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   "cell_type": "markdown",
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   "source": [
    "# How to select examples by similarity\n",
    "\n",
    "This object selects examples based on similarity to the inputs. It does this by finding the examples with the embeddings that have the greatest cosine similarity with the inputs.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "abc30764",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_chroma import Chroma\n",
    "from langchain_core.example_selectors import SemanticSimilarityExampleSelector\n",
    "from langchain_core.prompts import FewShotPromptTemplate, PromptTemplate\n",
    "from langchain_openai import OpenAIEmbeddings\n",
    "\n",
    "example_prompt = PromptTemplate(\n",
    "    input_variables=[\"input\", \"output\"],\n",
    "    template=\"Input: {input}\\nOutput: {output}\",\n",
    ")\n",
    "\n",
    "# Examples of a pretend task of creating antonyms.\n",
    "examples = [\n",
    "    {\"input\": \"happy\", \"output\": \"sad\"},\n",
    "    {\"input\": \"tall\", \"output\": \"short\"},\n",
    "    {\"input\": \"energetic\", \"output\": \"lethargic\"},\n",
    "    {\"input\": \"sunny\", \"output\": \"gloomy\"},\n",
    "    {\"input\": \"windy\", \"output\": \"calm\"},\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8a37fc84",
   "metadata": {},
   "outputs": [],
   "source": [
    "example_selector = SemanticSimilarityExampleSelector.from_examples(\n",
    "    # The list of examples available to select from.\n",
    "    examples,\n",
    "    # The embedding class used to produce embeddings which are used to measure semantic similarity.\n",
    "    OpenAIEmbeddings(),\n",
    "    # The VectorStore class that is used to store the embeddings and do a similarity search over.\n",
    "    Chroma,\n",
    "    # The number of examples to produce.\n",
    "    k=1,\n",
    ")\n",
    "similar_prompt = FewShotPromptTemplate(\n",
    "    # We provide an ExampleSelector instead of examples.\n",
    "    example_selector=example_selector,\n",
    "    example_prompt=example_prompt,\n",
    "    prefix=\"Give the antonym of every input\",\n",
    "    suffix=\"Input: {adjective}\\nOutput:\",\n",
    "    input_variables=[\"adjective\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eabd2020",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Give the antonym of every input\n",
      "\n",
      "Input: happy\n",
      "Output: sad\n",
      "\n",
      "Input: worried\n",
      "Output:\n"
     ]
    }
   ],
   "source": [
    "# Input is a feeling, so should select the happy/sad example\n",
    "print(similar_prompt.format(adjective=\"worried\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c02225a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Give the antonym of every input\n",
      "\n",
      "Input: tall\n",
      "Output: short\n",
      "\n",
      "Input: large\n",
      "Output:\n"
     ]
    }
   ],
   "source": [
    "# Input is a measurement, so should select the tall/short example\n",
    "print(similar_prompt.format(adjective=\"large\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "09836c64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Give the antonym of every input\n",
      "\n",
      "Input: enthusiastic\n",
      "Output: apathetic\n",
      "\n",
      "Input: passionate\n",
      "Output:\n"
     ]
    }
   ],
   "source": [
    "# You can add new examples to the SemanticSimilarityExampleSelector as well\n",
    "similar_prompt.example_selector.add_example(\n",
    "    {\"input\": \"enthusiastic\", \"output\": \"apathetic\"}\n",
    ")\n",
    "print(similar_prompt.format(adjective=\"passionate\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92e2c85f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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