{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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, 14655.15chunk/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, 41.25it/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",
      "[0.30s]   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",
      "[0.70s] Writing CSR HNSW graph data in FAISS-compatible order...\n",
      "   Pruning embeddings: Writing NULL storage marker.\n",
      "[0.80s] Conversion complete."
     ]
    },
    {
     "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": [
      "\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",
      "INFO: Terminating server process (PID: 26499) for backend leann_backend_hnsw.hnsw_embedding_server...\n",
      "INFO: Server process 26499 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: 27144\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",
      "[leann_backend_hnsw.hnsw_embedding_server LOG]: Starting HNSW server on port 5557 with model facebook/contriever-msmarco\n",
      "DEBUG: model_matches: True, passages_matches: True, overall: True\n",
      "✅ Existing server process (PID 27144) is compatible\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: 3.224629s\n",
      "  Generated embedding shape: (1, 768)\n",
      "  Embedding time: 4.262664318084717 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: 0.047393s\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: 0.1265699863433838 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",
      "[leann_backend_hnsw.hnsw_embedding_server LOG]: ⏱️  Distance calculation E2E time: 0.077312s\n",
      "    1. passage_id='0' -> SUCCESS: C# is a powerful programming language...\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": [
      "    2. passage_id='1' -> SUCCESS: Python is a powerful programming language and it is very popular...\n",
      "  Final enriched results: 2 passages\n",
      "LEANN Search results:  [SearchResult(id='0', score=np.float32(1.444752), text='C# is a powerful programming language', metadata={}), SearchResult(id='1', score=np.float32(1.394647), text='Python is a powerful programming language and it is 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: 0.050751s\n",
      "  Generated embedding shape: (1, 768)\n",
      "  Embedding time: 0.06844496726989746 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: 0.010474s\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",
      "  Search time: 0.02637481689453125 seconds\n",
      "  Backend returned: labels=2 results\n",
      "  Processing 2 passage IDs:\n",
      "[leann_backend_hnsw.hnsw_embedding_server LOG]: ⏱️  Distance calculation E2E time: 0.013796s\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\\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": [
      "    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...\n",
      "  Final enriched results: 2 passages\n",
      "LEANN Chat response:  Python has gained immense popularity significantly more so than C#, becoming one of the most widely used programming languages globally today.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[leann_backend_hnsw.hnsw_embedding_server LOG]: /Users/yichuan/.local/share/uv/python/cpython-3.11.12-macos-aarch64-none/lib/python3.11/multiprocessing/resource_tracker.py:254: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown\n",
      "[leann_backend_hnsw.hnsw_embedding_server LOG]: warnings.warn('resource_tracker: There appear to be %d '\n"
     ]
    }
   ],
   "source": [
    "from leann.api import LeannBuilder, LeannSearcher, LeannChat\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 and it is very popular\")\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(\"programming languages\", top_k=2, recompute_beighbor_embeddings=True)\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(\"LEANN Chat response: \", response)"
   ]
  }
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