# Task-03: Initialize Chroma Vector Store with HNSW Settings

**Status:** 🟠 Implemented
**Priority:** High
**Created:** 2025-01-12
**Parent:** [Goal 02: Semantic Transcript Search](../scratchpad.md)

---

## Objective

Initialize ChromaDB vector store with optimized HNSW configuration for transcript similarity search, including persistence options and proper integration with the embeddings module.

---

## Implementation Plan

1. [x] Test Chroma vector store initialization with default config
2. [x] Verify HNSW settings are applied via collection_configuration
3. [x] Test persistence directory creation and loading
4. [x] Test in-memory mode (persist_directory=None)
5. [x] Add unit tests for store module
6. [x] Verify integration with embeddings module

---

## Key Configuration

From `config.py`:
- `hnsw_space`: "cosine"
- `hnsw_max_neighbors`: 48
- `hnsw_ef_construction`: 200
- `hnsw_ef_search`: 128
- `collection_name`: "youtube_transcripts"
- `persist_directory`: XDG-compliant path or None

ChromaDB collection_configuration format:
```python
{
    "hnsw": {
        "space": "cosine",
        "max_neighbors": 48,
        "ef_construction": 200,
        "ef_search": 128,
    }
}
```

---

## Test Cases

1. **Basic store creation:**
   - Create vector store with default config
   - Verify collection is created with correct name
   - Verify HNSW settings are applied

2. **Persistence:**
   - Create store with persist_directory
   - Add documents, close, reopen
   - Verify documents are persisted

3. **In-memory mode:**
   - Create store with persist_directory=None
   - Verify it works without persistence

4. **Document operations:**
   - Add documents with metadata
   - Similarity search returns results
   - Metadata filtering works

---

## Acceptance Criteria

- [x] `get_vector_store()` returns working Chroma instance
- [x] HNSW settings correctly applied to collection
- [x] Persistence works (documents survive restart)
- [x] In-memory mode works when persist_directory=None
- [x] Unit tests added with mocking for CI (6 unit tests + 5 slow integration tests)
- [x] Integration with embeddings module verified

---

## Notes

- ChromaDB v1.4.0+ uses `collection_configuration` (not `collection_metadata`)
- HNSW param names: `max_neighbors` (not M), `ef_construction`, `ef_search`
- LangChain Chroma wrapper handles most complexity

---

## References

- [langchain-chroma source](https://github.com/langchain-ai/langchain/blob/master/libs/partners/chroma/langchain_chroma/vectorstores.py)
- [ChromaDB Collection Configuration](https://github.com/chroma-core/chroma/blob/main/chromadb/api/collection_configuration.py)

---

## Completion Notes (2025-01-12)

### What Was Done

1. **Tested vector store initialization:**
   - In-memory mode works (persist_directory=None)
   - Persistence mode works with temp directories
   - Custom collection names work

2. **Verified HNSW configuration:**
   - collection_configuration correctly passes HNSW settings
   - cosine similarity search works
   - max_neighbors, ef_construction, ef_search applied

3. **Tested metadata filtering:**
   - Filter by channel_id works
   - Filter by language works
   - Multiple documents with different metadata handled correctly

4. **Added comprehensive tests:**
   - `tests/test_semantic_store.py` with 6 unit tests + 5 slow integration tests
   - Tests cover: store creation, caching, HNSW config, persistence, filtering

5. **All tests pass:** 200 passed, linting clean

### Awaiting User Validation

Task is marked 🟠 Implemented. Awaiting user validation before marking 🟢 Complete.
