langchain/docs/modules/prompts/example_selectors/examples/mmr.ipynb
Tobias van der Werff 8d07ba0d51
Fix wrong class instantiation in docs MMR example (#5501)
# Fix wrong class instantiation in docs MMR example

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When looking at the Maximal Marginal Relevance ExampleSelector example
at
https://python.langchain.com/en/latest/modules/prompts/example_selectors/examples/mmr.html,
I noticed that there seems to be an error. Initially, the
`MaxMarginalRelevanceExampleSelector` class is used as an
`example_selector` argument to the `FewShotPromptTemplate` class. Then,
according to the text, a comparison is made to regular similarity
search. However, the `FewShotPromptTemplate` still uses the
`MaxMarginalRelevanceExampleSelector` class, so the output is the same.

To fix it, I added an instantiation of the
`SemanticSimilarityExampleSelector` class, because this seems to be what
is intended.


## Who can review?

@hwchase17
2023-05-31 17:30:59 -07:00

173 lines
5.3 KiB
Plaintext

{
"cells": [
{
"cell_type": "markdown",
"id": "bc35afd0",
"metadata": {},
"source": [
"# Maximal Marginal Relevance ExampleSelector\n",
"\n",
"The MaxMarginalRelevanceExampleSelector selects examples based on a combination of which examples are most similar to the inputs, while also optimizing for diversity. It does this by finding the examples with the embeddings that have the greatest cosine similarity with the inputs, and then iteratively adding them while penalizing them for closeness to already selected examples.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ac95c968",
"metadata": {},
"outputs": [],
"source": [
"from langchain.prompts.example_selector import MaxMarginalRelevanceExampleSelector, SemanticSimilarityExampleSelector\n",
"from langchain.vectorstores import FAISS\n",
"from langchain.embeddings import OpenAIEmbeddings\n",
"from langchain.prompts import FewShotPromptTemplate, PromptTemplate\n",
"\n",
"example_prompt = PromptTemplate(\n",
" input_variables=[\"input\", \"output\"],\n",
" template=\"Input: {input}\\nOutput: {output}\",\n",
")\n",
"\n",
"# These are a lot of 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": "db579bea",
"metadata": {},
"outputs": [],
"source": [
"example_selector = MaxMarginalRelevanceExampleSelector.from_examples(\n",
" # This is the list of examples available to select from.\n",
" examples, \n",
" # This is the embedding class used to produce embeddings which are used to measure semantic similarity.\n",
" OpenAIEmbeddings(), \n",
" # This is the VectorStore class that is used to store the embeddings and do a similarity search over.\n",
" FAISS, \n",
" # This is the number of examples to produce.\n",
" k=2\n",
")\n",
"mmr_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": "cd76e344",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Give the antonym of every input\n",
"\n",
"Input: happy\n",
"Output: sad\n",
"\n",
"Input: windy\n",
"Output: calm\n",
"\n",
"Input: worried\n",
"Output:\n"
]
}
],
"source": [
"# Input is a feeling, so should select the happy/sad example as the first one\n",
"print(mmr_prompt.format(adjective=\"worried\"))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "cf82956b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Give the antonym of every input\n",
"\n",
"Input: happy\n",
"Output: sad\n",
"\n",
"Input: sunny\n",
"Output: gloomy\n",
"\n",
"Input: worried\n",
"Output:\n"
]
}
],
"source": [
"# Let's compare this to what we would just get if we went solely off of similarity,\n",
"# by using SemanticSimilarityExampleSelector instead of MaxMarginalRelevanceExampleSelector.\n",
"example_selector = SemanticSimilarityExampleSelector.from_examples(\n",
" # This is the list of examples available to select from.\n",
" examples, \n",
" # This is the embedding class used to produce embeddings which are used to measure semantic similarity.\n",
" OpenAIEmbeddings(), \n",
" # This is the VectorStore class that is used to store the embeddings and do a similarity search over.\n",
" FAISS, \n",
" # This is the number of examples to produce.\n",
" k=2\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",
")\n",
"print(similar_prompt.format(adjective=\"worried\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "39f30097",
"metadata": {},
"outputs": [],
"source": []
}
],
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