scratch_fastembed.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jelle/Tools/pythagora-core/workspace/fabric-to-espanso/.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"
     ]
    }
   ],
   "source": [
    "from typing import List\n",
    "import numpy as np\n",
    "from fastembed import TextEmbedding\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'client' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mclient\u001b[49m\u001b[38;5;241m.\u001b[39mclose()\n",
      "\u001b[0;31mNameError\u001b[0m: name 'client' is not defined"
     ]
    }
   ],
   "source": [
    "client.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "documents: List[str] = [\n",
    "    \"FastEmbed is lighter than Transformers & Sentence-Transformers.\",\n",
    "    \"FastEmbed is supported by and maintained by Qdrant.\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fetching 5 files: 100%|██████████| 5/5 [00:00<00:00, 53092.46it/s]\n"
     ]
    }
   ],
   "source": [
    "embedding_model = TextEmbedding()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'BAAI/bge-small-en-v1.5'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding_model.model_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'fast-bge-small-en'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.get_vector_field_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "384"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings_generator = embedding_model.embed(documents)\n",
    "embeddings_list = list(embeddings_generator)\n",
    "len(embeddings_list[0])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qdrant_client import QdrantClient\n",
    "\n",
    "client = QdrantClient(\":memory:\")  # Qdrant is running from RAM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs = [\"Qdrant has a LangChain integration for chatbots.\", \"Qdrant has a LlamaIndex integration for agents.\"]\n",
    "metadata = [\n",
    "    {\"source\": \"langchain-docs\"},\n",
    "    {\"source\": \"llamaindex-docs\"},\n",
    "]\n",
    "ids = [42, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[42, 2]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.add(\n",
    "    collection_name=\"test_collection2\",\n",
    "    documents=docs,\n",
    "    metadata=metadata,\n",
    "    ids=ids\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "collection_info = client.get_collection(\"test_collection2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "henkt = client.get_fastembed_sparse_vector_params(\"test_collection2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.create_collection(\n",
    "    collection_name=\"testenblijven\",\n",
    "    vectors_config=client.get_fastembed_vector_params()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CollectionsResponse(collections=[CollectionDescription(name='test_collection2'), CollectionDescription(name='testenblijven')])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.get_collections()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "zomaar = client.get_collection(\"testenblijven\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CollectionConfig(params=CollectionParams(vectors={'fast-bge-small-en': VectorParams(size=384, distance=<Distance.COSINE: 'Cosine'>, hnsw_config=None, quantization_config=None, on_disk=None, datatype=None, multivector_config=None)}, shard_number=None, sharding_method=None, replication_factor=None, write_consistency_factor=None, read_fan_out_factor=None, on_disk_payload=None, sparse_vectors=None), hnsw_config=HnswConfig(m=16, ef_construct=100, full_scan_threshold=10000, max_indexing_threads=0, on_disk=None, payload_m=None), optimizer_config=OptimizersConfig(deleted_threshold=0.2, vacuum_min_vector_number=1000, default_segment_number=0, max_segment_size=None, memmap_threshold=None, indexing_threshold=20000, flush_interval_sec=5, max_optimization_threads=1), wal_config=WalConfig(wal_capacity_mb=32, wal_segments_ahead=0), quantization_config=None)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zomaar.config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[42, 2]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.add(\n",
    "    collection_name=\"testenblijven\",\n",
    "    documents=docs,\n",
    "    metadata=metadata,\n",
    "    ids=ids\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "henkt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[QueryResponse(id=2, embedding=None, sparse_embedding=None, metadata={'document': 'Qdrant has a LlamaIndex integration for agents.', 'source': 'llamaindex-docs'}, document='Qdrant has a LlamaIndex integration for agents.', score=0.8749180307571953), QueryResponse(id=42, embedding=None, sparse_embedding=None, metadata={'document': 'Qdrant has a LangChain integration for chatbots.', 'source': 'langchain-docs'}, document='Qdrant has a LangChain integration for chatbots.', score=0.8351846087729529)]\n"
     ]
    }
   ],
   "source": [
    "search_result = client.query(\n",
    "    collection_name=\"testenblijven\",\n",
    "    query_text=\"Which integration is best for agents?\"\n",
    ")\n",
    "print(search_result)"
   ]
  }
 ],
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