From 4a7b028597c3276e997430ed1d6ce8367b8c5175 Mon Sep 17 00:00:00 2001 From: Eric Furst Date: Sun, 22 Feb 2026 12:46:48 -0500 Subject: [PATCH] Update README for public branch with clippings search Remove references to files not in public branch (notebooks, archived/, devlog, NOTES.md, v2 scripts). Add clippings search documentation (build_clippings.py, retrieve_clippings.py, ChromaDB). --- README.md | 152 +++++++++++++++++++++--------------------------------- 1 file changed, 59 insertions(+), 93 deletions(-) diff --git a/README.md b/README.md index 823c7a4..c0fe5b2 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # 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. +Semantic search over a journal archive and a collection of clippings (articles, PDFs, web saves). Uses vector embeddings and a local LLM to find and synthesize information across dated journal entries and a pdf library of clippings files. ## How it works @@ -8,7 +8,7 @@ Semantic search over a personal journal archive. Uses vector embeddings and a lo 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. +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. @@ -17,23 +17,21 @@ Query → Embed (BAAI/bge-large-en-v1.5) → Vector similarity (top-30) → Cros ``` ssearch/ -├── build_exp_claude.py # Build/update vector store (incremental by default) +├── build_exp_claude.py # Build/update journal vector store (incremental) +├── build_clippings.py # Build/update clippings vector store (ChromaDB) ├── 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) +├── query_hybrid_bm25_v4.py # Hybrid BM25 + vector query +├── retrieve_raw.py # Verbatim journal chunk retrieval (no LLM) ├── retrieve_hybrid_raw.py # Hybrid verbatim retrieval (no LLM) +├── retrieve_clippings.py # Verbatim clippings chunk 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) +├── data/ # Symlink to journal .txt files +├── clippings/ # Symlink to clippings (PDFs, TXT, webarchive, RTF) +├── storage_exp/ # Persisted journal vector store (~242 MB) +├── storage_clippings/ # Persisted clippings vector store (ChromaDB) +├── models/ # Cached HuggingFace models (offline) +└── requirements.txt # Python dependencies ``` ## Setup @@ -47,7 +45,7 @@ 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. +The `data/` symlink should point to the journal archive (plain `.txt` files). The `clippings/` symlink should point to the clippings folder (PDFs, TXT, webarchive, RTF). 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 @@ -59,35 +57,37 @@ 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" -``` +**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. ## Usage -### Build the vector store +### Build the vector stores ```bash -# Incremental update (default): only processes new, modified, or deleted files +# Journal index -- incremental update (default) python build_exp_claude.py -# Full rebuild from scratch +# Journal index -- full rebuild python build_exp_claude.py --rebuild + +# Clippings index -- incremental update (default) +python build_clippings.py + +# Clippings index -- full rebuild +python build_clippings.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. +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. -### Search +`build_clippings.py` handles PDFs, TXT, webarchive, and RTF files. PDFs are validated before indexing -- those without extractable text (scanned, encrypted) are skipped and written to `ocr_needed.txt` for later OCR processing. + +### Search journals 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`.** +**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 @@ -106,103 +106,69 @@ python query_hybrid_bm25_v4.py "Louis Menand" #### 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.** +Same retrieval and re-ranking pipeline but outputs raw chunk text. **No Ollama needed.** -**Vector-only** (`retrieve_raw.py`): Top-30 vector retrieval, cross-encoder re-rank to top 15, raw output. +**Vector-only** (`retrieve_raw.py`): ```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]`. +**Hybrid BM25 + vector** (`retrieve_hybrid_raw.py`): 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.** +**`search_keywords.py`**: Extracts nouns and adjectives from the query using NLTK POS tagging, then greps the journal files for matches with surrounding context. ```bash python search_keywords.py "Discussions of Kondiaronk and the Wendats" ``` -### Output format +### Search clippings -``` -Response: - +**`retrieve_clippings.py`**: Verbatim chunk retrieval from the clippings index. Same embedding model and cross-encoder re-ranking. Outputs a summary of source files and rankings, then the full chunk text. **No Ollama needed.** -Source documents: -2024-03-15.txt ./data/2024-03-15.txt 0.683 -2023-11-02.txt ./data/2023-11-02.txt 0.651 -... +```bash +python retrieve_clippings.py "creativity and innovation" ``` +Output includes page numbers for PDF sources when available. + ## 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` | +| Embedding model | `BAAI/bge-large-en-v1.5` | all build and query scripts | +| Chunk size | 256 tokens | `build_exp_claude.py`, `build_clippings.py` | +| Chunk overlap | 25 tokens | `build_exp_claude.py`, `build_clippings.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 scripts | +| Temperature | 0.3 | query scripts | | 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 +- **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 ## 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. +- **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). +- **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 used for journals because the clippings index handles more diverse file types and benefits from ChromaDB's 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 are written to `ocr_needed.txt` for later OCR processing.