Update semsearch.py

semsearch.py

@@ -1,422 +1,423 @@
-import weaviate
-
-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)
-)
-
-
-#######################################################
-# 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.")
-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}")
-
-
-######################################################
-# Connect to the Weaviate vector database.
-logger.info("#### Create Weaviate db client connection.")
-client = weaviate.connect_to_custom(
-    http_host="127.0.0.1",
-    http_port=8080,
-    http_secure=False,
-    grpc_host="127.0.0.1",
-    grpc_port=50051,
-    grpc_secure=False
-    #read_timeout=600,
-    #write_timeout=90
-)
-client.connect() 
-
-
-######################################################
-# Create database webpage and chunks collections.
-#wpCollection = createWebpageCollection()
-#wpChunkCollection = createChunksCollection()
-logger.info("#### createWebpageCollection() entered.")
-if 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 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.
-logger.info("#### Create page/doc and chunk 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
-      }
-    )        
-    
-    # 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
-            }
-          }
-        )
-
-###############################################################################
-# text contains prompt for vector DB.
-text = "human-made computer cognitive ability"
-
-
-###############################################################################
-# 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')
-
-#################################################################
-# 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
-           )
-
-
-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.")
+import weaviate
+
+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)
+)
+
+
+#######################################################
+# 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.")
+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}")
+
+
+######################################################
+# Connect to the Weaviate vector database.
+logger.info("#### Create Weaviate db client connection.")
+client = weaviate.connect_to_custom(
+    http_host="127.0.0.1",
+    http_port=8080,
+    http_secure=False,
+    grpc_host="127.0.0.1",
+    grpc_port=50051,
+    grpc_secure=False,
+    timeout=[600,600]
+    #read_timeout=600,
+    #write_timeout=90
+)
+client.connect() 
+
+
+######################################################
+# Create database webpage and chunks collections.
+#wpCollection = createWebpageCollection()
+#wpChunkCollection = createChunksCollection()
+logger.info("#### createWebpageCollection() entered.")
+if 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 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.
+logger.info("#### Create page/doc and chunk 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
+      }
+    )        
+    
+    # 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
+            }
+          }
+        )
+
+###############################################################################
+# text contains prompt for vector DB.
+text = "human-made computer cognitive ability"
+
+
+###############################################################################
+# 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')
+
+#################################################################
+# 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
+           )
+
+
+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.")