{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {
                "tags": []
            },
            "source": [
                "# Embedding Distance\n",
                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/guides/evaluation/string/embedding_distance.ipynb)\n",
                "\n",
                "To measure semantic similarity (or dissimilarity) between a prediction and a reference label string, you could use a vector vector distance metric the two embedded representations using the `embedding_distance` evaluator.<a name=\"cite_ref-1\"></a>[<sup>[1]</sup>](#cite_note-1)\n",
                "\n",
                "\n",
                "**Note:** This returns a **distance** score, meaning that the lower the number, the **more** similar the prediction is to the reference, according to their embedded representation.\n",
                "\n",
                "Check out the reference docs for the [EmbeddingDistanceEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.embedding_distance.base.EmbeddingDistanceEvalChain.html#langchain.evaluation.embedding_distance.base.EmbeddingDistanceEvalChain) for more info."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "from langchain.evaluation import load_evaluator\n",
                "\n",
                "evaluator = load_evaluator(\"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_strings(prediction=\"I shall go\", reference=\"I shan't go\")"
            ]
        },
        {
            "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_strings(prediction=\"I shall go\", reference=\"I will go\")"
            ]
        },
        {
            "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": [
                "# You can load by enum or by raw python string\n",
                "evaluator = load_evaluator(\n",
                "    \"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": 6,
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "from langchain.embeddings import HuggingFaceEmbeddings\n",
                "\n",
                "embedding_model = HuggingFaceEmbeddings()\n",
                "hf_evaluator = load_evaluator(\"embedding_distance\", embeddings=embedding_model)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {
                "tags": []
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "{'score': 0.5486443280477362}"
                        ]
                    },
                    "execution_count": 7,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "hf_evaluator.evaluate_strings(prediction=\"I shall go\", reference=\"I shan't go\")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {
                "tags": []
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "{'score': 0.21018880025138598}"
                        ]
                    },
                    "execution_count": 8,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "hf_evaluator.evaluate_strings(prediction=\"I shall go\", reference=\"I will go\")"
            ]
        },
        {
            "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 [StringDistanceEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistanceEvalChain.html#langchain.evaluation.string_distance.base.StringDistanceEvalChain)), though it tends to be less reliable than evaluators that use the LLM directly (such as the [QAEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html#langchain.evaluation.qa.eval_chain.QAEvalChain) or [LabeledCriteriaEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html#langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain)) </i>"
            ]
        }
    ],
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