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update readme and 30 seconds example

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2025-07-15 23:18:01 -07:00
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README.md
View File

@@ -68,27 +68,24 @@ uv sync
```python
from leann.api import LeannBuilder, LeannSearcher
# 1. Build index (no embeddings stored!)
builder = LeannBuilder(backend_name="hnsw")
builder.add_text("C# is a powerful programming language")
builder.add_text("Python is a powerful programming language")
builder.add_text("Machine learning transforms industries")
builder.add_text("Neural networks process complex data")
builder.add_text("Leann is a great storage saving engine for RAG on your macbook")
builder.build_index("knowledge.leann")
# 2. Search with real-time embeddings
searcher = LeannSearcher("knowledge.leann")
results = searcher.search("programming languages", top_k=2)
for result in results:
print(f"Score: {result['score']:.3f} - {result['text']}")
results = searcher.search("C++ programming languages", top_k=2, recompute_beighbor_embeddings=True)
print(results)
```
### Run the Demo
### Run the Demo (support .pdf,.txt,.docx, .pptx, .csv, .md etc)
```bash
uv run examples/document_search.py
uv run ./examples/main_cli_example.py
```
or you want to use python
@@ -99,7 +96,7 @@ python ./examples/main_cli_example.py
```
**PDF RAG Demo (using LlamaIndex for document parsing and Leann for indexing/search)**
This demo showcases how to build a RAG system for PDF documents using Leann.
This demo showcases how to build a RAG system for PDF/md documents using Leann.
1. Place your PDF files (and other supported formats like .docx, .pptx, .xlsx) into the `examples/data/` directory.
2. Ensure you have an `OPENAI_API_KEY` set in your environment variables or in a `.env` file for the LLM to function.

487
demo.ipynb
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@@ -2,205 +2,64 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"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": [
"Initializing leann-backend-diskann...\n",
"INFO: Registering backend 'diskann'\n",
"INFO: DiskANN backend loaded successfully\n",
"INFO: LeannBuilder initialized with 'diskann' backend.\n"
"INFO: Computing embeddings for 1 chunks using SentenceTransformer model 'facebook/contriever-msmarco'...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ubuntu/LEANN_clean/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",
" from .autonotebook import tqdm as notebook_tqdm\n"
"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 6 chunks using 'sentence-transformers/all-mpnet-base-v2'...\n"
"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, 2.91it/s]\n"
"Batches: 100%|██████████| 1/1 [00:00<00:00, 50.38it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO: Building DiskANN index for 6 vectors with metric Metric.INNER_PRODUCT...\n",
"Using Inner Product search, so need to pre-process base data into temp file. Please ensure there is additional (n*(d+1)*4) bytes for storing pre-processed base vectors, apart from the interim indices created by DiskANN and the final index.\n",
"Pre-processing base file by adding extra coordinate\n",
"✅ DiskANN index built successfully at 'knowledge'\n",
"Writing bin: knowledge_disk.index_max_base_norm.bin\n",
"bin: #pts = 1, #dims = 1, size = 12B\n",
"Finished writing bin.\n",
"Time for preprocessing data for inner product: 0.000172 seconds\n",
"Reading max_norm_of_base from knowledge_disk.index_max_base_norm.bin\n",
"Reading bin file knowledge_disk.index_max_base_norm.bin ...\n",
"Opening bin file knowledge_disk.index_max_base_norm.bin... \n",
"Metadata: #pts = 1, #dims = 1...\n",
"done.\n",
"max_norm_of_base: 1\n",
"! Using prepped_base file at knowledge_prepped_base.bin\n",
"Starting index build: R=32 L=64 Query RAM budget: 4.02653e+09 Indexing ram budget: 8 T: 8\n",
"getting bin metadata\n",
"Time for getting bin metadata: 0.000019 seconds\n",
"Compressing 769-dimensional data into 512 bytes per vector.\n",
"Opened: knowledge_prepped_base.bin, size: 18464, cache_size: 18464\n",
"Training data with 6 samples loaded.\n",
"Reading bin file knowledge_pq_pivots.bin ...\n",
"Opening bin file knowledge_pq_pivots.bin... \n",
"Metadata: #pts = 256, #dims = 769...\n",
"done.\n",
"PQ pivot file exists. Not generating again\n",
"Opened: knowledge_prepped_base.bin, size: 18464, cache_size: 18464\n",
"Reading bin file knowledge_pq_pivots.bin ...\n",
"Opening bin file knowledge_pq_pivots.bin... \n",
"Metadata: #pts = 4, #dims = 1...\n",
"done.\n",
"Reading bin file knowledge_pq_pivots.bin ...\n",
"Opening bin file knowledge_pq_pivots.bin... \n",
"Metadata: #pts = 256, #dims = 769...\n",
"done.\n",
"Reading bin file knowledge_pq_pivots.bin ...\n",
"Opening bin file knowledge_pq_pivots.bin... \n",
"Metadata: #pts = 769, #dims = 1...\n",
"done.\n",
"Reading bin file knowledge_pq_pivots.bin ...\n",
"Opening bin file knowledge_pq_pivots.bin... \n",
"Metadata: #pts = 513, #dims = 1...\n",
"done.\n",
"Loaded PQ pivot information\n",
"Processing points [0, 6)...done.\n",
"Time for generating quantized data: 0.055587 seconds\n",
"Full index fits in RAM budget, should consume at most 2.03973e-05GiBs, so building in one shot\n",
"L2: Using AVX2 distance computation DistanceL2Float\n",
"Passed, empty search_params while creating index config\n",
"Using only first 6 from file.. \n",
"Starting index build with 6 points... \n",
"0% of index build completed.Starting final cleanup..done. Link time: 0.00011s\n",
"Index built with degree: max:5 avg:5 min:5 count(deg<2):0\n",
"Not saving tags as they are not enabled.\n",
"Time taken for save: 0.000148s.\n",
"Time for building merged vamana index: 0.000836 seconds\n",
"Opened: knowledge_prepped_base.bin, size: 18464, cache_size: 18464\n",
"Vamana index file size=168\n",
"Opened: knowledge_disk.index, cache_size: 67108864\n",
"medoid: 0B\n",
"max_node_len: 3100B\n",
"nnodes_per_sector: 1B\n",
"# sectors: 6\n",
"Sector #0written\n",
"Finished writing 28672B\n",
"Writing bin: knowledge_disk.index\n",
"bin: #pts = 9, #dims = 1, size = 80B\n",
"Finished writing bin.\n",
"Output disk index file written to knowledge_disk.index\n",
"Finished writing 28672B\n",
"Time for generating disk layout: 0.040268 seconds\n",
"Opened: knowledge_prepped_base.bin, size: 18464, cache_size: 18464\n",
"Loading base knowledge_prepped_base.bin. #points: 6. #dim: 769.\n",
"Wrote 1 points to sample file: knowledge_sample_data.bin\n",
"Indexing time: 0.0970594\n",
"INFO: Leann metadata saved to knowledge.leann.meta.json\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Opened file : knowledge_disk.index\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ DiskANN index loaded successfully.\n",
"INFO: LeannSearcher initialized with 'diskann' backend using index 'knowledge.leann'.\n",
"Since data is floating point, we assume that it has been appropriately pre-processed (normalization for cosine, and convert-to-l2 by adding extra dimension for MIPS). So we shall invoke an l2 distance function.\n",
"L2: Using AVX2 distance computation DistanceL2Float\n",
"L2: Using AVX2 distance computation DistanceL2Float\n",
"Before index load\n",
"Reading bin file knowledge_pq_compressed.bin ...\n",
"Opening bin file knowledge_pq_compressed.bin... \n",
"Metadata: #pts = 6, #dims = 512...\n",
"done.\n",
"Reading bin file knowledge_pq_pivots.bin ...\n",
"Opening bin file knowledge_pq_pivots.bin... \n",
"Metadata: #pts = 4, #dims = 1...\n",
"done.\n",
"Offsets: 4096 791560 794644 796704\n",
"Reading bin file knowledge_pq_pivots.bin ...\n",
"Opening bin file knowledge_pq_pivots.bin... \n",
"Metadata: #pts = 256, #dims = 769...\n",
"done.\n",
"Reading bin file knowledge_pq_pivots.bin ...\n",
"Opening bin file knowledge_pq_pivots.bin... \n",
"Metadata: #pts = 769, #dims = 1...\n",
"done.\n",
"Reading bin file knowledge_pq_pivots.bin ...\n",
"Opening bin file knowledge_pq_pivots.bin... \n",
"Metadata: #pts = 513, #dims = 1...\n",
"done.\n",
"Loaded PQ Pivots: #ctrs: 256, #dims: 769, #chunks: 512\n",
"Loaded PQ centroids and in-memory compressed vectors. #points: 6 #dim: 769 #aligned_dim: 776 #chunks: 512\n",
"Loading index metadata from knowledge_disk.index\n",
"Disk-Index File Meta-data: # nodes per sector: 1, max node len (bytes): 3100, max node degree: 5\n",
"Disk-Index Meta: nodes per sector: 1, max node len: 3100, max node degree: 5\n",
"Setting up thread-specific contexts for nthreads: 8\n",
"allocating ctx: 0x7a33f7204000 to thread-id:134367072315200\n",
"allocating ctx: 0x7a33f6805000 to thread-id:134355206802368\n",
"allocating ctx: 0x7a33f5e72000 to thread-id:134355217288000\n",
"allocating ctx: 0x7a33f5e61000 to thread-id:134355227773632\n",
"allocating ctx: 0x7a33f5e50000 to thread-id:134355196316736\n",
"allocating ctx: 0x7a33f5e3f000 to thread-id:134355164859840\n",
"allocating ctx: 0x7a33f5e2e000 to thread-id:134355175345472\n",
"allocating ctx: 0x7a33f5e1d000 to thread-id:134355185831104\n",
"Loading centroid data from medoids vector data of 1 medoid(s)\n",
"Reading bin file knowledge_disk.index_max_base_norm.bin ...\n",
"Opening bin file knowledge_disk.index_max_base_norm.bin... \n",
"Metadata: #pts = 1, #dims = 1...\n",
"done.\n",
"Setting re-scaling factor of base vectors to 1\n",
"load_from_separate_paths done.\n",
"Reading (with alignment) bin file knowledge_sample_data.bin ...Metadata: #pts = 1, #dims = 769, aligned_dim = 776... allocating aligned memory of 3104 bytes... done. Copying data to mem_aligned buffer... done.\n",
"reserve ratio: 1\n",
"Graph traversal completed, hops: 3\n",
"Loading the cache list into memory....done.\n",
"After index load\n",
"INFO: Computing embeddings for 1 chunks using 'sentence-transformers/all-mpnet-base-v2'...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Batches: 100%|██████████| 1/1 [00:00<00:00, 60.54it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO: DiskANN ZMQ mode enabled - ensuring embedding server is running\n",
"INFO: Starting session-level embedding server as a background process...\n",
"INFO: Running command from project root: /home/ubuntu/LEANN_clean/leann\n",
"INFO: Server process started with PID: 424761\n"
"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')... "
]
},
{
@@ -214,149 +73,187 @@
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Embedding server is up and ready for this session.\n",
"[EmbeddingServer LOG]: Initializing leann-backend-diskann...\n",
"[EmbeddingServer LOG]: WARNING: Could not import DiskANN backend: cannot import name '_diskannpy' from partially initialized module 'packages.leann-backend-diskann.leann_backend_diskann' (most likely due to a circular import) (/home/ubuntu/LEANN_clean/leann/packages/leann-backend-diskann/leann_backend_diskann/__init__.py)\n",
"[EmbeddingServer LOG]: INFO: Initializing embedding server thread on port 5555\n",
"[EmbeddingServer LOG]: INFO: Using CUDA device\n",
"[EmbeddingServer LOG]: INFO: Loading model sentence-transformers/all-mpnet-base-v2\n",
"[EmbeddingServer LOG]: INFO: Using FP16 precision with model: sentence-transformers/all-mpnet-base-v2\n",
"[EmbeddingServer LOG]: INFO: Loaded 6 demo documents\n",
"[EmbeddingServer LOG]: INFO: ZMQ ROUTER server listening on port 5555\n",
"[EmbeddingServer LOG]: INFO: Embedding server ready to serve requests\n",
"[EmbeddingServer LOG]: INFO: Received ZMQ request from client 006b8b45, size 3 bytes\n",
"[EmbeddingServer LOG]: INFO: Request for 1 node embeddings: [0]\n",
"[EmbeddingServer LOG]: DEBUG: Node ID range: 0 to 0\n",
"[EmbeddingServer LOG]: Time taken for text lookup: 0.000028 seconds\n",
"[EmbeddingServer LOG]: INFO: Total batch size: 1, max_batch_size: 128\n",
"[EmbeddingServer LOG]: INFO: Processing batch of size 1\n",
"[EmbeddingServer LOG]: Time taken for tokenization (batch): 0.019294 seconds\n",
"[EmbeddingServer LOG]: Batch size: 1, Sequence length: 256\n",
"[EmbeddingServer LOG]: Time taken for transfer to device (batch): 0.000210 seconds\n",
"[EmbeddingServer LOG]: Time taken for embedding (batch): 3.065444 seconds\n",
"[EmbeddingServer LOG]: Time taken for mean pooling (batch): 0.041810 seconds\n",
"[EmbeddingServer LOG]: INFO: Serialize time: 0.000194 seconds\n",
"[EmbeddingServer LOG]: INFO: ZMQ E2E time: 3.128073 seconds\n",
"[EmbeddingServer LOG]: INFO: Received ZMQ request from client 006b8b45, size 7 bytes\n",
"[EmbeddingServer LOG]: INFO: Request for 5 node embeddings: [1, 2, 3, 4, 5]\n",
"[EmbeddingServer LOG]: DEBUG: Node ID range: 1 to 5\n",
"[EmbeddingServer LOG]: Time taken for text lookup: 0.000042 seconds\n",
"[EmbeddingServer LOG]: INFO: Total batch size: 5, max_batch_size: 128\n",
"[EmbeddingServer LOG]: INFO: Processing batch of size 5\n",
"[EmbeddingServer LOG]: Time taken for tokenization (batch): 0.001791 seconds\n",
"[EmbeddingServer LOG]: Batch size: 5, Sequence length: 256\n",
"[EmbeddingServer LOG]: Time taken for transfer to device (batch): 0.000112 seconds\n",
"[EmbeddingServer LOG]: Time taken for embedding (batch): 3.674183 seconds\n",
"[EmbeddingServer LOG]: Time taken for mean pooling (batch): 0.000372 seconds\n",
"[EmbeddingServer LOG]: INFO: Serialize time: 0.000177 seconds\n",
"[EmbeddingServer LOG]: INFO: ZMQ E2E time: 3.677425 seconds\n",
"[EmbeddingServer LOG]: INFO: Received ZMQ request from client 006b8b45, size 7 bytes\n",
"[EmbeddingServer LOG]: INFO: Request for 5 node embeddings: [3, 4, 2, 1, 0]\n",
"[EmbeddingServer LOG]: DEBUG: Node ID range: 0 to 4\n",
"[EmbeddingServer LOG]: Time taken for text lookup: 0.000030 seconds\n",
"[EmbeddingServer LOG]: INFO: Total batch size: 5, max_batch_size: 128\n",
"[EmbeddingServer LOG]: INFO: Processing batch of size 5\n",
"[EmbeddingServer LOG]: Time taken for tokenization (batch): 0.001550 seconds\n",
"[EmbeddingServer LOG]: Batch size: 5, Sequence length: 256\n",
"[EmbeddingServer LOG]: Time taken for transfer to device (batch): 0.000097 seconds\n",
"[EmbeddingServer LOG]: Time taken for embedding (batch): 0.009335 seconds\n",
"[EmbeddingServer LOG]: Time taken for mean pooling (batch): 0.000154 seconds\n",
"[EmbeddingServer LOG]: INFO: Serialize time: 0.000073 seconds\n",
"[EmbeddingServer LOG]: INFO: ZMQ E2E time: 0.011773 seconds\n",
"[EmbeddingServer LOG]: INFO: Received ZMQ request from client 006b8b45, size 7 bytes\n",
"[EmbeddingServer LOG]: INFO: Request for 5 node embeddings: [0, 1, 2, 4, 5]\n",
"[EmbeddingServer LOG]: DEBUG: Node ID range: 0 to 5\n",
"[EmbeddingServer LOG]: Time taken for text lookup: 0.000020 seconds\n",
"[EmbeddingServer LOG]: INFO: Total batch size: 5, max_batch_size: 128\n",
"[EmbeddingServer LOG]: INFO: Processing batch of size 5\n",
"[EmbeddingServer LOG]: Time taken for tokenization (batch): 0.001041 seconds\n",
"[EmbeddingServer LOG]: Batch size: 5, Sequence length: 256\n",
"[EmbeddingServer LOG]: Time taken for transfer to device (batch): 0.000125 seconds\n",
"[EmbeddingServer LOG]: Time taken for embedding (batch): 0.008972 seconds\n",
"[EmbeddingServer LOG]: Time taken for mean pooling (batch): 0.000151 seconds\n",
"[EmbeddingServer LOG]: INFO: Serialize time: 0.000048 seconds\n",
"[EmbeddingServer LOG]: INFO: ZMQ E2E time: 0.010853 seconds\n",
"[EmbeddingServer LOG]: INFO: Received ZMQ request from client 006b8b45, size 7 bytes\n",
"[EmbeddingServer LOG]: INFO: Request for 5 node embeddings: [3, 1, 0, 2, 5]\n",
"[EmbeddingServer LOG]: DEBUG: Node ID range: 0 to 5\n",
"[EmbeddingServer LOG]: Time taken for text lookup: 0.000020 seconds\n",
"[EmbeddingServer LOG]: INFO: Total batch size: 5, max_batch_size: 128\n",
"[EmbeddingServer LOG]: INFO: Processing batch of size 5\n",
"[EmbeddingServer LOG]: Time taken for tokenization (batch): 0.001350 seconds\n",
"[EmbeddingServer LOG]: Batch size: 5, Sequence length: 256\n",
"[EmbeddingServer LOG]: Time taken for transfer to device (batch): 0.000088 seconds\n",
"[EmbeddingServer LOG]: Time taken for embedding (batch): 0.008869 seconds\n",
"[EmbeddingServer LOG]: Time taken for mean pooling (batch): 0.000146 seconds\n",
"[EmbeddingServer LOG]: INFO: Serialize time: 0.000063 seconds\n",
"[EmbeddingServer LOG]: INFO: ZMQ E2E time: 0.011054 seconds\n",
"[EmbeddingServer LOG]: INFO: Received ZMQ request from client 006b8b45, size 7 bytes\n",
"[EmbeddingServer LOG]: INFO: Request for 5 node embeddings: [0, 2, 3, 4, 5]\n",
"[EmbeddingServer LOG]: DEBUG: Node ID range: 0 to 5\n",
"[EmbeddingServer LOG]: Time taken for text lookup: 0.000022 seconds\n",
"[EmbeddingServer LOG]: INFO: Total batch size: 5, max_batch_size: 128\n",
"[EmbeddingServer LOG]: INFO: Processing batch of size 5\n",
"[EmbeddingServer LOG]: Time taken for tokenization (batch): 0.001195 seconds\n",
"[EmbeddingServer LOG]: Batch size: 5, Sequence length: 256\n",
"[EmbeddingServer LOG]: Time taken for transfer to device (batch): 0.000087 seconds\n",
"[EmbeddingServer LOG]: Time taken for embedding (batch): 0.008903 seconds\n",
"[EmbeddingServer LOG]: Time taken for mean pooling (batch): 0.000145 seconds\n",
"[EmbeddingServer LOG]: INFO: Serialize time: 0.000060 seconds\n",
"[EmbeddingServer LOG]: INFO: ZMQ E2E time: 0.010921 seconds\n",
"[EmbeddingServer LOG]: INFO: Received ZMQ request from client 006b8b45, size 7 bytes\n",
"[EmbeddingServer LOG]: INFO: Request for 5 node embeddings: [1, 0, 3, 4, 5]\n",
"[EmbeddingServer LOG]: DEBUG: Node ID range: 0 to 5\n",
"[EmbeddingServer LOG]: Time taken for text lookup: 0.000020 seconds\n",
"[EmbeddingServer LOG]: INFO: Total batch size: 5, max_batch_size: 128\n",
"[EmbeddingServer LOG]: INFO: Processing batch of size 5\n",
"[EmbeddingServer LOG]: Time taken for tokenization (batch): 0.001188 seconds\n",
"[EmbeddingServer LOG]: Batch size: 5, Sequence length: 256\n",
"[EmbeddingServer LOG]: Time taken for transfer to device (batch): 0.000087 seconds\n",
"[EmbeddingServer LOG]: Time taken for embedding (batch): 0.008858 seconds\n",
"[EmbeddingServer LOG]: Time taken for mean pooling (batch): 0.000153 seconds\n",
"[EmbeddingServer LOG]: INFO: Serialize time: 0.000052 seconds\n",
"[EmbeddingServer LOG]: INFO: ZMQ E2E time: 0.010886 seconds\n",
"reserve ratio: Score: -0.481 - C++ is a powerful programming language1\n",
"Graph traversal completed, hops: 3\n",
"\n",
"Score: -1.049 - Java is a powerful programming language\n"
"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": [
"[EmbeddingServer LOG]: INFO: ZMQ socket timeout, continuing to listen\n",
"[EmbeddingServer LOG]: INFO: ZMQ socket timeout, continuing to listen\n",
"[EmbeddingServer LOG]: INFO: ZMQ socket timeout, continuing to listen\n",
"[EmbeddingServer LOG]: INFO: ZMQ socket timeout, continuing to listen\n",
"[EmbeddingServer LOG]: INFO: ZMQ socket timeout, continuing to listen\n",
"[EmbeddingServer LOG]: INFO: ZMQ socket timeout, continuing to listen\n",
"[EmbeddingServer LOG]: INFO: ZMQ socket timeout, continuing to listen\n",
"[EmbeddingServer LOG]: INFO: ZMQ socket timeout, continuing to listen\n",
"[EmbeddingServer LOG]: INFO: ZMQ socket timeout, continuing to listen\n",
"[EmbeddingServer LOG]: INFO: ZMQ socket timeout, continuing to listen\n"
"\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",
"import leann_backend_diskann\n",
"# 1. Build index (no embeddings stored!)\n",
"builder = LeannBuilder(backend_name=\"diskann\")\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(\"Java is a powerful programming language\")\n",
"builder.add_text(\"C++ is a powerful programming language\")\n",
"builder.add_text(\"C# is a powerful programming language\")\n",
"builder.add_text(\"Leann is a great storage saving engine for RAG on your macbook\")\n",
"builder.build_index(\"knowledge.leann\")\n",
"\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",
"\n",
"for result in results:\n",
" print(f\"Score: {result['score']:.3f} - {result['text']}\")"
"results = searcher.search(\"C++ programming languages\", top_k=2, recompute_beighbor_embeddings=True)\n",
"print(results)"
]
}
],
@@ -376,7 +273,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
"version": "3.11.12"
}
},
"nbformat": 4,