_model.to(device)
-
-pipe = pipeline(
-"text-generation",
-model="google/gemma-2-2b-it",
-model_kwargs={"torch_dtype": torch.bfloat16},
-device=device,  
-)
-
-def strip_title(title):
-    if title.startswith('"'):
-        title = title[1:]
-    if title.endswith('"'):
-        title = title[:-1]
-    
-    return title
-
-
-def retrieved_info(query, rag_model = rag_model):
-    # Tokenize Query
-    retriever_input_ids = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
-        [query],
-        return_tensors = 'pt',
-        padding = True,
-        truncation = True,
-    )['input_ids'].to(device)
-
-    # Retrieve Documents
-    question_encoder_output = rag_model.rag.question_encoder(retriever_input_ids)
-    question_encoder_pool_output = question_encoder_output[0]
-
-    result = rag_model.retriever(
-        retriever_input_ids,
-        question_encoder_pool_output.cpu().detach().to(torch.float32).numpy(),
-        prefix = rag_model.rag.generator.config.prefix,
-        n_docs = rag_model.config.n_docs,
-        return_tensors = 'pt',
-    )
-
-    # Preparing query and retrieved docs for model
-    all_docs = rag_model.retriever.index.get_doc_dicts(result.doc_ids)
-    retrieved_context = []
-    for docs in all_docs:
-        titles = [strip_title(title) for title in docs['title']]
-        texts = docs['text']
-        for title, text in zip(titles, texts):
-            retrieved_context.append(f'{title}: {text}')
-
-    
-    # Generating answer using gemma model
-
-    messages = [
-        {"role": "user", "content": f"{query}"},
-        {"role": "system" , "content": f"Context: {retrieved_context}. Use the links and information from the Context to answer the query in brief. Provide links in the answer."}
-    ]
-
-    outputs = pipe(messages, max_new_tokens=256)
-    assistant_response = outputs[0]["generated_text"][-1]["content"].strip()
-    
-    return assistant_response
-
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens ,
-    temperature,
-    top_p,
-):
-    if message:  # If there's a user query
-        response = retrieved_info(message)  # Get the answer from your local FAISS and Q&A model
-        return response
-
-    # In case no message, return an empty string
-    return ""
-
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-# Custom title and description
-title = "🧠 Welcome to Your AI Knowledge Assistant"
-description = """
-HI!!, I am your loyal assistant, y functionality is based on RAG model, I retrieves relevant information and provide answers based on that. Ask me any question, and let me assist you.
-My capabilities are limited because I am still in development phase. I will do my best to assist you. SOOO LET'S BEGGINNNN......
-"""
-
-
-demo = gr.ChatInterface(
-    respond,
-    type = 'messages',
-    additional_inputs=[
-        gr.Textbox(value="You are a helpful and friendly assistant.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-    title=title,
-    description=description,
-    submit_btn = True,
-    textbox=gr.Textbox(placeholder=["'What is the future of AI?' or 'App Development'"]),
-    examples=[["Future of AI"], ["App Development"]],
-    theme="compact",
-)
-
-
-if __name__ == "__main__":
-    demo.launch(share = True )

app_RAG.py

@@ -0,0 +1,129 @@
+import torch
+import transformers
+from transformers import RagRetriever, RagSequenceForGeneration, AutoModelForCausalLM, pipeline
+import gradio as gr
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+dataset_path = "./5k_index_data/my_knowledge_dataset"
+index_path = "./5k_index_data/my_knowledge_dataset_hnsw_index.faiss"
+
+retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq", index_name="custom",
+                                            passages_path = dataset_path,
+                                            index_path = index_path,
+                                            n_docs = 5)
+rag_model = RagSequenceForGeneration.from_pretrained('facebook/rag-sequence-nq', retriever=retriever)
+rag_model.retriever.init_retrieval()
+rag_model.to(device)
+
+pipe = pipeline(
+"text-generation",
+model="google/gemma-2-2b-it",
+model_kwargs={"torch_dtype": torch.bfloat16},
+device=device,  
+)
+
+def strip_title(title):
+    if title.startswith('"'):
+        title = title[1:]
+    if title.endswith('"'):
+        title = title[:-1]
+    
+    return title
+    
+
+def retrieved_info(query, rag_model = rag_model):
+    # Tokenize Query
+    retriever_input_ids = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
+        [query],
+        return_tensors = 'pt',
+        padding = True,
+        truncation = True,
+    )['input_ids'].to(device)
+
+    # Retrieve Documents
+    question_encoder_output = rag_model.rag.question_encoder(retriever_input_ids)
+    question_encoder_pool_output = question_encoder_output[0]
+
+    result = rag_model.retriever(
+        retriever_input_ids,
+        question_encoder_pool_output.cpu().detach().to(torch.float32).numpy(),
+        prefix = rag_model.rag.generator.config.prefix,
+        n_docs = rag_model.config.n_docs,
+        return_tensors = 'pt',
+    )
+
+    # Preparing query and retrieved docs for model
+    all_docs = rag_model.retriever.index.get_doc_dicts(result.doc_ids)
+    retrieved_context = []
+    for docs in all_docs:
+        titles = [strip_title(title) for title in docs['title']]
+        texts = docs['text']
+        for title, text in zip(titles, texts):
+            retrieved_context.append(f'{title}: {text}')
+
+    
+    # Generating answer using gemma model
+
+    messages = [
+        {"role": "user", "content": f"{query}"},
+        {"role": "system" , "content": f"Context: {retrieved_context}. Use the links and information from the Context to answer the query in brief. Provide links in the answer."}
+    ]
+
+    outputs = pipe(messages, max_new_tokens=256)
+    assistant_response = outputs[0]["generated_text"][-1]["content"].strip()
+    
+    return assistant_response
+
+
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens ,
+    temperature,
+    top_p,
+):
+    if message:  # If there's a user query
+        response = retrieved_info(message)  # Get the answer from your local FAISS and Q&A model
+        return response
+
+    # In case no message, return an empty string
+    return ""
+
+
+"""
+For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+"""
+# Custom title and description
+title = "🧠 Welcome to Your AI Knowledge Assistant"
+description = """
+HI!!, I am your loyal assistant, y functionality is based on RAG model, I retrieves relevant information and provide answers based on that. Ask me any question, and let me assist you.
+My capabilities are limited because I am still in development phase. I will do my best to assist you. SOOO LET'S BEGGINNNN......
+"""
+
+demo = gr.ChatInterface(
+    respond,
+    type = 'messages',
+    additional_inputs=[
+        gr.Textbox(value="You are a helpful and friendly assistant.", label="System message"),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.95,
+            step=0.05,
+            label="Top-p (nucleus sampling)",
+        ),
+    ],
+    title=title,
+    description=description,
+    submit_btn = True,
+    textbox=gr.Textbox(placeholder=["'What is the future of AI?' or 'App Development'"]),
+    examples=[["Future of AI"], ["App Development"]],
+    theme="compact",
+)
+
+if __name__ == "__main__":
+    demo.launch(share = True )