clm-core 1.0.7

Natural Language compressor for LLMs (Compressed Language Model).

CLM

Semantic Token Encoding for LLMs

Compress transcripts, structured data, and system prompts — 60–95% fewer tokens, no model retraining.

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CLM is a patent-pending semantic compression library. It encodes verbose content into compact structured token sequences that LLMs interpret with equal or better accuracy, at a fraction of the token cost.

Three targets, one encoder:

Encoder	Input	Typical Compression
Thread	Support calls, chat transcripts, email threads	62–80%
Structured Data	Product catalogs, knowledge bases, business rules	40–85%
System Prompt	Task instructions, role definitions, agent configs	65–90%

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Installation

pip install clm-core

Install the spaCy model for your language:

python -m spacy download en_core_web_sm   # English
python -m spacy download pt_core_news_sm  # Portuguese
python -m spacy download es_core_news_sm  # Spanish
python -m spacy download fr_core_news_sm  # French

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Usage

All three encoders share the same interface. CLM auto-detects the input type.

from clm_core import CLMConfig, CLMEncoder

cfg = CLMConfig(lang="en")
encoder = CLMEncoder(cfg=cfg)

Thread Encoder — Transcripts

result = encoder.encode(input_=transcript, metadata={"channel": "voice"})
print(result.compressed)

[INTERACTION:SUPPORT:CHANNEL=VOICE] [DURATION=6m] [LANG=EN]
[DOMAIN:BILLING] [SERVICE:SUBSCRIPTION]
[CUSTOMER_INTENT:REPORT_DUPLICATE_CHARGE] [CONTEXT:EMAIL_PROVIDED]
[AGENT_ACTIONS:ACCOUNT_VERIFIED→DIAGNOSTIC_PERFORMED→REFUND_INITIATED]
[SYSTEM_ACTIONS:PAYMENT_RETRY_DETECTED]
[RESOLUTION:REFUND_INITIATED] [STATE:PENDING_CUSTOMER]
[COMMITMENT:REFUND_3-5_BUSINESS_DAYS] [ARTIFACT:REFUND_REF=RFD-908712]
[SENTIMENT:NEUTRAL→GRATEFUL]

Parse into a structured dict for downstream use:

data = result.to_dict()
# {"channel": "VOICE", "domain": "BILLING", "customerIntent": "REPORT_DUPLICATE_CHARGE",
#  "state": "PENDING_CUSTOMER", "agentActions": [...], "commitments": [...], ...}

Structured Data Encoder

catalog = [{"article_id": "KB-001", "title": "Reset Password", "content": "...", "tags": ["security"]}]
result = encoder.encode(catalog)
print(result.compressed)
# {article_id,title,content,tags}[KB-001,Reset Password,To reset your password...,security]

System Prompt Encoder

result = encoder.encode(system_prompt)
print(result.compressed)
# [REQ:ANALYZE] [TARGET:TRANSCRIPT:DOMAIN=QA]
# [EXTRACT:COMPLIANCE,DISCLOSURES,SOFT_SKILLS,SENTIMENT]
# [OUT_JSON:{summary,qa_scores,violations,recommendations}]

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Performance

Based on a dataset test across 5,000+ samples:

Thread Encoder

Metric	Value
Token reduction	72–80%
Latency improvement	Up to 56%
Semantic preservation	Validated via Shannon Entropy
Languages	EN, PT, ES, FR
Schema version	v2.0

Structured Data Encoder

Metric	Value
Token reduction	40–85%
Supports	Single objects, arrays, nested structures
Field filtering	Importance threshold + required/excluded
Per-field truncation	Configurable

System Prompt Encoder

Metric	Value
Token reduction	65–90%
Output	Hierarchical CLM token vocabulary
Type inference	Optional (infer_types=True)
Attribute preservation	Optional (add_attrs=True)

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Documentation

Topic	Link
Getting started	docs/index.md
Thread Encoder	docs/thread_encoder/index.md
Transcript encoding	docs/thread_encoder/transcript_encoder.md
Structured Data Encoder	docs/sd_encoder.md
System Prompt Encoder	docs/sys_prompt/index.md
CLM Configuration	docs/advanced/clm_configuration.md
Token hierarchy	docs/advanced/clm_tokenization.md
Output reference	docs/advanced/clm_output.md

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License

Dual-licensed:

• AGPL-3.0 — free for open source use (LICENSE-AGPL)
• Commercial — for proprietary products and SaaS (contact)

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