# ssearch

Semantic search over a personal journal archive and a collection of clippings. Uses vector embeddings and a local LLM to find and synthesize information across 1800+ dated text entries spanning 2000-2025, plus a library of PDFs, articles, and web saves.

## How it works

```
Query → Embed (BAAI/bge-large-en-v1.5) → Vector similarity (top-30) → Cross-encoder re-rank (top-15) → LLM synthesis (command-r7b via Ollama, or OpenAI API) → Response + sources
```

1. **Build**: Source files are chunked (256 tokens, 25-token overlap) and embedded into a vector store using LlamaIndex. The journal index uses LlamaIndex's JSON store; the clippings index uses ChromaDB. Both support incremental updates.
2. **Retrieve**: A user query is embedded with the same model and matched against stored vectors by cosine similarity, returning the top 30 candidate chunks.
3. **Re-rank**: A cross-encoder (`cross-encoder/ms-marco-MiniLM-L-12-v2`) scores each (query, chunk) pair jointly and keeps the top 15.
4. **Synthesize**: The re-ranked chunks are passed to a local LLM with a custom prompt that encourages multi-source synthesis, producing a grounded answer with file citations.

## Project structure

```
ssearch/
├── build_store.py              # Build/update journal vector store (incremental)
├── query_hybrid.py             # Hybrid BM25+vector query with LLM synthesis
├── retrieve.py                 # Verbatim hybrid retrieval (no LLM)
├── search_keywords.py          # Keyword search via POS-based term extraction
├── run_query.sh                # Interactive shell wrapper with timing and logging
├── clippings_search/
│   ├── build_clippings.py      # Build/update clippings vector store (ChromaDB)
│   ├── retrieve_clippings.py   # Verbatim clippings chunk retrieval
│   └── store_clippings/        # Persisted clippings vector store (ChromaDB)
├── data/                       # Symlink to journal .txt files
├── clippings/                  # Symlink to clippings (PDFs, TXT, webarchive, RTF)
├── store/                      # Persisted journal vector store
├── models/                     # Cached HuggingFace models (offline)
├── archived/                   # Superseded script versions
├── saved_output/               # Saved query results and model comparisons
├── requirements.txt            # Python dependencies
├── devlog.md                   # Development log and experimental findings
└── *.ipynb                     # Jupyter notebooks (HyDE, metrics, sandbox)
```

## Setup

**Prerequisites**: Python 3.12, [Ollama](https://ollama.com) with `command-r7b` pulled.

```bash
cd ssearch
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
```

The `data/` symlink should point to the journal archive (plain `.txt` files). The `clippings/` symlink should point to the clippings folder. The embedding model (`BAAI/bge-large-en-v1.5`) and cross-encoder (`cross-encoder/ms-marco-MiniLM-L-12-v2`) are cached in `./models/` for offline use.

### Offline model loading

All query scripts set three environment variables to prevent HuggingFace from making network requests:

```python
os.environ["TOKENIZERS_PARALLELISM"] = "false"
os.environ["SENTENCE_TRANSFORMERS_HOME"] = "./models"
os.environ["HF_HUB_OFFLINE"] = "1"
```

**These must appear before any imports that touch HuggingFace libraries.** The `huggingface_hub` library evaluates `HF_HUB_OFFLINE` once at import time (in `huggingface_hub/constants.py`). If the env var is set after imports, the library will still attempt network access and fail offline.

Alternatively, set the variable in your shell before running Python:
```bash
export HF_HUB_OFFLINE=1
python query_hybrid.py "your query"
```

## Usage

### Build the vector stores

```bash
# Journal index -- incremental update (default)
python build_store.py

# Journal index -- full rebuild
python build_store.py --rebuild

# Clippings index -- incremental update (default)
python clippings_search/build_clippings.py

# Clippings index -- full rebuild
python clippings_search/build_clippings.py --rebuild
```

The default incremental mode loads the existing index, compares file sizes and modification dates, and only re-indexes what changed. A full rebuild is only needed when chunk parameters or the embedding model change.

`build_clippings.py` handles PDFs, TXT, webarchive, and RTF files. PDFs are validated before indexing — those without extractable text are skipped and written to `ocr_needed.txt` for later OCR.

### Search journals

#### Semantic search with LLM synthesis

**Requires Ollama running with `command-r7b`.**

**Hybrid BM25 + vector** (`query_hybrid.py`): Retrieves top 20 by vector similarity and top 20 by BM25 term frequency, merges and deduplicates, re-ranks the union to top 15, synthesizes. Catches exact name/term matches that vector-only retrieval misses.
```bash
python query_hybrid.py "What does the author say about creativity?"
```

**Interactive wrapper** (`run_query.sh`): Loops for queries, displays timing, and appends queries to `query.log`.
```bash
./run_query.sh
```

#### Verbatim chunk retrieval (no LLM)

Same hybrid retrieval and re-ranking pipeline but outputs raw chunk text. Each chunk is annotated with its source: `[vector-only]`, `[bm25-only]`, or `[vector+bm25]`. **No Ollama needed.**

```bash
python retrieve.py "Kondiaronk and the Wendats"
```

#### Keyword search (no vector store, no LLM)

Extracts nouns and adjectives from the query using NLTK POS tagging, then greps journal files for matches with surrounding context.
```bash
python search_keywords.py "Discussions of Kondiaronk and the Wendats"
```

### Search clippings

Verbatim chunk retrieval from the clippings index. Same embedding model and cross-encoder re-ranking. Outputs a summary of source files and rankings, then full chunk text. Includes page numbers for PDF sources. **No Ollama needed.**

```bash
python clippings_search/retrieve_clippings.py "creativity and innovation"
```

### Output format

```
Response:
<LLM-synthesized answer citing specific files>

Source documents:
2024-03-15.txt  ./data/2024-03-15.txt  0.683
2023-11-02.txt  ./data/2023-11-02.txt  0.651
...
```

## Configuration

Key parameters (set in source files):

| Parameter | Value | Location |
|-----------|-------|----------|
| Embedding model | `BAAI/bge-large-en-v1.5` | all build and query scripts |
| Chunk size | 256 tokens | `build_store.py`, `clippings_search/build_clippings.py` |
| Chunk overlap | 25 tokens | `build_store.py`, `clippings_search/build_clippings.py` |
| Paragraph separator | `\n\n` | `build_store.py` |
| Initial retrieval | 30 chunks | query and retrieve scripts |
| Re-rank model | `cross-encoder/ms-marco-MiniLM-L-12-v2` | query and retrieve scripts |
| Re-rank top-n | 15 | query and retrieve scripts |
| LLM | `command-r7b` (Ollama) or `gpt-4o-mini` (OpenAI API) | `query_hybrid.py` |
| Temperature | 0.3 | `query_hybrid.py` |
| Context window | 8000 tokens | `query_hybrid.py` |
| Request timeout | 360 seconds | `query_hybrid.py` |

## Key dependencies

- **llama-index-core** (0.14.14) -- RAG framework
- **llama-index-embeddings-huggingface** -- embedding integration
- **llama-index-vector-stores-chroma** -- ChromaDB vector store for clippings
- **llama-index-llms-ollama** -- local LLM via Ollama
- **llama-index-llms-openai** -- OpenAI API LLM (optional)
- **llama-index-retrievers-bm25** -- BM25 sparse retrieval for hybrid search
- **chromadb** -- persistent vector store for clippings index
- **sentence-transformers** -- cross-encoder re-ranking
- **torch** -- ML runtime

## Notebooks

Three Jupyter notebooks document exploration and analysis:

- **`hyde.ipynb`** -- Experiments with HyDE (Hypothetical Document Embeddings) query rewriting. Finding: did not improve retrieval quality over direct prompt engineering.

- **`sandbox.ipynb`** -- Exploratory notebook for learning the LlamaIndex API.

- **`vs_metrics.ipynb`** -- Quantitative analysis of the vector store (embedding distributions, pairwise similarity, clustering, PCA/t-SNE projections).

## Design decisions

- **BAAI/bge-large-en-v1.5 over all-mpnet-base-v2**: Better semantic matching quality for journal text despite slower embedding.
- **256-token chunks**: Tested 512 and 384; 256 with 25-token overlap produced the highest quality matches.
- **command-r7b over llama3.1:8B**: Sticks closer to provided context with less hallucination at comparable speed.
- **Cross-encoder re-ranking**: Retrieve top-30 via bi-encoder, re-rank to top-15 with a cross-encoder that scores each (query, chunk) pair jointly. Tested three models; `ms-marco-MiniLM-L-12-v2` selected over `stsb-roberta-base` (wrong task) and `BAAI/bge-reranker-v2-m3` (slower, weak score tail).
- **HyDE query rewriting tested and dropped**: Did not improve results over direct prompt engineering.
- **Hybrid BM25 + vector retrieval**: BM25 nominates candidates with exact term matches that embeddings miss; the cross-encoder decides final relevance.
- **ChromaDB for clippings**: Persistent SQLite-backed store. Chosen over the JSON store for its metadata filtering and direct chunk-level operations for incremental updates.
- **PDF validation before indexing**: Pre-check each PDF with pypdf — skip if text extraction yields <100 chars or low printable ratio. Skipped files written to `ocr_needed.txt`.

## Development history

- **Aug 2025**: Initial implementation -- build pipeline, embedding model comparison, chunk size experiments, HyDE testing.
- **Jan 2026**: Command-line interface, prompt improvements, model comparison (command-r7b selected).
- **Feb 2026**: Cross-encoder re-ranking, hybrid BM25+vector retrieval, LlamaIndex upgrade to 0.14.14, OpenAI API backend, incremental updates, clippings search (ChromaDB), project reorganization.

See `devlog.md` for detailed development notes and experimental findings.