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a/ragas/compose.yml b/ragas/compose.yml new file mode 100644 index 0000000..ae24282 --- /dev/null +++ b/ragas/compose.yml @@ -0,0 +1,18 @@ +version: "3.7" +services: + qdrant: + image: qdrant/qdrant:v1.9.2 + ports: + - "6333:6333" + - "6334:6334" # grpc + volumes: + - qdrant-data:/qdrant/storage + redis: + image: redis:7-alpine + ports: + - 6397:6379 + volumes: + - redis-data:/data +volumes: + qdrant-data: + redis-data: diff --git a/ragas/notebook.ipynb b/ragas/notebook.ipynb new file mode 100644 index 0000000..467e9c3 --- /dev/null +++ b/ragas/notebook.ipynb @@ -0,0 +1,588 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "115ddde1", + "metadata": {}, + "source": [ + "# Swiftide evaluation with Ragas\n", + "\n", + "This notebook demonstrates how to evaluate a Swiftide query pipeline with Ragas. The rust implementation is a bit contrived to make it more configurable. However, the main thing is running the query pipeline with an evaluator, and then exporting the result of that evaluator to a JSON file compatible with a HF dataset.\n", + "\n", + "In the Rust code we use all features enabled as the base line, capturing the answers as the 'ground truth'.\n", + "\n", + "**tl&dr; pretty charts at the bottom**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8bbd3c1e-bc19-46ba-8b26-8881b2ef1001", + "metadata": { + "notebookRunGroups": { + "groupValue": "2" + } + }, + "outputs": [], + "source": [ + "# Run with all features and use the answers as the ground truths\n", + "!RUST_LOG=swiftide=info cargo run -- --language rust --path . --output base.json --record-ground-truth \"How is swiftide used?\" \"How are arguments passed?\" \"How is Ragas used?\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "605df33f", + "metadata": {}, + "outputs": [], + "source": [ + "# Run with chunking enabled and QA metadata disabled\n", + "!RUST_LOG=swiftide=info cargo run --no-default-features --features=chunk -- --language rust --path . --output metadata.json --record-ground-truth --file base.json" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c806589e", + "metadata": {}, + "outputs": [], + "source": [ + "# Run with chunking disabled \n", + "!RUST_LOG=swiftide=info cargo run --no-default-features --features=metadata -- --language rust --path . --output chunk.json --record-ground-truth --file base.json" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c42996ce", + "metadata": {}, + "outputs": [], + "source": [ + "# Run with chunking and metadata disabled\n", + "!RUST_LOG=swiftide=info cargo run --no-default-features -- --language rust --path . --output nothing.json --record-ground-truth --file base.json" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "60a56162", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "83505227bdd643beb7cb1d1685b49784", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Generating everything split: 0 examples [00:00, ? examples/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6344adc79ea14245bbdb032ea19a7a3d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Generating metadata split: 0 examples [00:00, ? examples/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4040a303cb86487783f7730f0afaf6ad", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Generating chunk split: 0 examples [00:00, ? examples/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9f95c181e259471391a26b0b07166051", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Generating nothing split: 0 examples [00:00, ? examples/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "DatasetDict({\n", + " everything: Dataset({\n", + " features: ['answer', 'contexts', 'ground_truth', 'question'],\n", + " num_rows: 3\n", + " })\n", + " metadata: Dataset({\n", + " features: ['answer', 'contexts', 'ground_truth', 'question'],\n", + " num_rows: 3\n", + " })\n", + " chunk: Dataset({\n", + " features: ['answer', 'contexts', 'ground_truth', 'question'],\n", + " num_rows: 3\n", + " })\n", + " nothing: Dataset({\n", + " features: ['answer', 'contexts', 'ground_truth', 'question'],\n", + " num_rows: 3\n", + " })\n", + "})" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from datasets import load_dataset\n", + "files = { \n", + " \"everything\": \"base.json\",\n", + " \"metadata\": \"metadata.json\",\n", + " \"chunk\": \"chunk.json\",\n", + " \"nothing\": \"nothing.json\"\n", + "}\n", + "dataset = load_dataset(\"json\", data_files=files)\n", + "\n", + "dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "fb3b137f", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3faf633f6d764dd7b2a6da87d1bd5720", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Evaluating: 0%| | 0/12 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
answercontextsground_truthquestionanswer_relevancyfaithfulnesscontext_recallcontext_precisiondataset
0```markdown\\nSwiftide is used for indexing cod...[\"use swiftide::{\\n indexing::{\\n lo...```markdown\\nSwiftide is used for indexing cod...How is swiftide used?0.9001421.0000001.001.0everything
1```markdown\\nArguments are passed in the conte...[\"\\nstruct Args {\\n #[arg(short, long)]\\n ...```markdown\\nArguments are passed in the conte...How are arguments passed?0.9147731.0000001.001.0everything
2```markdown\\nRagas is used in the context of t...[\"\\n\\nasync fn query(\\n questions: Evaluati...```markdown\\nRagas is used in the context of t...How is Ragas used?0.8949121.0000000.751.0everything
0```markdown\\nArguments are passed using a stru...[\"\\nstruct Args {\\n #[arg(short, long)]\\n ...```markdown\\nArguments are passed using a stru...How are arguments passed?0.9417461.0000001.001.0metadata
1```markdown\\nRagas is used in the context of e...[\"\\n\\nasync fn query(\\n questions: Evaluati...```markdown\\nRagas is used in the context of e...How is Ragas used?0.9221481.0000001.001.0metadata
2```markdown\\nSwiftide is used to index code an...[\"use swiftide::{\\n indexing::{\\n lo...```markdown\\nSwiftide is used to index code an...How is swiftide used?0.8601021.0000001.001.0metadata
0```markdown\\nRagas is used in the context of e...[\"use swiftide::{\\n indexing::{\\n lo...```markdown\\nRagas is used in the context of e...How is Ragas used?0.9306330.9285711.001.0chunk
1```markdown\\nSwiftide is used in the context o...[\"use swiftide::{\\n indexing::{\\n lo...```markdown\\nSwiftide is used in the context o...How is swiftide used?0.9270461.0000001.001.0chunk
2```markdown\\nArguments are passed in the conte...[\"use swiftide::{\\n indexing::{\\n lo...```markdown\\nArguments are passed in the conte...How are arguments passed?0.7915801.0000001.001.0chunk
0## How is Ragas used?\\n\\nRagas is used in the ...[\"use swiftide::{\\n indexing::{\\n lo...## How is Ragas used?\\n\\nRagas is used in the ...How is Ragas used?0.9134391.0000001.001.0nothing
1```markdown\\nSwiftide is used for indexing cod...[\"use swiftide::{\\n indexing::{\\n lo...```markdown\\nSwiftide is used for indexing cod...How is swiftide used?0.9270101.0000001.001.0nothing
2```markdown\\nArguments are passed in the conte...[\"use swiftide::{\\n indexing::{\\n lo...```markdown\\nArguments are passed in the conte...How are arguments passed?0.7852300.9444441.001.0nothing
\n", + "" + ], + "text/plain": [ + " answer \\\n", + "0 ```markdown\\nSwiftide is used for indexing cod... \n", + "1 ```markdown\\nArguments are passed in the conte... \n", + "2 ```markdown\\nRagas is used in the context of t... \n", + "0 ```markdown\\nArguments are passed using a stru... \n", + "1 ```markdown\\nRagas is used in the context of e... \n", + "2 ```markdown\\nSwiftide is used to index code an... \n", + "0 ```markdown\\nRagas is used in the context of e... \n", + "1 ```markdown\\nSwiftide is used in the context o... \n", + "2 ```markdown\\nArguments are passed in the conte... \n", + "0 ## How is Ragas used?\\n\\nRagas is used in the ... \n", + "1 ```markdown\\nSwiftide is used for indexing cod... \n", + "2 ```markdown\\nArguments are passed in the conte... \n", + "\n", + " contexts \\\n", + "0 [\"use swiftide::{\\n indexing::{\\n lo... \n", + "1 [\"\\nstruct Args {\\n #[arg(short, long)]\\n ... \n", + "2 [\"\\n\\nasync fn query(\\n questions: Evaluati... \n", + "0 [\"\\nstruct Args {\\n #[arg(short, long)]\\n ... \n", + "1 [\"\\n\\nasync fn query(\\n questions: Evaluati... \n", + "2 [\"use swiftide::{\\n indexing::{\\n lo... \n", + "0 [\"use swiftide::{\\n indexing::{\\n lo... \n", + "1 [\"use swiftide::{\\n indexing::{\\n lo... \n", + "2 [\"use swiftide::{\\n indexing::{\\n lo... \n", + "0 [\"use swiftide::{\\n indexing::{\\n lo... \n", + "1 [\"use swiftide::{\\n indexing::{\\n lo... \n", + "2 [\"use swiftide::{\\n indexing::{\\n lo... \n", + "\n", + " ground_truth \\\n", + "0 ```markdown\\nSwiftide is used for indexing cod... \n", + "1 ```markdown\\nArguments are passed in the conte... \n", + "2 ```markdown\\nRagas is used in the context of t... \n", + "0 ```markdown\\nArguments are passed using a stru... \n", + "1 ```markdown\\nRagas is used in the context of e... \n", + "2 ```markdown\\nSwiftide is used to index code an... \n", + "0 ```markdown\\nRagas is used in the context of e... \n", + "1 ```markdown\\nSwiftide is used in the context o... \n", + "2 ```markdown\\nArguments are passed in the conte... \n", + "0 ## How is Ragas used?\\n\\nRagas is used in the ... \n", + "1 ```markdown\\nSwiftide is used for indexing cod... \n", + "2 ```markdown\\nArguments are passed in the conte... \n", + "\n", + " question answer_relevancy faithfulness context_recall \\\n", + "0 How is swiftide used? 0.900142 1.000000 1.00 \n", + "1 How are arguments passed? 0.914773 1.000000 1.00 \n", + "2 How is Ragas used? 0.894912 1.000000 0.75 \n", + "0 How are arguments passed? 0.941746 1.000000 1.00 \n", + "1 How is Ragas used? 0.922148 1.000000 1.00 \n", + "2 How is swiftide used? 0.860102 1.000000 1.00 \n", + "0 How is Ragas used? 0.930633 0.928571 1.00 \n", + "1 How is swiftide used? 0.927046 1.000000 1.00 \n", + "2 How are arguments passed? 0.791580 1.000000 1.00 \n", + "0 How is Ragas used? 0.913439 1.000000 1.00 \n", + "1 How is swiftide used? 0.927010 1.000000 1.00 \n", + "2 How are arguments passed? 0.785230 0.944444 1.00 \n", + "\n", + " context_precision dataset \n", + "0 1.0 everything \n", + "1 1.0 everything \n", + "2 1.0 everything \n", + "0 1.0 metadata \n", + "1 1.0 metadata \n", + "2 1.0 metadata \n", + "0 1.0 chunk \n", + "1 1.0 chunk \n", + "2 1.0 chunk \n", + "0 1.0 nothing \n", + "1 1.0 nothing \n", + "2 1.0 nothing " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from ragas.metrics import (\n", + " answer_relevancy,\n", + " faithfulness,\n", + " context_recall,\n", + " context_precision,\n", + ")\n", + "\n", + "import pandas as pd\n", + "\n", + "from ragas import evaluate\n", + "\n", + "# Run evaluate on each dataset, add a column with the dataset name, then concat into single dataframe\n", + "all_results = []\n", + "for key in files:\n", + " result = evaluate(dataset[key], metrics=[answer_relevancy,faithfulness,context_recall,context_precision]).to_pandas()\n", + " result['dataset'] = key\n", + " all_results.append(result)\n", + " \n", + "df = pd.concat(all_results)\n", + "df\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "bf4e1a2a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Grouping by dataset and calculating the mean\n", + "df_grouped = df.groupby('dataset').mean(True).reset_index()\n", + "\n", + "# 1. Bar Chart\n", + "df_grouped.plot(x='dataset', kind='bar', figsize=(12, 6), title='Mean Performance Metrics per Dataset')\n", + "plt.ylabel('Mean Metric Value')\n", + "plt.show()\n", + "\n", + "# 2. Heatmap\n", + "plt.figure(figsize=(10, 6))\n", + "sns.heatmap(df_grouped.set_index('dataset').T, annot=True, cmap='Blues')\n", + "plt.title('Performance Heatmap per Dataset')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "819002f0", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ragas/poetry.lock b/ragas/poetry.lock new file mode 100644 index 0000000..30ebb7d --- /dev/null +++ b/ragas/poetry.lock @@ -0,0 +1,4416 @@ +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. + 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= "0.1.0" +description = "" +authors = ["Timon Vonk "] +readme = "README.md" +package-mode = false + +[tool.poetry.dependencies] +python = "^3.12" +datasets = "^2.21.0" +ragas = "^0.1.14" +pandas = "^2.2.2" +seaborn = "^0.13.2" +matplotlib = "^3.9.2" + + +[tool.poetry.group.dev.dependencies] +jupyter = "^1.0.0" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/ragas/src/main.rs b/ragas/src/main.rs new file mode 100644 index 0000000..c94acfc --- /dev/null +++ b/ragas/src/main.rs @@ -0,0 +1,196 @@ +use swiftide::{ + indexing::{ + loaders::FileLoader, + transformers::{ChunkCode, ChunkMarkdown, Embed, MetadataQACode, MetadataQAText}, + }, + query::{ + self, + answers::Simple, + evaluators::{self, ragas::EvaluationDataSet}, + query_transformers::{self, GenerateSubquestions}, + }, +}; + +use std::{path::PathBuf, str::FromStr}; + +use anyhow::{Context as _, Result}; +use clap::Parser; +use swiftide::{ + indexing::Pipeline, + integrations::{openai::OpenAI, qdrant::Qdrant, redis::Redis, treesitter::SupportedLanguages}, +}; + +const COLLECTION_NAME: &str = "swiftide-ragas"; + +#[derive(Parser, Debug)] +#[command(version, about, long_about = None)] +struct Args { + #[arg(short, long)] + /// Language of the code to index + language: String, + + #[arg(short, long, default_value = "./")] + /// Path to the code to index + path: PathBuf, + + #[command(flatten)] + dataset: DatasetArg, + + #[arg(short, long, default_value = "false")] + /// Records answers as ground truth + record_ground_truth: bool, + + #[arg(short, long)] + /// Output file to write the evaluation results to + output: PathBuf, +} + +#[derive(clap::Args, Debug, Clone)] +#[group(required = true, multiple = false)] +struct DatasetArg { + /// Dataset json file to load questions and ground truths from + #[arg(short, long, conflicts_with = "questions")] + file: Option, + /// List of questions to use for evaluation + questions: Option>, +} + +struct Context { + openai: OpenAI, + qdrant: Qdrant, +} + +#[tokio::main] +async fn main() -> Result<()> { + tracing_subscriber::fmt::init(); + + let args = Args::parse(); + + // Initialize the OpenAI client + let openai = OpenAI::builder() + .default_embed_model("text-embedding-3-small") + .default_prompt_model("gpt-4o-mini") + .build()?; + + // Initialize the Qdrant client + let qdrant = Qdrant::builder() + .vector_size(1536) + .collection_name(COLLECTION_NAME) + .build()?; + + let context = Context { openai, qdrant }; + + // Delete the collection if it already exists + force_delete_qdrant_collection(&context).await?; + + // Index the code + index_all(&args.language, &args.path, &context).await?; + + // Either load the dataset from a file or use the questions provided + // Then create the evaluation dataset to be used + let dataset: EvaluationDataSet = if let Some(path) = args.dataset.file { + std::fs::read_to_string(path)?.parse()? + } else { + args.dataset + .questions + .ok_or(anyhow::anyhow!("Expected questions"))? + .into() + }; + + // Query the indexed dataset and return the evaluation + let evaluation = query(dataset, args.record_ground_truth, &context).await?; + + // Write the evaluation to a json file so it can be used in the python notebook + let json = evaluation.to_json().await; + std::fs::write(args.output, json).context("Failed to write ragas.json")?; + + Ok(()) +} + +async fn index_all(language: &str, path: &PathBuf, context: &Context) -> Result<()> { + tracing::info!(path=?path, language, "Indexing code"); + + let language = SupportedLanguages::from_str(language)?; + let mut extensions = language.file_extensions().to_owned(); + extensions.push("md"); + + // Index all code and markdown files in the provided directory + let (mut markdown, mut code) = Pipeline::from_loader( + FileLoader::new(path).with_extensions(&extensions), + ) + .split_by(|node| { + // Any errors at this point we just pass to 'markdown' + let Ok(node) = node else { return true }; + + // On true we go 'markdown', on false we go 'code'. + node.path.extension().map_or(true, |ext| ext == "md") + }); + + // For each feature that we want to test, enable them conditionally + + if cfg!(feature = "chunk") { + code = code + // Uses tree-sitter to extract best effort blocks of code. We still keep the minimum + // fairly high and double the chunk size + .then_chunk(ChunkCode::try_for_language_and_chunk_size( + language, + 50..1024, + )?); + + markdown = markdown.then_chunk(ChunkMarkdown::from_chunk_range(50..1024)); + } + + if cfg!(feature = "metadata") { + code = code.then(MetadataQACode::new(context.openai.clone())); + markdown = markdown.then(MetadataQAText::new(context.openai.clone())); + } + + // Merge both pipelines and generate embeddings + code.merge(markdown) + .then_in_batch(50, Embed::new(context.openai.clone())) + .then_store_with(context.qdrant.clone()) + .run() + .await +} + +async fn query( + questions: EvaluationDataSet, + record_ground_truth: bool, + context: &Context, +) -> Result { + // Create a new evaluator with prepared questions, either from the input file or the provided + // questions + let ragas = evaluators::ragas::Ragas::from_prepared_questions(questions); + + // Run a query pipeline that answers all provided questions + let pipeline = query::Pipeline::default() + .evaluate_with(ragas.clone()) + .then_transform_query(GenerateSubquestions::from_client(context.openai.clone())) + .then_transform_query(query_transformers::Embed::from_client( + context.openai.clone(), + )) + .then_retrieve(context.qdrant.clone()) + .then_answer(Simple::from_client(context.openai.clone())); + + pipeline.query_all(ragas.questions().await).await?; + + // If the flag is set, record the answers as ground truth. + // Ragas needs to know the correct answers to evaluate certain metrics. + // + // Can also be set manually or have RAGAS handle it. There are pros and cons to each. + if record_ground_truth { + ragas.record_answers_as_ground_truth().await; + } + + Ok(ragas) +} + +async fn force_delete_qdrant_collection(context: &Context) -> Result<()> { + let _ = context + .qdrant + .client() + .delete_collection(COLLECTION_NAME) + .await; + + Ok(()) +}