adaptive-context-resolution 0.1.1

Adaptive Context Protocol - Multi-resolution data serialization for token-efficient LLM communication

Adaptive Context Resolution

Multi-resolution data serialization for token-efficient LLM communication.

Overview

Adaptive Context Resolution enables data sources to serve information at multiple resolution levels, dramatically reducing token consumption when communicating with LLMs.

Level	Name	Description	Typical Tokens
L0	Existence	Boolean presence check	1-3
L1	Summary	Natural language one-liner	10-30
L2	Key Facts	Structured essential fields	50-150
L3	Full Detail	Complete data	Unbounded

Results: 88-96% token reduction vs JSON for typical workloads.

Installation

pip install adaptive-context-resolution

With optional dependencies:

pip install adaptive-context-resolution[tiktoken]  # Accurate token counting
pip install adaptive-context-resolution[llm]       # LLM-assisted summaries

Quick Start

from acp import ACPDocument, ResolutionLevel

# Your data
user = {
    "id": "user-123",
    "name": "Alice Chen",
    "email": "[e-mail]",
    "role": "Senior Engineer",
    "department": "Platform",
    "status": "active",
    "skills": ["Python", "Go", "Kubernetes"],
    "preferences": {"theme": "dark", "notifications": True}
}

# Create ACP document (auto-generates all levels)
doc = ACPDocument.from_dict(user, entity="user", id="user-123")

# Get data at different resolutions
doc.get(level=ResolutionLevel.L0_EXISTENCE)  # "exists"
doc.get(level=ResolutionLevel.L1_SUMMARY)    # "Alice Chen, Senior Engineer, Platform, active"
doc.get(level=ResolutionLevel.L2_KEY_FACTS)  # {"name": "Alice Chen", "role": "Senior Engineer", ...}
doc.get(level=ResolutionLevel.L3_FULL)       # Full data

# Or specify a token budget
doc.get(token_budget=50)  # Returns highest level that fits

Token Counts

print(doc.token_counts)
# {'L0': 1, 'L1': 12, 'L2': 45, 'L3': 180}

Custom Level Generation

# Specify which fields matter for L2
doc = ACPDocument.from_dict(
    data=user,
    entity="user",
    id="user-123",
    key_fields=["name", "role", "email"],
    summary_template="{name} <{email}>"
)

MCP Integration

from acp.mcp import ACPServer, acp_resource

server = ACPServer()

@server.resource("user", key_fields=["name", "role"])
def get_user(user_id: str) -> dict:
    return database.get_user(user_id)

# Handle requests with resolution control
result = server.handle_request(
    "user",
    {"user_id": "123"},
    level=ResolutionLevel.L2_KEY_FACTS
)

ACP Format Output

print(doc.to_acp_format())

@acp 1.0
@entity: user
@id: user-123

L0: exists

L1: "Alice Chen, Senior Engineer, Platform, active"

L2:
  name: Alice Chen
  role: Senior Engineer
  department: Platform
  status: active

L3:
  {"id": "user-123", "name": "Alice Chen", ...}

@meta:
  tokens: {L0: 1, L1: 12, L2: 45, L3: 180}

License

MIT License - see LICENSE for details.

Contributing

Contributions welcome! Please read our contributing guidelines.