new repo after lfs issues

.gitignore

@@ -0,0 +1,4 @@
+Lib/
+Library/
+Scripts/
+pyvenv.cfg

app.py

@@ -1,634 +1,641 @@
-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(10)
-            #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('If needed, initialize 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
-        
-    
-    ####################################################################
-    # 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. 40+ seconds.)...'):
-        #with 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):
-        st.session_state.spinGenMsg = True
-        modelOutput = llm.create_chat_completion(
-           prompt
-           #max_tokens=max_tokens,
-           #temperature=temperature,
-           #top_p=top_p,
-           #echo=echoVal,
-           #stop=stop,
-        )
-        st.session_state.spinGenMsg = False
-        if modelOutput != "":
-            result = modelOutput["choices"][0]["message"]["content"]
-        else:
-            result = "No result returned."
-        #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
-
-    ##########################
-    # Display UI text areas. #
-    ##########################
-    col1, col2 = st.columns(2)
-    with col1:
-        if 'spinGenMsg' not in st.session_state or st.session_state.spinGenMsg == False:
-            placeHolder = st.empty()
-        else:
-            st.session_state.spinGenMsg = False;
-            with st.spinner('Generating Completion (but slowly. 40+ seconds.)...'):
-                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}")
-        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).")    
-
-
-    #####################################
-    # 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.spinGenMsg = True
-        
-        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}")
+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(10)
+            #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('If needed, initialize 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
+        
+    
+    ####################################################################
+    # 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. 40+ seconds.)...'):
+        #with 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):
+        st.session_state.spinGenMsg = True
+        modelOutput = llm.create_chat_completion(
+           prompt
+           #max_tokens=max_tokens,
+           #temperature=temperature,
+           #top_p=top_p,
+           #echo=echoVal,
+           #stop=stop,
+        )
+        st.session_state.spinGenMsg = False
+        if modelOutput != "":
+            result = modelOutput["choices"][0]["message"]["content"]
+        else:
+            result = "No result returned."
+        #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
+
+    ##########################
+    # Display UI text areas. #
+    ##########################
+    col1, col2 = st.columns(2)
+    with col1:
+        if 'spinGenMsg' not in st.session_state or st.session_state.spinGenMsg == False:
+            placeHolder = st.empty()
+        else:
+            st.session_state.spinGenMsg = False;
+            with st.spinner('Generating Completion (but slowly. 40+ seconds.)...'):
+                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}")
+                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 "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).")    
+
+
+    #####################################
+    # 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.spinGenMsg = True
+        
+        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}")