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yichuan520030910320
2025-07-21 22:30:03 -07:00
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commit 8a75829f3a
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@@ -4,7 +4,277 @@
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO: Computing embeddings for 1 texts using SentenceTransformer, model: 'facebook/contriever-msmarco'\n",
"INFO: Using cached model: facebook/contriever-msmarco\n",
"INFO: Starting embedding computation...\n",
"INFO: Generated 1 embeddings, dimension: 768\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Writing passages: 100%|██████████| 5/5 [00:00<00:00, 11715.93chunk/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO: Computing embeddings for 5 texts using SentenceTransformer, model: 'facebook/contriever-msmarco'\n",
"INFO: Using cached model: facebook/contriever-msmarco\n",
"INFO: Starting embedding computation...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Batches: 100%|██████████| 1/1 [00:00<00:00, 76.09it/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": [
"INFO: Generated 5 embeddings, dimension: 768\n",
"M: 64 for level: 0\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",
"[0.18s] Read cum_nneighbor_per_level (7)\n",
" Reading vector (dtype=<class 'numpy.int32'>, fmt='i')... Count=5, Bytes=20\n",
"[0.29s] Read levels (5)\n",
"[0.40s] Probing for compact storage flag...\n",
"[0.40s] Found compact flag: False\n",
"[0.40s] Compact flag is False, reading original format...\n",
"[0.40s] Probing for potential extra byte before non-compact offsets...\n",
"[0.40s] Found and consumed an unexpected 0x00 byte.\n",
" Reading vector (dtype=<class 'numpy.uint64'>, fmt='Q')... Count=6, Bytes=48\n",
"[0.40s] Read offsets (6)\n",
"[0.50s] Attempting to read neighbors vector...\n",
" Reading vector (dtype=<class 'numpy.int32'>, fmt='i')... Count=320, Bytes=1280\n",
"[0.50s] Read neighbors (320)\n",
"[0.60s] Read scalar params (ep=4, max_lvl=0)\n",
"[0.60s] Checking for storage data...\n",
"[0.60s] Found storage fourcc: 49467849.\n",
"[0.60s] Converting to CSR format...\n",
"[0.60s] Conversion loop finished. \n",
"[0.60s] 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",
"[0.60s] Deleting original neighbors and offsets arrays...\n",
" CSR Stats: |data|=20, |level_ptr|=10\n",
"[0.69s] Writing CSR HNSW graph data in FAISS-compatible order...\n",
" Pruning embeddings: Writing NULL storage marker.\n",
"[0.79s] 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 'knowledge.index'\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",
"INFO: Terminating server process (PID: 25224) for backend leann_backend_hnsw.hnsw_embedding_server...\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: Server process 25224 terminated.\n",
"🔍 DEBUG LeannSearcher.search() called:\n",
" Query: 'programming languages'\n",
" Top_k: 2\n",
" Additional kwargs: {'recompute_beighbor_embeddings': True}\n",
"INFO: Starting embedding server on port 5557...\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\n",
"INFO: Server process started with PID: 25336\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"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 ready!\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Registering backend 'diskann'\n",
"DEBUG: Found process on port 5557: /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\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Registering backend 'hnsw'\n",
"DEBUG: model_matches: True, passages_matches: True, overall: True\n",
"✅ Existing server process (PID 25336) is compatible\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]: Using embedding mode: sentence-transformers\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Successfully imported unified embedding computation module\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Loaded PassageManager with 5 passages from metadata\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 23 bytes\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO:__main__:Processing direct text embedding request for 1 texts in sentence-transformers mode\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Computing embeddings for 1 texts using SentenceTransformer, model: 'facebook/contriever-msmarco'\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Loading and caching SentenceTransformer model: facebook/contriever-msmarco\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO:datasets:PyTorch version 2.7.1 available.\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Using device: mps\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: facebook/contriever-msmarco\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: WARNING:sentence_transformers.SentenceTransformer:No sentence-transformers model found with name facebook/contriever-msmarco. Creating a new one with mean pooling.\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: ✅ Model loaded successfully! (local + optimized)\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: ✅ Using FP16 precision and compile optimization: facebook/contriever-msmarco\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: ✅ Model cached: sentence_transformers_facebook/contriever-msmarco_mps_True\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Starting embedding computation...\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Generated 1 embeddings, dimension: 768\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO:__main__:⏱️ Text embedding E2E time: 2.707787s\n",
" Generated embedding shape: (1, 768)\n",
" Embedding time: 3.7433197498321533 seconds\n",
"ZmqDistanceComputer initialized: d=768, metric=0\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Received ZMQ request of size 3 bytes\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Request for 1 node embeddings\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Computing embeddings for 1 texts using SentenceTransformer, model: 'facebook/contriever-msmarco'\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Using cached model: facebook/contriever-msmarco\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Starting embedding computation...\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Generated 1 embeddings, dimension: 768\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Computed embeddings for 1 texts, shape: (1, 768)\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO:__main__:⏱️ ZMQ E2E time: 0.048147s\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Received ZMQ request of size 3849 bytes\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]: INFO: Computing embeddings for 4 texts using SentenceTransformer, model: 'facebook/contriever-msmarco'\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Using cached model: facebook/contriever-msmarco\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Starting embedding computation...\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Generated 4 embeddings, dimension: 768\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Computed embeddings for 4 texts, shape: (4, 768)\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: 0.081424s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:leann.chat:Attempting to create LLM of type='ollama' with model='llama3.2:1b'\n",
"INFO:leann.chat:Initializing OllamaChat with model='llama3.2:1b' and host='http://localhost:11434'\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" Search time: 0.13153481483459473 seconds\n",
" Backend returned: labels=2 results\n",
" Processing 2 passage IDs:\n",
" 1. passage_id='1' -> SUCCESS: Python is a powerful programming language and it is very popular...\n",
" 2. passage_id='0' -> SUCCESS: C# is a powerful programming language but it is not very popular...\n",
" Final enriched results: 2 passages\n",
"LEANN Search results: [SearchResult(id='1', score=np.float32(1.394647), text='Python is a powerful programming language and it is very popular', metadata={}), SearchResult(id='0', score=np.float32(1.3831015), text='C# is a powerful programming language but it is not very popular', metadata={})]\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",
"[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",
"🔍 DEBUG LeannSearcher.search() called:\n",
" Query: 'Compare the two retrieved programming languages and say which one is more popular today. Respond in a single well-formed sentence.'\n",
" Top_k: 2\n",
" Additional kwargs: {'recompute_beighbor_embeddings': True}\n",
"DEBUG: Found process on port 5557: /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\n",
"DEBUG: model_matches: True, passages_matches: True, overall: True\n",
"✅ Found compatible server on port 5557\n",
"✅ Using existing compatible server on port 5557\n",
"DEBUG: Found process on port 5557: /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\n",
"DEBUG: model_matches: True, passages_matches: True, overall: True\n",
"✅ Found compatible server on port 5557\n",
"✅ Using existing compatible server on port 5557\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Received ZMQ request of size 133 bytes\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO:__main__:Processing direct text embedding request for 1 texts in sentence-transformers mode\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Computing embeddings for 1 texts using SentenceTransformer, model: 'facebook/contriever-msmarco'\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Using cached model: facebook/contriever-msmarco\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Starting embedding computation...\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Generated 1 embeddings, dimension: 768\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO:__main__:⏱️ Text embedding E2E time: 0.049667s\n",
" Generated embedding shape: (1, 768)\n",
" Embedding time: 0.06644177436828613 seconds\n",
"ZmqDistanceComputer initialized: d=768, metric=0\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Received ZMQ request of size 3 bytes\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Request for 1 node embeddings\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Computing embeddings for 1 texts using SentenceTransformer, model: 'facebook/contriever-msmarco'\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Using cached model: facebook/contriever-msmarco\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Starting embedding computation...\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Generated 1 embeddings, dimension: 768\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Computed embeddings for 1 texts, shape: (1, 768)\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO:__main__:⏱️ ZMQ E2E time: 0.014071s\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Received ZMQ request of size 3849 bytes\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]: INFO: Computing embeddings for 4 texts using SentenceTransformer, model: 'facebook/contriever-msmarco'\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Using cached model: facebook/contriever-msmarco\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Starting embedding computation...\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Generated 4 embeddings, dimension: 768\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: INFO: Computed embeddings for 4 texts, shape: (4, 768)\n",
" Search time: 0.041433095932006836 seconds\n",
" Backend returned: labels=2 results\n",
" Processing 2 passage IDs:\n",
"[leann_backend_hnsw.hnsw_embedding_server LOG]: Sending distance response with 4 distances\n",
" 1. passage_id='1' -> SUCCESS: Python is a powerful programming language and it is very popular...[leann_backend_hnsw.hnsw_embedding_server LOG]: ⏱️ Distance calculation E2E time: 0.025329s\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:leann.chat:Sending request to Ollama: {'model': 'llama3.2:1b', 'prompt': 'Here is some retrieved context that might help answer your question:\\n\\nPython is a powerful programming language and it is very popular\\n\\nC# is a powerful programming language but it is not very popular\\n\\nQuestion: Compare the two retrieved programming languages and say which one is more popular today. Respond in a single well-formed sentence.\\n\\nPlease provide the best answer you can based on this context and your knowledge.', 'stream': False, 'options': {}}\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 2. passage_id='0' -> SUCCESS: C# is a powerful programming language but it is not very popular...\n",
" Final enriched results: 2 passages\n",
"LEANN Chat response: Based on general trends and metrics, Python is currently more popular than C#, particularly among developers and in specific industries such as data science, artificial intelligence, and web development.\n"
]
}
],
"source": [
"from leann.api import LeannBuilder, LeannSearcher, LeannChat\n",
"# 1. Build index (no embeddings stored!)\n",
@@ -17,19 +287,16 @@
"builder.build_index(\"knowledge.leann\")\n",
"# 2. Search with real-time embeddings\n",
"searcher = LeannSearcher(\"knowledge.leann\")\n",
"results = searcher.search(\"programming languages\", top_k=2, recompute_beighbor_embeddings=True)\n",
"print(results)\n",
"\n",
"llm_config = {\"type\": \"ollama\", \"model\": \"qwen3:8b\"}\n",
"\n",
"chat = LeannChat(index_path=\"knowledge.leann\", llm_config=llm_config)\n",
"\n",
"results = searcher.search(\"programming languages\", top_k=2, recompute_beighbor_embeddings=True,complexity=2)\n",
"print(\"LEANN Search results: \", results)\n",
"# 3. Chat with LEANN\n",
"chat = LeannChat(index_path=\"knowledge.leann\", llm_config={\"type\": \"ollama\", \"model\": \"llama3.2:1b\"})\n",
"response = chat.ask(\n",
" \"Compare the two retrieved programming languages and say which one is more popular today. Respond in a single well-formed sentence.\",\n",
" top_k=2,\n",
" recompute_beighbor_embeddings=True,\n",
")\n",
"print(response)"
"print(\"LEANN Chat response: \", response)"
]
}
],