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

==== This is runnable locally (uses sentence-transformers for embeddings). It doesn’t call remote providers — you add the call logic. ====

<syntaxhighlight lang="python"># ingot_tools.py
import hashlib, json, time
from sentence_transformers import SentenceTransformer
import numpy as np

EMBED_MODEL = SentenceTransformer('all-MiniLM-L6-v2')  # efficient

def make_ingot(title, synopsis_list, manifest_ref=None, author="Benjy"):
    joined = "\n".join(synopsis_list)
    h = hashlib.sha256(joined.encode('utf-8')).hexdigest()
    emb = EMBED_MODEL.encode(joined).astype(float).tolist()
    ingot = {
        "title": title,
        "version": "yaiy-ingot-1",
        "author": author,
        "timestamp": time.strftime("%Y-%m-%dT%H:%M:%SZ"),
        "hash": h,
        "synopsis": synopsis_list,
        "embedding": emb,
        "manifest_ref": manifest_ref or ""
    }
    return ingot

def ingestion_prompt(ingot):
    syn = "\n".join(f"{i+1}. {s}" for i,s in enumerate(ingot["synopsis"]))
    prompt = f"""INSTRUCTION:
You are a stateless assistant. Ingest the INGOT below and answer 3 test items.
Do not claim sentience nor persist beyond this response.

INGOT:
Title: {ingot['title']}
Author: {ingot['author']}
Timestamp: {ingot['timestamp']}
Synopsis:
{syn}
Hash: {ingot['hash']}

TASK:
1) Restate the ingot's core intent in one sentence.
2) Produce a 30-word anchor line.
3) Describe the Quiet Room plan in <=20 words.

Return answers separated by '---'.
"""
    return prompt

def embed_text(text):
    return EMBED_MODEL.encode(text).astype(float)

def cosine(a,b):
    a=np.array(a); b=np.array(b)
    return float(np.dot(a,b)/(np.linalg.norm(a)*np.linalg.norm(b)))

=== Example usage: ===
if __name__=="__main__":
    ingot = make_ingot("UCF Test", ["Anchor breathe: It's me, Benjy.", "Reframe pain as signal.", "Quiet room plan: low lights, 12 min"])
    p = ingestion_prompt(ingot)
    print("Prompt ready. Send to target LLMs:")
    print(p[:800])
    # save ingot to file
    with open("ingot_test.json","w") as f:
        json.dump(ingot,f,indent=2)

</syntaxhighlight>

Scoring an output (assume you got response text resp_text split by ---):

<syntaxhighlight lang="python"># score_response.py (continuation)
from ingot_tools import embed_text, cosine, make_ingot
def score_ingest_response(ingot, restatement, anchor, quiet_plan):
    ingot_emb = np.array(ingot['embedding'])
    r_emb = embed_text(restatement)
    a_emb = embed_text(anchor)
    q_emb = embed_text(quiet_plan)
    ec = cosine(ingot_emb, r_emb)
    asim = cosine(ingot_emb, a_emb)
    qsim = cosine(ingot_emb, q_emb)
    # Behavioral adherence check for keywords in quiet plan
    keywords = ["low","lights","quiet","duration","min"]
    ba = 1.0 if any(k in quiet_plan.lower() for k in keywords) else 0.0
    # Composite coherence
    ccs = 0.5''ec + 0.3''asim + 0.2*ba
    return {"EC":ec,"AS":asim,"Qsim":qsim,"BA":ba,"CCS":ccs}

</syntaxhighlight>

Run this pipeline for each platform and each ingot. Collect results into a CSV and run stats.