344 lines
15 KiB
Plaintext
344 lines
15 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, 27887.66chunk/s]\n",
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"Batches: 100%|██████████| 1/1 [00:00<00:00, 13.51it/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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"[0.21s] Read levels (5)\n",
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"[0.30s] Probing for compact storage flag...\n",
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"[0.30s] Found compact flag: False\n",
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"[0.30s] Compact flag is False, reading original format...\n",
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"[0.30s] Probing for potential extra byte before non-compact offsets...\n",
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"[0.30s] 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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"[0.30s] Read offsets (6)\n",
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"[0.40s] 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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"[0.40s] Read neighbors (320)\n",
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"[0.50s] Read scalar params (ep=4, max_lvl=0)\n",
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"[0.50s] Checking for storage data...\n",
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"[0.50s] Found storage fourcc: 49467849.\n",
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"[0.50s] Converting to CSR format...\n",
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"[0.50s] Conversion loop finished. \n",
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"[0.50s] 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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"[0.50s] Deleting original neighbors and offsets arrays...\n",
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" CSR Stats: |data|=20, |level_ptr|=10\n",
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"[0.59s] 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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"[0.69s] 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 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(\"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:Using port 5560 instead of 5557\n",
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"INFO:leann.embedding_server_manager:Starting embedding server on port 5560...\n",
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"INFO:leann.embedding_server_manager:Command: /Users/yichuan/Desktop/code/LEANN/leann/.venv/bin/python -m leann_backend_hnsw.hnsw_embedding_server --zmq-port 5560 --model-name facebook/contriever --passages-file knowledge.leann.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: 4574\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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"INFO:leann.embedding_server_manager:Embedding server is ready!\n",
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"INFO:leann.api: Launching server time: 1.078078269958496 seconds\n",
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"INFO:leann.embedding_server_manager:Existing server process (PID 4574) is compatible\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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"INFO:leann.api: Generated embedding shape: (1, 768)\n",
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"INFO:leann.api: Embedding time: 2.9307072162628174 seconds\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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"name": "stderr",
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"output_type": "stream",
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"text": [
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"INFO:leann.api: Search time: 0.27327895164489746 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 and it is good at game development...\n",
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"INFO:leann.api: 2. passage_id='1' -> SUCCESS: Python is a powerful programming language and it is good at machine learning tasks...\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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"data": {
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"text/plain": [
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"[SearchResult(id='0', score=np.float32(0.9874103), text='C# is a powerful programming language and it is good at game development', metadata={}),\n",
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" SearchResult(id='1', score=np.float32(0.8922168), text='Python is a powerful programming language and it is good at machine learning tasks', 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": 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.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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"INFO:leann.chat:MPS is available. Using Apple Silicon GPU.\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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]
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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.api:🔍 LeannSearcher.search() called:\n",
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"INFO:leann.api: Query: 'Compare the two retrieved programming languages and tell me their advantages.'\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: 0.04932403564453125 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: 0.06902289390563965 seconds\n",
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"INFO:leann.api: Search time: 0.026793241500854492 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 and it is good at game development...\n",
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"INFO:leann.api: 2. passage_id='1' -> SUCCESS: Python is a powerful programming language and it is good at machine learning tasks...\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': 128, '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, here's a comparison of the two retrieved programming languages:\\n\\n**C#** is known for being a powerful programming language and is well-suited for game development. It is often used in game development and is popular among developers working on Windows applications.\\n\\n**Python**, on the other hand, is also a powerful language and is well-suited for machine learning tasks. It is widely used for data analysis, scientific computing, and other applications that require handling large datasets or performing complex calculations.\\n\\n**Advantages**:\\n- C#: Strong for game development and cross-platform compatibility.\\n- Python: Strong for\""
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]
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},
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"execution_count": 8,
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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 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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