GPT4all-langchain-demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5a005358",
   "metadata": {},
   "source": [
    "# GPT4All Langchain Demo\n",
    "\n",
    "Example of locally running [`GPT4All`](https://github.com/nomic-ai/gpt4all), a 4GB, *llama.cpp* based large language model (LLM) under [`langchain`](https://github.com/hwchase17/langchain), in a Jupyter notebook running a Python 3.10 kernel.\n",
    "\n",
    "*Tested on a mid-2015 16GB Macbook Pro, concurrently running Docker (a single container running a sepearate Jupyter server) and Chrome with approx. 40 open tabs).*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d226d19c",
   "metadata": {},
   "source": [
    "## Model preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dde35bcc",
   "metadata": {},
   "source": [
    "- download `gpt4all` model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5237686",
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://the-eye.eu/public/AI/models/nomic-ai/gpt4all/gpt4all-lora-quantized.bin"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57520ec5",
   "metadata": {},
   "source": [
    "- download `llama.cpp` 7B model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "37bed5fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#%pip install pyllama\n",
    "#!python3.10 -m llama.download --model_size 7B --folder llama/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c21e391",
   "metadata": {},
   "source": [
    "- transform `gpt4all` model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c11c606",
   "metadata": {},
   "outputs": [],
   "source": [
    "#%pip install pyllamacpp\n",
    "#!pyllamacpp-convert-gpt4all ./gpt4all-main/chat/gpt4all-lora-quantized.bin llama/tokenizer.model ./gpt4all-main/chat/gpt4all-lora-q-converted.bin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a858d90f",
   "metadata": {},
   "outputs": [],
   "source": [
    "GPT4ALL_MODEL_PATH = \"./gpt4all-main/chat/gpt4all-lora-q-converted.bin\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72d09e27",
   "metadata": {},
   "source": [
    "## `langchain` Demo\n",
    "\n",
    "Example of running a prompt using `langchain`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "65c9ca4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://python.langchain.com/en/latest/ecosystem/llamacpp.html\n",
    "#%pip uninstall -y langchain\n",
    "#%pip install --upgrade git+https://github.com/hwchase17/langchain.git\n",
    "\n",
    "from langchain.llms import LlamaCpp\n",
    "from langchain import PromptTemplate, LLMChain"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "500eb501",
   "metadata": {},
   "source": [
    "- set up prompt template:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "60202ce2",
   "metadata": {},
   "outputs": [],
   "source": [
    "template = \"\"\"\n",
    "Question: {question}\n",
    "Answer: Let's think step by step.\n",
    "\"\"\"\n",
    "\n",
    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad12a227",
   "metadata": {},
   "source": [
    "- load model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5d83c596",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "llama_model_load: loading model from './gpt4all-main/chat/gpt4all-lora-q-converted.bin' - please wait ...\n",
      "llama_model_load: n_vocab = 32001\n",
      "llama_model_load: n_ctx   = 512\n",
      "llama_model_load: n_embd  = 4096\n",
      "llama_model_load: n_mult  = 256\n",
      "llama_model_load: n_head  = 32\n",
      "llama_model_load: n_layer = 32\n",
      "llama_model_load: n_rot   = 128\n",
      "llama_model_load: f16     = 2\n",
      "llama_model_load: n_ff    = 11008\n",
      "llama_model_load: n_parts = 1\n",
      "llama_model_load: type    = 1\n",
      "llama_model_load: ggml map size = 4017.70 MB\n",
      "llama_model_load: ggml ctx size =  81.25 KB\n",
      "llama_model_load: mem required  = 5809.78 MB (+ 2052.00 MB per state)\n",
      "llama_model_load: loading tensors from './gpt4all-main/chat/gpt4all-lora-q-converted.bin'\n",
      "llama_model_load: model size =  4017.27 MB / num tensors = 291\n",
      "llama_init_from_file: kv self size  =  512.00 MB\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 572 ms, sys: 711 ms, total: 1.28 s\n",
      "Wall time: 1.42 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "llm = LlamaCpp(model_path=GPT4ALL_MODEL_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a40b30f6",
   "metadata": {},
   "source": [
    "- create language chain using prompt template and loaded model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "112a5b4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5351ee46",
   "metadata": {},
   "source": [
    "- run prompt:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "41b6cbbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5min 2s, sys: 4.17 s, total: 5min 6s\n",
      "Wall time: 43.7 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'1) The year Justin Bieber was born (2005):\\n2) Justin Bieber was born on March 1, 1994:\\n3) The Buffalo Bills won Super Bowl XXVIII over the Dallas Cowboys in 1994:\\nTherefore, the NFL team that won the Super Bowl in the year Justin Bieber was born is the Buffalo Bills.'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "question = \"What NFL team won the Super Bowl in the year Justin Bieber was born?\"\n",
    "\n",
    "llm_chain.run(question)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9b80c84",
   "metadata": {},
   "source": [
    "Another example..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a2946ade",
   "metadata": {},
   "outputs": [],
   "source": [
    "template = \"\"\"\n",
    "Question: {question}\n",
    "Answer: \n",
    "\"\"\"\n",
    "\n",
    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "12b59f4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3d56cc93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 14min 37s, sys: 5.56 s, total: 14min 42s\n",
      "Wall time: 2min 4s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"A relational database is a type of database management system (DBMS) that stores data in tables where each row represents one entity or object (e.g., customer, order, or product), and each column represents a property or attribute of the entity (e.g., first name, last name, email address, or shipping address).\\n\\nACID stands for Atomicity, Consistency, Isolation, Durability:\\n\\nAtomicity: The transaction's effects are either all applied or none at all; it cannot be partially applied. For example, if a customer payment is made but not authorized by the bank, then the entire transaction should fail and no changes should be committed to the database.\\nConsistency: Once a transaction has been committed, its effects should be durable (i.e., not lost), and no two transactions can access data in an inconsistent state. For example, if one transaction is in progress while another transaction attempts to update the same data, both transactions should fail.\\nIsolation: Each transaction should execute without interference from other concurrently executing transactions, thereby ensuring its properties are applied atomically and consistently. For example, two transactions cannot affect each other's data\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "question = \"What is a relational database and what is ACID in that context?\"\n",
    "\n",
    "llm_chain.run(question2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99219ee6",
   "metadata": {},
   "source": [
    "## Generating Embeddings\n",
    "\n",
    "We can also use the model to generate embddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "110cace5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "llama_model_load: loading model from './gpt4all-main/chat/gpt4all-lora-q-converted.bin' - please wait ...\n",
      "llama_model_load: n_vocab = 32001\n",
      "llama_model_load: n_ctx   = 512\n",
      "llama_model_load: n_embd  = 4096\n",
      "llama_model_load: n_mult  = 256\n",
      "llama_model_load: n_head  = 32\n",
      "llama_model_load: n_layer = 32\n",
      "llama_model_load: n_rot   = 128\n",
      "llama_model_load: f16     = 2\n",
      "llama_model_load: n_ff    = 11008\n",
      "llama_model_load: n_parts = 1\n",
      "llama_model_load: type    = 1\n",
      "llama_model_load: ggml map size = 4017.70 MB\n",
      "llama_model_load: ggml ctx size =  81.25 KB\n",
      "llama_model_load: mem required  = 5809.78 MB (+ 2052.00 MB per state)\n",
      "llama_model_load: loading tensors from './gpt4all-main/chat/gpt4all-lora-q-converted.bin'\n",
      "llama_model_load: model size =  4017.27 MB / num tensors = 291\n",
      "llama_init_from_file: kv self size  =  512.00 MB\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 548 ms, sys: 795 ms, total: 1.34 s\n",
      "Wall time: 1.36 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#https://abetlen.github.io/llama-cpp-python/\n",
    "#%pip uninstall -y llama-cpp-python\n",
    "#%pip install --upgrade llama-cpp-python\n",
    "\n",
    "from langchain.embeddings import LlamaCppEmbeddings\n",
    "\n",
    "llama_embeddings = LlamaCppEmbeddings(model_path=GPT4ALL_MODEL_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c90c768e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9.71 s, sys: 1.5 s, total: 11.2 s\n",
      "Wall time: 1.59 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "text = \"This is a test document.\"\n",
    "\n",
    "query_result = llama_embeddings.embed_query(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7e8efbf4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 10.4 s, sys: 59.7 ms, total: 10.4 s\n",
      "Wall time: 1.47 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "doc_result = llama.embed_documents([text])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20f62806",
   "metadata": {},
   "source": [
    "## Example Query Supported by a Document Based Knowledge Source\n",
    "\n",
    "Example document query using the example from the [`langchain` docs](https://python.langchain.com/en/latest/use_cases/question_answering.html).\n",
    "\n",
    "The idea is to run the query against a document source to retrieve some relevant context, and use that as part of the prompt context."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "84ca9bb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://python.langchain.com/en/latest/use_cases/question_answering.html\n",
    "\n",
    "template = \"\"\"\n",
    "Question: {question}\n",
    "Answer: \n",
    "\"\"\"\n",
    "\n",
    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
    "\n",
    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38c43eca",
   "metadata": {},
   "source": [
    "A naive prompt gives an irrelevant answer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "aa8c287c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 58.3 s, sys: 3.59 s, total: 1min 1s\n",
      "Wall time: 9.75 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nAnswer: The Pittsburgh Steelers'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
    "llm_chain.run(question)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d8fe74f",
   "metadata": {},
   "source": [
    "Now let's try with a source document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "62169830",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!wget https://raw.githubusercontent.com/hwchase17/langchainjs/main/examples/state_of_the_union.txt\n",
    "from langchain.document_loaders import TextLoader\n",
    "\n",
    "# Ideally....\n",
    "loader = TextLoader('./state_of_the_union.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2baf9db9",
   "metadata": {},
   "source": [
    "However, creating the embeddings is qute slow so I'm going to use a fragment of the text:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "b8d2d522",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. We can do both. At our border, we’ve installed new technology like cutting-edge"
     ]
    }
   ],
   "source": [
    "#ish via chatgpt...\n",
    "def search_context(src, phrase, buffer=100):\n",
    "    with open(src, 'r') as f:\n",
    "        txt=f.read()\n",
    "\n",
    "    words = txt.split()\n",
    "    index = words.index(phrase)\n",
    "    start_index = max(0, index - buffer)\n",
    "    end_index = min(len(words), index + buffer+1)\n",
    "    return ' '.join(words[start_index:end_index])\n",
    "\n",
    "fragment = './fragment.txt'\n",
    "with open(fragment, 'w') as fo:\n",
    "    _txt = search_context('./state_of_the_union.txt', \"Ketanji\")\n",
    "    fo.write(_txt)\n",
    "\n",
    "!cat $fragment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "10480de4",
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = TextLoader('./fragment.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "4d4dea15",
   "metadata": {},
   "outputs": [],
   "source": [
    "#%pip install chromadb\n",
    "from langchain.indexes import VectorstoreIndexCreator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92170711",
   "metadata": {},
   "source": [
    "Generate an index from the knowledge source text:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "3eff4ff5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using embedded DuckDB with persistence: data will be stored in: db\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2 µs, sys: 2 µs, total: 4 µs\n",
      "Wall time: 7.87 µs\n"
     ]
    }
   ],
   "source": [
    "%time\n",
    "# Time: ~0.5s per token\n",
    "# NOTE: \"You must specify a persist_directory oncreation to persist the collection.\"\n",
    "# TO DO: How do we load in an already generated and persisted index?\n",
    "index = VectorstoreIndexCreator(embedding=llama_embeddings,\n",
    "                                vectorstore_kwargs={\"persist_directory\": \"db\"}\n",
    "                               ).from_loaders([loader])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "21107c72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2 µs, sys: 2 µs, total: 4 µs\n",
      "Wall time: 10 µs\n"
     ]
    }
   ],
   "source": [
    "%time\n",
    "pass\n",
    "\n",
    "# The following errors...\n",
    "#index.query(query, llm=llm)\n",
    "\n",
    "# With the full SOTH text, I got:\n",
    "# Error: llama_tokenize: too many tokens;\n",
    "# Also getting:\n",
    "# ValueError: Requested tokens exceed context window of 512\n",
    "# If we do get passed that, \n",
    "# NotEnoughElementsException\n",
    "# For the latter, somehow need to set something like search_kwargs={\"k\": 1}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81059984",
   "metadata": {},
   "source": [
    "It seems the retriever is expecting, by default, 4 results documents. I can't see how to pass in a lower limit (a single response document is acceptable in this case), so we nd to roll our own chain..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "035d144a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using embedded DuckDB without persistence: data will be transient\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5min 59s, sys: 1.62 s, total: 6min 1s\n",
      "Wall time: 49.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Roll our own....\n",
    "\n",
    "#https://github.com/hwchase17/langchain/issues/2255\n",
    "from langchain.vectorstores import Chroma\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from langchain.chains import RetrievalQA\n",
    "\n",
    "documents = loader.load()\n",
    "\n",
    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n",
    "texts = text_splitter.split_documents(documents)\n",
    "\n",
    "# Again, we should persist the db and figure out how to reuse it\n",
    "docsearch = Chroma.from_documents(texts, llama_embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "b6c9d033",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 861 µs, sys: 2.97 ms, total: 3.83 ms\n",
      "Wall time: 7.09 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Just getting a single result document from the knowledge lookup is fine...\n",
    "MIN_DOCS = 1\n",
    "\n",
    "qa = RetrievalQA.from_chain_type(llm=llm, chain_type=\"stuff\",\n",
    "                                 retriever=docsearch.as_retriever(search_kwargs={\"k\": MIN_DOCS}))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bb80c55",
   "metadata": {},
   "source": [
    "What do we get in response to our original query now?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "05dcdc74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What did the president say about Ketanji Brown Jackson\n",
      "CPU times: user 7min 39s, sys: 2.59 s, total: 7min 42s\n",
      "Wall time: 1min 6s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "' The president honored Justice Stephen Breyer and acknowledged his service to this country before introducing Justice Ketanji Brown Jackson, who will be serving as the newest judge on the United States Court of Appeals for the District of Columbia Circuit.'"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "print(query)\n",
    "\n",
    "qa.run(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "edf7ca99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 10min 20s, sys: 4.2 s, total: 10min 24s\n",
      "Wall time: 1min 35s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "' The president said that she was nominated by Barack Obama to become the first African American woman to sit on the United States Court of Appeals for the District of Columbia Circuit. He also mentioned that she was an Army veteran, a Constitutional scholar, and is retiring Justice of the United States Supreme Court.'"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "query = \"Identify three things the president said about Ketanji Brown Jackson\"\n",
    "\n",
    "qa.run(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "be183a65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 12min 31s, sys: 4.24 s, total: 12min 35s\n",
      "Wall time: 1min 45s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"\\n\\nITEM 1: President Trump honored Justice Breyer for his service to this country, but did not specifically mention Ketanji Brown Jackson.\\n\\nITEM 2: The president did not identify any specific characteristics about Justice Breyer that would be useful in identifying her.\\n\\nITEM 3: The president did not make any reference to Justice Breyer's current or past judicial rulings or cases during his speech.\""
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "query = \"\"\"\n",
    "Identify three things the president said about Ketanji Brown Jackson. Provide the answer in the form: \n",
    "\n",
    "- ITEM 1\n",
    "- ITEM 2\n",
    "- ITEM 3\n",
    "\"\"\"\n",
    "\n",
    "qa.run(query)"
   ]
  }
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