Upload texttrail.py

texttrail.py

@@ -0,0 +1,388 @@
+# -*- coding: utf-8 -*-
+"""TextTrail.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/19FMO4hPcBUvq4whuvATRRXDxJ4ONqElV
+"""
+
+! nvidia-smi -L
+
+# Commented out IPython magic to ensure Python compatibility.
+# %%time
+# 
+# from IPython.display import clear_output
+# 
+# ! pip install sentence_transformers==2.2.2
+# 
+# ! pip install -qq -U langchain-community
+# ! pip install -U langchain-huggingface
+# ! pip install -qq -U tiktoken
+# ! pip install -qq -U pypdf
+# ! pip install -qq -U faiss-gpu
+# ! pip install -qq -U InstructorEmbedding
+# 
+# ! pip install -qq -U transformers
+# ! pip install -qq -U accelerate
+# ! pip install -qq -U bitsandbytes
+# 
+# clear_output()
+
+# Commented out IPython magic to ensure Python compatibility.
+# %%time
+# 
+# import warnings
+# warnings.filterwarnings("ignore")
+# 
+# import os
+# import glob
+# import textwrap
+# import time
+# 
+# import langchain
+# 
+# ### loaders
+# from langchain.document_loaders import PyPDFLoader, DirectoryLoader
+# 
+# ### splits
+# from langchain.text_splitter import RecursiveCharacterTextSplitter
+# 
+# ### prompts
+# from langchain import PromptTemplate, LLMChain
+# 
+# ### vector stores
+# from langchain.vectorstores import FAISS
+# 
+# ### models
+# from langchain.llms import HuggingFacePipeline
+# from langchain.embeddings import HuggingFaceInstructEmbeddings
+# 
+# ### retrievers
+# from langchain.chains import RetrievalQA
+# 
+# import torch
+# import transformers
+# from transformers import (
+#     AutoTokenizer, AutoModelForCausalLM,
+#     BitsAndBytesConfig,
+#     pipeline
+# )
+# 
+# clear_output()
+
+sorted(glob.glob('/content/anatomy_vol_*'))
+
+class CFG:
+    # LLMs
+    model_name = 'llama2-13b-chat' # wizardlm, llama2-7b-chat, llama2-13b-chat, mistral-7B
+    temperature = 0
+    top_p = 0.95
+    repetition_penalty = 1.15
+
+    # splitting
+    split_chunk_size = 800
+    split_overlap = 0
+
+    # embeddings
+    embeddings_model_repo = 'sentence-transformers/all-MiniLM-L6-v2'
+
+    # similar passages
+    k = 6
+
+    # paths
+    PDFs_path = '/content/'
+    Embeddings_path =  '/content/faiss-hp-sentence-transformers'
+    Output_folder = './rag-vectordb'
+
+def get_model(model = CFG.model_name):
+
+    print('\nDownloading model: ', model, '\n\n')
+
+    if model == 'wizardlm':
+        model_repo = 'TheBloke/wizardLM-7B-HF'
+
+        tokenizer = AutoTokenizer.from_pretrained(model_repo)
+
+        bnb_config = BitsAndBytesConfig(
+            load_in_4bit = True,
+            bnb_4bit_quant_type = "nf4",
+            bnb_4bit_compute_dtype = torch.float16,
+            bnb_4bit_use_double_quant = True,
+        )
+
+        model = AutoModelForCausalLM.from_pretrained(
+            model_repo,
+            quantization_config = bnb_config,
+            device_map = 'auto',
+            low_cpu_mem_usage = True
+        )
+
+        max_len = 1024
+
+    elif model == 'llama2-7b-chat':
+        model_repo = 'daryl149/llama-2-7b-chat-hf'
+
+        tokenizer = AutoTokenizer.from_pretrained(model_repo, use_fast=True)
+
+        bnb_config = BitsAndBytesConfig(
+            load_in_4bit = True,
+            bnb_4bit_quant_type = "nf4",
+            bnb_4bit_compute_dtype = torch.float16,
+            bnb_4bit_use_double_quant = True,
+        )
+
+        model = AutoModelForCausalLM.from_pretrained(
+            model_repo,
+            quantization_config = bnb_config,
+            device_map = 'auto',
+            low_cpu_mem_usage = True,
+            trust_remote_code = True
+        )
+
+        max_len = 2048
+
+    elif model == 'llama2-13b-chat':
+        model_repo = 'daryl149/llama-2-13b-chat-hf'
+
+        tokenizer = AutoTokenizer.from_pretrained(model_repo, use_fast=True)
+
+        bnb_config = BitsAndBytesConfig(
+            load_in_4bit = True,
+            bnb_4bit_quant_type = "nf4",
+            bnb_4bit_compute_dtype = torch.float16,
+            bnb_4bit_use_double_quant = True,
+        )
+
+        model = AutoModelForCausalLM.from_pretrained(
+            model_repo,
+            quantization_config = bnb_config,
+
+            low_cpu_mem_usage = True,
+            trust_remote_code = True
+        )
+
+        max_len = 2048  #8192
+        truncation=True,  # Explicitly enable truncation
+        padding="max_len"  # Optional: pad to max_length
+
+    elif model == 'mistral-7B':
+        model_repo = 'mistralai/Mistral-7B-v0.1'
+
+        tokenizer = AutoTokenizer.from_pretrained(model_repo)
+
+        bnb_config = BitsAndBytesConfig(
+            load_in_4bit = True,
+            bnb_4bit_quant_type = "nf4",
+            bnb_4bit_compute_dtype = torch.float16,
+            bnb_4bit_use_double_quant = True,
+        )
+
+        model = AutoModelForCausalLM.from_pretrained(
+            model_repo,
+            quantization_config = bnb_config,
+            device_map = 'auto',
+            low_cpu_mem_usage = True,
+        )
+
+        max_len = 1024
+
+    else:
+        print("Not implemented model (tokenizer and backbone)")
+
+    return tokenizer, model, max_len
+
+print(torch.cuda.is_available())
+print(torch.cuda.device_count())
+
+# Commented out IPython magic to ensure Python compatibility.
+# %%time
+# 
+# tokenizer, model, max_len = get_model(model = CFG.model_name)
+# 
+# clear_output()
+
+model.eval()
+
+### check how Accelerate split the model across the available devices (GPUs)
+model.hf_device_map
+
+### hugging face pipeline
+pipe = pipeline(
+    task = "text-generation",
+    model = model,
+    tokenizer = tokenizer,
+    pad_token_id = tokenizer.eos_token_id,
+#     do_sample = True,
+    max_length = max_len,
+    temperature = CFG.temperature,
+    top_p = CFG.top_p,
+    repetition_penalty = CFG.repetition_penalty
+)
+
+### langchain pipeline
+llm = HuggingFacePipeline(pipeline = pipe)
+
+llm
+
+query = "what are the structural organization of a human body"
+llm.invoke(query)
+
+"""Langchain"""
+
+CFG.model_name
+
+"""Loader"""
+
+# Commented out IPython magic to ensure Python compatibility.
+# %%time
+# 
+# loader = DirectoryLoader(
+#     CFG.PDFs_path,
+#     glob="./*.pdf",
+#     loader_cls=PyPDFLoader,
+#     show_progress=True,
+#     use_multithreading=True
+# )
+# 
+# documents = loader.load()
+
+print(f'We have {len(documents)} pages in total')
+
+documents[8].page_content
+
+"""Splitter"""
+
+text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size = CFG.split_chunk_size,
+    chunk_overlap = CFG.split_overlap
+)
+
+texts = text_splitter.split_documents(documents)
+
+print(f'We have created {len(texts)} chunks from {len(documents)} pages')
+
+"""Create Embeddings"""
+
+# Commented out IPython magic to ensure Python compatibility.
+# %%time
+# from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+# 
+# vectordb = FAISS.from_documents(
+#     texts,
+#     HuggingFaceEmbeddings(model_name='sentence-transformers/all-mpnet-base-v2')
+# )
+# 
+# ### persist vector database
+# vectordb.save_local(f"{CFG.Output_folder}/faiss_index_rag") # save in output folder
+# #     vectordb.save_local(f"{CFG.Embeddings_path}/faiss_index_hp") # save in input folder
+# 
+# clear_output()
+
+"""Prompt Template"""
+
+prompt_template = """
+Don't try to make up an answer, if you don't know just say that you don't know.
+Answer in the same language the question was asked.
+Use only the following pieces of context to answer the question at the end.
+
+{context}
+
+Question: {question}
+Answer:"""
+
+
+PROMPT = PromptTemplate(
+    template = prompt_template,
+    input_variables = ["context", "question"]
+)
+
+"""Retriever chain"""
+
+retriever = vectordb.as_retriever(search_kwargs = {"k": CFG.k, "search_type" : "similarity"})
+
+qa_chain = RetrievalQA.from_chain_type(
+    llm = llm,
+    chain_type = "stuff", # map_reduce, map_rerank, stuff, refine
+    retriever = retriever,
+    chain_type_kwargs = {"prompt": PROMPT},
+    return_source_documents = True,
+    verbose = False
+)
+
+question = "what are the structural organization of a human body"
+vectordb.max_marginal_relevance_search(question, k = CFG.k)
+
+### testing similarity search
+question = "what are the structural organization of a human body"
+vectordb.similarity_search(question, k = CFG.k)
+
+"""Post-process outputs"""
+
+def wrap_text_preserve_newlines(text, width=700):
+    # Split the input text into lines based on newline characters
+    lines = text.split('\n')
+
+    # Wrap each line individually
+    wrapped_lines = [textwrap.fill(line, width=width) for line in lines]
+
+    # Join the wrapped lines back together using newline characters
+    wrapped_text = '\n'.join(wrapped_lines)
+
+    return wrapped_text
+
+
+def process_llm_response(llm_response):
+    ans = wrap_text_preserve_newlines(llm_response['result'])
+
+    sources_used = ' \n'.join(
+        [
+            source.metadata['source'].split('/')[-1][:-4]
+            + ' - page: '
+            + str(source.metadata['page'])
+            for source in llm_response['source_documents']
+        ]
+    )
+
+    ans = ans + '\n\nSources: \n' + sources_used
+    return ans
+
+def llm_ans(query):
+    start = time.time()
+
+    llm_response = qa_chain.invoke(query)
+    ans = process_llm_response(llm_response)
+
+    end = time.time()
+
+    time_elapsed = int(round(end - start, 0))
+    time_elapsed_str = f'\n\nTime elapsed: {time_elapsed} s'
+    return ans + time_elapsed_str
+
+query =question = "what are the structural organization of a human body"
+print(llm_ans(query))
+
+"""Gradio Chat UI (Inspired from HinePo)"""
+
+import locale
+locale.getpreferredencoding = lambda: "UTF-8"
+
+! pip install --upgrade gradio -qq
+clear_output()
+
+def predict(message, history):
+     # output = message # debug mode
+
+     output = str(llm_ans(message)).replace("\n", "<br/>")
+     return output
+
+ demo = gr.ChatInterface(
+     predict,
+     title = f' Open-Source LLM ({CFG.model_name}) Question Answering'
+ )
+
+ demo.queue()
+ demo.launch()
+
+import gradio as gr
+print(gr.__version__)