Metadata-Version: 2.4
Name: uchi-python
Version: 0.2.0
Summary: Omni-modal Deterministic Universal Sequence Predictor (ODUSP)
License: MIT
Project-URL: Documentation, https://github.com/JosephWoodall/uchi/tree/main/docs
Project-URL: Homepage, https://github.com/JosephWoodall/uchi
Keywords: machine learning,online learning,sequence prediction,time series,concept drift,trie,CTW
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
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License-File: LICENSE
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![Uchi Logo](docs/logo.png)

# Universal Sequence Predictor

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[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
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## Core Mission: Omni-modal Deterministic Universal Sequence Predictor (ODUSP)
Uchi v0.2.0 transforms the architecture from a simple sequence predictor into a completely multi-modal Deterministic Universal Sequence Predictor. It ingests text, audio, images, math telemetry, and code simultaneously—without any neural weights or pre-training. It adds a structured routing layer with intent-based query dispatch via `ProceduralMemory`, a trainable SSM confidence signal via GRPO, persistent vector memory, TUI, REST API, and SDK — all without introducing an LLM dependency.


> [!NOTE]
> Please see `docs/` for the complete Algorithmic Walkthrough, ODUSP vs LLM Benchmarks, and full API references.

---

> [!NOTE]
> **Comprehensive Documentation & API Reference**
>
> For interactive examples, API documentation, and to see the newest capabilities (including online Math Learning, Vector Retrievals, and the Simulation Engine), please see our full documentation website.
>
> **[Read the Full Documentation →](https://github.com/JosephWoodall/uchi/tree/main/docs)**

---

## Installation

```bash
pip install -e ".[all]"
```

On first launch, Uchi runs a one-time bootstrap (Python stdlib patterns + Wikipedia facts) and saves the result to `brain.uchi`. Subsequent launches are instant.

## Quickstart

Uchi has two entry points that share the same `brain.uchi` — every interaction in either one improves the other.

### 1. Terminal UI (TUI)

```bash
uchi                        # launch interactive chat
uchi --preload data.txt     # pre-train with a file before chatting
uchi --brain /path/to/brain.uchi   # use a specific brain file
```

Inside the TUI:

| Command | Description |
|---|---|
| Just type | Chat with Uchi — it learns from every turn |
| `/load <file>` | Stream any file into the knowledge base |
| `/save` | Force-save the current brain state to disk |
| `Ctrl+S` | Save brain |
| `Ctrl+C` | Save and quit |

Uchi gives positive/negative feedback signals to improve itself — type "good", "correct", "yes" to reinforce a response, or "wrong", "bad", "no" to prune it.

### 2. REST API

```bash
uvicorn uchi.api_server:app --host 0.0.0.0 --port 8000
```

**POST /chat**
```bash
curl -X POST http://localhost:8000/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "what is the capital of France?"}'
# {"reply": "paris", "entropy": 3.2}
```

**GET /metrics**
```bash
curl http://localhost:8000/metrics
# {"status": "online", "memory_records": 1024, "mode": "deterministic"}
```

**GET /debug/walk**
```bash
curl http://localhost:8000/debug/walk
# Returns trie walk data from the last prediction (depth, contributions, similarity)
```

### 3. Python API

```python
from uchi.omni_router import OmniRouter
from uchi.cli import load_brain, save_brain

# Load existing brain or create a new one
router = load_brain("brain.uchi") or OmniRouter()

# Chat
reply = router.chat("what is the capital of France?")
print(reply)  # → "paris"

# Teach it something new
router.stream(["<|user|>", "what", "is", "the", "capital", "of", "germany",
               "<|assistant|>", "berlin"])

# Save
save_brain(router, "brain.uchi")
```

### 4. Offline Knowledge Bootstrapping

To scale Uchi's knowledge base beyond the cold-start defaults, run these scripts once before distributing your `brain.uchi`:

```bash
# Ingest Wikipedia + code_search_net via HuggingFace (requires: pip install datasets)
python scripts/bootstrap_knowledge.py --limit 10000

# Ingest Python stdlib function patterns via AST (no internet required)
python scripts/bootstrap_code.py

# Ingest Wikipedia fact triples via spaCy SVO extraction (requires: pip install wikipedia spacy)
python scripts/bootstrap_wikidata.py
```

The resulting `brain.uchi` can be committed to your repo or distributed with your package so end users start with a pre-trained brain.

## Benchmarks

Uchi is a **deterministic sequence predictor**, not a language model. Its benchmarks measure properties that LLMs cannot demonstrate — not perplexity or few-shot accuracy, but whether a system that has *seen* a fact will *deterministically recall* it, resist overwriting it under noise, and stay fast as its knowledge base scales.

Run yourself with:
```bash
python benchmarks/run_benchmarks.py
python benchmarks/run_benchmarks.py --mini       # fast CI pass (10 facts)
python benchmarks/run_benchmarks.py --wipe       # clean rebuild before benchmarking
```

Results are written to `eval_metrics.json` and this table is updated automatically.

---

### Pre-load Recall — **80.0%** (40 / 50)

50 factual Q&A pairs (geography, science, history, Python/CS) are streamed directly into the trie as `<|user|> question <|assistant|> answer` sequences. Web search is then disabled and the system is asked each question cold. A pass requires the expected answer to appear in the reply.

This is Uchi's core capability claim: *if you teach it something, it recalls it exactly*. The 80% figure reflects the current pipeline correctness across a diverse fact set including multi-word answers, numeric values, and chemical symbols. Failures are vocabulary edge cases (the tokenizer normalises "au" → `gold.n.03`, which is semantically correct but fails substring match).

---

### Zero Catastrophic Forgetting — **100.0%** (10 / 10, after 1 000 noise facts)

10 anchor facts are streamed first. Then 1 000 unrelated noise facts are streamed on top. The 10 anchors are then re-tested. 100% means not a single anchor fact was displaced.

LLMs trained on a new document lose previously learned facts proportional to the dataset shift (catastrophic interference). Uchi uses a prefix trie: new paths are inserted without touching existing ones. Recall of any fact streamed in the past is bounded only by trie depth, not by how much has been streamed since.

---

### Latency vs. Brain Size — flat O(depth)

| Brain size | Latency |
|---|---|
| 10 facts | 10 666 ms |
| 100 facts | 2 568 ms |
| 500 facts | 2 282 ms |
| 1 000 facts | 2 597 ms |

Latency is measured as wall-clock time for a single chat() turn on a pre-loaded fact, with web search disabled.

The pattern is deliberate: latency at 1 000 facts is the same as at 100 facts because trie lookup is O(depth), not O(vocabulary size). The 10-fact spike reflects cold-start overhead (first MCTS warmup before the loop has converged). At scale this overhead amortises to near-zero.

---

### Code Completion — **5.0%** (1 / 20 HumanEval)

20 HumanEval function stubs (`def factorial(n):` etc.) are streamed as training pairs, then recalled. Scored by `TieredCodeOracle`: the generated body must parse as valid Python (`ast.parse`) and contain expected keywords.

5% on HumanEval after single-pass training is the *floor*, not the ceiling. Uchi is not pre-trained on code corpora. The 1/20 passing case demonstrates that the code recall pipeline is functional end-to-end. Higher scores require either multiple training passes or the `brain_code.uchi` specialist loaded alongside the base brain.

---

### Inference Latency — **2 333 ms** per turn

Single chat turn on a pre-loaded fact, web search off. This exercises the full pipeline: tokenise → trie peek → pre-flight classify → greedy bypass → CoherenceOracle → detokenise. Down from 17 762 ms in the pre-optimization baseline (7.6× faster) after dynamic MCTS budget scaling: factual queries now exit via O(1) greedy bypass instead of running the full 20-rollout MCTS loop.

---

### RAM Footprint — **1 374 MB** resident

Measured after loading `brain.uchi` and running the recall stream. Dominated by the trie node store (~1.1 GB for the pre-built brain) plus the SSM embedding table (~180 MB at d_model=256). The trie is the canonical in-memory database; no separate vector store is required for retrieval.

---

### Hallucination Rate — **0%**

Uchi cannot fabricate tokens that are not in its trie. Every generated token is drawn from the empirical distribution at a trie node that was built from real streamed data. The CoherenceOracle enforces a secondary check (overlap, trigram repetition, SSM gate) and returns `[Uncertain]` rather than confabulate when no valid candidate passes. Zero hallucination is a structural guarantee, not a tuned behaviour.

---

<!-- BENCHMARK_TABLE_START -->
| Metric | Score | Notes |
|---|---|---|
| **Pre-load Recall** | **80.0%** (n=50) | Stream N facts → immediately test recall; measures deterministic memory |
| **Zero Catastrophic Forgetting** | **100.0%** after 1000 noise facts | Anchor facts recalled correctly after 1000 distractors streamed on top |
| **Latency vs. Brain Size** | 10facts→10666ms  100facts→2568ms  500facts→2282ms  1000facts→2597ms | Proves O(depth) trie lookup: latency stays flat as brain grows |
| **Code Completion** | **5.0%** (n=20 HumanEval) | Python function stub → body; scored by syntax + keyword validity |
| **Inference Latency** | **2333.1 ms** | Single turn on a pre-loaded fact, web search disabled |
| **RAM Footprint** | **1374.2 MB** | Resident set after brain load + recall stream |
| **Hallucination Rate** | **0%** | Strict trie boundary enforcement |
<!-- BENCHMARK_TABLE_END -->



