# ssearch

Semantic search over a personal journal archive. Uses vector embeddings and a local LLM to find and synthesize information across 1800+ dated text entries spanning 2000-2025.

## 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**: Journal entries in `./data` are chunked (256 tokens, 25-token overlap) and embedded into a vector store using LlamaIndex. Supports incremental updates (new/modified files only) or full rebuilds.
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_exp_claude.py             # Build/update vector store (incremental by default)
├── query_topk_prompt_engine_v3.py  # Main query engine (cross-encoder re-ranking)
├── query_topk_prompt_engine_v2.py  # Previous query engine (no re-ranking)
├── retrieve_raw.py                 # Verbatim chunk retrieval (no LLM)
├── query_hybrid_bm25_v4.py        # Hybrid BM25 + vector query (v4)
├── retrieve_hybrid_raw.py          # Hybrid verbatim retrieval (no LLM)
├── search_keywords.py              # Keyword search via POS-based term extraction
├── run_query.sh                    # Shell wrapper with timing and logging
├── data/                           # Symlink to ../text/ (journal .txt files)
├── storage_exp/                    # Persisted vector store (~242 MB)
├── models/                         # Cached HuggingFace models (embedding + cross-encoder, offline)
├── archived/                       # Earlier iterations and prototypes
├── saved_output/                   # Saved query results and model comparisons
├── requirements.txt                # Python dependencies (pip freeze)
├── NOTES.md                        # Similarity metric reference
├── devlog.txt                      # 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 `../text/` (the journal archive). 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. This is a common pitfall -- `llama_index.embeddings.huggingface` transitively imports `huggingface_hub`, so even indirect imports trigger the evaluation.

Alternatively, set the variable in your shell before running Python:
```bash
export HF_HUB_OFFLINE=1
python query_hybrid_bm25_v4.py "your query"
```

## Usage

### Build the vector store

```bash
# Incremental update (default): only processes new, modified, or deleted files
python build_exp_claude.py

# Full rebuild from scratch
python build_exp_claude.py --rebuild
```

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

### Search

Three categories of search are available, from heaviest (semantic + LLM) to lightest (grep).

#### Semantic search with LLM synthesis

These scripts embed the query, retrieve candidate chunks from the vector store, re-rank with a cross-encoder, and pass the top results to a local LLM that synthesizes a grounded answer with file citations. **Requires Ollama running with `command-r7b`.**

**Vector-only** (`query_topk_prompt_engine_v3.py`): Retrieves the top 30 chunks by cosine similarity, re-ranks to top 15, synthesizes.
```bash
python query_topk_prompt_engine_v3.py "What does the author say about creativity?"
```

**Hybrid BM25 + vector** (`query_hybrid_bm25_v4.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_bm25_v4.py "Louis Menand"
```

**Interactive wrapper** (`run_query.sh`): Loops for queries using the v3 engine, displays timing, and appends queries to `query.log`.
```bash
./run_query.sh
```

#### Verbatim chunk retrieval (no LLM)

These scripts run the same retrieval and re-ranking pipeline but output the raw chunk text instead of passing it to an LLM. Useful for inspecting what the retrieval pipeline finds, or when Ollama is not available. **No Ollama needed.**

**Vector-only** (`retrieve_raw.py`): Top-30 vector retrieval, cross-encoder re-rank to top 15, raw output.
```bash
python retrieve_raw.py "Kondiaronk and the Wendats"
```

**Hybrid BM25 + vector** (`retrieve_hybrid_raw.py`): Same hybrid retrieval as v4 but outputs raw chunks. Each chunk is annotated with its source: `[vector-only]`, `[bm25-only]`, or `[vector+bm25]`.
```bash
python retrieve_hybrid_raw.py "Louis Menand"
```

Pipe either to `less` for browsing.

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

**`search_keywords.py`**: Extracts nouns and adjectives from the query using NLTK POS tagging, then greps `./data/*.txt` for matches with surrounding context. A lightweight fallback when you want exact string matching without the vector store. **No vector store or Ollama needed.**
```bash
python search_keywords.py "Discussions of Kondiaronk and the Wendats"
```

### 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` | `build_exp_claude.py`, `query_topk_prompt_engine_v3.py` |
| Chunk size | 256 tokens | `build_exp_claude.py` |
| Chunk overlap | 25 tokens | `build_exp_claude.py` |
| Paragraph separator | `\n\n` | `build_exp_claude.py` |
| Initial retrieval | 30 chunks | `query_topk_prompt_engine_v3.py` |
| Re-rank model | `cross-encoder/ms-marco-MiniLM-L-12-v2` | `query_topk_prompt_engine_v3.py` |
| Re-rank top-n | 15 | `query_topk_prompt_engine_v3.py` |
| LLM | `command-r7b` (Ollama) or `gpt-4o-mini` (OpenAI API) | `query_topk_prompt_engine_v3.py`, `query_hybrid_bm25_v4.py` |
| Temperature | 0.3 (recommended for both local and API models) | `query_topk_prompt_engine_v3.py`, `query_hybrid_bm25_v4.py` |
| Context window | 8000 tokens | `query_topk_prompt_engine_v3.py` |
| Request timeout | 360 seconds | `query_topk_prompt_engine_v3.py` |

## Key dependencies

- **llama-index-core** (0.14.14) -- RAG framework
- **llama-index-embeddings-huggingface** (0.6.1) -- embedding integration
- **llama-index-llms-ollama** (0.9.1) -- local LLM via Ollama
- **llama-index-llms-openai** (0.6.18) -- OpenAI API LLM (optional, for API-based synthesis)
- **llama-index-readers-file** (0.5.6) -- file readers
- **llama-index-retrievers-bm25** (0.6.5) -- BM25 sparse retrieval for hybrid search
- **sentence-transformers** (5.1.0) -- embedding model support
- **torch** (2.8.0) -- ML runtime

## Notebooks

Three Jupyter notebooks document exploration and analysis:

- **`hyde.ipynb`** -- Experiments with HyDE (Hypothetical Document Embeddings) query rewriting. Tests whether generating a hypothetical answer to a query and embedding that instead improves retrieval. Uses LlamaIndex's `HyDEQueryTransform` with `llama3.1:8B`. Finding: the default HyDE prompt produced a rich hypothetical passage, but the technique did not improve retrieval quality over direct prompt engineering. This informed the decision to drop HyDE from the pipeline.

- **`sandbox.ipynb`** -- Exploratory notebook for learning the LlamaIndex API. Inspects the `llama_index.core` module (104 objects), lists available classes and methods, and reads the source of `VectorStoreIndex`. Useful as a quick reference for what LlamaIndex exposes.

- **`vs_metrics.ipynb`** -- Quantitative analysis of the vector store. Loads the persisted index (4,692 vectors, 1024 dimensions each from `BAAI/bge-large-en-v1.5`) and produces:
  - Distribution of embedding values (histogram)
  - Heatmap of the full embedding matrix
  - Embedding vector magnitude distribution
  - Per-dimension variance (which dimensions carry more signal)
  - Pairwise cosine similarity distribution and heatmap (subset)
  - Hierarchical clustering dendrogram (Ward linkage)
  - PCA and t-SNE 2D projections of the embedding space

## 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.
- **Top-k=15**: Wide enough to capture diverse perspectives, narrow enough to fit the context window.
- **Cross-encoder re-ranking (v3)**: Retrieve top-30 via bi-encoder, re-rank to top-15 with a cross-encoder that scores each (query, chunk) pair jointly. More accurate than bi-encoder similarity alone. Tested three models; `ms-marco-MiniLM-L-12-v2` selected over `stsb-roberta-base` (wrong task -- semantic similarity, not passage ranking) and `BAAI/bge-reranker-v2-m3` (50% slower, weak score tail).
- **HyDE query rewriting tested and dropped**: Did not improve results over direct prompt engineering.
- **V3 prompt**: Adapted for re-ranked context -- tells the LLM all excerpts have been curated, encourages examining every chunk and noting what each file contributes. Produces better multi-source synthesis than v2's prompt.
- **V2 prompt**: More flexible and query-adaptive than v1, which forced rigid structure (exactly 10 files, mandatory theme).
- **Verbatim retrieval (`retrieve_raw.py`)**: Uses LlamaIndex's `index.as_retriever()` instead of `index.as_query_engine()`. The retriever returns raw `NodeWithScore` objects (chunk text, metadata, scores) without invoking the LLM. The re-ranker is applied manually via `reranker.postprocess_nodes()`. This separation lets you inspect what the pipeline retrieves before synthesis.
- **Keyword search (`search_keywords.py`)**: NLTK POS tagging extracts nouns and adjectives from the query -- a middle ground between naive stopword removal and LLM-based term extraction. Catches exact names, places, and dates that vector similarity misses.
- **Hybrid BM25 + vector retrieval (v4)**: Runs two retrievers in parallel -- BM25 (top-20 by term frequency) and vector similarity (top-20 by cosine) -- merges and deduplicates candidates, then lets the cross-encoder re-rank the union to top-15. BM25 nominates candidates with exact term matches that embeddings miss; the cross-encoder decides final relevance. Uses `BM25Retriever.from_defaults(index=index)` from `llama-index-retrievers-bm25`, which indexes the nodes already stored in the persisted vector store.

## Development history

- **Aug 2025**: Initial implementation -- build pipeline, embedding model comparison, chunk size experiments, HyDE testing, prompt v1.
- **Jan 2026**: Command-line interface, v2 prompt, error handling improvements, model comparison (command-r7b selected).
- **Feb 2026**: Project tidy-up, cross-encoder re-ranking (v3), v3 prompt for multi-source synthesis, cross-encoder model comparison (L-12 selected), archived superseded scripts. Hybrid BM25 + vector retrieval (v4). Upgraded LlamaIndex from 0.13.1 to 0.14.14; added OpenAI API as optional LLM backend (`llama-index-llms-openai`). Incremental vector store updates (default mode in `build_exp_claude.py`). Fixed offline HuggingFace model loading (env vars must precede imports).

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