semantic-kinematics-mcp

A surface embedding-vector analysis toolkit. It takes the vectors an embedding model hands back and holds them up to different lights — semantic drift, trajectory dynamics, projection onto caller-defined axes, displacement-magnitude jolts — to see what structure falls out, cheaply. Everything is exposed as MCP tools for agentic integration.

Scope, honestly: this works on the embed model's output vectors only — no transformer hidden-state access. It is the cheap, surface-level instrument; the question it answers is "what can you measure with just the vectors?" Reaching into hidden states is a later, separate bet.

What it offers

Light	What it measures
Drift	Cosine distance between two texts — the simplest, most validated signal.
Trajectory	Text as a particle through embedding space: velocity, acceleration, curvature, and composite rhythm scores. Reflexive geometry — how a passage moves relative to itself.
Axis alignment	Project a passage onto a semantic direction you define (escalation, formality, certainty…), z-scored against an empirical null. Referential geometry — how strongly it marches along your axis.
Bearing / jolt (library-level, emerging — not yet a contracted MCP tool)	Axis-free displacement-magnitude jolt detection scored against a measured-displacement null.
Classification	Similarity-based document classification against caller-supplied exemplars.

Plus a bulk embedding engine (BulkEmbedder) for turning a corpus into vectors at scale, and three interchangeable embedding backends behind one adapter contract.

Method math, full per-tool request/response schemas, the layering invariant, and the data pipeline live in docs/ARCHITECTURE.md.

Quick Start

# MCP server only (lean install)
pip install -e .

# With Gradio UI
pip install -e ".[ui]"

# With GPU support (NV-Embed-v2, ~14GB VRAM)
pip install -e ".[gpu]"

# UI + GPU
pip install -e ".[ui,gpu]"

# Start MCP server
semantic-kinematics-mcp

# Or launch Gradio UI (requires [ui])
python -m semantic_kinematics

Docker

docker build -t mcp/semantic-kinematics .
docker run -i --rm mcp/semantic-kinematics
# or, for host networking + data mounts:
docker-compose up

Embedding Backends

Three interchangeable backends behind one EmbeddingAdapter (this is the "switching layer" — multiple providers' schemas normalized to one contract). Backend selection has two paths: the MCP server reads the EMBEDDING_BACKEND environment variable, while the embed_corpus.py bulk CLI selects via its --backend flag (it does not consult EMBEDDING_BACKEND):

Backend	Model	Dimensions	Notes
nv_embed	NV-Embed-v2	4096	GPU, fp16 (~14GB VRAM), highest quality. Custom BidirectionalMistralModel; resists GGUF/quantization — fp16 in-process only.
lmstudio	Any GGUF via OpenAI API	Varies	Local LM Studio / llama-server endpoint.
sentence_transformers	Any HuggingFace model	Varies	General purpose, CPU-friendly with a small model.

# .env
EMBEDDING_BACKEND=nv_embed

Path resolution is environment-driven (no hardcoded home directories — issue #34):

Variable	Used by	Default
NV_EMBED_MODEL_PATH	both in-process backends (nv_embed + sentence_transformers)	nvidia/NV-Embed-v2 (HuggingFace hub id; set to a local checkout to skip download)
THOUGHT_VAULT_VECTORS_DIR	null builders (build_displacement_null / build_conditioned_null) and smoke_jolt	/srv/dev/shanevcantwell/thought-vault-integration/output/vectors

MCP Tools

9 tools over JSON-RPC (stdio). Full request/response schemas and return-field tables are in ARCHITECTURE.md → Tool reference.

Tool	Description
embed_text	Embedding vector for text
calculate_drift	Cosine distance between two texts
classify_document	Similarity-based document classification
analyze_trajectory	Velocity / acceleration / curvature metrics for a passage
compare_trajectories	Fitness score comparing two passages structurally
analyze_axis_alignment	Project a passage onto a caller-defined axis, z-scored against a background null
model_status	Backend state (type, model, dimensions, cache)
model_load	Load a backend (slated for removal under ADR-003)
model_unload	Unload model, free memory (slated for removal under ADR-003)

Configure in Claude Code

{
  "mcpServers": {
    "semantic-kinematics": {
      "command": "semantic-kinematics-mcp",
      "env": { "EMBEDDING_BACKEND": "nv_embed" }
    }
  }
}

Bulk Embedding a Corpus

BulkEmbedder (semantic_kinematics/embeddings/bulk.py) turns a JSONL corpus into vectors at scale. It wraps any backend and adds windowed crash-resume (it streams prep + embed in windows of prep_window items, default 256 — checkpointing each embedded item to JSONL, so an interrupted run re-preps only the not-yet-checkpointed remainder and the whole run, prep included, reconstructs from the checkpoint rather than restarting from scratch), token-aware batching (packs requests under a token budget), sub-chunking with vector averaging (splits over-long items, averages the piece vectors), and backoff retries.

python scripts/embed_corpus.py corpus.jsonl \
    --checkpoint out.jsonl \
    --backend lmstudio \
    --base-url http://localhost:8082/v1 \
    --model embeddinggemma-300M-F32 \
    --text-field text \
    --id-field chunk_id \
    --max-tokens-per-request 3000 \
    --max-tokens-per-chunk 1500

For the in-process nv_embed backend (NV-Embed-v2 @ 4096-d), two things differ. The model is held resident for the whole run — embed_corpus.py sets unload_after_use=False automatically for nv_embed, since the per-call unload default would reload ~15GB of weights per request-group and make a corpus-scale run prohibitively slow. And bulk runs use larger token budgets — nv_embed's context is 32768, so the embeddinggemma-sized defaults (1500/3000) over-split the long tail; pass --max-tokens-per-request 8000 --max-tokens-per-chunk 8000 to keep ~99.7% of the corpus a single piece:

python scripts/embed_corpus.py corpus.jsonl \
    --checkpoint out.jsonl \
    --backend nv_embed \
    --max-tokens-per-request 8000 \
    --max-tokens-per-chunk 8000

For a full corpus, scripts/embed_full_corpus.sh is the canonical runner: it wraps the above with auto-restart on crash and a success-count-based completion signal (via scripts/embed_status.py) so a run that crashes mid-way resumes cleanly and never false-completes on _failed items. See scripts/embed_status.py to check progress at any time — it prints done failed pending total for a (corpus, checkpoint) pair.

• Input: a JSONL file, one object per line; --text-field / --id-field name the text and id keys (blank-text lines are skipped; missing ids default to line-N).
• Output: the --checkpoint JSONL — one record per embedded item. Re-running with the same checkpoint skips already-embedded items and retries only failures (idempotent resume). A sidecar <checkpoint>.meta.json records the producing model (model_name + dimensions); resuming a checkpoint built by a different model fails loud rather than silently merging incompatible vectors (#16).
• Where it sits: BulkEmbedder is a data-plane job on the shared adapter substrate — it never crosses the MCP contract boundary. Its output vectors feed downstream analysis, e.g. building the axis-alignment background null. Upstream corpus preparation (chat logs → chunked JSONL) lives in the sibling thought-vault-integration repo. See ARCHITECTURE.md → Data pipeline.

Gradio UI

Two tabs — Drift (pairwise cosine distance) and Trajectory (analyze one passage or compare two; interactive velocity/acceleration/curvature profiles, PCA projection, cosine-similarity heatmap, adjustable threshold + context-window smoothing).

python -m semantic_kinematics   # opens at http://localhost:7860

Project Structure

semantic_kinematics/
├── embeddings/        # EmbeddingAdapter + nv_embed / lmstudio / sentence_transformers backends
│                      # + BulkEmbedder (bulk.py): resumable, token-aware corpus embedding
│                      # + bearing/ : axis-free jolt + conditioned-atom (library-level, research)
├── mcp/
│   ├── server.py      # MCP entry point (sole contract door)
│   ├── state_manager.py
│   └── commands/      # embeddings, classification, trajectory, axis_alignment, model
├── ui/                # Gradio app + drift/trajectory tabs
└── utils/             # text cleaning, HTML extraction

scripts/build_axis_null.py   # build a background null cache for axis alignment
scripts/embed_corpus.py      # bulk-embed a corpus (resumable, token-aware)
scripts/embed_status.py      # report `done failed pending total` for a (corpus, checkpoint) pair
scripts/embed_full_corpus.sh # resumable full-corpus runner: auto-restart, success-count completion
docs/                        # ARCHITECTURE.md, axis-alignment.md, ADRs, HANDOFF.md
tests/                       # pytest suite

Documentation

• ARCHITECTURE.md — layering invariant, tool reference, analysis methods (the math), data pipeline, conformance gaps.
• axis-alignment.md — full math for the referential axis projection.
• ADRs — design decisions (ADR-001 axis alignment, ADR-002 unified adapter, ADR-003 stateless MCP).
• HANDOFF.md — cross-repo resume index.

Requirements

• Python 3.10+
• PyTorch 2.0+ (for the NV-Embed-v2 backend)
• See pyproject.toml for the full dependency list.

License

MIT