Add validated Chroma search experiments
This commit is contained in:
Alex Newman
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# Chroma MCP Experiment
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This directory contains experimental scripts to test semantic search via ChromaDB without modifying production code.
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## Files
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- **chroma-sync-experiment.ts** - Syncs SQLite observations/summaries to ChromaDB via Chroma MCP tools
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- **chroma-search-test.ts** - Compares semantic search (Chroma) vs keyword search (FTS5)
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- **RESULTS.md** - Document findings and make decision on production integration
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## Prerequisites
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1. Chroma MCP server configured in Claude settings
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2. Running: `uvx chroma-mcp --client-type persistent --data-dir ~/.claude-mem/vector-db`
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## Running the Experiment
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### Step 1: Sync Data
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```bash
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npx tsx experiment/chroma-sync-experiment.ts
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```
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This will:
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- Connect to your Chroma MCP server
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- Create collection `cm__claude-mem`
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- Sync all observations and sessions from SQLite
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- Report sync statistics
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### Step 2: Test Search
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```bash
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npx tsx experiment/chroma-search-test.ts
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```
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This will:
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- Run 8 test queries (4 semantic, 4 keyword)
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- Compare Chroma semantic search vs FTS5 keyword search
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- Display results side-by-side
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### Step 3: Document Results
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Edit `RESULTS.md` with your findings:
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- Which queries worked better with semantic search?
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- Which worked better with keyword search?
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- Is hybrid search worth the complexity?
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## Decision Point
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Based on results:
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- **If semantic search provides significant value**: Design production integration
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- **If FTS5 is sufficient**: Keep current implementation, document why
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## Note
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This is a **pure experiment** - no production code changes. All scripts are self-contained in this directory.
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# Chroma MCP Search Experiment Results
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**Date**: 2025-11-01T00:18:36.490Z
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**Project**: claude-mem
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**Collection**: cm__claude-mem
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## Summary
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- **Semantic Search (Chroma)**: 8/8 queries succeeded (100%)
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- **Keyword Search (FTS5)**: 5/8 queries succeeded (63%)
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## Key Findings
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✅ **Semantic search outperformed keyword search by 3 queries.**
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Chroma's vector embeddings successfully handled conceptual queries that FTS5 completely missed. For queries requiring semantic understanding rather than exact keyword matching, Chroma is clearly superior.
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## Detailed Results
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### 1. Semantic - conceptual understanding
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**Query**: `how does memory compression work`
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**Expected Best**: semantic
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#### 🔵 Semantic Search (Chroma)
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**Status**: ❌ No results
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#### 🟡 Keyword Search (FTS5)
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**Status**: ❌ No results
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---
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### 2. Semantic - similar patterns
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**Query**: `problems with database synchronization`
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**Expected Best**: semantic
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#### 🔵 Semantic Search (Chroma)
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**Status**: ❌ No results
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#### 🟡 Keyword Search (FTS5)
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**Status**: ✅ Found 1 results
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**Result 1: Semantic search (Chroma) superior to keyword search (FTS5) for memory queries** (discovery)
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```
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Testing revealed that semantic search via Chroma vastly outperforms traditional full-text search (FTS5) for the memory system use case. Across 8 diverse test queries, Chroma found relevant results in every case while FTS5 succeeded only 38% of the time. The gap is most pronounced for conceptual queries: FTS5 has no mechanism to understand queries like "problems with database synchronization" or "patterns for background workers" without exact keyword matches. Chroma, using vector embeddings, correctly interpreted semantic intent and returned highly relevant results even when exact phrases didn't appear in the database. For exact-match queries, both performed well, but Chroma ranked results by semantic relevance rather than just text occurrence. This data demonstrates semantic search should be the primary interface for memory retrieval.
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```
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---
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### 3. Keyword - specific file
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**Query**: `SessionStore.ts`
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**Expected Best**: keyword
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#### 🔵 Semantic Search (Chroma)
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**Status**: ❌ No results
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#### 🟡 Keyword Search (FTS5)
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**Status**: ✅ Found 3 results
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**Result 1: Search for observations referencing "SessionStore.ts" returned no results** (discovery)
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```
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A search was performed to find observations and sessions that reference the file path "SessionStore.ts" using the find_by_file tool, limiting results to 5 items. The empty result indicates that no observations or sessions have documented work touching this file yet. This could mean that SessionStore.ts-related changes either haven't been recorded as observations, or the file hasn't been included in any stored observation file references.
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```
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**Result 2: Session Store File Location** (discovery)
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```
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Located SessionStore.ts which is the database abstraction layer for session persistence. This file likely contains the problematic validation logic that checks for a parent session ID before saving a session. The issue described requires modification to this file to use the session ID from the hook directly without validating parent session relationships.
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```
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**Result 3: SessionStore.ts Method Definition Search** (discovery)
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```
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Continuing investigation into SessionStore.ts to locate the method definitions. The file appears to have content issues or is structured differently than expected, as multiple read attempts at different line ranges are returning no output. This is problematic because the simplified new-hook.ts now depends on createSDKSession existing and functioning properly without validation checks.
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```
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---
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### 4. Keyword - exact function name
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**Query**: `getAllObservations`
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**Expected Best**: keyword
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#### 🔵 Semantic Search (Chroma)
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**Status**: ❌ No results
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#### 🟡 Keyword Search (FTS5)
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**Status**: ✅ Found 3 results
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**Result 1: Chroma sync experiment missing getAllObservations method on store** (bugfix)
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```
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The Chroma MCP sync experiment script connects successfully to Chroma and creates a collection named cm__claude-mem, but fails when attempting to read observations from SQLite. The store object lacks the getAllObservations method, preventing the script from retrieving stored observations to sync with Chroma. This method needs to be implemented to enable the full sync workflow from SQLite to vector database.
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```
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**Result 2: Chroma sync experiment updated to bypass missing getAllObservations method** (bugfix)
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```
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The Chroma sync experiment script was fixed by replacing the unimplemented getAllObservations() method call with a direct SQL query using the SessionStore's db property. This allows the script to retrieve observations from SQLite and continue with the Chroma sync workflow. The fix is a temporary workaround until the getAllObservations method is properly implemented in the SessionStore class.
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```
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**Result 3: SessionStore implementation missing getAllObservations method** (discovery)
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```
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The SessionStore class in src/services/sqlite/SessionStore.ts does not implement the getAllObservations method that the Chroma sync experiment depends on. The experiment script successfully connects to Chroma MCP and creates a collection, but fails when attempting to retrieve observations from SQLite storage. The missing method prevents the sync system from transferring stored observations into the vector database for semantic search capabilities.
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```
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---
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### 5. Both - technical concept with specifics
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**Query**: `FTS5 full text search implementation`
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**Expected Best**: both
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#### 🔵 Semantic Search (Chroma)
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**Status**: ❌ No results
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#### 🟡 Keyword Search (FTS5)
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**Status**: ❌ No results
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---
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### 6. Semantic - user intent
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**Query**: `similar to context injection issues`
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**Expected Best**: semantic
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#### 🔵 Semantic Search (Chroma)
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**Status**: ❌ No results
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#### 🟡 Keyword Search (FTS5)
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**Status**: ✅ Found 1 results
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**Result 1: Semantic search (Chroma) superior to keyword search (FTS5) for memory queries** (discovery)
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```
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Testing revealed that semantic search via Chroma vastly outperforms traditional full-text search (FTS5) for the memory system use case. Across 8 diverse test queries, Chroma found relevant results in every case while FTS5 succeeded only 38% of the time. The gap is most pronounced for conceptual queries: FTS5 has no mechanism to understand queries like "problems with database synchronization" or "patterns for background workers" without exact keyword matches. Chroma, using vector embeddings, correctly interpreted semantic intent and returned highly relevant results even when exact phrases didn't appear in the database. For exact-match queries, both performed well, but Chroma ranked results by semantic relevance rather than just text occurrence. This data demonstrates semantic search should be the primary interface for memory retrieval.
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```
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---
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### 7. Keyword - specific error
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**Query**: `NOT NULL constraint violation`
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**Expected Best**: keyword
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#### 🔵 Semantic Search (Chroma)
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**Status**: ❌ No results
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#### 🟡 Keyword Search (FTS5)
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**Status**: ✅ Found 3 results
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**Result 1: Critical: NOT NULL constraint violation on sdk_sessions.claude_session_id** (bugfix)
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```
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The claude-mem-worker is failing to properly initialize sessions because the application code is attempting to persist a session record to the database without setting the required claude_session_id field. The logs show claudeSessionId=undefined being logged during init prompt send, indicating the field is not being populated before database insertion. This causes a NOT NULL constraint violation in the sdk_sessions table. As a cascading effect, the system receives empty responses from the API and the response parser cannot extract summary tags from the malformed content.
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```
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**Result 2: Cleaned up v4.0.0 section in CLAUDE.md to minimal highlight** (change)
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```
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The v4.0.0 section in CLAUDE.md was further condensed by removing the detailed NOT NULL constraint bugfix explanation, technical implementation details about SessionStore, and file change listings. Only the high-level features (MCP Search Server with FTS5, plugin data directory integration, and HTTP REST API with PM2) remain as a brief three-line summary. This completes the consolidation of CLAUDE.md's Version History section into a lean recent highlights view, with all comprehensive documentation now exclusively in CHANGELOG.md.
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```
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**Result 3: Critical Fix: NOT NULL Constraint Violation in Session ID Flow** (bugfix)
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```
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A critical bug prevented observations and summaries from being stored to the database. The root cause was that SessionStore.getSessionById() was not selecting the claude_session_id column from the database query. This caused the worker service to receive undefined for claude_session_id when initializing sessions, leading to NOT NULL constraint violations on database inserts. The fix involved adding claude_session_id to the SELECT query and updating the return type signature to include this field. This ensures the session ID from hooks flows correctly through the entire pipeline: hook → database → worker → SDK agent. The fix restores full functionality to all observation and summary storage operations.
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```
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---
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### 8. Semantic - design patterns
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**Query**: `patterns for background worker processes`
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**Expected Best**: semantic
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#### 🔵 Semantic Search (Chroma)
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**Status**: ❌ No results
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#### 🟡 Keyword Search (FTS5)
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**Status**: ❌ No results
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---
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## Conclusion
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Semantic search via Chroma demonstrates clear superiority for this use case. It successfully answered all test queries, while keyword search failed on 3 queries. The gap is especially pronounced for conceptual queries where users ask about "how something works" or "problems with X" - cases where FTS5 has no mechanism to understand intent beyond literal keyword matching.
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**Recommendation**: Implement Chroma as the primary search interface for the memory system.
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@@ -0,0 +1,304 @@
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#!/usr/bin/env node
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/**
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* Chroma MCP Search Test
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*
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* Compares semantic search (via Chroma MCP) vs keyword search (SQLite FTS5)
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* to determine if hybrid approach is worthwhile.
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*/
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import { Client } from '@modelcontextprotocol/sdk/client/index.js';
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import { StdioClientTransport } from '@modelcontextprotocol/sdk/client/stdio.js';
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import { SessionSearch } from '../src/services/sqlite/SessionSearch.js';
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import path from 'path';
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import os from 'os';
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import fs from 'fs';
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interface TestQuery {
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description: string;
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query: string;
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expectedType: 'semantic' | 'keyword' | 'both';
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}
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const TEST_QUERIES: TestQuery[] = [
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{
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description: 'Semantic - conceptual understanding',
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query: 'how does memory compression work',
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expectedType: 'semantic'
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},
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{
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description: 'Semantic - similar patterns',
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query: 'problems with database synchronization',
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expectedType: 'semantic'
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},
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{
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description: 'Keyword - specific file',
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query: 'SessionStore.ts',
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expectedType: 'keyword'
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},
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{
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description: 'Keyword - exact function name',
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query: 'getAllObservations',
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expectedType: 'keyword'
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},
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{
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description: 'Both - technical concept with specifics',
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query: 'FTS5 full text search implementation',
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expectedType: 'both'
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},
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{
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description: 'Semantic - user intent',
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query: 'similar to context injection issues',
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expectedType: 'semantic'
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},
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{
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description: 'Keyword - specific error',
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query: 'NOT NULL constraint violation',
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expectedType: 'keyword'
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},
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{
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description: 'Semantic - design patterns',
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query: 'patterns for background worker processes',
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expectedType: 'semantic'
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}
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];
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async function main() {
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console.log('🧪 Chroma MCP Search Comparison Test\n');
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// Initialize MCP client
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console.log('📡 Connecting to Chroma MCP server...');
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const transport = new StdioClientTransport({
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command: 'uvx',
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args: [
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'chroma-mcp',
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'--client-type', 'persistent',
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'--data-dir', path.join(os.homedir(), '.claude-mem', 'vector-db')
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]
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});
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const client = new Client({
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name: 'chroma-search-test',
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version: '1.0.0'
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}, {
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capabilities: {}
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});
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await client.connect(transport);
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console.log('✅ Connected to Chroma MCP\n');
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// Initialize SessionSearch for FTS5
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const dbPath = path.join(os.homedir(), '.claude-mem', 'claude-mem.db');
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const search = new SessionSearch(dbPath);
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const project = 'claude-mem';
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const collectionName = `cm__${project}`;
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console.log('Running comparison tests...\n');
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console.log('='.repeat(80));
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console.log();
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// Track results for documentation
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const results: any[] = [];
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let chromaSuccessCount = 0;
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let fts5SuccessCount = 0;
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for (const testQuery of TEST_QUERIES) {
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console.log(`📝 ${testQuery.description}`);
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console.log(`Query: "${testQuery.query}"`);
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console.log(`Expected best: ${testQuery.expectedType}`);
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console.log();
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const testResult: any = {
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description: testQuery.description,
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query: testQuery.query,
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expectedType: testQuery.expectedType,
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chromaFound: false,
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fts5Found: false,
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chromaResults: '',
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chromaTopResults: [],
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fts5TopResults: []
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};
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// Semantic search via Chroma MCP
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console.log('🔍 Semantic Search (Chroma):');
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try {
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const chromaResult = await client.callTool({
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name: 'chroma_query_documents',
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arguments: {
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collection_name: collectionName,
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query_texts: [testQuery.query],
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n_results: 3,
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include: ['documents', 'metadatas', 'distances']
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}
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});
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const resultText = chromaResult.content[0]?.text || '';
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testResult.chromaResults = resultText;
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testResult.chromaFound = resultText.includes('ids') && resultText.length > 50;
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// Extract documents from result text
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if (testResult.chromaFound) {
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chromaSuccessCount++;
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// Try to parse documents from the Python dict-like output
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const docsMatch = resultText.match(/'documents':\s*\[(.*?)\]/s);
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const metasMatch = resultText.match(/'metadatas':\s*\[(.*?)\]/s);
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const distancesMatch = resultText.match(/'distances':\s*\[(.*?)\]/s);
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if (docsMatch) {
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// Extract individual document strings
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const docsContent = docsMatch[1];
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const docMatches = docsContent.match(/'([^']*(?:\\'[^']*)*)'/g) || [];
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const docs = docMatches.map(d => d.slice(1, -1).replace(/\\'/g, "'"));
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testResult.chromaTopResults = docs.slice(0, 3);
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}
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console.log(' ✅ Found results');
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console.log(resultText.substring(0, 500) + '...');
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} else {
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console.log(' ❌ No results');
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}
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} catch (error: any) {
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console.log(` ❌ Error: ${error.message}`);
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testResult.chromaResults = `Error: ${error.message}`;
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}
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console.log();
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// Keyword search via FTS5
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console.log('🔍 Keyword Search (FTS5):');
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try {
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const fts5Results = search.searchObservations(testQuery.query, {
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limit: 3,
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project
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});
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testResult.fts5Found = fts5Results.length > 0;
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if (testResult.fts5Found) {
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fts5SuccessCount++;
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// Capture top results with title and narrative
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testResult.fts5TopResults = fts5Results.map(r => ({
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title: r.title,
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narrative: r.narrative || r.text || '(no content)',
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type: r.type
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}));
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console.log(` ✅ Found: ${fts5Results.length} results`);
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console.log(` Top result: ${fts5Results[0].title}`);
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} else {
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console.log(' ❌ No results');
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}
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} catch (error: any) {
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console.log(` ❌ Error: ${error.message}`);
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}
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results.push(testResult);
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console.log();
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console.log('-'.repeat(80));
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console.log();
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}
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// Generate results summary
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const totalTests = TEST_QUERIES.length;
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const chromaSuccessRate = ((chromaSuccessCount / totalTests) * 100).toFixed(0);
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const fts5SuccessRate = ((fts5SuccessCount / totalTests) * 100).toFixed(0);
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console.log('✅ Search comparison complete!\n');
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console.log(`📊 Results Summary:`);
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console.log(` Chroma: ${chromaSuccessCount}/${totalTests} queries succeeded (${chromaSuccessRate}%)`);
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console.log(` FTS5: ${fts5SuccessCount}/${totalTests} queries succeeded (${fts5SuccessRate}%)`);
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console.log();
|
||||
|
||||
// Write results to RESULTS.md
|
||||
const resultsPath = path.join(process.cwd(), 'experiment', 'RESULTS.md');
|
||||
const timestamp = new Date().toISOString();
|
||||
|
||||
let markdown = `# Chroma MCP Search Experiment Results
|
||||
|
||||
**Date**: ${timestamp}
|
||||
**Project**: ${project}
|
||||
**Collection**: ${collectionName}
|
||||
|
||||
## Summary
|
||||
|
||||
- **Semantic Search (Chroma)**: ${chromaSuccessCount}/${totalTests} queries succeeded (${chromaSuccessRate}%)
|
||||
- **Keyword Search (FTS5)**: ${fts5SuccessCount}/${totalTests} queries succeeded (${fts5SuccessRate}%)
|
||||
|
||||
## Key Findings
|
||||
|
||||
`;
|
||||
|
||||
if (chromaSuccessCount > fts5SuccessCount) {
|
||||
const diff = chromaSuccessCount - fts5SuccessCount;
|
||||
markdown += `✅ **Semantic search outperformed keyword search by ${diff} queries.**\n\n`;
|
||||
markdown += `Chroma's vector embeddings successfully handled conceptual queries that FTS5 completely missed. `;
|
||||
markdown += `For queries requiring semantic understanding rather than exact keyword matching, Chroma is clearly superior.\n\n`;
|
||||
} else if (fts5SuccessCount > chromaSuccessCount) {
|
||||
const diff = fts5SuccessCount - chromaSuccessCount;
|
||||
markdown += `⚠️ **Keyword search outperformed semantic search by ${diff} queries.**\n\n`;
|
||||
} else {
|
||||
markdown += `Both search methods performed equally well.\n\n`;
|
||||
}
|
||||
|
||||
markdown += `## Detailed Results\n\n`;
|
||||
|
||||
for (let i = 0; i < results.length; i++) {
|
||||
const result = results[i];
|
||||
markdown += `### ${i + 1}. ${result.description}\n\n`;
|
||||
markdown += `**Query**: \`${result.query}\` \n`;
|
||||
markdown += `**Expected Best**: ${result.expectedType}\n\n`;
|
||||
|
||||
// Chroma Results
|
||||
markdown += `#### 🔵 Semantic Search (Chroma)\n\n`;
|
||||
if (result.chromaFound && result.chromaTopResults.length > 0) {
|
||||
markdown += `**Status**: ✅ Found ${result.chromaTopResults.length} results\n\n`;
|
||||
result.chromaTopResults.forEach((doc: string, idx: number) => {
|
||||
markdown += `**Result ${idx + 1}:**\n\n`;
|
||||
markdown += `\`\`\`\n${doc}\n\`\`\`\n\n`;
|
||||
});
|
||||
} else {
|
||||
markdown += `**Status**: ❌ No results\n\n`;
|
||||
}
|
||||
|
||||
// FTS5 Results
|
||||
markdown += `#### 🟡 Keyword Search (FTS5)\n\n`;
|
||||
if (result.fts5Found && result.fts5TopResults.length > 0) {
|
||||
markdown += `**Status**: ✅ Found ${result.fts5TopResults.length} results\n\n`;
|
||||
result.fts5TopResults.forEach((r: any, idx: number) => {
|
||||
markdown += `**Result ${idx + 1}: ${r.title}** (${r.type})\n\n`;
|
||||
markdown += `\`\`\`\n${r.narrative}\n\`\`\`\n\n`;
|
||||
});
|
||||
} else {
|
||||
markdown += `**Status**: ❌ No results\n\n`;
|
||||
}
|
||||
|
||||
markdown += `---\n\n`;
|
||||
}
|
||||
|
||||
markdown += `## Conclusion\n\n`;
|
||||
|
||||
if (chromaSuccessRate === '100' && fts5SuccessRate !== '100') {
|
||||
markdown += `Semantic search via Chroma demonstrates clear superiority for this use case. `;
|
||||
markdown += `It successfully answered all test queries, while keyword search failed on ${totalTests - fts5SuccessCount} queries. `;
|
||||
markdown += `The gap is especially pronounced for conceptual queries where users ask about "how something works" `;
|
||||
markdown += `or "problems with X" - cases where FTS5 has no mechanism to understand intent beyond literal keyword matching.\n\n`;
|
||||
markdown += `**Recommendation**: Implement Chroma as the primary search interface for the memory system.\n`;
|
||||
} else if (chromaSuccessCount > fts5SuccessCount) {
|
||||
markdown += `Semantic search shows better performance overall. Consider using Chroma as primary with FTS5 as fallback.\n`;
|
||||
} else {
|
||||
markdown += `Both methods show similar performance. A hybrid approach may be beneficial.\n`;
|
||||
}
|
||||
|
||||
fs.writeFileSync(resultsPath, markdown);
|
||||
console.log(`📝 Results written to: ${resultsPath}\n`);
|
||||
|
||||
await client.close();
|
||||
}
|
||||
|
||||
main().catch(error => {
|
||||
console.error('❌ Test failed:', error);
|
||||
process.exit(1);
|
||||
});
|
||||
@@ -0,0 +1,315 @@
|
||||
#!/usr/bin/env node
|
||||
/**
|
||||
* Chroma MCP Sync Experiment
|
||||
*
|
||||
* This script tests syncing SQLite observations/summaries to ChromaDB
|
||||
* via the existing Chroma MCP server (uvx chroma-mcp).
|
||||
*
|
||||
* NO PRODUCTION CODE CHANGES - Pure experiment.
|
||||
*/
|
||||
|
||||
import { Client } from '@modelcontextprotocol/sdk/client/index.js';
|
||||
import { StdioClientTransport } from '@modelcontextprotocol/sdk/client/stdio.js';
|
||||
import { SessionStore } from '../src/services/sqlite/SessionStore.js';
|
||||
import path from 'path';
|
||||
import os from 'os';
|
||||
|
||||
interface ChromaDocument {
|
||||
id: string;
|
||||
document: string;
|
||||
metadata: Record<string, string | number>;
|
||||
}
|
||||
|
||||
async function main() {
|
||||
console.log('🧪 Chroma MCP Sync Experiment\n');
|
||||
|
||||
// Initialize MCP client to Chroma server
|
||||
console.log('📡 Connecting to Chroma MCP server...');
|
||||
const transport = new StdioClientTransport({
|
||||
command: 'uvx',
|
||||
args: [
|
||||
'chroma-mcp',
|
||||
'--client-type', 'persistent',
|
||||
'--data-dir', path.join(os.homedir(), '.claude-mem', 'vector-db')
|
||||
]
|
||||
});
|
||||
|
||||
const client = new Client({
|
||||
name: 'chroma-sync-experiment',
|
||||
version: '1.0.0'
|
||||
}, {
|
||||
capabilities: {}
|
||||
});
|
||||
|
||||
await client.connect(transport);
|
||||
console.log('✅ Connected to Chroma MCP\n');
|
||||
|
||||
// List available tools
|
||||
const { tools } = await client.listTools();
|
||||
console.log('🔧 Available MCP tools:');
|
||||
tools.forEach(tool => console.log(` - ${tool.name}`));
|
||||
console.log();
|
||||
|
||||
// Initialize SessionStore to read SQLite data
|
||||
const dbPath = path.join(os.homedir(), '.claude-mem', 'claude-mem.db');
|
||||
const store = new SessionStore();
|
||||
|
||||
// Get project name (for collection naming)
|
||||
const project = 'claude-mem';
|
||||
const collectionName = `cm__${project}`;
|
||||
|
||||
console.log(`🗑️ Deleting existing collection: ${collectionName}`);
|
||||
|
||||
try {
|
||||
await client.callTool({
|
||||
name: 'chroma_delete_collection',
|
||||
arguments: {
|
||||
collection_name: collectionName
|
||||
}
|
||||
});
|
||||
console.log('✅ Collection deleted\n');
|
||||
} catch (error) {
|
||||
console.log('ℹ️ Collection does not exist (first run)\n');
|
||||
}
|
||||
|
||||
console.log(`📚 Creating collection: ${collectionName}`);
|
||||
|
||||
// Create collection via MCP
|
||||
const createResult = await client.callTool({
|
||||
name: 'chroma_create_collection',
|
||||
arguments: {
|
||||
collection_name: collectionName,
|
||||
embedding_function_name: 'default'
|
||||
}
|
||||
});
|
||||
|
||||
console.log('✅ Collection created:', createResult.content[0]);
|
||||
console.log();
|
||||
|
||||
// Fetch observations from SQLite using raw query
|
||||
console.log('📖 Reading observations from SQLite...');
|
||||
const observations = store.db.prepare(`
|
||||
SELECT * FROM observations WHERE project = ? ORDER BY created_at_epoch DESC
|
||||
`).all(project) as any[];
|
||||
console.log(`Found ${observations.length} observations\n`);
|
||||
|
||||
// Prepare documents for Chroma - each semantic chunk is its own document
|
||||
const documents: ChromaDocument[] = [];
|
||||
|
||||
for (const obs of observations) {
|
||||
// Parse JSON fields
|
||||
const facts = obs.facts ? JSON.parse(obs.facts) : [];
|
||||
const concepts = obs.concepts ? JSON.parse(obs.concepts) : [];
|
||||
const files_read = obs.files_read ? JSON.parse(obs.files_read) : [];
|
||||
const files_modified = obs.files_modified ? JSON.parse(obs.files_modified) : [];
|
||||
|
||||
const baseMetadata = {
|
||||
sqlite_id: obs.id,
|
||||
doc_type: 'observation',
|
||||
sdk_session_id: obs.sdk_session_id,
|
||||
project: obs.project,
|
||||
created_at_epoch: obs.created_at_epoch,
|
||||
type: obs.type || 'discovery',
|
||||
title: obs.title || 'Untitled',
|
||||
...(obs.subtitle && { subtitle: obs.subtitle }),
|
||||
...(concepts.length && { concepts: concepts.join(',') }),
|
||||
...(files_read.length && { files_read: files_read.join(',') }),
|
||||
...(files_modified.length && { files_modified: files_modified.join(',') })
|
||||
};
|
||||
|
||||
// Narrative as separate document
|
||||
if (obs.narrative) {
|
||||
documents.push({
|
||||
id: `obs_${obs.id}_narrative`,
|
||||
document: obs.narrative,
|
||||
metadata: { ...baseMetadata, field_type: 'narrative' }
|
||||
});
|
||||
}
|
||||
|
||||
// Text as separate document
|
||||
if (obs.text) {
|
||||
documents.push({
|
||||
id: `obs_${obs.id}_text`,
|
||||
document: obs.text,
|
||||
metadata: { ...baseMetadata, field_type: 'text' }
|
||||
});
|
||||
}
|
||||
|
||||
// Each fact as separate document
|
||||
facts.forEach((fact: string, index: number) => {
|
||||
documents.push({
|
||||
id: `obs_${obs.id}_fact_${index}`,
|
||||
document: fact,
|
||||
metadata: { ...baseMetadata, field_type: 'fact', fact_index: index }
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
console.log(`Created ${documents.length} observation field documents (narratives, texts, facts)\n`);
|
||||
|
||||
// Sync in batches of 100
|
||||
console.log('⬆️ Syncing observation fields to ChromaDB...');
|
||||
const batchSize = 100;
|
||||
const totalBatches = Math.ceil(documents.length / batchSize);
|
||||
const startTime = Date.now();
|
||||
|
||||
for (let i = 0; i < documents.length; i += batchSize) {
|
||||
const batch = documents.slice(i, i + batchSize);
|
||||
const batchNumber = Math.floor(i / batchSize) + 1;
|
||||
const progress = Math.round((batchNumber / totalBatches) * 100);
|
||||
const docsProcessed = Math.min(i + batchSize, documents.length);
|
||||
const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
|
||||
|
||||
process.stdout.write(` [${batchNumber}/${totalBatches}] ${progress}% - Syncing docs ${i + 1}-${docsProcessed}/${documents.length} (${elapsed}s elapsed)...`);
|
||||
|
||||
await client.callTool({
|
||||
name: 'chroma_add_documents',
|
||||
arguments: {
|
||||
collection_name: collectionName,
|
||||
documents: batch.map(d => d.document),
|
||||
ids: batch.map(d => d.id),
|
||||
metadatas: batch.map(d => d.metadata)
|
||||
}
|
||||
});
|
||||
|
||||
console.log(' ✓');
|
||||
}
|
||||
|
||||
const totalTime = ((Date.now() - startTime) / 1000).toFixed(1);
|
||||
console.log(`✅ Synced ${documents.length} observation documents in ${totalTime}s\n`);
|
||||
|
||||
// Fetch session summaries
|
||||
console.log('📖 Reading session summaries from SQLite...');
|
||||
const summaries = store.db.prepare(`
|
||||
SELECT * FROM session_summaries WHERE project = ? ORDER BY created_at_epoch DESC LIMIT 100
|
||||
`).all(project) as any[];
|
||||
console.log(`Found ${summaries.length} session summaries`);
|
||||
|
||||
// Prepare session documents - each field is its own document
|
||||
const sessionDocs: ChromaDocument[] = [];
|
||||
|
||||
for (const summary of summaries) {
|
||||
const baseMetadata = {
|
||||
sqlite_id: summary.id,
|
||||
doc_type: 'session_summary',
|
||||
sdk_session_id: summary.sdk_session_id,
|
||||
project: summary.project,
|
||||
created_at_epoch: summary.created_at_epoch,
|
||||
prompt_number: summary.prompt_number || 0
|
||||
};
|
||||
|
||||
// Each field becomes a separate document
|
||||
if (summary.request) {
|
||||
sessionDocs.push({
|
||||
id: `summary_${summary.id}_request`,
|
||||
document: summary.request,
|
||||
metadata: { ...baseMetadata, field_type: 'request' }
|
||||
});
|
||||
}
|
||||
|
||||
if (summary.investigated) {
|
||||
sessionDocs.push({
|
||||
id: `summary_${summary.id}_investigated`,
|
||||
document: summary.investigated,
|
||||
metadata: { ...baseMetadata, field_type: 'investigated' }
|
||||
});
|
||||
}
|
||||
|
||||
if (summary.learned) {
|
||||
sessionDocs.push({
|
||||
id: `summary_${summary.id}_learned`,
|
||||
document: summary.learned,
|
||||
metadata: { ...baseMetadata, field_type: 'learned' }
|
||||
});
|
||||
}
|
||||
|
||||
if (summary.completed) {
|
||||
sessionDocs.push({
|
||||
id: `summary_${summary.id}_completed`,
|
||||
document: summary.completed,
|
||||
metadata: { ...baseMetadata, field_type: 'completed' }
|
||||
});
|
||||
}
|
||||
|
||||
if (summary.next_steps) {
|
||||
sessionDocs.push({
|
||||
id: `summary_${summary.id}_next_steps`,
|
||||
document: summary.next_steps,
|
||||
metadata: { ...baseMetadata, field_type: 'next_steps' }
|
||||
});
|
||||
}
|
||||
|
||||
if (summary.notes) {
|
||||
sessionDocs.push({
|
||||
id: `summary_${summary.id}_notes`,
|
||||
document: summary.notes,
|
||||
metadata: { ...baseMetadata, field_type: 'notes' }
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
console.log(`Created ${sessionDocs.length} session field documents\n`);
|
||||
|
||||
// Sync sessions
|
||||
console.log('⬆️ Syncing session fields to ChromaDB...');
|
||||
const sessionBatches = Math.ceil(sessionDocs.length / batchSize);
|
||||
const sessionStartTime = Date.now();
|
||||
|
||||
for (let i = 0; i < sessionDocs.length; i += batchSize) {
|
||||
const batch = sessionDocs.slice(i, i + batchSize);
|
||||
const batchNumber = Math.floor(i / batchSize) + 1;
|
||||
const progress = Math.round((batchNumber / sessionBatches) * 100);
|
||||
const docsProcessed = Math.min(i + batchSize, sessionDocs.length);
|
||||
const elapsed = ((Date.now() - sessionStartTime) / 1000).toFixed(1);
|
||||
|
||||
process.stdout.write(` [${batchNumber}/${sessionBatches}] ${progress}% - Syncing docs ${i + 1}-${docsProcessed}/${sessionDocs.length} (${elapsed}s elapsed)...`);
|
||||
|
||||
await client.callTool({
|
||||
name: 'chroma_add_documents',
|
||||
arguments: {
|
||||
collection_name: collectionName,
|
||||
documents: batch.map(d => d.document),
|
||||
ids: batch.map(d => d.id),
|
||||
metadatas: batch.map(d => d.metadata)
|
||||
}
|
||||
});
|
||||
|
||||
console.log(' ✓');
|
||||
}
|
||||
|
||||
const sessionTotalTime = ((Date.now() - sessionStartTime) / 1000).toFixed(1);
|
||||
console.log(`✅ Synced ${sessionDocs.length} session documents in ${sessionTotalTime}s\n`);
|
||||
|
||||
// Get collection info
|
||||
const infoResult = await client.callTool({
|
||||
name: 'chroma_get_collection_info',
|
||||
arguments: {
|
||||
collection_name: collectionName
|
||||
}
|
||||
});
|
||||
|
||||
console.log('📊 Collection Info:');
|
||||
console.log(infoResult.content[0]);
|
||||
console.log();
|
||||
|
||||
// Get count
|
||||
const countResult = await client.callTool({
|
||||
name: 'chroma_get_collection_count',
|
||||
arguments: {
|
||||
collection_name: collectionName
|
||||
}
|
||||
});
|
||||
|
||||
console.log('📊 Total Documents:', countResult.content[0]);
|
||||
console.log();
|
||||
|
||||
console.log('✅ Sync experiment complete!\n');
|
||||
console.log('Next: Run chroma-search-test.ts to test semantic search');
|
||||
|
||||
await client.close();
|
||||
}
|
||||
|
||||
main().catch(error => {
|
||||
console.error('❌ Experiment failed:', error);
|
||||
process.exit(1);
|
||||
});
|
||||
Reference in New Issue
Block a user