319 lines
13 KiB
Plaintext
319 lines
13 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"
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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 areusing colab\n",
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"! pip install 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": 1,
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"metadata": {},
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"outputs": [
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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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"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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"/Users/yichuan/Desktop/code/LEANN/leann/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n",
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"INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: facebook/contriever\n",
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"WARNING:sentence_transformers.SentenceTransformer:No sentence-transformers model found with name facebook/contriever. Creating a new one with mean pooling.\n",
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"Writing passages: 100%|██████████| 5/5 [00:00<00:00, 31254.13chunk/s]\n",
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"Batches: 100%|██████████| 1/1 [00:00<00:00, 12.19it/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: knowledge.index -> knowledge.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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"[0.11s] 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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"[0.23s] Read levels (5)\n",
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"[0.34s] Probing for compact storage flag...\n",
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"[0.34s] Found compact flag: False\n",
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"[0.34s] Compact flag is False, reading original format...\n",
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"[0.34s] Probing for potential extra byte before non-compact offsets...\n",
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"[0.34s] 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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"[0.34s] Read offsets (6)\n",
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"[0.44s] 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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"[0.44s] 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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"[0.73s] 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 'knowledge.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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"\n",
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"builder = LeannBuilder(backend_name=\"hnsw\")\n",
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"builder.add_text(\"C# is a powerful programming language\")\n",
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"builder.add_text(\"Python is a powerful programming language and it is very popular\")\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(\"knowledge.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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"## 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": 2,
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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:Found compatible server on port 5560\n",
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"INFO:leann.embedding_server_manager:Using existing compatible server on port 5560\n",
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"INFO:leann.api: Launching server time: 0.05758476257324219 seconds\n",
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"INFO:leann.embedding_server_manager:Found compatible server on port 5560\n",
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"INFO:leann.embedding_server_manager:Using existing compatible server on port 5560\n",
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"INFO:leann.api: Generated embedding shape: (1, 768)\n",
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"INFO:leann.api: Embedding time: 0.05983591079711914 seconds\n",
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"INFO:leann.api: Search time: 0.039762258529663086 seconds\n",
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"INFO:leann.api: Backend returned: labels=2 results\n",
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"INFO:leann.api: Processing 2 passage IDs:\n",
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"INFO:leann.api: 1. passage_id='0' -> SUCCESS: C# is a powerful programming language...\n",
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"INFO:leann.api: 2. passage_id='1' -> SUCCESS: Python is a powerful programming language and it is very popular...\n",
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"INFO:leann.api: Final enriched results: 2 passages\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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"ZmqDistanceComputer initialized: d=768, metric=0\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[SearchResult(id='0', score=np.float32(0.9646692), text='C# is a powerful programming language', metadata={}),\n",
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" SearchResult(id='1', score=np.float32(0.91955304), text='Python is a powerful programming language and it is very popular', metadata={})]"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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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(\"knowledge.leann\")\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": 1,
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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.chat:Attempting to create LLM of type='hf' with model='Qwen/Qwen3-0.6B'\n",
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"INFO:leann.chat:Initializing HFChat with model='Qwen/Qwen3-0.6B'\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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"INFO: Registering backend 'hnsw'\n",
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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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]
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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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"/Users/yichuan/Desktop/code/LEANN/leann/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n",
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"INFO:leann.chat:MPS is available. Using Apple Silicon GPU.\n",
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"INFO:leann.api:🔍 LeannSearcher.search() called:\n",
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"INFO:leann.api: Query: 'Compare the two retrieved programming languages and say which one is more popular today.'\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:Found compatible server on port 5560\n",
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"INFO:leann.embedding_server_manager:Using existing compatible server on port 5560\n",
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"INFO:leann.api: Launching server time: 0.11421084403991699 seconds\n",
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"INFO:leann.embedding_server_manager:Found compatible server on port 5560\n",
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"INFO:leann.embedding_server_manager:Using existing compatible server on port 5560\n",
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"INFO:leann.api: Generated embedding shape: (1, 768)\n",
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"INFO:leann.api: Embedding time: 0.1147918701171875 seconds\n",
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"INFO:leann.api: Search time: 0.05468583106994629 seconds\n",
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"INFO:leann.api: Backend returned: labels=2 results\n",
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"INFO:leann.api: Processing 2 passage IDs:\n",
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"INFO:leann.api: 1. passage_id='1' -> SUCCESS: Python is a powerful programming language and it is very popular...\n",
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"INFO:leann.api: 2. passage_id='0' -> SUCCESS: C# is a powerful programming language...\n",
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"INFO:leann.api: Final enriched results: 2 passages\n",
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"INFO:leann.chat:Generating with HuggingFace model, config: {'max_new_tokens': 512, 'temperature': 0.7, 'top_p': 0.9, 'do_sample': True, 'pad_token_id': 151645, 'eos_token_id': 151645}\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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"ZmqDistanceComputer initialized: d=768, metric=0\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'<think>\\n\\n</think>\\n\\nBased on the context provided, both Python and C# are mentioned as powerful programming languages, but no specific information is given about their popularity today. However, generally, Python is more popular for data science, web development, and other tasks, while C# is widely used in enterprise applications and game development. Since the context does not explicitly state which is more popular, but Python is often considered more popular in many cases, the best answer would be:\\n\\n**Python is more popular today.**'"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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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=\"knowledge.leann\", llm_config=llm_config)\n",
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"response = chat.ask(\n",
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" \"Compare the two retrieved programming languages and say which one is more popular today.\",\n",
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" top_k=2,\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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