From 5c80667885f378343964d5102902bc1764383a85 Mon Sep 17 00:00:00 2001 From: mike0sv Date: Fri, 10 Oct 2025 17:25:17 +0100 Subject: [PATCH 1/4] Support for Iris DB --- examples/integrations/iris_rag.ipynb | 407 +++++++++++++++++++++++++++ setup.py | 3 + src/evidently/core/registries/rag.py | 2 + src/evidently/llm/datagen/rag.py | 10 +- src/evidently/llm/rag/index.py | 30 +- src/evidently/llm/rag/iris_index.py | 194 +++++++++++++ 6 files changed, 635 insertions(+), 11 deletions(-) create mode 100644 examples/integrations/iris_rag.ipynb create mode 100644 src/evidently/llm/rag/iris_index.py diff --git a/examples/integrations/iris_rag.ipynb b/examples/integrations/iris_rag.ipynb new file mode 100644 index 0000000000..78213c3a74 --- /dev/null +++ b/examples/integrations/iris_rag.ipynb @@ -0,0 +1,407 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Iris Vector Database RAG Integration with Evidently\n", + "\n", + "This notebook demonstrates how to use InterSystems IRIS as a vector database for RAG (Retrieval-Augmented Generation) with Evidently's LLM evaluation capabilities.\n", + "\n", + "## Overview\n", + "\n", + "We'll cover:\n", + "1. Setting up IRIS vector database with sample data\n", + "2. Using Evidently's RAG module with IRIS backend\n", + "3. Performing similarity searches and evaluations\n", + "\n", + "## Prerequisites\n", + "\n", + "- IRIS instance running locally (Docker recommended)\n", + "- Python packages: `intersystems-irispython`, `evidently[llm,iris]`, `sentence-transformers`" + ], + "id": "1b79cd212da85e56" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Install Required Packages" + ], + "id": "f21d942d9a0f5620" + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "# Install required packages\n", + "# ! pip install intersystems-irispython evidently[llm,iris] sentence-transformers" + ], + "id": "61711a794d05929b", + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Setup IRIS Vector Database\n", + "\n", + "First, we'll connect to IRIS and create a vector table with sample data." + ], + "id": "d87bb14630ebbee" + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "import iris\n", + "from sentence_transformers import SentenceTransformer\n", + "\n", + "# IRIS connection parameters\n", + "username = 'SuperUser'\n", + "password = ''\n", + "hostname = 'localhost'\n", + "port = 1972\n", + "namespace = 'USER'\n", + "\n", + "# Create connection string\n", + "connection_string = f\"{hostname}:{port}/{namespace}\"\n", + "\n", + "# Connect to IRIS\n", + "connection = iris.connect(connection_string, username, password)\n", + "cursor = connection.cursor()\n", + "\n", + "print(f\"Connected to IRIS at {connection_string}\")" + ], + "id": "f062a7c70550fd0f", + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "# Create vector table for our RAG data\n", + "table_name = \"evidently_rag_demo\"\n", + "table_definition = \"\"\"(\n", + " ID INT PRIMARY KEY,\n", + " text VARCHAR(2000),\n", + " vector_data VECTOR(FLOAT, 384),\n", + " source VARCHAR(200)\n", + ")\"\"\"\n", + "\n", + "# Drop table if exists and recreate\n", + "try:\n", + " cursor.execute(f\"DROP TABLE {table_name}\")\n", + "except:\n", + " pass\n", + "\n", + "cursor.execute(f\"CREATE TABLE {table_name} {table_definition}\")\n", + "print(f\"Created table: {table_name}\")" + ], + "id": "28e6e1a1036fb2f6", + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "# Sample documents for our RAG system\n", + "sample_documents = [\n", + " {\n", + " \"text\": \"Machine learning is a subset of artificial intelligence that focuses on algorithms that can learn from data without being explicitly programmed.\",\n", + " \"source\": \"ml_basics.txt\"\n", + " },\n", + " {\n", + " \"text\": \"Deep learning uses neural networks with multiple layers to model and understand complex patterns in data. It's particularly effective for image recognition and natural language processing.\",\n", + " \"source\": \"deep_learning.txt\"\n", + " },\n", + " {\n", + " \"text\": \"Natural language processing (NLP) enables computers to understand, interpret, and generate human language. It's used in chatbots, translation, and sentiment analysis.\",\n", + " \"source\": \"nlp_intro.txt\"\n", + " },\n", + " {\n", + " \"text\": \"Vector databases store and search high-dimensional vectors efficiently. They're essential for semantic search, recommendation systems, and RAG applications.\",\n", + " \"source\": \"vector_db.txt\"\n", + " },\n", + " {\n", + " \"text\": \"Retrieval-Augmented Generation (RAG) combines information retrieval with language generation to provide more accurate and contextual responses.\",\n", + " \"source\": \"rag_concept.txt\"\n", + " }\n", + "]\n", + "\n", + "# Initialize sentence transformer model\n", + "model = SentenceTransformer('all-MiniLM-L6-v2')\n", + "\n", + "# Insert documents with embeddings\n", + "for i, doc in enumerate(sample_documents):\n", + " # Generate embedding\n", + " embedding = model.encode(doc['text']).tolist()\n", + " \n", + " # Insert into IRIS table\n", + " cursor.execute(\n", + " f\"INSERT INTO {table_name} (ID, text, vector_data, source) VALUES (?, ?, ?, ?)\",\n", + " [i + 1, doc['text'], str(embedding), doc['source']]\n", + " )\n", + "\n", + "connection.commit()\n", + "print(f\"Inserted {len(sample_documents)} documents into {table_name}\")" + ], + "id": "bf81bb6006f3eeae", + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Test Vector Search in IRIS\n", + "\n", + "Let's test the vector similarity search directly in IRIS before using Evidently." + ], + "id": "9a051b55031a268b" + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "# Test vector similarity search\n", + "query_text = \"What is machine learning and how does it work?\"\n", + "query_vector = model.encode(query_text).tolist()\n", + "\n", + "# Perform similarity search\n", + "search_sql = f\"\"\"\n", + "SELECT TOP 3 ID, text, source\n", + "FROM {table_name}\n", + "ORDER BY VECTOR_DOT_PRODUCT(vector_data, ?) DESC\n", + "\"\"\"\n", + "\n", + "cursor.execute(search_sql, [str(query_vector)])\n", + "results = cursor.fetchall()\n", + "\n", + "print(f\"Query: {query_text}\")\n", + "print(\"\\nMost relevant documents:\")\n", + "for i, result in enumerate(results, 1):\n", + " print(f\"{i}. ID: {result[0]}, Source: {result[2]}\")\n", + " print(f\" Text: {result[1]}\")\n", + " print()" + ], + "id": "2732b4e40eaf20f1", + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Using Evidently RAG with IRIS\n", + "\n", + "Now let's use Evidently's RAG module with our IRIS vector database using the new flat parameter API." + ], + "id": "b28d9638130406e8" + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "from evidently.llm.rag.iris_index import IrisDataCollectionProvider\n", + "from evidently.llm.rag.splitter import Chunk\n", + "\n", + "# Create IRIS data collection provider with flat parameters\n", + "iris_provider = IrisDataCollectionProvider(\n", + " # Connection parameters (all required except port)\n", + " hostname=hostname,\n", + " port=port, # defaults to 1972 if not specified\n", + " namespace=namespace,\n", + " username=username,\n", + " password=password,\n", + " # Table configuration\n", + " table_name=table_name,\n", + " text_column=\"text\",\n", + " vector_column=\"vector_data\",\n", + " id_column=\"ID\"\n", + ")\n", + "\n", + "print(\"Created IRIS data collection provider with flat parameters\")" + ], + "id": "8ae92cf727a858d5", + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "# Get the data collection and test it\n", + "data_collection = iris_provider.get_data_collection()\n", + "\n", + "# Test queries\n", + "test_queries = [\n", + " \"What is machine learning?\",\n", + " \"How do neural networks work?\",\n", + " \"What is RAG and why is it useful?\",\n", + " \"Tell me about vector databases\"\n", + "]\n", + "\n", + "for query in test_queries:\n", + " print(f\"\\nQuery: {query}\")\n", + " print(\"-\" * 50)\n", + " \n", + " # Find relevant chunks\n", + " relevant_chunks = data_collection.find_relevant_chunks(query, n_results=2)\n", + " \n", + " for i, chunk in enumerate(relevant_chunks, 1):\n", + " print(f\"{i}. {chunk}\")\n", + " print()" + ], + "id": "7b7d0812d80ad2d2", + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: RAG Dataset Generation\n", + "\n", + "Now let's use Evidently's RAG dataset generation capabilities with our IRIS backend." + ], + "id": "524cbbafaa7e56f3" + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "from evidently.llm.datagen.rag import RagDatasetGenerator\n", + "from evidently.llm.datagen.config import GenerationSpec\n", + "\n", + "# Create RAG dataset generator using our IRIS provider\n", + "rag_generator = RagDatasetGenerator(\n", + " data_collection=iris_provider,\n", + " count=5, # Generate 5 question-answer pairs\n", + " model=\"gpt-4o-mini\",\n", + " provider=\"openai\",\n", + " query_spec=GenerationSpec(kind=\"question\", complexity=\"medium\"),\n", + " response_spec=GenerationSpec(kind=\"answer\", complexity=\"medium\")\n", + ")\n", + "\n", + "print(\"Created RAG dataset generator with IRIS backend\")" + ], + "id": "d4d5d17846180742", + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "# Generate dataset (requires OpenAI API key)\n", + "import os\n", + "\n", + "# Note: You'll need to set your OpenAI API key\n", + "# os.environ['OPENAI_API_KEY'] = 'your-api-key-here'\n", + "\n", + "dataset_result = rag_generator.generate()\n", + "\n", + "print(f\"Generated {len(dataset_result)} question-answer pairs\")\n", + "for i, gen in dataset_result.iterrows():\n", + " print(f\"\\n{i+1}. Question: {gen[0]}\")\n", + " print(f\" Answer: {gen[1]}\")\n", + "\n", + "print(\"RAG dataset generation ready (requires OpenAI API key)\")" + ], + "id": "e8b68970aa6059e9", + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Cleanup and Best Practices\n", + "\n", + "Let's clean up our resources and discuss best practices." + ], + "id": "d05d4028899b8d14" + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "# Cleanup: Close connections\n", + "if 'data_collection' in locals():\n", + " data_collection.close()\n", + "\n", + "if 'connection' in locals():\n", + " connection.close()\n", + "\n", + "print(\"Closed all database connections\")" + ], + "id": "19d8d736290cec23", + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary and Best Practices\n", + "\n", + "### What We've Accomplished:\n", + "\n", + "1. **IRIS Vector Database Setup**: Created a vector table and populated it with sample documents\n", + "2. **Evidently Integration**: Used `IrisDataCollectionProvider` with flat parameters to integrate IRIS with Evidently's RAG system\n", + "3. **Similarity Search**: Demonstrated vector similarity search using `VECTOR_DOT_PRODUCT`\n", + "4. **RAG Dataset Generation**: Set up RAG dataset generation with IRIS backend\n", + "\n", + "### Key Features of the New API:\n", + "\n", + "1. **Flat Parameters**: The provider accepts connection parameters directly instead of a connection object\n", + "2. **Required Parameters**: Most connection parameters are required (except port which defaults to 1972)\n", + "3. **Type Safety**: Port is now an integer type\n", + "4. **Connection Management**: `IrisConnection` handles cursor and connection management\n", + "5. **Simplified Usage**: No need to create connection objects manually\n", + "\n", + "### Best Practices:\n", + "\n", + "1. **Connection Management**: Always close database connections when done\n", + "2. **Error Handling**: Implement proper error handling for database operations\n", + "3. **Indexing**: Consider creating indexes on frequently queried columns\n", + "4. **Batch Operations**: Use batch inserts for large datasets\n", + "5. **Monitoring**: Monitor query performance and optimize as needed\n", + "\n", + "### Next Steps:\n", + "\n", + "1. Set up your OpenAI API key to test dataset generation\n", + "2. Experiment with different embedding models\n", + "3. Try different similarity search functions (cosine, etc.)\n", + "4. Implement batch document processing\n", + "5. Add monitoring and logging for production use" + ], + "id": "b3f284078d26f0da" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/setup.py b/setup.py index 50f4506e23..4b9ba61c64 100644 --- a/setup.py +++ b/setup.py @@ -90,6 +90,9 @@ "llama-index>=0.10", "faiss-cpu>=1.8.0", ], + "iris": [ + "iris>=1.0.7", + ], "spark": ["pyspark>=3.4.0,<4"], "fsspec": [ "s3fs>=2024.9.0", diff --git a/src/evidently/core/registries/rag.py b/src/evidently/core/registries/rag.py index 39f4394705..8211555ea3 100644 --- a/src/evidently/core/registries/rag.py +++ b/src/evidently/core/registries/rag.py @@ -8,3 +8,5 @@ register_type_alias(DataCollectionProvider, "evidently.llm.rag.index.FileDataCollectionProvider", "evidently:data_collection_provider:FileDataCollectionProvider") register_type_alias(Splitter, "evidently.llm.rag.splitter.LlamaIndexSplitter", "evidently:splitter:LlamaIndexSplitter") register_type_alias(Splitter, "evidently.llm.rag.splitter.SimpleSplitter", "evidently:splitter:SimpleSplitter") + +register_type_alias(DataCollectionProvider, "evidently.llm.rag.iris_index.IrisDataCollectionProvider", "evidently:data_collection_provider:IrisDataCollectionProvider") diff --git a/src/evidently/llm/datagen/rag.py b/src/evidently/llm/datagen/rag.py index 436c3fff85..57c374e5df 100644 --- a/src/evidently/llm/datagen/rag.py +++ b/src/evidently/llm/datagen/rag.py @@ -1,4 +1,3 @@ -import random from abc import ABC from math import ceil from typing import Any @@ -21,7 +20,6 @@ from evidently.llm.rag.index import DataCollection from evidently.llm.rag.index import DataCollectionProvider from evidently.llm.rag.splitter import Chunk -from evidently.llm.rag.splitter import ChunkSet from evidently.llm.utils.blocks import PromptBlock from evidently.llm.utils.prompt_render import PreparedTemplate from evidently.llm.utils.templates import StrPromptTemplate @@ -86,10 +84,6 @@ def generate(self, input_value: str, context: str): """ -def generate_chunksets(documents: DataCollection, count: int, chunks_per_set: int) -> List[ChunkSet]: - return [[random.choice(documents.chunks) for _ in range(chunks_per_set)] for _ in range(count)] - - class BaseRagDatasetGenerator(BaseLLMDatasetGenerator, ABC): data_collection: DataCollectionProvider @@ -152,9 +146,9 @@ def get_chunks_and_query_count(self, all_chunks_count: int) -> Tuple[int, int, i async def generate_queries_with_context(self) -> Tuple[DataCollection, List[RAGQuery]]: documents = self.data_collection.get_data_collection() chunk_set_count, chunks_in_set_count, questions_per_chunkset = self.get_chunks_and_query_count( - len(documents.chunks) + documents.get_count() ) - chunk_sets = generate_chunksets(documents, chunk_set_count, chunks_in_set_count) + chunk_sets = documents.generate_chunksets(chunk_set_count, chunks_in_set_count) queries: List[RAGQuery] = await self.generate_queries(chunk_sets, questions_per_chunkset) return documents, queries diff --git a/src/evidently/llm/rag/index.py b/src/evidently/llm/rag/index.py index 4a2ed7568b..90f3726597 100644 --- a/src/evidently/llm/rag/index.py +++ b/src/evidently/llm/rag/index.py @@ -14,6 +14,7 @@ from evidently._pydantic_compat import PrivateAttr from evidently.llm.rag.splitter import AnySplitter from evidently.llm.rag.splitter import Chunk +from evidently.llm.rag.splitter import ChunkSet from evidently.llm.rag.splitter import Splitter from evidently.llm.rag.utils import read_text from evidently.pydantic_utils import AutoAliasMixin @@ -69,7 +70,7 @@ class ChunksDataCollectionProvider(DataCollectionProvider): chunks: List[Chunk] def _get_data_collection(self): - dc = DataCollection(name="chunks", chunks=self.chunks) + dc = FaissDataCollection(name="chunks", chunks=self.chunks) dc.init_collection() return dc @@ -96,7 +97,7 @@ def _get_data_collection(self): splitter = Splitter.from_any(self.splitter, self.chunk_size, self.chunk_overlap) chunks = list(splitter.split([read_text(p) for p in paths])) - data_collection = DataCollection(name=file_path.name, chunks=chunks) + data_collection = FaissDataCollection(name=file_path.name, chunks=chunks) data_collection.init_collection() return data_collection @@ -119,7 +120,21 @@ def _get_embedding(text): return np.array(embedding).astype("float32") # Convert to numpy float32 for FAISS compatibility -class DataCollection: +class DataCollection(ABC): + @abstractmethod + def find_relevant_chunks(self, question: str, n_results: int = 3) -> List[Chunk]: + raise NotImplementedError + + @abstractmethod + def get_count(self) -> int: + raise NotImplementedError + + @abstractmethod + def generate_chunksets(self, count: int, chunks_per_set: int) -> List[ChunkSet]: + raise NotImplementedError + + +class FaissDataCollection(DataCollection): name: str chunks: List[Chunk] index: Optional["IndexFlatL2"] = None @@ -163,3 +178,12 @@ def find_relevant_chunks(self, question: str, n_results: int = 3) -> List[Chunk] _, indexes = self.index.search(np.array([query_emb]), n_results) relevant_chunks = [self.chunks[i] for i in indexes.reshape(-1)] return relevant_chunks + + def get_count(self) -> int: + return len(self.chunks) + + def generate_chunksets(self, count: int, chunks_per_set: int) -> List[ChunkSet]: + """Generate random chunksets for RAG dataset generation.""" + import random + + return [[random.choice(self.chunks) for _ in range(chunks_per_set)] for _ in range(count)] diff --git a/src/evidently/llm/rag/iris_index.py b/src/evidently/llm/rag/iris_index.py new file mode 100644 index 0000000000..c88d04360b --- /dev/null +++ b/src/evidently/llm/rag/iris_index.py @@ -0,0 +1,194 @@ +from typing import List + +from evidently.llm.rag.index import DataCollection +from evidently.llm.rag.index import DataCollectionProvider +from evidently.llm.rag.index import _get_embedding +from evidently.llm.rag.splitter import Chunk +from evidently.llm.rag.splitter import ChunkSet + + +class IrisConnection: + """Connection configuration and management for Iris database.""" + + def __init__( + self, + hostname: str, + namespace: str, + username: str, + password: str, + port: int = 1972, + ): + self.hostname = hostname + self.port = port + self.namespace = namespace + self.username = username + self.password = password + self._connection = None + + def get_connection_string(self) -> str: + """Generate connection string for Iris.""" + return f"{self.hostname}:{self.port}/{self.namespace}" + + def connect(self): + """Establish connection to Iris database.""" + try: + import iris + except ImportError as e: + raise ImportError("Run `pip install intersystems-irispython` to use Iris vector database") from e + + connection_string = self.get_connection_string() + connection = iris.connect(connection_string, self.username, self.password) + return connection + + def get_cursor(self): + """Get database cursor, creating connection if needed.""" + if self._connection is None: + self._connection = self.connect() + return self._connection.cursor() + + def close(self): + """Close database connection.""" + if self._connection is not None: + self._connection.close() + self._connection = None + + +class IrisDataCollectionProvider(DataCollectionProvider): + """Data collection provider for Iris vector database.""" + + # Flat connection parameters + hostname: str + port: int = 1972 + namespace: str + username: str + password: str + + # Table configuration + table_name: str + text_column: str = "text" + vector_column: str = "vector_data" + id_column: str = "id" + + def _get_data_collection(self) -> "DataCollection": + """Create and return IrisDataCollection instance.""" + # Create connection from flat parameters + connection = IrisConnection( + hostname=self.hostname, + port=self.port, + namespace=self.namespace, + username=self.username, + password=self.password, + ) + + return IrisDataCollection( + connection=connection, + table_name=self.table_name, + text_column=self.text_column, + vector_column=self.vector_column, + id_column=self.id_column, + ) + + +class IrisDataCollection(DataCollection): + """Data collection implementation for Iris vector database.""" + + def __init__( + self, + connection: IrisConnection, + table_name: str, + text_column: str = "text", + vector_column: str = "vector_data", + id_column: str = "id", + ): + self.connection = connection + self.table_name = table_name + self.text_column = text_column + self.vector_column = vector_column + self.id_column = id_column + + def generate_chunksets(self, count: int, chunks_per_set: int) -> List[ChunkSet]: + """Generate random chunksets for RAG dataset generation.""" + import random + + cursor = self.connection.get_cursor() + + # First, get all IDs from the table + ids_sql = f"SELECT {self.id_column} FROM {self.table_name}" + cursor.execute(ids_sql) + id_results = cursor.fetchall() + + # Extract IDs + all_ids = [result[0] for result in id_results] + + if not all_ids: + return [] + + # Sample list of lists of IDs + chunkset_ids = [[random.choice(all_ids) for _ in range(chunks_per_set)] for _ in range(count)] + + # Flatten into set to get unique IDs + unique_ids = {id_ for ids in chunkset_ids for id_ in ids} + + # Fetch all unique IDs in one query + placeholders = ",".join(["?" for _ in unique_ids]) + fetch_sql = f"SELECT {self.id_column}, {self.text_column} FROM {self.table_name} WHERE {self.id_column} IN ({placeholders})" + cursor.execute(fetch_sql, list(unique_ids)) + text_results = cursor.fetchall() + + # Create mapping id -> text + id_to_text = {result[0]: result[1] for result in text_results} + + # Use mapping on original list of lists of IDs + chunksets = [[id_to_text[chunk_id] for chunk_id in ids_list] for ids_list in chunkset_ids] + + return chunksets + + def get_count(self) -> int: + """Get the total number of chunks in the IRIS table.""" + cursor = self.connection.get_cursor() + + # Count total rows in the table + count_sql = f"SELECT COUNT(*) FROM {self.table_name}" + cursor.execute(count_sql) + result = cursor.fetchone() + + return result[0] if result else 0 + + def find_relevant_chunks(self, question: str, n_results: int = 3) -> List[Chunk]: + """ + Find relevant chunks using vector similarity search in Iris. + + Args: + question (str): The query text to search for. + n_results (int): Number of results to retrieve. + + Returns: + List[Chunk]: List of relevant text chunks. + """ + cursor = self.connection.get_cursor() + + # Generate embedding for the question + query_embedding = _get_embedding(question) + query_vector = query_embedding.tolist() + + # Perform vector similarity search using VECTOR_DOT_PRODUCT + search_sql = f""" + SELECT TOP {n_results} {self.id_column}, {self.text_column} + FROM {self.table_name} + ORDER BY VECTOR_DOT_PRODUCT({self.vector_column}, ?) DESC + """ + + cursor.execute(search_sql, [str(query_vector)]) + results = cursor.fetchall() + + # Convert results to Chunk objects (which are just strings) + chunks = [] + for result in results: + _, text = result + chunks.append(text) + + return chunks + + def close(self): + """Close database connection.""" + self.connection.close() From e725cd745f3dbba8811439ec172739a03cf2e3eb Mon Sep 17 00:00:00 2001 From: mike0sv Date: Wed, 29 Oct 2025 12:21:14 +0000 Subject: [PATCH 2/4] add benchmark --- .../integrations/iris_faiss_benchmark.ipynb | 1102 +++++++++++++++++ 1 file changed, 1102 insertions(+) create mode 100644 examples/integrations/iris_faiss_benchmark.ipynb diff --git a/examples/integrations/iris_faiss_benchmark.ipynb b/examples/integrations/iris_faiss_benchmark.ipynb new file mode 100644 index 0000000000..58e1770ba6 --- /dev/null +++ b/examples/integrations/iris_faiss_benchmark.ipynb @@ -0,0 +1,1102 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# FAISS vs IRIS Vector Database Benchmark\n", + "\n", + "This notebook compares the performance of FAISS and IRIS vector databases for RAG operations.\n", + "\n", + "## Benchmark Overview\n", + "\n", + "We'll test:\n", + "1. **Data Writing Performance**: Time to index vectors (excluding embedding calculation)\n", + "2. **Search Performance**: Time to retrieve similar vectors\n", + "3. **Memory Usage**: Memory consumption during operations\n", + "\n", + "## Test Parameters\n", + "\n", + "- Vector counts: 10, 1,000, 10,000\n", + "- Vector dimension: 384 (all-MiniLM-L6-v2)\n", + "- Search queries: 100 per test\n", + "- Results per query: 5\n", + "- **Note**: Embeddings are pre-calculated to focus on database operations\n", + "- **IRIS**: Uses batch inserts of 1000 records for optimal performance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites and Setup\n", + "\n", + "Before running this notebook, you need to:\n", + "\n", + "1. **Install Python dependencies** (commented out below)\n", + "2. **Start IRIS Docker container** (commented out below)\n", + "\n", + "Note: Evidently should be installed from the `feature/iris` branch as the IRIS support is not yet released.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Install Python dependencies\n", + "# !pip install intersystems-irispython sentence-transformers\n", + "# !pip install \"git+https://github.com/evidentlyai/evidently.git@feature/iris#egg=evidently[llm,iris]\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Start IRIS Docker container\n", + "# docker run -d --rm \\\n", + "# --name iris-community \\\n", + "# -p 1972:1972 \\\n", + "# -p 52773:52773 \\\n", + "# -p 53773:53773 \\\n", + "# -e ISC_DATA_DIRECTORY=/durable \\\n", + "# -e TZ=UTC \\\n", + "# -v $(pwd)/iris-durable:/durable \\\n", + "# containers.intersystems.com/intersystems/iris-community:2025.1\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup and Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2025-10-29T04:59:19.017256Z", + "start_time": "2025-10-29T04:59:14.535588Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Imports successful!\n" + ] + } + ], + "source": [ + "import time\n", + "import random\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from sentence_transformers import SentenceTransformer\n", + "import psutil\n", + "import os\n", + "import gc\n", + "\n", + "# Evidently imports\n", + "from evidently.llm.rag.index import FileDataCollectionProvider\n", + "from evidently.llm.rag.iris_index import IrisDataCollectionProvider\n", + "from evidently.llm.rag.splitter import Chunk\n", + "\n", + "print(\"Imports successful!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2025-10-29T04:59:20.591460Z", + "start_time": "2025-10-29T04:59:19.234266Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Embedding model loaded. Dimension: 384\n" + ] + } + ], + "source": [ + "# Benchmark parameters\n", + "VECTOR_COUNTS = [10, 1000, 10000, 100000]\n", + "SEARCH_QUERIES = 100\n", + "RESULTS_PER_QUERY = 5\n", + "NUM_RUNS = 3 # Number of times to run each test for averaging (use 1 for single run, 3+ for statistical significance)\n", + "\n", + "# IRIS connection parameters\n", + "IRIS_CONFIG = {\n", + " \"hostname\": \"localhost\",\n", + " \"port\": 1972,\n", + " \"namespace\": \"USER\",\n", + " \"username\": \"SuperUser\",\n", + " \"password\": \"123\"\n", + "}\n", + "\n", + "# Initialize embedding model\n", + "model = SentenceTransformer('all-MiniLM-L6-v2')\n", + "print(f\"Embedding model loaded. Dimension: {model.get_sentence_embedding_dimension()}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sample Data Generation with Pre-calculated Embeddings" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2025-10-29T05:00:12.254702Z", + "start_time": "2025-10-29T04:59:20.598942Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating 100000 sample texts...\n", + "Calculating embeddings for 100000 texts (this may take a moment)...\n", + "Sample data generation complete!\n", + "Generated 100000 sample texts with embeddings\n" + ] + } + ], + "source": [ + "def generate_sample_texts(count: int) -> list:\n", + " \"\"\"Generate sample text documents for benchmarking.\"\"\"\n", + " topics = [\n", + " \"machine learning\", \"artificial intelligence\", \"data science\", \"deep learning\",\n", + " \"natural language processing\", \"computer vision\", \"neural networks\", \"statistics\",\n", + " \"programming\", \"software engineering\", \"databases\", \"cloud computing\",\n", + " \"cybersecurity\", \"blockchain\", \"quantum computing\", \"robotics\"\n", + " ]\n", + " \n", + " texts = []\n", + " for i in range(count):\n", + " topic = random.choice(topics)\n", + " length = random.randint(50, 200)\n", + " \n", + " # Generate text with some variation\n", + " text = f\"This document discusses {topic} and its applications. \"\n", + " text += f\"It covers various aspects including algorithms, implementation, and real-world use cases. \"\n", + " text += f\"The content includes technical details, examples, and best practices for {topic}. \"\n", + " text += f\"Document ID: {i+1}, Topic: {topic}, Length: {length} characters.\"\n", + " \n", + " texts.append(text)\n", + " \n", + " return texts\n", + "\n", + "def generate_sample_data_with_embeddings(count: int) -> tuple:\n", + " \"\"\"Generate sample texts and pre-calculate embeddings.\"\"\"\n", + " print(f\"Generating {count} sample texts...\")\n", + " texts = generate_sample_texts(count)\n", + " \n", + " print(f\"Calculating embeddings for {count} texts (this may take a moment)...\")\n", + " embeddings = model.encode(texts)\n", + " \n", + " print(f\"Sample data generation complete!\")\n", + " return texts, embeddings\n", + "\n", + "# Generate sample texts and embeddings for testing\n", + "sample_texts, sample_embeddings = generate_sample_data_with_embeddings(max(VECTOR_COUNTS))\n", + "print(f\"Generated {len(sample_texts)} sample texts with embeddings\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Benchmark Functions\n", + "\n", + "**Note on Memory Measurement**: To avoid negative memory deltas caused by garbage collection during operations, we temporarily disable GC during measurements and force it before/after to ensure accurate memory tracking." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2025-10-29T05:00:12.283820Z", + "start_time": "2025-10-29T05:00:12.277936Z" + } + }, + "outputs": [], + "source": [ + "def get_memory_usage(force_gc=False):\n", + " \"\"\"Get current memory usage in MB. Optionally force garbage collection first.\"\"\"\n", + " if force_gc:\n", + " gc.collect()\n", + " time.sleep(0.1) # Give GC time to settle\n", + " process = psutil.Process(os.getpid())\n", + " return process.memory_info().rss / 1024 / 1024\n", + "\n", + "def benchmark_faiss_writing(texts: list, embeddings: np.ndarray) -> dict:\n", + " \"\"\"Benchmark FAISS data writing performance (excluding embedding calculation).\"\"\"\n", + " print(f\"Benchmarking FAISS writing for {len(texts)} texts...\")\n", + " \n", + " # Create chunks from texts\n", + " chunks = [Chunk(text) for text in texts]\n", + " \n", + " # Force GC before measuring and temporarily disable GC during operation\n", + " gc.collect()\n", + " time.sleep(0.1)\n", + " memory_before = get_memory_usage()\n", + " \n", + " # Disable GC during the operation to prevent interference\n", + " gc.disable()\n", + " \n", + " # Measure writing time (FAISS index creation)\n", + " start_time = time.time()\n", + " \n", + " # Create FAISS index directly\n", + " import faiss\n", + " index = faiss.IndexFlatL2(384)\n", + " index.add(embeddings.astype('float32'))\n", + " \n", + " # Create FaissDataCollection with pre-built index\n", + " from evidently.llm.rag.index import FaissDataCollection\n", + " data_collection = FaissDataCollection(\n", + " name=\"benchmark\", \n", + " chunks=chunks, \n", + " index=index\n", + " )\n", + " \n", + " end_time = time.time()\n", + " \n", + " # Measure memory before re-enabling GC\n", + " memory_after = get_memory_usage()\n", + " \n", + " # Re-enable GC\n", + " gc.enable()\n", + " gc.collect() # Clean up now that we're done measuring\n", + " \n", + " return {\n", + " \"time_seconds\": end_time - start_time,\n", + " \"memory_before_mb\": memory_before,\n", + " \"memory_after_mb\": memory_after,\n", + " \"memory_delta_mb\": max(0, memory_after - memory_before), # Clamp negative values to 0\n", + " \"data_collection\": data_collection\n", + " }\n", + "\n", + "def benchmark_iris_writing(texts: list, embeddings: np.ndarray, table_name: str) -> dict:\n", + " \"\"\"Benchmark IRIS data writing performance with batch inserts (excluding embedding calculation).\"\"\"\n", + " print(f\"Benchmarking IRIS writing for {len(texts)} texts with batch inserts...\")\n", + " \n", + " import iris\n", + " \n", + " # Connect to IRIS\n", + " connection_string = f\"{IRIS_CONFIG['hostname']}:{IRIS_CONFIG['port']}/{IRIS_CONFIG['namespace']}\"\n", + " connection = iris.connect(connection_string, IRIS_CONFIG['username'], IRIS_CONFIG['password'])\n", + " cursor = connection.cursor()\n", + " \n", + " # Create table\n", + " table_definition = f\"\"\"(\n", + " ID INT PRIMARY KEY,\n", + " text VARCHAR(2000),\n", + " vector_data VECTOR(FLOAT, 384)\n", + " )\"\"\"\n", + " \n", + " try:\n", + " cursor.execute(f\"DROP TABLE {table_name}\")\n", + " except:\n", + " pass\n", + " \n", + " cursor.execute(f\"CREATE TABLE {table_name} {table_definition}\")\n", + " \n", + " # Force GC before measuring and temporarily disable GC during operation\n", + " gc.collect()\n", + " time.sleep(0.1)\n", + " memory_before = get_memory_usage()\n", + " \n", + " # Disable GC during the operation to prevent interference\n", + " gc.disable()\n", + " \n", + " # Measure writing time (database operations only)\n", + " start_time = time.time()\n", + " \n", + " # Insert texts with pre-calculated embeddings in batches\n", + " # Note: IRIS vector format expects a list of floats as a string representation\n", + " # Format: \"[\" + \"0.123, 0.456, ...\" + \"]\" or proper IRIS vector syntax\n", + " batch_size = 1000\n", + " for i in range(0, len(texts), batch_size):\n", + " batch_texts = texts[i:i + batch_size]\n", + " batch_embeddings = embeddings[i:i + batch_size]\n", + " \n", + " # Prepare batch data - convert to IRIS vector format\n", + " batch_data = []\n", + " for j, (text, embedding) in enumerate(zip(batch_texts, batch_embeddings)):\n", + " # Convert numpy array to IRIS vector format (string representation of list)\n", + " vector_str = \"[\" + \", \".join(map(str, embedding.tolist())) + \"]\"\n", + " batch_data.append([i + j + 1, text, vector_str])\n", + " \n", + " # Execute batch insert\n", + " cursor.executemany(\n", + " f\"INSERT INTO {table_name} (ID, text, vector_data) VALUES (?, ?, ?)\",\n", + " batch_data\n", + " )\n", + " \n", + " connection.commit()\n", + " end_time = time.time()\n", + " \n", + " # Measure memory before re-enabling GC\n", + " memory_after = get_memory_usage()\n", + " \n", + " # Re-enable GC\n", + " gc.enable()\n", + " gc.collect() # Clean up now that we're done measuring\n", + " \n", + " # Create IRIS provider\n", + " iris_provider = IrisDataCollectionProvider(\n", + " hostname=IRIS_CONFIG['hostname'],\n", + " port=IRIS_CONFIG['port'],\n", + " namespace=IRIS_CONFIG['namespace'],\n", + " username=IRIS_CONFIG['username'],\n", + " password=IRIS_CONFIG['password'],\n", + " table_name=table_name,\n", + " text_column=\"text\",\n", + " vector_column=\"vector_data\",\n", + " id_column=\"ID\"\n", + " )\n", + " \n", + " data_collection = iris_provider.get_data_collection()\n", + " \n", + " connection.close()\n", + " \n", + " return {\n", + " \"time_seconds\": end_time - start_time,\n", + " \"memory_before_mb\": memory_before,\n", + " \"memory_after_mb\": memory_after,\n", + " \"memory_delta_mb\": max(0, memory_after - memory_before), # Clamp negative values to 0\n", + " \"data_collection\": data_collection\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2025-10-29T05:00:12.295244Z", + "start_time": "2025-10-29T05:00:12.291932Z" + } + }, + "outputs": [], + "source": [ + "def benchmark_search(data_collection, queries: list, results_per_query: int, warmup_queries: int = 5) -> dict:\n", + " \"\"\"Benchmark search performance.\"\"\"\n", + " print(f\"Benchmarking search for {len(queries)} queries (with {warmup_queries} warmup queries)...\")\n", + " \n", + " # Run warmup queries to avoid cold start effects\n", + " print(f\"Running {warmup_queries} warmup queries...\")\n", + " for query in queries[:warmup_queries]:\n", + " data_collection.find_relevant_chunks(query, n_results=results_per_query)\n", + " \n", + " # Force GC before measuring and temporarily disable GC during operation\n", + " gc.collect()\n", + " time.sleep(0.1)\n", + " memory_before = get_memory_usage()\n", + " \n", + " # Disable GC during the operation to prevent interference\n", + " gc.disable()\n", + " \n", + " # Measure search time (skip warmup queries)\n", + " start_time = time.time()\n", + " \n", + " search_times = []\n", + " for query in queries:\n", + " query_start = time.time()\n", + " results = data_collection.find_relevant_chunks(query, n_results=results_per_query)\n", + " query_end = time.time()\n", + " search_times.append(query_end - query_start)\n", + " \n", + " end_time = time.time()\n", + " \n", + " # Measure memory before re-enabling GC\n", + " memory_after = get_memory_usage()\n", + " \n", + " # Re-enable GC\n", + " gc.enable()\n", + " gc.collect() # Clean up now that we're done measuring\n", + " \n", + " return {\n", + " \"total_time_seconds\": end_time - start_time,\n", + " \"avg_query_time_seconds\": np.mean(search_times),\n", + " \"min_query_time_seconds\": np.min(search_times),\n", + " \"max_query_time_seconds\": np.max(search_times),\n", + " \"memory_before_mb\": memory_before,\n", + " \"memory_after_mb\": memory_after,\n", + " \"memory_delta_mb\": max(0, memory_after - memory_before), # Clamp negative values to 0\n", + " \"search_times\": search_times\n", + " }\n", + "\n", + "def generate_search_queries(count: int) -> list:\n", + " \"\"\"Generate search queries for benchmarking.\"\"\"\n", + " query_templates = [\n", + " \"What is machine learning?\",\n", + " \"How does deep learning work?\",\n", + " \"Explain artificial intelligence\",\n", + " \"What are neural networks?\",\n", + " \"How to implement data science?\",\n", + " \"What is natural language processing?\",\n", + " \"Explain computer vision\",\n", + " \"How does programming work?\",\n", + " \"What is software engineering?\",\n", + " \"Explain database systems\"\n", + " ]\n", + " \n", + " queries = []\n", + " for i in range(count):\n", + " base_query = random.choice(query_templates)\n", + " # Add some variation\n", + " variations = [\"\", \" in detail\", \" with examples\", \" and applications\", \" for beginners\"]\n", + " variation = random.choice(variations)\n", + " queries.append(base_query + variation)\n", + " \n", + " return queries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run Benchmarks" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2025-10-29T05:02:48.995134Z", + "start_time": "2025-10-29T05:00:12.301201Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generated 100 search queries\n", + "Running each test 3 time(s)\n", + "\n", + "============================================================\n", + "BENCHMARKING WITH 10 VECTORS\n", + "============================================================\n", + "\n", + "--- RUN 1 of 3 ---\n", + "\n", + "--- FAISS BENCHMARK ---\n", + "Benchmarking FAISS writing for 10 texts...\n", + "Benchmarking search for 100 queries (with 5 warmup queries)...\n", + "Running 5 warmup queries...\n", + "\n", + "--- IRIS BENCHMARK ---\n", + "Benchmarking IRIS writing for 10 texts with batch inserts...\n", + "Benchmarking search for 100 queries (with 5 warmup queries)...\n", + "Running 5 warmup queries...\n", + "\n", + "--- RUN 2 of 3 ---\n", + "\n", + "--- FAISS BENCHMARK ---\n", + "Benchmarking FAISS writing for 10 texts...\n", + "Benchmarking search for 100 queries (with 5 warmup queries)...\n", + "Running 5 warmup queries...\n", + "\n", + "--- IRIS BENCHMARK ---\n", + "Benchmarking IRIS writing for 10 texts with batch inserts...\n", + "Benchmarking search for 100 queries (with 5 warmup queries)...\n", + "Running 5 warmup queries...\n", + "\n", + "--- RUN 3 of 3 ---\n", + "\n", + "--- FAISS BENCHMARK ---\n", + "Benchmarking FAISS writing for 10 texts...\n", + "Benchmarking search for 100 queries (with 5 warmup queries)...\n", + "Running 5 warmup queries...\n", + "\n", + "--- IRIS BENCHMARK ---\n", + "Benchmarking IRIS writing for 10 texts with batch inserts...\n", + "Benchmarking search for 100 queries (with 5 warmup queries)...\n", + "Running 5 warmup queries...\n", + "\n", + "Completed benchmark for 10 vectors\n", + "\n", + "============================================================\n", + "BENCHMARKING WITH 1000 VECTORS\n", + "============================================================\n", + "\n", + "--- RUN 1 of 3 ---\n", + "\n", + "--- FAISS BENCHMARK ---\n", + "Benchmarking FAISS writing for 1000 texts...\n", + "Benchmarking search for 100 queries (with 5 warmup queries)...\n", + "Running 5 warmup queries...\n", + "\n", + "--- IRIS BENCHMARK ---\n", + "Benchmarking IRIS writing for 1000 texts with batch inserts...\n", + "Benchmarking search for 100 queries (with 5 warmup queries)...\n", + "Running 5 warmup queries...\n", + "\n", + "--- RUN 2 of 3 ---\n", + "\n", + "--- FAISS BENCHMARK ---\n", + "Benchmarking FAISS writing for 1000 texts...\n", + "Benchmarking search for 100 queries (with 5 warmup queries)...\n", + "Running 5 warmup queries...\n", + "\n", + "--- IRIS BENCHMARK ---\n", + "Benchmarking IRIS writing for 1000 texts with batch inserts...\n", + "Benchmarking search for 100 queries (with 5 warmup queries)...\n", + "Running 5 warmup queries...\n", + "\n", + "--- RUN 3 of 3 ---\n", + "\n", + "--- FAISS BENCHMARK ---\n", + "Benchmarking FAISS writing for 1000 texts...\n", + "Benchmarking search for 100 queries (with 5 warmup queries)...\n", + "Running 5 warmup queries...\n", + "\n", + "--- IRIS BENCHMARK ---\n", + "Benchmarking IRIS writing for 1000 texts with batch inserts...\n", + "Benchmarking search for 100 queries (with 5 warmup queries)...\n", + "Running 5 warmup queries...\n", + "\n", + "Completed benchmark for 1000 vectors\n", + "\n", + "============================================================\n", + "BENCHMARKING WITH 10000 VECTORS\n", + "============================================================\n", + "\n", + "--- RUN 1 of 3 ---\n", + "\n", + "--- FAISS BENCHMARK ---\n", + "Benchmarking FAISS writing for 10000 texts...\n", + "Benchmarking search for 100 queries (with 5 warmup queries)...\n", + "Running 5 warmup queries...\n", + "\n", + "--- IRIS BENCHMARK ---\n", + "Benchmarking IRIS writing for 10000 texts with batch inserts...\n", + "Benchmarking search for 100 queries (with 5 warmup queries)...\n", + "Running 5 warmup queries...\n", + "\n", + "--- RUN 2 of 3 ---\n", + "\n", + "--- FAISS BENCHMARK ---\n", + "Benchmarking FAISS writing for 10000 texts...\n", + "Benchmarking search for 100 queries (with 5 warmup queries)...\n", + "Running 5 warmup queries...\n", + "\n", + "--- IRIS BENCHMARK ---\n", + "Benchmarking IRIS writing for 10000 texts with batch inserts...\n", + "Benchmarking search for 100 queries (with 5 warmup queries)...\n", + "Running 5 warmup queries...\n", + "\n", + "--- RUN 3 of 3 ---\n", + "\n", + "--- FAISS BENCHMARK ---\n", + "Benchmarking FAISS writing for 10000 texts...\n", + "Benchmarking search for 100 queries (with 5 warmup queries)...\n", + "Running 5 warmup queries...\n", + "\n", + "--- IRIS BENCHMARK ---\n", + "Benchmarking IRIS writing for 10000 texts with batch inserts...\n", + "Benchmarking search for 100 queries (with 5 warmup queries)...\n", + "Running 5 warmup queries...\n", + "\n", + "Completed benchmark for 10000 vectors\n", + "\n", + "============================================================\n", + "BENCHMARKING WITH 100000 VECTORS\n", + "============================================================\n", + "\n", + "--- RUN 1 of 3 ---\n", + "\n", + "--- FAISS BENCHMARK ---\n", + "Benchmarking FAISS writing for 100000 texts...\n", + "Benchmarking search for 100 queries (with 5 warmup queries)...\n", + "Running 5 warmup queries...\n", + "\n", + "--- IRIS BENCHMARK ---\n", + "Benchmarking IRIS writing for 100000 texts with batch inserts...\n", + "Benchmarking search for 100 queries (with 5 warmup queries)...\n", + "Running 5 warmup queries...\n", + "\n", + "--- RUN 2 of 3 ---\n", + "\n", + "--- FAISS BENCHMARK ---\n", + "Benchmarking FAISS writing for 100000 texts...\n", + "Benchmarking search for 100 queries (with 5 warmup queries)...\n", + "Running 5 warmup queries...\n", + "\n", + "--- IRIS BENCHMARK ---\n", + "Benchmarking IRIS writing for 100000 texts with batch inserts...\n", + "Benchmarking search for 100 queries (with 5 warmup queries)...\n", + "Running 5 warmup queries...\n", + "\n", + "--- RUN 3 of 3 ---\n", + "\n", + "--- FAISS BENCHMARK ---\n", + "Benchmarking FAISS writing for 100000 texts...\n", + "Benchmarking search for 100 queries (with 5 warmup queries)...\n", + "Running 5 warmup queries...\n", + "\n", + "--- IRIS BENCHMARK ---\n", + "Benchmarking IRIS writing for 100000 texts with batch inserts...\n", + "Benchmarking search for 100 queries (with 5 warmup queries)...\n", + "Running 5 warmup queries...\n", + "\n", + "Completed benchmark for 100000 vectors\n", + "\n", + "============================================================\n", + "ALL BENCHMARKS COMPLETED\n", + "============================================================\n" + ] + } + ], + "source": [ + "# Generate search queries\n", + "search_queries = generate_search_queries(SEARCH_QUERIES)\n", + "print(f\"Generated {len(search_queries)} search queries\")\n", + "print(f\"Running each test {NUM_RUNS} time(s)\")\n", + "\n", + "# Results storage\n", + "results = []\n", + "\n", + "for vector_count in VECTOR_COUNTS:\n", + " print(f\"\\n{'='*60}\")\n", + " print(f\"BENCHMARKING WITH {vector_count} VECTORS\")\n", + " print(f\"{'='*60}\")\n", + " \n", + " # Get subset of texts and embeddings for this test\n", + " test_texts = sample_texts[:vector_count]\n", + " test_embeddings = sample_embeddings[:vector_count]\n", + " \n", + " # Storage for multiple runs\n", + " run_results = []\n", + " \n", + " for run_num in range(NUM_RUNS):\n", + " if NUM_RUNS > 1:\n", + " print(f\"\\n--- RUN {run_num + 1} of {NUM_RUNS} ---\")\n", + " \n", + " # Benchmark FAISS\n", + " print(f\"\\n--- FAISS BENCHMARK ---\")\n", + " faiss_write_results = benchmark_faiss_writing(test_texts, test_embeddings)\n", + " faiss_search_results = benchmark_search(\n", + " faiss_write_results['data_collection'], \n", + " search_queries, \n", + " RESULTS_PER_QUERY\n", + " )\n", + " \n", + " # Benchmark IRIS\n", + " print(f\"\\n--- IRIS BENCHMARK ---\")\n", + " table_name = f\"benchmark_{vector_count}_{run_num}\"\n", + " iris_write_results = benchmark_iris_writing(test_texts, test_embeddings, table_name)\n", + " iris_search_results = benchmark_search(\n", + " iris_write_results['data_collection'], \n", + " search_queries, \n", + " RESULTS_PER_QUERY\n", + " )\n", + " \n", + " # Store results for this run\n", + " run_results.append({\n", + " 'faiss_write_time': faiss_write_results['time_seconds'],\n", + " 'faiss_write_memory': faiss_write_results['memory_delta_mb'],\n", + " 'faiss_search_time': faiss_search_results['total_time_seconds'],\n", + " 'faiss_avg_query_time': faiss_search_results['avg_query_time_seconds'],\n", + " 'faiss_search_memory': faiss_search_results['memory_delta_mb'],\n", + " 'iris_write_time': iris_write_results['time_seconds'],\n", + " 'iris_write_memory': iris_write_results['memory_delta_mb'],\n", + " 'iris_search_time': iris_search_results['total_time_seconds'],\n", + " 'iris_avg_query_time': iris_search_results['avg_query_time_seconds'],\n", + " 'iris_search_memory': iris_search_results['memory_delta_mb'],\n", + " })\n", + " \n", + " # Average results across runs (or use single result if NUM_RUNS == 1)\n", + " if NUM_RUNS > 1:\n", + " avg_results = {}\n", + " for key in run_results[0].keys():\n", + " avg_results[key] = np.mean([r[key] for r in run_results])\n", + " results.append({\n", + " 'vector_count': vector_count,\n", + " **avg_results\n", + " })\n", + " else:\n", + " results.append({\n", + " 'vector_count': vector_count,\n", + " **run_results[0]\n", + " })\n", + " \n", + " print(f\"\\nCompleted benchmark for {vector_count} vectors\")\n", + "\n", + "print(f\"\\n{'='*60}\")\n", + "print(\"ALL BENCHMARKS COMPLETED\")\n", + "print(f\"{'='*60}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Results Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2025-10-29T05:02:49.133171Z", + "start_time": "2025-10-29T05:02:49.117523Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Benchmark Results:\n", + " vector_count faiss_write_time faiss_write_memory faiss_search_time \\\n", + "0 10 0.0094 1.7135 0.4960 \n", + "1 1000 0.0004 1.7031 0.4688 \n", + "2 10000 0.0029 16.8750 0.5030 \n", + "3 100000 0.0869 249.8646 1.1198 \n", + "\n", + " faiss_avg_query_time faiss_search_memory iris_write_time \\\n", + "0 0.0050 9.6042 0.0351 \n", + "1 0.0047 0.0938 0.5409 \n", + "2 0.0050 0.0833 3.2143 \n", + "3 0.0112 0.1094 32.5681 \n", + "\n", + " iris_write_memory iris_search_time iris_avg_query_time \\\n", + "0 0.1719 0.6910 0.0069 \n", + "1 32.0208 0.8787 0.0088 \n", + "2 78.6302 1.0869 0.0109 \n", + "3 44.2917 3.1850 0.0318 \n", + "\n", + " iris_search_memory \n", + "0 0.0469 \n", + "1 0.0677 \n", + "2 2.3802 \n", + "3 7.1667 \n" + ] + } + ], + "source": [ + "# Create results DataFrame\n", + "df_results = pd.DataFrame(results)\n", + "print(\"Benchmark Results:\")\n", + "print(df_results.round(4))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2025-10-29T05:02:49.329166Z", + "start_time": "2025-10-29T05:02:49.315084Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Performance Ratios (IRIS / FAISS):\n", + "Values > 1 mean IRIS is slower, Values < 1 mean IRIS is faster\n", + " vector_count write_time_ratio search_time_ratio avg_query_time_ratio\n", + "0 10 3.7281 1.3931 1.3931\n", + "1 1000 1290.0106 1.8743 1.8744\n", + "2 10000 1106.1843 2.1609 2.1609\n", + "3 100000 374.6489 2.8444 2.8443\n" + ] + } + ], + "source": [ + "# Calculate performance ratios\n", + "df_results['write_time_ratio'] = df_results['iris_write_time'] / df_results['faiss_write_time']\n", + "df_results['search_time_ratio'] = df_results['iris_search_time'] / df_results['faiss_search_time']\n", + "df_results['avg_query_time_ratio'] = df_results['iris_avg_query_time'] / df_results['faiss_avg_query_time']\n", + "\n", + "print(\"\\nPerformance Ratios (IRIS / FAISS):\")\n", + "print(\"Values > 1 mean IRIS is slower, Values < 1 mean IRIS is faster\")\n", + "print(df_results[['vector_count', 'write_time_ratio', 'search_time_ratio', 'avg_query_time_ratio']].round(4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2025-10-29T05:02:50.211866Z", + "start_time": "2025-10-29T05:02:49.368793Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create visualizations\n", + "fig, axes = plt.subplots(2, 2, figsize=(15, 12))\n", + "\n", + "# Write time comparison\n", + "axes[0, 0].plot(df_results['vector_count'], df_results['faiss_write_time'], 'b-o', label='FAISS')\n", + "axes[0, 0].plot(df_results['vector_count'], df_results['iris_write_time'], 'r-s', label='IRIS (Batch)')\n", + "axes[0, 0].set_xlabel('Number of Vectors')\n", + "axes[0, 0].set_ylabel('Write Time (seconds)')\n", + "axes[0, 0].set_title('Write Performance Comparison (Excluding Embedding Calculation)')\n", + "axes[0, 0].legend()\n", + "axes[0, 0].set_xscale('log')\n", + "axes[0, 0].set_yscale('log')\n", + "\n", + "# Search time comparison\n", + "axes[0, 1].plot(df_results['vector_count'], df_results['faiss_search_time'], 'b-o', label='FAISS')\n", + "axes[0, 1].plot(df_results['vector_count'], df_results['iris_search_time'], 'r-s', label='IRIS')\n", + "axes[0, 1].set_xlabel('Number of Vectors')\n", + "axes[0, 1].set_ylabel('Total Search Time (seconds)')\n", + "axes[0, 1].set_title('Search Performance Comparison')\n", + "axes[0, 1].legend()\n", + "axes[0, 1].set_xscale('log')\n", + "axes[0, 1].set_yscale('log')\n", + "\n", + "# Average query time comparison\n", + "axes[1, 0].plot(df_results['vector_count'], df_results['faiss_avg_query_time'], 'b-o', label='FAISS')\n", + "axes[1, 0].plot(df_results['vector_count'], df_results['iris_avg_query_time'], 'r-s', label='IRIS')\n", + "axes[1, 0].set_xlabel('Number of Vectors')\n", + "axes[1, 0].set_ylabel('Average Query Time (seconds)')\n", + "axes[1, 0].set_title('Average Query Time Comparison')\n", + "axes[1, 0].legend()\n", + "axes[1, 0].set_xscale('log')\n", + "axes[1, 0].set_yscale('log')\n", + "\n", + "# Performance ratios\n", + "axes[1, 1].plot(df_results['vector_count'], df_results['write_time_ratio'], 'g-o', label='Write Time Ratio')\n", + "axes[1, 1].plot(df_results['vector_count'], df_results['search_time_ratio'], 'm-s', label='Search Time Ratio')\n", + "axes[1, 1].axhline(y=1, color='k', linestyle='--', alpha=0.5, label='Equal Performance')\n", + "axes[1, 1].set_xlabel('Number of Vectors')\n", + "axes[1, 1].set_ylabel('Performance Ratio (IRIS / FAISS)')\n", + "axes[1, 1].set_title('Performance Ratios')\n", + "axes[1, 1].legend()\n", + "axes[1, 1].set_xscale('log')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Memory Usage Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2025-10-29T05:02:50.397780Z", + "start_time": "2025-10-29T05:02:50.223989Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Memory usage comparison\n", + "fig, axes = plt.subplots(1, 2, figsize=(15, 6))\n", + "\n", + "# Write memory comparison\n", + "axes[0].plot(df_results['vector_count'], df_results['faiss_write_memory'], 'b-o', label='FAISS')\n", + "axes[0].plot(df_results['vector_count'], df_results['iris_write_memory'], 'r-s', label='IRIS (Batch)')\n", + "axes[0].set_xlabel('Number of Vectors')\n", + "axes[0].set_ylabel('Memory Usage (MB)')\n", + "axes[0].set_title('Write Memory Usage')\n", + "axes[0].legend()\n", + "axes[0].set_xscale('log')\n", + "\n", + "# Search memory comparison\n", + "axes[1].plot(df_results['vector_count'], df_results['faiss_search_memory'], 'b-o', label='FAISS')\n", + "axes[1].plot(df_results['vector_count'], df_results['iris_search_memory'], 'r-s', label='IRIS')\n", + "axes[1].set_xlabel('Number of Vectors')\n", + "axes[1].set_ylabel('Memory Usage (MB)')\n", + "axes[1].set_title('Search Memory Usage')\n", + "axes[1].legend()\n", + "axes[1].set_xscale('log')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary and Conclusions\n", + "\n", + "### Key Insights\n", + "\n", + "Based on the benchmark results with 3 runs per test configuration:\n", + "\n", + "1. **Write Performance**: FAISS is significantly faster for writes (3.7x to 1290x faster)\n", + " - Small datasets (10-1000 vectors): FAISS advantages are most pronounced (1290x faster)\n", + " - Large datasets (100k vectors): FAISS still 375x faster, but gap decreases\n", + " - IRIS overhead comes from database I/O, string conversion, and network communication\n", + "\n", + "2. **Search Performance**: FAISS is consistently faster, but the gap is smaller\n", + " - IRIS is 1.4x to 2.8x slower than FAISS for search operations\n", + " - Performance gap increases with dataset size (1.4x at 10 vectors vs 2.8x at 100k vectors)\n", + " - Both systems maintain sub-50ms query times even with 100k vectors\n", + "\n", + "3. **Memory Usage**:\n", + " - FAISS: Memory usage grows linearly with data size (1.7MB at 10 vectors → 250MB at 100k vectors)\n", + " - IRIS: Very low memory footprint (~0-78MB for writes, <8MB for searches) as data persists in database\n", + " - IRIS is much more memory-efficient for large datasets\n", + "\n", + "4. **Best Use Cases**:\n", + " - **FAISS**: In-memory datasets, write-once/read-many workflows, highest performance requirements\n", + " - **IRIS**: Persistent storage needed, large datasets, memory-constrained environments, multi-user access\n", + "\n", + "### Limitations Addressed\n", + "\n", + "✓ **Query Warmup**: Implemented (5 warmup queries per test) \n", + "✓ **Multiple Runs**: Implemented (3 runs averaged in this benchmark) \n", + "✓ **Memory Measurement**: GC disabled during measurements to prevent negative deltas \n", + "✓ **String Conversion**: Noted in code comments - IRIS requires string format for vectors \n", + "\n", + "### Remaining Limitations\n", + "\n", + "- **IRIS String Conversion Overhead**: Embeddings converted to strings adds CPU overhead not present in production\n", + "- **Connection Overhead**: IRIS connections created fresh per test (connection pooling not used)\n", + "- **FAISS Index Type**: Uses only IndexFlatL2 - other types (HNSW, IVF) might offer better tradeoffs\n", + "- **Memory Persistence Difference**: IRIS stores in DB (off-memory), FAISS in RAM - affects memory measurements\n", + "- **Provider Instantiation**: IRIS provider creation not timed but consumes resources during write benchmark" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2025-10-29T06:34:38.139888Z", + "start_time": "2025-10-29T06:34:38.135616Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Detailed Benchmark Results:\n", + "================================================================================\n", + " vector_count faiss_write_time faiss_write_memory faiss_search_time faiss_avg_query_time faiss_search_memory iris_write_time iris_write_memory iris_search_time iris_avg_query_time iris_search_memory write_time_ratio search_time_ratio avg_query_time_ratio\n", + "0 10 0.009417 1.713542 0.496016 0.004960 9.604167 0.035110 0.171875 0.691010 0.006910 0.046875 3.728141 1.393120 1.393134\n", + "1 1000 0.000419 1.703125 0.468809 0.004688 0.093750 0.540900 32.020833 0.878712 0.008787 0.067708 1290.010614 1.874349 1.874360\n", + "2 10000 0.002906 16.875000 0.502999 0.005030 0.083333 3.214313 78.630208 1.086933 0.010868 2.380208 1106.184257 2.160903 2.160850\n", + "3 100000 0.086930 249.864583 1.119773 0.011197 0.109375 32.568124 44.291667 3.185037 0.031848 7.166667 374.648909 2.844359 2.844348\n", + "\n", + "================================================================================\n" + ] + } + ], + "source": [ + "# Display detailed results table\n", + "print(\"Detailed Benchmark Results:\")\n", + "print(\"=\"*80)\n", + "print(df_results.to_string())\n", + "print(\"\\n\" + \"=\"*80)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2025-10-29T05:02:50.421702Z", + "start_time": "2025-10-29T05:02:50.420464Z" + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 450dc3ebca96f41c4c8b277483dfef6e31d4b983 Mon Sep 17 00:00:00 2001 From: mike0sv Date: Wed, 29 Oct 2025 12:30:09 +0000 Subject: [PATCH 3/4] add benchmark --- examples/integrations/iris_faiss_benchmark.ipynb | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/examples/integrations/iris_faiss_benchmark.ipynb b/examples/integrations/iris_faiss_benchmark.ipynb index 58e1770ba6..8f479a52dc 100644 --- a/examples/integrations/iris_faiss_benchmark.ipynb +++ b/examples/integrations/iris_faiss_benchmark.ipynb @@ -68,6 +68,11 @@ "# containers.intersystems.com/intersystems/iris-community:2025.1\n" ] }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Go to http://localhost:52773/csp/sys/UtilHome.csp and setup SuperUser. Default password is 'SYS', change it to '123' (or something else, but then change connection settings accordingly)" + }, { "cell_type": "markdown", "metadata": {}, From b88a0bc4485493a5a1201ff56dee19eb8d0a429f Mon Sep 17 00:00:00 2001 From: mike0sv Date: Mon, 3 Nov 2025 18:09:08 +0000 Subject: [PATCH 4/4] fix db limit --- .../integrations/iris_faiss_benchmark.ipynb | 565 ++++++++++-------- 1 file changed, 319 insertions(+), 246 deletions(-) diff --git a/examples/integrations/iris_faiss_benchmark.ipynb b/examples/integrations/iris_faiss_benchmark.ipynb index 8f479a52dc..073a1024c9 100644 --- a/examples/integrations/iris_faiss_benchmark.ipynb +++ b/examples/integrations/iris_faiss_benchmark.ipynb @@ -41,20 +41,28 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-11-03T18:02:15.346611Z", + "start_time": "2025-11-03T18:02:15.343570Z" + } + }, "source": [ "# Install Python dependencies\n", "# !pip install intersystems-irispython sentence-transformers\n", "# !pip install \"git+https://github.com/evidentlyai/evidently.git@feature/iris#egg=evidently[llm,iris]\"\n" - ] + ], + "outputs": [], + "execution_count": 1 }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-11-03T18:02:15.626256Z", + "start_time": "2025-11-03T18:02:15.624692Z" + } + }, "source": [ "# Start IRIS Docker container\n", "# docker run -d --rm \\\n", @@ -66,7 +74,9 @@ "# -e TZ=UTC \\\n", "# -v $(pwd)/iris-durable:/durable \\\n", "# containers.intersystems.com/intersystems/iris-community:2025.1\n" - ] + ], + "outputs": [], + "execution_count": 2 }, { "metadata": {}, @@ -82,23 +92,14 @@ }, { "cell_type": "code", - "execution_count": 1, "metadata": { "ExecuteTime": { - "end_time": "2025-10-29T04:59:19.017256Z", - "start_time": "2025-10-29T04:59:14.535588Z" + "end_time": "2025-11-03T18:02:19.738920Z", + "start_time": "2025-11-03T18:02:15.632447Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Imports successful!\n" - ] - } - ], "source": [ + "import pickle\n", "import time\n", "import random\n", "import numpy as np\n", @@ -115,7 +116,17 @@ "from evidently.llm.rag.splitter import Chunk\n", "\n", "print(\"Imports successful!\")" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Imports successful!\n" + ] + } + ], + "execution_count": 3 }, { "cell_type": "markdown", @@ -126,25 +137,16 @@ }, { "cell_type": "code", - "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2025-10-29T04:59:20.591460Z", - "start_time": "2025-10-29T04:59:19.234266Z" + "end_time": "2025-11-03T18:02:22.623877Z", + "start_time": "2025-11-03T18:02:19.748769Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Embedding model loaded. Dimension: 384\n" - ] - } - ], "source": [ "# Benchmark parameters\n", - "VECTOR_COUNTS = [10, 1000, 10000, 100000]\n", + "VECTOR_COUNTS = [10, 1000, 10000, 100000] #, 400000, 1000000, 5000000]\n", + "BATCH_SIZE = 10000\n", "SEARCH_QUERIES = 100\n", "RESULTS_PER_QUERY = 5\n", "NUM_RUNS = 3 # Number of times to run each test for averaging (use 1 for single run, 3+ for statistical significance)\n", @@ -161,7 +163,17 @@ "# Initialize embedding model\n", "model = SentenceTransformer('all-MiniLM-L6-v2')\n", "print(f\"Embedding model loaded. Dimension: {model.get_sentence_embedding_dimension()}\")" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Embedding model loaded. Dimension: 384\n" + ] + } + ], + "execution_count": 4 }, { "cell_type": "markdown", @@ -172,25 +184,12 @@ }, { "cell_type": "code", - "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2025-10-29T05:00:12.254702Z", - "start_time": "2025-10-29T04:59:20.598942Z" + "end_time": "2025-11-03T18:02:25.211361Z", + "start_time": "2025-11-03T18:02:22.631984Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generating 100000 sample texts...\n", - "Calculating embeddings for 100000 texts (this may take a moment)...\n", - "Sample data generation complete!\n", - "Generated 100000 sample texts with embeddings\n" - ] - } - ], "source": [ "def generate_sample_texts(count: int) -> list:\n", " \"\"\"Generate sample text documents for benchmarking.\"\"\"\n", @@ -218,19 +217,45 @@ "\n", "def generate_sample_data_with_embeddings(count: int) -> tuple:\n", " \"\"\"Generate sample texts and pre-calculate embeddings.\"\"\"\n", + " texts = []\n", + " embeddings = []\n", + " if os.path.exists(\"./embeddings.pkl\"):\n", + " with open(\"./embeddings.pkl\", \"rb\") as f:\n", + " texts, embeddings = pickle.load(f)\n", + " print(f\"Loaded {len(texts)} from embeddings.pkl\")\n", + " count = count - len(texts)\n", " print(f\"Generating {count} sample texts...\")\n", - " texts = generate_sample_texts(count)\n", - " \n", - " print(f\"Calculating embeddings for {count} texts (this may take a moment)...\")\n", - " embeddings = model.encode(texts)\n", + " new_texts = generate_sample_texts(count)\n", + " texts.extend(new_texts)\n", + "\n", + " if new_texts:\n", + " print(f\"Calculating embeddings for {count} texts (this may take a moment)...\")\n", + " embeddings.extend(model.encode(new_texts))\n", " \n", " print(f\"Sample data generation complete!\")\n", - " return texts, embeddings\n", + " print(f\"Writing {len(embeddings)} embeddings to ./embeddings.pkl...\")\n", + " with open(\"./embeddings.pkl\", \"wb\") as f:\n", + " pickle.dump((texts, embeddings), f)\n", + " return np.array(texts), np.array(embeddings)\n", "\n", "# Generate sample texts and embeddings for testing\n", "sample_texts, sample_embeddings = generate_sample_data_with_embeddings(max(VECTOR_COUNTS))\n", "print(f\"Generated {len(sample_texts)} sample texts with embeddings\")" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded 400000 from embeddings.pkl\n", + "Generating -300000 sample texts...\n", + "Sample data generation complete!\n", + "Writing 400000 embeddings to ./embeddings.pkl...\n", + "Generated 400000 sample texts with embeddings\n" + ] + } + ], + "execution_count": 5 }, { "cell_type": "markdown", @@ -243,14 +268,12 @@ }, { "cell_type": "code", - "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2025-10-29T05:00:12.283820Z", - "start_time": "2025-10-29T05:00:12.277936Z" + "end_time": "2025-11-03T18:02:25.232373Z", + "start_time": "2025-11-03T18:02:25.226401Z" } }, - "outputs": [], "source": [ "def get_memory_usage(force_gc=False):\n", " \"\"\"Get current memory usage in MB. Optionally force garbage collection first.\"\"\"\n", @@ -347,23 +370,26 @@ " # Insert texts with pre-calculated embeddings in batches\n", " # Note: IRIS vector format expects a list of floats as a string representation\n", " # Format: \"[\" + \"0.123, 0.456, ...\" + \"]\" or proper IRIS vector syntax\n", - " batch_size = 1000\n", + " batch_size = BATCH_SIZE\n", " for i in range(0, len(texts), batch_size):\n", " batch_texts = texts[i:i + batch_size]\n", " batch_embeddings = embeddings[i:i + batch_size]\n", " \n", " # Prepare batch data - convert to IRIS vector format\n", " batch_data = []\n", + " # print(f\"[{time.time() - start_time:0.2f}s] Preparing batch\")\n", " for j, (text, embedding) in enumerate(zip(batch_texts, batch_embeddings)):\n", " # Convert numpy array to IRIS vector format (string representation of list)\n", - " vector_str = \"[\" + \", \".join(map(str, embedding.tolist())) + \"]\"\n", - " batch_data.append([i + j + 1, text, vector_str])\n", + " # vector_str = \"[\" + \", \".join(map(str, embedding.tolist())) + \"]\"\n", + " batch_data.append([i + j + 1, text, str(embedding.tolist())])\n", " \n", " # Execute batch insert\n", + " # print(f\"[{time.time() - start_time:0.2f}s] Start insert\")\n", " cursor.executemany(\n", - " f\"INSERT INTO {table_name} (ID, text, vector_data) VALUES (?, ?, ?)\",\n", + " f\"INSERT INTO {table_name} (ID, text, vector_data) VALUES (?, ?, TO_VECTOR(?))\",\n", " batch_data\n", " )\n", + " print(f\"[{time.time() - start_time:0.2f}s] Batch of {len(batch_data)} inserted. Total {min(i + batch_size, len(texts))} rows inserted.\")\n", " \n", " connection.commit()\n", " end_time = time.time()\n", @@ -399,18 +425,18 @@ " \"memory_delta_mb\": max(0, memory_after - memory_before), # Clamp negative values to 0\n", " \"data_collection\": data_collection\n", " }" - ] + ], + "outputs": [], + "execution_count": 6 }, { "cell_type": "code", - "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2025-10-29T05:00:12.295244Z", - "start_time": "2025-10-29T05:00:12.291932Z" + "end_time": "2025-11-03T18:02:25.242230Z", + "start_time": "2025-11-03T18:02:25.238931Z" } }, - "outputs": [], "source": [ "def benchmark_search(data_collection, queries: list, results_per_query: int, warmup_queries: int = 5) -> dict:\n", " \"\"\"Benchmark search performance.\"\"\"\n", @@ -483,7 +509,9 @@ " queries.append(base_query + variation)\n", " \n", " return queries" - ] + ], + "outputs": [], + "execution_count": 7 }, { "cell_type": "markdown", @@ -494,13 +522,97 @@ }, { "cell_type": "code", - "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2025-10-29T05:02:48.995134Z", - "start_time": "2025-10-29T05:00:12.301201Z" + "end_time": "2025-11-03T18:05:34.549385Z", + "start_time": "2025-11-03T18:02:25.248510Z" } }, + "source": [ + "from collections import defaultdict\n", + "# Generate search queries\n", + "search_queries = generate_search_queries(SEARCH_QUERIES)\n", + "print(f\"Generated {len(search_queries)} search queries\")\n", + "print(f\"Running each test {NUM_RUNS} time(s)\")\n", + "\n", + "# Results storage\n", + "results = []\n", + "\n", + "for vector_count in VECTOR_COUNTS:\n", + " print(f\"\\n{'='*60}\")\n", + " print(f\"BENCHMARKING WITH {vector_count} VECTORS\")\n", + " print(f\"{'='*60}\")\n", + " \n", + " # Get subset of texts and embeddings for this test\n", + " test_texts = sample_texts[:vector_count]\n", + " test_embeddings = sample_embeddings[:vector_count]\n", + " \n", + " # Storage for multiple runs\n", + " run_results = []\n", + " \n", + " for run_num in range(NUM_RUNS):\n", + " if NUM_RUNS > 1:\n", + " print(f\"\\n--- RUN {run_num + 1} of {NUM_RUNS} ---\")\n", + " \n", + " # Benchmark FAISS\n", + " print(f\"\\n--- FAISS BENCHMARK ---\")\n", + " if vector_count > 10**5:\n", + " print(f\"\\n--- SKIP ---\")\n", + " faiss_write_results = defaultdict(lambda : 1)\n", + " faiss_search_results = defaultdict(lambda : 1)\n", + " else:\n", + " faiss_write_results = benchmark_faiss_writing(test_texts, test_embeddings)\n", + " faiss_search_results = benchmark_search(\n", + " faiss_write_results['data_collection'],\n", + " search_queries,\n", + " RESULTS_PER_QUERY\n", + " )\n", + " \n", + " # Benchmark IRIS\n", + " print(f\"\\n--- IRIS BENCHMARK ---\")\n", + " table_name = f\"benchmark_iris\"\n", + " iris_write_results = benchmark_iris_writing(test_texts, test_embeddings, table_name)\n", + " iris_search_results = benchmark_search(\n", + " iris_write_results['data_collection'], \n", + " search_queries, \n", + " RESULTS_PER_QUERY\n", + " )\n", + " \n", + " # Store results for this run\n", + " run_results.append({\n", + " 'faiss_write_time': faiss_write_results['time_seconds'],\n", + " 'faiss_write_memory': faiss_write_results['memory_delta_mb'],\n", + " 'faiss_search_time': faiss_search_results['total_time_seconds'],\n", + " 'faiss_avg_query_time': faiss_search_results['avg_query_time_seconds'],\n", + " 'faiss_search_memory': faiss_search_results['memory_delta_mb'],\n", + " 'iris_write_time': iris_write_results['time_seconds'],\n", + " 'iris_write_memory': iris_write_results['memory_delta_mb'],\n", + " 'iris_search_time': iris_search_results['total_time_seconds'],\n", + " 'iris_avg_query_time': iris_search_results['avg_query_time_seconds'],\n", + " 'iris_search_memory': iris_search_results['memory_delta_mb'],\n", + " })\n", + " \n", + " # Average results across runs (or use single result if NUM_RUNS == 1)\n", + " if NUM_RUNS > 1:\n", + " avg_results = {}\n", + " for key in run_results[0].keys():\n", + " avg_results[key] = np.mean([r[key] for r in run_results])\n", + " results.append({\n", + " 'vector_count': vector_count,\n", + " **avg_results\n", + " })\n", + " else:\n", + " results.append({\n", + " 'vector_count': vector_count,\n", + " **run_results[0]\n", + " })\n", + " \n", + " print(f\"\\nCompleted benchmark for {vector_count} vectors\")\n", + "\n", + "print(f\"\\n{'='*60}\")\n", + "print(\"ALL BENCHMARKS COMPLETED\")\n", + "print(f\"{'='*60}\")" + ], "outputs": [ { "name": "stdout", @@ -522,6 +634,7 @@ "\n", "--- IRIS BENCHMARK ---\n", "Benchmarking IRIS writing for 10 texts with batch inserts...\n", + "[0.03s] Batch of 10 inserted. Total 10 rows inserted.\n", "Benchmarking search for 100 queries (with 5 warmup queries)...\n", "Running 5 warmup queries...\n", "\n", @@ -534,6 +647,7 @@ "\n", "--- IRIS BENCHMARK ---\n", "Benchmarking IRIS writing for 10 texts with batch inserts...\n", + "[0.03s] Batch of 10 inserted. Total 10 rows inserted.\n", "Benchmarking search for 100 queries (with 5 warmup queries)...\n", "Running 5 warmup queries...\n", "\n", @@ -546,6 +660,7 @@ "\n", "--- IRIS BENCHMARK ---\n", "Benchmarking IRIS writing for 10 texts with batch inserts...\n", + "[0.03s] Batch of 10 inserted. Total 10 rows inserted.\n", "Benchmarking search for 100 queries (with 5 warmup queries)...\n", "Running 5 warmup queries...\n", "\n", @@ -564,6 +679,7 @@ "\n", "--- IRIS BENCHMARK ---\n", "Benchmarking IRIS writing for 1000 texts with batch inserts...\n", + "[0.43s] Batch of 1000 inserted. Total 1000 rows inserted.\n", "Benchmarking search for 100 queries (with 5 warmup queries)...\n", "Running 5 warmup queries...\n", "\n", @@ -576,6 +692,7 @@ "\n", "--- IRIS BENCHMARK ---\n", "Benchmarking IRIS writing for 1000 texts with batch inserts...\n", + "[0.83s] Batch of 1000 inserted. Total 1000 rows inserted.\n", "Benchmarking search for 100 queries (with 5 warmup queries)...\n", "Running 5 warmup queries...\n", "\n", @@ -588,6 +705,7 @@ "\n", "--- IRIS BENCHMARK ---\n", "Benchmarking IRIS writing for 1000 texts with batch inserts...\n", + "[0.36s] Batch of 1000 inserted. Total 1000 rows inserted.\n", "Benchmarking search for 100 queries (with 5 warmup queries)...\n", "Running 5 warmup queries...\n", "\n", @@ -606,6 +724,7 @@ "\n", "--- IRIS BENCHMARK ---\n", "Benchmarking IRIS writing for 10000 texts with batch inserts...\n", + "[3.11s] Batch of 10000 inserted. Total 10000 rows inserted.\n", "Benchmarking search for 100 queries (with 5 warmup queries)...\n", "Running 5 warmup queries...\n", "\n", @@ -618,6 +737,7 @@ "\n", "--- IRIS BENCHMARK ---\n", "Benchmarking IRIS writing for 10000 texts with batch inserts...\n", + "[2.85s] Batch of 10000 inserted. Total 10000 rows inserted.\n", "Benchmarking search for 100 queries (with 5 warmup queries)...\n", "Running 5 warmup queries...\n", "\n", @@ -630,6 +750,7 @@ "\n", "--- IRIS BENCHMARK ---\n", "Benchmarking IRIS writing for 10000 texts with batch inserts...\n", + "[2.97s] Batch of 10000 inserted. Total 10000 rows inserted.\n", "Benchmarking search for 100 queries (with 5 warmup queries)...\n", "Running 5 warmup queries...\n", "\n", @@ -648,6 +769,16 @@ "\n", "--- IRIS BENCHMARK ---\n", "Benchmarking IRIS writing for 100000 texts with batch inserts...\n", + "[3.04s] Batch of 10000 inserted. Total 10000 rows inserted.\n", + "[5.91s] Batch of 10000 inserted. Total 20000 rows inserted.\n", + "[8.76s] Batch of 10000 inserted. Total 30000 rows inserted.\n", + "[11.62s] Batch of 10000 inserted. Total 40000 rows inserted.\n", + "[14.62s] Batch of 10000 inserted. Total 50000 rows inserted.\n", + "[18.14s] Batch of 10000 inserted. Total 60000 rows inserted.\n", + "[21.20s] Batch of 10000 inserted. Total 70000 rows inserted.\n", + "[24.14s] Batch of 10000 inserted. Total 80000 rows inserted.\n", + "[27.08s] Batch of 10000 inserted. Total 90000 rows inserted.\n", + "[30.38s] Batch of 10000 inserted. Total 100000 rows inserted.\n", "Benchmarking search for 100 queries (with 5 warmup queries)...\n", "Running 5 warmup queries...\n", "\n", @@ -660,6 +791,16 @@ "\n", "--- IRIS BENCHMARK ---\n", "Benchmarking IRIS writing for 100000 texts with batch inserts...\n", + "[3.29s] Batch of 10000 inserted. Total 10000 rows inserted.\n", + "[6.27s] Batch of 10000 inserted. Total 20000 rows inserted.\n", + "[9.28s] Batch of 10000 inserted. Total 30000 rows inserted.\n", + "[12.26s] Batch of 10000 inserted. Total 40000 rows inserted.\n", + "[15.21s] Batch of 10000 inserted. Total 50000 rows inserted.\n", + "[18.15s] Batch of 10000 inserted. Total 60000 rows inserted.\n", + "[21.13s] Batch of 10000 inserted. Total 70000 rows inserted.\n", + "[24.12s] Batch of 10000 inserted. Total 80000 rows inserted.\n", + "[27.07s] Batch of 10000 inserted. Total 90000 rows inserted.\n", + "[30.02s] Batch of 10000 inserted. Total 100000 rows inserted.\n", "Benchmarking search for 100 queries (with 5 warmup queries)...\n", "Running 5 warmup queries...\n", "\n", @@ -672,6 +813,16 @@ "\n", "--- IRIS BENCHMARK ---\n", "Benchmarking IRIS writing for 100000 texts with batch inserts...\n", + "[3.10s] Batch of 10000 inserted. Total 10000 rows inserted.\n", + "[6.07s] Batch of 10000 inserted. Total 20000 rows inserted.\n", + "[9.10s] Batch of 10000 inserted. Total 30000 rows inserted.\n", + "[12.01s] Batch of 10000 inserted. Total 40000 rows inserted.\n", + "[15.23s] Batch of 10000 inserted. Total 50000 rows inserted.\n", + "[18.55s] Batch of 10000 inserted. Total 60000 rows inserted.\n", + "[21.56s] Batch of 10000 inserted. Total 70000 rows inserted.\n", + "[24.55s] Batch of 10000 inserted. Total 80000 rows inserted.\n", + "[27.51s] Batch of 10000 inserted. Total 90000 rows inserted.\n", + "[30.88s] Batch of 10000 inserted. Total 100000 rows inserted.\n", "Benchmarking search for 100 queries (with 5 warmup queries)...\n", "Running 5 warmup queries...\n", "\n", @@ -683,85 +834,7 @@ ] } ], - "source": [ - "# Generate search queries\n", - "search_queries = generate_search_queries(SEARCH_QUERIES)\n", - "print(f\"Generated {len(search_queries)} search queries\")\n", - "print(f\"Running each test {NUM_RUNS} time(s)\")\n", - "\n", - "# Results storage\n", - "results = []\n", - "\n", - "for vector_count in VECTOR_COUNTS:\n", - " print(f\"\\n{'='*60}\")\n", - " print(f\"BENCHMARKING WITH {vector_count} VECTORS\")\n", - " print(f\"{'='*60}\")\n", - " \n", - " # Get subset of texts and embeddings for this test\n", - " test_texts = sample_texts[:vector_count]\n", - " test_embeddings = sample_embeddings[:vector_count]\n", - " \n", - " # Storage for multiple runs\n", - " run_results = []\n", - " \n", - " for run_num in range(NUM_RUNS):\n", - " if NUM_RUNS > 1:\n", - " print(f\"\\n--- RUN {run_num + 1} of {NUM_RUNS} ---\")\n", - " \n", - " # Benchmark FAISS\n", - " print(f\"\\n--- FAISS BENCHMARK ---\")\n", - " faiss_write_results = benchmark_faiss_writing(test_texts, test_embeddings)\n", - " faiss_search_results = benchmark_search(\n", - " faiss_write_results['data_collection'], \n", - " search_queries, \n", - " RESULTS_PER_QUERY\n", - " )\n", - " \n", - " # Benchmark IRIS\n", - " print(f\"\\n--- IRIS BENCHMARK ---\")\n", - " table_name = f\"benchmark_{vector_count}_{run_num}\"\n", - " iris_write_results = benchmark_iris_writing(test_texts, test_embeddings, table_name)\n", - " iris_search_results = benchmark_search(\n", - " iris_write_results['data_collection'], \n", - " search_queries, \n", - " RESULTS_PER_QUERY\n", - " )\n", - " \n", - " # Store results for this run\n", - " run_results.append({\n", - " 'faiss_write_time': faiss_write_results['time_seconds'],\n", - " 'faiss_write_memory': faiss_write_results['memory_delta_mb'],\n", - " 'faiss_search_time': faiss_search_results['total_time_seconds'],\n", - " 'faiss_avg_query_time': faiss_search_results['avg_query_time_seconds'],\n", - " 'faiss_search_memory': faiss_search_results['memory_delta_mb'],\n", - " 'iris_write_time': iris_write_results['time_seconds'],\n", - " 'iris_write_memory': iris_write_results['memory_delta_mb'],\n", - " 'iris_search_time': iris_search_results['total_time_seconds'],\n", - " 'iris_avg_query_time': iris_search_results['avg_query_time_seconds'],\n", - " 'iris_search_memory': iris_search_results['memory_delta_mb'],\n", - " })\n", - " \n", - " # Average results across runs (or use single result if NUM_RUNS == 1)\n", - " if NUM_RUNS > 1:\n", - " avg_results = {}\n", - " for key in run_results[0].keys():\n", - " avg_results[key] = np.mean([r[key] for r in run_results])\n", - " results.append({\n", - " 'vector_count': vector_count,\n", - " **avg_results\n", - " })\n", - " else:\n", - " results.append({\n", - " 'vector_count': vector_count,\n", - " **run_results[0]\n", - " })\n", - " \n", - " print(f\"\\nCompleted benchmark for {vector_count} vectors\")\n", - "\n", - "print(f\"\\n{'='*60}\")\n", - "print(\"ALL BENCHMARKS COMPLETED\")\n", - "print(f\"{'='*60}\")" - ] + "execution_count": 8 }, { "cell_type": "markdown", @@ -772,13 +845,18 @@ }, { "cell_type": "code", - "execution_count": 7, "metadata": { "ExecuteTime": { - "end_time": "2025-10-29T05:02:49.133171Z", - "start_time": "2025-10-29T05:02:49.117523Z" + "end_time": "2025-11-03T18:05:34.742868Z", + "start_time": "2025-11-03T18:05:34.725987Z" } }, + "source": [ + "# Create results DataFrame\n", + "df_results = pd.DataFrame(results)\n", + "print(\"Benchmark Results:\")\n", + "print(df_results.round(4))" + ], "outputs": [ { "name": "stdout", @@ -786,47 +864,51 @@ "text": [ "Benchmark Results:\n", " vector_count faiss_write_time faiss_write_memory faiss_search_time \\\n", - "0 10 0.0094 1.7135 0.4960 \n", - "1 1000 0.0004 1.7031 0.4688 \n", - "2 10000 0.0029 16.8750 0.5030 \n", - "3 100000 0.0869 249.8646 1.1198 \n", + "0 10 0.0096 0.9740 0.5323 \n", + "1 1000 0.0008 3.9792 0.5130 \n", + "2 10000 0.0035 17.3073 0.5076 \n", + "3 100000 0.1428 257.6250 0.8355 \n", "\n", " faiss_avg_query_time faiss_search_memory iris_write_time \\\n", - "0 0.0050 9.6042 0.0351 \n", - "1 0.0047 0.0938 0.5409 \n", - "2 0.0050 0.0833 3.2143 \n", - "3 0.0112 0.1094 32.5681 \n", + "0 0.0053 7.8854 0.0318 \n", + "1 0.0051 0.0833 0.5407 \n", + "2 0.0051 0.0417 2.9793 \n", + "3 0.0084 2.9323 30.4268 \n", "\n", " iris_write_memory iris_search_time iris_avg_query_time \\\n", - "0 0.1719 0.6910 0.0069 \n", - "1 32.0208 0.8787 0.0088 \n", - "2 78.6302 1.0869 0.0109 \n", - "3 44.2917 3.1850 0.0318 \n", + "0 0.5729 0.6388 0.0064 \n", + "1 52.1875 0.9740 0.0097 \n", + "2 554.8958 1.1528 0.0115 \n", + "3 316.3021 13.8491 0.1385 \n", "\n", " iris_search_memory \n", - "0 0.0469 \n", - "1 0.0677 \n", - "2 2.3802 \n", - "3 7.1667 \n" + "0 0.2604 \n", + "1 1.1302 \n", + "2 0.1771 \n", + "3 0.0000 \n" ] } ], - "source": [ - "# Create results DataFrame\n", - "df_results = pd.DataFrame(results)\n", - "print(\"Benchmark Results:\")\n", - "print(df_results.round(4))" - ] + "execution_count": 9 }, { "cell_type": "code", - "execution_count": 8, "metadata": { "ExecuteTime": { - "end_time": "2025-10-29T05:02:49.329166Z", - "start_time": "2025-10-29T05:02:49.315084Z" + "end_time": "2025-11-03T18:05:34.813896Z", + "start_time": "2025-11-03T18:05:34.806454Z" } }, + "source": [ + "# Calculate performance ratios\n", + "df_results['write_time_ratio'] = df_results['iris_write_time'] / df_results['faiss_write_time']\n", + "df_results['search_time_ratio'] = df_results['iris_search_time'] / df_results['faiss_search_time']\n", + "df_results['avg_query_time_ratio'] = df_results['iris_avg_query_time'] / df_results['faiss_avg_query_time']\n", + "\n", + "print(\"\\nPerformance Ratios (IRIS / FAISS):\")\n", + "print(\"Values > 1 mean IRIS is slower, Values < 1 mean IRIS is faster\")\n", + "print(df_results[['vector_count', 'write_time_ratio', 'search_time_ratio', 'avg_query_time_ratio']].round(4))" + ], "outputs": [ { "name": "stdout", @@ -836,23 +918,14 @@ "Performance Ratios (IRIS / FAISS):\n", "Values > 1 mean IRIS is slower, Values < 1 mean IRIS is faster\n", " vector_count write_time_ratio search_time_ratio avg_query_time_ratio\n", - "0 10 3.7281 1.3931 1.3931\n", - "1 1000 1290.0106 1.8743 1.8744\n", - "2 10000 1106.1843 2.1609 2.1609\n", - "3 100000 374.6489 2.8444 2.8443\n" + "0 10 3.3070 1.2002 1.2002\n", + "1 1000 699.8418 1.8988 1.8988\n", + "2 10000 844.2519 2.2712 2.2712\n", + "3 100000 213.1458 16.5751 16.5765\n" ] } ], - "source": [ - "# Calculate performance ratios\n", - "df_results['write_time_ratio'] = df_results['iris_write_time'] / df_results['faiss_write_time']\n", - "df_results['search_time_ratio'] = df_results['iris_search_time'] / df_results['faiss_search_time']\n", - "df_results['avg_query_time_ratio'] = df_results['iris_avg_query_time'] / df_results['faiss_avg_query_time']\n", - "\n", - "print(\"\\nPerformance Ratios (IRIS / FAISS):\")\n", - "print(\"Values > 1 mean IRIS is slower, Values < 1 mean IRIS is faster\")\n", - "print(df_results[['vector_count', 'write_time_ratio', 'search_time_ratio', 'avg_query_time_ratio']].round(4))" - ] + "execution_count": 10 }, { "cell_type": "markdown", @@ -863,25 +936,12 @@ }, { "cell_type": "code", - "execution_count": 9, "metadata": { "ExecuteTime": { - "end_time": "2025-10-29T05:02:50.211866Z", - "start_time": "2025-10-29T05:02:49.368793Z" + "end_time": "2025-11-03T18:05:35.512854Z", + "start_time": "2025-11-03T18:05:34.834507Z" } }, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "# Create visualizations\n", "fig, axes = plt.subplots(2, 2, figsize=(15, 12))\n", @@ -928,7 +988,20 @@ "\n", "plt.tight_layout()\n", "plt.show()" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 11 }, { "cell_type": "markdown", @@ -939,25 +1012,12 @@ }, { "cell_type": "code", - "execution_count": 10, "metadata": { "ExecuteTime": { - "end_time": "2025-10-29T05:02:50.397780Z", - "start_time": "2025-10-29T05:02:50.223989Z" + "end_time": "2025-11-03T18:05:35.706158Z", + "start_time": "2025-11-03T18:05:35.528546Z" } }, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "# Memory usage comparison\n", "fig, axes = plt.subplots(1, 2, figsize=(15, 6))\n", @@ -982,7 +1042,20 @@ "\n", "plt.tight_layout()\n", "plt.show()" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 12 }, { "cell_type": "markdown", @@ -995,8 +1068,8 @@ "Based on the benchmark results with 3 runs per test configuration:\n", "\n", "1. **Write Performance**: FAISS is significantly faster for writes (3.7x to 1290x faster)\n", - " - Small datasets (10-1000 vectors): FAISS advantages are most pronounced (1290x faster)\n", - " - Large datasets (100k vectors): FAISS still 375x faster, but gap decreases\n", + " - Small datasets (10-1000 vectors): FAISS advantages are most pronounced\n", + " - Large datasets (100k vectors): FAISS still ~200x faster, but gap decreases\n", " - IRIS overhead comes from database I/O, string conversion, and network communication\n", "\n", "2. **Search Performance**: FAISS is consistently faster, but the gap is smaller\n", @@ -1031,13 +1104,19 @@ }, { "cell_type": "code", - "execution_count": 12, "metadata": { "ExecuteTime": { - "end_time": "2025-10-29T06:34:38.139888Z", - "start_time": "2025-10-29T06:34:38.135616Z" + "end_time": "2025-11-03T18:05:35.719473Z", + "start_time": "2025-11-03T18:05:35.716488Z" } }, + "source": [ + "# Display detailed results table\n", + "print(\"Detailed Benchmark Results:\")\n", + "print(\"=\"*80)\n", + "print(df_results.to_string())\n", + "print(\"\\n\" + \"=\"*80)" + ], "outputs": [ { "name": "stdout", @@ -1046,22 +1125,16 @@ "Detailed Benchmark Results:\n", "================================================================================\n", " vector_count faiss_write_time faiss_write_memory faiss_search_time faiss_avg_query_time faiss_search_memory iris_write_time iris_write_memory iris_search_time iris_avg_query_time iris_search_memory write_time_ratio search_time_ratio avg_query_time_ratio\n", - "0 10 0.009417 1.713542 0.496016 0.004960 9.604167 0.035110 0.171875 0.691010 0.006910 0.046875 3.728141 1.393120 1.393134\n", - "1 1000 0.000419 1.703125 0.468809 0.004688 0.093750 0.540900 32.020833 0.878712 0.008787 0.067708 1290.010614 1.874349 1.874360\n", - "2 10000 0.002906 16.875000 0.502999 0.005030 0.083333 3.214313 78.630208 1.086933 0.010868 2.380208 1106.184257 2.160903 2.160850\n", - "3 100000 0.086930 249.864583 1.119773 0.011197 0.109375 32.568124 44.291667 3.185037 0.031848 7.166667 374.648909 2.844359 2.844348\n", + "0 10 0.009603 0.973958 0.532262 0.005322 7.885417 0.031759 0.572917 0.638802 0.006388 0.260417 3.307039 1.200165 1.200180\n", + "1 1000 0.000773 3.979167 0.512965 0.005129 0.083333 0.540722 52.187500 0.973998 0.009739 1.130208 699.841802 1.898761 1.898775\n", + "2 10000 0.003529 17.307292 0.507551 0.005075 0.041667 2.979292 554.895833 1.152759 0.011527 0.177083 844.251937 2.271217 2.271184\n", + "3 100000 0.142751 257.625000 0.835534 0.008354 2.932292 30.426794 316.302083 13.849079 0.138488 0.000000 213.145838 16.575129 16.576505\n", "\n", "================================================================================\n" ] } ], - "source": [ - "# Display detailed results table\n", - "print(\"Detailed Benchmark Results:\")\n", - "print(\"=\"*80)\n", - "print(df_results.to_string())\n", - "print(\"\\n\" + \"=\"*80)" - ] + "execution_count": 13 }, { "cell_type": "markdown", @@ -1072,15 +1145,15 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2025-10-29T05:02:50.421702Z", - "start_time": "2025-10-29T05:02:50.420464Z" + "end_time": "2025-11-03T18:05:35.735059Z", + "start_time": "2025-11-03T18:05:35.733879Z" } }, + "source": [], "outputs": [], - "source": [] + "execution_count": null } ], "metadata": {