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'', 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("Vector Database RAG Proof of Concept
", \
unsafe_allow_html=True)
st.markdown("V1
", 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""" [INST] <> {systemTextArea.value} > 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}")