langchain/docs/docs/integrations/retrievers/tf_idf.ipynb
ccurme c010ec8b71
patch: deprecate (a)get_relevant_documents (#20477)
- `.get_relevant_documents(query)` -> `.invoke(query)`
- `.get_relevant_documents(query=query)` -> `.invoke(query)`
- `.get_relevant_documents(query, callbacks=callbacks)` ->
`.invoke(query, config={"callbacks": callbacks})`
- `.get_relevant_documents(query, **kwargs)` -> `.invoke(query,
**kwargs)`

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
2024-04-22 11:14:53 -04:00

222 lines
4.8 KiB
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{
"cells": [
{
"cell_type": "markdown",
"id": "ab66dd43",
"metadata": {},
"source": [
"# TF-IDF\n",
"\n",
">[TF-IDF](https://scikit-learn.org/stable/modules/feature_extraction.html#tfidf-term-weighting) means term-frequency times inverse document-frequency.\n",
"\n",
"This notebook goes over how to use a retriever that under the hood uses [TF-IDF](https://en.wikipedia.org/wiki/Tf%E2%80%93idf) using `scikit-learn` package.\n",
"\n",
"For more information on the details of TF-IDF see [this blog post](https://medium.com/data-science-bootcamp/tf-idf-basics-of-information-retrieval-48de122b2a4c)."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a801b57c",
"metadata": {},
"outputs": [],
"source": [
"%pip install --upgrade --quiet scikit-learn"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "393ac030",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain_community.retrievers import TFIDFRetriever"
]
},
{
"cell_type": "markdown",
"id": "aaf80e7f",
"metadata": {},
"source": [
"## Create New Retriever with Texts"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "98b1c017",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"retriever = TFIDFRetriever.from_texts([\"foo\", \"bar\", \"world\", \"hello\", \"foo bar\"])"
]
},
{
"cell_type": "markdown",
"id": "c016b266",
"metadata": {},
"source": [
"## Create a New Retriever with Documents\n",
"\n",
"You can now create a new retriever with the documents you created."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "53af4f00",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.documents import Document\n",
"\n",
"retriever = TFIDFRetriever.from_documents(\n",
" [\n",
" Document(page_content=\"foo\"),\n",
" Document(page_content=\"bar\"),\n",
" Document(page_content=\"world\"),\n",
" Document(page_content=\"hello\"),\n",
" Document(page_content=\"foo bar\"),\n",
" ]\n",
")"
]
},
{
"cell_type": "markdown",
"id": "08437fa2",
"metadata": {},
"source": [
"## Use Retriever\n",
"\n",
"We can now use the retriever!"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "c0455218",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"result = retriever.invoke(\"foo\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "7dfa5c29",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='foo', metadata={}),\n",
" Document(page_content='foo bar', metadata={}),\n",
" Document(page_content='hello', metadata={}),\n",
" Document(page_content='world', metadata={})]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result"
]
},
{
"cell_type": "markdown",
"id": "363f3c04",
"metadata": {},
"source": [
"## Save and load\n",
"\n",
"You can easily save and load this retriever, making it handy for local development!"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "10c90d03",
"metadata": {},
"outputs": [],
"source": [
"retriever.save_local(\"testing.pkl\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "fb3b153c",
"metadata": {},
"outputs": [],
"source": [
"retriever_copy = TFIDFRetriever.load_local(\"testing.pkl\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "c03ff3c7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='foo', metadata={}),\n",
" Document(page_content='foo bar', metadata={}),\n",
" Document(page_content='hello', metadata={}),\n",
" Document(page_content='world', metadata={})]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"retriever_copy.invoke(\"foo\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2d7c5728",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}