Reorganize project: rename scripts, archive superseded, add clippings_search/
- Rename build_exp_claude.py → build_store.py - Rename query_hybrid_bm25_v4.py → query_hybrid.py - Rename retrieve_hybrid_raw.py → retrieve.py - Archive query_topk_prompt_engine_v3.py (superseded by hybrid) - Archive retrieve_raw.py (superseded by hybrid) - Move build_clippings.py, retrieve_clippings.py → clippings_search/ - Update run_query.sh, README.md, CLAUDE.md for new names
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README.md
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README.md
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# ssearch
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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.
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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.
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## How it works
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@ -8,7 +8,7 @@ Semantic search over a personal journal archive. Uses vector embeddings and a lo
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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
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```
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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.
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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.
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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.
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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.
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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.
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@ -17,23 +17,24 @@ Query → Embed (BAAI/bge-large-en-v1.5) → Vector similarity (top-30) → Cros
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```
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ssearch/
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├── build_exp_claude.py # Build/update vector store (incremental by default)
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├── query_topk_prompt_engine_v3.py # Main query engine (cross-encoder re-ranking)
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├── query_topk_prompt_engine_v2.py # Previous query engine (no re-ranking)
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├── retrieve_raw.py # Verbatim chunk retrieval (no LLM)
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├── query_hybrid_bm25_v4.py # Hybrid BM25 + vector query (v4)
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├── retrieve_hybrid_raw.py # Hybrid verbatim retrieval (no LLM)
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├── search_keywords.py # Keyword search via POS-based term extraction
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├── run_query.sh # Shell wrapper with timing and logging
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├── data/ # Symlink to ../text/ (journal .txt files)
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├── storage_exp/ # Persisted vector store (~242 MB)
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├── models/ # Cached HuggingFace models (embedding + cross-encoder, offline)
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├── archived/ # Earlier iterations and prototypes
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├── saved_output/ # Saved query results and model comparisons
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├── requirements.txt # Python dependencies (pip freeze)
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├── NOTES.md # Similarity metric reference
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├── devlog.txt # Development log and experimental findings
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└── *.ipynb # Jupyter notebooks (HyDE, metrics, sandbox)
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├── build_store.py # Build/update journal vector store (incremental)
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├── query_hybrid.py # Hybrid BM25+vector query with LLM synthesis
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├── retrieve.py # Verbatim hybrid retrieval (no LLM)
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├── search_keywords.py # Keyword search via POS-based term extraction
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├── run_query.sh # Interactive shell wrapper with timing and logging
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├── clippings_search/
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│ ├── build_clippings.py # Build/update clippings vector store (ChromaDB)
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│ └── retrieve_clippings.py # Verbatim clippings chunk retrieval
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├── data/ # Symlink to journal .txt files
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├── clippings/ # Symlink to clippings (PDFs, TXT, webarchive, RTF)
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├── storage_exp/ # Persisted journal vector store (~242 MB)
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├── storage_clippings/ # Persisted clippings vector store (ChromaDB)
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├── models/ # Cached HuggingFace models (offline)
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├── archived/ # Superseded script versions
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├── saved_output/ # Saved query results and model comparisons
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├── requirements.txt # Python dependencies
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├── devlog.txt # Development log and experimental findings
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└── *.ipynb # Jupyter notebooks (HyDE, metrics, sandbox)
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```
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## Setup
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@ -47,7 +48,7 @@ source .venv/bin/activate
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pip install -r requirements.txt
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```
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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.
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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.
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### Offline model loading
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@ -59,74 +60,75 @@ os.environ["SENTENCE_TRANSFORMERS_HOME"] = "./models"
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os.environ["HF_HUB_OFFLINE"] = "1"
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```
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**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.
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**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.
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Alternatively, set the variable in your shell before running Python:
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```bash
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export HF_HUB_OFFLINE=1
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python query_hybrid_bm25_v4.py "your query"
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python query_hybrid.py "your query"
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```
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## Usage
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### Build the vector store
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### Build the vector stores
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```bash
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# Incremental update (default): only processes new, modified, or deleted files
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python build_exp_claude.py
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# Journal index -- incremental update (default)
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python build_store.py
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# Full rebuild from scratch
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python build_exp_claude.py --rebuild
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# Journal index -- full rebuild
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python build_store.py --rebuild
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# Clippings index -- incremental update (default)
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python clippings_search/build_clippings.py
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# Clippings index -- full rebuild
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python clippings_search/build_clippings.py --rebuild
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```
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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.
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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.
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### Search
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`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.
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Three categories of search are available, from heaviest (semantic + LLM) to lightest (grep).
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### Search journals
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#### Semantic search with LLM synthesis
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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`.**
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**Requires Ollama running with `command-r7b`.**
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**Vector-only** (`query_topk_prompt_engine_v3.py`): Retrieves the top 30 chunks by cosine similarity, re-ranks to top 15, synthesizes.
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**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.
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```bash
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python query_topk_prompt_engine_v3.py "What does the author say about creativity?"
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python query_hybrid.py "What does the author say about creativity?"
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```
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**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.
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```bash
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python query_hybrid_bm25_v4.py "Louis Menand"
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```
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**Interactive wrapper** (`run_query.sh`): Loops for queries using the v3 engine, displays timing, and appends queries to `query.log`.
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**Interactive wrapper** (`run_query.sh`): Loops for queries, displays timing, and appends queries to `query.log`.
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```bash
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./run_query.sh
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```
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#### Verbatim chunk retrieval (no LLM)
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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.**
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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.**
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**Vector-only** (`retrieve_raw.py`): Top-30 vector retrieval, cross-encoder re-rank to top 15, raw output.
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```bash
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python retrieve_raw.py "Kondiaronk and the Wendats"
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python retrieve.py "Kondiaronk and the Wendats"
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```
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**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]`.
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```bash
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python retrieve_hybrid_raw.py "Louis Menand"
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```
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Pipe either to `less` for browsing.
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#### Keyword search (no vector store, no LLM)
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**`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.**
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Extracts nouns and adjectives from the query using NLTK POS tagging, then greps journal files for matches with surrounding context.
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```bash
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python search_keywords.py "Discussions of Kondiaronk and the Wendats"
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```
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### Search clippings
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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.**
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```bash
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python clippings_search/retrieve_clippings.py "creativity and innovation"
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```
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### Output format
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```
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@ -145,64 +147,55 @@ Key parameters (set in source files):
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| Parameter | Value | Location |
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|-----------|-------|----------|
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| Embedding model | `BAAI/bge-large-en-v1.5` | `build_exp_claude.py`, `query_topk_prompt_engine_v3.py` |
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| Chunk size | 256 tokens | `build_exp_claude.py` |
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| Chunk overlap | 25 tokens | `build_exp_claude.py` |
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| Paragraph separator | `\n\n` | `build_exp_claude.py` |
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| Initial retrieval | 30 chunks | `query_topk_prompt_engine_v3.py` |
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| Re-rank model | `cross-encoder/ms-marco-MiniLM-L-12-v2` | `query_topk_prompt_engine_v3.py` |
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| Re-rank top-n | 15 | `query_topk_prompt_engine_v3.py` |
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| LLM | `command-r7b` (Ollama) or `gpt-4o-mini` (OpenAI API) | `query_topk_prompt_engine_v3.py`, `query_hybrid_bm25_v4.py` |
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| Temperature | 0.3 (recommended for both local and API models) | `query_topk_prompt_engine_v3.py`, `query_hybrid_bm25_v4.py` |
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| Context window | 8000 tokens | `query_topk_prompt_engine_v3.py` |
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| Request timeout | 360 seconds | `query_topk_prompt_engine_v3.py` |
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| Embedding model | `BAAI/bge-large-en-v1.5` | all build and query scripts |
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| Chunk size | 256 tokens | `build_store.py`, `clippings_search/build_clippings.py` |
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| Chunk overlap | 25 tokens | `build_store.py`, `clippings_search/build_clippings.py` |
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| Paragraph separator | `\n\n` | `build_store.py` |
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| Initial retrieval | 30 chunks | query and retrieve scripts |
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| Re-rank model | `cross-encoder/ms-marco-MiniLM-L-12-v2` | query and retrieve scripts |
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| Re-rank top-n | 15 | query and retrieve scripts |
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| LLM | `command-r7b` (Ollama) or `gpt-4o-mini` (OpenAI API) | `query_hybrid.py` |
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| Temperature | 0.3 | `query_hybrid.py` |
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| Context window | 8000 tokens | `query_hybrid.py` |
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| Request timeout | 360 seconds | `query_hybrid.py` |
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## Key dependencies
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- **llama-index-core** (0.14.14) -- RAG framework
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- **llama-index-embeddings-huggingface** (0.6.1) -- embedding integration
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- **llama-index-llms-ollama** (0.9.1) -- local LLM via Ollama
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- **llama-index-llms-openai** (0.6.18) -- OpenAI API LLM (optional, for API-based synthesis)
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- **llama-index-readers-file** (0.5.6) -- file readers
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- **llama-index-retrievers-bm25** (0.6.5) -- BM25 sparse retrieval for hybrid search
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- **sentence-transformers** (5.1.0) -- embedding model support
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- **torch** (2.8.0) -- ML runtime
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- **llama-index-embeddings-huggingface** -- embedding integration
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- **llama-index-vector-stores-chroma** -- ChromaDB vector store for clippings
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- **llama-index-llms-ollama** -- local LLM via Ollama
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- **llama-index-llms-openai** -- OpenAI API LLM (optional)
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- **llama-index-retrievers-bm25** -- BM25 sparse retrieval for hybrid search
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- **chromadb** -- persistent vector store for clippings index
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- **sentence-transformers** -- cross-encoder re-ranking
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- **torch** -- ML runtime
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## Notebooks
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Three Jupyter notebooks document exploration and analysis:
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- **`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.
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- **`hyde.ipynb`** -- Experiments with HyDE (Hypothetical Document Embeddings) query rewriting. Finding: did not improve retrieval quality over direct prompt engineering.
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- **`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.
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- **`sandbox.ipynb`** -- Exploratory notebook for learning the LlamaIndex API.
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- **`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:
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- Distribution of embedding values (histogram)
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- Heatmap of the full embedding matrix
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- Embedding vector magnitude distribution
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- Per-dimension variance (which dimensions carry more signal)
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- Pairwise cosine similarity distribution and heatmap (subset)
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- Hierarchical clustering dendrogram (Ward linkage)
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- PCA and t-SNE 2D projections of the embedding space
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- **`vs_metrics.ipynb`** -- Quantitative analysis of the vector store (embedding distributions, pairwise similarity, clustering, PCA/t-SNE projections).
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## Design decisions
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- **BAAI/bge-large-en-v1.5 over all-mpnet-base-v2**: Better semantic matching quality for journal text despite slower embedding.
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- **256-token chunks**: Tested 512 and 384; 256 with 25-token overlap produced the highest quality matches.
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- **command-r7b over llama3.1:8B**: Sticks closer to provided context with less hallucination at comparable speed.
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- **Top-k=15**: Wide enough to capture diverse perspectives, narrow enough to fit the context window.
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- **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).
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- **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).
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- **HyDE query rewriting tested and dropped**: Did not improve results over direct prompt engineering.
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- **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.
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- **V2 prompt**: More flexible and query-adaptive than v1, which forced rigid structure (exactly 10 files, mandatory theme).
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- **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.
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- **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.
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- **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.
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- **Hybrid BM25 + vector retrieval**: BM25 nominates candidates with exact term matches that embeddings miss; the cross-encoder decides final relevance.
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- **ChromaDB for clippings**: Persistent SQLite-backed store. Chosen over the JSON store for its metadata filtering and direct chunk-level operations for incremental updates.
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- **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`.
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## Development history
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- **Aug 2025**: Initial implementation -- build pipeline, embedding model comparison, chunk size experiments, HyDE testing, prompt v1.
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- **Jan 2026**: Command-line interface, v2 prompt, error handling improvements, model comparison (command-r7b selected).
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- **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).
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- **Aug 2025**: Initial implementation -- build pipeline, embedding model comparison, chunk size experiments, HyDE testing.
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- **Jan 2026**: Command-line interface, prompt improvements, model comparison (command-r7b selected).
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- **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.
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See `devlog.txt` for detailed development notes and experimental findings.
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# Usage: ./run_query.sh
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QUERY_SCRIPT="query_hybrid_bm25_v4.py"
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QUERY_SCRIPT="query_hybrid.py"
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echo -e "Current query engine is $QUERY_SCRIPT\n"
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