5 commits

Author	SHA1	Message	Date
Francisco Arceo	37b6da37ba	docs: Document sqlite-vec faiss comparison (#1821) ... # What does this PR do? This PR documents and benchmarks the performance tradeoffs between sqlite-vec and FAISS inline VectorDB providers. # Closes https://github.com/meta-llama/llama-stack/issues/1165 ## Test Plan The test was run using this script: <details> <summary>CLICK TO SHOW SCRIPT 👋 </summary> ```python import cProfile import os import uuid import time import random import string import matplotlib.pyplot as plt import pandas as pd from termcolor import cprint from llama_stack_client.types import Document from llama_stack.distribution.library_client import LlamaStackAsLibraryClient from memory_profiler import profile from line_profiler import LineProfiler os.environ["INFERENCE_MODEL"] = "llama3.2:3b-instruct-fp16" os.environ["LLAMA_STACK_CONFIG"] = "ollama" def generate_random_chars(count=400): return ''.join(random.choices(string.ascii_letters, k=count)) def generate_documents(num_docs: int, num_chars: int): documents = [ Document( document_id=f"doc-{i}", content=f"Document content for document {i} - {generate_random_chars(count=num_chars)}", mime_type="text/plain", metadata={}, ) for i in range(num_docs) ] return documents @profile def benchmark_write(client, vector_db_id, documents, batch_size=100): write_times = [] for i in range(0, len(documents), batch_size): batch = documents[i:i + batch_size] start_time = time.time() client.tool_runtime.rag_tool.insert( documents=batch, vector_db_id=vector_db_id, chunk_size_in_tokens=512, ) end_time = time.time() write_times.append(end_time - start_time) return write_times @profile def benchmark_read(client, provider_id, vector_db_id, user_prompts): response_times = [] for prompt in user_prompts: start_time = time.time() response = client.vector_io.query( vector_db_id=vector_db_id, query=prompt, ) end_time = time.time() response_times.append(end_time - start_time) return response_times def profile_functions(): profiler = LineProfiler() profiler.add_function(benchmark_write) profiler.add_function(benchmark_read) return profiler def plot_results(output, batch_size): # Create a DataFrame for easy manipulation df_sqlite = pd.DataFrame(output['sqlite-vec']) df_faiss = pd.DataFrame(output['faiss']) df_sqlite['write_times'] *= 1000 df_faiss['write_times'] *= 1000 avg_write_sqlite = df_sqlite['write_times'].mean() avg_write_faiss = df_faiss['write_times'].mean() avg_read_sqlite = df_sqlite['read_times'].mean() avg_read_faiss = df_faiss['read_times'].mean() plt.figure(figsize=(12, 6)) plt.hist(df_sqlite['write_times'], bins=10, alpha=0.5, color='blue', label='sqlite-vec Write Times') plt.hist(df_faiss['write_times'], bins=10, alpha=0.5, color='red', label='faiss Write Times') plt.axvline(avg_write_sqlite, color='blue', linestyle='--', label=f'Average Write Time (sqlite-vec): {avg_write_sqlite:.3f} ms') plt.axvline(avg_write_faiss, color='red', linestyle='--', label=f'Average Write Time (faiss): {avg_write_faiss:.3f} ms') plt.title(f'Histogram of Write Times for sqlite-vec and faiss\nn = {df_faiss.shape[0]} with batch size = {batch_size}') plt.xlabel('Time (milliseconds)') plt.ylabel('Density') plt.legend() plt.savefig('write_time_comparison.png') plt.close() plt.figure(figsize=(12, 6)) plt.hist(df_sqlite['read_times'], bins=10, alpha=0.5, color='blue', label='sqlite-vec Read Times') plt.hist(df_faiss['read_times'], bins=10, alpha=0.5, color='red', label='faiss Read Times') plt.axvline(avg_read_sqlite, color='blue', linestyle='--', label=f'Average Read Time (sqlite-vec): {avg_read_sqlite:.3f} ms') plt.axvline(avg_read_faiss, color='red', linestyle='--', label=f'Average Read Time (faiss): {avg_read_faiss:.3f} ms') plt.title(f'Histogram of Read Times for sqlite-vec and faiss\nn = {df_faiss.shape[0]}') plt.xlabel('Time (milliseconds)') plt.ylabel('Density') plt.legend() plt.savefig('read_time_comparison.png') plt.close() plt.figure(figsize=(12, 6)) plt.hist(df_sqlite['read_times'], bins=10, alpha=0.5, color='blue', label='sqlite-vec Read Times') plt.hist(df_faiss['read_times'], bins=10, alpha=0.5, color='red', label='faiss Read Times') plt.axvline(avg_read_sqlite, color='blue', linestyle='--', label=f'Average Read Time (sqlite-vec): {avg_read_sqlite:.3f} ms') plt.axvline(avg_read_faiss, color='red', linestyle='--', label=f'Average Read Time (faiss): {avg_read_faiss:.3f} ms') plt.title(f'Histogram of Read Times for sqlite-vec and faiss\nn = {df_faiss.shape[0]}') plt.xlabel('Time (milliseconds)') plt.ylabel('Density') plt.legend() plt.savefig('read_time_comparison.png') plt.close() plt.figure(figsize=(12, 6)) plt.plot(df_sqlite.index, df_sqlite['write_times'], marker='o', markersize=4, linestyle='-', color='blue', label='sqlite-vec Write Times') plt.plot(df_faiss.index, df_faiss['write_times'], marker='x', markersize=4, linestyle='-', color='red', label='faiss Write Times') plt.title(f'Write Times by Operation Sequence\n(batch size = {batch_size})') plt.xlabel('Write Operation Sequence') plt.ylabel('Time (milliseconds)') plt.legend() plt.grid(True, linestyle='--', alpha=0.7) plt.tight_layout() plt.savefig('write_time_sequence.png') plt.close() # Print out the summary table print("\nPerformance Summary for sqlite-vec:") print(df_sqlite) # Print out the summary table print("\nPerformance Summary for faiss:") print(df_faiss) def main(): # Initialize the client client = LlamaStackAsLibraryClient("ollama") vector_db_id = f"test-vector-db-{uuid.uuid4().hex}" _ = client.initialize() # Generate a large dataset num_chars = 50 num_docs = 100 num_writes = 100 write_batch_size = 100 num_reads = 100 documents = generate_documents(num_docs * write_batch_size, num_chars) user_prompts = [ f"Tell me about document {i}" for i in range(1, num_reads + 1) ] providers = ["sqlite-vec", "faiss"] output = { provider_id: {"write_times": None, "read_times": None} for provider_id in providers } # Benchmark writes and reads for SQLite and Faiss for provider_id in providers: cprint(f"Benchmarking provider: {provider_id}", "yellow") client.vector_dbs.register( provider_id=provider_id, vector_db_id=vector_db_id, embedding_model="all-MiniLM-L6-v2", embedding_dimension=384, ) write_times = benchmark_write(client, vector_db_id, documents, write_batch_size) average_write_time_ms = sum(write_times) / len(write_times) * 1000. cprint(f"Average write time for {provider_id} is {average_write_time_ms:.2f} milliseconds for {num_writes} runs", "blue") cprint(f"Benchmarking reads for provider: {provider_id}", "yellow") read_times = benchmark_read(client, provider_id, vector_db_id, user_prompts) average_read_time_ms = sum(read_times) / len(read_times) * 1000. cprint(f"Average read time for {provider_id} is {average_read_time_ms:.2f} milliseconds for {num_reads} runs", "blue") client.vector_dbs.unregister(vector_db_id=vector_db_id) output[provider_id]['write_times'] = write_times output[provider_id]['read_times'] = read_times # Generate plots and summary plot_results(output, write_batch_size) if __name__ == "__main__": cProfile.run('main()', 'profile_output.prof') ``` </details> --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-03-28 17:41:33 +01:00
Daniele Martinoli	cca9bd6cc3	feat: Qdrant inline provider (#1273) ... # What does this PR do? Removed local execution option from the remote Qdrant provider and introduced an explicit inline provider for the embedded execution. Updated the ollama template to include this option: this part can be reverted in case we don't want to have two default `vector_io` providers. (Closes #1082) ## Test Plan Build and run an ollama distro: ```bash llama stack build --template ollama --image-type conda llama stack run --image-type conda ollama ``` Run one of the sample ingestionapplicatinos like [rag_with_vector_db.py](https://github.com/meta-llama/llama-stack-apps/blob/main/examples/agents/rag_with_vector_db.py), but replace this line: ```py selected_vector_provider = vector_providers[0] ``` with the following, to use the `qdrant` provider: ```py selected_vector_provider = vector_providers[1] ``` After running the test code, verify the timestamp of the Qdrant store: ```bash % ls -ltr ~/.llama/distributions/ollama/qdrant.db/collection/test_vector_db_* total 784 -rw-r--r--@ 1 dmartino staff 401408 Feb 26 10:07 storage.sqlite ``` [//]: # (## Documentation) --------- Signed-off-by: Daniele Martinoli <dmartino@redhat.com> Co-authored-by: Francisco Arceo <farceo@redhat.com>	2025-03-18 14:04:21 -07:00
Ashwin Bharambe	330cc9d09d	feat: add Milvus vectorDB (#1467) ... # What does this PR do? See https://github.com/meta-llama/llama-stack/pull/1171 which is the original PR. Author: @zc277584121 feat: add [Milvus](https://milvus.io/) vectorDB note: I use the MilvusClient to implement it instead of AsyncMilvusClient, because when I tested AsyncMilvusClient, it would raise issues about evenloop, which I think AsyncMilvusClient SDK is not robust enough to be compatible with llama_stack framework. ## Test Plan have passed the unit test and ene2end test Here is my end2end test logs, including the client code, client log, server logs from inline and remote settings [test_end2end_logs.zip](https://github.com/user-attachments/files/18964391/test_end2end_logs.zip) --------- Signed-off-by: ChengZi <chen.zhang@zilliz.com> Co-authored-by: Cheney Zhang <chen.zhang@zilliz.com>	2025-03-06 20:59:31 -08:00
Ashwin Bharambe	8bbd52bb9f	chore: remove dependency on llama_models completely (#1344)	2025-03-01 12:48:08 -08:00
Francisco Arceo	19ae4b35d9	docs: Adding Provider sections to docs (#1195) ... # What does this PR do? Adding Provider sections to docs (some of these will be empty and need updating). This PR is still a draft while I seek feedback from other contributors. I opened it to make the structure visible in the linked GitHub Issue. # Closes https://github.com/meta-llama/llama-stack/issues/1189 - Providers Overview Page  - SQLite-Vec specific page  ## Test Plan N/A [//]: # (## Documentation) --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-02-22 11:59:34 -08:00