debug

app.py

@@ -329,7 +329,7 @@ try:
                     use_mlock=False,
                     kv_overrides=None,
                     seed=llama_cpp.LLAMA_DEFAULT_SEED,
-                    n_ctx=512,
+                    n_ctx=2048,
                     n_batch=512,
                     n_threads=8,
                     n_threads_batch=16,
@@ -350,13 +350,13 @@ try:
                     lora_scale=1.0,
                     lora_path=None,
                     numa=False,
-                    chat_format=None,
+                    chat_format="llama-2",
                     chat_handler=None,
                     draft_model=None,
                     tokenizer=None,
                     type_k=None,
                     type_v=None,
-                    verbose=True
+                    verbose=False
                    )
         st.session_state.llm = llm
         logger.info("### Initializing LLM exited.")
@@ -468,11 +468,19 @@ try:
             userPrompt = pprompt + "\n" + ragPrompt
             prompt = userPrompt
             userPrompt = "Using this information: " + ragPrompt \
-                 + "process the following statement or question and produce a response" \
+                 + "process the following statement or question and produce a response. " \
                  + pprompt
         else:
-            userPrompt = st.session_state.sysTA + " " + pprompt
+            #userPrompt = st.session_state.sysTA + " " + pprompt
+            userPrompt = pprompt
         #prompt = f""" <s> [INST] <<SYS>> {systemTextArea.value} </SYS>> Q: {userPrompt} A: [/INST]"""
+        messages = [
+          {"role": "system", "content": st.session_state.sysTA},
+          {
+              "role": "user",
+              "content": userPrompt
+          }
+      ]
         logger.info("setPrompt exited.")
         logger.info(f"### userPrompt: {userPrompt}")
         return userPrompt

app_Hld01.py

@@ -0,0 +1,513 @@
+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
+
+
+try:
+    if 'logging' not in st.session_state:
+        weaviate_logger = logging.getLogger("httpx")
+        weaviate_logger.setLevel(logging.WARNING)
+        logger = logging.getLogger(__name__)
+        logging.basicConfig(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
+
+
+    def runStartup():
+        logger.info("### Running startup.sh")
+        try:
+            #result = subprocess.run("/app/startup.sh",shell=False,capture_output=None,
+            # text=None,timeout=300)
+            #logger.info(f"startup.sh stdout:  {result.stdout}")
+            #logger.info(f"startup.sh stderr:  {result.stderr}")
+            #logger.info(f"Return code: {result.returncode}")
+            subprocess.Popen(["/app/startup.sh"])
+            time.sleep(180)
+        except Exception as e:
+            emsg = str(e)
+            logger.ERROR(f"subprocess.run  EXCEPTION. e: {emsg}")
+            try:
+                with open("/app/startup.log", "r") as file:
+                    content = file.read()
+                    print(content)
+            except Exception as e2:
+                emsg = str(e2)
+                logger.ERROR(f"#### Displaying startup.log EXCEPTION. e2: {emsg}")
+        logger.info("### Running startup.sh complete")
+    if 'runStartup' not in st.session_state:
+        st.session_state.runStartup = True
+        runStartup()
+
+
+
+    ######################################################################
+    # MAINLINE
+    #
+    logger.info("#### MAINLINE ENTERED.")
+
+    # Function to load the CSS 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.")
+
+    # Load the custom CSS
+    if 'load_css' not in st.session_state:
+        load_css(".streamlit/main.css")
+        st.session_state.load_css = True
+
+    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)
+
+    #pathString = "/Users/660565/KPSAllInOne/ProgramFilesX86/WebCopy/DownloadedWebSites/LLMPOC_HTML"
+    pathString = "/app/inputDocs"
+    chunks = []
+    webpageDocNames = []
+    page_contentArray = []
+    webpageChunks = []
+    webpageTitles = []
+    webpageChunksDocNames = []
+
+    ######################################################
+    # Connect to the Weaviate vector database.
+    #if 'client' not in st.session_state:
+    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
+            )
+        )
+        client.connect()
+        st.session_state.client = client
+        logger.info("#### Create Weaviate db client connection exited.")
+    else:
+        client = st.session_state.client
+
+
+    #######################################################
+    # Read each text input file, parse it into a document,
+    # chunk it, collect chunks and document name.
+    if not client.collections.exists("Documents") or not client.collections.exists("Chunks") :
+        logger.info("#### Read and chunk input text 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.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}")
+        logger.info("#### Read and chunk input text files exited.")
+
+
+
+    ######################################################
+    # Create database webpage and chunks collections.
+    #wpCollection = createWebpageCollection()
+    #wpChunksCollection = createChunksCollection()
+    if not client.collections.exists("Documents"):
+        logger.info("#### createWebpageCollection() entered.")
+        #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)
+        st.session_state.wpCollection = wpCollection
+        logger.info("#### createWebpageCollection() exited.")
+    else:
+        wpCollection = client.collections.get("Documents")
+        st.session_state.wpCollection = wpCollection
+
+
+    if not client.collections.exists("Chunks"):
+        logger.info("#### createChunksCollection() entered.")
+        #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("#### createChunksCollection() exited.")
+    else:
+        wpChunksCollection = client.collections.get("Chunks")
+        st.session_state.wpChunksCollection = wpChunksCollection
+        
+        
+
+
+    ###########################################################
+    # 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
+              }
+            )
+        logger.info("#### Create page/doc/db/objects exited.")
+        
+    if not client.collections.exists("Chunks") :
+        logger.info("#### Create chunk db objects.")
+        # Insert the chunks for the document.
+        for i2, chunk in enumerate(webpageChunks):
+            chunk_uuid = wpChunksCollection.data.insert(
+              {
+                "title": title,
+                "chunk": chunk,
+                "chunk_index": i2,
+                "references":
+                {
+                  "webpage": wpCollectionObj_uuid
+                }
+              }
+            )
+        logger.info("#### Create chunk db objects exited.")
+
+
+    #################################################################
+    # 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=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
+                   )
+        st.session_state.llm = llm
+        logger.info("### Initializing LLM exited.")
+    else:
+        llm = st.session_state.llm
+
+    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)
+        vectorList = []
+
+        logger.debug("#### Print vectors.")
+        for vec in vector:
+            vectorList.append(vec)
+        logger.debug(f"vectorList: {vectorList[2]}")
+
+        # 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
+        )
+        logger.debug(f"### semChunks[0]: {semChunks}")
+
+        # 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.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")
+        logger.info("#### getRagData() exited.")
+        return  ragData
+
+
+    # Display UI
+    col1, col2 = st.columns(2)
+
+    with col1:
+        if "sysTA" not in st.session_state:
+            st.session_state.sysTA = st.text_area(label="sysTA",value="fdsaf fsdafdsa")
+        elif "sysTAtext" in st.session_state:
+            st.session_state.sysTA = st.text_area(label="sysTA",value=st.session_state.sysTAtext)
+        else:
+            st.session_state.sysTA = st.text_area(label="sysTA",value=st.session_state.sysTA)
+            
+        if "userpTA" not in st.session_state:
+            st.session_state.userpTA = st.text_area(label="userpTA",value="fdsaf fsdafdsa")
+        elif "userpTAtext" in st.session_state:
+            st.session_state.userpTA = st.text_area (label="userpTA",value=st.session_state.userpTAtext)
+        else:
+            st.session_state.userpTA = st.text_area(label="userpTA",value=st.session_state.userpTA)
+
+    with col2:
+        if "ragpTA" not in st.session_state:
+            st.session_state.ragpTA = st.text_area(label="ragpTA",value="fdsaf fsdafdsa")
+        elif "ragpTAtext" in st.session_state:
+            st.session_state.ragpTA = st.text_area(label="ragpTA",value=st.session_state.ragpTAtext)
+        else:
+            st.session_state.ragpTA = st.text_area(label="ragpTA",value=st.session_state.ragpTA)
+
+        if "rspTA" not in st.session_state:
+            st.session_state.rspTA = st.text_area(label="rspTA",value="fdsaf fsdafdsa")
+        elif "rspTAtext" in st.session_state:
+            st.session_state.rspTA = st.text_area(label="rspTA",value=st.session_state.rspTAtext)
+        else:
+            st.session_state.rspTA = st.text_area(label="rspTA",value=st.session_state.rspTA)
+
+    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 = llm(
+           prompt,
+           max_tokens=max_tokens,
+           temperature=temperature,
+           top_p=top_p,
+           echo=echoVal,
+           stop=stop,
+        )
+        result = modelOutput["choices"][0]["text"].strip()
+        logger.info(f"### llmResult: {result}")
+        logger.info("### runLLM exited.")
+        return result
+
+    def setPrompt(pprompt,ragFlag):
+        logger = st.session_state.logger
+        logger.info(f"\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 response. " \
+                 + pprompt
+        else:
+            userPrompt = st.session_state.sysTA + " " + pprompt
+        #prompt = f""" <s> [INST] <<SYS>> {systemTextArea.value} </SYS>> Q: {userPrompt} A: [/INST]"""
+        logger.info("setPrompt exited.")
+        logger.info(f"### userPrompt: {userPrompt}")
+        return userPrompt
+
+
+    def on_submitButton_clicked():
+        logger = st.session_state.logger
+        logger.info("### on_submitButton_clicked entered.")
+        st.session_state.sysTAtext = st.session_state.sysTA
+        logger.info(f"sysTAtext: {st.session_state.sysTAtext}")
+        
+        #st.session_state.userpTAtext = st.session_state.userpTA
+        st.session_state.userpTAtext = setPrompt(st.session_state.userpTA,st.selectRag)
+        st.session_state.userpTA = st.session_state.userpTAtext
+        logger.info(f"userpTAtext: {st.session_state.userpTAtext}")
+        
+        st.session_state.rspTAtext = runLLM(st.session_state.userpTAtext)
+        st.session_state.rspTA = st.session_state.rspTAtext
+        logger.info(f"rspTAtext: {st.session_state.rspTAtext}")
+        
+        logger.info("### on_submitButton_clicked exited.")
+
+
+    with st.sidebar:
+        st.selectRag    = st.checkbox("Enable Query With RAG",value=False,key="selectRag",help=None,on_change=None,args=None,kwargs=None,disabled=False,label_visibility="visible")
+        st.submitButton = st.button("Run LLM Query",key=None,help=None,on_click=on_submitButton_clicked,args=None,kwargs=None,type="secondary",disabled=False,use_container_width=False)
+
+    logger.info("#### semsearch.py end of code.")
+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}")