Integrations
Add the significance channel to your existing chatbot in 20 lines
Your chatbot handles factual queries well. Significance queries — "when was I most afraid?" — return nothing.
The Problem
Your chatbot handles factual queries well — dates, names, events. But significance queries — "when was I most afraid?", "what made me happiest with mum?" — return nothing because BM25 and semantic search match words, not meaning.
How It Works
The significance channel intercepts queries and routes them by what mattered, not keyword frequency.
query arrives
↓
your existing retrieval (BM25/semantic/temporal)
↓
significance channel (/v1/activate)
→ returns field filters
→ apply to your memory store
↓
merge candidates
↓
LLM receives significance-weighted contextThe Code
Wrap your existing /chat endpoint with a significance channel call before retrieval:
// Express.js — wrap your existing /chat endpoint
app.post('/chat', async (req, res) => {
const { query, userId } = req.body;
// 1. Call significance channel
const activation = await fetch('https://deepadata.com/api/v1/activate', {
method: 'POST',
headers: {
'Authorization': `Bearer ${process.env.DEEPADATA_API_KEY}`,
'Content-Type': 'application/json'
},
body: JSON.stringify({ query })
}).then(r => r.json());
// 2. Apply EDM filters to your existing memory store
const filters = activation.data?.field_filters || {};
const memories = await yourMemoryStore.search(query, { filters });
// 3. Continue with your existing LLM call
const response = await yourLLM.chat({ query, context: memories });
res.json({ response });
});That's it. Your existing retrieval pipeline gains significance awareness with one API call.
What You Get
Before
"when was I happiest with mum"
→ No results. Semantic search finds "mum" mentions but can't rank by emotional significance.
After
"when was I happiest with mum"
→ EDM routes to arc_type: bond, emotional_weight ≥ 0.7 → finds it.
Why it works:queries like this share no words with the stored text — "what mattered" is typed into the record as fields (arc, weight, state), so it can be addressed directly instead of hoped-for via embedding proximity.