269 lines
14 KiB
Plaintext
269 lines
14 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Quick Start in 30s\n",
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"\n",
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"**Home GitHub Repository:** [LEANN on GitHub](https://github.com/yichuan-w/LEANN)\n",
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"\n",
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"**Important for Colab users:** Set your runtime type to T4 GPU for optimal performance. Go to Runtime → Change runtime type → Hardware accelerator → T4 GPU."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# install this if you are using colab\n",
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"! uv pip install leann-core leann-backend-hnsw --no-deps\n",
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"! uv pip install leann --no-deps\n",
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"# For Colab environment, we need to set some environment variables\n",
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"import os\n",
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"os.environ['LEANN_LOG_LEVEL'] = 'INFO' # Enable more detailed logging"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from pathlib import Path\n",
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"INDEX_DIR = Path(\"./\").resolve()\n",
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"INDEX_PATH = str(INDEX_DIR / \"demo.leann\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Build the index"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Writing passages: 100%|██████████| 5/5 [00:00<00:00, 17077.79chunk/s]\n",
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"Batches: 100%|██████████| 1/1 [00:00<00:00, 36.43it/s]\n",
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"WARNING:leann_backend_hnsw.hnsw_backend:Converting data to float32, shape: (5, 768)\n",
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"INFO:leann_backend_hnsw.hnsw_backend:INFO: Converting HNSW index to CSR-pruned format...\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"M: 64 for level: 0\n",
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"Starting conversion: index.index -> index.csr.tmp\n",
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"[0.00s] Reading Index HNSW header...\n",
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"[0.00s] Header read: d=768, ntotal=5\n",
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"[0.00s] Reading HNSW struct vectors...\n",
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" Reading vector (dtype=<class 'numpy.float64'>, fmt='d')... Count=6, Bytes=48\n",
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"[0.00s] Read assign_probas (6)\n",
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" Reading vector (dtype=<class 'numpy.int32'>, fmt='i')... Count=7, Bytes=28\n",
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"[0.14s] Read cum_nneighbor_per_level (7)\n",
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" Reading vector (dtype=<class 'numpy.int32'>, fmt='i')... Count=5, Bytes=20\n",
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"[0.24s] Read levels (5)\n",
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"[0.33s] Probing for compact storage flag...\n",
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"[0.33s] Found compact flag: False\n",
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"[0.33s] Compact flag is False, reading original format...\n",
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"[0.33s] Probing for potential extra byte before non-compact offsets...\n",
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"[0.33s] Found and consumed an unexpected 0x00 byte.\n",
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" Reading vector (dtype=<class 'numpy.uint64'>, fmt='Q')... Count=6, Bytes=48\n",
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"[0.33s] Read offsets (6)\n",
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"[0.41s] Attempting to read neighbors vector...\n",
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" Reading vector (dtype=<class 'numpy.int32'>, fmt='i')... Count=320, Bytes=1280\n",
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"[0.41s] Read neighbors (320)\n",
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"[0.54s] Read scalar params (ep=4, max_lvl=0)\n",
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"[0.54s] Checking for storage data...\n",
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"[0.54s] Found storage fourcc: 49467849.\n",
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"[0.54s] Converting to CSR format...\n",
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"[0.54s] Conversion loop finished. \n",
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"[0.54s] Running validation checks...\n",
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" Checking total valid neighbor count...\n",
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" OK: Total valid neighbors = 20\n",
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" Checking final pointer indices...\n",
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" OK: Final pointers match data size.\n",
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"[0.54s] Deleting original neighbors and offsets arrays...\n",
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" CSR Stats: |data|=20, |level_ptr|=10\n",
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"[0.63s] Writing CSR HNSW graph data in FAISS-compatible order...\n",
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" Pruning embeddings: Writing NULL storage marker.\n",
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"[0.71s] Conversion complete.\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"INFO:leann_backend_hnsw.hnsw_backend:✅ CSR conversion successful.\n",
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"INFO:leann_backend_hnsw.hnsw_backend:INFO: Replaced original index with CSR-pruned version at 'index.index'\n"
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]
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}
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],
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"source": [
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"from leann.api import LeannBuilder\n",
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"builder = LeannBuilder(backend_name=\"hnsw\")\n",
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"builder.add_text(\"C# is a powerful programming language and it is good at game development\")\n",
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"builder.add_text(\"Python is a powerful programming language and it is good at machine learning tasks\")\n",
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"builder.add_text(\"Machine learning transforms industries\")\n",
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"builder.add_text(\"Neural networks process complex data\")\n",
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"builder.add_text(\"Leann is a great storage saving engine for RAG on your MacBook\")\n",
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"builder.build_index(INDEX_PATH)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Search with real-time embeddings"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"INFO:leann.api:🔍 LeannSearcher.search() called:\n",
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"INFO:leann.api: Query: 'programming languages'\n",
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"INFO:leann.api: Top_k: 2\n",
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"INFO:leann.api: Additional kwargs: {}\n",
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"INFO:leann.embedding_server_manager:Port 5557 has incompatible server, trying next port...\n",
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"INFO:leann.embedding_server_manager:Port 5558 has incompatible server, trying next port...\n",
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"INFO:leann.embedding_server_manager:Port 5559 has incompatible server, trying next port...\n",
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"INFO:leann.embedding_server_manager:Port 5560 has incompatible server, trying next port...\n",
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"INFO:leann.embedding_server_manager:Port 5561 has incompatible server, trying next port...\n",
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"INFO:leann.embedding_server_manager:Port 5562 has incompatible server, trying next port...\n",
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"INFO:leann.embedding_server_manager:Starting embedding server on port 5563...\n",
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"INFO:leann.embedding_server_manager:Command: /Users/yichuan/Desktop/code/test_leann_pip/LEANN/.venv/bin/python -m leann_backend_hnsw.hnsw_embedding_server --zmq-port 5563 --model-name facebook/contriever --passages-file /Users/yichuan/Desktop/code/test_leann_pip/LEANN/content/index.meta.json\n",
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"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
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"To disable this warning, you can either:\n",
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"\t- Avoid using `tokenizers` before the fork if possible\n",
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"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
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"INFO:leann.embedding_server_manager:Server process started with PID: 31699\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[read_HNSW - CSR NL v4] Reading metadata & CSR indices (manual offset)...\n",
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"[read_HNSW NL v4] Read levels vector, size: 5\n",
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"[read_HNSW NL v4] Reading Compact Storage format indices...\n",
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"[read_HNSW NL v4] Read compact_level_ptr, size: 10\n",
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"[read_HNSW NL v4] Read compact_node_offsets, size: 6\n",
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"[read_HNSW NL v4] Read entry_point: 4, max_level: 0\n",
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"[read_HNSW NL v4] Read storage fourcc: 0x6c6c756e\n",
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"[read_HNSW NL v4 FIX] Detected FileIOReader. Neighbors size field offset: 326\n",
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"[read_HNSW NL v4] Reading neighbors data into memory.\n",
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"[read_HNSW NL v4] Read neighbors data, size: 20\n",
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"[read_HNSW NL v4] Finished reading metadata and CSR indices.\n",
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"INFO: Skipping external storage loading, since is_recompute is true.\n",
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"INFO: Registering backend 'hnsw'\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Traceback (most recent call last):\n",
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" File \"<frozen runpy>\", line 198, in _run_module_as_main\n",
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" File \"<frozen runpy>\", line 88, in _run_code\n",
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" File \"/Users/yichuan/Desktop/code/test_leann_pip/LEANN/.venv/lib/python3.11/site-packages/leann_backend_hnsw/hnsw_embedding_server.py\", line 323, in <module>\n",
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" create_hnsw_embedding_server(\n",
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" File \"/Users/yichuan/Desktop/code/test_leann_pip/LEANN/.venv/lib/python3.11/site-packages/leann_backend_hnsw/hnsw_embedding_server.py\", line 98, in create_hnsw_embedding_server\n",
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" passages = PassageManager(passage_sources)\n",
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" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
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" File \"/Users/yichuan/Desktop/code/test_leann_pip/LEANN/.venv/lib/python3.11/site-packages/leann/api.py\", line 127, in __init__\n",
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" raise FileNotFoundError(f\"Passage index file not found: {index_file}\")\n",
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"FileNotFoundError: Passage index file not found: /Users/yichuan/Desktop/code/test_leann_pip/LEANN/index.passages.idx\n",
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"ERROR:leann.embedding_server_manager:Server terminated during startup.\n"
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]
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},
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{
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"ename": "RuntimeError",
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"evalue": "Failed to start embedding server on port 5563",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mRuntimeError\u001b[39m Traceback (most recent call last)",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mleann\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mapi\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m LeannSearcher\n\u001b[32m 3\u001b[39m searcher = LeannSearcher(\u001b[33m\"\u001b[39m\u001b[33mindex\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m results = \u001b[43msearcher\u001b[49m\u001b[43m.\u001b[49m\u001b[43msearch\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mprogramming languages\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtop_k\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 5\u001b[39m results\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/code/test_leann_pip/LEANN/.venv/lib/python3.11/site-packages/leann/api.py:439\u001b[39m, in \u001b[36mLeannSearcher.search\u001b[39m\u001b[34m(self, query, top_k, complexity, beam_width, prune_ratio, recompute_embeddings, pruning_strategy, expected_zmq_port, **kwargs)\u001b[39m\n\u001b[32m 437\u001b[39m start_time = time.time()\n\u001b[32m 438\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m recompute_embeddings:\n\u001b[32m--> \u001b[39m\u001b[32m439\u001b[39m zmq_port = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mbackend_impl\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_ensure_server_running\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 440\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmeta_path_str\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 441\u001b[39m \u001b[43m \u001b[49m\u001b[43mport\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpected_zmq_port\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 442\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 443\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 444\u001b[39m \u001b[38;5;28;01mdel\u001b[39;00m expected_zmq_port\n\u001b[32m 445\u001b[39m zmq_time = time.time() - start_time\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/code/test_leann_pip/LEANN/.venv/lib/python3.11/site-packages/leann/searcher_base.py:81\u001b[39m, in \u001b[36mBaseSearcher._ensure_server_running\u001b[39m\u001b[34m(self, passages_source_file, port, **kwargs)\u001b[39m\n\u001b[32m 72\u001b[39m server_started, actual_port = \u001b[38;5;28mself\u001b[39m.embedding_server_manager.start_server(\n\u001b[32m 73\u001b[39m port=port,\n\u001b[32m 74\u001b[39m model_name=\u001b[38;5;28mself\u001b[39m.embedding_model,\n\u001b[32m (...)\u001b[39m\u001b[32m 78\u001b[39m enable_warmup=kwargs.get(\u001b[33m\"\u001b[39m\u001b[33menable_warmup\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m),\n\u001b[32m 79\u001b[39m )\n\u001b[32m 80\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m server_started:\n\u001b[32m---> \u001b[39m\u001b[32m81\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[32m 82\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mFailed to start embedding server on port \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mactual_port\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 83\u001b[39m )\n\u001b[32m 85\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m actual_port\n",
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"\u001b[31mRuntimeError\u001b[39m: Failed to start embedding server on port 5563"
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]
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}
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],
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"source": [
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"from leann.api import LeannSearcher\n",
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"\n",
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"searcher = LeannSearcher(INDEX_PATH)\n",
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"results = searcher.search(\"programming languages\", top_k=2)\n",
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"results"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Chat with LEANN using retrieved results"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from leann.api import LeannChat\n",
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"\n",
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"llm_config = {\n",
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" \"type\": \"hf\",\n",
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" \"model\": \"Qwen/Qwen3-0.6B\",\n",
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"}\n",
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"\n",
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"chat = LeannChat(index_path=INDEX_PATH, llm_config=llm_config)\n",
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"response = chat.ask(\n",
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" \"Compare the two retrieved programming languages and tell me their advantages.\",\n",
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" top_k=2,\n",
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" llm_kwargs={\"max_tokens\": 128}\n",
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")\n",
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"response"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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