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fix emaple and add pypi example

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yichuan520030910320
2025-07-27 18:20:13 -07:00
parent 48207c3b69
commit 455f93fb7c
4 changed files with 26 additions and 173 deletions
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35
README.md
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@@ -114,32 +114,23 @@ Our declarative API makes RAG as easy as writing a config file.
[Try in this ipynb file →](demo.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yichuan-w/LEANN/blob/main/demo.ipynb)
```python
from leann.api import LeannBuilder, LeannSearcher, LeannChat
from leann import LeannBuilder, LeannSearcher, LeannChat
from pathlib import Path
INDEX_PATH = str(Path("./").resolve() / "demo.leann")
# 1. Build the index (no embeddings stored!)
# Build an index
builder = LeannBuilder(backend_name="hnsw")
builder.add_text("C# is a powerful programming language")
builder.add_text("Python is a powerful programming language and it is very popular")
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")
builder.add_text("LEANN saves 97% storage compared to traditional vector databases.")
builder.add_text("Tung Tung Tung Sahur called—they need their banana‑crocodile hybrid back")
builder.build_index(INDEX_PATH)
# 2. Search with real-time embeddings
searcher = LeannSearcher("knowledge.leann")
results = searcher.search("programming languages", top_k=2)
# Search
searcher = LeannSearcher(INDEX_PATH)
results = searcher.search("fantastical AI-generated creatures", top_k=1)
# 3. Chat with LEANN using retrieved results
llm_config = {
"type": "ollama",
"model": "llama3.2:1b"
}
chat = LeannChat(index_path="knowledge.leann", llm_config=llm_config)
response = chat.ask(
"Compare the two retrieved programming languages and say which one is more popular today.",
top_k=2,
)
# Chat with your data
chat = LeannChat(INDEX_PATH, llm_config={"type": "hf", "model": "Qwen/Qwen3-0.6B"})
response = chat.ask("How much storage does LEANN save?", top_k=1)
```
## RAG on Everything!

141
demo.ipynb
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@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Quick Start in 30s\n",
"# Quick Start \n",
"\n",
"**Home GitHub Repository:** [LEANN on GitHub](https://github.com/yichuan-w/LEANN)\n",
"\n",
@@ -49,68 +49,7 @@
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Writing passages: 100%|██████████| 5/5 [00:00<00:00, 17077.79chunk/s]\n",
"Batches: 100%|██████████| 1/1 [00:00<00:00, 36.43it/s]\n",
"WARNING:leann_backend_hnsw.hnsw_backend:Converting data to float32, shape: (5, 768)\n",
"INFO:leann_backend_hnsw.hnsw_backend:INFO: Converting HNSW index to CSR-pruned format...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"M: 64 for level: 0\n",
"Starting conversion: index.index -> index.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",
"[0.14s] Read cum_nneighbor_per_level (7)\n",
" Reading vector (dtype=<class 'numpy.int32'>, fmt='i')... Count=5, Bytes=20\n",
"[0.24s] Read levels (5)\n",
"[0.33s] Probing for compact storage flag...\n",
"[0.33s] Found compact flag: False\n",
"[0.33s] Compact flag is False, reading original format...\n",
"[0.33s] Probing for potential extra byte before non-compact offsets...\n",
"[0.33s] Found and consumed an unexpected 0x00 byte.\n",
" Reading vector (dtype=<class 'numpy.uint64'>, fmt='Q')... Count=6, Bytes=48\n",
"[0.33s] Read offsets (6)\n",
"[0.41s] Attempting to read neighbors vector...\n",
" Reading vector (dtype=<class 'numpy.int32'>, fmt='i')... Count=320, Bytes=1280\n",
"[0.41s] Read neighbors (320)\n",
"[0.54s] Read scalar params (ep=4, max_lvl=0)\n",
"[0.54s] Checking for storage data...\n",
"[0.54s] Found storage fourcc: 49467849.\n",
"[0.54s] Converting to CSR format...\n",
"[0.54s] Conversion loop finished. \n",
"[0.54s] 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",
"[0.54s] Deleting original neighbors and offsets arrays...\n",
" CSR Stats: |data|=20, |level_ptr|=10\n",
"[0.63s] Writing CSR HNSW graph data in FAISS-compatible order...\n",
" Pruning embeddings: Writing NULL storage marker.\n",
"[0.71s] Conversion complete.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:leann_backend_hnsw.hnsw_backend:✅ CSR conversion successful.\n",
"INFO:leann_backend_hnsw.hnsw_backend:INFO: Replaced original index with CSR-pruned version at 'index.index'\n"
]
}
],
"outputs": [],
"source": [
"from leann.api import LeannBuilder\n",
"\n",
@@ -136,81 +75,7 @@
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:leann.api:🔍 LeannSearcher.search() called:\n",
"INFO:leann.api: Query: 'programming languages'\n",
"INFO:leann.api: Top_k: 2\n",
"INFO:leann.api: Additional kwargs: {}\n",
"INFO:leann.embedding_server_manager:Port 5557 has incompatible server, trying next port...\n",
"INFO:leann.embedding_server_manager:Port 5558 has incompatible server, trying next port...\n",
"INFO:leann.embedding_server_manager:Port 5559 has incompatible server, trying next port...\n",
"INFO:leann.embedding_server_manager:Port 5560 has incompatible server, trying next port...\n",
"INFO:leann.embedding_server_manager:Port 5561 has incompatible server, trying next port...\n",
"INFO:leann.embedding_server_manager:Port 5562 has incompatible server, trying next port...\n",
"INFO:leann.embedding_server_manager:Starting embedding server on port 5563...\n",
"INFO:leann.embedding_server_manager:Command: /Users/yichuan/Desktop/code/test_leann_pip/LEANN/.venv/bin/python -m leann_backend_hnsw.hnsw_embedding_server --zmq-port 5563 --model-name facebook/contriever --passages-file /Users/yichuan/Desktop/code/test_leann_pip/LEANN/content/index.meta.json\n",
"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",
"INFO:leann.embedding_server_manager:Server process started with PID: 31699\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[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",
"[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: Registering backend 'hnsw'\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Traceback (most recent call last):\n",
" File \"<frozen runpy>\", line 198, in _run_module_as_main\n",
" File \"<frozen runpy>\", line 88, in _run_code\n",
" File \"/Users/yichuan/Desktop/code/test_leann_pip/LEANN/.venv/lib/python3.11/site-packages/leann_backend_hnsw/hnsw_embedding_server.py\", line 323, in <module>\n",
" create_hnsw_embedding_server(\n",
" File \"/Users/yichuan/Desktop/code/test_leann_pip/LEANN/.venv/lib/python3.11/site-packages/leann_backend_hnsw/hnsw_embedding_server.py\", line 98, in create_hnsw_embedding_server\n",
" passages = PassageManager(passage_sources)\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"/Users/yichuan/Desktop/code/test_leann_pip/LEANN/.venv/lib/python3.11/site-packages/leann/api.py\", line 127, in __init__\n",
" raise FileNotFoundError(f\"Passage index file not found: {index_file}\")\n",
"FileNotFoundError: Passage index file not found: /Users/yichuan/Desktop/code/test_leann_pip/LEANN/index.passages.idx\n",
"ERROR:leann.embedding_server_manager:Server terminated during startup.\n"
]
},
{
"ename": "RuntimeError",
"evalue": "Failed to start embedding server on port 5563",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mRuntimeError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mleann\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mapi\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m LeannSearcher\n\u001b[32m 3\u001b[39m searcher = LeannSearcher(\u001b[33m\"\u001b[39m\u001b[33mindex\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m results = \u001b[43msearcher\u001b[49m\u001b[43m.\u001b[49m\u001b[43msearch\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mprogramming languages\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtop_k\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 5\u001b[39m results\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/code/test_leann_pip/LEANN/.venv/lib/python3.11/site-packages/leann/api.py:439\u001b[39m, in \u001b[36mLeannSearcher.search\u001b[39m\u001b[34m(self, query, top_k, complexity, beam_width, prune_ratio, recompute_embeddings, pruning_strategy, expected_zmq_port, **kwargs)\u001b[39m\n\u001b[32m 437\u001b[39m start_time = time.time()\n\u001b[32m 438\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m recompute_embeddings:\n\u001b[32m--> \u001b[39m\u001b[32m439\u001b[39m zmq_port = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mbackend_impl\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_ensure_server_running\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 440\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmeta_path_str\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 441\u001b[39m \u001b[43m \u001b[49m\u001b[43mport\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpected_zmq_port\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 442\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 443\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 444\u001b[39m \u001b[38;5;28;01mdel\u001b[39;00m expected_zmq_port\n\u001b[32m 445\u001b[39m zmq_time = time.time() - start_time\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/code/test_leann_pip/LEANN/.venv/lib/python3.11/site-packages/leann/searcher_base.py:81\u001b[39m, in \u001b[36mBaseSearcher._ensure_server_running\u001b[39m\u001b[34m(self, passages_source_file, port, **kwargs)\u001b[39m\n\u001b[32m 72\u001b[39m server_started, actual_port = \u001b[38;5;28mself\u001b[39m.embedding_server_manager.start_server(\n\u001b[32m 73\u001b[39m port=port,\n\u001b[32m 74\u001b[39m model_name=\u001b[38;5;28mself\u001b[39m.embedding_model,\n\u001b[32m (...)\u001b[39m\u001b[32m 78\u001b[39m enable_warmup=kwargs.get(\u001b[33m\"\u001b[39m\u001b[33menable_warmup\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m),\n\u001b[32m 79\u001b[39m )\n\u001b[32m 80\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m server_started:\n\u001b[32m---> \u001b[39m\u001b[32m81\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[32m 82\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mFailed to start embedding server on port \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mactual_port\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 83\u001b[39m )\n\u001b[32m 85\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m actual_port\n",
"\u001b[31mRuntimeError\u001b[39m: Failed to start embedding server on port 5563"
]
}
],
"outputs": [],
"source": [
"from leann.api import LeannSearcher\n",
"\n",

17
packages/leann/README.md
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@@ -16,25 +16,24 @@ uv pip install leann[diskann]
```python
from leann import LeannBuilder, LeannSearcher, LeannChat
from pathlib import Path
INDEX_PATH = str(Path("./").resolve() / "demo.leann")
# Build an index
builder = LeannBuilder(backend_name="hnsw")
builder.add_text("LEANN saves 97% storage compared to traditional vector databases.")
builder.build_index("my_index.leann")
builder.add_text("Tung Tung Tung Sahur called—they need their banana‑crocodile hybrid back")
builder.build_index(INDEX_PATH)
# Search
searcher = LeannSearcher("my_index.leann")
results = searcher.search("storage savings", top_k=3)
searcher = LeannSearcher(INDEX_PATH)
results = searcher.search("fantastical AI-generated creatures", top_k=1)
# Chat with your data
chat = LeannChat("my_index.leann", llm_config={"type": "ollama", "model": "llama3.2:1b"})
response = chat.ask("How much storage does LEANN save?")
chat = LeannChat(INDEX_PATH, llm_config={"type": "hf", "model": "Qwen/Qwen3-0.6B"})
response = chat.ask("How much storage does LEANN save?", top_k=1)
```
## Documentation
For full documentation, visit [https://leann.readthedocs.io](https://leann.readthedocs.io)
## License
MIT License

6
packages/leann/pyproject.toml
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@@ -36,7 +36,5 @@ diskann = [
]
[project.urls]
Homepage = "https://github.com/yourusername/leann"
Documentation = "https://leann.readthedocs.io"
Repository = "https://github.com/yourusername/leann"
Issues = "https://github.com/yourusername/leann/issues"
Repository = "https://github.com/yichuan-w/LEANN"
Issues = "https://github.com/yichuan-w/LEANN/issues"