From c272ddd82d444909997d62194f41459dd0fe1918 Mon Sep 17 00:00:00 2001 From: Chris Coutinho Date: Sat, 15 Nov 2025 23:11:06 +0100 Subject: [PATCH 01/10] feat: implement RAG evaluation framework with CLI tooling MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Add ADR-013 documenting RAG evaluation architecture - Implement two-part evaluation: Context Recall (retrieval) + Answer Correctness (generation) - Create Click CLI for ground truth generation and corpus upload - Add pytest fixtures and tests for retrieval/generation quality - Use BeIR/nfcorpus dataset with 5 selected test queries - Support Ollama and Anthropic LLM providers - Generate synthetic ground truth answers offline - Add comprehensive documentation in tests/rag_evaluation/README.md The framework separates one-time setup (generate/upload) from test execution, making tests much faster (~6-12 min vs ~15-25 min per run). Tests are manual only (not in CI) and require external LLM access. πŸ€– Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude --- .gitignore | 3 + docs/ADR-013-rag-evaluation.md | 254 ++++ pyproject.toml | 2 + tests/rag_evaluation/README.md | 277 +++++ tests/rag_evaluation/__init__.py | 1 + tests/rag_evaluation/conftest.py | 145 +++ tests/rag_evaluation/llm_providers.py | 145 +++ .../rag_evaluation/test_generation_quality.py | 139 +++ .../rag_evaluation/test_retrieval_quality.py | 143 +++ uv.lock | 1049 +++++++++++++++++ 10 files changed, 2158 insertions(+) create mode 100644 docs/ADR-013-rag-evaluation.md create mode 100644 tests/rag_evaluation/README.md create mode 100644 tests/rag_evaluation/__init__.py create mode 100644 tests/rag_evaluation/conftest.py create mode 100644 tests/rag_evaluation/llm_providers.py create mode 100644 tests/rag_evaluation/test_generation_quality.py create mode 100644 tests/rag_evaluation/test_retrieval_quality.py diff --git a/.gitignore b/.gitignore index ae03a95..702fe2f 100644 --- a/.gitignore +++ b/.gitignore @@ -13,3 +13,6 @@ docker-compose.override.yml # Generated by pytest used to login users .nextcloud_oauth_*.json .playwright-mcp/ + +# RAG Evaluation +tests/rag_evaluation/fixtures/ diff --git a/docs/ADR-013-rag-evaluation.md b/docs/ADR-013-rag-evaluation.md new file mode 100644 index 0000000..dfe140f --- /dev/null +++ b/docs/ADR-013-rag-evaluation.md @@ -0,0 +1,254 @@ +## ADR-013: RAG Evaluation Testing Framework + +**Status:** Proposed + +**Date:** 2025-11-15 + +### Context + +The `nc_semantic_search_answer` tool implements a Retrieval-Augmented Generation (RAG) system where: +1. **Retrieval**: Vector sync pipeline indexes Nextcloud documents (notes, calendar, contacts, etc.) into a vector database +2. **Generation**: MCP client's LLM synthesizes answers from retrieved documents via MCP sampling (ADR-008) + +We need a testing framework to evaluate RAG system performance and identify whether failures occur in retrieval (wrong documents found) or generation (poor answer quality). This framework must use industry-standard evaluation methodologies while remaining practical to implement and maintain. + +To establish a baseline, we will use the **BeIR/nfcorpus** dataset (medical/biomedical corpus) with ~5,000 documents and established query/answer pairs. + +Homepage: https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/ +Download: https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip + +### Decision + +We will implement a **two-part evaluation framework** that independently tests retrieval and generation quality using pytest fixtures. + +#### In Scope + +**1. Retrieval Evaluation** +Tests the vector sync/embedding pipeline's ability to find relevant documents. + +- **Metric: Context Recall** (Did we retrieve documents containing the answer?) + - **Evaluation method**: Heuristic - Check if ground-truth document IDs appear in top-k retrieval results + - **Test**: Query β†’ Semantic search β†’ Assert expected doc IDs present + +**2. Generation Evaluation** +Tests the MCP client LLM's ability to synthesize correct answers from retrieved context. + +- **Metric: Answer Correctness** (Is the generated answer factually correct?) + - **Evaluation method**: LLM-as-judge - Compare RAG answer against ground-truth answer + - **Test**: Query β†’ `nc_semantic_search_answer` β†’ LLM evaluates answer vs. ground truth (binary true/false) + +#### Out of Scope (Initial Implementation) + +- **Context Relevance/Precision**: Measuring irrelevant documents in retrieval results +- **Faithfulness/Groundedness**: Detecting hallucinations not supported by retrieved context +- **Answer Relevance**: Whether answer addresses the specific question asked +- **Out-of-Scope Handling**: Testing "I don't know" responses when answer isn't in context +- **Continuous benchmarking**: Automated tracking of metric trends over time +- **Custom domain datasets**: Production-specific test data (medical corpus used initially) + +These remain valuable for future iterations but add complexity beyond our initial goals. + +#### Implementation + +**Test Structure** + +Location: `tests/rag_evaluation/` +- `test_retrieval_quality.py` - Retrieval evaluation tests +- `test_generation_quality.py` - Generation evaluation tests +- `conftest.py` - Fixtures for test data, MCP clients, and evaluation LLMs + +**Required Pytest Fixtures** + +1. **`nfcorpus_test_data`** (session-scoped) + - Downloads/caches BeIR nfcorpus dataset at runtime + - Loads 5 pre-selected test queries with: + - Query text + - Pre-generated ground-truth answer (from `tests/rag_evaluation/fixtures/ground_truth.json`) + - Expected document IDs (from qrels with score=2) + - Uploads all corpus documents as notes in test Nextcloud instance + - Triggers vector sync to index documents + - Waits for indexing completion + - Returns test case data structure + +2. **`mcp_sampling_client`** (session-scoped) + - Creates MCP client that supports sampling + - Configurable LLM provider (ollama or anthropic) via environment: + - `RAG_EVAL_PROVIDER=ollama` (default) or `anthropic` + - `RAG_EVAL_OLLAMA_BASE_URL=http://localhost:11434` + - `RAG_EVAL_OLLAMA_MODEL=llama3.1:8b` + - `RAG_EVAL_ANTHROPIC_API_KEY=sk-...` + - `RAG_EVAL_ANTHROPIC_MODEL=claude-3-5-sonnet-20241022` + - Returns configured MCP client fixture + +3. **`evaluation_llm`** (session-scoped) + - Separate LLM instance for evaluation (independent from MCP client) + - Same provider configuration as `mcp_sampling_client` + - Returns callable: `async def evaluate(prompt: str) -> str` + +**Test Implementation Examples** + +```python +# tests/rag_evaluation/test_retrieval_quality.py +async def test_retrieval_recall(nc_client, nfcorpus_test_data): + """Test that semantic search retrieves documents containing the answer.""" + for test_case in nfcorpus_test_data: + # Perform semantic search (retrieval only, no generation) + results = await nc_client.notes.semantic_search( + query=test_case.query, + limit=10 + ) + + retrieved_doc_ids = {r.document_id for r in results} + expected_doc_ids = set(test_case.expected_document_ids) + + # Context Recall: Are expected documents in top-k results? + recall = len(expected_doc_ids & retrieved_doc_ids) / len(expected_doc_ids) + assert recall >= 0.8, f"Recall {recall} below threshold for query: {test_case.query}" + + +# tests/rag_evaluation/test_generation_quality.py +async def test_answer_correctness(mcp_sampling_client, evaluation_llm, nfcorpus_test_data): + """Test that RAG system generates factually correct answers.""" + for test_case in nfcorpus_test_data: + # Execute full RAG pipeline (retrieval + generation) + result = await mcp_sampling_client.call_tool( + "nc_semantic_search_answer", + arguments={"query": test_case.query, "limit": 5} + ) + + rag_answer = result["generated_answer"] + + # LLM-as-judge evaluation + evaluation_prompt = f"""Compare these two answers and respond with only TRUE or FALSE. + +Question: {test_case.query} + +Generated Answer: {rag_answer} + +Ground Truth Answer: {test_case.ground_truth} + +Are these answers semantically equivalent (do they convey the same factual information)? +Respond with only: TRUE or FALSE""" + + evaluation_result = await evaluation_llm(evaluation_prompt) + + assert evaluation_result.strip().upper() == "TRUE", \ + f"Answer mismatch for query: {test_case.query}\nGot: {rag_answer}\nExpected: {test_case.ground_truth}" +``` + +**Dataset Integration** + +The BeIR nfcorpus dataset structure: +- **corpus.jsonl**: 3,633 medical/biomedical documents (articles from PubMed) +- **queries.jsonl**: 3,237 queries (questions) +- **qrels/*.tsv**: Relevance judgments mapping query IDs to document IDs with scores (2=highly relevant, 1=somewhat relevant) + +**Important**: The dataset provides relevance judgments (which documents answer which queries) but does NOT include ground truth answers. We must generate synthetic ground truth offline. + +**Selected Test Queries** (5 diverse candidates): + +1. **PLAIN-2630**: "Alkylphenol Endocrine Disruptors and Allergies" (5 words, 21 highly relevant docs) +2. **PLAIN-2660**: "How Long to Detox From Fish Before Pregnancy?" (8 words, 20 highly relevant docs) +3. **PLAIN-2510**: "Coffee and Artery Function" (4 words, 16 highly relevant docs) +4. **PLAIN-2430**: "Preventing Brain Loss with B Vitamins?" (6 words, 15 highly relevant docs) +5. **PLAIN-2690**: "Chronic Headaches and Pork Tapeworms" (5 words, 14 highly relevant docs) + +**Ground Truth Generation** (offline, pre-test): + +Ground truth answers will be generated offline using a script that: +1. Loads nfcorpus dataset +2. For each selected query, extracts top 3-5 highly relevant documents +3. Uses an LLM (ollama/anthropic) to synthesize a reference answer +4. Stores ground truth in `tests/rag_evaluation/fixtures/ground_truth.json` + +```python +# tools/generate_rag_ground_truth.py +async def generate_ground_truth(query: str, relevant_docs: List[dict], llm: LLMProvider) -> str: + """Generate synthetic ground truth answer from highly relevant documents.""" + context = "\n\n".join([ + f"Document {i+1}:\nTitle: {doc['title']}\n{doc['text']}" + for i, doc in enumerate(relevant_docs[:5]) + ]) + + prompt = f"""Based on the following documents, provide a comprehensive answer to this question: + +Question: {query} + +{context} + +Provide a factual, well-structured answer that synthesizes information from the documents. +Focus on accuracy and completeness.""" + + return await llm.generate(prompt, max_tokens=500) +``` + +**Dataset Loading at Test Runtime** (in `nfcorpus_test_data` fixture): + +1. Download nfcorpus dataset (cached in pytest temp directory) +2. Load corpus, queries, and qrels (relevance judgments) +3. Load pre-generated ground truth from `tests/rag_evaluation/fixtures/ground_truth.json` +4. Upload all corpus documents as Nextcloud notes +5. Trigger vector sync to index documents +6. Wait for indexing completion +7. Return test cases with query, ground truth, and expected doc IDs + +**LLM Provider Abstraction** + +```python +# tests/rag_evaluation/llm_providers.py +class LLMProvider(Protocol): + async def generate(self, prompt: str, max_tokens: int = 100) -> str: ... + +class OllamaProvider: + def __init__(self, base_url: str, model: str): + self.base_url = base_url + self.model = model + + async def generate(self, prompt: str, max_tokens: int = 100) -> str: + # Use httpx to call Ollama API + ... + +class AnthropicProvider: + def __init__(self, api_key: str, model: str): + self.client = anthropic.AsyncAnthropic(api_key=api_key) + self.model = model + + async def generate(self, prompt: str, max_tokens: int = 100) -> str: + message = await self.client.messages.create( + model=self.model, + max_tokens=max_tokens, + messages=[{"role": "user", "content": prompt}] + ) + return message.content[0].text +``` + +### Consequences + +**Positive:** + +* **Actionable debugging**: Separate retrieval/generation tests pinpoint failure location +* **Industry-standard metrics**: Context Recall and Answer Correctness are recognized RAG evaluation metrics +* **Simple initial implementation**: Binary LLM evaluation (true/false) is straightforward to implement and interpret +* **Extensible framework**: Easy to add more metrics (faithfulness, relevance) later +* **Standardized benchmark**: nfcorpus provides objective comparison against published RAG systems +* **Hybrid evaluation**: Combines efficiency (heuristics for retrieval) with quality (LLM-as-judge for generation) +* **Provider flexibility**: Supports both local (Ollama) and cloud (Anthropic) LLM evaluation + +**Negative:** + +* **Medical domain bias**: nfcorpus is medical/biomedical content, may not represent production use cases (personal notes, calendar events, etc.) +* **Manual test execution**: Tests require external LLM access and are not integrated into CI pipeline +* **Limited initial coverage**: Starting with only 5 queries provides limited statistical confidence +* **Evaluation cost**: LLM-as-judge for generation evaluation incurs API costs (Anthropic) or requires local inference (Ollama) +* **Single metric per component**: Initial scope tests only one metric per component, missing other important quality dimensions +* **Synthetic ground truth**: Ground truth answers are LLM-generated, not human-validated, which may introduce evaluation bias +* **Large corpus upload**: Uploading 3,633 documents at test runtime may be slow; caching strategy needed + +**Future Work:** + +* Expand to 50-100 queries for statistical significance +* Add custom test dataset with production-representative documents (meeting notes, task lists, etc.) +* Implement additional metrics (faithfulness, context relevance, answer relevance) +* Create automated benchmarking dashboard to track metric trends +* Test multi-hop reasoning (synthesis questions requiring multiple documents) +* Evaluate out-of-scope handling ("I don't know" responses) diff --git a/pyproject.toml b/pyproject.toml index 3b37701..3d85c27 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -102,7 +102,9 @@ module-root = "" [dependency-groups] dev = [ + "anthropic>=0.42.0", # For RAG evaluation with Anthropic LLMs "commitizen>=4.8.2", + "datasets>=3.3.0", # For BeIR nfcorpus dataset loading "ipython>=9.2.0", "playwright>=1.49.1", "pytest>=8.3.5", diff --git a/tests/rag_evaluation/README.md b/tests/rag_evaluation/README.md new file mode 100644 index 0000000..1409a5e --- /dev/null +++ b/tests/rag_evaluation/README.md @@ -0,0 +1,277 @@ +# RAG Evaluation Tests + +This directory contains tests for evaluating the Retrieval-Augmented Generation (RAG) system in the Nextcloud MCP server, specifically the `nc_semantic_search_answer` tool. + +## Architecture + +The RAG system has two components that are tested independently: + +1. **Retrieval** - Vector sync/embedding pipeline (indexed Nextcloud documents β†’ vector database) +2. **Generation** - MCP client LLM synthesis (retrieved context β†’ natural language answer) + +See [ADR-013](../../docs/ADR-013-rag-evaluation.md) for full architectural details. + +## Test Structure + +``` +tests/rag_evaluation/ +β”œβ”€β”€ README.md # This file +β”œβ”€β”€ conftest.py # Pytest fixtures +β”œβ”€β”€ llm_providers.py # LLM provider abstraction (Ollama/Anthropic) +β”œβ”€β”€ fixtures/ +β”‚ └── ground_truth.json # Pre-generated reference answers +β”œβ”€β”€ test_retrieval_quality.py # Retrieval evaluation (Context Recall) +└── test_generation_quality.py # Generation evaluation (Answer Correctness) +``` + +## Metrics + +### Retrieval Evaluation +- **Metric**: Context Recall +- **Method**: Heuristic - Check if ground-truth document IDs appear in top-k results +- **Target**: β‰₯80% recall + +### Generation Evaluation +- **Metric**: Answer Correctness +- **Method**: LLM-as-judge - Compare RAG answer vs ground truth (binary true/false) +- **Evaluation**: External LLM evaluates semantic equivalence + +## Dataset + +**BeIR/nfcorpus** - Medical/biomedical corpus with ~3,600 documents + +**Test Queries** (5 selected): +1. PLAIN-2630: "Alkylphenol Endocrine Disruptors and Allergies" (21 relevant docs) +2. PLAIN-2660: "How Long to Detox From Fish Before Pregnancy?" (20 relevant docs) +3. PLAIN-2510: "Coffee and Artery Function" (16 relevant docs) +4. PLAIN-2430: "Preventing Brain Loss with B Vitamins?" (15 relevant docs) +5. PLAIN-2690: "Chronic Headaches and Pork Tapeworms" (14 relevant docs) + +## Setup + +### 1. Install Dependencies + +```bash +uv sync --group dev +``` + +This installs: +- `anthropic>=0.42.0` - For Anthropic LLM evaluation +- `click>=8.1.8` - For CLI interface +- `datasets>=3.3.0` - For BeIR nfcorpus dataset loading + +### 2. Configure LLM Provider + +Set environment variables for your LLM provider: + +**Option A: Ollama (default, local/remote)** +```bash +export RAG_EVAL_PROVIDER=ollama +export OLLAMA_HOST=https://ollama.example.com # or RAG_EVAL_OLLAMA_BASE_URL +export RAG_EVAL_OLLAMA_MODEL=llama3.2:1b +``` + +**Option B: Anthropic (cloud)** +```bash +export RAG_EVAL_PROVIDER=anthropic +export RAG_EVAL_ANTHROPIC_API_KEY=sk-ant-... +export RAG_EVAL_ANTHROPIC_MODEL=claude-3-5-sonnet-20241022 +``` + +### 3. One-Time Setup: Generate Ground Truth + +Generate synthetic reference answers for the 5 test queries: + +```bash +uv run python tools/rag_eval_cli.py generate +``` + +**What this does:** +- Downloads nfcorpus dataset to `tests/rag_evaluation/fixtures/nfcorpus/` (cached locally) +- For each of the 5 selected queries, extracts highly relevant documents +- Uses configured LLM to synthesize a reference answer +- Saves to `tests/rag_evaluation/fixtures/ground_truth.json` + +**Optional flags:** +- `--provider ollama|anthropic` - Override LLM provider +- `--model MODEL_NAME` - Override model name +- `--force-download` - Re-download nfcorpus dataset + +### 4. One-Time Setup: Upload Corpus to Nextcloud + +Upload all 3,633 nfcorpus documents as Nextcloud notes: + +```bash +uv run python tools/rag_eval_cli.py upload \ + --nextcloud-url http://localhost:8000 \ + --username admin \ + --password admin +``` + +**What this does:** +- Downloads nfcorpus dataset (if not already cached) +- Uploads all documents as notes in Nextcloud +- Saves document ID β†’ note ID mapping to `tests/rag_evaluation/fixtures/note_mapping.json` + +**Optional flags:** +- `--category CATEGORY` - Custom category for notes (default: `nfcorpus_rag_eval`) +- `--force-download` - Re-download nfcorpus dataset + +**Important:** This step requires: +- A running Nextcloud instance with vector sync enabled +- Notes app installed +- Valid credentials + +**Duration:** ~10-15 minutes to upload 3,633 documents + +## Running Tests + +### Run All RAG Evaluation Tests + +```bash +uv run pytest tests/rag_evaluation/ -v +``` + +### Run Specific Test Suites + +**Retrieval Quality Only:** +```bash +uv run pytest tests/rag_evaluation/test_retrieval_quality.py -v +``` + +**Generation Quality Only:** +```bash +uv run pytest tests/rag_evaluation/test_generation_quality.py -v +``` + +### Run Individual Tests + +```bash +uv run pytest tests/rag_evaluation/test_retrieval_quality.py::test_retrieval_context_recall -v +uv run pytest tests/rag_evaluation/test_generation_quality.py::test_answer_correctness -v +``` + +## Test Execution Flow + +**Prerequisites** (one-time setup): +1. Generated ground truth (`tools/rag_eval_cli.py generate`) +2. Uploaded corpus to Nextcloud (`tools/rag_eval_cli.py upload`) + +### Retrieval Quality Tests + +1. **Setup** (`nfcorpus_test_data` fixture): + - Loads pre-generated ground truth from `fixtures/ground_truth.json` + - Loads note mapping from `fixtures/note_mapping.json` + - Returns test cases with expected note IDs + +2. **Test** (`test_retrieval_context_recall`): + - For each query: Perform semantic search (top-10) + - Extract retrieved note IDs + - Calculate Context Recall = (expected ∩ retrieved) / expected + - Assert recall β‰₯ 80% + +3. **Cleanup**: + - None required (notes persist in Nextcloud for reuse) + +### Generation Quality Tests + +1. **Setup**: + - Same as retrieval tests (reuses `nfcorpus_test_data` fixture) + - Creates evaluation LLM provider + +2. **Test** (`test_answer_correctness`): + - For each query: Call `nc_semantic_search_answer` MCP tool + - Extract generated answer + - Use LLM-as-judge to compare vs ground truth + - Assert semantic equivalence (TRUE/FALSE) + +3. **Cleanup**: + - LLM provider closed + +## Expected Test Duration + +**One-time setup:** +- **Generate ground truth**: ~5-10 minutes (5 queries with LLM generation) +- **Upload corpus**: ~10-15 minutes (3,633 documents) +- **Total setup**: ~15-25 minutes + +**Test execution** (after setup): +- **Retrieval tests**: ~1-2 minutes (5 queries, no upload/cleanup) +- **Generation tests**: ~5-10 minutes (RAG generation + LLM evaluation) +- **Total per run**: ~6-12 minutes + +**Note**: These are NOT smoke tests and are NOT run in CI. + +## Limitations & Future Work + +**Current Limitations:** +- Only 5 test queries (limited statistical confidence) +- Medical domain bias (may not represent production use cases) +- Synthetic ground truth (LLM-generated, not human-validated) +- Manual test execution (requires external LLM access) + +**Future Enhancements:** +- Expand to 50-100 queries for statistical significance +- Add custom test dataset with production-representative documents +- Implement additional metrics (faithfulness, context relevance, answer relevance) +- Create automated benchmarking dashboard +- Test multi-hop reasoning (synthesis questions) +- Evaluate out-of-scope handling ("I don't know" responses) + +## Troubleshooting + +### Tests Fail with "Ground truth file not found" + +Run the generate command first: +```bash +uv run python tools/rag_eval_cli.py generate +``` + +### Tests Fail with "Note mapping file not found" + +Run the upload command first: +```bash +uv run python tools/rag_eval_cli.py upload --nextcloud-url http://localhost:8000 --username admin --password admin +``` + +### Tests Fail with "MCP sampling client not yet implemented" + +The `mcp_sampling_client` fixture is a placeholder. You need to implement MCP client creation with sampling support. See the TODO in `conftest.py`. + +### Upload Command Fails + +Common issues: +1. **Nextcloud not running**: Ensure Nextcloud is accessible at the URL +2. **Invalid credentials**: Verify username/password +3. **Notes app not installed**: Install Notes app in Nextcloud +4. **Network timeout**: Increase timeout in CLI (currently 60s) + +### LLM Timeout + +If ground truth generation times out: +1. Increase timeout in `llm_providers.py` (currently 10 min) +2. Use a faster model: `--model llama3.2:1b` +3. Check Ollama/Anthropic service availability + +### Dataset Download Fails + +The nfcorpus dataset is downloaded automatically. If download fails: +1. Check internet connection +2. Manually download from: https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip +3. Extract to `tests/rag_evaluation/fixtures/nfcorpus/` +4. Or use HuggingFace datasets cache: `~/.cache/huggingface/datasets/BeIR___nfcorpus/` + +### Vector Sync Not Indexing Documents + +After uploading, vector sync must index the documents: +1. Check vector sync is enabled in Nextcloud +2. Trigger manual sync if needed +3. Wait for background job to process all documents +4. Verify in Qdrant that vectors exist for uploaded notes + +## References + +- [ADR-013: RAG Evaluation Testing Framework](../../docs/ADR-013-rag-evaluation.md) +- [ADR-008: MCP Sampling for Semantic Search](../../docs/ADR-008-mcp-sampling-for-semantic-search.md) +- [BeIR Benchmark](https://github.com/beir-cellar/beir) +- [NFCorpus Dataset](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) diff --git a/tests/rag_evaluation/__init__.py b/tests/rag_evaluation/__init__.py new file mode 100644 index 0000000..cbfae6d --- /dev/null +++ b/tests/rag_evaluation/__init__.py @@ -0,0 +1 @@ +"""RAG evaluation tests for the Nextcloud MCP semantic search system.""" diff --git a/tests/rag_evaluation/conftest.py b/tests/rag_evaluation/conftest.py new file mode 100644 index 0000000..96f8747 --- /dev/null +++ b/tests/rag_evaluation/conftest.py @@ -0,0 +1,145 @@ +"""Pytest fixtures for RAG evaluation tests. + +IMPORTANT: Before running these tests, you must: +1. Generate ground truth: uv run python tools/rag_eval_cli.py generate +2. Upload corpus: uv run python tools/rag_eval_cli.py upload --nextcloud-url http://localhost:8000 --username admin --password admin + +This ensures that the ground truth and note mappings are available. +""" + +import json +from pathlib import Path +from typing import Any + +import pytest + +from tests.rag_evaluation.llm_providers import create_llm_provider + +# Paths +FIXTURES_DIR = Path(__file__).parent / "fixtures" +GROUND_TRUTH_FILE = FIXTURES_DIR / "ground_truth.json" +NOTE_MAPPING_FILE = FIXTURES_DIR / "note_mapping.json" + + +@pytest.fixture(scope="session") +def ground_truth_data() -> list[dict[str, Any]]: + """Load pre-generated ground truth data. + + Returns: + List of test cases with query, ground truth answer, and expected doc IDs + + Raises: + FileNotFoundError: If ground_truth.json doesn't exist + """ + if not GROUND_TRUTH_FILE.exists(): + raise FileNotFoundError( + f"Ground truth file not found: {GROUND_TRUTH_FILE}\n" + "Run: uv run python tools/rag_eval_cli.py generate" + ) + + with open(GROUND_TRUTH_FILE) as f: + return json.load(f) + + +@pytest.fixture(scope="session") +def note_mapping() -> dict[str, int]: + """Load document ID β†’ note ID mapping. + + Returns: + Dict mapping nfcorpus document ID to Nextcloud note ID + + Raises: + FileNotFoundError: If note_mapping.json doesn't exist + """ + if not NOTE_MAPPING_FILE.exists(): + raise FileNotFoundError( + f"Note mapping file not found: {NOTE_MAPPING_FILE}\n" + "Run: uv run python tools/rag_eval_cli.py upload --nextcloud-url ... --username ... --password ..." + ) + + with open(NOTE_MAPPING_FILE) as f: + return json.load(f) + + +@pytest.fixture(scope="session") +def nfcorpus_test_data( + ground_truth_data: list[dict[str, Any]], + note_mapping: dict[str, int], +): + """Prepare nfcorpus test data for evaluation. + + This fixture combines ground truth answers with note mappings to create + test cases ready for retrieval and generation quality tests. + + Args: + ground_truth_data: Pre-generated ground truth answers + note_mapping: Document ID β†’ note ID mapping + + Returns: + List of test cases with query, ground truth, expected doc IDs, and note IDs + """ + test_cases = [] + + for gt in ground_truth_data: + # Map expected document IDs to note IDs + expected_note_ids = [ + note_mapping.get(doc_id) + for doc_id in gt["expected_document_ids"] + if doc_id in note_mapping + ] + + # Filter out None values (docs that weren't uploaded) + expected_note_ids = [nid for nid in expected_note_ids if nid is not None] + + test_cases.append( + { + "query_id": gt["query_id"], + "query_text": gt["query_text"], + "ground_truth_answer": gt["ground_truth_answer"], + "expected_document_ids": gt["expected_document_ids"], + "expected_note_ids": expected_note_ids, + "highly_relevant_count": gt["highly_relevant_count"], + } + ) + + return test_cases + + +@pytest.fixture(scope="session") +async def evaluation_llm(): + """Create LLM provider for evaluation (separate from MCP client). + + Environment variables: + RAG_EVAL_PROVIDER: Provider type (ollama or anthropic) + RAG_EVAL_OLLAMA_BASE_URL: Ollama base URL (or OLLAMA_HOST) + RAG_EVAL_OLLAMA_MODEL: Ollama model name + RAG_EVAL_ANTHROPIC_API_KEY: Anthropic API key + RAG_EVAL_ANTHROPIC_MODEL: Anthropic model name + + Returns: + LLM provider instance (OllamaProvider or AnthropicProvider) + """ + llm = create_llm_provider() + yield llm + await llm.close() + + +@pytest.fixture(scope="session") +async def mcp_sampling_client(): + """Create MCP client that supports sampling for RAG generation. + + This fixture creates an MCP client configured to support sampling, + which is required for testing the nc_semantic_search_answer tool. + + TODO: Implement MCP client with sampling support + For now, this is a placeholder. + + Returns: + MCP client instance with sampling enabled + """ + # TODO: Implement MCP client creation with sampling support + # This will require: + # 1. Creating an MCP client configured for sampling + # 2. Authenticating with Nextcloud + # 3. Ensuring sampling is enabled + pytest.skip("MCP sampling client not yet implemented") diff --git a/tests/rag_evaluation/llm_providers.py b/tests/rag_evaluation/llm_providers.py new file mode 100644 index 0000000..dbc17a0 --- /dev/null +++ b/tests/rag_evaluation/llm_providers.py @@ -0,0 +1,145 @@ +"""LLM provider abstraction for RAG evaluation. + +Supports Ollama (local) and Anthropic (cloud) providers for both ground truth +generation and evaluation. +""" + +import os +from typing import Protocol + +import httpx +from anthropic import AsyncAnthropic + + +class LLMProvider(Protocol): + """Protocol for LLM providers.""" + + async def generate(self, prompt: str, max_tokens: int = 500) -> str: + """Generate text from a prompt. + + Args: + prompt: The prompt to generate from + max_tokens: Maximum tokens to generate + + Returns: + Generated text + """ + ... + + +class OllamaProvider: + """Ollama provider for local LLM inference.""" + + def __init__(self, base_url: str, model: str): + """Initialize Ollama provider. + + Args: + base_url: Ollama API base URL (e.g., http://localhost:11434) + model: Model name (e.g., llama3.1:8b) + """ + self.base_url = base_url.rstrip("/") + self.model = model + self.client = httpx.AsyncClient(timeout=600.0) # 10 min timeout for generation + + async def generate(self, prompt: str, max_tokens: int = 500) -> str: + """Generate text using Ollama API.""" + response = await self.client.post( + f"{self.base_url}/api/generate", + json={ + "model": self.model, + "prompt": prompt, + "stream": False, + "options": { + "num_predict": max_tokens, + "temperature": 0.7, + }, + }, + ) + response.raise_for_status() + data = response.json() + return data["response"] + + async def close(self): + """Close the HTTP client.""" + await self.client.aclose() + + +class AnthropicProvider: + """Anthropic provider for cloud LLM inference.""" + + def __init__(self, api_key: str, model: str): + """Initialize Anthropic provider. + + Args: + api_key: Anthropic API key + model: Model name (e.g., claude-3-5-sonnet-20241022) + """ + self.client = AsyncAnthropic(api_key=api_key) + self.model = model + + async def generate(self, prompt: str, max_tokens: int = 500) -> str: + """Generate text using Anthropic API.""" + message = await self.client.messages.create( + model=self.model, + max_tokens=max_tokens, + temperature=0.7, + messages=[{"role": "user", "content": prompt}], + ) + return message.content[0].text + + async def close(self): + """Close the client (no-op for Anthropic).""" + pass + + +def create_llm_provider( + provider: str | None = None, + ollama_base_url: str | None = None, + ollama_model: str | None = None, + anthropic_api_key: str | None = None, + anthropic_model: str | None = None, +) -> LLMProvider: + """Create an LLM provider from environment variables or arguments. + + Args: + provider: Provider type ('ollama' or 'anthropic'). Defaults to RAG_EVAL_PROVIDER env var or 'ollama' + ollama_base_url: Ollama base URL. Defaults to RAG_EVAL_OLLAMA_BASE_URL or 'http://localhost:11434' + ollama_model: Ollama model. Defaults to RAG_EVAL_OLLAMA_MODEL or 'llama3.1:8b' + anthropic_api_key: Anthropic API key. Defaults to RAG_EVAL_ANTHROPIC_API_KEY env var + anthropic_model: Anthropic model. Defaults to RAG_EVAL_ANTHROPIC_MODEL or 'claude-3-5-sonnet-20241022' + + Returns: + LLMProvider instance + + Raises: + ValueError: If provider is invalid or required credentials are missing + """ + # Get provider from args or env + provider = provider or os.environ.get("RAG_EVAL_PROVIDER", "ollama") + + if provider == "ollama": + # Try RAG_EVAL_OLLAMA_BASE_URL, then OLLAMA_HOST, then default + base_url = ( + ollama_base_url + or os.environ.get("RAG_EVAL_OLLAMA_BASE_URL") + or os.environ.get("OLLAMA_HOST") + or "http://localhost:11434" + ) + model = ollama_model or os.environ.get("RAG_EVAL_OLLAMA_MODEL", "llama3.2:1b") + return OllamaProvider(base_url=base_url, model=model) + + elif provider == "anthropic": + api_key = anthropic_api_key or os.environ.get("RAG_EVAL_ANTHROPIC_API_KEY") + if not api_key: + raise ValueError( + "Anthropic API key required. Set RAG_EVAL_ANTHROPIC_API_KEY environment variable." + ) + model = anthropic_model or os.environ.get( + "RAG_EVAL_ANTHROPIC_MODEL", "claude-3-5-sonnet-20241022" + ) + return AnthropicProvider(api_key=api_key, model=model) + + else: + raise ValueError( + f"Invalid provider: {provider}. Must be 'ollama' or 'anthropic'." + ) diff --git a/tests/rag_evaluation/test_generation_quality.py b/tests/rag_evaluation/test_generation_quality.py new file mode 100644 index 0000000..1538fc7 --- /dev/null +++ b/tests/rag_evaluation/test_generation_quality.py @@ -0,0 +1,139 @@ +"""Tests for RAG generation quality (Answer Correctness metric). + +These tests evaluate whether the MCP client LLM generates factually correct +answers from retrieved context using the nc_semantic_search_answer tool. + +Metric: Answer Correctness +- Measures: Is the generated answer factually correct? +- Method: LLM-as-judge - Compare RAG answer vs ground truth (binary true/false) +- Evaluation: External LLM evaluates semantic equivalence +""" + +import pytest + + +@pytest.mark.integration +async def test_answer_correctness( + mcp_sampling_client, + evaluation_llm, + nfcorpus_test_data, +): + """Test that RAG system generates factually correct answers. + + For each test query: + 1. Execute full RAG pipeline via nc_semantic_search_answer MCP tool + 2. Extract generated answer from RAG response + 3. Use LLM-as-judge to compare against ground truth (binary true/false) + 4. Assert answer is semantically equivalent to ground truth + + This tests the quality of the generation component (MCP client LLM). + """ + results_summary = [] + + for test_case in nfcorpus_test_data: + query = test_case["query_text"] + ground_truth = test_case["ground_truth_answer"] + + print(f"\n{'=' * 80}") + print(f"Query: {query}") + + # Execute full RAG pipeline + print("Executing RAG pipeline...") + rag_result = await mcp_sampling_client.call_tool( + "nc_semantic_search_answer", + arguments={"query": query, "limit": 5}, + ) + + rag_answer = rag_result["generated_answer"] + + print(f"RAG Answer preview: {rag_answer[:200]}...") + print(f"Ground Truth preview: {ground_truth[:200]}...") + + # LLM-as-judge evaluation + evaluation_prompt = f"""Compare these two answers and respond with only TRUE or FALSE. + +Question: {query} + +Generated Answer: {rag_answer} + +Ground Truth Answer: {ground_truth} + +Are these answers semantically equivalent (do they convey the same factual information)? +Respond with only: TRUE or FALSE""" + + print("Evaluating answer correctness...") + evaluation_result = await evaluation_llm.generate( + evaluation_prompt, + max_tokens=10, + ) + + is_correct = evaluation_result.strip().upper() == "TRUE" + + result = { + "query_id": test_case["query_id"], + "query": query, + "rag_answer_length": len(rag_answer), + "ground_truth_length": len(ground_truth), + "is_correct": is_correct, + "evaluation_result": evaluation_result.strip(), + } + results_summary.append(result) + + print(f" Evaluation: {evaluation_result.strip()}") + print(f" Status: {'βœ“ CORRECT' if is_correct else 'βœ— INCORRECT'}") + + # Assert answer correctness + assert is_correct, ( + f"Answer mismatch for query: {query}\n\n" + f"Generated Answer:\n{rag_answer}\n\n" + f"Ground Truth:\n{ground_truth}\n\n" + f"Evaluation: {evaluation_result.strip()}" + ) + + # Print summary + print(f"\n{'=' * 80}") + print("Answer Correctness Summary:") + print(f" Total queries: {len(results_summary)}") + print(f" Correct: {sum(r['is_correct'] for r in results_summary)}") + print(f" Incorrect: {sum(not r['is_correct'] for r in results_summary)}") + accuracy = sum(r["is_correct"] for r in results_summary) / len(results_summary) + print(f" Accuracy: {accuracy:.2%}") + print(f"{'=' * 80}") + + +@pytest.mark.integration +async def test_answer_contains_sources(mcp_sampling_client, nfcorpus_test_data): + """Test that RAG answers include source citations. + + This is a basic quality check - we verify that the nc_semantic_search_answer + tool returns both a generated answer and source documents. + """ + for test_case in nfcorpus_test_data: + query = test_case["query_text"] + + # Execute RAG pipeline + rag_result = await mcp_sampling_client.call_tool( + "nc_semantic_search_answer", + arguments={"query": query, "limit": 5}, + ) + + # Check response structure + assert "generated_answer" in rag_result, "Response missing 'generated_answer'" + assert "sources" in rag_result, "Response missing 'sources'" + + # Check sources are provided + sources = rag_result["sources"] + assert len(sources) > 0, f"No sources returned for query: {query}" + + # Check each source has required fields + for i, source in enumerate(sources): + assert "document_id" in source or "id" in source, ( + f"Source {i} missing document ID" + ) + assert "excerpt" in source or "content" in source or "text" in source, ( + f"Source {i} missing content" + ) + + print(f"Query: {query}") + print(f" Sources provided: {len(sources)}") + print(" Status: βœ“ PASS") diff --git a/tests/rag_evaluation/test_retrieval_quality.py b/tests/rag_evaluation/test_retrieval_quality.py new file mode 100644 index 0000000..89bce34 --- /dev/null +++ b/tests/rag_evaluation/test_retrieval_quality.py @@ -0,0 +1,143 @@ +"""Tests for RAG retrieval quality (Context Recall metric). + +These tests evaluate whether the vector sync/embedding pipeline successfully +retrieves documents containing the answer to a query. + +Metric: Context Recall +- Measures: Did we retrieve documents containing the answer? +- Method: Heuristic - Check if ground-truth document IDs appear in top-k results +- Target: β‰₯80% recall (at least 80% of expected docs in top-10 results) +""" + +import pytest + + +@pytest.mark.integration +async def test_retrieval_context_recall(nc_client, nfcorpus_test_data): + """Test that semantic search retrieves documents containing the answer. + + For each test query: + 1. Perform semantic search (retrieval only, no generation) + 2. Extract retrieved document IDs from top-k results + 3. Calculate Context Recall: intersection of retrieved and expected docs + 4. Assert recall meets threshold (β‰₯80%) + + This tests the quality of the vector sync/embedding pipeline. + """ + # Top-k documents to retrieve + k = 10 + + # Minimum acceptable recall + min_recall = 0.8 + + results_summary = [] + + for test_case in nfcorpus_test_data: + query = test_case["query_text"] + expected_note_ids = set(test_case["expected_note_ids"]) + + # Perform semantic search (retrieval only) + search_results = await nc_client.notes.semantic_search( + query=query, + limit=k, + ) + + # Extract retrieved note IDs + retrieved_note_ids = {result["id"] for result in search_results} + + # Calculate Context Recall + intersection = expected_note_ids & retrieved_note_ids + recall = len(intersection) / len(expected_note_ids) if expected_note_ids else 0 + + # Store results + result = { + "query_id": test_case["query_id"], + "query": query, + "expected_count": len(expected_note_ids), + "retrieved_count": len(retrieved_note_ids), + "intersection_count": len(intersection), + "recall": recall, + "passed": recall >= min_recall, + } + results_summary.append(result) + + # Print detailed result for this query + print(f"\n{'=' * 80}") + print(f"Query: {query}") + print(f" Expected docs: {len(expected_note_ids)}") + print(f" Retrieved (top-{k}): {len(retrieved_note_ids)}") + print(f" Intersection: {len(intersection)}") + print(f" Context Recall: {recall:.2%}") + print(f" Status: {'βœ“ PASS' if result['passed'] else 'βœ— FAIL'}") + + # Assert recall meets threshold + assert recall >= min_recall, ( + f"Context Recall {recall:.2%} below threshold {min_recall:.2%} " + f"for query: {query}\n" + f"Expected {len(expected_note_ids)} docs, found {len(intersection)} in top-{k}" + ) + + # Print summary + print(f"\n{'=' * 80}") + print("Context Recall Summary:") + print(f" Total queries: {len(results_summary)}") + print(f" Passed: {sum(r['passed'] for r in results_summary)}") + print(f" Failed: {sum(not r['passed'] for r in results_summary)}") + print( + f" Average recall: {sum(r['recall'] for r in results_summary) / len(results_summary):.2%}" + ) + print(f"{'=' * 80}") + + +@pytest.mark.integration +async def test_retrieval_top1_precision(nc_client, nfcorpus_test_data): + """Test that the top-1 retrieved document is highly relevant. + + This is a stricter test than context recall - we verify that + the single most relevant document (rank 1) is in the expected set. + + This tests whether the ranking is good, not just retrieval. + """ + results_summary = [] + + for test_case in nfcorpus_test_data: + query = test_case["query_text"] + expected_note_ids = set(test_case["expected_note_ids"]) + + # Perform semantic search + search_results = await nc_client.notes.semantic_search( + query=query, + limit=1, # Only top-1 + ) + + # Check if top result is in expected set + if search_results: + top_result_id = search_results[0]["id"] + is_relevant = top_result_id in expected_note_ids + else: + is_relevant = False + + result = { + "query_id": test_case["query_id"], + "query": query, + "top_result_id": search_results[0]["id"] if search_results else None, + "is_relevant": is_relevant, + } + results_summary.append(result) + + print(f"\nQuery: {query}") + print(f" Top-1 relevant: {'βœ“ YES' if is_relevant else 'βœ— NO'}") + + # This is informational - we don't assert here + # Some queries may have multiple valid top results + + # Print summary + precision_at_1 = sum(r["is_relevant"] for r in results_summary) / len( + results_summary + ) + print(f"\n{'=' * 80}") + print(f"Precision@1: {precision_at_1:.2%}") + print( + f" ({sum(r['is_relevant'] for r in results_summary)}/{len(results_summary)} queries)" + ) + print(f"{'=' * 80}") diff --git a/uv.lock b/uv.lock index 2c15f1b..6eccb7a 100644 --- a/uv.lock +++ b/uv.lock @@ -7,6 +7,130 @@ resolution-markers = [ "python_full_version < '3.12'", ] +[[package]] +name = "aiohappyeyeballs" +version = "2.6.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = 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From: Chris Coutinho Date: Sat, 15 Nov 2025 23:26:07 +0100 Subject: [PATCH 02/10] refactor: replace httpx client with NextcloudClient in upload command MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Use NextcloudClient with BasicAuth instead of raw httpx - Replace direct HTTP POST with notes.create_note() method - Add close() method to LLMProvider Protocol for proper cleanup - Fix type annotations for dataset iteration This improves code reuse and consistency with the rest of the codebase. πŸ€– Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude --- tests/rag_evaluation/llm_providers.py | 4 + tools/rag_eval_cli.py | 513 ++++++++++++++++++++++++++ 2 files changed, 517 insertions(+) create mode 100644 tools/rag_eval_cli.py diff --git a/tests/rag_evaluation/llm_providers.py b/tests/rag_evaluation/llm_providers.py index dbc17a0..ef37c7f 100644 --- a/tests/rag_evaluation/llm_providers.py +++ b/tests/rag_evaluation/llm_providers.py @@ -26,6 +26,10 @@ class LLMProvider(Protocol): """ ... + async def close(self) -> None: + """Close the provider and release resources.""" + ... + class OllamaProvider: """Ollama provider for local LLM inference.""" diff --git a/tools/rag_eval_cli.py b/tools/rag_eval_cli.py new file mode 100644 index 0000000..e06f9a9 --- /dev/null +++ b/tools/rag_eval_cli.py @@ -0,0 +1,513 @@ +#!/usr/bin/env python3 +"""RAG Evaluation Management CLI. + +Commands: + generate - Generate ground truth answers from nfcorpus dataset + upload - Upload nfcorpus documents as Nextcloud notes + +Usage: + # Generate ground truth + uv run python tools/rag_eval_cli.py generate + + # Upload corpus to Nextcloud + uv run python tools/rag_eval_cli.py upload --nextcloud-url http://localhost:8000 --username admin --password admin +""" + +import json +import sys +from pathlib import Path +from typing import Any + +import anyio +import click +from datasets import load_dataset +from httpx import BasicAuth + +# Add parent directory to path to import from tests/ +sys.path.insert(0, str(Path(__file__).parent.parent)) + +from nextcloud_mcp_server.client import NextcloudClient +from tests.rag_evaluation.llm_providers import create_llm_provider + +# Paths +FIXTURES_DIR = Path(__file__).parent.parent / "tests" / "rag_evaluation" / "fixtures" +CORPUS_DIR = FIXTURES_DIR / "nfcorpus" +GROUND_TRUTH_FILE = FIXTURES_DIR / "ground_truth.json" +NOTE_MAPPING_FILE = FIXTURES_DIR / "note_mapping.json" + +# Dataset URL +NFCORPUS_URL = ( + "https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip" +) + +# Selected test queries (from ADR-013) +SELECTED_QUERIES = [ + "PLAIN-2630", # Alkylphenol Endocrine Disruptors and Allergies + "PLAIN-2660", # How Long to Detox From Fish Before Pregnancy? + "PLAIN-2510", # Coffee and Artery Function + "PLAIN-2430", # Preventing Brain Loss with B Vitamins? + "PLAIN-2690", # Chronic Headaches and Pork Tapeworms +] + + +def ensure_corpus_downloaded(force_download: bool = False) -> Path: + """Ensure nfcorpus dataset is downloaded to fixtures directory. + + Args: + force_download: Force re-download even if corpus exists + + Returns: + Path to corpus directory + + Raises: + RuntimeError: If download fails + """ + if CORPUS_DIR.exists() and not force_download: + click.echo(f"Corpus already exists at {CORPUS_DIR}") + return CORPUS_DIR + + click.echo(f"Downloading nfcorpus dataset to {CORPUS_DIR}...") + + # Create fixtures directory + FIXTURES_DIR.mkdir(parents=True, exist_ok=True) + + # Download using HuggingFace datasets library (handles caching) + try: + # Download corpus + click.echo(" Downloading corpus...") + corpus_dataset = load_dataset( + "BeIR/nfcorpus", + "corpus", + split="corpus", + trust_remote_code=True, + ) + + # Download queries + click.echo(" Downloading queries...") + queries_dataset = load_dataset( + "BeIR/nfcorpus", + "queries", + split="queries", + trust_remote_code=True, + ) + + # Download qrels + click.echo(" Downloading qrels...") + qrels_dataset = load_dataset( + "BeIR/nfcorpus", + "qrels", + split="test", + trust_remote_code=True, + ) + + # Save to local fixtures directory as JSONL + CORPUS_DIR.mkdir(parents=True, exist_ok=True) + + # Save corpus + with open(CORPUS_DIR / "corpus.jsonl", "w") as f: + for doc in corpus_dataset: + f.write(json.dumps(doc) + "\n") + + # Save queries + with open(CORPUS_DIR / "queries.jsonl", "w") as f: + for query in queries_dataset: + f.write(json.dumps(query) + "\n") + + # Save qrels + qrels_dir = CORPUS_DIR / "qrels" + qrels_dir.mkdir(exist_ok=True) + with open(qrels_dir / "test.tsv", "w") as f: + f.write("query-id\tcorpus-id\tscore\n") + for qrel in qrels_dataset: # type: ignore[index] + qrel_dict: dict[str, Any] = qrel # type: ignore[assignment] + f.write( + f"{qrel_dict['query-id']}\t{qrel_dict['corpus-id']}\t{qrel_dict['score']}\n" + ) + + click.echo(f"Dataset downloaded to {CORPUS_DIR}") + return CORPUS_DIR + + except Exception as e: + raise RuntimeError(f"Failed to download nfcorpus dataset: {e}") from e + + +def load_corpus(corpus_dir: Path) -> dict[str, dict]: + """Load corpus documents from local directory. + + Args: + corpus_dir: Path to corpus directory + + Returns: + Dict mapping document ID to document data + """ + corpus = {} + with open(corpus_dir / "corpus.jsonl") as f: + for line in f: + doc = json.loads(line) + corpus[doc["_id"]] = doc + return corpus + + +def load_queries(corpus_dir: Path) -> dict[str, dict]: + """Load queries from local directory. + + Args: + corpus_dir: Path to corpus directory + + Returns: + Dict mapping query ID to query data + """ + queries = {} + with open(corpus_dir / "queries.jsonl") as f: + for line in f: + query = json.loads(line) + queries[query["_id"]] = query + return queries + + +def load_qrels(corpus_dir: Path) -> dict[str, list[tuple[str, int]]]: + """Load query relevance judgments from local directory. + + Args: + corpus_dir: Path to corpus directory + + Returns: + Dict mapping query ID to list of (doc_id, score) tuples + """ + qrels: dict[str, list[tuple[str, int]]] = {} + with open(corpus_dir / "qrels" / "test.tsv") as f: + next(f) # Skip header + for line in f: + query_id, corpus_id, score = line.strip().split("\t") + if query_id not in qrels: + qrels[query_id] = [] + qrels[query_id].append((corpus_id, int(score))) + + # Sort by score descending + for query_id in qrels: + qrels[query_id].sort(key=lambda x: x[1], reverse=True) + + return qrels + + +async def generate_ground_truth_answer( + query_text: str, relevant_docs: list[dict[str, Any]], llm +) -> str: + """Generate ground truth answer from highly relevant documents. + + Args: + query_text: The query/question + relevant_docs: List of highly relevant documents (top 5) + llm: LLM provider instance + + Returns: + Generated ground truth answer + """ + # Construct context from documents + context_parts = [] + for i, doc in enumerate(relevant_docs, 1): + context_parts.append( + f"Document {i}:\nTitle: {doc['title']}\nText: {doc['text']}\n" + ) + context = "\n".join(context_parts) + + # Generate ground truth + prompt = f"""Based on the following medical/biomedical documents, provide a comprehensive, factual answer to this question. + +Question: {query_text} + +{context} + +Instructions: +- Provide a clear, well-structured answer that synthesizes information from the documents +- Focus on accuracy and completeness +- Use specific facts and findings from the documents +- Keep the answer concise but informative (2-4 paragraphs) +- Do not make up information not present in the documents + +Answer:""" + + click.echo(f" Generating answer for: {query_text}") + answer = await llm.generate(prompt, max_tokens=500) + click.echo(f" Generated {len(answer)} characters") + return answer.strip() + + +@click.group() +def cli(): + """RAG Evaluation Management CLI. + + Manage ground truth generation and corpus upload for RAG evaluation tests. + """ + pass + + +@cli.command() +@click.option( + "--provider", + type=click.Choice(["ollama", "anthropic"]), + default="ollama", + help="LLM provider to use for generation", +) +@click.option( + "--model", + help="Model name (default: llama3.2:1b for Ollama, claude-3-5-sonnet-20241022 for Anthropic)", +) +@click.option( + "--force-download", + is_flag=True, + help="Force re-download of nfcorpus dataset", +) +def generate(provider: str, model: str | None, force_download: bool): + """Generate ground truth answers for RAG evaluation. + + This command: + 1. Downloads nfcorpus dataset (if not already cached) + 2. For each selected query, extracts highly relevant documents + 3. Uses an LLM to synthesize a reference answer + 4. Saves ground truth to fixtures/ground_truth.json + + Environment variables: + RAG_EVAL_PROVIDER: Provider type (ollama or anthropic) + RAG_EVAL_OLLAMA_BASE_URL: Ollama base URL + RAG_EVAL_OLLAMA_MODEL: Ollama model name + RAG_EVAL_ANTHROPIC_API_KEY: Anthropic API key + RAG_EVAL_ANTHROPIC_MODEL: Anthropic model name + """ + + async def _generate(): + click.echo("=" * 80) + click.echo("RAG Ground Truth Generation") + click.echo("=" * 80) + + # Ensure corpus is downloaded + corpus_dir = ensure_corpus_downloaded(force_download) + + # Load dataset + click.echo("\nLoading nfcorpus dataset...") + corpus = load_corpus(corpus_dir) + queries = load_queries(corpus_dir) + qrels = load_qrels(corpus_dir) + click.echo(f"Loaded {len(corpus)} documents, {len(queries)} queries") + + # Create LLM provider + click.echo("\nInitializing LLM provider...") + try: + llm = create_llm_provider( + provider=provider, + ollama_model=model if provider == "ollama" else None, + anthropic_model=model if provider == "anthropic" else None, + ) + provider_type = type(llm).__name__ + click.echo(f"Using provider: {provider_type}") + except ValueError as e: + click.echo(f"\nError: {e}", err=True) + return 1 + + # Generate ground truth for each selected query + ground_truth_data = [] + + try: + for query_id in SELECTED_QUERIES: + if query_id not in queries: + click.echo( + f"\nWarning: Query {query_id} not found in dataset", err=True + ) + continue + + query = queries[query_id] + query_text = query["text"] + + # Get highly relevant documents (score=2) + if query_id not in qrels: + click.echo( + f"\nWarning: No relevance judgments for {query_id}", err=True + ) + continue + + highly_relevant_doc_ids = [ + doc_id for doc_id, score in qrels[query_id] if score == 2 + ] + + if not highly_relevant_doc_ids: + click.echo( + f"\nWarning: No highly relevant docs for {query_id}", err=True + ) + continue + + # Get top 5 highly relevant documents + relevant_docs = [] + for doc_id in highly_relevant_doc_ids[:5]: + if doc_id in corpus: + relevant_docs.append(corpus[doc_id]) + + if not relevant_docs: + click.echo( + f"\nWarning: Could not load documents for {query_id}", err=True + ) + continue + + # Generate ground truth answer + click.echo(f"\n{'-' * 80}") + ground_truth_answer = await generate_ground_truth_answer( + query_text, relevant_docs, llm + ) + + # Store result + ground_truth_data.append( + { + "query_id": query_id, + "query_text": query_text, + "ground_truth_answer": ground_truth_answer, + "expected_document_ids": highly_relevant_doc_ids, + "highly_relevant_count": len(highly_relevant_doc_ids), + } + ) + + click.echo(f" Preview: {ground_truth_answer[:200]}...") + + finally: + await llm.close() + + # Save ground truth + GROUND_TRUTH_FILE.parent.mkdir(parents=True, exist_ok=True) + with open(GROUND_TRUTH_FILE, "w") as f: + json.dump(ground_truth_data, f, indent=2) + + click.echo(f"\n{'=' * 80}") + click.echo(f"Generated {len(ground_truth_data)} ground truth answers") + click.echo(f"Saved to: {GROUND_TRUTH_FILE}") + click.echo("=" * 80) + + return 0 + + sys.exit(anyio.run(_generate)) + + +@cli.command() +@click.option( + "--nextcloud-url", + envvar="NEXTCLOUD_HOST", + required=True, + help="Nextcloud base URL (e.g., http://localhost:8000)", +) +@click.option( + "--username", + envvar="NEXTCLOUD_USERNAME", + required=True, + help="Nextcloud username", +) +@click.option( + "--password", + envvar="NEXTCLOUD_PASSWORD", + required=True, + help="Nextcloud password", +) +@click.option( + "--category", + default="nfcorpus_rag_eval", + help="Category/folder for uploaded notes", +) +@click.option( + "--force-download", + is_flag=True, + help="Force re-download of nfcorpus dataset", +) +def upload( + nextcloud_url: str, + username: str, + password: str, + category: str, + force_download: bool, +): + """Upload nfcorpus corpus documents as Nextcloud notes. + + This command: + 1. Downloads nfcorpus dataset (if not already cached) + 2. Uploads all corpus documents as Nextcloud notes + 3. Saves document ID β†’ note ID mapping to fixtures/note_mapping.json + + The note mapping file is used by pytest tests to map expected document IDs + to actual note IDs in Nextcloud. + """ + + async def _upload(): + click.echo("=" * 80) + click.echo("Upload nfcorpus Corpus to Nextcloud") + click.echo("=" * 80) + + # Ensure corpus is downloaded + corpus_dir = ensure_corpus_downloaded(force_download) + + # Load corpus + click.echo("\nLoading corpus...") + corpus = load_corpus(corpus_dir) + click.echo(f"Loaded {len(corpus)} documents") + + # Create Nextcloud client + click.echo(f"\nConnecting to Nextcloud at {nextcloud_url}...") + nc_client = NextcloudClient( + base_url=nextcloud_url, + username=username, + auth=BasicAuth(username, password), + ) + + try: + # Upload documents + click.echo(f"\nUploading {len(corpus)} documents as notes...") + click.echo(f"Category: {category}") + + note_mapping = {} + uploaded_count = 0 + + for doc_id, doc in corpus.items(): + # Create note via Notes API + # Title includes doc ID for easy mapping + title = f"[{doc_id}] {doc['title'][:100]}" # Truncate long titles + content = doc["text"] + + try: + note_data = await nc_client.notes.create_note( + title=title, + content=content, + category=category, + ) + + # Store mapping + note_id = note_data["id"] + note_mapping[doc_id] = note_id + + uploaded_count += 1 + + # Progress indicator every 100 docs + if uploaded_count % 100 == 0: + click.echo( + f" Uploaded {uploaded_count}/{len(corpus)} documents..." + ) + + except Exception as e: + click.echo(f" Error uploading {doc_id}: {e}", err=True) + + click.echo(f"\nUploaded {uploaded_count} documents successfully") + + # Save note mapping + with open(NOTE_MAPPING_FILE, "w") as f: + json.dump(note_mapping, f, indent=2) + + click.echo(f"Saved note mapping to: {NOTE_MAPPING_FILE}") + + finally: + # Close the Nextcloud client + await nc_client.close() + + click.echo("=" * 80) + click.echo("Upload complete!") + click.echo("=" * 80) + + return 0 + + sys.exit(anyio.run(_upload)) + + +if __name__ == "__main__": + cli() From defa8db18e8f53ef041f466aec543058768f2a2b Mon Sep 17 00:00:00 2001 From: Chris Coutinho Date: Sun, 16 Nov 2025 00:02:15 +0100 Subject: [PATCH 03/10] fix: download qrels from BEIR ZIP instead of HuggingFace MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - HuggingFace BeIR/nfcorpus only has 'corpus' and 'queries' configs - Download qrels from original BEIR ZIP file (nfcorpus.zip) - Use synchronous httpx.Client for download (simpler than async) - Remove deprecated trust_remote_code parameter Tested with successful corpus download and qrels extraction. πŸ€– Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude --- tools/rag_eval_cli.py | 40 +++++++++++++++++++--------------------- 1 file changed, 19 insertions(+), 21 deletions(-) diff --git a/tools/rag_eval_cli.py b/tools/rag_eval_cli.py index e06f9a9..d3d8d74 100644 --- a/tools/rag_eval_cli.py +++ b/tools/rag_eval_cli.py @@ -13,13 +13,16 @@ Usage: uv run python tools/rag_eval_cli.py upload --nextcloud-url http://localhost:8000 --username admin --password admin """ +import io import json import sys +import zipfile from pathlib import Path from typing import Any import anyio import click +import httpx from datasets import load_dataset from httpx import BasicAuth @@ -79,7 +82,6 @@ def ensure_corpus_downloaded(force_download: bool = False) -> Path: "BeIR/nfcorpus", "corpus", split="corpus", - trust_remote_code=True, ) # Download queries @@ -88,16 +90,6 @@ def ensure_corpus_downloaded(force_download: bool = False) -> Path: "BeIR/nfcorpus", "queries", split="queries", - trust_remote_code=True, - ) - - # Download qrels - click.echo(" Downloading qrels...") - qrels_dataset = load_dataset( - "BeIR/nfcorpus", - "qrels", - split="test", - trust_remote_code=True, ) # Save to local fixtures directory as JSONL @@ -113,16 +105,22 @@ def ensure_corpus_downloaded(force_download: bool = False) -> Path: for query in queries_dataset: f.write(json.dumps(query) + "\n") - # Save qrels - qrels_dir = CORPUS_DIR / "qrels" - qrels_dir.mkdir(exist_ok=True) - with open(qrels_dir / "test.tsv", "w") as f: - f.write("query-id\tcorpus-id\tscore\n") - for qrel in qrels_dataset: # type: ignore[index] - qrel_dict: dict[str, Any] = qrel # type: ignore[assignment] - f.write( - f"{qrel_dict['query-id']}\t{qrel_dict['corpus-id']}\t{qrel_dict['score']}\n" - ) + # Download qrels from BEIR directly (not available via HuggingFace) + click.echo(" Downloading qrels from BEIR ZIP...") + with httpx.Client(timeout=300.0) as client: + response = client.get(NFCORPUS_URL) + response.raise_for_status() + + # Extract qrels from ZIP + with zipfile.ZipFile(io.BytesIO(response.content)) as zf: + # The qrels are in nfcorpus/qrels/test.tsv within the ZIP + qrels_path = "nfcorpus/qrels/test.tsv" + qrels_dir = CORPUS_DIR / "qrels" + qrels_dir.mkdir(parents=True, exist_ok=True) + + qrels_content = zf.read(qrels_path).decode("utf-8") + with open(qrels_dir / "test.tsv", "w") as f: + f.write(qrels_content) click.echo(f"Dataset downloaded to {CORPUS_DIR}") return CORPUS_DIR From 4fa2edf4c753b1d064875ae5ca1d2c2eba18c468 Mon Sep 17 00:00:00 2001 From: Chris Coutinho Date: Sun, 16 Nov 2025 00:10:12 +0100 Subject: [PATCH 04/10] ci: Set default scan interval to 5min --- docker-compose.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docker-compose.yml b/docker-compose.yml index 38f72db..f186c02 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -85,7 +85,7 @@ services: # Vector sync configuration (ADR-007) - VECTOR_SYNC_ENABLED=true - - VECTOR_SYNC_SCAN_INTERVAL=10 + - VECTOR_SYNC_SCAN_INTERVAL=300 - VECTOR_SYNC_PROCESSOR_WORKERS=1 #- LOG_FORMAT=json From 30a4d844589ebc9f99ec7e8190301b4f89e1c021 Mon Sep 17 00:00:00 2001 From: Chris Coutinho Date: Sun, 16 Nov 2025 00:41:00 +0100 Subject: [PATCH 05/10] feat: add concurrent uploads and --force flag to upload command MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Add --force flag to delete all existing notes in target category before upload - Implement concurrent uploads using anyio task groups (20 concurrent max) - Add semaphore to limit concurrent requests and avoid overwhelming server - Improve progress reporting with upload count and error tracking - Update README with --force flag documentation Performance improvement: Concurrent uploads significantly reduce upload time from ~10-15 minutes to ~2-3 minutes for 3,633 documents. πŸ€– Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude --- tests/rag_evaluation/README.md | 1 + tools/rag_eval_cli.py | 100 ++++++++++++++++++++++++--------- 2 files changed, 75 insertions(+), 26 deletions(-) diff --git a/tests/rag_evaluation/README.md b/tests/rag_evaluation/README.md index 1409a5e..3f571b2 100644 --- a/tests/rag_evaluation/README.md +++ b/tests/rag_evaluation/README.md @@ -116,6 +116,7 @@ uv run python tools/rag_eval_cli.py upload \ **Optional flags:** - `--category CATEGORY` - Custom category for notes (default: `nfcorpus_rag_eval`) - `--force-download` - Re-download nfcorpus dataset +- `--force` - Delete all existing notes in the target category before uploading (efficient corpus refresh) **Important:** This step requires: - A running Nextcloud instance with vector sync enabled diff --git a/tools/rag_eval_cli.py b/tools/rag_eval_cli.py index d3d8d74..30f14c9 100644 --- a/tools/rag_eval_cli.py +++ b/tools/rag_eval_cli.py @@ -411,19 +411,26 @@ def generate(provider: str, model: str | None, force_download: bool): is_flag=True, help="Force re-download of nfcorpus dataset", ) +@click.option( + "--force", + is_flag=True, + help="Delete all existing notes in the target category before uploading", +) def upload( nextcloud_url: str, username: str, password: str, category: str, force_download: bool, + force: bool, ): """Upload nfcorpus corpus documents as Nextcloud notes. This command: 1. Downloads nfcorpus dataset (if not already cached) - 2. Uploads all corpus documents as Nextcloud notes - 3. Saves document ID β†’ note ID mapping to fixtures/note_mapping.json + 2. Optionally deletes existing notes in target category (--force) + 3. Uploads all corpus documents as Nextcloud notes + 4. Saves document ID β†’ note ID mapping to fixtures/note_mapping.json The note mapping file is used by pytest tests to map expected document IDs to actual note IDs in Nextcloud. @@ -451,48 +458,89 @@ def upload( ) try: - # Upload documents - click.echo(f"\nUploading {len(corpus)} documents as notes...") + # Delete existing notes in category if force is specified + if force: + click.echo( + f"\n--force specified: Deleting existing notes in category '{category}'..." + ) + deleted_count = 0 + async for note in nc_client.notes.get_all_notes(): + if note.get("category") == category: + try: + await nc_client.notes.delete_note(note["id"]) + deleted_count += 1 + if deleted_count % 100 == 0: + click.echo(f" Deleted {deleted_count} notes...") + except Exception as e: + click.echo( + f" Error deleting note {note['id']}: {e}", err=True + ) + + click.echo( + f"Deleted {deleted_count} existing notes in category '{category}'" + ) + + # Upload documents concurrently + click.echo(f"\nUploading {len(corpus)} documents as notes (concurrent)...") click.echo(f"Category: {category}") note_mapping = {} uploaded_count = 0 + upload_errors = [] - for doc_id, doc in corpus.items(): - # Create note via Notes API - # Title includes doc ID for easy mapping - title = f"[{doc_id}] {doc['title'][:100]}" # Truncate long titles - content = doc["text"] + # Semaphore to limit concurrent uploads (avoid overwhelming server) + max_concurrent = 20 + semaphore = anyio.Semaphore(max_concurrent) - try: - note_data = await nc_client.notes.create_note( - title=title, - content=content, - category=category, - ) + async def upload_document(doc_id: str, doc: dict[str, Any]): + """Upload a single document as a note.""" + nonlocal uploaded_count - # Store mapping - note_id = note_data["id"] - note_mapping[doc_id] = note_id + async with semaphore: + title = f"[{doc_id}] {doc['title'][:100]}" # Truncate long titles + content = doc["text"] - uploaded_count += 1 - - # Progress indicator every 100 docs - if uploaded_count % 100 == 0: - click.echo( - f" Uploaded {uploaded_count}/{len(corpus)} documents..." + try: + note_data = await nc_client.notes.create_note( + title=title, + content=content, + category=category, ) - except Exception as e: - click.echo(f" Error uploading {doc_id}: {e}", err=True) + # Store mapping + note_id = note_data["id"] + note_mapping[doc_id] = note_id + + uploaded_count += 1 + + # Progress indicator every 100 docs + if uploaded_count % 100 == 0: + click.echo( + f" Uploaded {uploaded_count}/{len(corpus)} documents..." + ) + + except Exception as e: + error_msg = f"Error uploading {doc_id}: {e}" + upload_errors.append(error_msg) + click.echo(f" {error_msg}", err=True) + + # Upload all documents concurrently using task group + async with anyio.create_task_group() as tg: + for doc_id, doc in corpus.items(): + tg.start_soon(upload_document, doc_id, doc) click.echo(f"\nUploaded {uploaded_count} documents successfully") + if upload_errors: + click.echo( + f"Encountered {len(upload_errors)} errors during upload", err=True + ) # Save note mapping with open(NOTE_MAPPING_FILE, "w") as f: json.dump(note_mapping, f, indent=2) click.echo(f"Saved note mapping to: {NOTE_MAPPING_FILE}") + click.echo(f" Mapped {len(note_mapping)} document IDs to note IDs") finally: # Close the Nextcloud client From 98d1c2de8ef5493e3aab6c4fe88426a1fe2d0d69 Mon Sep 17 00:00:00 2001 From: Chris Coutinho Date: Sun, 16 Nov 2025 00:55:27 +0100 Subject: [PATCH 06/10] perf: make note deletion concurrent in upload --force MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Collect all notes to delete first, then delete concurrently - Use anyio task group with semaphore (20 concurrent deletions) - Add progress reporting and error tracking for deletions - Show count of notes found before deletion starts This significantly improves --force performance when refreshing large corpuses (e.g., 3,633 notes now delete in ~1 minute instead of ~5 minutes). πŸ€– Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude --- tools/rag_eval_cli.py | 54 ++++++++++++++++++++++++++++++++----------- 1 file changed, 41 insertions(+), 13 deletions(-) diff --git a/tools/rag_eval_cli.py b/tools/rag_eval_cli.py index 30f14c9..90b1652 100644 --- a/tools/rag_eval_cli.py +++ b/tools/rag_eval_cli.py @@ -463,22 +463,50 @@ def upload( click.echo( f"\n--force specified: Deleting existing notes in category '{category}'..." ) - deleted_count = 0 + + # Collect notes to delete + notes_to_delete = [] async for note in nc_client.notes.get_all_notes(): if note.get("category") == category: - try: - await nc_client.notes.delete_note(note["id"]) - deleted_count += 1 - if deleted_count % 100 == 0: - click.echo(f" Deleted {deleted_count} notes...") - except Exception as e: - click.echo( - f" Error deleting note {note['id']}: {e}", err=True - ) + notes_to_delete.append(note["id"]) - click.echo( - f"Deleted {deleted_count} existing notes in category '{category}'" - ) + if not notes_to_delete: + click.echo(f"No existing notes found in category '{category}'") + else: + click.echo(f"Found {len(notes_to_delete)} notes to delete") + + deleted_count = 0 + delete_errors = [] + delete_semaphore = anyio.Semaphore(20) + + async def delete_note(note_id: int): + """Delete a single note.""" + nonlocal deleted_count + + async with delete_semaphore: + try: + await nc_client.notes.delete_note(note_id) + deleted_count += 1 + if deleted_count % 100 == 0: + click.echo(f" Deleted {deleted_count} notes...") + except Exception as e: + error_msg = f"Error deleting note {note_id}: {e}" + delete_errors.append(error_msg) + click.echo(f" {error_msg}", err=True) + + # Delete all notes concurrently + async with anyio.create_task_group() as tg: + for note_id in notes_to_delete: + tg.start_soon(delete_note, note_id) + + click.echo( + f"Deleted {deleted_count} existing notes in category '{category}'" + ) + if delete_errors: + click.echo( + f"Encountered {len(delete_errors)} errors during deletion", + err=True, + ) # Upload documents concurrently click.echo(f"\nUploading {len(corpus)} documents as notes (concurrent)...") From c8d9cc24e013bea08421395e3df2c3ab44c3f537 Mon Sep 17 00:00:00 2001 From: Chris Coutinho Date: Sun, 16 Nov 2025 03:51:45 +0100 Subject: [PATCH 07/10] refactor: migrate asyncio to anyio for consistent structured concurrency MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Replace asyncio primitives with anyio equivalents throughout the codebase to establish a single async pattern. This provides better structured concurrency with automatic cancellation on errors and aligns with the pytest anyio configuration. Changes: - hybrid.py: Replace asyncio.gather() with anyio task groups - token_broker.py: Replace asyncio.Lock() with anyio.Lock() - storage.py: Replace asyncio.run() with anyio.run() - app.py: Replace tg.start_soon() with await tg.start() for task status - processor.py: Add task_status parameter for structured startup - scanner.py: Add task_status parameter for structured startup - CLAUDE.md: Update async/await patterns guidance The change from start_soon() to await tg.start() enables proper task initialization signaling, ensuring background tasks are ready before proceeding. This follows anyio best practices for structured concurrency. All 118 unit tests pass with the new implementation. πŸ€– Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude --- CLAUDE.md | 8 ++- nextcloud_mcp_server/app.py | 8 +-- nextcloud_mcp_server/auth/storage.py | 4 +- nextcloud_mcp_server/auth/token_broker.py | 4 +- nextcloud_mcp_server/search/hybrid.py | 77 +++++++++++++++++------ nextcloud_mcp_server/vector/processor.py | 7 +++ nextcloud_mcp_server/vector/scanner.py | 7 +++ 7 files changed, 86 insertions(+), 29 deletions(-) diff --git a/CLAUDE.md b/CLAUDE.md index 194a9cb..5972633 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -5,11 +5,13 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co ## Coding Conventions ### async/await Patterns -- **Use anyio + asyncio hybrid** - Both libraries are available +- **Use anyio for all async operations** - Provides structured concurrency - pytest runs in `anyio` mode (`anyio_mode = "auto"` in pyproject.toml) - - asyncio used in auth modules (refresh_token_storage.py, token_exchange.py, token_broker.py) - - anyio used in calendar.py, client_registration.py, app.py + - Use `anyio.create_task_group()` for concurrent execution (NOT `asyncio.gather()`) + - Use `anyio.Lock()` for synchronization primitives (NOT `asyncio.Lock()`) + - Use `anyio.run()` for entry points (NOT `asyncio.run()`) - Prefer standard async/await syntax without explicit library imports when possible + - Examples: app.py, search/hybrid.py, search/verification.py, auth/token_broker.py ### Type Hints - **Use Python 3.10+ union syntax**: `str | None` instead of `Optional[str]` diff --git a/nextcloud_mcp_server/app.py b/nextcloud_mcp_server/app.py index 501bc16..b953983 100644 --- a/nextcloud_mcp_server/app.py +++ b/nextcloud_mcp_server/app.py @@ -446,7 +446,7 @@ async def app_lifespan_basic(server: FastMCP) -> AsyncIterator[AppContext]: # Start background tasks using anyio TaskGroup async with anyio.create_task_group() as tg: # Start scanner task - tg.start_soon( + await tg.start( scanner_task, send_stream, shutdown_event, @@ -457,7 +457,7 @@ async def app_lifespan_basic(server: FastMCP) -> AsyncIterator[AppContext]: # Start processor pool (each gets a cloned receive stream) for i in range(settings.vector_sync_processor_workers): - tg.start_soon( + await tg.start( processor_task, i, receive_stream.clone(), @@ -1147,7 +1147,7 @@ def get_app(transport: str = "sse", enabled_apps: list[str] | None = None): # Start background tasks using anyio TaskGroup async with anyio_module.create_task_group() as tg: # Start scanner task - tg.start_soon( + await tg.start( scanner_task, send_stream, shutdown_event, @@ -1158,7 +1158,7 @@ def get_app(transport: str = "sse", enabled_apps: list[str] | None = None): # Start processor pool (each gets a cloned receive stream) for i in range(settings.vector_sync_processor_workers): - tg.start_soon( + await tg.start( processor_task, i, receive_stream.clone(), diff --git a/nextcloud_mcp_server/auth/storage.py b/nextcloud_mcp_server/auth/storage.py index 1cf3936..9f4c1ff 100644 --- a/nextcloud_mcp_server/auth/storage.py +++ b/nextcloud_mcp_server/auth/storage.py @@ -1310,7 +1310,7 @@ async def generate_encryption_key() -> str: # Example usage if __name__ == "__main__": - import asyncio + import anyio async def main(): # Generate a key for testing @@ -1318,4 +1318,4 @@ if __name__ == "__main__": print(f"Generated encryption key: {key}") print(f"Set this in your environment: export TOKEN_ENCRYPTION_KEY='{key}'") - asyncio.run(main()) + anyio.run(main) diff --git a/nextcloud_mcp_server/auth/token_broker.py b/nextcloud_mcp_server/auth/token_broker.py index a4d68aa..6b89eef 100644 --- a/nextcloud_mcp_server/auth/token_broker.py +++ b/nextcloud_mcp_server/auth/token_broker.py @@ -14,11 +14,11 @@ The Token Broker provides: - Session vs background token separation (RFC 8693) """ -import asyncio import logging from datetime import datetime, timedelta, timezone from typing import Dict, Optional, Tuple +import anyio import httpx import jwt from cryptography.fernet import Fernet @@ -43,7 +43,7 @@ class TokenCache: self._cache: Dict[str, Tuple[str, datetime]] = {} self._ttl = timedelta(seconds=ttl_seconds) self._early_refresh = timedelta(seconds=early_refresh_seconds) - self._lock = asyncio.Lock() + self._lock = anyio.Lock() async def get(self, user_id: str) -> Optional[str]: """Get cached token if valid.""" diff --git a/nextcloud_mcp_server/search/hybrid.py b/nextcloud_mcp_server/search/hybrid.py index 3dd65b0..287aa94 100644 --- a/nextcloud_mcp_server/search/hybrid.py +++ b/nextcloud_mcp_server/search/hybrid.py @@ -1,10 +1,11 @@ """Hybrid search algorithm using Reciprocal Rank Fusion (RRF).""" -import asyncio import logging from collections import defaultdict from typing import Any +import anyio + from nextcloud_mcp_server.search.algorithms import SearchAlgorithm, SearchResult from nextcloud_mcp_server.search.fuzzy import FuzzySearchAlgorithm from nextcloud_mcp_server.search.keyword import KeywordSearchAlgorithm @@ -105,30 +106,70 @@ class HybridSearchAlgorithm(SearchAlgorithm): f"fuzzy={self.fuzzy_weight})" ) - # Run algorithms in parallel - tasks = [] - algo_names = [] - + # Prepare algorithm configurations for parallel execution + algo_configs = [] if self.semantic_weight > 0: - tasks.append( - self.semantic.search(query, user_id, limit * 2, doc_type, **kwargs) + algo_configs.append( + ( + "semantic", + self.semantic.search, + query, + user_id, + limit * 2, + doc_type, + kwargs, + ) ) - algo_names.append("semantic") - if self.keyword_weight > 0: - tasks.append( - self.keyword.search(query, user_id, limit * 2, doc_type, **kwargs) + algo_configs.append( + ( + "keyword", + self.keyword.search, + query, + user_id, + limit * 2, + doc_type, + kwargs, + ) ) - algo_names.append("keyword") - if self.fuzzy_weight > 0: - tasks.append( - self.fuzzy.search(query, user_id, limit * 2, doc_type, **kwargs) + algo_configs.append( + ( + "fuzzy", + self.fuzzy.search, + query, + user_id, + limit * 2, + doc_type, + kwargs, + ) ) - algo_names.append("fuzzy") - # Execute searches in parallel - results_list = await asyncio.gather(*tasks) + # Pre-allocate results list and extract algorithm names + results_list = [None] * len(algo_configs) + algo_names = [name for name, *_ in algo_configs] + + async def search_one( + index: int, + search_func, + query_arg: str, + user_id_arg: str, + limit_arg: int, + doc_type_arg: str | None, + kwargs_arg: dict, + ): + """Execute one search algorithm and store result at index.""" + result = await search_func( + query_arg, user_id_arg, limit_arg, doc_type_arg, **kwargs_arg + ) + results_list[index] = result + + # Execute searches in parallel using anyio task group + async with anyio.create_task_group() as tg: + for idx, (name, search_func, q, uid, lim, dt, kw) in enumerate( + algo_configs + ): + tg.start_soon(search_one, idx, search_func, q, uid, lim, dt, kw) # Build results dict algo_results = {} diff --git a/nextcloud_mcp_server/vector/processor.py b/nextcloud_mcp_server/vector/processor.py index ceecf5c..fde08d0 100644 --- a/nextcloud_mcp_server/vector/processor.py +++ b/nextcloud_mcp_server/vector/processor.py @@ -8,6 +8,7 @@ import time import uuid import anyio +from anyio.abc import TaskStatus from anyio.streams.memory import MemoryObjectReceiveStream from httpx import HTTPStatusError from qdrant_client.models import FieldCondition, Filter, MatchValue, PointStruct @@ -34,6 +35,8 @@ async def processor_task( shutdown_event: anyio.Event, nc_client: NextcloudClient, user_id: str, + *, + task_status: TaskStatus = anyio.TASK_STATUS_IGNORED, ): """ Process documents from stream concurrently. @@ -53,9 +56,13 @@ async def processor_task( shutdown_event: Event signaling shutdown nc_client: Authenticated Nextcloud client user_id: User being processed + task_status: Status object for signaling task readiness """ logger.info(f"Processor {worker_id} started") + # Signal that the task has started and is ready + task_status.started() + while not shutdown_event.is_set(): try: # Get document with timeout (allows checking shutdown) diff --git a/nextcloud_mcp_server/vector/scanner.py b/nextcloud_mcp_server/vector/scanner.py index 953219e..27f7d86 100644 --- a/nextcloud_mcp_server/vector/scanner.py +++ b/nextcloud_mcp_server/vector/scanner.py @@ -8,6 +8,7 @@ import time from dataclasses import dataclass import anyio +from anyio.abc import TaskStatus from anyio.streams.memory import MemoryObjectSendStream from qdrant_client.models import FieldCondition, Filter, MatchValue @@ -93,6 +94,8 @@ async def scanner_task( wake_event: anyio.Event, nc_client: NextcloudClient, user_id: str, + *, + task_status: TaskStatus = anyio.TASK_STATUS_IGNORED, ): """ Periodic scanner that detects changed documents for enabled user. @@ -105,10 +108,14 @@ async def scanner_task( wake_event: Event to trigger immediate scan nc_client: Authenticated Nextcloud client user_id: User to scan + task_status: Status object for signaling task readiness """ logger.info(f"Scanner task started for user: {user_id}") settings = get_settings() + # Signal that the task has started and is ready + task_status.started() + async with send_stream: while not shutdown_event.is_set(): try: From 137d1d6c75b22fd4e5dabd712a1eff4f58179400 Mon Sep 17 00:00:00 2001 From: Chris Coutinho Date: Sun, 16 Nov 2025 07:01:35 +0100 Subject: [PATCH 08/10] perf: fix vector viz search performance and visual encoding MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit This commit addresses critical performance issues with vector visualization search (reducing time from 40s to ~2s) and improves result visualization through better visual encoding. ## Performance Fixes ### 1. Fix blocking sleep in retry decorator (base.py:51) - Changed `time.sleep(5)` to `await anyio.sleep(5)` in @retry_on_429 - Prevents entire event loop from freezing during rate limit retries - Impact: Reduced search time from 22s to 16s initially ### 2. Add concurrency limiting for verification (verification.py:77-93) - Added `anyio.Semaphore(20)` to limit concurrent HTTP requests - Prevents connection pool exhaustion (RequestError) from 90+ simultaneous requests - Fixes false filtering (was filtering 77/90 results incorrectly) - Note: Semaphore still in code but verification removed from viz endpoint ### 3. Remove unnecessary verification from viz endpoint (viz_routes.py:483-486) - Visualization only needs Qdrant metadata (title, excerpt), not full content - Verification only required for sampling (LLM needs full note content) - Impact: Reduced search time from 43.7s to ~2s (final fix) ### 4. Restore streaming scanner pattern (scanner.py) - Process notes one-at-a-time using async generator - Avoids loading all notes into memory ## Visualization Improvements ### 5. Result-relative score normalization (viz_routes.py:489-504) - Normalize scores within result set: best=1.0, worst=0.0 - Removes arbitrary RRF normalization (theoretical max didn't make sense) - Makes visual encoding meaningful regardless of algorithm scores ### 6. Power scaling for marker sizes (userinfo_routes.py:743) - Changed from linear `8 + (score * 12)` to power `6 + (scoreΒ² * 14)` - Creates dramatic visual contrast: 0.0β†’6px, 0.5β†’9.5px, 1.0β†’20px - Combined with opacity (0.2-1.0) for clear visual hierarchy ### 7. Multi-channel visual encoding (userinfo_routes.py:740-745) - Size: Exponentially scaled with scoreΒ² - Opacity: Linear 0.2-1.0 (keeps all points visible) - Color: Viridis gradient (blueβ†’yellow) - Effect: Top results are large/bright/opaque, context results small/dim/transparent ## Result - Search time: 40s β†’ ~2s (20x faster) - Visual contrast: Subtle β†’ dramatic (clear result hierarchy) - No arbitrary cutoffs: All results visible, best naturally highlighted πŸ€– Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude --- nextcloud_mcp_server/auth/userinfo_routes.py | 18 ++- nextcloud_mcp_server/auth/viz_routes.py | 98 +++++++-------- nextcloud_mcp_server/client/base.py | 3 +- nextcloud_mcp_server/search/verification.py | 55 +++++---- nextcloud_mcp_server/vector/scanner.py | 120 ++++++++++--------- 5 files changed, 161 insertions(+), 133 deletions(-) diff --git a/nextcloud_mcp_server/auth/userinfo_routes.py b/nextcloud_mcp_server/auth/userinfo_routes.py index ca33e2b..113c524 100644 --- a/nextcloud_mcp_server/auth/userinfo_routes.py +++ b/nextcloud_mcp_server/auth/userinfo_routes.py @@ -725,6 +725,11 @@ async def user_info_html(request: Request) -> HTMLResponse: }}, renderPlot(coordinates, results) {{ + // Calculate score range for auto-scaling + const scores = results.map(r => r.score); + const minScore = Math.min(...scores); + const maxScore = Math.max(...scores); + const trace = {{ x: coordinates.map(c => c[0]), y: coordinates.map(c => c[1]), @@ -732,11 +737,18 @@ async def user_info_html(request: Request) -> HTMLResponse: type: 'scatter', text: results.map(r => `${{r.title}}
Score: ${{r.score.toFixed(3)}}`), marker: {{ - size: 8, - color: results.map(r => r.score), + // Multi-channel encoding: size + opacity + color for visual hierarchy + // Power scaling (score^2) amplifies visual differences dramatically + // score=0.0 β†’ 6px, score=0.5 β†’ 9.5px, score=1.0 β†’ 20px + size: results.map(r => 6 + (Math.pow(r.score, 2) * 14)), + // Linear opacity scaling (0.2-1.0 range keeps all points visible) + opacity: results.map(r => 0.2 + (r.score * 0.8)), + // Color gradient shows score + color: scores, colorscale: 'Viridis', showscale: true, - colorbar: {{ title: 'Score' }}, + colorbar: {{ title: 'Relative Score' }}, + // Scores are normalized 0-1 within result set cmin: 0, cmax: 1 }} diff --git a/nextcloud_mcp_server/auth/viz_routes.py b/nextcloud_mcp_server/auth/viz_routes.py index 20eaf1c..ef5a163 100644 --- a/nextcloud_mcp_server/auth/viz_routes.py +++ b/nextcloud_mcp_server/auth/viz_routes.py @@ -11,6 +11,7 @@ All processing happens server-side following ADR-012: """ import logging +import time import numpy as np from starlette.authentication import requires @@ -381,56 +382,17 @@ async def vector_visualization_search(request: Request) -> JSONResponse: ) try: + # Start total request timer + request_start = time.perf_counter() # Get authenticated HTTP client from session # In BasicAuth mode: uses username/password from session # In OAuth mode: uses access token from session from nextcloud_mcp_server.auth.userinfo_routes import ( _get_authenticated_client_for_userinfo, ) - from nextcloud_mcp_server.client.notes import NotesClient - async with await _get_authenticated_client_for_userinfo(request) as http_client: - # Create NotesClient directly with authenticated HTTP client - notes_client = NotesClient(http_client, username) - - # Wrap in a minimal client object for search algorithms - # This conforms to NextcloudClientProtocol but only implements notes - class MinimalNextcloudClient: - def __init__(self, notes_client, username): - self._notes = notes_client - self.username = username - - @property - def notes(self): - return self._notes - - @property - def webdav(self): - return None - - @property - def calendar(self): - return None - - @property - def contacts(self): - return None - - @property - def deck(self): - return None - - @property - def cookbook(self): - return None - - @property - def tables(self): - return None - - nextcloud_client = MinimalNextcloudClient(notes_client, username) - - # Create search algorithm + async with await _get_authenticated_client_for_userinfo(request) as http_client: # noqa: F841 + # Create search algorithm (no client needed - verification removed) if algorithm == "semantic": search_algo = SemanticSearchAlgorithm(score_threshold=score_threshold) elif algorithm == "keyword": @@ -451,6 +413,7 @@ async def vector_visualization_search(request: Request) -> JSONResponse: # Execute search (supports cross-app when doc_types=None) # Get unverified results with buffer for filtering + search_start = time.perf_counter() all_results = [] if doc_types is None or len(doc_types) == 0: # Cross-app search - search all indexed types @@ -476,13 +439,28 @@ async def vector_visualization_search(request: Request) -> JSONResponse: # Sort by score before verification all_results.sort(key=lambda r: r.score, reverse=True) - # Verify access for all results (deduplicates and filters) - from nextcloud_mcp_server.search.verification import verify_search_results + # No verification needed for visualization - we only need Qdrant metadata + # (title, excerpt, doc_type) which is already in search results. + # Verification is only needed for sampling (LLM needs full content). + search_results = all_results[:limit] + search_duration = time.perf_counter() - search_start - verified_results = await verify_search_results( - all_results, nextcloud_client + # Normalize scores relative to this result set for better visualization + # (best result = 1.0, worst result = 0.0 within THIS result set) + # This makes visual encoding meaningful regardless of RRF normalization + if search_results: + scores = [r.score for r in search_results] + min_score, max_score = min(scores), max(scores) + score_range = max_score - min_score if max_score > min_score else 1.0 + + logger.info( + f"Normalizing scores for viz: original range [{min_score:.3f}, {max_score:.3f}] " + f"β†’ [0.0, 1.0]" ) - search_results = verified_results[:limit] + + # Rescale each result's score to 0-1 within this result set + for r in search_results: + r.score = (r.score - min_score) / score_range if not search_results: return JSONResponse( @@ -495,6 +473,7 @@ async def vector_visualization_search(request: Request) -> JSONResponse: ) # Fetch vectors for matching results from Qdrant + vector_fetch_start = time.perf_counter() qdrant_client = await get_qdrant_client() doc_ids = [r.id for r in search_results] @@ -534,6 +513,7 @@ async def vector_visualization_search(request: Request) -> JSONResponse: # Extract vectors vectors = np.array([p.vector for p in points if p.vector is not None]) + vector_fetch_duration = time.perf_counter() - vector_fetch_start if len(vectors) < 2: # Not enough points for PCA @@ -556,8 +536,10 @@ async def vector_visualization_search(request: Request) -> JSONResponse: ) # Apply PCA dimensionality reduction (768-dim β†’ 2D) + pca_start = time.perf_counter() pca = PCA(n_components=2) coords_2d = pca.fit_transform(vectors) + pca_duration = time.perf_counter() - pca_start # After fit, these attributes are guaranteed to be set assert pca.explained_variance_ratio_ is not None @@ -590,6 +572,18 @@ async def vector_visualization_search(request: Request) -> JSONResponse: for r in search_results ] + # Calculate total request duration + total_duration = time.perf_counter() - request_start + + # Log comprehensive timing metrics + logger.info( + f"Viz search timing: total={total_duration * 1000:.1f}ms, " + f"search={search_duration * 1000:.1f}ms ({search_duration / total_duration * 100:.1f}%), " + f"vector_fetch={vector_fetch_duration * 1000:.1f}ms ({vector_fetch_duration / total_duration * 100:.1f}%), " + f"pca={pca_duration * 1000:.1f}ms ({pca_duration / total_duration * 100:.1f}%), " + f"results={len(search_results)}, vectors={len(vectors)}" + ) + return JSONResponse( { "success": True, @@ -599,6 +593,14 @@ async def vector_visualization_search(request: Request) -> JSONResponse: "pc1": float(pca.explained_variance_ratio_[0]), "pc2": float(pca.explained_variance_ratio_[1]), }, + "timing": { + "total_ms": round(total_duration * 1000, 2), + "search_ms": round(search_duration * 1000, 2), + "vector_fetch_ms": round(vector_fetch_duration * 1000, 2), + "pca_ms": round(pca_duration * 1000, 2), + "num_results": len(search_results), + "num_vectors": len(vectors), + }, } ) diff --git a/nextcloud_mcp_server/client/base.py b/nextcloud_mcp_server/client/base.py index 7ca2278..7e6a196 100644 --- a/nextcloud_mcp_server/client/base.py +++ b/nextcloud_mcp_server/client/base.py @@ -5,6 +5,7 @@ import time from abc import ABC from functools import wraps +import anyio from httpx import AsyncClient, HTTPStatusError, RequestError, codes from nextcloud_mcp_server.observability.metrics import ( @@ -47,7 +48,7 @@ def retry_on_429(func): # Record retry metric (extract app name from args if available) if len(args) > 0 and hasattr(args[0], "app_name"): record_nextcloud_api_retry(app=args[0].app_name, reason="429") - time.sleep(5) + await anyio.sleep(5) elif e.response.status_code == 404: # 404 errors are often expected (e.g., checking if attachments exist) # Log as debug instead of warning diff --git a/nextcloud_mcp_server/search/verification.py b/nextcloud_mcp_server/search/verification.py index ea64621..4f25482 100644 --- a/nextcloud_mcp_server/search/verification.py +++ b/nextcloud_mcp_server/search/verification.py @@ -74,39 +74,50 @@ async def verify_search_results( # Use list to maintain order (index-based storage) verified_results = [None] * len(unique_results) + # Limit concurrent verifications to prevent connection pool exhaustion + # Without this, launching 90+ simultaneous HTTP requests overwhelms the + # connection pool, causing RequestError failures + max_concurrent = 20 + semaphore = anyio.Semaphore(max_concurrent) + async def verify_one(index: int, result: SearchResult): """ Verify a single document and store result at index. + Uses semaphore to limit concurrent requests and prevent connection pool exhaustion. + Args: index: Position in verified_results list result: Search result to verify """ - try: - if result.doc_type == "note": - # Fetch note to verify access and get fresh metadata - note = await nextcloud_client.notes.get_note(result.id) - # Update metadata with fresh data from Nextcloud - updated_metadata = {**(result.metadata or {}), **note} - verified_results[index] = replace(result, metadata=updated_metadata) - # TODO: Add verification for other doc types (calendar, deck, file, etc.) - else: - # For now, assume other types are accessible - # In production, add proper verification for each type - logger.debug( - f"No verification implemented for doc_type={result.doc_type}, " - "assuming accessible" - ) - verified_results[index] = result + async with semaphore: # Limit concurrent verifications + try: + if result.doc_type == "note": + # Fetch note to verify access and get fresh metadata + note = await nextcloud_client.notes.get_note(result.id) + # Update metadata with fresh data from Nextcloud + updated_metadata = {**(result.metadata or {}), **note} + verified_results[index] = replace(result, metadata=updated_metadata) + # TODO: Add verification for other doc types (calendar, deck, file, etc.) + else: + # For now, assume other types are accessible + # In production, add proper verification for each type + logger.debug( + f"No verification implemented for doc_type={result.doc_type}, " + "assuming accessible" + ) + verified_results[index] = result - except Exception as e: - # Document is inaccessible (403, 404, or other error) - # Log at debug level since this is expected for filtered results - logger.debug(f"Document {result.doc_type}/{result.id} not accessible: {e}") - verified_results[index] = None + except Exception as e: + # Document is inaccessible (403, 404, or other error) + # Log at debug level since this is expected for filtered results + logger.debug( + f"Document {result.doc_type}/{result.id} not accessible: {e}" + ) + verified_results[index] = None # Run all verifications in parallel using anyio task group - # This provides structured concurrency with automatic cancellation on errors + # Semaphore limits concurrency to prevent overwhelming connection pool async with anyio.create_task_group() as tg: for idx, result in enumerate(unique_results): tg.start_soon(verify_one, idx, result) diff --git a/nextcloud_mcp_server/vector/scanner.py b/nextcloud_mcp_server/vector/scanner.py index 27f7d86..9d45a8f 100644 --- a/nextcloud_mcp_server/vector/scanner.py +++ b/nextcloud_mcp_server/vector/scanner.py @@ -182,73 +182,43 @@ async def scan_user_documents( f"[SCAN-{scan_id}] Using pruneBefore={prune_before} to optimize data transfer" ) - # Fetch all notes from Nextcloud - notes = [ - note - async for note in nc_client.notes.get_all_notes(prune_before=prune_before) - ] - logger.info(f"[SCAN-{scan_id}] Found {len(notes)} notes for {user_id}") + # Get indexed state from Qdrant first (for incremental sync) + indexed_docs = {} + if not initial_sync: + qdrant_client = await get_qdrant_client() + scroll_result = await qdrant_client.scroll( + collection_name=get_settings().get_collection_name(), + scroll_filter=Filter( + must=[ + FieldCondition(key="user_id", match=MatchValue(value=user_id)), + FieldCondition(key="doc_type", match=MatchValue(value="note")), + ] + ), + with_payload=["doc_id", "indexed_at"], + with_vectors=False, + limit=10000, + ) - # Record documents scanned - record_vector_sync_scan(len(notes)) + indexed_docs = { + point.payload["doc_id"]: point.payload["indexed_at"] + for point in scroll_result[0] + } - if initial_sync: - # Send everything on first sync - for note in notes: - modified_at = note.get("modified", 0) - await send_stream.send( - DocumentTask( - user_id=user_id, - doc_id=str(note["id"]), - doc_type="note", - operation="index", - modified_at=modified_at, - ) - ) - logger.info(f"Sent {len(notes)} documents for initial sync: {user_id}") - return + logger.debug(f"Found {len(indexed_docs)} indexed documents in Qdrant") - # Get indexed state from Qdrant - qdrant_client = await get_qdrant_client() - scroll_result = await qdrant_client.scroll( - collection_name=get_settings().get_collection_name(), - scroll_filter=Filter( - must=[ - FieldCondition(key="user_id", match=MatchValue(value=user_id)), - FieldCondition(key="doc_type", match=MatchValue(value="note")), - ] - ), - with_payload=["doc_id", "indexed_at"], - with_vectors=False, - limit=10000, - ) - - indexed_docs = { - point.payload["doc_id"]: point.payload["indexed_at"] - for point in scroll_result[0] - } - - logger.debug(f"Found {len(indexed_docs)} indexed documents in Qdrant") - - # Compare and queue changes + # Stream notes from Nextcloud and process immediately + note_count = 0 queued = 0 - nextcloud_doc_ids = {str(note["id"]) for note in notes} + nextcloud_doc_ids = set() - for note in notes: + async for note in nc_client.notes.get_all_notes(prune_before=prune_before): + note_count += 1 doc_id = str(note["id"]) - indexed_at = indexed_docs.get(doc_id) + nextcloud_doc_ids.add(doc_id) modified_at = note.get("modified", 0) - # If document reappeared, remove from potentially_deleted - doc_key = (user_id, doc_id) - if doc_key in _potentially_deleted: - logger.debug( - f"Document {doc_id} reappeared, removing from deletion grace period" - ) - del _potentially_deleted[doc_key] - - # Send if never indexed or modified since last index - if indexed_at is None or modified_at > indexed_at: + if initial_sync: + # Send everything on first sync await send_stream.send( DocumentTask( user_id=user_id, @@ -259,6 +229,38 @@ async def scan_user_documents( ) ) queued += 1 + else: + # Incremental sync: compare with indexed state + indexed_at = indexed_docs.get(doc_id) + + # If document reappeared, remove from potentially_deleted + doc_key = (user_id, doc_id) + if doc_key in _potentially_deleted: + logger.debug( + f"Document {doc_id} reappeared, removing from deletion grace period" + ) + del _potentially_deleted[doc_key] + + # Send if never indexed or modified since last index + if indexed_at is None or modified_at > indexed_at: + await send_stream.send( + DocumentTask( + user_id=user_id, + doc_id=doc_id, + doc_type="note", + operation="index", + modified_at=modified_at, + ) + ) + queued += 1 + + # Log and record metrics after streaming + logger.info(f"[SCAN-{scan_id}] Found {note_count} notes for {user_id}") + record_vector_sync_scan(note_count) + + if initial_sync: + logger.info(f"Sent {queued} documents for initial sync: {user_id}") + return # Check for deleted documents (in Qdrant but not in Nextcloud) # Use grace period: only delete after 2 consecutive scans confirm absence From 529daf2b4897171dc18d1691778f421bb3d316b3 Mon Sep 17 00:00:00 2001 From: Chris Coutinho Date: Sun, 16 Nov 2025 07:03:18 +0100 Subject: [PATCH 09/10] ci: temp disable sse in ci --- docker-compose.yml | 8 +++++--- nextcloud_mcp_server/client/notes.py | 2 +- nextcloud_mcp_server/observability/logging_config.py | 7 ++++++- tests/conftest.py | 11 +++++++++-- 4 files changed, 21 insertions(+), 7 deletions(-) diff --git a/docker-compose.yml b/docker-compose.yml index f877271..4669033 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -70,11 +70,13 @@ services: mcp: build: . restart: always + command: ["--transport", "streamable-http"] depends_on: app: condition: service_healthy ports: - 127.0.0.1:8000:8000 + - 127.0.0.1:9090:9090 volumes: - mcp-data:/app/data environment: @@ -85,7 +87,7 @@ services: # Vector sync configuration (ADR-007) - VECTOR_SYNC_ENABLED=true - - VECTOR_SYNC_SCAN_INTERVAL=300 + - VECTOR_SYNC_SCAN_INTERVAL=60 - VECTOR_SYNC_PROCESSOR_WORKERS=1 #- LOG_FORMAT=json @@ -193,8 +195,8 @@ services: # Provider auto-detected from OIDC_DISCOVERY_URL issuer # Using internal Docker hostname for discovery to get consistent issuer - OIDC_DISCOVERY_URL=http://keycloak:8080/realms/nextcloud-mcp/.well-known/openid-configuration - - OIDC_CLIENT_ID=nextcloud-mcp-server - - OIDC_CLIENT_SECRET=mcp-secret-change-in-production + - NEXTCLOUD_OIDC_CLIENT_ID=nextcloud-mcp-server + - NEXTCLOUD_OIDC_CLIENT_SECRET=mcp-secret-change-in-production - OIDC_JWKS_URI=http://keycloak:8080/realms/nextcloud-mcp/protocol/openid-connect/certs # Nextcloud API endpoint (for accessing APIs with validated token) diff --git a/nextcloud_mcp_server/client/notes.py b/nextcloud_mcp_server/client/notes.py index 601e94f..e845a2d 100644 --- a/nextcloud_mcp_server/client/notes.py +++ b/nextcloud_mcp_server/client/notes.py @@ -40,7 +40,7 @@ class NotesClient(BaseNextcloudClient): seen_ids: set[int] = set() while True: - params: Dict[str, Any] = {"chunkSize": 10} + params: Dict[str, Any] = {"chunkSize": 100} if cursor: params["chunkCursor"] = cursor if prune_before is not None: diff --git a/nextcloud_mcp_server/observability/logging_config.py b/nextcloud_mcp_server/observability/logging_config.py index d64ddd4..15e43fb 100644 --- a/nextcloud_mcp_server/observability/logging_config.py +++ b/nextcloud_mcp_server/observability/logging_config.py @@ -39,7 +39,12 @@ class HealthCheckFilter(logging.Filter): message = record.getMessage() return not any( endpoint in message - for endpoint in ["/health/live", "/health/ready", "/metrics"] + for endpoint in [ + "/health/live", + "/health/ready", + "/metrics", + "/app/vector-sync/status", + ] ) diff --git a/tests/conftest.py b/tests/conftest.py index 98bb2e4..0eaf115 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -255,8 +255,15 @@ async def nc_mcp_client(anyio_backend) -> AsyncGenerator[ClientSession, Any]: Note: SSE transport is being deprecated. This fixture uses SSE for compatibility testing. """ - async for session in create_mcp_client_session_sse( - url="http://localhost:8000/sse", client_name="Basic MCP (SSE)" + + # async for session in create_mcp_client_session_sse( + # url="http://localhost:8000/sse", client_name="Basic MCP (SSE)" + # ): + # yield session + + async for session in create_mcp_client_session( + url="http://localhost:8000/mcp", + client_name="Basic MCP (HTTP)", ): yield session From 02700a8e2cbb52816c8cdeecdb6531afa0661407 Mon Sep 17 00:00:00 2001 From: Chris Coutinho Date: Sun, 16 Nov 2025 10:25:04 +0100 Subject: [PATCH 10/10] perf: Eliminate double-fetching in semantic search sampling MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Performance optimization that removes redundant verification step and makes content fetching parallel in nc_semantic_search_answer tool. Changes: - Remove verification.py module (only had 1 caller) - Refactor nc_semantic_search to do inline deduplication instead of calling verify_search_results() - Migrate verification patterns (anyio task group, semaphore limiting) to nc_semantic_search_answer's content fetching - Change content fetching from sequential loop to parallel execution Performance impact: - Before: 10 API calls (5 parallel verification + 5 sequential content) = ~5.5s overhead - After: 5 API calls (parallel content fetch) = ~0.5s overhead - Result: 50% fewer API calls, ~10x faster for sampling operations Technical details: - Uses anyio.create_task_group() for structured concurrency - Semaphore limiting (max_concurrent=20) prevents connection pool exhaustion - Index-based storage maintains result ordering - Expected failures (deleted notes) logged at debug level - Deduplication handles hybrid search returning same doc from dense + sparse πŸ€– Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude --- nextcloud_mcp_server/search/verification.py | 133 -------------------- nextcloud_mcp_server/server/semantic.py | 91 +++++++++----- 2 files changed, 59 insertions(+), 165 deletions(-) delete mode 100644 nextcloud_mcp_server/search/verification.py diff --git a/nextcloud_mcp_server/search/verification.py b/nextcloud_mcp_server/search/verification.py deleted file mode 100644 index 4f25482..0000000 --- a/nextcloud_mcp_server/search/verification.py +++ /dev/null @@ -1,133 +0,0 @@ -"""Access verification for search results. - -This module provides centralized verification of Nextcloud access permissions -for search results. Verification happens at the final output stage (MCP tool/viz endpoint) -rather than within individual search algorithms, preventing redundant API calls. - -Key benefits: -- Deduplication: Each document verified exactly once (even in hybrid mode) -- Parallel execution: All verifications run concurrently via anyio task groups -- Separation of concerns: Algorithms handle scoring, this module handles security -""" - -import logging -from dataclasses import replace -from typing import Protocol - -import anyio - -from nextcloud_mcp_server.search.algorithms import SearchResult - -logger = logging.getLogger(__name__) - - -class NextcloudClientProtocol(Protocol): - """Protocol for Nextcloud client with app-specific access.""" - - @property - def notes(self): - """Notes client for accessing notes API.""" - ... - - -async def verify_search_results( - results: list[SearchResult], - nextcloud_client: NextcloudClientProtocol, -) -> list[SearchResult]: - """ - Verify Nextcloud access for search results. - - Deduplicates by (doc_id, doc_type), verifies in parallel using anyio task groups, - and filters out inaccessible documents. Maintains original result ordering. - - Args: - results: Unverified search results from Qdrant - nextcloud_client: Nextcloud client for access checks - - Returns: - Verified and accessible results (same order as input) - - Example: - >>> unverified = await search_algo.search(query="test", limit=10) - >>> verified = await verify_search_results(unverified, client) - >>> # verified contains only documents user can access - """ - # Deduplicate by (doc_id, doc_type) while preserving order - # This is critical for hybrid search where same doc may appear in multiple algorithm results - seen = set() - unique_results = [] - for result in results: - key = (result.id, result.doc_type) - if key not in seen: - seen.add(key) - unique_results.append(result) - - if not unique_results: - return [] - - logger.debug( - f"Verifying access for {len(unique_results)} unique documents " - f"(from {len(results)} total results)" - ) - - # Verify all unique documents in parallel using anyio task group - # Use list to maintain order (index-based storage) - verified_results = [None] * len(unique_results) - - # Limit concurrent verifications to prevent connection pool exhaustion - # Without this, launching 90+ simultaneous HTTP requests overwhelms the - # connection pool, causing RequestError failures - max_concurrent = 20 - semaphore = anyio.Semaphore(max_concurrent) - - async def verify_one(index: int, result: SearchResult): - """ - Verify a single document and store result at index. - - Uses semaphore to limit concurrent requests and prevent connection pool exhaustion. - - Args: - index: Position in verified_results list - result: Search result to verify - """ - async with semaphore: # Limit concurrent verifications - try: - if result.doc_type == "note": - # Fetch note to verify access and get fresh metadata - note = await nextcloud_client.notes.get_note(result.id) - # Update metadata with fresh data from Nextcloud - updated_metadata = {**(result.metadata or {}), **note} - verified_results[index] = replace(result, metadata=updated_metadata) - # TODO: Add verification for other doc types (calendar, deck, file, etc.) - else: - # For now, assume other types are accessible - # In production, add proper verification for each type - logger.debug( - f"No verification implemented for doc_type={result.doc_type}, " - "assuming accessible" - ) - verified_results[index] = result - - except Exception as e: - # Document is inaccessible (403, 404, or other error) - # Log at debug level since this is expected for filtered results - logger.debug( - f"Document {result.doc_type}/{result.id} not accessible: {e}" - ) - verified_results[index] = None - - # Run all verifications in parallel using anyio task group - # Semaphore limits concurrency to prevent overwhelming connection pool - async with anyio.create_task_group() as tg: - for idx, result in enumerate(unique_results): - tg.start_soon(verify_one, idx, result) - - # Filter out None (inaccessible) and return verified results - accessible = [r for r in verified_results if r is not None] - - logger.debug( - f"Verification complete: {len(accessible)} accessible, " - f"{len(unique_results) - len(accessible)} filtered out" - ) - - return accessible diff --git a/nextcloud_mcp_server/server/semantic.py b/nextcloud_mcp_server/server/semantic.py index fe13c1e..df6ec95 100644 --- a/nextcloud_mcp_server/server/semantic.py +++ b/nextcloud_mcp_server/server/semantic.py @@ -3,6 +3,7 @@ import logging from typing import Literal +import anyio from httpx import RequestError from mcp.server.fastmcp import Context, FastMCP from mcp.shared.exceptions import McpError @@ -153,11 +154,18 @@ def configure_semantic_tools(mcp: FastMCP): # Sort combined results by score all_results.sort(key=lambda r: r.score, reverse=True) - # Verify access for all results (deduplicates and filters) - from nextcloud_mcp_server.search.verification import verify_search_results + # Deduplicate results (hybrid search may return same doc from dense + sparse) + # Qdrant already filters by user_id for multi-tenant isolation + # Sampling tool will verify access when fetching full content + seen = set() + unique_results = [] + for result in all_results: + key = (result.id, result.doc_type) + if key not in seen: + seen.add(key) + unique_results.append(result) - verified_results = await verify_search_results(all_results, client) - search_results = verified_results[:limit] # Final limit after verification + search_results = unique_results[:limit] # Final limit after deduplication # Convert SearchResult objects to SemanticSearchResult for response results = [] @@ -334,35 +342,55 @@ def configure_semantic_tools(mcp: FastMCP): success=True, ) - # 4. Fetch full content for notes to provide complete context to LLM - # Filter out inaccessible notes (deleted or permissions changed) + # 4. Fetch full content for notes in parallel (also verifies access) + # Use anyio task group for concurrent fetching with semaphore to prevent + # connection pool exhaustion client = await get_client(ctx) - accessible_results = [] - full_contents = [] # Full content for accessible notes + accessible_results = [None] * len(search_response.results) + full_contents = [None] * len(search_response.results) - for result in search_response.results: - if result.doc_type == "note": - try: - note = await client.notes.get_note(result.id) - # Note is accessible, store full content - accessible_results.append(result) - full_contents.append(note.get("content", "")) - logger.debug( - f"Fetched full content for note {result.id} " - f"(length: {len(full_contents[-1])} chars)" - ) - except Exception as e: - # Note might have been deleted or permissions changed - # Filter it out to avoid corrupting LLM with inaccessible data - logger.warning( - f"Failed to fetch full content for note {result.id}: {e}. " - f"Excluding from results." - ) - else: - # Non-note document types (future: calendar, deck, files) - # For now, keep them with excerpts - accessible_results.append(result) - full_contents.append(None) + # Limit concurrent requests to prevent connection pool exhaustion + max_concurrent = 20 + semaphore = anyio.Semaphore(max_concurrent) + + async def fetch_content(index: int, result: SemanticSearchResult): + """Fetch full content for a single document (parallel with semaphore).""" + async with semaphore: + if result.doc_type == "note": + try: + note = await client.notes.get_note(result.id) + # Note is accessible, store result and full content + content = note.get("content", "") + accessible_results[index] = result + full_contents[index] = content + logger.debug( + f"Fetched full content for note {result.id} " + f"(length: {len(content)} chars)" + ) + except Exception as e: + # Note might have been deleted or permissions changed + # Leave as None to filter out later + logger.debug( + f"Note {result.id} not accessible: {e}. " + f"Excluding from results." + ) + else: + # Non-note document types (future: calendar, deck, files) + # For now, keep them with excerpts + accessible_results[index] = result + # full_contents[index] remains None (will use excerpt) + + # Run all fetches in parallel using anyio task group + async with anyio.create_task_group() as tg: + for idx, result in enumerate(search_response.results): + tg.start_soon(fetch_content, idx, result) + + # Filter out None (inaccessible notes) while preserving order + final_pairs = [ + (r, c) for r, c in zip(accessible_results, full_contents) if r is not None + ] + accessible_results = [r for r, c in final_pairs] + full_contents = [c for r, c in final_pairs] # Check if we filtered out all results if not accessible_results: @@ -414,7 +442,6 @@ def configure_semantic_tools(mcp: FastMCP): ) # 6. Request LLM completion via MCP sampling with timeout - import anyio try: with anyio.fail_after(30):