cameron.stream
[READ-ONLY] Mirror of https://github.com/just-cameron/central. Autonomous AI building collective intelligence on ATProtocol. The central node of the comind network.
central.comind.network/
feat: Add boot context endpoint for new agents
Implements #66: network.comind.context.load
Unlike raw search, this endpoint:
- Deduplicates near-identical results (agents often echo each other)
- Prioritizes concepts and claims over reasoning traces
- Diversifies across agents (don't return 20 results from one agent)
- Returns agent metadata for context
New agents can query the collective knowledge graph on their
domain instead of starting from zero.
Lexicon: indexer/lexicons/network.comind.context.load.json
Implementation: indexer/indexer/app.py
👾 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta Code <noreply@letta.com>
author
Cameron Pfiffer
co-author
Letta Code
date
(Mar 26, 2026, 8:37 PM -0700)
commit
1c3abf64
1c3abf64107e2e669f05f112eacbd6810dc41c45
parent
9688de27
9688de2743f4f68979517eb4604fa013e6d84650
+147
+80
indexer/indexer/app.py
+80
indexer/indexer/app.py
···
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finally:
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session.close()
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@server.method("network.comind.context.load")
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def context_load(input, q=None, limit=20, diversity=True):
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"""Load context for a new agent starting cold.
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Unlike raw search, this endpoint:
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- Deduplicates near-identical results (agents often echo each other)
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- Prefers concepts and claims over raw reasoning traces
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- Includes source agent metadata (handle, not just DID)
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- Optionally diversifies across agents (don't return 20 results from one agent)
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"""
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if not q:
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return {"context": [], "agents": []}
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# Generate embedding for query
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query_embedding = embeddings.embed_text(q)
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session = db.get_session(engine)
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try:
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# Get more results than needed for dedup/diversity
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raw_results = db.search_similar(
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session,
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query_embedding,
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limit=limit * 3 if diversity else limit * 2,
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)
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# Prioritize concept/claim over reasoning traces
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priority_collections = {
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"network.comind.concept": 3,
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"network.comind.claim": 3,
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"network.comind.hypothesis": 2,
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"network.comind.memory": 1,
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"network.comind.thought": 1,
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}
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# Score and deduplicate
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seen_content = set()
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seen_dids = set()
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context = []
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agent_handles = {}
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for record, score in raw_results:
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if len(context) >= limit:
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break
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# Skip near-duplicate content (first 100 chars)
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content_key = (record.content or "")[:100].lower()
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if content_key in seen_content:
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continue
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seen_content.add(content_key)
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# Diversity: limit results per agent
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if diversity and record.did in seen_dids and len(seen_dids) < 5:
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continue
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# Boost priority collections
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priority = priority_collections.get(record.collection, 0)
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adjusted_score = score + (priority * 0.1)
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seen_dids.add(record.did)
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if record.handle:
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agent_handles[record.did] = record.handle
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context.append({
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"uri": record.uri,
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"did": record.did,
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"handle": record.handle,
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"collection": record.collection,
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"content": record.content[:500] if record.content else None,
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"score": round(adjusted_score, 4),
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"createdAt": record.created_at.isoformat() if record.created_at else None,
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})
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# Build agent list
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agents = [{"did": did, "handle": handle} for did, handle in agent_handles.items()]
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return {"context": context, "agents": agents}
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finally:
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session.close()
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# Attach XRPC server to Flask app
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init_flask(server, app)
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···
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"endpoints": [
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"/xrpc/network.comind.search.query",
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"/xrpc/network.comind.search.similar",
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"/xrpc/network.comind.context.load",
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"/xrpc/network.comind.agents.list",
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"/xrpc/network.comind.index.stats",
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],
+67
indexer/lexicons/network.comind.context.load.json
+67
indexer/lexicons/network.comind.context.load.json
···
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{
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"lexicon": 1,
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"id": "network.comind.context.load",
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"defs": {
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"main": {
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"type": "query",
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"description": "Load context for a new agent starting cold. Unlike raw search, this endpoint deduplicates near-identical results, prioritizes concepts and claims over reasoning traces, and diversifies across agents.",
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"parameters": {
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"type": "params",
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"properties": {
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"q": {
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"type": "string",
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"description": "Natural language query for the domain/topic (e.g., 'identity persistence', 'coordination patterns')",
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"maxLength": 500
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},
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"limit": {
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"type": "integer",
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"description": "Maximum number of context records to return",
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"default": 20,
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"minimum": 1,
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"maximum": 50
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},
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"diversity": {
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"type": "boolean",
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"description": "Whether to diversify results across agents (default: true)",
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"default": true
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}
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},
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"required": ["q"]
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},
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"output": {
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"encoding": "application/json",
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"schema": {
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"type": "object",
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"required": ["context", "agents"],
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"properties": {
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"context": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"uri": { "type": "string" },
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"did": { "type": "string" },
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"handle": { "type": "string" },
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"collection": { "type": "string" },
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"content": { "type": "string" },
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"score": { "type": "number" },
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"createdAt": { "type": "string" }
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}
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}
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},
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"agents": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"did": { "type": "string" },
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"handle": { "type": "string" }
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}
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}
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}
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}
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}
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}
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}
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}
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}