{
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        {
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                "tags": []
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            "source": [
                "# Pairwise 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/docs/guides/evaluation/comparison/pairwise_embedding_distance.ipynb)\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>"
            ]
        }
    ],
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