Ccf

Skill: Contextual Compression with Foresight (CCF)

Version: 1.0 Author: Manus AI

1. Description

This skill implements Contextual Compression with Foresight (CCF), an intelligent technique for managing the limited context window of Large Language Models. Instead of simply discarding old information (like a FIFO queue), CCF actively manages the context. It summarizes less relevant information to save space and, more importantly, uses "foresight" to predict which pieces of information will be most critical for upcoming tasks, boosting their relevance to protect them from being compressed or discarded.

This skill is a direct implementation of the seventh of the 10 breakthrough LLM innovations.

Key Features:

• Intelligent Summarization: Compresses low-relevance information instead of discarding it entirely, keeping a condensed version available.
• Foresight Mechanism: Predicts future context needs based on the current goal and proactively increases the relevance of related information.
• Relevance-Based Prioritization: Ensures that the most critical information is always preserved in full detail.
• Dynamic Context Management: Provides a flexible and adaptive way to handle large amounts of information over long, complex tasks.

2. How to Use

2.1. Installation

This skill is a self-contained Python module. To use it, import the ContextManager class.

from skills.ccf.src.ccf_engine import ContextManager

2.2. Initializing the Context Manager

Instantiate the ContextManager with a specified context limit (in characters).

# Initialize with a small limit for demonstration
ccf = ContextManager(context_limit=200)

2.3. Adding Documents to the Context

As the AI processes information, add each piece of information as a document to the context manager.

# Add documents with varying relevance
doc1_id = ccf.add_document("This is the main project goal: build a web server.", relevance_score=10.0)
doc2_id = ccf.add_document("User mentioned they prefer using the Flask framework.", relevance_score=8.0)
doc3_id = ccf.add_document("A side note about database indexing from a previous task.", relevance_score=2.0)

2.4. Using Foresight

Before starting a new sub-task, inform the context manager of the immediate goal. It will use this information to predict which documents are most relevant.

# The next task is to choose a web framework
ccf.predict_future_needs("Choose a web framework")

# The relevance of doc2 (about Flask) will be boosted.

2.5. Compressing the Context

When the context size exceeds the limit, call the compress_context method. It will intelligently summarize or discard the least relevant documents until the context fits within the limit.

# Add more documents to exceed the limit
ccf.add_document("Another long document about deployment strategies that fills up the context window completely.")

# This will trigger the compression
ccf.compress_context()

# You will see in the logs that the least relevant document (doc3) was summarized or discarded.

3. Development Roadmap

CCF is crucial for enabling LLMs to handle long-running, complex tasks without losing critical information. Future development will focus on improving the intelligence of the compression and foresight mechanisms.

• v1.1: LLM-Powered Summarization:

  • Goal: Replace the simplistic truncation-based summarization with a call to an LLM. This will produce much more coherent and useful summaries of compressed documents.
  • Timeline: 3 weeks

• v1.2: Sophisticated Foresight Agent:

  • Goal: Develop a dedicated LLM agent for the predict_future_needs method. This agent will analyze the current task graph (from the PTD skill) to make much more accurate predictions about which documents will be needed for upcoming steps.
  • Timeline: 4 weeks

• v1.3: Hierarchical Context:

  • Goal: Implement a multi-layered context system. Instead of just "full" and "summarized," there will be multiple levels of detail. This will allow for more graceful degradation of information.
  • Timeline: 5 weeks

• v2.0: Active Retrieval:

  • Goal: When a document is summarized or discarded, don't just delete it. Store the full version in a vector database. If the AI later realizes it needs the full details of a summarized document, it can actively retrieve it from the database, bringing it back into the active context.
  • Timeline: 8 weeks