forked from Archives/langchain
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
372 lines
19 KiB
Plaintext
372 lines
19 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "fc0db1bc",
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"metadata": {},
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"source": [
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"# Contextual Compression\n",
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"\n",
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"This notebook introduces the concept of DocumentCompressors and the ContextualCompressionRetriever. The core idea is simple: given a specific query, we should be able to return only the documents relevant to that query, and only the parts of those documents that are relevant. The ContextualCompressionsRetriever is a wrapper for another retriever that iterates over the initial output of the base retriever and filters and compresses those initial documents, so that only the most relevant information is returned."
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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": 1,
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"id": "28e8dc12",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Helper function for printing docs\n",
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"\n",
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"def pretty_print_docs(docs):\n",
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" print(f\"\\n{'-' * 100}\\n\".join([f\"Document {i+1}:\\n\\n\" + d.page_content for i, d in enumerate(docs)]))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6fa3d916",
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"metadata": {},
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"source": [
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"## Using a vanilla vector store retriever\n",
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"Let's start by initializing a simple vector store retriever and storing the 2023 State of the Union speech (in chunks). We can see that given an example question our retriever returns one or two relevant docs and a few irrelevant docs. And even the relevant docs have a lot of irrelevant information in them."
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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": 2,
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"id": "9fbcc58f",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Document 1:\n",
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"\n",
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"Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
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"\n",
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"Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
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"\n",
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"One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
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"\n",
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"And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
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"----------------------------------------------------------------------------------------------------\n",
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"Document 2:\n",
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"\n",
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"A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
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"\n",
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"And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n",
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"\n",
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"We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling. \n",
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"\n",
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"We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \n",
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"\n",
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"We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n",
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"\n",
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"We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.\n",
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"----------------------------------------------------------------------------------------------------\n",
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"Document 3:\n",
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"\n",
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"And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n",
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"\n",
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"As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n",
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"\n",
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"While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \n",
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"\n",
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"And soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n",
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"\n",
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"So tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together. \n",
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"\n",
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"First, beat the opioid epidemic.\n",
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"----------------------------------------------------------------------------------------------------\n",
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"Document 4:\n",
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"\n",
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"Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \n",
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"\n",
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"And as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \n",
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"\n",
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"That ends on my watch. \n",
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"\n",
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"Medicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \n",
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"\n",
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"We’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \n",
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"\n",
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"Let’s pass the Paycheck Fairness Act and paid leave. \n",
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"\n",
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"Raise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \n",
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"\n",
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"Let’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.\n"
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]
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}
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],
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"source": [
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.embeddings import OpenAIEmbeddings\n",
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"from langchain.document_loaders import TextLoader\n",
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"from langchain.vectorstores import FAISS\n",
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"\n",
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"documents = TextLoader('../../../state_of_the_union.txt').load()\n",
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
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"texts = text_splitter.split_documents(documents)\n",
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"retriever = FAISS.from_documents(texts, OpenAIEmbeddings()).as_retriever()\n",
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"\n",
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"docs = retriever.get_relevant_documents(\"What did the president say about Ketanji Brown Jackson\")\n",
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"pretty_print_docs(docs)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b7648612",
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"metadata": {},
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"source": [
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"## Adding contextual compression with an `LLMChainExtractor`\n",
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"Now let's wrap our base retriever with a `ContextualCompressionRetriever`. We'll add an `LLMChainExtractor`, which will iterate over the initially returned documents and extract from each only the content that is relevant to the query."
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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": "9a658023",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Document 1:\n",
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"\n",
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"\"One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
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"\n",
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"And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\"\n",
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"----------------------------------------------------------------------------------------------------\n",
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"Document 2:\n",
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"\n",
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"\"A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\"\n"
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]
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}
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],
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"source": [
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"from langchain.llms import OpenAI\n",
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"from langchain.retrievers import ContextualCompressionRetriever\n",
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"from langchain.retrievers.document_compressors import LLMChainExtractor\n",
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"\n",
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"llm = OpenAI(temperature=0)\n",
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"compressor = LLMChainExtractor.from_llm(llm)\n",
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"compression_retriever = ContextualCompressionRetriever(base_compressor=compressor, base_retriever=retriever)\n",
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"\n",
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"compressed_docs = compression_retriever.get_relevant_documents(\"What did the president say about Ketanji Jackson Brown\")\n",
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"pretty_print_docs(compressed_docs)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2cd38f3a",
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"metadata": {},
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"source": [
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"## More built-in compressors: filters\n",
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"### `LLMChainFilter`\n",
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"The `LLMChainFilter` is slightly simpler but more robust compressor that uses an LLM chain to decide which of the initially retrieved documents to filter out and which ones to return, without manipulating the document contents."
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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": "b216a767",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Document 1:\n",
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"\n",
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"Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
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"\n",
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"Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
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"\n",
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"One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
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"\n",
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"And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n"
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]
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}
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],
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"source": [
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"from langchain.retrievers.document_compressors import LLMChainFilter\n",
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"\n",
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"_filter = LLMChainFilter.from_llm(llm)\n",
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"compression_retriever = ContextualCompressionRetriever(base_compressor=_filter, base_retriever=retriever)\n",
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"\n",
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"compressed_docs = compression_retriever.get_relevant_documents(\"What did the president say about Ketanji Jackson Brown\")\n",
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"pretty_print_docs(compressed_docs)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8c709598",
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"metadata": {},
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"source": [
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"### `EmbeddingsFilter`\n",
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"\n",
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"Making an extra LLM call over each retrieved document is expensive and slow. The `EmbeddingsFilter` provides a cheaper and faster option by embedding the documents and query and only returning those documents which have sufficiently similar embeddings to the query."
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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": "6fbc801f",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Document 1:\n",
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"\n",
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"Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
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||
"\n",
|
||
"Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
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"\n",
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||
"One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
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"\n",
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"And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
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"----------------------------------------------------------------------------------------------------\n",
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"Document 2:\n",
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"\n",
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"A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
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"\n",
|
||
"And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n",
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"\n",
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"We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling. \n",
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"\n",
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"We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \n",
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"\n",
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"We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n",
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"\n",
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"We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.\n",
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"----------------------------------------------------------------------------------------------------\n",
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"Document 3:\n",
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"\n",
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"And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n",
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"\n",
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"As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n",
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"\n",
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"While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \n",
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"\n",
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"And soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n",
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"\n",
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"So tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together. \n",
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"\n",
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"First, beat the opioid epidemic.\n"
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]
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}
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],
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"source": [
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"from langchain.embeddings import OpenAIEmbeddings\n",
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"from langchain.retrievers.document_compressors import EmbeddingsFilter\n",
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"\n",
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"embeddings = OpenAIEmbeddings()\n",
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"embeddings_filter = EmbeddingsFilter(embeddings=embeddings, similarity_threshold=0.76)\n",
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"compression_retriever = ContextualCompressionRetriever(base_compressor=embeddings_filter, base_retriever=retriever)\n",
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"\n",
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"compressed_docs = compression_retriever.get_relevant_documents(\"What did the president say about Ketanji Jackson Brown\")\n",
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"pretty_print_docs(compressed_docs)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "07365d36",
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"metadata": {},
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"source": [
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"# Stringing compressors and document transformers together\n",
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"Using the `DocumentCompressorPipeline` we can also easily combine multiple compressors in sequence. Along with compressors we can add `BaseDocumentTransformer`s to our pipeline, which don't perform any contextual compression but simply perform some transformation on a set of documents. For example `TextSplitter`s can be used as document transformers to split documents into smaller pieces, and the `EmbeddingsRedundantFilter` can be used to filter out redundant documents based on embedding similarity between documents.\n",
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"\n",
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"Below we create a compressor pipeline by first splitting our docs into smaller chunks, then removing redundant documents, and then filtering based on relevance to the query."
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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": "2a150a63",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.document_transformers import EmbeddingsRedundantFilter\n",
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"from langchain.retrievers.document_compressors import DocumentCompressorPipeline\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"\n",
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"splitter = CharacterTextSplitter(chunk_size=300, chunk_overlap=0, separator=\". \")\n",
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"redundant_filter = EmbeddingsRedundantFilter(embeddings=embeddings)\n",
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"relevant_filter = EmbeddingsFilter(embeddings=embeddings, similarity_threshold=0.76)\n",
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"pipeline_compressor = DocumentCompressorPipeline(\n",
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" transformers=[splitter, redundant_filter, relevant_filter]\n",
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")"
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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": 7,
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"id": "3ceab64a",
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||
"metadata": {},
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||
"outputs": [
|
||
{
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||
"name": "stdout",
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||
"output_type": "stream",
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"text": [
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"Document 1:\n",
|
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"\n",
|
||
"One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
|
||
"\n",
|
||
"And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson\n",
|
||
"----------------------------------------------------------------------------------------------------\n",
|
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"Document 2:\n",
|
||
"\n",
|
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"As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n",
|
||
"\n",
|
||
"While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year\n",
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"----------------------------------------------------------------------------------------------------\n",
|
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"Document 3:\n",
|
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"\n",
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"A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder\n"
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]
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}
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],
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"source": [
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"compression_retriever = ContextualCompressionRetriever(base_compressor=pipeline_compressor, base_retriever=retriever)\n",
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"\n",
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"compressed_docs = compression_retriever.get_relevant_documents(\"What did the president say about Ketanji Jackson Brown\")\n",
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"pretty_print_docs(compressed_docs)"
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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": "8cfd9fc5",
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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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}
|