Contextual compression in LangChain - Mini Project: Build & Apply

Contextual Compression with LangChain

📖 Scenario: You are building a simple text processing tool using LangChain to compress context before sending it to a language model. This helps reduce the amount of text while keeping the important meaning.

🎯 Goal: Create a LangChain pipeline that compresses a given text context using a compression chain and a language model.

📋 What You'll Learn

Create a variable text_context with the exact string: "LangChain helps build applications with language models."

Create a variable compression_prompt with the exact string: "Summarize the following text concisely:"

Use LangChain's LLMChain with a mock OpenAI model and the compression_prompt to create a compression chain called compressor.

Create a ContextualCompressionChain named compression_chain using the compressor.

Run the compression_chain on text_context and store the result in compressed_text.

💡 Why This Matters

🌍 Real World

Contextual compression helps reduce the size of text data sent to language models, saving costs and improving speed in applications like chatbots and summarizers.

💼 Career

Understanding how to build and use compression chains with LangChain is useful for developers working on AI-powered text applications, improving efficiency and user experience.

1

DATA SETUP: Create the initial text context

Create a variable called text_context and assign it the exact string "LangChain helps build applications with language models."

LangChain

# Your code here

Hint

Use a simple assignment with the exact string inside double quotes.

2

CONFIGURATION: Define the compression prompt

Create a variable called compression_prompt and assign it the exact string "Summarize the following text concisely:"

LangChain

text_context = "LangChain helps build applications with language models."
# Your code here

Hint

Assign the prompt string exactly as shown to the variable compression_prompt.

3

CORE LOGIC: Create the compression chain using LangChain

Import OpenAI and LLMChain from langchain, then create a variable compressor as an LLMChain using OpenAI() as the language model and compression_prompt as the prompt template.

LangChain

text_context = "LangChain helps build applications with language models."
compression_prompt = "Summarize the following text concisely:"
# Your code here

Hint

Import the classes, then create compressor with LLMChain passing OpenAI() and compression_prompt.

4

COMPLETION: Use ContextualCompressionChain and compress the text

Import ContextualCompressionChain from langchain.chains, create a variable compression_chain using ContextualCompressionChain(compressor=compressor), then run compression_chain.compress(text_context) and assign the result to compressed_text.

LangChain

from langchain.llms import OpenAI
from langchain.chains import LLMChain

text_context = "LangChain helps build applications with language models."
compression_prompt = "Summarize the following text concisely:"

compressor = LLMChain(llm=OpenAI(), prompt=compression_prompt)
# Your code here

Hint

Import ContextualCompressionChain, create compression_chain with compressor, then compress text_context.

Practice

1. What is the main purpose of Contextual compression in langchain?

easy

A. To check grammar and spelling errors

B. To translate text into multiple languages

C. To generate random text from a prompt

D. To reduce text size while keeping its important meaning

Solution

1. Step 1: Understand the goal of contextual compression

   Contextual compression aims to shrink text but keep its main meaning intact.

2. Step 2: Compare options with this goal

   Only To reduce text size while keeping its important meaning matches this goal; others describe different tasks.

3. Final Answer:

   To reduce text size while keeping its important meaning -> Option D

4. Quick Check:

   Contextual compression = reduce text size + keep meaning [OK]

Hint: Focus on meaning retention while reducing text size [OK]

Common Mistakes:

• Confusing compression with translation
• Thinking it generates new text
• Assuming it checks grammar

2. Which of the following is the correct way to create a ContextualCompressionRetriever in langchain?

easy

A. ContextualCompressionRetriever(base_retriever, llm)

B. ContextualCompressionRetriever(llm, base_retriever)

C. ContextualCompressionRetriever()

D. ContextualCompressionRetriever(llm)

Solution

1. Step 1: Recall the constructor parameters order

   The correct order is base_retriever first, then llm.

2. Step 2: Match options with correct order

   Only ContextualCompressionRetriever(base_retriever, llm) matches this order; others have wrong order or missing arguments.

3. Final Answer:

   ContextualCompressionRetriever(base_retriever, llm) -> Option A

4. Quick Check:

   Constructor order = base_retriever, llm [OK]

Hint: Remember: retriever first, then language model [OK]

Common Mistakes:

• Swapping the order of parameters
• Omitting required arguments
• Using empty constructor

3. Given this code snippet, what will be the output type of compressed_docs?

from langchain.retrievers import ContextualCompressionRetriever
compressed_retriever = ContextualCompressionRetriever(base_retriever, llm)
compressed_docs = compressed_retriever.get_relevant_documents(query)

medium

A. A list of compressed Document objects

B. A single compressed string

C. A dictionary with original and compressed texts

D. An error because get_relevant_documents is undefined

Solution

1. Step 1: Understand what get_relevant_documents returns

   It returns a list of Document objects relevant to the query.

2. Step 2: Consider compression effect

   ContextualCompressionRetriever compresses content inside Document objects but still returns a list of Documents.

3. Final Answer:

   A list of compressed Document objects -> Option A

4. Quick Check:

   get_relevant_documents returns list of Documents [OK]

Hint: Retriever methods return lists of Documents, not strings [OK]

Common Mistakes:

• Expecting a single string instead of list
• Thinking it returns a dictionary
• Assuming method does not exist

4. You wrote this code but get an error: TypeError: ContextualCompressionRetriever() missing 1 required positional argument. What is the likely cause?

compressed_retriever = ContextualCompressionRetriever(llm)

medium

A. You passed arguments in the wrong order

B. You forgot to pass the base retriever argument

C. You need to import ContextualCompressionRetriever differently

D. The llm argument is invalid

Solution

1. Step 1: Check constructor requirements

   ContextualCompressionRetriever requires both base_retriever and llm arguments.

2. Step 2: Analyze the error message and code

   The code passes only llm, missing base_retriever, causing the error.

3. Final Answer:

   You forgot to pass the base retriever argument -> Option B

4. Quick Check:

   Missing base retriever causes TypeError [OK]

Hint: Always pass base retriever first, then llm [OK]

Common Mistakes:

• Passing only one argument
• Swapping argument order
• Ignoring error message details

5. You want to speed up document retrieval by compressing context but keep key info. Which approach best applies ContextualCompressionRetriever correctly in a langchain app?

hard

A. Replace your retriever with only an LLM that compresses text manually

B. Use ContextualCompressionRetriever without a base retriever to compress all documents at once

C. Wrap your existing retriever with ContextualCompressionRetriever passing your LLM, then query it

D. Compress documents before adding them to the retriever, then use a normal retriever

Solution

1. Step 1: Understand how ContextualCompressionRetriever works

   It wraps an existing retriever and uses an LLM to compress retrieved documents on the fly.

2. Step 2: Evaluate options for correct usage

   Wrap your existing retriever with ContextualCompressionRetriever passing your LLM, then query it correctly wraps the retriever with compression; others either omit retriever or do manual steps outside the pattern.

3. Final Answer:

   Wrap your existing retriever with ContextualCompressionRetriever passing your LLM, then query it -> Option C

4. Quick Check:

   Wrap retriever + LLM = ContextualCompressionRetriever usage [OK]

Hint: Wrap retriever with compression, don't replace or skip it [OK]

Common Mistakes:

• Skipping base retriever wrapping
• Trying to compress all docs at once without retriever
• Replacing retriever with only LLM