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LEANN/demo.ipynb
yichuan520030910320 c1bc2603a2 update readme and 30 seconds example
2025-07-15 23:18:01 -07:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:sentence_transformers.SentenceTransformer:Use pytorch device_name: mps\n",
"INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: facebook/contriever-msmarco\n",
"WARNING:sentence_transformers.SentenceTransformer:No sentence-transformers model found with name facebook/contriever-msmarco. Creating a new one with mean pooling.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO: Computing embeddings for 1 chunks using SentenceTransformer model 'facebook/contriever-msmarco'...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Batches: 100%|██████████| 1/1 [00:00<00:00, 65.35it/s]\n",
"INFO:sentence_transformers.SentenceTransformer:Use pytorch device_name: mps\n",
"INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: facebook/contriever-msmarco\n",
"WARNING:sentence_transformers.SentenceTransformer:No sentence-transformers model found with name facebook/contriever-msmarco. Creating a new one with mean pooling.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO: Computing embeddings for 5 chunks using SentenceTransformer model 'facebook/contriever-msmarco'...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Batches: 100%|██████████| 1/1 [00:00<00:00, 50.38it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"M: 64 for level: 0\n",
"INFO: Converting HNSW index to CSR-pruned format...\n",
"Starting conversion: knowledge.index -> knowledge.csr.tmp\n",
"[0.00s] Reading Index HNSW header...\n",
"[0.00s] Header read: d=768, ntotal=5\n",
"[0.00s] Reading HNSW struct vectors...\n",
" Reading vector (dtype=<class 'numpy.float64'>, fmt='d')... Count=6, Bytes=48\n",
"[0.00s] Read assign_probas (6)\n",
" Reading vector (dtype=<class 'numpy.int32'>, fmt='i')... Count=7, Bytes=28\n",
"[0.12s] Read cum_nneighbor_per_level (7)\n",
" Reading vector (dtype=<class 'numpy.int32'>, fmt='i')... "
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Count=5, Bytes=20\n",
"[0.21s] Read levels (5)\n",
"[0.30s] Probing for compact storage flag...\n",
"[0.30s] Found compact flag: False\n",
"[0.30s] Compact flag is False, reading original format...\n",
"[0.30s] Probing for potential extra byte before non-compact offsets...\n",
"[0.30s] Found and consumed an unexpected 0x00 byte.\n",
" Reading vector (dtype=<class 'numpy.uint64'>, fmt='Q')... Count=6, Bytes=48\n",
"[0.30s] Read offsets (6)\n",
"[0.39s] Attempting to read neighbors vector...\n",
" Reading vector (dtype=<class 'numpy.int32'>, fmt='i')... Count=320, Bytes=1280\n",
"[0.39s] Read neighbors (320)\n",
"[0.47s] Read scalar params (ep=4, max_lvl=0)\n",
"[0.47s] Checking for storage data...\n",
"[0.47s] Found storage fourcc: 49467849.\n",
"[0.47s] Converting to CSR format...\n",
"[0.47s] Conversion loop finished. \n",
"[0.47s] Running validation checks...\n",
" Checking total valid neighbor count...\n",
" OK: Total valid neighbors = 20\n",
" Checking final pointer indices...\n",
" OK: Final pointers match data size.\n",
"[0.47s] Deleting original neighbors and offsets arrays...\n",
" CSR Stats: |data|=20, |level_ptr|=10\n",
"[0.56s] Writing CSR HNSW graph data in FAISS-compatible order...\n",
" Pruning embeddings: Writing NULL storage marker.\n",
"[0.64s] Conversion complete."
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:sentence_transformers.SentenceTransformer:Use pytorch device_name: mps\n",
"INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: facebook/contriever-msmarco\n",
"WARNING:sentence_transformers.SentenceTransformer:No sentence-transformers model found with name facebook/contriever-msmarco. Creating a new one with mean pooling.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"✅ CSR conversion successful.\n",
"INFO: Replaced original index with CSR-pruned version at 'knowledge.index'\n",
"[read_HNSW - CSR NL v4] Reading metadata & CSR indices (manual offset)...\n",
"[read_HNSW NL v4] Read levels vector, size: 5\n",
"[read_HNSW NL v4] Reading Compact Storage format indices...\n",
"[read_HNSW NL v4] Read compact_level_ptr, size: 10\n",
"INFO: Terminating session server process (PID: 21439)...\n",
"[read_HNSW NL v4] Read compact_node_offsets, size: 6\n",
"[read_HNSW NL v4] Read entry_point: 4, max_level: 0\n",
"[read_HNSW NL v4] Read storage fourcc: 0x6c6c756e\n",
"[read_HNSW NL v4 FIX] Detected FileIOReader. Neighbors size field offset: 326\n",
"[read_HNSW NL v4] Reading neighbors data into memory.\n",
"[read_HNSW NL v4] Read neighbors data, size: 20\n",
"[read_HNSW NL v4] Finished reading metadata and CSR indices.\n",
"INFO: Skipping external storage loading, since is_recompute is true.\n",
"INFO: Server process terminated.\n",
"🔍 DEBUG LeannSearcher.search() called:\n",
" Query: 'C++ programming languages'\n",
" Top_k: 2\n",
" Search kwargs: {'recompute_beighbor_embeddings': True}\n",
"INFO: Computing embeddings for 1 chunks using SentenceTransformer model 'facebook/contriever-msmarco'...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Batches: 100%|██████████| 1/1 [00:00<00:00, 85.08it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" Generated embedding shape: (1, 768)\n",
"🔍 DEBUG Query embedding first 10 values: [ 0.04288 -0.04135 0.0666 0.02197 -0.0881 -0.04367 -0.02835 -0.0408\n",
" -0.1254 -0.08594]\n",
"🔍 DEBUG Query embedding norm: 1.3876953125\n",
"INFO: Starting session-level embedding server for 'leann_backend_hnsw.hnsw_embedding_server'...\n",
"INFO: Running command from project root: /Users/yichuan/Desktop/code/LEANN/leann\n",
"INFO: Command: /Users/yichuan/Desktop/code/LEANN/leann/.venv/bin/python -m leann_backend_hnsw.hnsw_embedding_server --zmq-port 5557 --model-name facebook/contriever-msmarco --passages-file knowledge.leann.meta.json --disable-warmup\n",
"INFO: Server process started with PID: 21622\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
"To disable this warning, you can either:\n",
"\t- Avoid using `tokenizers` before the fork if possible\n",
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Embedding server is up and ready for this session.\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Starting backend auto-discovery...\n",
"ZmqDistanceComputer initialized: d=768, metric=0\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Registering backend 'diskann'\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Backend auto-discovery finished.\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Registering backend 'hnsw'\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Loading tokenizer for facebook/contriever-msmarco...\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Tokenizer loaded successfully!\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: MPS available: True\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: CUDA available: False\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Using MPS device (Apple Silicon)\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Starting HNSW server on port 5557 with model facebook/contriever-msmarco\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Loading model facebook/contriever-msmarco... (this may take a while if downloading)\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Model facebook/contriever-msmarco loaded successfully!\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Loaded label map with 5 entries\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Initialized lazy passage loading for 5 passages\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Warmup disabled or no passages available (enable_warmup=False, passages=5)\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: HNSW ZMQ server listening on port 5557\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Started HNSW ZMQ server thread on port 5557\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Received ZMQ request of size 3 bytes\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: DEBUG: request_payload length: 1\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: DEBUG: request_payload[0]: <class 'list'> - [4]\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Request for 1 node embeddings\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Total batch size: 1, max_batch_size: 128\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: DEBUG zmq_server_thread: Final 'hidden' array | Shape: (1, 768) | Dtype: float32 | Has NaN/Inf: False\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Serialize time: 0.000150 seconds\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: ZMQ E2E time: 0.142946 seconds\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Received ZMQ request of size 3849 bytes\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: DEBUG: request_payload length: 2\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: DEBUG: request_payload[0]: <class 'list'> - [0, 1, 2, 3]\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: DEBUG: request_payload[1]: <class 'list'> - [0.042877197265625, -0.041351318359375, 0.06658935546875, 0.02197265625, -0.08807373046875, -0.04367...\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: DEBUG: Distance calculation request received\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Node IDs: [0, 1, 2, 3]\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Query vector dim: 768\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Passages loaded: 5\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: DEBUG: Looking up passage ID 0\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: DEBUG: Found text for ID 0, length: 37\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: DEBUG: Looking up passage ID 1\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: DEBUG: Found text for ID 1, length: 41\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: DEBUG: Looking up passage ID 2\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: DEBUG: Found text for ID 2, length: 38\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: DEBUG: Looking up passage ID 3\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: DEBUG: Found text for ID 3, length: 36\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Sending distance response with 4 distances\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Distance calculation E2E time: 0.173929 seconds\n",
" Backend returned: labels=2 results\n",
" Processing 2 passage IDs:\n",
" 1. passage_id='28b7b6b9-d0a4-408d-9e7f-9a7fcb7d8186' -> SUCCESS: C# is a powerful programming language...\n",
" 2. passage_id='1bccf691-a571-4e9d-aaed-424a30ba8604' -> SUCCESS: Python is a powerful programming language...\n",
" Final enriched results: 2 passages\n",
"[SearchResult(id='28b7b6b9-d0a4-408d-9e7f-9a7fcb7d8186', score=np.float32(1.5213046), text='C# is a powerful programming language', metadata={}), SearchResult(id='1bccf691-a571-4e9d-aaed-424a30ba8604', score=np.float32(1.2999034), text='Python is a powerful programming language', metadata={})]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[leann_backend_hnsw.hnsw_embedding_server LOG]: ZMQ socket timeout, continuing to listen\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: ZMQ socket timeout, continuing to listen\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: ZMQ socket timeout, continuing to listen\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: ZMQ socket timeout, continuing to listen\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: ZMQ socket timeout, continuing to listen\n"
]
}
],
"source": [
"from leann.api import LeannBuilder, LeannSearcher\n",
"# 1. Build index (no embeddings stored!)\n",
"builder = LeannBuilder(backend_name=\"hnsw\")\n",
"builder.add_text(\"C# is a powerful programming language\")\n",
"builder.add_text(\"Python is a powerful programming language\")\n",
"builder.add_text(\"Machine learning transforms industries\") \n",
"builder.add_text(\"Neural networks process complex data\")\n",
"builder.add_text(\"Leann is a great storage saving engine for RAG on your macbook\")\n",
"builder.build_index(\"knowledge.leann\")\n",
"# 2. Search with real-time embeddings\n",
"searcher = LeannSearcher(\"knowledge.leann\")\n",
"results = searcher.search(\"C++ programming languages\", top_k=2, recompute_beighbor_embeddings=True)\n",
"print(results)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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