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HF Deploy.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n",
+      "Created 915 chunks from 2 PDF files\n",
+      "Query: What are the key principles of the AI Bill of Rights?\n",
+      "\n",
+      "Response:\n",
+      "The key principles of the AI Bill of Rights are civil rights, civil liberties, and privacy.\n",
+      "\n",
+      "Context used:\n",
+      "1. use, and deployment of automated systems to protect the rights of the American public in the age of ...\n",
+      "2. civil rights, civil liberties, and privacy. The Blueprint for an AI Bill of Rights includes this For...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Cell 1: Install required packages\n",
+    "%pip install langchain openai chromadb PyPDF2 tiktoken -qU\n",
+    "\n",
+    "# Cell 2: Import necessary modules\n",
+    "import os\n",
+    "import tempfile\n",
+    "import aiohttp\n",
+    "import asyncio\n",
+    "import getpass\n",
+    "from io import BytesIO\n",
+    "from typing import List\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain.document_loaders import PyPDFLoader\n",
+    "from langchain.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate\n",
+    "from langchain.vectorstores import Chroma\n",
+    "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from PyPDF2 import PdfReader\n",
+    "\n",
+    "\n",
+    "# Cell 4: Set up prompts\n",
+    "system_template = \"Use the following context to answer a user's question. If you cannot find the answer in the context, say you don't know the answer.\"\n",
+    "system_role_prompt = SystemMessagePromptTemplate.from_template(system_template)\n",
+    "\n",
+    "user_prompt_template = \"Context:\\n{context}\\n\\nQuestion:\\n{question}\"\n",
+    "user_role_prompt = HumanMessagePromptTemplate.from_template(user_prompt_template)\n",
+    "\n",
+    "# Cell 5: Define RetrievalAugmentedQAPipeline class\n",
+    "class RetrievalAugmentedQAPipeline:\n",
+    "    def __init__(self, llm: ChatOpenAI, vector_db: Chroma) -> None:\n",
+    "        self.llm = llm\n",
+    "        self.vector_db = vector_db\n",
+    "\n",
+    "    async def arun_pipeline(self, user_query: str):\n",
+    "        context_docs = self.vector_db.similarity_search(user_query, k=2)  # Reduced from 4 to 2\n",
+    "        context_list = [doc.page_content for doc in context_docs]\n",
+    "        context_prompt = \"\\n\".join(context_list)\n",
+    "        \n",
+    "        # Implement a simple truncation to ensure we don't exceed token limit\n",
+    "        max_context_length = 12000  # Adjust this value as needed\n",
+    "        if len(context_prompt) > max_context_length:\n",
+    "            context_prompt = context_prompt[:max_context_length]\n",
+    "        \n",
+    "        formatted_system_prompt = system_role_prompt.format()\n",
+    "        formatted_user_prompt = user_role_prompt.format(question=user_query, context=context_prompt)\n",
+    "\n",
+    "        async def generate_response():\n",
+    "            async for chunk in self.llm.astream([formatted_system_prompt, formatted_user_prompt]):\n",
+    "                yield chunk.content\n",
+    "\n",
+    "        return {\"response\": generate_response(), \"context\": context_list}\n",
+    "\n",
+    "# Cell 6: PDF processing functions\n",
+    "async def fetch_pdf(session, url):\n",
+    "    async with session.get(url) as response:\n",
+    "        if response.status == 200:\n",
+    "            return await response.read()\n",
+    "        else:\n",
+    "            print(f\"Failed to fetch PDF from {url}\")\n",
+    "            return None\n",
+    "\n",
+    "async def process_pdf(pdf_content):\n",
+    "    pdf_reader = PdfReader(BytesIO(pdf_content))\n",
+    "    text = \"\\n\".join([page.extract_text() for page in pdf_reader.pages])\n",
+    "    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=40)\n",
+    "    return text_splitter.split_text(text)\n",
+    "\n",
+    "# Cell 7: Main execution\n",
+    "async def main():\n",
+    "    # Ensure API key is set\n",
+    "    api_key = get_openai_api_key()\n",
+    "\n",
+    "    # List of PDF URLs\n",
+    "    pdf_urls = [\n",
+    "        \"https://www.whitehouse.gov/wp-content/uploads/2022/10/Blueprint-for-an-AI-Bill-of-Rights.pdf\",\n",
+    "        \"https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf\",\n",
+    "    ]\n",
+    "\n",
+    "    all_chunks = []\n",
+    "    async with aiohttp.ClientSession() as session:\n",
+    "        pdf_contents = await asyncio.gather(*[fetch_pdf(session, url) for url in pdf_urls])\n",
+    "        \n",
+    "    for pdf_content in pdf_contents:\n",
+    "        if pdf_content:\n",
+    "            chunks = await process_pdf(pdf_content)\n",
+    "            all_chunks.extend(chunks)\n",
+    "\n",
+    "    print(f\"Created {len(all_chunks)} chunks from {len(pdf_urls)} PDF files\")\n",
+    "\n",
+    "    embeddings = OpenAIEmbeddings(openai_api_key=api_key)\n",
+    "    vector_db = Chroma.from_texts(all_chunks, embeddings)\n",
+    "    \n",
+    "    chat_openai = ChatOpenAI(openai_api_key=api_key)\n",
+    "    retrieval_augmented_qa_pipeline = RetrievalAugmentedQAPipeline(vector_db=vector_db, llm=chat_openai)\n",
+    "    \n",
+    "    # Example query\n",
+    "    query = \"What are the key principles of the AI Bill of Rights?\"\n",
+    "    result = await retrieval_augmented_qa_pipeline.arun_pipeline(query)\n",
+    "    \n",
+    "    print(\"Query:\", query)\n",
+    "    print(\"\\nResponse:\")\n",
+    "    async for chunk in result[\"response\"]:\n",
+    "        print(chunk, end=\"\")\n",
+    "    print(\"\\n\\nContext used:\")\n",
+    "    for i, context in enumerate(result[\"context\"], 1):\n",
+    "        print(f\"{i}. {context[:100]}...\")\n",
+    "\n",
+    "# Cell 8: Run the main function\n",
+    "await main()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

requirements.txt

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+streamlit
+langchain
+openai
+chromadb
+PyPDF2
+tiktoken
+aiohttp