debug

app.py

@@ -1,426 +0,0 @@
-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 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")
-weaviate_logger.setLevel(logging.WARNING)
-
-logger = logging.getLogger(__name__)
-logging.basicConfig(level=logging.INFO)
-
-# Function to load the CSS file
-def load_css(file_name):
-    with open(file_name) as f:
-        st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
-
-# Load the custom CSS
-load_css(".streamlit/main.css")
-
-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)
-
-######################################################################
-# MAINLINE
-#
-logger.info("#### MAINLINE ENTERED.")
-
-#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.
-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
-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:
-        userTextArea = 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:
-        ragPromptTextArea = 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.ragTA = st.text_area(label="ragTA",value=st.session_state.ragTA)
-
-    if "rspTA" not in st.session_state:
-        responseTextArea = 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):
-    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):
-    logger.debug("\n### on_submitButton_clicked")
-    st.session_state.sysTAtext = st.session_state.sysTA
-    logger.info(f"sysTAtext: {st.session_state.sysTAtext}")
-    
-    st.session_state.userpTAtext = setPrompt("","")
-    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}")
-
-
-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("#### Closing client db connection.")
-#client.close()
-#logger.info("#### Program terminating.")

semsearch.py

@@ -18,6 +18,7 @@ import llama_cpp
 from llama_cpp import Llama
 
 import streamlit as st
+import subprocess
 
 
 st.markdown("<h1 style='text-align: center; color: #666666;'>Vector Database RAG Proof of Concept</h1>", unsafe_allow_html=True)
@@ -30,6 +31,15 @@ weaviate_logger.setLevel(logging.WARNING)
 logger = logging.getLogger(__name__)
 logging.basicConfig(level=logging.INFO)
 
+
+def runStartup():
+    result = subprocess.run(["bash", "startup.sh"], capture_output=True, text=True)
+    #print("Output:", result.stdout)
+    #print("Error:", result.stderr)
+    logger(f"Return code: {result.returncode}")
+logger("### Running startup.sh")
+runStartup()
+
 # Function to load the CSS file
 def load_css(file_name):
     with open(file_name) as f:

startup.sh

@@ -62,8 +62,9 @@ env
 
 echo "#### Before sleep."
 sleep 30
+echo "#### startup.sh exiting."
 
-echo "#### Before /app/semsearch.py"
+#echo "#### Before /app/semsearch.py"
 #python /app/semsearch.py &  #2>& 1 | tee /data/var/lib/weaviate/ss.log &
 #streamlit run /app/semsearch.py &