Embedding Model Swap Broke Semantic Search Silently
TL;DR - Session insights
- Switching embedding models broke the service silently: When upgrading from
all-MiniLM-L6-v2tomulti-qa-mpnet-base-dot-v1, the model loaded successfully but every inference failed with “Invalid input name: token_type_ids” - the service fell back to random pseudo-embeddings without user-visible errors, producing meaningless search results - Semantic search scores don’t mean what you think: A query for “drill_down” returned the node literally containing “drill_down” in 3rd place (score 0.52), while unrelated content ranked higher - this is by design, as semantic search matches conceptual meaning, not keywords
- Dynamic ONNX input detection required ~100 lines of C: The fix used
SessionGetInputCountandSessionGetInputNameAPIs to introspect model inputs at load time, then conditionally built input tensors - a pattern that should be standard for any multi-model embedding service - Q&A-optimized models excel at natural language queries: “what is the scoring algorithm” correctly returned the ranking formula node as #1 (score 0.58), while “drill_down” as a code identifier performed worse than with the simpler MiniLM model
Session Log
USER: I have memory-service (the server equivalent of ‘memory’ MCP) running on localhost:8080. The documentation for memory service is available in ../grid/memory-service/ directory. Look at it and write sample messages to the memory service
AI: Let me explore the memory-service documentation first.
TOOL-CALL: Glob
{"pattern":"../grid/memory-service/**/*"}TOOL:
No files foundTOOL-CALL: Bash
{"command":"ls -la ../grid/memory-service/","description":"List memory-service directory contents"}TOOL:
total 80 drwxr-xr-x 15 user staff 480 Jan 13 21:45 . drwxr-xr-x@ 9 user staff 288 Jan 13 21:45 .. -rw-r--r-- 1 user staff 40 Jan 13 09:53 .dockerignore drwxr-xr-x@ 4 user staff 128 Jan 13 21:33 build drwxr-xr-x@ 5 user staff 160 Jan 13 21:45 data -rw-r--r-- 1 user staff 156 Jan 13 10:05 docker-compose.yml -rw-r--r-- 1 user staff 2397 Jan 13 11:56 Dockerfile drwxr-xr-x 5 user staff 160 Jan 13 07:12 include -rw-r--r-- 1 user staff 12694 Jan 13 14:21 Makefile drwxr-xr-x 3 user staff 96 Jan 13 12:58 models -rw-r--r-- 1 user staff 11537 Jan 13 12:57 README.md drwxr-xr-x 3 user staff 96 Jan 13 12:57 scripts drwxr-xr-x 13 user staff 416 Jan 13 21:22 src drwxr-xr-x 8 user staff 256 Jan 13 12:48 tests drwxr-xr-x 3 user staff 96 Jan 12 22:26 third_partyTOOL-CALL: Read
{"file_path":"./grid/memory-service/README.md"}TOOL:
1→# Memory Service 2→ 3→A high-performance hierarchical memory store for AI agents, providing semantic search across conversation history with sub-10ms query latency. 4→ 5→## Overview 6→ 7→Memory Service stores agent conversations in a 4-level hierarchy: 8→ 9→``` 10→session <- Agent work session (e.g., "auth-system") 11→ └── message <- Single message/turn in conversation 12→ └── block <- Logical section (code, explanation, etc.) 13→ └── statement <- Individual sentence or line 14→``` 15→ 16→Each level is independently indexed for semantic search, allowing agents to: 17→- Find relevant context at any granularity 18→- Drill down from high-level topics to specific details 19→- Zoom out from details to broader context 20→ 21→## Features 22→ 23→- **Hierarchical Storage**: 4-level tree structure (Session → Message → Block → Statement) 24→- **Semantic Search**: ONNX Runtime embeddings with HNSW indexing 25→- **Level Filtering**: Search specific levels or ranges 26→- **Text Filtering**: Filter drill-down results by content 27→- **Cross-Platform**: Builds on Linux (Docker) and macOS (native with CoreML) 28→- **JSON-RPC 2.0**: Standard API over HTTP 29→ 30→## Building [...370 more lines...]AI: The memory-service uses JSON-RPC 2.0 over HTTP. Let me verify it’s running and then send some sample messages.
TOOL-CALL: Bash
{"command":"curl -s http://localhost:8080/health","description":"Check if memory-service is healthy"}TOOL:
{"status":"healthy","node_count":0,"uptime_ms":108596,"request_count":1}AI: The service is running. Let me send sample messages to demonstrate the API.
TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc \\\n -H \"Content-Type: application/json\" \\\n -d '{\"jsonrpc\": \"2.0\", \"method\": \"list_sessions\", \"params\": {}, \"id\": 1}' | jq .","description":"List existing sessions"}TOOL:
curl: option : blank argument where content is expected curl: try 'curl --help' or 'curl --manual' for more informationTOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"list_sessions\", \"params\": {}, \"id\": 1}' | jq .","description":"List existing sessions"}TOOL:
{ "jsonrpc": "2.0", "id": 1, "result": { "sessions": [], "total": 0 } }AI: No sessions yet. Let me store some sample data.
TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"store\", \"params\": {\"session_id\": \"blog-development\", \"agent_id\": \"claude-code\", \"content\": \"Working on the 52vibes blog project - implementing a Hugo-based static site with a tmux-inspired design and Gruvbox color scheme\"}, \"id\": 1}' | jq .","description":"Store first message in a new session"}CLI-MESSAGE:
The user doesn't want to proceed with this tool use. The tool use was rejected (eg. if it was a file edit, the new_string was NOT written to the file). STOP what you are doing and wait for the user toUSER: [Request interrupted by user for tool use]
USER: Push all the chat messages from this session to memory-service instead of sample messages.
AI: I’ll push our actual conversation messages to the memory-service.
TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"store\", \"params\": {\"session_id\": \"claude-code-session-2026-01-13\", \"agent_id\": \"claude-opus-4.5\", \"content\": \"User request: Look at memory-service documentation in ../grid/memory-service/ directory and write sample messages to the memory service running on localhost:8080\"}, \"id\": 1}' | jq .","description":"Store user's initial request"}TOOL:
{ "jsonrpc": "2.0", "id": 1, "result": { "message_id": 1, "session_id": 0, "sequence_num": 1768369756544572000, "new_session": true } }TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"store\", \"params\": {\"session_id\": \"claude-code-session-2026-01-13\", \"agent_id\": \"claude-opus-4.5\", \"content\": \"Explored memory-service directory structure and read README.md documentation. The service uses JSON-RPC 2.0 over HTTP with hierarchical storage: session -> message -> block -> statement. Key methods: store, store_block, store_statement, query,...TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"store\", \"params\": {\"session_id\": \"claude-code-session-2026-01-13\", \"agent_id\": \"claude-opus-4.5\", \"content\": \"Verified memory-service health check at localhost:8080 - service is healthy with 0 nodes initially. Listed sessions and found none existing.\"}, \"id\": 3}' | jq .","description":"Store health check message"}TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"store\", \"params\": {\"session_id\": \"claude-code-session-2026-01-13\", \"agent_id\": \"claude-opus-4.5\", \"content\": \"User updated request: Push all the actual chat messages from this session to memory-service instead of fabricated sample messages.\"}, \"id\": 4}' | jq .","description":"Store updated user request"}TOOL:
{ "jsonrpc": "2.0", "id": 2, "result": { "message_id": 3, "session_id": 2, "sequence_num": 1768369772376906000, "new_session": true } }TOOL:
{ "jsonrpc": "2.0", "id": 3, "result": { "message_id": 5, "session_id": 4, "sequence_num": 1768369774531426000, "new_session": true } }TOOL:
{ "jsonrpc": "2.0", "id": 4, "result": { "message_id": 7, "session_id": 6, "sequence_num": 1768369780250675000, "new_session": true } }AI: Now let me add some blocks with more detail under the documentation message (message_id: 3).
TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"store_block\", \"params\": {\"parent_id\": 3, \"content\": \"Memory-service API methods discovered: store (message), store_block, store_statement, query (semantic search with level filtering), drill_down (get children), zoom_out (get ancestors), list_sessions, get_session\"}, \"id\": 5}' | jq .","description":"Store block with API methods"}TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"store_block\", \"params\": {\"parent_id\": 3, \"content\": \"Hierarchy levels: session (top) -> message -> block -> statement (bottom). Each level independently indexed for semantic search.\"}, \"id\": 6}' | jq .","description":"Store block with hierarchy info"}TOOL:
{ "jsonrpc": "2.0", "id": 5, "result": { "block_id": 8, "parent_id": 3 } }TOOL: ``
TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"store_statement\", \"params\": {\"parent_id\": 8, \"content\": \"Search ranking formula: score = 0.6 * relevance + 0.3 * recency + 0.1 * level_boost\"}, \"id\": 7}' | jq .","description":"Store statement with ranking formula"}TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"list_sessions\", \"params\": {}, \"id\": 8}' | jq .","description":"List all sessions to verify"}TOOL:
{ "jsonrpc": "2.0", "id": 7, "result": { "statement_id": 10, "parent_id": 8 } }TOOL:
{ "jsonrpc": "2.0", "id": 8, "result": { "sessions": [ { "node_id": 0, "session_id": "claude-code-session-2026-01-13", "agent_id": "claude-opus-4.5", "message_count": 1 }, { "node_id": 2, "session_id": "claude-code-session-2026-01-13", "agent_id": "claude-opus-4.5", "message_count": 1 }, { "node_id": 4, "session_id": "claude-code-session-2026-01-13", "agent_id": "claude-opus-4.5", "message_count": 1 }, { "node_id": 6, "session_id": "claude-code-session-2026-01-13", "agent_id": "claude-opus-4.5", "message_count": 1 } ], [...3 more lines...]AI: Now let me demonstrate querying the stored messages.
TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"query\", \"params\": {\"query\": \"memory service documentation API\", \"max_results\": 5}, \"id\": 9}' | jq .","description":"Semantic search for API documentation"}TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"drill_down\", \"params\": {\"id\": 3, \"max_results\": 10}, \"id\": 10}' | jq .","description":"Drill down from message to see blocks"}TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"zoom_out\", \"params\": {\"id\": 10}, \"id\": 11}' | jq .","description":"Zoom out from statement to see ancestors"}TOOL:
{ "jsonrpc": "2.0", "id": 9, "result": { "results": [ { "node_id": 8, "level": "block", "score": 0.6568242311477661, "content": "Memory-service API methods discovered: store (message), store_block, store_statement, query (semantic search with level filtering), drill_down (get children), zoom_out (get ancestors), list_sessions, get_session", "children_count": 1 }, { "node_id": 1, "level": "message", "score": 0.6542071104049683, "content": "User request: Look at memory-service documentation in ../grid/memory-service/ directory and write sample messages to the memory service running on localhost:8080", "children_count": 0 }, { "node_id": 3, "level": "message", "score": 0.6499037146568298, "content": "Explored memory-service directory structure and read README.md documentation. The service uses JSON-RPC 2.0 over HTTP with hierarchical storage: session -> message -> block -> statement. Key methods: store, store_block, store_statement, query, drill_down, zoom_out, list_sessions, get_session.", "children_count": 1 }, { "node_id": 5, "level": "message", "score": 0.5735046863555908, [...17 more lines...]TOOL:
{ "jsonrpc": "2.0", "id": 10, "result": { "node_id": 3, "level": "message", "children": [ { "node_id": 8, "level": "block", "content": "Memory-service API methods discovered: store (message), store_block, store_statement, query (semantic search with level filtering), drill_down (get children), zoom_out (get ancestors), list_sessions, get_session", "children_count": 1 }, { "node_id": 9, "level": "block", "content": "Hierarchy levels: session (top) -> message -> block -> statement (bottom). Each level independently indexed for semantic search.", "children_count": 0 } ], "children_count": 2, "total_children": 2 } }TOOL:
{ "jsonrpc": "2.0", "id": 11, "result": { "node_id": 10, "level": "statement", "content": "Search ranking formula: score = 0.6 * relevance + 0.3 * recency + 0.1 * level_boost", "ancestors": [ { "node_id": 8, "level": "block", "content": "Memory-service API methods discovered: store (message), store_block, store_statement, query (semantic search with level filtering), drill_down (get children), zoom_out (get ancestors), list_sessions, get_session" }, { "node_id": 3, "level": "message", "content": "Explored memory-service directory structure and read README.md documentation. The service uses JSON-RPC 2.0 over HTTP with hierarchical storage: session -> message -> block -> statement. Key methods: store, store_block, store_statement, query, drill_down, zoom_out, list_sessions, get_session." }, { "node_id": 2, "level": "session" } ], "siblings": [] } }AI: Done. I’ve pushed our conversation to memory-service. Here’s a summary:
Stored Messages (4 messages across the session):
- User’s initial request to explore memory-service documentation
- My exploration of the README and discovery of the API
- Health check verification and empty session list
- User’s updated request to push actual chat messages
Stored Blocks (2 blocks under message 3):
- API methods list (store, query, drill_down, zoom_out, etc.)
- Hierarchy explanation (session → message → block → statement)
Stored Statement (1 statement under block 8):
- Search ranking formula
Verification:
- Semantic search for “memory service documentation API” returned all 5 nodes ranked by relevance (scores 0.54-0.66)
drill_downon message 3 shows its 2 child blockszoom_outfrom statement 10 shows full ancestor chain: statement → block → message → session
USER: Query for “leak”.
TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"query\", \"params\": {\"query\": \"leak\", \"max_results\": 10}, \"id\": 1}' | jq .","description":"Semantic search for \"leak\""}TOOL:
{ "jsonrpc": "2.0", "id": 1, "result": { "results": [ { "node_id": 7, "level": "message", "score": 0.4619942903518677, "content": "User updated request: Push all the actual chat messages from this session to memory-service instead of fabricated sample messages.", "children_count": 0 }, { "node_id": 5, "level": "message", "score": 0.43514707684516907, "content": "Verified memory-service health check at localhost:8080 - service is healthy with 0 nodes initially. Listed sessions and found none existing.", "children_count": 0 }, { "node_id": 3, "level": "message", "score": 0.4211381673812866, "content": "Explored memory-service directory structure and read README.md documentation. The service uses JSON-RPC 2.0 over HTTP with hierarchical storage: session -> message -> block -> statement. Key methods: store, store_block, store_statement, query, drill_down, zoom_out, list_sessions, get_session.", "children_count": 1 }, { "node_id": 1, "level": "message", "score": 0.41879650950431824, [...31 more lines...]AI: No content about “leak” exists in the stored messages. The results show low relevance scores (0.38-0.46) - these are just the closest semantic matches the model could find, but none actually contain leak-related content.
USER: Query for “Memory leak”
TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"query\", \"params\": {\"query\": \"Memory leak\", \"max_results\": 10}, \"id\": 1}' | jq .","description":"Semantic search for \"Memory leak\""}TOOL:
{ "jsonrpc": "2.0", "id": 1, "result": { "results": [ { "node_id": 5, "level": "message", "score": 0.5452277064323425, "content": "Verified memory-service health check at localhost:8080 - service is healthy with 0 nodes initially. Listed sessions and found none existing.", "children_count": 0 }, { "node_id": 1, "level": "message", "score": 0.5173182487487793, "content": "User request: Look at memory-service documentation in ../grid/memory-service/ directory and write sample messages to the memory service running on localhost:8080", "children_count": 0 }, { "node_id": 7, "level": "message", "score": 0.5069347620010376, "content": "User updated request: Push all the actual chat messages from this session to memory-service instead of fabricated sample messages.", "children_count": 0 }, { "node_id": 8, "level": "block", "score": 0.4957166016101837, [...31 more lines...]AI: Slightly higher scores (0.35-0.55) because “memory” semantically matches the “memory-service” content. The top result (node 5) mentions “memory-service health check” - the model is picking up on the word “memory” but there’s still no actual leak-related content stored.
USER: query for “curl”
TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"query\", \"params\": {\"query\": \"curl\", \"max_results\": 10}, \"id\": 1}' | jq .","description":"Semantic search for \"curl\""}TOOL:
{ "jsonrpc": "2.0", "id": 1, "result": { "results": [ { "node_id": 7, "level": "message", "score": 0.438824325799942, "content": "User updated request: Push all the actual chat messages from this session to memory-service instead of fabricated sample messages.", "children_count": 0 }, { "node_id": 3, "level": "message", "score": 0.42578738927841187, "content": "Explored memory-service directory structure and read README.md documentation. The service uses JSON-RPC 2.0 over HTTP with hierarchical storage: session -> message -> block -> statement. Key methods: store, store_block, store_statement, query, drill_down, zoom_out, list_sessions, get_session.", "children_count": 1 }, { "node_id": 8, "level": "block", "score": 0.40884992480278015, "content": "Memory-service API methods discovered: store (message), store_block, store_statement, query (semantic search with level filtering), drill_down (get children), zoom_out (get ancestors), list_sessions, get_session", "children_count": 1 }, { "node_id": 5, "level": "message", "score": 0.4000113010406494, [...31 more lines...]AI: Low scores again (0.38-0.44). No content about “curl” was stored - I used curl to make the API calls but didn’t document that in the stored messages. The results are just generic closest matches.
USER: What terms do you suggest I query for?
AI: Based on what’s stored, these queries should return high relevance scores:
High match potential:
"JSON-RPC"- mentioned in API description"hierarchical storage"- key architecture concept"semantic search"- mentioned multiple times"health check"- in the verification message"drill_down zoom_out"- specific API methods"ranking formula"- stored as a statement"recency relevance"- part of the scoring formula
Concept matches:
"API methods"- should hit the block listing methods"session message block statement"- hierarchy levels"README documentation"- exploration message"localhost 8080"- service endpoint
Likely poor matches (not stored):
"curl","leak","error","bug"- no related content"Hugo","blog","Gruvbox"- from CLAUDE.md but not stored in memory-service
USER: query for “drill_down”
TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"query\", \"params\": {\"query\": \"drill_down\", \"max_results\": 10}, \"id\": 1}' | jq .","description":"Semantic search for \"drill_down\""}TOOL:
{ "jsonrpc": "2.0", "id": 1, "result": { "results": [ { "node_id": 10, "level": "statement", "score": 0.4460861384868622, "content": "Search ranking formula: score = 0.6 * relevance + 0.3 * recency + 0.1 * level_boost", "children_count": 0 }, { "node_id": 8, "level": "block", "score": 0.4413498640060425, "content": "Memory-service API methods discovered: store (message), store_block, store_statement, query (semantic search with level filtering), drill_down (get children), zoom_out (get ancestors), list_sessions, get_session", "children_count": 1 }, { "node_id": 9, "level": "block", "score": 0.435371458530426, "content": "Hierarchy levels: session (top) -> message -> block -> statement (bottom). Each level independently indexed for semantic search.", "children_count": 0 }, { "node_id": 3, "level": "message", "score": 0.4232420325279236, [...31 more lines...]AI: Interesting result. Even though “drill_down” appears literally in node 8 and node 3, they scored only 0.44 and 0.42 respectively. Node 10 (ranking formula) scored highest at 0.45 despite not containing “drill_down” at all.
This shows semantic search ≠ keyword search. The embedding model is matching on conceptual similarity, not exact text. The underscore in
drill_downmay also affect tokenization.USER: query for “drill down”
TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"query\", \"params\": {\"query\": \"drill down\", \"max_results\": 10}, \"id\": 1}' | jq .","description":"Semantic search for \"drill down\" (no underscore)"}TOOL:
{ "jsonrpc": "2.0", "id": 1, "result": { "results": [ { "node_id": 10, "level": "statement", "score": 0.41677314043045044, "content": "Search ranking formula: score = 0.6 * relevance + 0.3 * recency + 0.1 * level_boost", "children_count": 0 }, { "node_id": 9, "level": "block", "score": 0.41649129986763, "content": "Hierarchy levels: session (top) -> message -> block -> statement (bottom). Each level independently indexed for semantic search.", "children_count": 0 }, { "node_id": 8, "level": "block", "score": 0.40578457713127136, "content": "Memory-service API methods discovered: store (message), store_block, store_statement, query (semantic search with level filtering), drill_down (get children), zoom_out (get ancestors), list_sessions, get_session", "children_count": 1 }, { "node_id": 3, "level": "message", "score": 0.4051898419857025, [...31 more lines...]AI: Scores actually dropped slightly without the underscore:
- Node 8: 0.44 → 0.41
- Node 3: 0.42 → 0.41
The
drill_downquery matched better thandrill down. The model likely tokenized the storeddrill_downsimilarly and found closer similarity with the underscore version.Still, neither query produces a strong match (>0.6) despite exact text presence - the embedding model’s semantic understanding dominates over lexical matching.
USER: Ok. Do I need a better embedding model then!
AI: It depends on what you want:
If you need exact/keyword matching:
- Semantic search isn’t designed for that - consider adding a hybrid approach (semantic + BM25/inverted index)
- The README mentions the service has an inverted index - might be worth checking if there’s keyword search support
If you want better semantic matching: The README lists model options:
Model Quality Tradeoff all-MiniLM-L6-v2(current)Good Fast, 80MB all-MiniLM-L12-v2Better 120MB all-mpnet-base-v2Best Slow, 420MB multi-qa-mpnet-base-dot-v1Best for Q&A 420MB My take: The 0.4-0.5 scores aren’t necessarily bad - they just show low semantic similarity between “drill_down” (a method name) and stored content. The model correctly ranked the nodes containing “drill_down” near the top despite low absolute scores.
For code/technical content,
multi-qa-mpnet-base-dot-v1might perform better. Want me to check what model is currently loaded?USER: Yes
TOOL-CALL: Bash
{"command":"curl -s http://localhost:8080/health | jq .","description":"Check health endpoint for model info"}TOOL:
{ "status": "healthy", "node_count": 11, "uptime_ms": 758242, "request_count": 19 }AI: Health endpoint doesn’t show model info. Let me check the running process.
TOOL-CALL: Bash
{"command":"ps aux | grep memory-service | grep -v grep","description":"Check memory-service process arguments"}TOOL:
vinay 75249 0.0 0.6 411251184 95424 s008 S+ 9:45PM 0:03.76 ./build/bin/memory-service --data ./data --model ./models/all-MiniLM-L6-v2/model.onnxAI: Running
all-MiniLM-L6-v2(the default fast/small model).To try a better model for technical content:
# Download the Q&A-optimized model ./scripts/download-model.sh multi-qa-mpnet-base-dot-v1 # Restart service with new model ./build/bin/memory-service --data ./data --model ./models/multi-qa-mpnet-base-dot-v1/model.onnxOr for the highest general quality:
./scripts/download-model.sh all-mpnet-base-v2Note: Larger models will increase latency but should give more meaningful similarity scores, especially for technical/code content.
USER: Ok. I’ve loaded the model. Now run the same query -
drill_downTOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"query\", \"params\": {\"query\": \"drill_down\", \"max_results\": 10}, \"id\": 1}' | jq .","description":"Semantic search for \"drill_down\" with new model"}TOOL:
{ "jsonrpc": "2.0", "id": 1, "result": { "results": [ { "node_id": 9, "level": "block", "score": 0.4038603603839874, "children_count": 0 }, { "node_id": 0, "level": "session", "score": 0.4000000059604645, "children_count": 1 }, { "node_id": 2, "level": "session", "score": 0.4000000059604645, "children_count": 1 }, { "node_id": 4, "level": "session", "score": 0.4000000059604645, "children_count": 1 }, { [...42 more lines...]USER: [Request interrupted by user]
USER: Ok. We need to refill the messages into the service (that is using the new model). Do that first.
TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"store\", \"params\": {\"session_id\": \"claude-code-session-2026-01-13-v2\", \"agent_id\": \"claude-opus-4.5\", \"content\": \"User request: Look at memory-service documentation in ../grid/memory-service/ directory and write sample messages to the memory service running on localhost:8080\"}, \"id\": 1}' | jq .","description":"Store message 1 with new model"}TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"store\", \"params\": {\"session_id\": \"claude-code-session-2026-01-13-v2\", \"agent_id\": \"claude-opus-4.5\", \"content\": \"Explored memory-service directory structure and read README.md documentation. The service uses JSON-RPC 2.0 over HTTP with hierarchical storage: session -> message -> block -> statement. Key methods: store, store_block, store_statement, que...TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"store\", \"params\": {\"session_id\": \"claude-code-session-2026-01-13-v2\", \"agent_id\": \"claude-opus-4.5\", \"content\": \"Verified memory-service health check at localhost:8080 - service is healthy with 0 nodes initially. Listed sessions and found none existing.\"}, \"id\": 3}' | jq .","description":"Store message 3 with new model"}TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"store\", \"params\": {\"session_id\": \"claude-code-session-2026-01-13-v2\", \"agent_id\": \"claude-opus-4.5\", \"content\": \"User updated request: Push all the actual chat messages from this session to memory-service instead of fabricated sample messages.\"}, \"id\": 4}' | jq .","description":"Store message 4 with new model"}TOOL:
{ "jsonrpc": "2.0", "id": 1, "result": { "message_id": 1, "session_id": 0, "sequence_num": 1768370489128274000, "new_session": true } }TOOL:
{ "jsonrpc": "2.0", "id": 2, "result": { "message_id": 3, "session_id": 2, "sequence_num": 1768370508179856000, "new_session": true } }TOOL:
{ "jsonrpc": "2.0", "id": 3, "result": { "message_id": 5, "session_id": 4, "sequence_num": 1768370527387891000, "new_session": true } }TOOL:
{ "jsonrpc": "2.0", "id": 4, "result": { "message_id": 7, "session_id": 6, "sequence_num": 1768370529115523000, "new_session": true } }TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"store_block\", \"params\": {\"parent_id\": 3, \"content\": \"Memory-service API methods discovered: store (message), store_block, store_statement, query (semantic search with level filtering), drill_down (get children), zoom_out (get ancestors), list_sessions, get_session\"}, \"id\": 5}' | jq .","description":"Store block with API methods"}TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"store_block\", \"params\": {\"parent_id\": 3, \"content\": \"Hierarchy levels: session (top) -> message -> block -> statement (bottom). Each level independently indexed for semantic search.\"}, \"id\": 6}' | jq .","description":"Store block with hierarchy info"}TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"store_statement\", \"params\": {\"parent_id\": 8, \"content\": \"Search ranking formula: score = 0.6 * relevance + 0.3 * recency + 0.1 * level_boost\"}, \"id\": 7}' | jq .","description":"Store statement with ranking formula"}TOOL:
{ "jsonrpc": "2.0", "id": 5, "result": { "block_id": 8, "parent_id": 3 } }TOOL: ``
TOOL:
{ "jsonrpc": "2.0", "id": 7, "result": { "statement_id": 10, "parent_id": 8 } }AI: Done. Now querying for “drill_down” with the new embeddings.
TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"query\", \"params\": {\"query\": \"drill_down\", \"max_results\": 10}, \"id\": 1}' | jq .","description":"Semantic search for \"drill_down\" with new model"}TOOL:
{ "jsonrpc": "2.0", "id": 1, "result": { "results": [ { "node_id": 5, "level": "message", "score": 0.4174763560295105, "content": "Verified memory-service health check at localhost:8080 - service is healthy with 0 nodes initially. Listed sessions and found none existing.", "children_count": 0 }, { "node_id": 3, "level": "message", "score": 0.414652556180954, "content": "Explored memory-service directory structure and read README.md documentation. The service uses JSON-RPC 2.0 over HTTP with hierarchical storage: session -> message -> block -> statement. Key methods: store, store_block, store_statement, query, drill_down, zoom_out, list_sessions, get_session.", "children_count": 1 }, { "node_id": 7, "level": "message", "score": 0.4033713638782501, "content": "User updated request: Push all the actual chat messages from this session to memory-service instead of fabricated sample messages.", "children_count": 0 }, { "node_id": 1, "level": "message", "score": 0.3985072672367096, [...31 more lines...]AI: Interesting - the new model (
multi-qa-mpnet-base-dot-v1) actually performed worse for this query:Node Contains “drill_down”? Old Model New Model 8 (block) ✓ 0.44 (2nd) 0.38 (last) 3 (message) ✓ 0.42 (4th) 0.41 (2nd) The Q&A model is optimized for natural language questions like “how do I navigate the hierarchy?” rather than code identifiers like
drill_down.For method name / keyword matching, you likely need:
- Hybrid search (semantic + BM25/inverted index), or
- A code-specific model like
all-roberta-large-v1, or - Accept that semantic search isn’t meant for exact keyword matching
USER: Actually, the model was not used. I found a bunch of errors -
[📂 memory-service]▶ ./build/bin/memory-service --data ./data --model ./models/multi-qa-mpnet-base-dot-v1/model.onnx 2026-01-13 22:00:48.257 INFO Memory Service v0.1.0 starting... 2026-01-13 22:00:48.257 INFO Data directory: ./data 2026-01-13 22:00:48.257 INFO No existing data found, creating new hierarchy 2026-01-13 22:00:48.257 INFO Relations store created at ./data/relations with capacity 10000 2026-01-13 22:00:48.258 INFO Embeddings store created at ./data/embeddings with capacity 10000 per level 2026-01-13 22:00:48.258 INFO Hierarchy created at ./data with capacity 10000 2026-01-13 22:00:48.260 INFO Tokenizer loaded vocabulary with 30527 tokens 2026-01-13 22:00:48.590 INFO ONNX embedding engine initialized (provider=CPU, model=./models/multi-qa-mpnet-base-dot-v1/model.onnx) 2026-01-13 22:00:48.591 INFO Search engine created 2026-01-13 22:00:48.591 INFO API server started on port 8080 2026-01-13 22:00:48.591 INFO API server listening on http://localhost:8080 2026-01-13 22:00:48.591 INFO POST /rpc - JSON-RPC 2.0 endpoint 2026-01-13 22:00:48.591 INFO GET /health - Health check 2026-01-13 22:00:48.591 INFO GET /metrics - Prometheus metrics 2026-01-13 22:00:48.591 INFO Memory Service ready (capacity: 10000 nodes) 2026-01-13 22:01:29.128 INFO RPC request: method=store 2026-01-13 22:01:29.128 ERROR ONNX Runtime error: Invalid input name: token_type_ids 2026-01-13 22:01:29.128 WARN ONNX inference failed, falling back to pseudo-embedding 2026-01-13 22:01:29.128 INFO RPC response: method=store status=ok elapsed=0.08ms [hierarchy=0.02 embed=0.04] 2026-01-13 22:01:48.179 INFO RPC request: method=store 2026-01-13 22:01:48.179 ERROR ONNX Runtime error: Invalid input name: token_type_ids 2026-01-13 22:01:48.179 WARN ONNX inference failed, falling back to pseudo-embedding 2026-01-13 22:01:48.179 INFO RPC response: method=store status=ok elapsed=0.10ms [hierarchy=0.02 embed=0.05] 2026-01-13 22:02:07.387 INFO RPC request: method=store 2026-01-13 22:02:07.387 ERROR ONNX Runtime error: Invalid input name: token_type_ids 2026-01-13 22:02:07.387 WARN ONNX inference failed, falling back to pseudo-embedding 2026-01-13 22:02:07.387 INFO RPC response: method=store status=ok elapsed=0.17ms [embed=0.14 index=0.01] 2026-01-13 22:02:09.115 INFO RPC request: method=store 2026-01-13 22:02:09.115 ERROR ONNX Runtime error: Invalid input name: token_type_ids 2026-01-13 22:02:09.115 WARN ONNX inference failed, falling back to pseudo-embedding 2026-01-13 22:02:09.115 INFO RPC response: method=store status=ok elapsed=0.10ms [embed=0.07 index=0.01] 2026-01-13 22:02:27.535 INFO RPC request: method=store_block 2026-01-13 22:02:27.536 ERROR ONNX Runtime error: Invalid input name: token_type_ids 2026-01-13 22:02:27.536 WARN ONNX inference failed, falling back to pseudo-embedding 2026-01-13 22:02:27.536 INFO RPC response: method=store_block status=ok elapsed=0.10ms [hierarchy=0.03 embed=0.05 index=0.01] 2026-01-13 22:02:28.790 INFO RPC request: method=store_block 2026-01-13 22:02:28.790 ERROR ONNX Runtime error: Invalid input name: token_type_ids 2026-01-13 22:02:28.790 WARN ONNX inference failed, falling back to pseudo-embedding 2026-01-13 22:02:28.790 INFO RPC response: method=store_block status=ok elapsed=0.07ms [embed=0.04] 2026-01-13 22:02:29.281 INFO RPC request: method=store_statement 2026-01-13 22:02:29.281 ERROR ONNX Runtime error: Invalid input name: token_type_ids 2026-01-13 22:02:29.281 WARN ONNX inference failed, falling back to pseudo-embedding 2026-01-13 22:02:29.281 INFO RPC response: method=store_statement status=ok elapsed=0.11ms [embed=0.08 index=0.01] 2026-01-13 22:02:37.225 INFO RPC request: method=query 2026-01-13 22:02:37.225 ERROR ONNX Runtime error: Invalid input name: token_type_ids 2026-01-13 22:02:37.225 WARN ONNX inference failed, falling back to pseudo-embedding 2026-01-13 22:02:37.225 INFO RPC response: method=query status=ok matches=7 levels=statement,block,message elapsed=0.07ms [embed=0.04]AI: The issue is the model’s input requirements. The
multi-qa-mpnet-base-dot-v1model expects atoken_type_idsinput tensor that the memory-service isn’t providing.ERROR ONNX Runtime error: Invalid input name: token_type_ids WARN ONNX inference failed, falling back to pseudo-embeddingWhat happened:
- The model loaded successfully
- But every inference call failed because
token_type_idswasn’t passed - Service fell back to pseudo-embeddings (random vectors) → meaningless search results
Model input requirements:
Model Inputs Required all-MiniLM-L6-v2input_ids,attention_maskmulti-qa-mpnet-base-dot-v1input_ids,attention_mask,token_type_idsOptions:
- Use a compatible model - stick with MiniLM variants that don’t need
token_type_ids - Fix memory-service - add
token_type_idssupport to the ONNX embedding code
Want me to look at the embedding code in
src/embedding/to see what it would take to addtoken_type_idssupport?USER: Before that, will every new embedding model I download have this kind of input list differences!!
AI: Unfortunately, yes - there’s variation, though most follow similar patterns.
Common input patterns for sentence-transformer ONNX models:
Pattern Inputs Models Standard input_ids,attention_maskMiniLM, DistilBERT variants BERT-style input_ids,attention_mask,token_type_idsMPNet, RoBERTa, full BERT Output variations too:
- Some output
last_hidden_state(needs pooling) - Some output
sentence_embedding(already pooled)
How to check before using a model:
# Install onnx python package (one-time) pip install onnx # Inspect model inputs/outputs python -c " import onnx model = onnx.load('./models/multi-qa-mpnet-base-dot-v1/model.onnx') print('Inputs:', [i.name for i in model.graph.input]) print('Outputs:', [o.name for o in model.graph.output]) "The practical reality:
- The MiniLM models (
all-MiniLM-L6-v2,all-MiniLM-L12-v2) are the safest - widely used, simple interface - Larger/specialized models often have quirks
- A robust embedding service should either detect inputs dynamically or document supported models
Want me to check the memory-service code to see how hard it would be to add dynamic input detection?
- Some output
USER: yes check it
TOOL-CALL: Glob
{"pattern":"../grid/memory-service/src/embedding/**/*.c"}TOOL:
No files foundTOOL-CALL: Bash
{"command":"ls -la ./grid/memory-service/src/","description":"List src directory structure"}TOOL:
total 64 drwxr-xr-x 13 user staff 416 Jan 13 21:22 . drwxr-xr-x 15 user staff 480 Jan 13 22:03 .. drwxr-xr-x 5 user staff 160 Jan 13 21:22 api drwxr-xr-x 6 user staff 192 Jan 13 12:17 core drwxr-xr-x 8 user staff 256 Jan 13 14:07 embedding drwxr-xr-x 4 user staff 128 Jan 13 10:00 events -rw-r--r-- 1 user staff 7667 Jan 13 14:09 main.c -rw-r--r-- 1 user staff 21837 Jan 13 21:22 mcp.c drwxr-xr-x 5 user staff 160 Jan 13 12:18 platform drwxr-xr-x 8 user staff 256 Jan 13 14:10 search drwxr-xr-x 6 user staff 192 Jan 13 10:00 session drwxr-xr-x 10 user staff 320 Jan 13 12:12 storage drwxr-xr-x 6 user staff 192 Jan 13 11:55 utilTOOL-CALL: Bash
{"command":"ls -la ./grid/memory-service/src/embedding/","description":"List embedding directory"}TOOL:
total 112 drwxr-xr-x 8 user staff 256 Jan 13 14:07 . drwxr-xr-x 13 user staff 416 Jan 13 21:22 .. -rw-r--r-- 1 user staff 18574 Jan 13 14:07 embedding.c -rw-r--r-- 1 user staff 3018 Jan 12 22:43 embedding.h -rw-r--r-- 1 user staff 5328 Jan 13 09:50 pooling.c -rw-r--r-- 1 user staff 2254 Jan 12 22:43 pooling.h -rw-r--r-- 1 user staff 12430 Jan 13 09:50 tokenizer.c -rw-r--r-- 1 user staff 3477 Jan 13 07:19 tokenizer.hTOOL-CALL: Read
{"file_path":"./grid/memory-service/src/embedding/embedding.c"}TOOL:
1→/* 2→ * Memory Service - Embedding Generation Implementation 3→ * 4→ * When ONNX Runtime is available (HAVE_ONNXRUNTIME defined), 5→ * uses real model inference. Otherwise, uses stub implementation. 6→ */ 7→ 8→#include "embedding.h" 9→#include "tokenizer.h" 10→#include "../util/log.h" 11→ 12→#include <stdlib.h> 13→#include <string.h> 14→#include <math.h> 15→ 16→#ifdef HAVE_ONNXRUNTIME 17→#include <onnxruntime_c_api.h> 18→#ifdef __APPLE__ 19→/* CoreML provider header - may not exist in all ONNX Runtime builds */ 20→#if __has_include(<coreml_provider_factory.h>) 21→#include <coreml_provider_factory.h> 22→#define HAVE_COREML_PROVIDER 1 23→#endif 24→#endif 25→#endif 26→ 27→struct embedding_engine { 28→ embedding_config_t config; 29→ bool onnx_available; 30→ tokenizer_t* tokenizer; [...555 more lines...]AI: Found the issue. Looking at
run_inference(lines 324-328):/* Run inference */ const char* input_names[] = {"input_ids", "attention_mask", "token_type_ids"}; const char* output_names[] = {"last_hidden_state"}; OrtValue* inputs[] = {input_ids_tensor, attention_mask_tensor, token_type_ids_tensor}; status = api->Run(engine->session, NULL, input_names, (const OrtValue* const*)inputs, 3, output_names, 1, &output_tensor);The problem: Input names are hardcoded. It always passes all 3 inputs regardless of what the model expects.
What’s needed: Query the model at load time to detect its input names dynamically.
The fix would use ONNX Runtime’s session introspection API:
// At session creation time (around line 200): size_t input_count; api->SessionGetInputCount(session, &input_count); for (size_t i = 0; i < input_count; i++) { char* name; api->SessionGetInputName(session, i, allocator, &name); // Store name, check if "token_type_ids" exists api->AllocatorFree(allocator, name); }Then at inference time, only pass the inputs the model declared.
Scope of change:
- ~30-50 lines in
embedding_engine_createto detect inputs - ~20 lines in
run_inferenceto conditionally build input arrays - Add a flag like
bool has_token_type_idsto the engine struct
Want me to implement this fix?
- ~30-50 lines in
USER: yes
TOOL-CALL: TodoWrite
{"todos":[{"content":"Add input detection fields to embedding_engine struct","status":"in_progress","activeForm":"Adding input detection fields to embedding_engine struct"},{"content":"Add input introspection in embedding_engine_create","status":"pending","activeForm":"Adding input introspection in embedding_engine_create"},{"content":"Update run_inference to conditionally pass inputs","status":"pending","activeForm":"Updating run_inference to conditionally pass inputs"}]}TOOL:
Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicableTOOL-CALL: Edit
{"replace_all":false,"file_path":"./grid/memory-service/src/embedding/embedding.c","old_string":"struct embedding_engine {\n embedding_config_t config;\n bool onnx_available;\n tokenizer_t* tokenizer;\n\n#ifdef HAVE_ONNXRUNTIME\n const OrtApi* api;\n OrtEnv* env;\n OrtSession* session;\n OrtSessionOptions* session_options;\n OrtAllocator* allocator;\n OrtMemoryInfo* memory_info;\n#endif\n};","new_string":"struct embedding_engine {\...TOOL:
The file ./grid/memory-service/src/embedding/embedding.c has been updated successfully.TOOL-CALL: TodoWrite
{"todos":[{"content":"Add input detection fields to embedding_engine struct","status":"completed","activeForm":"Adding input detection fields to embedding_engine struct"},{"content":"Add input introspection in embedding_engine_create","status":"in_progress","activeForm":"Adding input introspection in embedding_engine_create"},{"content":"Update run_inference to conditionally pass inputs","status":"pending","activeForm":"Updating run_inference to conditionally pass inputs"}]}TOOL:
Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicableTOOL-CALL: Edit
{"replace_all":false,"file_path":"./grid/memory-service/src/embedding/embedding.c","old_string":" /* Create session */\n const char* model_path = e->config.model_path; /* Save before potential free */\n status = api->CreateSession(e->env, model_path,\n e->session_options, &e->session);\n if (status) {\n check_ort_status(status, api);\n api->ReleaseSessionOptions(e->session_options);\n api->Rele...TOOL:
The file ./grid/memory-service/src/embedding/embedding.c has been updated successfully.AI: Now I need to remove the duplicate allocator code and update the cleanup to free output_name. Let me read the current state around the allocator section.
TOOL-CALL: Read
{"file_path":"./grid/memory-service/src/embedding/embedding.c","offset":265,"limit":50}TOOL:
265→ e->output_name = strdup(output_name); 266→ api->AllocatorFree(e->allocator, output_name); 267→ LOG_DEBUG("Model output detected: %s", e->output_name); 268→ } 269→ } 270→ 271→ /* Validate required inputs */ 272→ if (!e->has_input_ids || !e->has_attention_mask) { 273→ LOG_WARN("Model missing required inputs (input_ids=%d, attention_mask=%d)", 274→ e->has_input_ids, e->has_attention_mask); 275→ } 276→ 277→ /* Note: allocator already obtained above */ 278→ status = api->GetAllocatorWithDefaultOptions(&e->allocator); 279→ if (status) { 280→ check_ort_status(status, api); 281→ api->ReleaseSession(e->session); 282→ api->ReleaseSessionOptions(e->session_options); 283→ api->ReleaseEnv(e->env); 284→ tokenizer_destroy(e->tokenizer); 285→ free(e); 286→ MEM_RETURN_ERROR(MEM_ERR_ONNX, "failed to get ONNX allocator"); 287→ } 288→ 289→ /* Create CPU memory info */ 290→ status = api->CreateCpuMemoryInfo(OrtArenaAllocator, OrtMemTypeDefault, &e->memory_info); 291→ if (status) { 292→ check_ort_status(status, api); 293→ api->ReleaseSession(e->session); 294→ api->ReleaseSessionOptions(e->session_options); [...25 more lines...]TOOL-CALL: Edit
{"replace_all":false,"file_path":"./grid/memory-service/src/embedding/embedding.c","old_string":" /* Validate required inputs */\n if (!e->has_input_ids || !e->has_attention_mask) {\n LOG_WARN(\"Model missing required inputs (input_ids=%d, attention_mask=%d)\",\n e->has_input_ids, e->has_attention_mask);\n }\n\n /* Note: allocator already obtained above */\n status = api->GetAllocatorWithDefaultOptions(&e->allocator);\n ...TOOL:
The file ./grid/memory-service/src/embedding/embedding.c has been updated successfully.TOOL-CALL: Read
{"file_path":"./grid/memory-service/src/embedding/embedding.c","offset":290,"limit":30}TOOL:
290→ *engine = e; 291→ LOG_INFO("ONNX embedding engine initialized (provider=%s, model=%s)", 292→ provider_name, e->config.model_path); 293→ return MEM_OK; 294→} 295→ 296→void embedding_engine_destroy(embedding_engine_t* engine) { 297→ if (!engine) return; 298→ 299→ if (engine->tokenizer) { 300→ tokenizer_destroy(engine->tokenizer); 301→ } 302→ 303→ if (engine->onnx_available && engine->api) { 304→ const OrtApi* api = engine->api; 305→ if (engine->memory_info) api->ReleaseMemoryInfo(engine->memory_info); 306→ if (engine->session) api->ReleaseSession(engine->session); 307→ if (engine->session_options) api->ReleaseSessionOptions(engine->session_options); 308→ if (engine->env) api->ReleaseEnv(engine->env); 309→ } 310→ 311→ free(engine); 312→} 313→ 314→mem_error_t embedding_generate(embedding_engine_t* engine, 315→ const char* text, size_t text_len, 316→ float* output) { 317→ return embedding_generate_batch(engine, &text, &text_len, 1, output); 318→} 319→ [...5 more lines...]TOOL-CALL: Edit
{"replace_all":false,"file_path":"./grid/memory-service/src/embedding/embedding.c","old_string":"void embedding_engine_destroy(embedding_engine_t* engine) {\n if (!engine) return;\n\n if (engine->tokenizer) {\n tokenizer_destroy(engine->tokenizer);\n }\n\n if (engine->onnx_available && engine->api) {\n const OrtApi* api = engine->api;\n if (engine->memory_info) api->ReleaseMemoryInfo(engine->memory_info);\n if (engine-...TOOL:
The file ./grid/memory-service/src/embedding/embedding.c has been updated successfully.TOOL-CALL: TodoWrite
{"todos":[{"content":"Add input detection fields to embedding_engine struct","status":"completed","activeForm":"Adding input detection fields to embedding_engine struct"},{"content":"Add input introspection in embedding_engine_create","status":"completed","activeForm":"Adding input introspection in embedding_engine_create"},{"content":"Update run_inference to conditionally pass inputs","status":"in_progress","activeForm":"Updating run_inference to conditionally pass inputs"}]}TOOL:
Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicableTOOL-CALL: Read
{"file_path":"./grid/memory-service/src/embedding/embedding.c","offset":330,"limit":80}TOOL:
330→ size_t seq_len, 331→ float* outputs) { 332→ const OrtApi* api = engine->api; 333→ OrtStatus* status; 334→ mem_error_t result = MEM_OK; 335→ 336→ /* Prepare input tensors */ 337→ size_t input_size = batch_size * seq_len; 338→ int64_t input_shape[2] = {(int64_t)batch_size, (int64_t)seq_len}; 339→ 340→ /* Allocate and fill input arrays */ 341→ int64_t* input_ids_data = malloc(input_size * sizeof(int64_t)); 342→ int64_t* attention_mask_data = malloc(input_size * sizeof(int64_t)); 343→ int64_t* token_type_ids_data = malloc(input_size * sizeof(int64_t)); 344→ 345→ if (!input_ids_data || !attention_mask_data || !token_type_ids_data) { 346→ free(input_ids_data); 347→ free(attention_mask_data); 348→ free(token_type_ids_data); 349→ MEM_RETURN_ERROR(MEM_ERR_NOMEM, "failed to allocate input tensors"); 350→ } 351→ 352→ /* Copy tokenized data to contiguous arrays */ 353→ for (size_t b = 0; b < batch_size; b++) { 354→ for (size_t s = 0; s < seq_len; s++) { 355→ size_t idx = b * seq_len + s; 356→ input_ids_data[idx] = tokens[b].input_ids[s]; 357→ attention_mask_data[idx] = tokens[b].attention_mask[s]; 358→ token_type_ids_data[idx] = tokens[b].token_type_ids[s]; 359→ } [...55 more lines...]TOOL-CALL: Edit
{"replace_all":false,"file_path":"./grid/memory-service/src/embedding/embedding.c","old_string":" /* Allocate and fill input arrays */\n int64_t* input_ids_data = malloc(input_size * sizeof(int64_t));\n int64_t* attention_mask_data = malloc(input_size * sizeof(int64_t));\n int64_t* token_type_ids_data = malloc(input_size * sizeof(int64_t));\n\n if (!input_ids_data || !attention_mask_data || !token_type_ids_data) {\n free(input_ids_data)...TOOL:
The file ./grid/memory-service/src/embedding/embedding.c has been updated successfully.TOOL-CALL: TodoWrite
{"todos":[{"content":"Add input detection fields to embedding_engine struct","status":"completed","activeForm":"Adding input detection fields to embedding_engine struct"},{"content":"Add input introspection in embedding_engine_create","status":"completed","activeForm":"Adding input introspection in embedding_engine_create"},{"content":"Update run_inference to conditionally pass inputs","status":"completed","activeForm":"Updating run_inference to conditionally pass inputs"},{"content":"Build and ...TOOL:
Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicableTOOL-CALL: Bash
{"command":"cd ./grid/memory-service && make 2>&1","description":"Build memory-service with changes","timeout":120000}CLI-MESSAGE:
Exit code 2 gcc -Wall -Wextra -Werror -std=c11 -D_GNU_SOURCE -I./grid/memory-service/include -I./grid/memory-service/src -mcAI: Need to handle the return values from
AllocatorFree. Let me fix those warnings.TOOL-CALL: Read
{"file_path":"./grid/memory-service/src/embedding/embedding.c","offset":240,"limit":35}TOOL:
240→ e->has_input_ids = true; 241→ } else if (strcmp(input_name, "attention_mask") == 0) { 242→ e->has_attention_mask = true; 243→ } else if (strcmp(input_name, "token_type_ids") == 0) { 244→ e->has_token_type_ids = true; 245→ } 246→ api->AllocatorFree(e->allocator, input_name); 247→ } 248→ } 249→ e->input_count = num_inputs; 250→ } 251→ 252→ LOG_DEBUG("Model inputs detected: input_ids=%d attention_mask=%d token_type_ids=%d", 253→ e->has_input_ids, e->has_attention_mask, e->has_token_type_ids); 254→ 255→ /* Introspect model outputs */ 256→ e->output_name = NULL; 257→ size_t num_outputs; 258→ status = api->SessionGetOutputCount(e->session, &num_outputs); 259→ if (status) { 260→ check_ort_status(status, api); 261→ } else if (num_outputs > 0) { 262→ char* output_name = NULL; 263→ status = api->SessionGetOutputName(e->session, 0, e->allocator, &output_name); 264→ if (!status && output_name) { 265→ e->output_name = strdup(output_name); 266→ api->AllocatorFree(e->allocator, output_name); 267→ LOG_DEBUG("Model output detected: %s", e->output_name); 268→ } 269→ } [...10 more lines...]TOOL-CALL: Edit
{"replace_all":false,"file_path":"./grid/memory-service/src/embedding/embedding.c","old_string":" api->AllocatorFree(e->allocator, input_name);","new_string":" (void)api->AllocatorFree(e->allocator, input_name);"}TOOL:
The file ./grid/memory-service/src/embedding/embedding.c has been updated successfully.TOOL-CALL: Edit
{"replace_all":false,"file_path":"./grid/memory-service/src/embedding/embedding.c","old_string":" api->AllocatorFree(e->allocator, output_name);","new_string":" (void)api->AllocatorFree(e->allocator, output_name);"}TOOL:
The file ./grid/memory-service/src/embedding/embedding.c has been updated successfully.TOOL-CALL: Bash
{"command":"cd ./grid/memory-service && make 2>&1","description":"Build memory-service with fixes","timeout":120000}TOOL:
gcc -Wall -Wextra -Werror -std=c11 -D_GNU_SOURCE -I./grid/memory-service/include -I./grid/memory-service/src -mcpu=apple-m1 -I/opt/homebrew/Cellar/libmicrohttpd/1.0.2/include -DHAVE_MICROHTTPD -I/opt/homebrew/Cellar/onnxruntime/1.23.2_1/include/onnxruntime -DHAVE_ONNXRUNTIME -O2 -DNDEBUG -c -o build/obj/embedding/embedding.o src/embedding/embedding.c gcc -Wall -Wextra -Werror -std=c11 -D_GNU_SOURCE -I./grid/memory-service/include -I./grid/memory-service/src -mcpu=apple-m1 -I/opt/homebrew/Cellar/libmicrohttpd/1.0.2/include -DHAVE_MICROHTTPD -I/opt/homebrew/Cellar/onnxruntime/1.23.2_1/include/onnxruntime -DHAVE_ONNXRUNTIME -O2 -DNDEBUG -o build/bin/memory-service build/obj/core/arena.o build/obj/core/hierarchy.o build/obj/util/log.o build/obj/util/time.o build/obj/storage/embeddings.o build/obj/storage/metadata.o build/obj/storage/relations.o build/obj/storage/wal.o build/obj/embedding/embedding.o build/obj/embedding/pooling.o build/obj/embedding/tokenizer.o build/obj/search/hnsw.o build/obj/search/inverted_index.o build/obj/search/search.o build/obj/api/http_server.o build/obj/api/json_rpc.o build/obj/session/keywords.o build/obj/session/session.o build/obj/events/emitter.o build/obj/platform/platform_darwin.o build/obj/main.o build/obj/yyjson.o -lpthread -lm -L/opt/homebrew/Cellar/libmicrohttpd/1.0.2/lib -lmicrohttpd -L/opt/homebrew/Cellar/onnxruntime/1.23.2_1/lib -lonnxruntime gcc -Wall -Wextra -Werror -std=c11 -D_GNU_SOURCE -I./grid/memory-service/include -I./grid/memory-service/src -mcpu=apple-m1 -I/opt/homebrew/Cellar/libmicrohttpd/1.0.2/include -DHAVE_MICROHTTPD -I/opt/homebrew/Cellar/onnxruntime/1.23.2_1/include/onnxruntime -DHAVE_ONNXRUNTIME -O2 -DNDEBUG -o build/bin/memory-mcp build/obj/core/arena.o build/obj/core/hierarchy.o build/obj/util/log.o build/obj/util/time.o build/obj/storage/embeddings.o build/obj/storage/metadata.o build/obj/storage/relations.o build/obj/storage/wal.o build/obj/embedding/embedding.o build/obj/embedding/pooling.o build/obj/embedding/tokenizer.o build/obj/search/hnsw.o build/obj/search/inverted_index.o build/obj/search/search.o build/obj/api/http_server.o build/obj/api/json_rpc.o build/obj/session/keywords.o build/obj/session/session.o build/obj/events/emitter.o build/obj/platform/platform_darwin.o build/obj/mcp.o build/obj/yyjson.o -lpthread -lm -L/opt/homebrew/Cellar/libmicrohttpd/1.0.2/lib -lmicrohttpd -L/opt/homebrew/Cellar/onnxruntime/1.23.2_1/lib -lonnxruntime Shell cwd was reset to ./blogAI: Build succeeded. Now restart the service with the new binary:
cd ../grid/memory-service ./build/bin/memory-service --data ./data --model ./models/multi-qa-mpnet-base-dot-v1/model.onnxYou should see new log lines like:
DEBUG Model inputs detected: input_ids=1 attention_mask=1 token_type_ids=0 DEBUG Model output detected: last_hidden_stateThe service will now only pass the inputs the model expects. Let me know when it’s running and I’ll re-store the messages and test the query again.
TOOL-CALL: TodoWrite
{"todos":[{"content":"Add input detection fields to embedding_engine struct","status":"completed","activeForm":"Adding input detection fields to embedding_engine struct"},{"content":"Add input introspection in embedding_engine_create","status":"completed","activeForm":"Adding input introspection in embedding_engine_create"},{"content":"Update run_inference to conditionally pass inputs","status":"completed","activeForm":"Updating run_inference to conditionally pass inputs"},{"content":"Build and ...TOOL:
Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicableUSER: Ok. Lets restart and fill it up with messages.
TOOL-CALL: Bash
{"command":"curl -s http://localhost:8080/health | jq .","description":"Verify service is running"}TOOL:
{ "status": "healthy", "node_count": 0, "uptime_ms": 58473, "request_count": 1 }AI: Service is up. Storing messages now.
TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"store\", \"params\": {\"session_id\": \"claude-session-mpnet\", \"agent_id\": \"claude-opus-4.5\", \"content\": \"User request: Look at memory-service documentation in ../grid/memory-service/ directory and write sample messages to the memory service running on localhost:8080\"}, \"id\": 1}' | jq .","description":"Store message 1"}TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"store\", \"params\": {\"session_id\": \"claude-session-mpnet\", \"agent_id\": \"claude-opus-4.5\", \"content\": \"Explored memory-service directory structure and read README.md documentation. The service uses JSON-RPC 2.0 over HTTP with hierarchical storage: session -> message -> block -> statement. Key methods: store, store_block, store_statement, query, drill_dow...TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"store\", \"params\": {\"session_id\": \"claude-session-mpnet\", \"agent_id\": \"claude-opus-4.5\", \"content\": \"Verified memory-service health check at localhost:8080 - service is healthy with 0 nodes initially. Listed sessions and found none existing.\"}, \"id\": 3}' | jq .","description":"Store message 3"}TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"store\", \"params\": {\"session_id\": \"claude-session-mpnet\", \"agent_id\": \"claude-opus-4.5\", \"content\": \"User updated request: Push all the actual chat messages from this session to memory-service instead of fabricated sample messages.\"}, \"id\": 4}' | jq .","description":"Store message 4"}TOOL:
{ "jsonrpc": "2.0", "id": 1, "result": { "message_id": 1, "session_id": 0, "sequence_num": 1768371052661512000, "new_session": true } }TOOL:
{ "jsonrpc": "2.0", "id": 2, "result": { "message_id": 3, "session_id": 2, "sequence_num": 1768371056224395000, "new_session": true } }TOOL:
{ "jsonrpc": "2.0", "id": 3, "result": { "message_id": 5, "session_id": 4, "sequence_num": 1768371058277564000, "new_session": true } }TOOL:
{ "jsonrpc": "2.0", "id": 4, "result": { "message_id": 7, "session_id": 6, "sequence_num": 1768371060534927000, "new_session": true } }TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"store_block\", \"params\": {\"parent_id\": 3, \"content\": \"Memory-service API methods discovered: store (message), store_block, store_statement, query (semantic search with level filtering), drill_down (get children), zoom_out (get ancestors), list_sessions, get_session\"}, \"id\": 5}' | jq .","description":"Store block - API methods"}TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"store_block\", \"params\": {\"parent_id\": 3, \"content\": \"Hierarchy levels: session (top) -> message -> block -> statement (bottom). Each level independently indexed for semantic search.\"}, \"id\": 6}' | jq .","description":"Store block - hierarchy"}TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"store_statement\", \"params\": {\"parent_id\": 8, \"content\": \"Search ranking formula: score = 0.6 * relevance + 0.3 * recency + 0.1 * level_boost\"}, \"id\": 7}' | jq .","description":"Store statement - ranking formula"}TOOL:
{ "jsonrpc": "2.0", "id": 5, "result": { "block_id": 8, "parent_id": 3 } }TOOL: ``
TOOL:
{ "jsonrpc": "2.0", "id": 7, "result": { "statement_id": 10, "parent_id": 8 } }AI: All stored successfully. Now querying for “drill_down”:
TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"query\", \"params\": {\"query\": \"drill_down\", \"max_results\": 10}, \"id\": 1}' | jq .","description":"Query for drill_down with mpnet model"}TOOL:
{ "jsonrpc": "2.0", "id": 1, "result": { "results": [ { "node_id": 10, "level": "statement", "score": 0.5371643304824829, "content": "Search ranking formula: score = 0.6 * relevance + 0.3 * recency + 0.1 * level_boost", "children_count": 0 }, { "node_id": 1, "level": "message", "score": 0.5347201228141785, "content": "User request: Look at memory-service documentation in ../grid/memory-service/ directory and write sample messages to the memory service running on localhost:8080", "children_count": 0 }, { "node_id": 8, "level": "block", "score": 0.5243363976478577, "content": "Memory-service API methods discovered: store (message), store_block, store_statement, query (semantic search with level filtering), drill_down (get children), zoom_out (get ancestors), list_sessions, get_session", "children_count": 1 }, { "node_id": 9, "level": "block", "score": 0.5239226222038269, [...31 more lines...]AI: It works. Comparing scores:
Model Node 8 (contains “drill_down”) Score Range MiniLM (old) 0.44 (2nd place) 0.39-0.45 MPNet (broken) 0.38 (last place) 0.37-0.42 MPNet (fixed) 0.52 (3rd place) 0.45-0.54 Scores improved ~20% across the board now that real embeddings are being used.
Node 8 (which literally contains “drill_down”) still isn’t ranked #1 - that’s the nature of semantic search. The Q&A model finds “ranking formula” and “documentation request” more semantically similar to “drill_down” as a concept.
Want to try a more natural query like “how do I navigate to children nodes”?
USER: yes
TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"query\", \"params\": {\"query\": \"how do I navigate to children nodes\", \"max_results\": 10}, \"id\": 1}' | jq .","description":"Natural language query for navigation"}TOOL:
{ "jsonrpc": "2.0", "id": 1, "result": { "results": [ { "node_id": 9, "level": "block", "score": 0.5554893016815186, "content": "Hierarchy levels: session (top) -> message -> block -> statement (bottom). Each level independently indexed for semantic search.", "children_count": 0 }, { "node_id": 1, "level": "message", "score": 0.5345324277877808, "content": "User request: Look at memory-service documentation in ../grid/memory-service/ directory and write sample messages to the memory service running on localhost:8080", "children_count": 0 }, { "node_id": 3, "level": "message", "score": 0.4924192726612091, "content": "Explored memory-service directory structure and read README.md documentation. The service uses JSON-RPC 2.0 over HTTP with hierarchical storage: session -> message -> block -> statement. Key methods: store, store_block, store_statement, query, drill_down, zoom_out, list_sessions, get_session.", "children_count": 1 }, { "node_id": 10, "level": "statement", "score": 0.488545298576355, [...31 more lines...]AI: Interesting results:
Rank Node Content Score 1st 9 (block) “Hierarchy levels: session → message → block → statement…” 0.555 6th 8 (block) “…drill_down (get children)…” 0.487 The Q&A model correctly identified that “hierarchy levels” is semantically related to “navigating to children nodes” - even though node 8 literally says “get children”.
This is the Q&A model doing its job: understanding intent rather than matching keywords.
Try another: “what is the scoring algorithm”
TOOL-CALL: Bash
{"command":"curl -s -X POST http://localhost:8080/rpc -H \"Content-Type: application/json\" -d '{\"jsonrpc\": \"2.0\", \"method\": \"query\", \"params\": {\"query\": \"what is the scoring algorithm\", \"max_results\": 5}, \"id\": 1}' | jq .","description":"Query about scoring algorithm"}TOOL:
{ "jsonrpc": "2.0", "id": 1, "result": { "results": [ { "node_id": 10, "level": "statement", "score": 0.5821560621261597, "content": "Search ranking formula: score = 0.6 * relevance + 0.3 * recency + 0.1 * level_boost", "children_count": 0 }, { "node_id": 8, "level": "block", "score": 0.5182095766067505, "content": "Memory-service API methods discovered: store (message), store_block, store_statement, query (semantic search with level filtering), drill_down (get children), zoom_out (get ancestors), list_sessions, get_session", "children_count": 1 }, { "node_id": 3, "level": "message", "score": 0.5173745155334473, "content": "Explored memory-service directory structure and read README.md documentation. The service uses JSON-RPC 2.0 over HTTP with hierarchical storage: session -> message -> block -> statement. Key methods: store, store_block, store_statement, query, drill_down, zoom_out, list_sessions, get_session.", "children_count": 1 }, { "node_id": 1, "level": "message", "score": 0.5135051608085632, [...17 more lines...]AI: Node 10 (“Search ranking formula: score = 0.6 * relevance…”) ranked #1 with score 0.58 - exactly right.
The Q&A model excels at natural language questions. The dynamic input detection fix is working correctly.
USER: Great. Lets create a MCP bash script that performs all the querying operations (query, zoom out, drill down, etc.) supported by the memory-service server.
AI: I’ll create a CLI script for memory-service operations.
TOOL-CALL: TodoWrite
{"todos":[{"content":"Create memory-service CLI bash script","status":"in_progress","activeForm":"Creating memory-service CLI bash script"}]}TOOL:
Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicableTOOL-CALL: Bash
{"command":"ls -la ./grid/memory-service/scripts/","description":"Check existing scripts"}TOOL:
total 8 drwxr-xr-x 3 user staff 96 Jan 13 12:57 . drwxr-xr-x 15 user staff 480 Jan 13 22:09 .. -rwxr-xr-x 1 user staff 2192 Jan 13 12:57 download-model.shTOOL-CALL: Write
{"file_path":"./grid/memory-service/scripts/mem","content":"#!/usr/bin/env bash\n#\n# mem - CLI for memory-service\n#\n# Usage:\n# mem query \"search text\" [--level LEVEL] [--top LEVEL] [--bottom LEVEL] [--max N]\n# mem drill ID [--filter TEXT] [--max N]\n# mem zoom ID\n# mem sessions [SESSION_ID]\n# mem store message SESSION_ID AGENT_ID \"content\"\n# mem store block PARENT_ID \"content\"\n# mem store statement PARENT_ID \"content\"\n#\n# Env...TOOL:
File created successfully at: ./grid/memory-service/scripts/memTOOL-CALL: Bash
{"command":"chmod +x ./grid/memory-service/scripts/mem","description":"Make script executable"}TOOL: ``
TOOL-CALL: Bash
{"command":"./grid/memory-service/scripts/mem health","description":"Test health command"}TOOL:
● healthy Nodes: 11 Requests: 12 Uptime: 5m 46sTOOL-CALL: Bash
{"command":"./grid/memory-service/scripts/mem query \"scoring algorithm\" --max 3","description":"Test query command"}TOOL:
Query: scoring algorithm Found 3 matches #10 statement (0 children) ████████████░░░░░░░░ 62% Search ranking formula: score = 0.6 * relevance + 0.3 * recency + 0.1 * level_boost #9 block (0 children) ██████████░░░░░░░░░░ 52% Hierarchy levels: session (top) -> message -> block -> statement (bottom). Each level independent... #8 block (1 children) ██████████░░░░░░░░░░ 50% Memory-service API methods discovered: store (message), store_block, store_statement, query (sema...TOOL-CALL: Bash
{"command":"./grid/memory-service/scripts/mem drill 3","description":"Test drill command"}TOOL:
Node #3 message 2/2 children #8 block (1 children) Memory-service API methods discovered: store (message), store_block, store_statement, q... #9 block (0 children) Hierarchy levels: session (top) -> message -> block -> statement (bottom). Each level i...TOOL-CALL: Bash
{"command":"./grid/memory-service/scripts/mem zoom 10","description":"Test zoom command"}CLI-MESSAGE: `Exit code 1 Node #10 statement Search ranking formula: score = 0.6 * relevance + 0.3 * recency + 0.1 * level_boost
Ancestors: └─ #8 block Memory-service API methods discovered: store (messag`
TOOL-CALL: Bash
{"command":"./grid/memory-service/scripts/mem sessions","description":"Test sessions command"}TOOL:
Sessions (4 total) #0 claude-session-mpnet Agent: claude-opus-4.5 Messages: 1 #2 claude-session-mpnet Agent: claude-opus-4.5 Messages: 1 #4 claude-session-mpnet Agent: claude-opus-4.5 Messages: 1 #6 claude-session-mpnet Agent: claude-opus-4.5 Messages: 1TOOL-CALL: Bash
{"command":"./grid/memory-service/scripts/mem help 2>&1 | head -30","description":"Test help command"}TOOL:
mem - CLI for memory-service USAGE mem <command> [options] COMMANDS query TEXT [options] Semantic search --level LEVEL Search single level (session|message|block|statement) --top LEVEL Highest level to search (default: session) --bottom LEVEL Lowest level to search (default: statement) --max N Max results (default: 10) drill ID [options] Get children of a node --filter TEXT Filter children by text --max N Max results (default: 100) zoom ID Get ancestors of a node sessions [ID] List sessions or get specific session store TYPE ... Store content store message SESSION AGENT "content" store block PARENT_ID "content" store statement PARENT_ID "content" health Check service health EXAMPLES mem query "authentication token" mem query "API methods" --level block --max 5AI: Let me fix the exit code issue with empty while loops.
TOOL-CALL: Edit
{"replace_all":false,"file_path":"./grid/memory-service/scripts/mem","old_string":" echo \"$result\" | jq -r '.results[] | @base64' | while read -r item; do\n local decoded=$(echo \"$item\" | base64 -d)\n local node_id=$(echo \"$decoded\" | jq -r '.node_id')\n local level=$(echo \"$decoded\" | jq -r '.level')\n local score=$(echo \"$decoded\" | jq -r '.score')\n local content=$(echo \"$decoded\" | jq -r '.content // \"(n...TOOL:
The file ./grid/memory-service/scripts/mem has been updated successfully.TOOL-CALL: Edit
{"replace_all":false,"file_path":"./grid/memory-service/scripts/mem","old_string":" echo \"$result\" | jq -r '.children[]? | @base64' | while read -r item; do\n local decoded=$(echo \"$item\" | base64 -d)\n local child_id=$(echo \"$decoded\" | jq -r '.node_id')\n local child_level=$(echo \"$decoded\" | jq -r '.level')\n local content=$(echo \"$decoded\" | jq -r '.content // \"(no content)\"')\n local children=$(echo \"$d...TOOL:
The file ./grid/memory-service/scripts/mem has been updated successfully.TOOL-CALL: Edit
{"replace_all":false,"file_path":"./grid/memory-service/scripts/mem","old_string":" echo -e \"${DIM}Ancestors:${RESET}\"\n echo \"$result\" | jq -r '.ancestors[]? | @base64' | while read -r item; do\n local decoded=$(echo \"$item\" | base64 -d)\n local anc_id=$(echo \"$decoded\" | jq -r '.node_id')\n local anc_level=$(echo \"$decoded\" | jq -r '.level')\n local anc_content=$(echo \"$decoded\" | jq -r '.content // \"(no conte...TOOL:
The file ./grid/memory-service/scripts/mem has been updated successfully.TOOL-CALL: Edit
{"replace_all":false,"file_path":"./grid/memory-service/scripts/mem","old_string":" echo \"$result\" | jq -r '.sessions[]? | @base64' | while read -r item; do\n local decoded=$(echo \"$item\" | base64 -d)\n local node_id=$(echo \"$decoded\" | jq -r '.node_id')\n local sid=$(echo \"$decoded\" | jq -r '.session_id')\n local agent=$(echo \"$decoded\" | jq -r '.agent_id')\n local count=$(echo \"$decod...TOOL:
The file ./grid/memory-service/scripts/mem has been updated successfully.TOOL-CALL: Bash
{"command":"./grid/memory-service/scripts/mem zoom 10 && echo \"Exit code: $?\"","description":"Test zoom with exit code check"}TOOL:
Node #10 statement Search ranking formula: score = 0.6 * relevance + 0.3 * recency + 0.1 * level_boost Ancestors: └─ #8 block Memory-service API methods discovered: store (message), store_block, store_stateme... └─ #3 message Explored memory-service directory structure and read README.md documentation. The ... └─ #2 session Exit code: 0TOOL-CALL: TodoWrite
{"todos":[{"content":"Create memory-service CLI bash script","status":"completed","activeForm":"Creating memory-service CLI bash script"}]}TOOL:
Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicableAI: Done. Created
/scripts/memCLI with these commands:Command Description mem query TEXTSemantic search with optional --level,--top,--bottom,--maxmem drill IDGet children with optional --filter,--maxmem zoom IDGet ancestor chain mem sessionsList all sessions mem sessions IDGet specific session mem store message SESSION AGENT "content"Store message mem store block PARENT_ID "content"Store block mem store statement PARENT_ID "content"Store statement mem healthService health check Usage:
# Add to PATH or use directly ./scripts/mem query "how to navigate hierarchy" ./scripts/mem drill 3 --filter "API" ./scripts/mem zoom 10 ./scripts/mem healthFeatures:
- Colored output with score visualization bars
- Level-colored labels (session/message/block/statement)
- Truncated content preview
MEMORY_SERVICE_HOSTenv var support
USER: I asked for a MCP, not a CLI script!!!
AI: Sorry! Let me create a proper MCP server that exposes memory-service operations as tools.
TOOL-CALL: TodoWrite
{"todos":[{"content":"Create MCP server for memory-service","status":"in_progress","activeForm":"Creating MCP server for memory-service"}]}TOOL:
Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicableTOOL-CALL: Write
{"file_path":"./grid/memory-service/scripts/memory-mcp","content":"#!/usr/bin/env bash\n#\n# memory-mcp - MCP server for memory-service\n#\n# Exposes memory-service operations as MCP tools for AI assistants.\n#\n# Environment:\n# MEMORY_SERVICE_HOST Service endpoint (default: localhost:8080)\n#\n\nset -u\n\nHOST=\"${MEMORY_SERVICE_HOST:-localhost:8080}\"\nENDPOINT=\"http://${HOST}/rpc\"\n\n# Log to stderr (not visible to MCP client)\nlog() {\n echo \"[...TOOL:
File created successfully at: ./grid/memory-service/scripts/memory-mcpTOOL-CALL: Bash
{"command":"chmod +x ./grid/memory-service/scripts/memory-mcp","description":"Make MCP server executable"}TOOL: ``
TOOL-CALL: Bash
{"command":"echo '{\"jsonrpc\":\"2.0\",\"method\":\"initialize\",\"id\":1}' | ./grid/memory-service/scripts/memory-mcp 2>/dev/null","description":"Test MCP initialize"}TOOL:
{"jsonrpc":"2.0","id":1,"result":{ "protocolVersion": "2024-11-05", "capabilities": { "tools": {} }, "serverInfo": { "name": "memory-mcp", "version": "1.0.0" } }}TOOL-CALL: Bash
{"command":"echo '{\"jsonrpc\":\"2.0\",\"method\":\"tools/list\",\"id\":2}' | ./grid/memory-service/scripts/memory-mcp 2>/dev/null | jq '.result.tools[].name'","description":"Test MCP tools/list"}TOOL:
"memory_query" "memory_drill_down" "memory_zoom_out" "memory_list_sessions" "memory_get_session" "memory_store" "memory_store_block" "memory_store_statement"TOOL-CALL: Bash
{"command":"echo '{\"jsonrpc\":\"2.0\",\"method\":\"tools/call\",\"params\":{\"name\":\"memory_query\",\"arguments\":{\"query\":\"scoring algorithm\",\"max_results\":2}},\"id\":3}' | ./grid/memory-service/scripts/memory-mcp 2>/dev/null | jq '.'","description":"Test MCP tools/call query"}TOOL:
{ "jsonrpc": "2.0", "id": 3, "result": { "content": [ { "type": "text", "text": "{\"results\":[{\"node_id\":10,\"level\":\"statement\",\"score\":0.6210741996765137,\"content\":\"Search ranking formula: score = 0.6 * relevance + 0.3 * recency + 0.1 * level_boost\",\"children_count\":0},{\"node_id\":9,\"level\":\"block\",\"score\":0.5218445062637329,\"content\":\"Hierarchy levels: session (top) -> message -> block -> statement (bottom). Each level independently indexed for semantic search.\",\"children_count\":0}],\"total_matches\":2,\"top_level\":\"session\",\"bottom_level\":\"statement\",\"truncated\":false}\n" } ] } }TOOL-CALL: Bash
{"command":"echo '{\"jsonrpc\":\"2.0\",\"method\":\"tools/call\",\"params\":{\"name\":\"memory_zoom_out\",\"arguments\":{\"id\":10}},\"id\":4}' | ./grid/memory-service/scripts/memory-mcp 2>/dev/null | jq '.result.content[0].text | fromjson'","description":"Test MCP zoom_out"}TOOL:
{ "node_id": 10, "level": "statement", "content": "Search ranking formula: score = 0.6 * relevance + 0.3 * recency + 0.1 * level_boost", "ancestors": [ { "node_id": 8, "level": "block", "content": "Memory-service API methods discovered: store (message), store_block, store_statement, query (semantic search with level filtering), drill_down (get children), zoom_out (get ancestors), list_sessions, get_session" }, { "node_id": 3, "level": "message", "content": "Explored memory-service directory structure and read README.md documentation. The service uses JSON-RPC 2.0 over HTTP with hierarchical storage: session -> message -> block -> statement. Key methods: store, store_block, store_statement, query, drill_down, zoom_out, list_sessions, get_session." }, { "node_id": 2, "level": "session" } ], "siblings": [] }TOOL-CALL: TodoWrite
{"todos":[{"content":"Create MCP server for memory-service","status":"completed","activeForm":"Creating MCP server for memory-service"}]}TOOL:
Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicableAI: Done! Created
/scripts/memory-mcp- an MCP server exposing these tools:Tool Description memory_querySemantic search with level filtering memory_drill_downGet children of a node memory_zoom_outGet ancestors of a node memory_list_sessionsList all sessions memory_get_sessionGet specific session memory_storeStore a message memory_store_blockStore a block memory_store_statementStore a statement To use it, add to your MCP config:
{ "mcpServers": { "memory": { "command": "./grid/memory-service/scripts/memory-mcp", "env": { "MEMORY_SERVICE_HOST": "localhost:8080" } } } }For Claude Code, add to
~/.claude/claude_desktop_config.jsonor your project’s.mcp.json.