langchain/docs/examples/evaluation/data_augmented_question_answering.ipynb

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  {
   "cell_type": "markdown",
   "id": "e78b7bb1",
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   "source": [
    "# Data Augmented Question Answering\n",
    "\n",
    "This notebook uses some generic prompts/language models to evaluate an question answering system that uses other sources of data besides what is in the model. For example, this can be used to evaluate a question answering system over your propritary data.\n",
    "\n",
    "## Setup\n",
    "Let's set up an example with our favorite example - the state of the union address."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ab4a6931",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
    "from langchain.vectorstores.faiss import FAISS\n",
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "from langchain import OpenAI, VectorDBQA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4fdc211d",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../state_of_the_union.txt') as f:\n",
    "    state_of_the_union = f.read()\n",
    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
    "texts = text_splitter.split_text(state_of_the_union)\n",
    "\n",
    "embeddings = OpenAIEmbeddings()\n",
    "docsearch = FAISS.from_texts(texts, embeddings)\n",
    "qa = VectorDBQA.from_llm(llm=OpenAI(), vectorstore=docsearch)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30fd72f2",
   "metadata": {},
   "source": [
    "## Examples\n",
    "Now we need some examples to evaluate. We can do this in two ways:\n",
    "\n",
    "1. Hard code some examples ourselves\n",
    "2. Generate examples automatically, using a language model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3459b001",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hard-coded examples\n",
    "examples = [\n",
    "    {\n",
    "        \"query\": \"What did the president say about Ketanji Brown Jackson\",\n",
    "        \"answer\": \"He praised her legal ability and said he nominated her for the supreme court.\"\n",
    "    },\n",
    "    {\n",
    "        \"query\": \"What did the president say about Michael Jackson\",\n",
    "        \"answer\": \"Nothing\"\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b9c3fa75",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generated examples\n",
    "from langchain.evaluation.qa import QAGenerateChain\n",
    "example_gen_chain = QAGenerateChain.from_llm(OpenAI())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c24543a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_examples = example_gen_chain.apply_and_parse([{\"doc\": t} for t in texts[:5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a2d27560",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'query': 'What did Vladimir Putin seek to do according to the document?',\n",
       "  'answer': 'Vladimir Putin sought to shake the foundations of the free world and make it bend to his menacing ways.'},\n",
       " {'query': 'What did President Zelenskyy say in his speech to the European Parliament?',\n",
       "  'answer': 'President Zelenskyy said \"Light will win over darkness.\"'},\n",
       " {'query': \"How many countries joined the European Union in opposing Putin's attack on Ukraine?\",\n",
       "  'answer': '27'},\n",
       " {'query': 'What is the U.S. Department of Justice assembling in response to the Russian oligarchs?',\n",
       "  'answer': 'A dedicated task force.'},\n",
       " {'query': 'How much direct assistance is the US providing to Ukraine?',\n",
       "  'answer': 'The US is providing more than $1 Billion in direct assistance to Ukraine.'}]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "558da6f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine examples\n",
    "examples += new_examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "443dc34e",
   "metadata": {},
   "source": [
    "## Evaluate\n",
    "Now that we have examples, we can use the question answering evaluator to evaluate our question answering chain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "782169a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.evaluation.qa import QAEvalChain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1bb77416",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = qa.apply(examples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bcd0ad7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "llm = OpenAI(temperature=0)\n",
    "eval_chain = QAEvalChain.from_llm(llm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2e6af79a",
   "metadata": {},
   "outputs": [],
   "source": [
    "graded_outputs = eval_chain.evaluate(examples, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "32fac2dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'text': ' CORRECT'},\n",
       " {'text': ' CORRECT'},\n",
       " {'text': ' INCORRECT'},\n",
       " {'text': ' CORRECT'},\n",
       " {'text': ' CORRECT'},\n",
       " {'text': ' CORRECT'},\n",
       " {'text': ' CORRECT'}]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graded_outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bb9bc7e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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