p-chat
This commit is contained in:
Ransford Okpoti
353
week5/community-contributions/ranskills-week5-p-chat.ipynb
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353
week5/community-contributions/ranskills-week5-p-chat.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "1f2969c8",
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"metadata": {},
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"source": [
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"# P-Chat 🔒💬\n",
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"\n",
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"A privacy-focused bring-your-own-document (BYOD) solution that empowers you to leverage the power of LLMs to interact with your documents. Nothing is persisted, and it exists entirely in ephemeral memory.\n",
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"\n",
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"## Features\n",
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"- Parent-child chunking used to enrich the context\n",
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"- Chunk augmentation with some parent data for structured documents\n",
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"- Streamed responses for better user experience\n",
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"- Secure by design; no data is stored permanently\n",
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"- Uses locally-running Ollama for total privacy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "df7609cf",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install -qU langchain_ollama langchain_chroma langchain_community"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "144bdf7c",
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"metadata": {},
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"outputs": [],
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"source": [
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"import logging\n",
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"import sys\n",
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"from pathlib import Path\n",
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"from enum import StrEnum\n",
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"\n",
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"import gradio as gr\n",
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"from langchain_core.documents import Document\n",
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"from langchain_text_splitters import RecursiveCharacterTextSplitter, MarkdownHeaderTextSplitter\n",
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"from langchain_ollama import OllamaEmbeddings, ChatOllama\n",
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"from langchain.storage import InMemoryStore\n",
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"from langchain_chroma import Chroma\n",
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"from langchain_community.document_loaders import TextLoader"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "dfdb143d",
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"metadata": {},
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"outputs": [],
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"source": [
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"logger = logging.getLogger('rag')\n",
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"logger.setLevel(logging.DEBUG)\n",
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"\n",
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"if not logger.handlers:\n",
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" handler = logging.StreamHandler(sys.stdout)\n",
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" formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n",
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" handler.setFormatter(formatter)\n",
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" logger.addHandler(handler)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0e2f176b",
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"metadata": {},
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"source": [
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"## RAG Pipeline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "78f2a554",
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"metadata": {},
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"outputs": [],
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"source": [
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"def pretty_print(l: list[Document | tuple[Document, float]]):\n",
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" for i,item in enumerate(l, start=1):\n",
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" logger.debug('-' * 80 + '\\n')\n",
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"\n",
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" if isinstance(item, tuple):\n",
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" doc, score = item\n",
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" logger.debug(f'{i}. characters: {len(doc.page_content)}\\n')\n",
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" logger.debug(f'Score: {score}\\nMetadata: {doc.metadata}\\nContent: {doc.page_content}')\n",
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" else:\n",
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" logger.debug(f'{i}. characters: {len(item.page_content)}\\n')\n",
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" logger.debug(f'Metadata: {item.metadata}\\nContent: {item.page_content}')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "42893f0b",
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"metadata": {},
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"source": [
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"### Indexing\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "20ad0e80",
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"metadata": {},
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"outputs": [],
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"source": [
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"model_id = 'qwen3:0.6b'\n",
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"embedding_model = 'nomic-embed-text:latest'\n",
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"\n",
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"embeddings = OllamaEmbeddings(model=embedding_model)\n",
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"model = ChatOllama(model=model_id, temperature=0.1)\n",
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"\n",
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"vectorstore = Chroma(\n",
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" collection_name='p-chat',\n",
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" embedding_function=embeddings,\n",
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")\n",
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"docstore = InMemoryStore()\n",
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"\n",
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"class Metadata(StrEnum):\n",
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" ID = 'id'\n",
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" PARENT_ID = 'parent_id'\n",
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" SOURCE = 'source'\n",
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" FILE_TYPE = 'file_type'\n",
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"\n",
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"\n",
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"LOADER_MAPPING = {\n",
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" '.md': TextLoader,\n",
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" '.txt': TextLoader, \n",
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"}\n",
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"\n",
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"def load_documents(file_path: Path) -> list[Document]:\n",
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" # p = Path(file_path)\n",
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" extension = file_path.suffix\n",
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" logger.info(f'Loading loader for {extension}')\n",
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" loader_cls = LOADER_MAPPING.get(extension)\n",
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"\n",
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" if loader_cls is None:\n",
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" logger.warning(f'No loader configured for {extension}')\n",
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" return []\n",
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" \n",
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" loader = loader_cls(file_path)\n",
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" documents = loader.load()\n",
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" logger.info(f'{len(documents)} loaded for {file_path.name}')\n",
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"\n",
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" return documents\n",
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"\n",
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"\n",
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"def preprocess(documents: list[Document]) -> list[Document]:\n",
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" # Perform any cleaning, etc.\n",
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" import uuid\n",
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"\n",
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" for doc in documents:\n",
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" metadata = doc.metadata\n",
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" shortened_source = metadata.get('source').split('/')[-1]\n",
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"\n",
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" metadata[Metadata.ID] = str(uuid.uuid4())\n",
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" metadata[Metadata.SOURCE] = shortened_source\n",
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" metadata[Metadata.FILE_TYPE] = shortened_source.split('.')[-1]\n",
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"\n",
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" return documents\n",
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"\n",
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"\n",
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"def index_document(file_path):\n",
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" documents = load_documents(Path(file_path))\n",
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" preprocessed_docs = preprocess(documents)\n",
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" logger.debug([doc.metadata for doc in preprocessed_docs])\n",
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"\n",
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" for doc in preprocessed_docs:\n",
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" chunks = chunk_documents(doc)\n",
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"\n",
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" vectorstore.add_documents(chunks)\n",
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" docstore.mset([(doc.metadata.get(Metadata.ID) , doc)])\n",
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"\n",
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"\n",
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"def chunk_documents(parent: Document) -> list[Document]:\n",
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" if parent.metadata.get(Metadata.FILE_TYPE) == '.md':\n",
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" headers_to_split_on = [\n",
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" ('#', 'employee_name'),\n",
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" ('##', 'section'),\n",
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" ('###', 'Header 3'),\n",
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" ] \n",
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" markdown_splitter = MarkdownHeaderTextSplitter(\n",
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" headers_to_split_on=headers_to_split_on\n",
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" )\n",
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" chunks = markdown_splitter.split_text(parent.page_content) \n",
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" else:\n",
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" text_splitter = RecursiveCharacterTextSplitter(\n",
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" chunk_size=400,\n",
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" chunk_overlap=80,\n",
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" separators=['\\n\\n', '\\n', ' ', '']\n",
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" )\n",
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" chunks = text_splitter.split_text(parent.page_content)\n",
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"\n",
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" children = []\n",
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" parent_id = parent.metadata.get(Metadata.ID)\n",
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" for i, chunk in enumerate(chunks, start=1):\n",
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" if isinstance(chunk, Document):\n",
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" metadata = {**parent.metadata, **chunk.metadata}\n",
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" augmented_text = f'[Employee: {metadata.get('employee_name')}] '\n",
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" content = augmented_text + chunk.page_content\n",
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" else:\n",
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" # chunk is a text\n",
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" metadata = parent.metadata.copy()\n",
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" content = chunk\n",
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"\n",
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" metadata.update({\n",
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" Metadata.ID: f'{parent_id}-{i}',\n",
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" Metadata.PARENT_ID: parent_id,\n",
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" })\n",
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" children.append(Document(page_content=content, metadata=metadata))\n",
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"\n",
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" logger.debug(f'Number chunks: {len(children)}, Parent ID: {parent_id}')\n",
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" \n",
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" return children"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a90db6ee",
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"metadata": {},
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"source": [
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"### LLM Interaction"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e2e15e99",
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"metadata": {},
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"outputs": [],
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"source": [
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"def retrieve_context(query) -> str:\n",
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" results = vectorstore.similarity_search(query)\n",
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" logger.info(f'Matching records: {len(results)}')\n",
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" selected_parents = {}\n",
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" for result in results:\n",
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" parent_id = result.metadata.get('parent_id')\n",
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" if parent_id in selected_parents:\n",
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" continue\n",
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"\n",
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" parents = docstore.mget([parent_id])\n",
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" selected_parents[parent_id] = parents[0]\n",
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"\n",
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" logger.info(f'Selected documents for query: {query} ids:{selected_parents.keys()}')\n",
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" context = '\\n\\n'.join([doc.page_content for _,doc in selected_parents.items() if doc is not None])\n",
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"\n",
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" return context\n",
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"\n",
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" \n",
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"def ask(message, history):\n",
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" context = retrieve_context(message)\n",
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" prompt = f'''\n",
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" You are helpful assistant that answers a question based on the provided context.\n",
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" If the context is not helpful to you in answering the question, say so.\n",
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" Be concise with your responses.\n",
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"\n",
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" Context:\n",
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" {context}\n",
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" '''\n",
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"\n",
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" messages = [\n",
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" ('system', prompt),\n",
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" ('user', message)\n",
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" ]\n",
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"\n",
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" stream = model.stream(messages)\n",
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" response_text = ''\n",
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"\n",
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" for chunk in stream:\n",
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" response_text += chunk.content or ''\n",
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" if not response_text:\n",
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" continue\n",
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"\n",
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" yield response_text"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c3e632dc-9e87-4510-9fcd-aa699c27e82b",
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"metadata": {
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"jp-MarkdownHeadingCollapsed": true
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},
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"source": [
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"## Gradio UI"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a3d68a74",
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"metadata": {},
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"outputs": [],
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"source": [
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"def chat(message, history):\n",
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" if message is None:\n",
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" return ''\n",
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"\n",
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" text_input = message.get('text', '')\n",
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" files_uploaded = message.get('files', [])\n",
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" \n",
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" latest_file_path = files_uploaded[-1] if files_uploaded else None\n",
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" if latest_file_path:\n",
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" index_document(latest_file_path)\n",
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"\n",
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"\n",
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" if not text_input:\n",
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" yield '✅ Indexed document'\n",
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" return\n",
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"\n",
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" for chunk in ask(text_input, history):\n",
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" yield chunk\n",
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"\n",
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"title = 'P-Chat 🔒💬'\n",
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"with gr.Blocks(title=title, fill_height=True) as ui:\n",
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" gr.Markdown(f'# {title}')\n",
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" gr.Markdown('## Privacy-focused bring-your-own-document (BYOD) solution 🤫.')\n",
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"\n",
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" gr.ChatInterface(\n",
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" fn=chat,\n",
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" type='messages',\n",
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" textbox=gr.MultimodalTextbox(file_types=['text', '.txt', '.md'], autofocus=True),\n",
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" )\n",
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"\n",
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"ui.launch(debug=True)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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Block a user