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semsearch_Hld02.py

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+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
+import time
+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)
+
+
+
+#################################################################
+# Connect to Weaviate vector database.
+#################################################################
+client = ""
+def connectToWeaviateDB():
+    ######################################################
+    # 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
+    )
+    client.connect() 
+
+
+#######################################################
+# Read each text input file, parse it into a document,
+# chunk it, collect chunks and document name.
+#######################################################
+webpageDocNames       = []
+page_contentArray     = []
+webpageTitles         = []
+webpageChunks         = []
+webpageChunksDocNames = []
+
+def readParseChunkFiles():
+    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}")
+
+#################################################################
+# Create the chunks collection for the Weaviate database.
+#################################################################
+def createChunksCollection():
+    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
+                    }
+                }  
+            }
+        ]
+    }
+    return(client.collections.create_from_dict(class_obj))
+
+
+#####################################################################
+# Create the document collection for the Weaviate database.
+#####################################################################
+def createWebpageCollection():
+    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"
+                    }
+                }
+            }
+        ]  
+    }
+    return(client.collections.create_from_dict(class_obj))
+
+
+#################################################################
+# Create document and chunk objects in database.
+#################################################################
+def createDatabaseObjects():
+    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
+                }
+              }
+            )
+
+
+#################################################################
+# Create display widgets.
+#################################################################
+output_widget = ""
+systemTextArea = ""
+userTextArea = ""
+ragPromptTextArea = ""
+responseTextArea = ""
+selectRag = ""
+submitButton = ""
+def createWidgets():
+    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)
+    )
+
+
+######################################################################
+# MAINLINE
+######################################################################
+logger.info("#### MAINLINE ENTERED.")
+
+#pathString = "/Users/660565/KPSAllInOne/ProgramFilesX86/WebCopy/DownloadedWebSites/LLMPOC_HTML" 
+pathString = "/app/inputDocs"
+chunks = []
+webpageDocNames = []
+page_contentArray = []
+webpageChunks = []
+webpageTitles = []
+webpageChunksDocNames = []
+
+#connectToWeaviateDB()
+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
+)
+client.connect() 
+
+readParseChunkFiles()
+wpCollection = createWebpageCollection()
+wpChunkCollection = createChunksCollection()
+
+#createDatabaseObjects()
+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']}")
+
+logger.info("#### Closing client db connection.")
+client.close()
+
+logger.info("#### Program terminating.")