From laptop

Dockerfile

@@ -1,29 +1,76 @@
-FROM python:3.8.9
-
-WORKDIR /app
-
-COPY ./requirements.txt /app/requirements.txt
-COPY ./packages.txt /app/packages.txt
-
-RUN apt-get update && xargs -r -a /app/packages.txt apt-get install -y && rm -rf /var/lib/apt/lists/*
-RUN pip3 install --no-cache-dir -r /app/requirements.txt
-
-# User
-RUN useradd -m -u 1000 user
-USER user
-ENV HOME /home/user
-ENV PATH $HOME/.local/bin:$PATH
-
-WORKDIR $HOME
-RUN mkdir app
-WORKDIR $HOME/app
-COPY . $HOME/app
-
-#
-
-EXPOSE 8501
-CMD streamlit run apphf.py \
-    --server.headless true \
-    --server.enableCORS false \
-    --server.enableXsrfProtection false \
-    --server.fileWatcherType none
+###############################################################################
+#python environment, main app and startup script.
+FROM python:3.11.5
+#FROM python:3.11.9-slim
+#FROM python:3.11.9-alpine
+#FROM python:3.11-bookworm
+
+RUN echo "\n\n############################################# Dockerfile DbgUI ######################################\n\n"
+
+#ENTRYPOINT ["/app/startup.sh"]
+#RUN apt-get update && \
+#    apt-get install -y libc6 && \
+#    rm -rf /var/lib/apt/lists/*
+WORKDIR /app
+
+#RUN  ls -l / || ls -l /lib || ls -l /usr || ls -l /usr/lib6 || echo "### An ls failed."
+
+COPY ./requirements.txt /app/requirements.txt
+COPY ./semsearch.py     /app/semsearch.py
+COPY ./startup.sh       /app/startup.sh
+COPY ./semsearchDbgUI.py     /app/semsearchDbgUI.py
+COPY ./startupDbgUI.sh       /app/startupDbgUI.sh
+RUN  chmod 755          /app/startup.sh /app/startupDbgUI.sh
+
+COPY ./multi-qa-MiniLM-L6-cos-v1 /app/multi-qa-MiniLM-L6-cos-v1
+
+RUN mkdir -p /app/inputDocs
+COPY ./inputDocs/*      /app/inputDocs
+RUN pip install --no-cache-dir --upgrade -r /app/requirements.txt
+RUN pip install https://files.pythonhosted.org/packages/13/87/e0cb08c2d4bd7d38ab63816b306c8b1e7cfdc0e59bd54462e8b0df069078/semantic_text_splitter-0.6.3-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+RUN pip show semantic-text-splitter
+
+RUN pip install llama_cpp_python
+
+##############################################################################
+# Install Weaviate
+WORKDIR /app/weaviate
+RUN wget -qO- https://github.com/weaviate/weaviate/releases/download/v1.24.10/weaviate-v1.24.10-linux-amd64.tar.gz | tar -xzf -
+RUN ls -al /app/weaviate
+
+# Set environment variables for Weaviate
+ENV PATH="/app:/app/weaviate-v1.24.10-linux-x86_64:${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="/app/text2vec-transformers:/app/text2vec-transformers/bin:${PATH}"
+#RUN pip install -r requirements.txt
+#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
+
+#############################################
+# Specify /data volume.
+#VOLUME /data
+
+##############################################################################
+# 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/startup.sh"]
+CMD ["/app/startupDbgUI.sh"]

DockerfileMoreLessGood

@@ -0,0 +1,76 @@
+###############################################################################
+#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
+RUN  chmod 755          /app/startup.sh /app/startupDbgUI.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:${PATH}"
+# Expose the Weaviate port
+EXPOSE 8080
+
+#COPY Llama-2-7B-Chat-GGUF/llama-2-7b-chat.Q4_0.gguf /app
+RUN cd /app; wget -v https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/resolve/main/llama-2-7b-chat.Q4_0.gguf
+
+##############################################################################
+# Install text2vec-transformers
+WORKDIR /app/text2vec-transformers
+COPY --from=semitechnologies/transformers-inference:sentence-transformers-multi-qa-MiniLM-L6-cos-v1 /app /app/text2vec-transformers
+COPY --from=semitechnologies/transformers-inference:sentence-transformers-multi-qa-MiniLM-L6-cos-v1 /usr/local/bin /app/text2vec-transformers/bin
+
+COPY ./multi-qa-MiniLM-L6-cos-v1 /app/app/text2vec-transformers
+
+ENV PATH="/app/text2vec-transformers:/app/text2vec-transformers/bin:${PATH}"
+#RUN pip install -r requirements.txt
+#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
+
+#############################################
+# Specify /data volume.
+#VOLUME /data
+
+##############################################################################
+# 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/startup.sh"]
+CMD ["/app/startupDbgUI.sh"]

Dockerfile_Apphf

@@ -0,0 +1,29 @@
+FROM python:3.8.9
+
+WORKDIR /app
+
+COPY ./requirements.txt /app/requirements.txt
+COPY ./packages.txt /app/packages.txt
+
+RUN apt-get update && xargs -r -a /app/packages.txt apt-get install -y && rm -rf /var/lib/apt/lists/*
+RUN pip3 install --no-cache-dir -r /app/requirements.txt
+
+# User
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME /home/user
+ENV PATH $HOME/.local/bin:$PATH
+
+WORKDIR $HOME
+RUN mkdir app
+WORKDIR $HOME/app
+COPY . $HOME/app
+
+#
+
+EXPOSE 8501
+CMD streamlit run apphf.py \
+    --server.headless true \
+    --server.enableCORS false \
+    --server.enableXsrfProtection false \
+    --server.fileWatcherType none

requirements_DbgUI.txt

@@ -0,0 +1 @@
+streamlit

requirements_Full.txt

@@ -0,0 +1,18 @@
+weaviate-client==4.*
+sentence-transformers
+langchain
+langchain_community
+lxml
+beautifulsoup4
+
+transformers==4.34.1
+fastapi==0.103.2
+uvicorn==0.23.2
+nltk==3.8.1
+torch==2.0.1
+sentencepiece==0.1.99
+sentence-transformers==2.2.2
+optimum==1.13.2
+onnxruntime==1.16.1
+onnx==1.14.1
+

semsearch.py

@@ -17,8 +17,11 @@ import logging
 import llama_cpp
 from llama_cpp import Llama
 
-import ipywidgets as widgets
-from IPython.display import display, clear_output
+import streamlit as st
+
+
+st.markdown("<h1 style='text-align: center; color: #666666;'>Vector Database RAG Proof of Concept</h1>", unsafe_allow_html=True)
+st.markdown("<h6 style='text-align: center; color: #666666;'>V1</h6>", unsafe_allow_html=True)
 
 
 weaviate_logger = logging.getLogger("httpx")
@@ -28,7 +31,6 @@ logger = logging.getLogger(__name__)
 logging.basicConfig(level=logging.INFO)
 
 
-
 ######################################################################
 # MAINLINE
 #
@@ -45,56 +47,22 @@ webpageChunksDocNames = []
 
 #####################################################################
 # Create UI widgets.
-output_widget = widgets.Output()
-with output_widget:
-    print("### Create widgets entered.")
-
-systemTextArea = widgets.Textarea(
-    value='',
-    placeholder='Enter System Prompt.',
-    description='Sys Prompt: ',
-    disabled=False,
-    layout=widgets.Layout(width='300px', height='80px')
-)
-
-userTextArea = widgets.Textarea(
-    value='',
-    placeholder='Enter User Prompt.',
-    description='User Prompt: ',
-    disabled=False,
-    layout=widgets.Layout(width='435px', height='110px')
-)
-
-ragPromptTextArea = widgets.Textarea(
-    value='',
-    placeholder='App generated prompt with RAG information.',
-    description='RAG Prompt: ',
-    disabled=False,
-    layout=widgets.Layout(width='580px', height='180px')
-)
-
-responseTextArea = widgets.Textarea(
-    value='',
-    placeholder='LLM generated response.',
-    description='LLM Resp: ',
-    disabled=False,
-    layout=widgets.Layout(width='780px', height='200px')
-)
+def runLLM(prompt):
+    logger.debug("### runLLM")
+    result = ""
+    return(result)
 
-selectRag = widgets.Checkbox(
-    value=False,
-    description='Use RAG',
-    disabled=False
-)
+def setPrompt(pprompt,ragFlag):
+    logger.debug("### setPrompt")
+    userPrompt = ""
+    return userPrompt
 
-submitButton = widgets.Button(
-    description='Run Model.',
-    disabled=False,
-    button_style='', # 'success', 'info', 'warning', 'danger' or ''
-    tooltip='Click',
-    icon='check' # (FontAwesome names without the `fa-` prefix)
-)
 
+def on_submitButton_clicked():
+    logger.debug("### on_submitButton_clicked")
+    prompt = setPrompt("","")
+    runLLM(prompt)
+    
 
 ######################################################
 # Connect to the Weaviate vector database.

semsearch.py,Bkup02

@@ -0,0 +1,437 @@
+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 logging
+
+import llama_cpp
+from llama_cpp import Llama
+
+import ipywidgets as widgets
+from IPython.display import display, clear_output
+
+
+weaviate_logger = logging.getLogger("httpx")
+weaviate_logger.setLevel(logging.WARNING)
+
+logger = logging.getLogger(__name__)
+logging.basicConfig(level=logging.INFO)
+
+
+
+######################################################################
+# MAINLINE
+#
+logger.info("#### MAINLINE ENTERED.")
+
+#pathString = "/Users/660565/KPSAllInOne/ProgramFilesX86/WebCopy/DownloadedWebSites/LLMPOC_HTML" 
+pathString = "/app/inputDocs"
+chunks = []
+webpageDocNames = []
+page_contentArray = []
+webpageChunks = []
+webpageTitles = []
+webpageChunksDocNames = []
+
+#####################################################################
+# Create UI widgets.
+output_widget = widgets.Output()
+with output_widget:
+    print("### Create widgets entered.")
+
+systemTextArea = widgets.Textarea(
+    value='',
+    placeholder='Enter System Prompt.',
+    description='Sys Prompt: ',
+    disabled=False,
+    layout=widgets.Layout(width='300px', height='80px')
+)
+
+userTextArea = widgets.Textarea(
+    value='',
+    placeholder='Enter User Prompt.',
+    description='User Prompt: ',
+    disabled=False,
+    layout=widgets.Layout(width='435px', height='110px')
+)
+
+ragPromptTextArea = widgets.Textarea(
+    value='',
+    placeholder='App generated prompt with RAG information.',
+    description='RAG Prompt: ',
+    disabled=False,
+    layout=widgets.Layout(width='580px', height='180px')
+)
+
+responseTextArea = widgets.Textarea(
+    value='',
+    placeholder='LLM generated response.',
+    description='LLM Resp: ',
+    disabled=False,
+    layout=widgets.Layout(width='780px', height='200px')
+)
+
+selectRag = widgets.Checkbox(
+    value=False,
+    description='Use RAG',
+    disabled=False
+)
+
+submitButton = widgets.Button(
+    description='Run Model.',
+    disabled=False,
+    button_style='', # 'success', 'info', 'warning', 'danger' or ''
+    tooltip='Click',
+    icon='check' # (FontAwesome names without the `fa-` prefix)
+)
+
+
+######################################################
+# Connect to the Weaviate vector database.
+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
+    )
+)
+client.connect() 
+
+
+#######################################################
+# Read each text input file, parse it into a document,
+# chunk it, collect chunks and document name.
+logger.info("#### Read and chunk input text files.")
+if not client.collections.exists("Documents") or not client.collections.exists("Documents") :
+    for filename in os.listdir(pathString):
+        logger.info(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.debug(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.debug(f"### filename, title: {filename}, {title}")
+        logger.debug(f"### webpageDocNames: {webpageDocNames}")
+
+
+
+######################################################
+# Create database webpage and chunks collections.
+#wpCollection = createWebpageCollection()
+#wpChunkCollection = createChunksCollection()
+logger.info("#### createWebpageCollection() entered.")
+if not client.collections.exists("Documents"):
+    #client.collections.delete("Documents")
+    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)
+
+logger.info("#### createChunksCollection() entered.")
+if not client.collections.exists("Chunks"):
+    #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
+                    }
+                }  
+            }
+        ]
+    }
+    wpChunkCollection = client.collections.create_from_dict(class_obj)
+
+
+###########################################################
+# Create document and chunks objects in the database.
+if not client.collections.exists("Documents") :
+    logger.info("#### Create page/doc db objects.")
+    for i, className in enumerate(webpageDocNames):
+        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
+          }
+        )
+    
+if not client.collections.exists("Chunks") :
+    logger.info("#### Create chunk db objects.")
+    # Insert the chunks for the document.
+    for i2, chunk in enumerate(webpageChunks[i]):
+        chunk_uuid = wpChunkCollection.data.insert(
+          {
+            "title": title,
+            "chunk": chunk,
+            "chunk_index": i2,
+            "references":
+            {
+              "webpage": wpCollectionObj_uuid
+            }
+          }
+        )
+
+
+#################################################################
+# Initialize the LLM.
+model_path = "/app/llama-2-7b-chat.Q4_0.gguf"
+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=512,
+            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=None,
+            chat_handler=None,
+            draft_model=None,
+            tokenizer=None,
+            type_k=None,
+            type_v=None,
+            verbose=True
+           )
+
+
+def getRagData(promptText):
+    ###############################################################################
+    # Initial the the sentence transformer and encode the query prompt.
+    logger.info(f"#### Encode text query prompt to create vectors. {text}")
+    model = SentenceTransformer('/app/multi-qa-MiniLM-L6-cos-v1')
+
+    vector = model.encode(promptText)
+    vectorList = []
+
+    logger.debug("#### Print vectors.")
+    for vec in vector:
+        vectorList.append(vec)
+    logger.debug(f"vectorList: {vectorList[2]}")
+
+    # Fetch chunks and print chunks.
+    logger.info("#### Retrieve semchunks from db using vectors from prompt.")
+    semChunks = wpChunkCollection.query.near_vector(
+        near_vector=vectorList,
+        distance=0.7,
+        limit=3
+    )
+    logger.debug(f"### semChunks[0]: {semChunks}")
+
+    # Print chunks, corresponding document and document title.
+    ragData = ""
+    logger.info("#### Print individual retrieved chunks.")
+    for chunk in enumerate(semChunks.objects):
+        logger.info(f"#### chunk: {chunk}")
+        ragData = ragData + "\n" + chunk[0]
+        webpage_uuid = chunk[1].properties['references']['webpage']
+        logger.info(f"webpage_uuid: {webpage_uuid}")
+        wpFromChunk = wpCollection.query.fetch_object_by_id(webpage_uuid)
+        logger.info(f"### wpFromChunk title: {wpFromChunk.properties['title']}")
+    #collection = client.collections.get("Chunks")
+    return  ragData
+
+# Display UI
+display(systemTextArea)
+display(userTextArea)
+display(ragPromptTextArea)
+display(responseTextArea)
+display(selectRag)
+display(submitButton)
+
+def runLLM(prompt):
+    max_tokens = 1000
+    temperature = 0.3
+    top_p = 0.1
+    echo = True
+    stop = ["Q", "\n"]
+
+    modelOutput = llm(
+       prompt,
+       max_tokens=max_tokens,
+       temperature=temperature,
+       top_p=top_p,
+       echo=echo,
+       stop=stop,
+    )
+    result = modelOutput["choices"][0]["text"].strip()
+    return(result)
+
+def setPrompt(pprompt,ragFlag):
+    print("\n### setPrompt() entered. ragFlag: ",ragFlag)
+    if ragFlag:
+        ragPrompt = getRagData(pprompt)
+        userPrompt = pprompt + "\n" + ragPrompt
+        prompt = userPrompt
+        userPrompt = "Using this information: " + ragPrompt \
+             + "process the following statement or question and produce a a response" \
+             + intialPrompt
+    else:
+        userPrompt = pprompt
+    #prompt = f""" <s> [INST] <<SYS>> {systemTextArea.value} </SYS>> Q: {userPrompt} A: [/INST]"""
+    return userPrompt
+
+
+def on_submitButton_clicked(b):
+    with output_widget:
+        clear_output(wait=True)
+        ragPromptTextArea.value = ""
+        responseTextArea.value = ""
+        log.debug(f"### selectRag: {selectRag.value}")
+        prompt = setPrompt(userTextArea.value,selectRag.value)
+        log.debug("### prompt: " + prompt)
+        runLLM(prompt)
+
+submitButton.on_click(on_submitButton_clicked)
+display(output_widget)
+
+
+#logger.info("#### Closing client db connection.")
+#client.close()
+#logger.info("#### Program terminating.")

semsearch.py.Bkup01

@@ -0,0 +1,431 @@
+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 logging
+
+import llama_cpp
+from llama_cpp import Llama
+import ipywidgets as widgets
+from IPython.display import display, clear_output
+
+
+weaviate_logger = logging.getLogger("httpx")
+weaviate_logger.setLevel(logging.WARNING)
+
+logger = logging.getLogger(__name__)
+logging.basicConfig(level=logging.INFO)
+
+
+
+######################################################################
+# MAINLINE
+#
+logger.info("#### MAINLINE ENTERED.")
+
+#pathString = "/Users/660565/KPSAllInOne/ProgramFilesX86/WebCopy/DownloadedWebSites/LLMPOC_HTML" 
+pathString = "/app/inputDocs"
+chunks = []
+webpageDocNames = []
+page_contentArray = []
+webpageChunks = []
+webpageTitles = []
+webpageChunksDocNames = []
+
+#####################################################################
+# Create UI widgets.
+output_widget = widgets.Output()
+with output_widget:
+    print("### Create widgets entered.")
+
+systemTextArea = widgets.Textarea(
+    value='',
+    placeholder='Enter System Prompt.',
+    description='Sys Prompt: ',
+    disabled=False,
+    layout=widgets.Layout(width='300px', height='80px')
+)
+
+userTextArea = widgets.Textarea(
+    value='',
+    placeholder='Enter User Prompt.',
+    description='User Prompt: ',
+    disabled=False,
+    layout=widgets.Layout(width='435px', height='110px')
+)
+
+ragPromptTextArea = widgets.Textarea(
+    value='',
+    placeholder='App generated prompt with RAG information.',
+    description='RAG Prompt: ',
+    disabled=False,
+    layout=widgets.Layout(width='580px', height='180px')
+)
+
+responseTextArea = widgets.Textarea(
+    value='',
+    placeholder='LLM generated response.',
+    description='LLM Resp: ',
+    disabled=False,
+    layout=widgets.Layout(width='780px', height='200px')
+)
+
+selectRag = widgets.Checkbox(
+    value=False,
+    description='Use RAG',
+    disabled=False
+)
+
+submitButton = widgets.Button(
+    description='Run Model.',
+    disabled=False,
+    button_style='', # 'success', 'info', 'warning', 'danger' or ''
+    tooltip='Click',
+    icon='check' # (FontAwesome names without the `fa-` prefix)
+)
+
+
+######################################################
+# Connect to the Weaviate vector database.
+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
+   )
+)
+client.connect() 
+
+
+#######################################################
+# Read each text input file, parse it into a document,
+# chunk it, collect chunks and document name.
+logger.info("#### Read and chunk input text files.")
+if not client.collections.exists("Documents") || not client.collections.exists("Documents") :
+    for filename in os.listdir(pathString):
+        logger.info(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.debug(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.debug(f"### filename, title: {filename}, {title}")
+        logger.debug(f"### webpageDocNames: {webpageDocNames}")
+
+
+
+######################################################
+# Create database webpage and chunks collections.
+#wpCollection = createWebpageCollection()
+#wpChunkCollection = createChunksCollection()
+logger.info("#### createWebpageCollection() entered.")
+if not client.collections.exists("Documents"):
+    #client.collections.delete("Documents")
+    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)
+
+logger.info("#### createChunksCollection() entered.")
+if not client.collections.exists("Chunks"):
+    #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
+                    }
+                }  
+            }
+        ]
+    }
+    wpChunkCollection = client.collections.create_from_dict(class_obj)
+
+
+###########################################################
+# Create document and chunks objects in the database.
+if not client.collections.exists("Documents") :
+    logger.info("#### Create page/doc db objects.")
+    for i, className in enumerate(webpageDocNames):
+        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
+          }
+        )
+    
+if not client.collections.exists("Chunks") :
+    logger.info("#### Create chunk db objects.")
+    # Insert the chunks for the document.
+    for i2, chunk in enumerate(webpageChunks[i]):
+        chunk_uuid = wpChunkCollection.data.insert(
+          {
+            "title": title,
+            "chunk": chunk,
+            "chunk_index": i2,
+            "references":
+            {
+              "webpage": wpCollectionObj_uuid
+            }
+          }
+        )
+
+
+#################################################################
+# Initialize the LLM.
+model_path = "/app/llama-2-7b-chat.Q4_0.gguf"
+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=512,
+            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=None,
+            chat_handler=None,
+            draft_model=None,
+            tokenizer=None,
+            type_k=None,
+            type_v=None,
+            verbose=True
+           )
+
+
+###############################################################################
+# text contains prompt for vector DB.
+text = "human-made computer cognitive ability"
+
+
+def getRagData(text):
+    ###############################################################################
+    # Initial the the sentence transformer and encode the query prompt.
+    logger.info(f"#### Encode text query prompt to create vectors. {text}")
+    model = SentenceTransformer('/app/multi-qa-MiniLM-L6-cos-v1')
+
+    vector = model.encode(text)
+    vectorList = []
+
+    logger.debug("#### Print vectors.")
+    for vec in vector:
+        vectorList.append(vec)
+    logger.debug(f"vectorList: {vectorList[2]}")
+
+    # Fetch chunks and print chunks.
+    logger.info("#### Retrieve semchunks from db using vectors from prompt.")
+    semChunks = wpChunkCollection.query.near_vector(
+        near_vector=vectorList,
+        distance=0.7,
+        limit=3
+    )
+    logger.debug(f"### semChunks[0]: {semChunks}")
+
+    # Print chunks, corresponding document and document title.
+    logger.info("#### Print individual retrieved chunks.")
+    for chunk in enumerate(semChunks.objects):
+        logger.info(f"#### chunk: {chunk}")
+        webpage_uuid = chunk[1].properties['references']['webpage']
+        logger.info(f"webpage_uuid: {webpage_uuid}")
+        wpFromChunk = wpCollection.query.fetch_object_by_id(webpage_uuid)
+        logger.info(f"### wpFromChunk title: {wpFromChunk.properties['title']}")
+
+
+
+    ####################################################################
+    # 
+    collection = client.collections.get("Chunks")
+    #model = SentenceTransformer('../multi-qa-MiniLM-L6-cos-v1')
+
+display(systemTextArea)
+display(userTextArea)
+display(ragPromptTextArea)
+display(responseTextArea)
+display(selectRag)
+display(submitButton)
+
+def setPrompt(pprompt,ragFlag):
+    print("\n### setPrompt() entered. ragFlag: ",ragFlag)
+    if ragFlag:
+        ragPrompt = setRagPrompt(pprompt)
+        userPrompt = pprompt + "\n" + ragPrompt
+        prompt = userPrompt
+    else:
+        userPrompt = pprompt
+    prompt = f""" <s> [INST] <<SYS>> {systemTextArea.value} </SYS>> Q: {userPrompt} A: [/INST]"""
+    return prompt
+
+def runModel(prompt):
+    output = llm.create_completion(
+                  prompt, # Prompt
+                  max_tokens=4096, # Generate up to 32 tokens
+                  #stop = ["Q:", "\n"], # Stop generating just before the model would generate a new question
+                  echo = False # Echo the prompt back in the output
+                )
+    responseTextArea.value = output["choices"][0]["text"]
+
+def on_submitButton_clicked(b):
+    with output_widget:
+        clear_output(wait=True)
+        ragPromptTextArea.value = ""
+        responseTextArea.value = ""
+        log.debug(f"### selectRag: {selectRag.value}")
+        prompt = setPrompt(userTextArea.value,selectRag.value)
+        log.debug("### prompt: " + prompt)
+        runModel(prompt)
+
+submitButton.on_click(on_submitButton_clicked)
+display(output_widget)
+
+
+#logger.info("#### Closing client db connection.")
+#client.close()
+#logger.info("#### Program terminating.")

semsearchDbgUI.py

@@ -9,19 +9,19 @@ logger = logging.getLogger(__name__)
 logging.basicConfig(level=logging.DEBUG)
 
 def runLLM(prompt):
-    log.debug("### runLLM")
+    logger.debug("### runLLM")
     result = ""
     return(result)
 
 def setPrompt(pprompt,ragFlag):
-    log.debug("### setPrompt")
+    logger.debug("### setPrompt")
     userPrompt = ""
     return userPrompt
 
 
-def on_submitButton_clicked(b):
-    log.debug("### on_submitButton_clicked")
-    setPrompt("","")
+def on_submitButton_clicked():
+    logger.debug("### on_submitButton_clicked")
+    prompt = setPrompt("","")
     runLLM(prompt)