Update app.py

app.py

@@ -1,285 +1,207 @@
-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
-)
-import gradio as gr
-import locale
-import time
-from langchain.embeddings.huggingface import HuggingFaceEmbeddings
-
-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 = './'
-    Embeddings_path =  './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
-
-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
-
-def predict(message, history):
-     output = str(llm_ans(message)).replace("\n", "<br/>")
-     return output
-
-
-
-
-tokenizer, model, max_len = get_model(model = CFG.model_name)
-
-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)
-
-loader = DirectoryLoader(
-    CFG.PDFs_path,
-    glob="./*.pdf",
-    loader_cls=PyPDFLoader,
-    show_progress=True,
-    use_multithreading=True
-)
-
-documents = loader.load()
-text_splitter = RecursiveCharacterTextSplitter(
-    chunk_size = CFG.split_chunk_size,
-    chunk_overlap = CFG.split_overlap
-)
-
-texts = text_splitter.split_documents(documents)
-
-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")
-
-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
-)
-
-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"]
-)
-
-
-locale.getpreferredencoding = lambda: "UTF-8"
-
-demo = gr.ChatInterface(
-     predict,
-     title = f' Open-Source LLM ({CFG.model_name}) Question Answering'
- )
-
-demo.queue()
+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
+)
+import gradio as gr
+import locale
+import time
+from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+
+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 = './'
+    Embeddings_path =  './faiss-hp-sentence-transformers'
+    Output_folder = './rag-vectordb'
+
+def get_model(model=CFG.model_name):
+    print('\nDownloading model: ', model, '\n\n')
+    model_repo = None
+
+    if model == 'llama2-13b-chat':
+        model_repo = 'daryl149/llama-2-13b-chat-hf'
+
+    if model_repo:
+        tokenizer = AutoTokenizer.from_pretrained(model_repo, use_fast=True)
+        model = AutoModelForCausalLM.from_pretrained(
+            model_repo,
+            device_map="auto",
+            trust_remote_code=True
+        )
+        max_len = 2048
+    else:
+        raise ValueError("Model not implemented: " + model)
+
+    return tokenizer, model, max_len
+
+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
+
+def predict(message, history):
+     output = str(llm_ans(message)).replace("\n", "<br/>")
+     return output
+
+
+
+
+tokenizer, model, max_len = get_model(model = CFG.model_name)
+
+pipe = pipeline(
+    task="text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    pad_token_id=tokenizer.eos_token_id,
+    max_length=max_len,
+    temperature=CFG.temperature,
+    top_p=CFG.top_p,
+    repetition_penalty=CFG.repetition_penalty
+)
+
+### langchain pipeline
+llm = HuggingFacePipeline(pipeline = pipe)
+
+loader = DirectoryLoader(
+    CFG.PDFs_path,
+    glob="./*.pdf",
+    loader_cls=PyPDFLoader,
+    show_progress=True,
+    use_multithreading=True
+)
+
+documents = loader.load()
+text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size = CFG.split_chunk_size,
+    chunk_overlap = CFG.split_overlap
+)
+
+texts = text_splitter.split_documents(documents)
+
+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")
+
+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
+)
+
+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"]
+)
+
+
+locale.getpreferredencoding = lambda: "UTF-8"
+
+demo = gr.ChatInterface(
+     predict,
+     title = f' Open-Source LLM ({CFG.model_name}) Question Answering'
+ )
+
+demo.queue()
 demo.launch()