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ishaan-jaff 2023-07-29 07:00:13 -07:00
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"# Embedding Distance\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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"display_name": "Python 3 (ipykernel)",
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