versions/2023/llm/python/doc-query-chroma.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Doc Query Using Chroma\n",
    "\n",
    "`VectorstoreIndexCreator` already uses Chroma vectorstore by default (to be verified).\n",
    "\n",
    "In this example, the Chroma vectorstore is created and initialized from a document. \n",
    "\n",
    "Let us install/update the required packages.\n",
    "\n",
    "```python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "! pip install langchain --upgrade\n",
    "! pip install openai --upgrade\n",
    "! pip install unstructured --upgrade\n",
    "! pip install pypdf --upgrade\n",
    "! pip install chromadb --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.document_loaders import PyPDFLoader\n",
    "from langchain.vectorstores import Chroma\n",
    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
    "import os\n",
    "import textwrap\n",
    "from langchain.llms import OpenAI"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since OpenAI embeddings will be used, the `OpenAIEmbeddings` instance is created. This requires that a valid API key is supplied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = input(\"OpenAI API Key: \")\n",
    "os.environ[\"OPENAI_API_KEY\"] = query\n",
    "embeddings = OpenAIEmbeddings()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a Chroma vectorstore from a document (pdf)\n",
    "\n",
    "After initializing the OpenAI embeddings, the chroma vectorstore is created by loading a pdf document and parsing it into pages.\n",
    "\n",
    "In this example, the `metadata` is set to the document name and page number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using embedded DuckDB without persistence: data will be transient\n",
      "No embedding_function provided, using default embedding function: SentenceTransformerEmbeddingFunction\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UP_Academic_Information.pdf number of pages = 69\n"
     ]
    }
   ],
   "source": [
    "# sample pdf: https://ac.upd.edu.ph/acmedia/images/newpdfs/UP_Academic_Information.pdf\n",
    "\n",
    "pdf_url = input(\"Enter pdf url: \")\n",
    "loader = PyPDFLoader(pdf_url)\n",
    "document = loader.load()      \n",
    "pages = loader.load_and_split()\n",
    "\n",
    "# for the url, get the document name\n",
    "document_name = pdf_url.split(\"/\")[-1]\n",
    "document_len = len(pages)\n",
    "print(f\"{document_name} number of pages = {document_len}\")\n",
    "\n",
    "vectorstore = Chroma.from_documents(documents=pages, embeddings=embeddings, \\\n",
    "                                    metadatas=[{\"source\": f\"{document_name}:page-{i}\"} for i in range(document_len)])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a QA Chain\n",
    "\n",
    "A chain for document question answering is created. This will be used to process queries about the document and pass them to the LLM to retrieve the answer.\n",
    "\n",
    "There are several chain_types. In this example, `map_rerank` is used to overcome the limitations of max tokens by breaking the document into chunks (eg pages) and reranking the results as returned by LLM. The result with the highest score is returned. \n",
    "\n",
    "More info can be found in the [LangChain documentation](https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html) and this [twitter post](https://twitter.com/wooing0306/status/1645092115914063872).\n",
    "\n",
    "```python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm = OpenAI(temperature=0)\n",
    "\n",
    "from langchain.chains.qa_with_sources import load_qa_with_sources_chain\n",
    "chain = load_qa_with_sources_chain(llm, chain_type=\"map_rerank\",)\n",
    "\n",
    "# if w/o sources\n",
    "#from langchain.chains.question_answering import load_qa_chain\n",
    "#chain = load_qa_chain(llm, chain_type=\"stuff\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Human: : How many years to finish a bachelor's degree?\n",
      "AI:  Four years\n",
      "Human: : What is APE?\n",
      "AI:  APE stands for Advanced Placement Examinations and is usually a written\n",
      "examination that tests the student’s knowledge and skills of the course for\n",
      "which he/she would want advanced credits in.\n",
      "Human: : Are all freshman students required to take APE?\n",
      "AI:  No, APE is only for students who wish to be considered for advanced credits\n",
      "in a course.\n",
      "Human: : When does the first semester start?\n",
      "AI:  The first semester starts in August.\n",
      "Human: : How many weeks per semester?\n",
      "AI:  16 weeks\n",
      "Human: : bye\n",
      "AI: Bye!\n"
     ]
    }
   ],
   "source": [
    "while True:\n",
    "    input_prompt = \"Human: \"\n",
    "    query = input(input_prompt)\n",
    "\n",
    "    if query.lower() == \"bye\":\n",
    "        text = f\"{input_prompt}: {query}\"\n",
    "        print(textwrap.fill(text, width=80))\n",
    "        print(\"AI: Bye!\")\n",
    "        break\n",
    "\n",
    "    docs = vectorstore.similarity_search(query, include_metadata=True)\n",
    "    result = chain.run(input_documents=docs, question=query, return_sources=True)\n",
    "    text = f\"{input_prompt}: {query}\"\n",
    "    print(textwrap.fill(text, width=80))\n",
    "    text = f\"AI: {result}\"\n",
    "    print(textwrap.fill(text, width=80))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Human: : What is CRS?\n",
      "AI: {'answer': ' The Computerized Registration System (CRS) allows UP Diliman\n",
      "enrollees, faculty, and staff to access records online. It includes features for\n",
      "creating and updating student records during and after admission, encoding of\n",
      "new course offerings, submission of class schedules, pre-enlistment,\n",
      "registration, change of matriculation, dropping, online submission of grades,\n",
      "online viewing of grades, automated assessment of fees, tagging of\n",
      "scholarship/privileges (UP faculty/employees and dependents) and STFAP brackets,\n",
      "encoding of ineligibilities and accountabilities, students’ evaluation of\n",
      "teacher, management of enlistment priority, online advising, and generation of\n",
      "reports.', 'sources': ''}\n",
      "Human: : What is role of a registrar?\n",
      "AI: {'answer': ' The registrar is responsible for overseeing the registration\n",
      "process, including creating and updating student records, encoding of new course\n",
      "offerings, submission of class schedules, pre-enlistment, registration, change\n",
      "of matriculation, dropping, online submission of grades, online viewing of\n",
      "grades, automated assessment of fees, tagging of scholarship/privileges,\n",
      "encoding of ineligibilities and accountabilities, students’ evaluation of\n",
      "teacher, management of enlistment priority, online advising, and generation of\n",
      "reports.', 'sources': ''}\n",
      "Human: : bye\n",
      "AI: Bye!\n"
     ]
    }
   ],
   "source": [
    "# this is an alternative chain for QA with sources\n",
    "\n",
    "from langchain.chains import RetrievalQAWithSourcesChain\n",
    "\n",
    "chain = RetrievalQAWithSourcesChain.from_chain_type(OpenAI(temperature=0), chain_type=\"map_rerank\", \\\n",
    "    retriever=vectorstore.as_retriever())\n",
    "\n",
    "\n",
    "while True:\n",
    "    input_prompt = \"Human: \"\n",
    "    query = input(input_prompt)\n",
    "\n",
    "    if query.lower() == \"bye\":\n",
    "        text = f\"{input_prompt}: {query}\"\n",
    "        print(textwrap.fill(text, width=80))\n",
    "        print(\"AI: Bye!\")\n",
    "        break\n",
    "\n",
    "    result = chain({\"question\": query}, return_only_outputs=True)\n",
    "    text = f\"{input_prompt}: {query}\"\n",
    "    print(textwrap.fill(text, width=80))\n",
    "    text = f\"AI: {result}\"\n",
    "    print(textwrap.fill(text, width=80))"
   ]
  }
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