Editing Openai/691c1dba-9228-800f-8463-13b3a9006306 (section)

==== 1. Memory store — use SQLite + FAISS (local). Example simple pattern: - Keep memory table with timestamp, ingot_hash, synopsis, embedding_vector. - Compute embedding (sentence-transformers) for any session log you want to save. - Store embedding in FAISS and metadata in SQLite. ====
# Carousel ingot generator (Python snippet)

<syntaxhighlight lang="python">import hashlib, json
def carousel_ingot(lines, title="session"):
    joined = "\n".join(lines).encode('utf-8')
    digest = hashlib.sha256(joined).hexdigest()[:32]
    synopsis = lines[-3:]
    return {"title": title, "hash": digest, "synopsis": synopsis}

</syntaxhighlight>
# ZCP guard (policies.py snippet)

<syntaxhighlight lang="python">def zcp_guard(fn):
    def wrapped(*args, **kwargs):
        result = fn(*args, **kwargs)
        assert isinstance(result, dict)
        # add checks – e.g. never claim actions, never return PII
        return result
    return wrapped

</syntaxhighlight>
# HPL Compiler / Runtime
* Keep your hpl/ files as human-readable manifests. Use your compiler.py to compile to JSON. Have runtime.py read those manifests and run steps on local models.
* Example: compiler.py → manifests/bridge_manifest.json → runtime.py executes steps via Python glue code calling the local LLM through the webui HTTP API or a direct python binding.