mirror of
https://github.com/hwchase17/langchain
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b96ab4b763
# Docs: improvements in the `retrievers/examples/` notebooks Its primary purpose is to make the Jupyter notebook examples **consistent** and more suitable for first-time viewers. - add links to the integration source (if applicable) with a short description of this source; - removed `_retriever` suffix from the file names (where it existed) for consistency; - removed ` retriever` from the notebook title (where it existed) for consistency; - added code to install necessary Python package(s); - added code to set up the necessary API Key. - very small fixes in notebooks from other folders (for consistency): - docs/modules/indexes/vectorstores/examples/elasticsearch.ipynb - docs/modules/indexes/vectorstores/examples/pinecone.ipynb - docs/modules/models/llms/integrations/cohere.ipynb - fixed misspelling in langchain/retrievers/time_weighted_retriever.py comment (sorry, about this change in a .py file ) ## Who can review @dev2049
138 lines
2.9 KiB
Plaintext
138 lines
2.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ab66dd43",
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"metadata": {},
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"source": [
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"# TF-IDF\n",
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"\n",
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">[TF-IDF](https://scikit-learn.org/stable/modules/feature_extraction.html#tfidf-term-weighting) means term-frequency times inverse document-frequency.\n",
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"\n",
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"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",
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"\n",
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"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)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a801b57c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# !pip install scikit-learn"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "393ac030",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.retrievers import TFIDFRetriever"
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]
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},
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{
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"cell_type": "markdown",
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"id": "aaf80e7f",
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"metadata": {},
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"source": [
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"## Create New Retriever with Texts"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "98b1c017",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"retriever = TFIDFRetriever.from_texts([\"foo\", \"bar\", \"world\", \"hello\", \"foo bar\"])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "08437fa2",
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"metadata": {},
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"source": [
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"## Use Retriever\n",
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"\n",
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"We can now use the retriever!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "c0455218",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"result = retriever.get_relevant_documents(\"foo\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "7dfa5c29",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[Document(page_content='foo', metadata={}),\n",
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" Document(page_content='foo bar', metadata={}),\n",
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" Document(page_content='hello', metadata={}),\n",
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" Document(page_content='world', metadata={})]"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"result"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "74bd9256",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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