8vcpu invalid instruction

Dockerfile

@@ -5,7 +5,9 @@ FROM python:3.11.5
 #FROM python:3.11.9-alpine
 #FROM python:3.11-bookworm
 
-RUN echo "\n\n############################################# Dockerfile DbgUI ######################################\n\n"
+RUN echo "\n\n############################################# Dockerfile ######################################\n\n"
+RUN echo "### cat /proc/cpuinfo"
+RUN cat /proc/cpuinfo
 
 #ENTRYPOINT ["/app/startup.sh"]
 #RUN apt-get update && \
@@ -18,14 +20,9 @@ WORKDIR /app
 COPY ./requirements.txt    /app/requirements.txt
 COPY ./semsearch.py        /app/semsearch.py
 COPY ./startup.sh          /app/startup.sh
-COPY ./semsearchDbgUI.py   /app/semsearchDbgUI.py
-COPY ./startupDbgUI.sh     /app/startupDbgUI.sh
 COPY ./.streamlit/main.css /app/.streamlit/main.css
 COPY ./app.py              /app/app.py
-COPY ./cmd.sh              /app/cmd.sh
-RUN  chmod 755             /app/startup.sh /app/cmd.sh
-
-COPY ./multi-qa-MiniLM-L6-cos-v1 /app/multi-qa-MiniLM-L6-cos-v1
+RUN  chmod 755             /app/startup.sh
 
 RUN mkdir -p /app/inputDocs
 COPY ./inputDocs/*      /app/inputDocs
@@ -58,8 +55,6 @@ COPY --from=semitechnologies/transformers-inference:sentence-transformers-multi-
 COPY ./multi-qa-MiniLM-L6-cos-v1 /app/app/text2vec-transformers
 
 ENV PATH="/usr/bin/local:/app/text2vec-transformers:/app/text2vec-transformers/bin:${PATH}"
-#RUN pip install -r requirements.txt--server.port=8501 --server.address=0.0.0.0
-#RUN pip install nltk==3.8.1 optimum==1.13.2 onnxruntime==1.16.1 onnx==1.14.1
 RUN ./custom_prerequisites.py
 
 ##############################
@@ -69,25 +64,12 @@ RUN chmod -R 755  /app
 RUN chown -R user /app
 RUN chgrp -R user /app
 
-#############################################
-# Specify /data volume.
-#VOLUME /data
-
 WORKDIR /app
 USER user
 
-##############################################################################
-# Start the weaviate vector database, text2vec-transformers and the semantic search app.
-#RUN /app/startup.sh
-#RUN --mount=type=cache,target=/data,mode=777 /app/startup.sh
-#RUN --mount=type=cache,target=/data,mode=777 echo "### Mounting /data"
-#CMD ["/app/startupDbgUI.sh"]
 EXPOSE 8501
-#CMD /app/startup.sh; /usr/local/bin/streamlit run semsearch.py --server.port=8501 --server.address=0.0.0.0
 CMD streamlit run /app/app.py \
     --server.headless true \
     --server.enableCORS false \
     --server.enableXsrfProtection false \
     --server.fileWatcherType none
-
-#CMD python apptst.py

Dockerfile.Hld02

@@ -0,0 +1,93 @@
+###############################################################################
+#python environment, main app and startup script.
+FROM python:3.11.5
+#FROM python:3.11.9-slim
+#FROM python:3.11.9-alpine
+#FROM python:3.11-bookworm
+
+RUN echo "\n\n############################################# Dockerfile DbgUI ######################################\n\n"
+
+#ENTRYPOINT ["/app/startup.sh"]
+#RUN apt-get update && \
+#    apt-get install -y libc6 && \
+#    rm -rf /var/lib/apt/lists/*
+WORKDIR /app
+
+#RUN  ls -l / || ls -l /lib || ls -l /usr || ls -l /usr/lib6 || echo "### An ls failed."
+
+COPY ./requirements.txt    /app/requirements.txt
+COPY ./semsearch.py        /app/semsearch.py
+COPY ./startup.sh          /app/startup.sh
+COPY ./semsearchDbgUI.py   /app/semsearchDbgUI.py
+COPY ./startupDbgUI.sh     /app/startupDbgUI.sh
+COPY ./.streamlit/main.css /app/.streamlit/main.css
+COPY ./app.py              /app/app.py
+COPY ./cmd.sh              /app/cmd.sh
+RUN  chmod 755             /app/startup.sh /app/cmd.sh
+
+COPY ./multi-qa-MiniLM-L6-cos-v1 /app/multi-qa-MiniLM-L6-cos-v1
+
+RUN mkdir -p /app/inputDocs
+COPY ./inputDocs/*      /app/inputDocs
+RUN pip install --no-cache-dir --upgrade -r /app/requirements.txt
+RUN pip install https://files.pythonhosted.org/packages/13/87/e0cb08c2d4bd7d38ab63816b306c8b1e7cfdc0e59bd54462e8b0df069078/semantic_text_splitter-0.6.3-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+RUN pip show semantic-text-splitter
+
+RUN pip install llama_cpp_python
+
+##############################################################################
+# Install Weaviate
+WORKDIR /app/weaviate
+RUN wget -qO- https://github.com/weaviate/weaviate/releases/download/v1.24.10/weaviate-v1.24.10-linux-amd64.tar.gz | tar -xzf -
+RUN ls -al /app/weaviate
+
+# Set environment variables for Weaviate
+ENV PATH="/app:/app/weaviate-v1.24.10-linux-x86_64:${PATH}"
+# Expose the Weaviate port
+EXPOSE 8080
+
+#COPY Llama-2-7B-Chat-GGUF/llama-2-7b-chat.Q4_0.gguf /app
+RUN cd /app; wget -v https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/resolve/main/llama-2-7b-chat.Q4_0.gguf
+
+##############################################################################
+# Install text2vec-transformers
+WORKDIR /app/text2vec-transformers
+COPY --from=semitechnologies/transformers-inference:sentence-transformers-multi-qa-MiniLM-L6-cos-v1 /app /app/text2vec-transformers
+COPY --from=semitechnologies/transformers-inference:sentence-transformers-multi-qa-MiniLM-L6-cos-v1 /usr/local/bin /app/text2vec-transformers/bin
+
+COPY ./multi-qa-MiniLM-L6-cos-v1 /app/app/text2vec-transformers
+
+ENV PATH="/usr/bin/local:/app/text2vec-transformers:/app/text2vec-transformers/bin:${PATH}"
+#RUN pip install -r requirements.txt--server.port=8501 --server.address=0.0.0.0
+#RUN pip install nltk==3.8.1 optimum==1.13.2 onnxruntime==1.16.1 onnx==1.14.1
+RUN ./custom_prerequisites.py
+
+##############################
+RUN useradd -m -u 1000 user
+
+RUN chmod -R 755  /app
+RUN chown -R user /app
+RUN chgrp -R user /app
+
+#############################################
+# Specify /data volume.
+#VOLUME /data
+
+WORKDIR /app
+USER user
+
+##############################################################################
+# Start the weaviate vector database, text2vec-transformers and the semantic search app.
+#RUN /app/startup.sh
+#RUN --mount=type=cache,target=/data,mode=777 /app/startup.sh
+#RUN --mount=type=cache,target=/data,mode=777 echo "### Mounting /data"
+#CMD ["/app/startupDbgUI.sh"]
+EXPOSE 8501
+#CMD /app/startup.sh; /usr/local/bin/streamlit run semsearch.py --server.port=8501 --server.address=0.0.0.0
+CMD streamlit run /app/app.py \
+    --server.headless true \
+    --server.enableCORS false \
+    --server.enableXsrfProtection false \
+    --server.fileWatcherType none
+
+#CMD python apptst.py

app.py

@@ -510,7 +510,7 @@ try:
         stop = ["Q", "\n"]
 
         modelOutput = ""
-        with st.spinner('Generating Completion...'):
+        with st.spinner('Generating Completion (but slowly)...'):
             modelOutput = llm.create_chat_completion(
                prompt
                #max_tokens=max_tokens,

app.py.Hld02

@@ -0,0 +1,622 @@
+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}")

startup.sh

@@ -1,5 +1,8 @@
 #! /bin/bash 
 
+echo "### cat /proc/cpuinfo"
+cat /proc/cpuinfo
+
 #####################################
 # Start text2vec-transformers and   #
 # Weaviate DB to run asynchronously #

startup.sh.Hld02

@@ -0,0 +1,60 @@
+#! /bin/bash 
+
+#####################################
+# Start text2vec-transformers and   #
+# Weaviate DB to run asynchronously #
+# and wait.                         #
+#####################################
+exec &> /app/startup.log
+
+echo "#### startup.sh entered."
+echo "### ps -ef 1"; ps -ef
+
+# Is startup.sh already running?
+echo " "
+echo "### before ps and grep startup.sh"
+ps -ef | grep -i startup.sh
+cnt=$(ps -ef | grep -i startup.sh | wc -l)
+echo "### cnt: $cnt"
+if [ $cnt -gt 3 ];then
+  echo "#### startup.sh already running. Exiting."
+  exit 0
+fi
+
+# Make sure Weaviate DB directory exists.
+echo "### Before mkdir -p ~/data/var/lib/weaviate"
+weaviateDir=~/data/var/lib/weaviate
+mkdir -p $weaviateDir
+chmod -R 777 $weaviateDir
+
+
+# Start tex2vec-transformers
+echo "#### Before /app/text2vec-transformers"
+cd /app/text2vec-transformers
+/app/text2vec-transformers/bin/uvicorn app:app --host 0.0.0.0 --port 8081 --log-level warning --timeout-keep-alive 1440 & 
+echo "### After text2vec start. RC=$?"
+cd /app
+
+
+# Start the weaviate vector database server.
+echo "#### Before /app/weaviate"
+
+export AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED=true \
+       PERSISTENCE_DATA_PATH=$weaviateDir \
+       DEFAULT_VECTORIZER_MODULE=text2vec-transformers \
+	   ENABLE_MODULES=text2vec-transformers \
+	   TRANSFORMERS_INFERENCE_API=http://127.0.0.1:8081 \
+	   LOG_LEVEL=warning \
+       MODULES_CLIENT_TIMEOUT=600s
+/app/weaviate/weaviate --host 127.0.0.1 --port 8080 --scheme http --write-timeout 600s & 
+echo "### After Weaviate DB start. RC=$?"
+
+#echo "### Before sleep 120"
+#sleep 120
+
+echo "### Before wait."
+echo "### ps -ef 2: "; ps -ef
+wait
+
+
+