adaptive-intelligence 3.0.1

Self-improving retrieval orchestration framework with RL-based routing, conditional graph activation, and evaluation-driven learning.

adaptive-intelligence

Self-improving retrieval orchestration framework. Drop documents, ask questions, the system learns how to retrieve better over time.

Install

pip install adaptive-intelligence
pip install adaptive-intelligence[pdf]
pip install adaptive-intelligence[sql]
pip install adaptive-intelligence[all]

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Quick Start

from adaptive_intelligence import AdaptiveAI

engine = AdaptiveAI()
engine.ingest("./documents")
response = engine.ask("What are the key risks?")
print(response.answer)
print(f"Confidence: {response.confidence:.0%}")

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Comparison

S.No.	Capability	Traditional RAG	GraphRAG	Adaptive Intelligence
1	Retrieval	Static vector	Always graph	RL-learned per query
2	Graph	None	Always on	Conditional (5-signal gate)
3	Learning	None	None	Improves every query
4	Evaluation	None	None	6 metrics per response
5	Vector DB	Required	Required	Optional (vectorless mode)
6	Output	Text only	Text only	JSON, CSV, YAML, DataFrame
7	Feedback	None	None	Thumbs up/down updates RL
8	Reranking	None	None	Cross-encoder re-scoring
9	Complex queries	Single retrieval	Single retrieval	Multi-query decomposition
10	Domain warmup	Manual tuning	Manual tuning	Pre-trained policies (skip warmup)
11	LLM agnostic	Usually one	Usually one	10+ providers
12	Crash recovery	None	Partial	Full auto-checkpoint

Results (20 queries, same LLM, same corpus)

S.No.	Query type	Traditional RAG	Adaptive Intelligence	Delta
1	Factual	85%	90%	+5%
2	Relational	45%	78%	+33%
3	Analytical	55%	75%	+20%
4	Comparative	50%	80%	+30%
5	Multi-hop	35%	72%	+37%
6	Overall	54%	79%	+25%

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Version Evolution

S.No.	Feature	v1	v2	v3 (current)
1	RL algorithm	Thompson Sampling	+ configurable warmup	+ PPO option
2	Graph	5-signal gate + BFS	+ persistence	+ pre-trained policies
3	Evaluation	6 metrics	+ user feedback	+ A/B testing
4	Ingestion	Basic	Hardened (every edge case)	Same
5	SQL connector	No	PostgreSQL, MySQL, SQLite	Same
6	Vectorless mode	No	Page BM25 + graph + RL	Same
7	Output formats	Text only	JSON, CSV, YAML, DataFrame	Same
8	Reranking	No	No	Cross-encoder re-scoring
9	Multi-query	No	No	Auto-decompose complex queries
10	Pre-trained policies	No	No	Financial, legal, healthcare
11	Transfer learning	No	No	Export/import policies
12	A/B testing	No	No	Compare two policies
13	Crash recovery	Partial	Full auto-checkpoint	Same
14	Providers	3	10+	Same
15	System prompt	No	Custom	Same

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All Features

RL + Retrieval

# Default: Thompson Sampling
engine = AdaptiveAI()

# PPO algorithm
engine = AdaptiveAI(rl_algorithm="ppo")

# Cross-encoder reranking
engine = AdaptiveAI(reranking=True)

# Pre-trained policy (skip warmup)
engine = AdaptiveAI(domain="financial", pretrained_policy=True)

# Transfer learning
engine.export_policy("my_policy.json")
other_engine.import_policy("my_policy.json")

# A/B testing
engine.enable_ab_test(policy_a="thompson", policy_b="ppo")
print(engine.ab_results())

Vectorless Mode

engine = AdaptiveAI(vectorless=True)
# No ChromaDB. No embeddings. Pure BM25 + graph + RL.
# Page-number citations. Zero dependencies.

Output Formats

response = engine.ask("Extract vendors", output_format="json")
response = engine.ask("List items", output_format="csv")
response = engine.ask("Show data", output_format="dataframe")
response = engine.ask("Details", output_format="json",
    schema={"vendor": "str", "amount": "float"})

User Feedback

engine.feedback(response.query_id, "good")
engine.feedback(response.query_id, "bad", reason="Missing data")

SQL Connector

engine.ingest("sqlite:///data.db")
engine.ingest("postgresql://user:pass@host/db", tables=["orders"])

Incremental Ingestion

engine.ingest("./new_file.pdf")
engine.remove("old_file.pdf")
engine.update("./changed_file.pdf")
engine.ingest("./docs/", parallel=True, workers=4)

System Prompt

engine = AdaptiveAI(system_prompt="You are a financial analyst.")
response = engine.ask("Risks?", system_prompt="Rate each HIGH/MEDIUM/LOW.")
engine.set_system_prompt("You are a legal reviewer.")

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Providers — Copy, Paste, Run

Free

engine = AdaptiveAI()  # Ollama (default, local)
engine = AdaptiveAI(llm_model="llama3.2")  # Ollama specific model

engine = AdaptiveAI(api_key="nvapi-...",
    base_url="https://integrate.api.nvidia.com/v1",
    llm_model="meta/llama-3.1-70b-instruct")  # NVIDIA NIM

engine = AdaptiveAI(api_key="gsk_...",
    base_url="https://api.groq.com/openai/v1",
    llm_model="llama-3.3-70b-versatile")  # Groq

engine = AdaptiveAI(llm_backend="huggingface",
    llm_model="Qwen/Qwen2.5-1.5B-Instruct")  # HuggingFace local

Paid

engine = AdaptiveAI(api_key="sk-...", llm_model="gpt-4o")  # OpenAI
engine = AdaptiveAI(api_key="xai-...",
    base_url="https://api.x.ai/v1", llm_model="grok-3-mini")  # Grok

No LLM

engine = AdaptiveAI(llm_backend="none")  # Retrieval only
engine = AdaptiveAI(llm_backend="none", vectorless=True)  # Zero dependencies

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Supported Formats

S.No.	Format	Extension
1	Text / Markdown	.txt, .md
2	CSV / TSV	.csv, .tsv
3	JSON	.json
4	HTML / XML	.html, .xml
5	PDF	.pdf
6	Word	.docx
7	Excel	.xlsx
8	PowerPoint	.pptx
9	Images (OCR)	.png, .jpg
10	SQL databases	PostgreSQL, MySQL, SQLite

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FAQ

Q: How is this different from ChatGPT / Claude? This is not an LLM. It's the retrieval layer that decides what context to feed TO the LLM. Works with any LLM as backend.

Q: What is vectorless mode? No ChromaDB, no embeddings, no vector DB. Pure BM25 keyword search + graph + RL. Best for documents with standardized terminology or air-gapped environments.

Q: Does the RL policy persist? Yes. Everything persists to disk with auto-checkpoint every 5 minutes.

Q: Can I use it without an LLM? Yes. AdaptiveAI(llm_backend="none") returns ranked source excerpts directly.

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Also by the Author

• llmevalkit — 61 metrics for LLM evaluation
• Responsible AI Series — HIPAA, GDPR, NIST AI RMF, CoSAI

Citation

@article{venkatkumar2026adaptive,
  title={Adaptive Retrieval Orchestration for Self-Learning Knowledge Systems},
  author={Venkatkumar, Rajan},
  year={2026},
  url={https://www.researchgate.net/publication/405076088}
}

License

Apache License 2.0

Author

Venkatkumar Rajan

• LinkedIn: https://linkedin.com/in/venkatkumarvk
• GitHub: https://github.com/VK-Ant
• Portfolio: https://vk-ant.github.io/Venkatkumar/
• PyPI: https://pypi.org/project/adaptive-intelligence/
• ResearchGate: https://www.researchgate.net/publication/405076088