{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Pairwise Embedding Distance \n",
    "\n",
    "One way to measure the similarity (or dissimilarity) between two predictions on a shared or similar input is to embed the predictions and compute a vector distance between the two embeddings.<a name=\"cite_ref-1\"></a>[<sup>[1]</sup>](#cite_note-1)\n",
    "\n",
    "You can load the `pairwise_embedding_distance` evaluator to do this.\n",
    "\n",
    "**Note:** This returns a **distance** score, meaning that the lower the number, the **more** similar the outputs are, according to their embedded representation.\n",
    "\n",
    "Check out the reference docs for the [PairwiseEmbeddingDistanceEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain.html#langchain.evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain) for more info."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.evaluation import load_evaluator\n",
    "\n",
    "evaluator = load_evaluator(\"pairwise_embedding_distance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'score': 0.0966466944859925}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluator.evaluate_string_pairs(\n",
    "    prediction=\"Seattle is hot in June\", prediction_b=\"Seattle is cool in June.\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'score': 0.03761174337464557}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluator.evaluate_string_pairs(\n",
    "    prediction=\"Seattle is warm in June\", prediction_b=\"Seattle is cool in June.\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select the Distance Metric\n",
    "\n",
    "By default, the evalutor uses cosine distance. You can choose a different distance metric if you'd like. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<EmbeddingDistance.COSINE: 'cosine'>,\n",
       " <EmbeddingDistance.EUCLIDEAN: 'euclidean'>,\n",
       " <EmbeddingDistance.MANHATTAN: 'manhattan'>,\n",
       " <EmbeddingDistance.CHEBYSHEV: 'chebyshev'>,\n",
       " <EmbeddingDistance.HAMMING: 'hamming'>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain.evaluation import EmbeddingDistance\n",
    "\n",
    "list(EmbeddingDistance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "evaluator = load_evaluator(\n",
    "    \"pairwise_embedding_distance\", distance_metric=EmbeddingDistance.EUCLIDEAN\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select Embeddings to Use\n",
    "\n",
    "The constructor uses `OpenAI` embeddings by default, but you can configure this however you want. Below, use huggingface local embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.embeddings import HuggingFaceEmbeddings\n",
    "\n",
    "embedding_model = HuggingFaceEmbeddings()\n",
    "hf_evaluator = load_evaluator(\"pairwise_embedding_distance\", embeddings=embedding_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'score': 0.5486443280477362}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hf_evaluator.evaluate_string_pairs(\n",
    "    prediction=\"Seattle is hot in June\", prediction_b=\"Seattle is cool in June.\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'score': 0.21018880025138598}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hf_evaluator.evaluate_string_pairs(\n",
    "    prediction=\"Seattle is warm in June\", prediction_b=\"Seattle is cool in June.\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"cite_note-1\"></a><i>1. Note: When it comes to semantic similarity, this often gives better results than older string distance metrics (such as those in the `PairwiseStringDistanceEvalChain`), though it tends to be less reliable than evaluators that use the LLM directly (such as the `PairwiseStringEvalChain`) </i>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}