app.py.Hld02

import weaviate
from weaviate.connect import ConnectionParams
from weaviate.classes.init import AdditionalConfig, Timeout

from sentence_transformers import SentenceTransformer
from langchain_community.document_loaders import BSHTMLLoader
from pathlib import Path
from lxml import html
import logging
from semantic_text_splitter import HuggingFaceTextSplitter
from tokenizers import Tokenizer
import json
import os
import re

import llama_cpp
from llama_cpp import Llama

import streamlit as st
import subprocess
import time
import pprint
import io



try:
    #############################################
    # Logging setup including weaviate logging. #
    #############################################
    if 'logging' not in st.session_state:
        weaviate_logger = logging.getLogger("httpx")
        weaviate_logger.setLevel(logging.WARNING)
        logger = logging.getLogger(__name__)
        logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s',level=logging.INFO)
        st.session_state.weaviate_logger = weaviate_logger
        st.session_state.logger = logger
    else:
        weaviate_logger = st.session_state.weaviate_logger
        logger = st.session_state.logger

    
    logger.info("###################### Program Entry ############################")
    
    ##########################################################################
    # Asynchonously run startup.sh which run text2vec-transformers           #
    # asynchronously and the Weaviate Vector Database server asynchronously. #
    ##########################################################################
    def runStartup():
        logger.info("### Running startup.sh")
        try:
            subprocess.Popen(["/app/startup.sh"])
            # Wait for text2vec-transformers and Weaviate DB to initialize.
            time.sleep(180)
            #subprocess.run(["/app/cmd.sh 'ps -ef'"])
        except Exception as e:
            emsg = str(e)
            logger.error(f"### subprocess.run  EXCEPTION. e: {emsg}")
        logger.info("### Running startup.sh complete")
    if 'runStartup' not in st.session_state:
        st.session_state.runStartup = False
        if 'runStartup' not in st.session_state:
            logger.info("### runStartup still not in st.session_state after setting variable.")
        with st.spinner('Initializing Weaviate DB and text2vec-transformer...'):
            runStartup()
        try:
            logger.info("### Displaying /app/startup.log")
            with open("/app/startup.log", "r") as file:
               line = file.readline().rstrip()
               while line:
                   logger.info(line)
                   line = file.readline().rstrip()
        except Exception as e2:
            emsg = str(e2)
            logger.error(f"#### Displaying startup.log EXCEPTION. e2: {emsg}")


    #########################################
    # Function to load the CSS syling file. #
    #########################################
    def load_css(file_name):
        logger.info("#### load_css entered.")
        with open(file_name) as f:
            st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
        logger.info("#### load_css exited.")
    if 'load_css' not in st.session_state:
        load_css(".streamlit/main.css")
        st.session_state.load_css = True

    # Display UI heading.
    st.markdown("<h1 style='text-align: center; color: #666666;'>LLM with RAG Prompting <br style='page-break-after: always;'>Proof of Concept</h1>",
                unsafe_allow_html=True)
   
    pathString = "/app/inputDocs"
    chunks = []
    webpageDocNames = []
    page_contentArray = []
    webpageChunks = []
    webpageTitles = []
    webpageChunksDocNames = []


    ############################################
    # Connect to the Weaviate vector database. #
    ############################################
    if 'client' not in st.session_state:
        logger.info("#### Create Weaviate db client connection.")
        client = weaviate.WeaviateClient(
            connection_params=ConnectionParams.from_params(
                http_host="localhost",
                http_port="8080",
                http_secure=False,
                grpc_host="localhost",
                grpc_port="50051",
                grpc_secure=False
            ),
            additional_config=AdditionalConfig(
                timeout=Timeout(init=60, query=1800, insert=1800),  # Values in seconds
            )
        )
        for i in range(3):
            try:
                client.connect()
                st.session_state.client = client
                logger.info("#### Create Weaviate db client connection exited.")
                break
            except Exception as e:
                emsg = str(e)
                logger.error(f"### client.connect() EXCEPTION. e2: {emsg}")
                time.sleep(45)
        if i >= 3:
            raise Exception("client.connect retries exhausted.")
    else:
        client = st.session_state.client


    ########################################################
    # Read each text input file, parse it into a document, #
    # chunk it, collect chunks and document names.         #
    ########################################################
    if not client.collections.exists("Documents") or not client.collections.exists("Chunks") :
        logger.info("#### Read and chunk input RAG document files.")
        for filename in os.listdir(pathString):
            logger.debug(filename)
            path = Path(pathString + "/" + filename)
            filename = filename.rstrip(".html")
            webpageDocNames.append(filename)
            htmlLoader = BSHTMLLoader(path,"utf-8")
            htmlData = htmlLoader.load()

            title   = htmlData[0].metadata['title']
            page_content = htmlData[0].page_content
            
            # Clean data. Remove multiple newlines, etc.
            page_content = re.sub(r'\n+', '\n',page_content)

            page_contentArray.append(page_content)
            webpageTitles.append(title)
            max_tokens = 1000
            tokenizer = Tokenizer.from_pretrained("bert-base-uncased")
            logger.info(f"### tokenizer: {tokenizer}")
            splitter = HuggingFaceTextSplitter(tokenizer, trim_chunks=True)
            chunksOnePage = splitter.chunks(page_content, chunk_capacity=50)

            chunks = []
            for chnk in chunksOnePage:
                logger.debug(f"#### chnk in file: {chnk}")
                chunks.append(chnk)
                logger.debug(f"chunks: {chunks}")
            webpageChunks.append(chunks)
            webpageChunksDocNames.append(filename + "Chunks")

            logger.info(f"### filename, title: {filename}, {title}")
            logger.info(f"### webpageDocNames: {webpageDocNames}")
        logger.info("#### Read and chunk input RAG document files.")


    #############################################################
    # Create database documents and chunks schemas/collections. #
    # Each chunk schema points to its corresponding document.   #
    #############################################################
    if not client.collections.exists("Documents"):
        logger.info("#### Create documents schema/collection started.")
        class_obj = {
          "class": "Documents",
          "description": "For first attempt at loading a Weviate database.",
          "vectorizer": "text2vec-transformers",
          "moduleConfig": {
            "text2vec-transformers": {
            "vectorizeClassName": False
            }
          },
          "vectorIndexType": "hnsw",  
          "vectorIndexConfig": {
              "distance": "cosine",
          },
          "properties": [
                {
                    "name": "title",
                    "dataType": ["text"],
                    "description": "HTML doc title.",
                    "vectorizer": "text2vec-transformers",
                    "moduleConfig": {
                        "text2vec-transformers": {
                            "vectorizePropertyName": True,
                            "skip": False,
                            "tokenization": "lowercase"
                        }
                    },
                    "invertedIndexConfig": {
                        "bm25": {
                            "b": 0.75,
                            "k1": 1.2
                        },
                    }
                },
                {
                    "name": "content",
                    "dataType": ["text"],
                    "description": "HTML page content.",
                    "moduleConfig": {
                        "text2vec-transformers": {
                            "vectorizePropertyName": True,
                            "tokenization": "whitespace"
                        }
                    }
                }
            ]  
        }
        wpCollection = client.collections.create_from_dict(class_obj)
        st.session_state.wpCollection = wpCollection
        logger.info("#### Create documents schema/collection ended.")
    else:
        wpCollection = client.collections.get("Documents")
        st.session_state.wpCollection = wpCollection

    # Create chunks in db.
    if not client.collections.exists("Chunks"):
        logger.info("#### create document chunks schema/collection started.")
        #client.collections.delete("Chunks")
        class_obj = {
            "class": "Chunks",
            "description": "Collection for document chunks.",
            "vectorizer": "text2vec-transformers",
            "moduleConfig": {
                "text2vec-transformers": {
                    "vectorizeClassName": True
                }
            },
            "vectorIndexType": "hnsw",  
            "vectorIndexConfig": {
                "distance": "cosine"
            },
            "properties": [
                {
                    "name": "chunk",
                    "dataType": ["text"],
                    "description": "Single webpage chunk.",
                    "vectorizer": "text2vec-transformers",
                    "moduleConfig": {
                        "text2vec-transformers": {
                          "vectorizePropertyName": False,
                          "skip": False,
                          "tokenization": "lowercase"
                        }
                    }
                },
                {
                    "name": "chunk_index",
                    "dataType": ["int"]
                },
                {
                    "name": "webpage",
                    "dataType": ["Documents"],
                    "description": "Webpage content chunks.",
                
                    "invertedIndexConfig": {
                        "bm25": {
                            "b": 0.75,
                            "k1": 1.2
                        }
                    }  
                }
            ]
        }
        wpChunksCollection = client.collections.create_from_dict(class_obj)
        st.session_state.wpChunksCollection = wpChunksCollection
        logger.info("#### create document chunks schedma/collection ended.")
    else:
        wpChunksCollection = client.collections.get("Chunks")
        st.session_state.wpChunksCollection = wpChunksCollection        


    ##################################################################
    # Create the actual document and chunks objects in the database. #
    ##################################################################
    if 'dbObjsCreated' not in st.session_state:
        logger.info("#### Create db document and chunk objects started.")
        st.session_state.dbObjsCreated = True
        for i, className in enumerate(webpageDocNames):
            logger.info("#### Creating document object.")
            title = webpageTitles[i]
            logger.debug(f"## className, title: {className}, {title}")
            # Create Webpage Object
            page_content = page_contentArray[i]
            # Insert the document.
            wpCollectionObj_uuid = wpCollection.data.insert(
              {
                "name": className,
                "title": title,
                "content": page_content
              }
            )
            logger.info("#### Document object created.")
            
            logger.info("#### Create chunk db objects.")
            st.session_state.wpChunksCollection = wpChunksCollection
            # Insert the chunks for the document.
            for i2, chunk in enumerate(webpageChunks[i]):
                chunk_uuid = wpChunksCollection.data.insert(
                  {
                    "title": title,
                    "chunk": chunk,
                    "chunk_index": i2,
                    "references":
                    {
                      "webpage": wpCollectionObj_uuid
                    }
                  }
                )
            logger.info("#### Create chunk db objects created.")
        logger.info("#### Create db document and chunk objects ended.")


    #######################
    # Initialize the LLM. #
    #######################
    model_path = "/app/llama-2-7b-chat.Q4_0.gguf"
    if 'llm' not in st.session_state:
        logger.info("### Initializing LLM.")
        llm = Llama(model_path,
                    #*,
                    n_gpu_layers=0,
                    split_mode=llama_cpp.LLAMA_SPLIT_MODE_LAYER,
                    main_gpu=0,
                    tensor_split=None,
                    vocab_only=False,
                    use_mmap=True,
                    use_mlock=False,
                    kv_overrides=None,
                    seed=llama_cpp.LLAMA_DEFAULT_SEED,
                    n_ctx=2048,
                    n_batch=512,
                    n_threads=8,
                    n_threads_batch=16,
                    rope_scaling_type=llama_cpp.LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED,
                    pooling_type=llama_cpp.LLAMA_POOLING_TYPE_UNSPECIFIED,
                    rope_freq_base=0.0,
                    rope_freq_scale=0.0,
                    yarn_ext_factor=-1.0,
                    yarn_attn_factor=1.0,
                    yarn_beta_fast=32.0,
                    yarn_beta_slow=1.0,
                    yarn_orig_ctx=0,
                    logits_all=False,
                    embedding=False,
                    offload_kqv=True,
                    last_n_tokens_size=64,
                    lora_base=None,
                    lora_scale=1.0,
                    lora_path=None,
                    numa=False,
                    chat_format="llama-2",
                    chat_handler=None,
                    draft_model=None,
                    tokenizer=None,
                    type_k=None,
                    type_v=None,
                    verbose=False
                   )
        st.session_state.llm = llm
        logger.info("### Initializing LLM completed.")
    else:
        llm = st.session_state.llm


    #####################################################
    # Get RAG data from vector db based on user prompt. #
    #####################################################
    def getRagData(promptText):
        logger.info("#### getRagData() entered.")
        ###############################################################################
        # Initial the the sentence transformer and encode the query prompt.
        logger.debug(f"#### Encode text query prompt to create vectors. {promptText}")
        model = SentenceTransformer('/app/multi-qa-MiniLM-L6-cos-v1')
        vector = model.encode(promptText)
        
        logLevel = logger.getEffectiveLevel()
        if logLevel >= logging.DEBUG:
            wrks = str(vector)
            logger.debug(f"### vector: {wrks}")

        
        vectorList = []
        for vec in vector:
            vectorList.append(vec)
        
        if logLevel >= logging.DEBUG:
            logger.debug("#### Print vectors.")
            wrks = str(vectorList)
            logger.debug(f"vectorList: {wrks}")

        # Fetch chunks and print chunks.
        logger.debug("#### Retrieve semchunks from db using vectors from prompt.")
        wpChunksCollection = st.session_state.wpChunksCollection
        semChunks = wpChunksCollection.query.near_vector(
            near_vector=vectorList,
            distance=0.7,
            limit=3
        )
        
        if logLevel >= logging.DEBUG:
            wrks = str(semChunks)
            logger.debug(f"### semChunks[0]: {wrks}")

        # Print chunks, corresponding document and document title.
        ragData = ""
        logger.debug("#### Print individual retrieved chunks.")
        wpCollection = st.session_state.wpCollection
        for chunk in enumerate(semChunks.objects):
            logger.debug(f"#### chunk: {chunk}")
            ragData = ragData + chunk[1].properties['chunk'] + "\n"
            webpage_uuid = chunk[1].properties['references']['webpage']
            logger.debug(f"webpage_uuid: {webpage_uuid}")
            wpFromChunk = wpCollection.query.fetch_object_by_id(webpage_uuid)
            logger.debug(f"### wpFromChunk title: {wpFromChunk.properties['title']}")
        #collection = client.collections.get("Chunks")
        logger.debug("#### ragData: {ragData}")
        if ragData == "" or ragData == None:
            ragData = "None found."
        logger.info("#### getRagData() exited.")
        return  ragData
     
     
    #################################################
    # Retrieve all RAG data for the user to review. #
    #################################################
    def getAllRagData():
        logger.info("#### getAllRagData() entered.")
        
        chunksCollection = client.collections.get("Chunks")
        response = chunksCollection.query.fetch_objects()
        wstrObjs = str(response.objects)
        logger.debug(f"### response.objects: {wstrObjs}")
        for o in response.objects:
            wstr = o.properties
            logger.debug(f"### o.properties: {wstr}") 
        logger.info("#### getAllRagData() exited.")            
        return  wstrObjs

    ##########################
    # Display UI text areas. #
    ##########################
    col1, col2 = st.columns(2)
    with col1:
        if "sysTA" not in st.session_state:
            st.session_state.sysTA = st.text_area(label="System Prompt",placeholder="You are a helpful AI assistant", help="Instruct the LLM about how to handle the user prompt.")
        elif "sysTAtext" in st.session_state:
            st.session_state.sysTA = st.text_area(label="System Prompt",value=st.session_state.sysTAtext,placeholder="You are a helpful AI assistant", help="Instruct the LLM about how to handle the user prompt.")
        else:
            st.session_state.sysTA = st.text_area(label="System Prompt",value=st.session_state.sysTA,placeholder="You are a helpful AI assistant", help="Instruct the LLM about how to handle the user prompt.")
            
        if "userpTA" not in st.session_state:
            st.session_state.userpTA = st.text_area(label="User Prompt",placeholder="Prompt the LLM with a question or instruction.", \
            help="Enter a prompt for the LLM. No special characters needed.")
        elif "userpTAtext" in st.session_state:
            st.session_state.userpTA = st.text_area (label="User Prompt",value=st.session_state.userpTAtext,placeholder="Prompt the LLM with a question or instruction.", \
            help="Enter a prompt for the LLM. No special characters needed.")
        else:
            st.session_state.userpTA = st.text_area(label="User Prompt",value=st.session_state.userpTA,placeholder="Prompt the LLM with a question or instruction.", \
            help="Enter a prompt for the LLM. No special characters needed.")

    with col2:
        if "ragpTA" not in st.session_state:
            st.session_state.ragpTA = st.text_area(label="RAG Response",placeholder="Output if RAG selected.",help="RAG output if enabled.")
        elif "ragpTAtext" in st.session_state:
            st.session_state.ragpTA = st.text_area(label="RAG Response",value=st.session_state.ragpTAtext,placeholder="Output if RAG selected.",help="RAG output if enabled.")
        else:
            st.session_state.ragpTA = st.text_area(label="RAG Response",value=st.session_state.ragpTA,placeholder="Output if RAG selected.",help="RAG output if enabled.")

        if "rspTA" not in st.session_state:
            st.session_state.rspTA = st.text_area(label="LLM Completion",placeholder="LLM completion.",help="Output area for LLM completion (response).")
        elif "rspTAtext" in st.session_state:
            st.session_state.rspTA = st.text_area(label="LLM Completion",value=st.session_state.rspTAtext,placeholder="LLM completion.",help="Output area for LLM completion (response).")
        else:
            st.session_state.rspTA = st.text_area(label="LLM Completion",value=st.session_state.rspTA,placeholder="LLM completion.",help="Output area for LLM completion (response).")
    
    
    ####################################################################
    # Prompt the LLM with the user's input and return the completion.  #
    ####################################################################
    def runLLM(prompt):
        logger = st.session_state.logger
        logger.info("### runLLM entered.")
        
        max_tokens = 1000
        temperature = 0.3
        top_p = 0.1
        echoVal = True
        stop = ["Q", "\n"]

        modelOutput = ""
        with st.spinner('Generating Completion (but slowly)...'):
            modelOutput = llm.create_chat_completion(
               prompt
               #max_tokens=max_tokens,
               #temperature=temperature,
               #top_p=top_p,
               #echo=echoVal,
               #stop=stop,
            )
        result = modelOutput["choices"][0]["message"]["content"]
        #result = str(modelOutput)
        logger.debug(f"### llmResult: {result}")
        logger.info("### runLLM exited.")
        return result


    ##########################################################################
    # Build a llama-2 prompt from the user prompt and RAG input if selected. #
    ##########################################################################
    def setPrompt(pprompt,ragFlag):
        logger = st.session_state.logger
        logger.info(f"### setPrompt() entered. ragFlag: {ragFlag}")
        if ragFlag:
            ragPrompt = getRagData(pprompt)
            st.session_state.ragpTA = ragPrompt
            if ragFlag != "None found.":
                userPrompt = pprompt + " " \
                               + "Also, combine the following information with information in the LLM itself. " \
                               + "Use the combined information to generate the response. " \
                               + ragPrompt + " "
            else:
                userPrompt = pprompt
        else:
            userPrompt = pprompt
        
        fullPrompt = [
          {"role": "system", "content": st.session_state.sysTA},
          {"role": "user", "content": userPrompt}
        ]
          
        logger.debug(f"### userPrompt: {userPrompt}")
        logger.info("setPrompt exited.")
        return fullPrompt


    #####################################
    # Run the LLM with the user prompt. #
    #####################################
    def on_runLLMButton_Clicked():
        logger = st.session_state.logger
        logger.info("### on_runLLMButton_Clicked entered.")
        st.session_state.sysTAtext = st.session_state.sysTA
        logger.debug(f"sysTAtext: {st.session_state.sysTAtext}")
        
        wrklist = setPrompt(st.session_state.userpTA,st.selectRag)
        st.session_state.userpTA = wrklist[1]["content"]
        logger.debug(f"userpTAtext: {st.session_state.userpTA}")
        
        rsp = runLLM(wrklist)
        st.session_state.rspTA = rsp
        logger.debug(f"rspTAtext: {st.session_state.rspTA}")
        
        logger.info("### on_runLLMButton_Clicked exited.")


    #########################################
    # Get all the RAG data for user review. #
    #########################################
    def on_getAllRagDataButton_Clicked():
        logger = st.session_state.logger
        logger.info("### on_getAllRagButton_Clicked entered.")
        st.session_state.ragpTA = getAllRagData();
        logger.info("### on_getAllRagButton_Clicked exited.")


    #######################################
    # Reset all the input, output fields. #
    #######################################
    def on_resetButton_Clicked():
        logger = st.session_state.logger
        logger.info("### on_resetButton_Clicked entered.")
        st.session_state.sysTA     = ""
        st.session_state.userpTA   = ""
        st.session_state.ragpTA    = ""
        st.session_state.rspTA     = ""
        logger.info("### on_resetButton_Clicked exited.")
      

    ###########################################
    # Display the sidebar with a checkbox and #
    # text areas.                             #
    ###########################################
    with st.sidebar:
        st.selectRag = st.checkbox("Enable RAG",value=False,key="selectRag",help=None,on_change=None,args=None,kwargs=None,disabled=False,label_visibility="visible")
        st.runLLMButton        = st.button("Run LLM Prompt",key=None,help=None,on_click=on_runLLMButton_Clicked,args=None,kwargs=None,type="secondary",disabled=False,use_container_width=False)
        st.getAllRagDataButton = st.button("Get All Rag Data",key=None,help=None,on_click=on_getAllRagDataButton_Clicked,args=None,kwargs=None,type="secondary",disabled=False,use_container_width=False)
        st.resetButton         = st.button("Reset",key=None,help=None,on_click=on_resetButton_Clicked,args=None,kwargs=None,type="secondary",disabled=False,use_container_width=False)

    logger.info("#### Program End Execution.")
    
except Exception as e:
    try:
        emsg = str(e)
        logger.error(f"Program-wide EXCEPTION. e: {emsg}")
        with open("/app/startup.log", "r") as file:
            content = file.read()
            logger.debug(content)
    except Exception as e2:
        emsg = str(e2)
        logger.error(f"#### Displaying startup.log EXCEPTION. e2: {emsg}")