diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index dd0c74b..b0200a1 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -18,8 +18,19 @@ Thank you for considering contributing to Tapio Assistant! This document provide
- [Testing Guidelines](#testing-guidelines)
- [Running Tests](#running-tests)
- [Code Coverage](#code-coverage)
+ - [Test Categories](#test-categories)
+ - [Test Fixtures](#test-fixtures)
- [Project Structure](#project-structure)
+ - [Programmatic API](#programmatic-api)
+ - [Using Factory Pattern (Recommended)](#using-factory-pattern-recommended)
+ - [Manual Dependency Injection (Advanced)](#manual-dependency-injection-advanced)
+ - [Key Components](#key-components)
- [Configuration System](#configuration-system)
+ - [Default Settings](#default-settings)
+ - [Site Configurations](#site-configurations)
+ - [Configuration Structure](#configuration-structure)
+ - [Required vs Optional Fields](#required-vs-optional-fields)
+ - [Adding New Sites](#adding-new-sites)
- [Ollama for LLM Inference](#ollama-for-llm-inference)
- [Pull Request Process](#pull-request-process)
@@ -241,7 +252,7 @@ uv run pytest
### Code Coverage
-We aim for high test coverage. When submitting code:
+We aim for high test coverage (minimum 80%). When submitting code:
1. Check your coverage with:
@@ -263,6 +274,41 @@ uv run pytest --cov=tapio.utils tests/utils/
Aim for at least 80% coverage for new code. The HTML coverage report can be found in the `htmlcov` directory. Open `htmlcov/index.html` in your browser to view it.
+### Test Categories
+
+We maintain different types of tests:
+
+**Unit Tests** - Fast, isolated tests with mocked dependencies:
+```bash
+uv run pytest -m "not integration"
+```
+
+**Integration Tests** - Tests using real components (marked with `@pytest.mark.integration`):
+```bash
+uv run pytest -m integration
+```
+
+**All Tests**:
+```bash
+uv run pytest
+```
+
+### Test Fixtures
+
+Common mock fixtures are available in `tests/conftest.py`:
+- `mock_embeddings` - Mocked HuggingFace embeddings
+- `mock_chroma_store` - Mocked ChromaDB vector store
+- `mock_llm_service` - Mocked LLM service
+- `mock_doc_retrieval_service` - Mocked document retrieval service
+- `mock_rag_orchestrator` - Mocked RAG orchestrator
+
+Use these fixtures in your tests for consistent mocking:
+```python
+def test_my_feature(mock_rag_orchestrator):
+ # Test uses mocked orchestrator
+ pass
+```
+
## Project Structure
The project has been designed with a clear separation of concerns:
@@ -270,11 +316,70 @@ The project has been designed with a clear separation of concerns:
- `crawler/`: Module responsible for crawling websites and saving HTML content
- `parsers/`: Module responsible for parsing HTML content into structured formats
- `vectorstore/`: Module responsible for vectorizing content and storing in ChromaDB
+- `services/`: RAG orchestration and LLM services
- `config/`: Configuration settings for the project
-- `gradio_app.py`: Gradio interface for the RAG chatbot
+- `app.py`: Gradio interface for the RAG chatbot
+- `cli.py`: Command-line interface
+- `factories.py`: Factory classes for dependency injection
- `utils/`: Utility modules for embedding generation, markdown processing, etc.
- `tests/`: Test suite for all modules
+## Programmatic API
+
+For developers who want to use Tapio as a library or extend its functionality:
+
+### Using Factory Pattern (Recommended)
+
+```python
+from tapio import RAGConfig, RAGOrchestratorFactory
+
+# Create configuration
+config = RAGConfig(
+ collection_name="my_docs",
+ persist_directory="./db",
+ llm_model_name="llama3.2",
+ max_tokens=1024,
+ num_results=5
+)
+
+# Create orchestrator using factory
+factory = RAGOrchestratorFactory(config)
+orchestrator = factory.create_orchestrator()
+
+# Query the system
+response, documents = orchestrator.query("What are the visa requirements?")
+print(response)
+```
+
+### Manual Dependency Injection (Advanced)
+
+For full control over component creation:
+
+```python
+from langchain_huggingface import HuggingFaceEmbeddings
+from tapio.vectorstore.chroma_store import ChromaStore
+from tapio.services.document_retrieval_service import DocumentRetrievalService
+from tapio.services.llm_service import LLMService
+from tapio.services.rag_orchestrator import RAGOrchestrator
+
+# Create dependencies
+embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+chroma_store = ChromaStore("my_docs", embeddings, "./db")
+doc_service = DocumentRetrievalService(chroma_store, num_results=5)
+llm_service = LLMService(model_name="llama3.2", max_tokens=1024)
+
+# Create orchestrator
+orchestrator = RAGOrchestrator(doc_service, llm_service)
+```
+
+### Key Components
+
+- **RAGOrchestrator**: Main orchestrator that coordinates document retrieval and LLM generation
+- **DocumentRetrievalService**: Handles vector-based document retrieval
+- **LLMService**: Manages LLM interactions via Ollama
+- **ChromaStore**: Vector database abstraction layer
+- **Factories**: Simplify dependency wiring with sensible defaults
+
## Configuration System
The application uses a centralized configuration system:
@@ -291,6 +396,95 @@ When adding new features that require configuration values:
3. Avoid hardcoding values that might need to change in the future
4. Use descriptive keys for configuration values
+### Default Settings
+
+Centralized configuration in `tapio/config/settings.py`:
+
+```python
+DEFAULT_DIRS = {
+ "CRAWLED_DIR": "content/crawled", # HTML storage
+ "PARSED_DIR": "content/parsed", # Markdown storage
+ "CHROMA_DIR": "chroma_db", # Vector database
+}
+
+DEFAULT_CHROMA_COLLECTION = "tapio" # ChromaDB collection name
+DEFAULT_EMBEDDING_MODEL = "all-MiniLM-L6-v2"
+DEFAULT_LLM_MODEL = "llama3.2"
+DEFAULT_MAX_TOKENS = 1024
+DEFAULT_NUM_RESULTS = 5
+```
+
+## Site Configurations
+
+Site configurations define how to crawl and parse specific websites. They're stored in `tapio/config/site_configs.yaml` and used by both crawl and parse commands.
+
+### Configuration Structure
+
+```yaml
+sites:
+ migri:
+ base_url: "https://migri.fi" # Used for crawling and converting relative links
+ description: "Finnish Immigration Service website"
+ crawler_config: # Crawling behavior
+ delay_between_requests: 1.0 # Seconds between requests
+ max_concurrent: 3 # Concurrent request limit
+ parser_config: # Parser-specific configuration
+ title_selector: "//title" # XPath for page titles
+ content_selectors: # Priority-ordered content extraction
+ - '//div[@id="main-content"]'
+ - "//main"
+ - "//article"
+ - '//div[@class="content"]'
+ fallback_to_body: true # Use if selectors fail
+ markdown_config: # HTML-to-Markdown options
+ ignore_links: false
+ body_width: 0 # No text wrapping
+ protect_links: true
+ unicode_snob: true
+ ignore_images: false
+ ignore_tables: false
+```
+
+### Required vs Optional Fields
+
+**Required:**
+- `base_url` - Base URL for the site (used for crawling and link resolution)
+
+**Optional (with defaults):**
+- `description` - Human-readable description
+- `parser_config` - Parser-specific settings (uses defaults if omitted)
+ - `title_selector` - Page title XPath (default: "//title")
+ - `content_selectors` - XPath selectors for content extraction (default: ["//main", "//article", "//body"])
+ - `fallback_to_body` - Use full-body content if selectors fail (default: true)
+ - `markdown_config` - HTML conversion settings (uses defaults if omitted)
+- `crawler_config` - Crawling behavior settings (uses defaults if omitted)
+ - `delay_between_requests` - Delay between requests in seconds (default: 1.0)
+ - `max_concurrent` - Maximum concurrent requests (default: 5)
+
+### Adding New Sites
+
+1. Analyze the target website's structure
+2. Identify XPath selectors for content extraction
+3. Add configuration to `site_configs.yaml`:
+
+```yaml
+sites:
+ my_site:
+ base_url: "https://example.com"
+ description: "Example site configuration"
+ parser_config:
+ content_selectors:
+ - '//div[@class="main-content"]'
+```
+
+4. Use with commands:
+```bash
+uv run -m tapio.cli crawl my_site
+uv run -m tapio.cli parse my_site
+uv run -m tapio.cli vectorize
+uv run -m tapio.cli tapio-app
+```
+
## Ollama for LLM Inference
We use Ollama for local LLM inference.
diff --git a/README.md b/README.md
index 9b88b18..8b31159 100644
--- a/README.md
+++ b/README.md
@@ -90,92 +90,7 @@ To view detailed site configurations:
uv run -m tapio.cli list-sites --verbose
```
-## Site Configurations
-
-Site configurations define how to crawl and parse specific websites. They're stored in `tapio/config/site_configs.yaml` and used by both crawl and parse commands.
-
-### Configuration Structure
-
-```yaml
-sites:
- migri:
- base_url: "https://migri.fi" # Used for crawling and converting relative links
- description: "Finnish Immigration Service website"
- crawler_config: # Crawling behavior
- delay_between_requests: 1.0 # Seconds between requests
- max_concurrent: 3 # Concurrent request limit
- parser_config: # Parser-specific configuration
- title_selector: "//title" # XPath for page titles
- content_selectors: # Priority-ordered content extraction
- - '//div[@id="main-content"]'
- - "//main"
- - "//article"
- - '//div[@class="content"]'
- fallback_to_body: true # Use if selectors fail
- markdown_config: # HTML-to-Markdown options
- ignore_links: false
- body_width: 0 # No text wrapping
- protect_links: true
- unicode_snob: true
- ignore_images: false
- ignore_tables: false
-```
-
-### Required vs Optional Fields
-
-**Required:**
-- `base_url` - Base URL for the site (used for crawling and link resolution)
-
-**Optional (with defaults):**
-- `description` - Human-readable description
-- `parser_config` - Parser-specific settings (uses defaults if omitted)
- - `title_selector` - Page title XPath (default: "//title")
- - `content_selectors` - XPath selectors for content extraction (default: ["//main", "//article", "//body"])
- - `fallback_to_body` - Use full body content if selectors fail (default: true)
- - `markdown_config` - HTML conversion settings (uses defaults if omitted)
-- `crawler_config` - Crawling behavior settings (uses defaults if omitted)
- - `delay_between_requests` - Delay between requests in seconds (default: 1.0)
- - `max_concurrent` - Maximum concurrent requests (default: 5)
-
-### Adding New Sites
-
-1. Analyze the target website's structure
-2. Identify XPath selectors for content extraction
-3. Add configuration to `site_configs.yaml`:
-
-```yaml
-sites:
- my_site:
- base_url: "https://example.com"
- description: "Example site configuration"
- parser_config:
- content_selectors:
- - '//div[@class="main-content"]'
-```
-
-4. Use with commands:
-```bash
-uv run -m tapio.cli crawl my_site
-uv run -m tapio.cli parse my_site
-uv run -m tapio.cli vectorize
-uv run -m tapio.cli tapio-app
-```
-
-## Configuration
-
-Tapio uses centralized configuration in `tapio/config/settings.py`:
-
-```python
-DEFAULT_DIRS = {
- "CRAWLED_DIR": "content/crawled", # HTML storage
- "PARSED_DIR": "content/parsed", # Markdown storage
- "CHROMA_DIR": "chroma_db", # Vector database
-}
-
-DEFAULT_CHROMA_COLLECTION = "tapio" # ChromaDB collection name
-```
-
-Site-specific configurations are in `tapio/config/site_configs.yaml` and automatically handle content extraction and directory organization based on the site's domain.
+For technical details on site configurations, programmatic API usage, and adding new sites, see [CONTRIBUTING.md](CONTRIBUTING.md).
## Contributing
diff --git a/pyproject.toml b/pyproject.toml
index cc0118c..1b7c3b3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
[project]
name = "tapio"
-version = "0.1.0"
+version = "2.0.0"
description = "An assistant for Finnish immigrants"
readme = "README.md"
requires-python = ">=3.10"
diff --git a/pytest.ini b/pytest.ini
index fcf6403..a6f4b03 100644
--- a/pytest.ini
+++ b/pytest.ini
@@ -5,3 +5,7 @@ filterwarnings =
ignore::DeprecationWarning:websockets.legacy:
ignore::DeprecationWarning:.*:
ignore::DeprecationWarning:gradio.utils:
+
+markers =
+ integration: Integration tests that use real components (slower, may download models)
+
diff --git a/tapio/__init__.py b/tapio/__init__.py
index c329f0c..8b3f585 100644
--- a/tapio/__init__.py
+++ b/tapio/__init__.py
@@ -1 +1,9 @@
"""This file initializes the tapio package."""
+
+from tapio.config.config_models import RAGConfig
+from tapio.factories import RAGOrchestratorFactory
+
+__all__ = [
+ "RAGConfig",
+ "RAGOrchestratorFactory",
+]
diff --git a/tapio/app.py b/tapio/app.py
index 470517a..c2fd10f 100644
--- a/tapio/app.py
+++ b/tapio/app.py
@@ -12,61 +12,44 @@
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
-# Default constants (can be overridden by CLI)
-DEFAULT_COLLECTION_NAME = "migri_docs"
-DEFAULT_CHROMA_DB_PATH = "chroma_db"
-DEFAULT_MODEL_NAME = "llama3.2"
-DEFAULT_MAX_TOKENS = 1024
-DEFAULT_NUM_RESULTS = 5
-
class TapioAssistantApp:
- """Class representing the Tapio Assistant Gradio application."""
+ """Class representing the Tapio Assistant Gradio application.
+
+ This class provides a web interface for interacting with the RAG system.
+ The RAG orchestrator is injected to enable testing and configuration.
+ """
def __init__(
self,
- collection_name: str = DEFAULT_COLLECTION_NAME,
- persist_directory: str = DEFAULT_CHROMA_DB_PATH,
- model_name: str = DEFAULT_MODEL_NAME,
- max_tokens: int = DEFAULT_MAX_TOKENS,
- num_results: int = DEFAULT_NUM_RESULTS,
+ rag_orchestrator: RAGOrchestrator,
) -> None:
"""Initialize the Tapio Assistant application.
Args:
- collection_name: Name of the ChromaDB collection
- persist_directory: Directory where the ChromaDB database is stored
- model_name: Name of the LLM model to use
- max_tokens: Maximum number of tokens to generate
- num_results: Number of documents to retrieve from the vector store
+ rag_orchestrator: Configured RAG orchestrator for query handling
+
+ Example:
+ >>> from tapio.factories import RAGOrchestratorFactory
+ >>> from tapio.config.config_models import RAGConfig
+ >>>
+ >>> config = RAGConfig(llm_model_name="llama3.2")
+ >>> factory = RAGOrchestratorFactory(config)
+ >>> orchestrator = factory.create_orchestrator()
+ >>> app = TapioAssistantApp(rag_orchestrator=orchestrator)
"""
- self.collection_name = collection_name
- self.persist_directory = persist_directory
- self.model_name = model_name
- self.max_tokens = max_tokens
- self.num_results = num_results
- self.rag_orchestrator: RAGOrchestrator | None = None
+ self.rag_orchestrator = rag_orchestrator
self.demo = self._build_interface()
- def _init_rag_orchestrator(self) -> RAGOrchestrator:
- """Initialize the RAG orchestrator if not already done.
+ def check_model_availability(self) -> None:
+ """Check if the LLM model is available.
- Returns:
- Initialized RAGOrchestrator instance
+ Raises:
+ SystemExit: If the model is not available
"""
- if self.rag_orchestrator is None:
- logger.info(
- f"Initializing RAG orchestrator with {self.model_name} model",
- )
- self.rag_orchestrator = RAGOrchestrator(
- collection_name=self.collection_name,
- persist_directory=self.persist_directory,
- model_name=self.model_name,
- max_tokens=self.max_tokens,
- num_results=self.num_results,
- )
-
- return self.rag_orchestrator
+ if not self.rag_orchestrator.check_model_availability():
+ logger.error("Required LLM model is not available")
+ raise SystemExit(1)
def generate_rag_response(
self,
@@ -83,17 +66,14 @@ def generate_rag_response(
Tuple containing the response and formatted documents for display
"""
try:
- # Initialize RAG orchestrator if not already done
- orchestrator = self._init_rag_orchestrator()
-
# Get response and retrieved docs from the RAG orchestrator
- response, retrieved_docs = orchestrator.query(
+ response, retrieved_docs = self.rag_orchestrator.query(
query_text=query,
history=history,
)
# Format documents for display
- formatted_docs = orchestrator.format_documents_for_display(
+ formatted_docs = self.rag_orchestrator.format_documents_for_display(
retrieved_docs,
)
@@ -131,11 +111,8 @@ def respond_stream(
yield "", chat_history, "Retrieving relevant documents..."
try:
- # Initialize RAG orchestrator if not already done
- rag_orchestrator = self._init_rag_orchestrator()
-
# Get streaming response and retrieved docs from the RAG orchestrator
- response_stream, retrieved_docs = rag_orchestrator.query_stream(
+ response_stream, retrieved_docs = self.rag_orchestrator.query_stream(
query_text=message,
history=chat_history,
)
@@ -178,7 +155,7 @@ def respond_stream(
# Format documents for display once we have them
if retrieved_docs and formatted_docs == "Retrieving relevant documents...":
- formatted_docs = rag_orchestrator.format_documents_for_display(
+ formatted_docs = self.rag_orchestrator.format_documents_for_display(
retrieved_docs,
)
@@ -191,7 +168,7 @@ def respond_stream(
# Ensure documents are formatted for final display
if retrieved_docs:
- formatted_docs = rag_orchestrator.format_documents_for_display(
+ formatted_docs = self.rag_orchestrator.format_documents_for_display(
retrieved_docs,
)
@@ -320,58 +297,37 @@ def _build_interface(self) -> gr.Blocks:
return demo
- def check_model_availability(self) -> bool:
- """Check if the Ollama model is available.
-
- Returns:
- True if the model is available, False otherwise
- """
- orchestrator = self._init_rag_orchestrator()
- return orchestrator.check_model_availability()
-
def launch(self, share: bool = False) -> None:
"""Launch the Gradio app.
Args:
share: Whether to create a shareable link for the app
"""
- # Check model availability
- if not self.check_model_availability():
- logger.warning(
- f"Could not find {self.model_name} model in Ollama. "
- f"The app will start, but responses may not work correctly.",
- )
-
# Launch the Gradio app
self.demo.launch(share=share)
def main(
- collection_name: str = DEFAULT_COLLECTION_NAME,
- persist_directory: str = DEFAULT_CHROMA_DB_PATH,
- model_name: str = DEFAULT_MODEL_NAME,
- max_tokens: int = DEFAULT_MAX_TOKENS,
- num_results: int = DEFAULT_NUM_RESULTS,
+ rag_orchestrator: RAGOrchestrator,
share: bool = False,
) -> None:
- """Run the Tapio Assistant app with the specified parameters.
+ """Run the Tapio Assistant app with the specified RAG orchestrator.
Args:
- collection_name: Name of the ChromaDB collection
- persist_directory: Directory where the ChromaDB database is stored
- model_name: Name of the LLM model to use
- max_tokens: Maximum number of tokens to generate
- num_results: Number of documents to retrieve from the vector store
+ rag_orchestrator: Configured RAG orchestrator instance
share: Whether to create a shareable link for the app
+
+ Example:
+ >>> from tapio.factories import RAGOrchestratorFactory
+ >>> from tapio.config.config_models import RAGConfig
+ >>>
+ >>> config = RAGConfig(llm_model_name="llama3.2")
+ >>> factory = RAGOrchestratorFactory(config)
+ >>> orchestrator = factory.create_orchestrator()
+ >>> main(rag_orchestrator=orchestrator, share=False)
"""
- # Create the app
- app = TapioAssistantApp(
- collection_name=collection_name,
- persist_directory=persist_directory,
- model_name=model_name,
- max_tokens=max_tokens,
- num_results=num_results,
- )
+ # Create the app with injected orchestrator
+ app = TapioAssistantApp(rag_orchestrator=rag_orchestrator)
# Check model availability
app.check_model_availability()
@@ -381,4 +337,11 @@ def main(
if __name__ == "__main__":
- main()
+ # Create RAG orchestrator using factory for standalone execution
+ from tapio.config.config_models import RAGConfig
+ from tapio.factories import RAGOrchestratorFactory
+
+ config = RAGConfig()
+ factory = RAGOrchestratorFactory(config=config)
+ rag_orch = factory.create_orchestrator()
+ main(rag_orchestrator=rag_orch)
diff --git a/tapio/cli.py b/tapio/cli.py
index b51717b..5a8795e 100644
--- a/tapio/cli.py
+++ b/tapio/cli.py
@@ -2,10 +2,21 @@
import os
import typer
+from langchain_chroma import Chroma # type: ignore[import-not-found]
+from langchain_huggingface import HuggingFaceEmbeddings # type: ignore[import-not-found]
+from langchain_text_splitters import MarkdownTextSplitter # type: ignore[import-not-found]
from tapio.config import ConfigManager
-from tapio.config.settings import DEFAULT_CHROMA_COLLECTION, DEFAULT_CONTENT_DIR, DEFAULT_DIRS
+from tapio.config.config_models import RAGConfig
+from tapio.config.settings import (
+ DEFAULT_CHROMA_COLLECTION,
+ DEFAULT_CONTENT_DIR,
+ DEFAULT_DIRS,
+ DEFAULT_EMBEDDING_MODEL,
+ DEFAULT_NUM_RESULTS,
+)
from tapio.crawler.runner import CrawlerRunner
+from tapio.factories import RAGOrchestratorFactory
from tapio.parser import Parser
from tapio.vectorstore.vectorizer import MarkdownVectorizer
@@ -195,9 +206,20 @@ def parse(
# Parse a specific site
if site in available_sites:
typer.echo(f"🔧 Using configuration for site: {site}")
+
+ # Get site config from config manager
+ site_config = config_manager.get_site_config(site)
+
+ # Determine input and output directories
+ input_dir = os.path.join(DEFAULT_CONTENT_DIR, site, DEFAULT_DIRS["CRAWLED_DIR"])
+ output_dir = os.path.join(DEFAULT_CONTENT_DIR, site, DEFAULT_DIRS["PARSED_DIR"])
+
+ # Create parser with dependency injection
parser = Parser(
site_name=site,
- config_path=config_path,
+ site_config=site_config,
+ input_dir=input_dir,
+ output_dir=output_dir,
)
results = parser.parse_all()
@@ -247,9 +269,19 @@ def parse(
total_results = []
for site_name in sites_to_parse:
typer.echo(f"🔧 Parsing site: {site_name}")
+ # Get site configuration
+ site_config = config_manager.get_site_config(site_name)
+
+ # Build directory paths
+ input_dir = os.path.join(DEFAULT_CONTENT_DIR, site_name, DEFAULT_DIRS["CRAWLED_DIR"])
+ output_dir = os.path.join(DEFAULT_CONTENT_DIR, site_name, DEFAULT_DIRS["PARSED_DIR"])
+
+ # Create parser with injected dependencies
parser = Parser(
site_name=site_name,
- config_path=config_path,
+ site_config=site_config,
+ input_dir=input_dir,
+ output_dir=output_dir,
)
site_results = parser.parse_all()
@@ -327,14 +359,23 @@ def vectorize(
typer.echo(f"📑 Using collection name: {collection_name}")
try:
- # Initialize vectorizer
- vectorizer = MarkdownVectorizer(
- collection_name=collection_name,
- persist_directory=db_dir,
- embedding_model_name=embedding_model,
+ # Create dependencies
+ embeddings = HuggingFaceEmbeddings(model_name=embedding_model)
+ text_splitter = MarkdownTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
+ vector_db = Chroma(
+ collection_name=collection_name,
+ embedding_function=embeddings,
+ persist_directory=db_dir,
+ )
+
+ # Initialize vectorizer with injected dependencies
+ vectorizer = MarkdownVectorizer(
+ vector_db=vector_db,
+ text_splitter=text_splitter,
+ )
# Process files in the directory
typer.echo("⚙️ Processing markdown files...")
@@ -443,12 +484,23 @@ def tapio_app(
if share:
typer.echo("🔗 Creating a shareable link")
- # Launch the Gradio app with CLI parameters
- launch_app(
+ # Create RAG configuration
+ rag_config = RAGConfig(
collection_name=collection_name,
persist_directory=db_dir,
- model_name=model_name,
+ embedding_model_name=DEFAULT_EMBEDDING_MODEL,
+ llm_model_name=model_name,
max_tokens=max_tokens,
+ num_results=DEFAULT_NUM_RESULTS,
+ )
+
+ # Create RAG orchestrator using factory
+ factory = RAGOrchestratorFactory(rag_config)
+ orchestrator = factory.create_orchestrator()
+
+ # Launch the Gradio app with orchestrator
+ launch_app(
+ rag_orchestrator=orchestrator,
share=share,
)
diff --git a/tapio/config/config_models.py b/tapio/config/config_models.py
index 72d3acc..41ff39c 100644
--- a/tapio/config/config_models.py
+++ b/tapio/config/config_models.py
@@ -5,11 +5,20 @@
conversion settings.
"""
+from dataclasses import dataclass
from typing import Annotated, Any
from urllib.parse import urlparse
from pydantic import BaseModel, Field, HttpUrl
+from tapio.config.settings import (
+ DEFAULT_CHROMA_COLLECTION,
+ DEFAULT_EMBEDDING_MODEL,
+ DEFAULT_LLM_MODEL,
+ DEFAULT_MAX_TOKENS,
+ DEFAULT_NUM_RESULTS,
+)
+
class HtmlToMarkdownConfig(BaseModel):
"""Configuration settings for HTML to Markdown conversion.
@@ -117,3 +126,34 @@ class ParserConfigRegistry(BaseModel):
"""Registry of all site parser configurations."""
sites: dict[str, SiteConfig]
+
+
+@dataclass
+class RAGConfig:
+ """Configuration for RAG (Retrieval Augmented Generation) system.
+
+ Groups all configuration parameters for the RAG orchestrator and its
+ dependencies, including vector store settings, embedding model, and
+ LLM configuration.
+
+ Args:
+ collection_name: Name of the ChromaDB collection
+ persist_directory: Directory path for ChromaDB persistence
+ embedding_model_name: Name of the HuggingFace embedding model
+ llm_model_name: Name of the Ollama LLM model
+ max_tokens: Maximum tokens for LLM generation
+ num_results: Number of documents to retrieve for context
+
+ Example:
+ >>> config = RAGConfig(
+ ... collection_name="my_docs",
+ ... llm_model_name="llama3.2:latest"
+ ... )
+ """
+
+ collection_name: str = DEFAULT_CHROMA_COLLECTION
+ persist_directory: str = "chroma_db"
+ embedding_model_name: str = DEFAULT_EMBEDDING_MODEL
+ llm_model_name: str = DEFAULT_LLM_MODEL
+ max_tokens: int = DEFAULT_MAX_TOKENS
+ num_results: int = DEFAULT_NUM_RESULTS
diff --git a/tapio/config/settings.py b/tapio/config/settings.py
index 1ec7c46..cb453af 100644
--- a/tapio/config/settings.py
+++ b/tapio/config/settings.py
@@ -16,3 +16,11 @@
DEFAULT_CHROMA_COLLECTION = "tapio_knowledge"
DEFAULT_CRAWLER_TIMEOUT = 30
+
+# Embedding model configuration
+DEFAULT_EMBEDDING_MODEL = "all-MiniLM-L6-v2"
+
+# RAG configuration defaults
+DEFAULT_LLM_MODEL = "llama3.2"
+DEFAULT_MAX_TOKENS = 1024
+DEFAULT_NUM_RESULTS = 5
diff --git a/tapio/factories.py b/tapio/factories.py
new file mode 100644
index 0000000..774ea0e
--- /dev/null
+++ b/tapio/factories.py
@@ -0,0 +1,126 @@
+"""Factory classes for creating complex object graphs with dependency injection.
+
+This module provides factory classes that handle the wiring of dependencies
+for the RAG system, making it easy to create properly configured service
+instances without tight coupling.
+"""
+
+from langchain_core.embeddings import Embeddings # type: ignore[import-not-found]
+from langchain_huggingface import HuggingFaceEmbeddings
+
+from tapio.config.config_models import RAGConfig
+from tapio.services.document_retrieval_service import DocumentRetrievalService
+from tapio.services.llm_service import LLMService
+from tapio.services.rag_orchestrator import RAGOrchestrator
+from tapio.vectorstore.chroma_store import ChromaStore
+
+
+class RAGOrchestratorFactory:
+ """Factory for creating RAGOrchestrator instances with all dependencies.
+
+ This factory handles the complete dependency graph for a RAG orchestrator,
+ including embeddings, vector store, document retrieval service, and LLM
+ service. It ensures all components are properly configured and wired
+ together.
+
+ Args:
+ config: Configuration object containing all RAG settings
+
+ Example:
+ >>> from tapio.config.config_models import RAGConfig
+ >>> config = RAGConfig(collection_name="my_docs")
+ >>> factory = RAGOrchestratorFactory(config)
+ >>> orchestrator = factory.create_orchestrator()
+ """
+
+ def __init__(self, config: RAGConfig) -> None:
+ """Initialize the factory with configuration.
+
+ Args:
+ config: RAGConfig instance containing all configuration parameters
+ """
+ self.config = config
+
+ def create_embeddings(self) -> HuggingFaceEmbeddings:
+ """Create embeddings instance.
+
+ Returns:
+ Configured HuggingFaceEmbeddings instance
+ """
+ return HuggingFaceEmbeddings(model_name=self.config.embedding_model_name)
+
+ def create_chroma_store(self, embeddings: Embeddings | None = None) -> ChromaStore:
+ """Create ChromaDB vector store.
+
+ Args:
+ embeddings: Optional embeddings instance. If None, creates new instance.
+
+ Returns:
+ Configured ChromaStore instance
+ """
+ if embeddings is None:
+ embeddings = self.create_embeddings()
+
+ return ChromaStore(
+ collection_name=self.config.collection_name,
+ embeddings=embeddings,
+ persist_directory=self.config.persist_directory,
+ )
+
+ def create_document_retrieval_service(self, chroma_store: ChromaStore | None = None) -> DocumentRetrievalService:
+ """Create document retrieval service.
+
+ Args:
+ chroma_store: Optional ChromaStore instance. If None, creates new instance.
+
+ Returns:
+ Configured DocumentRetrievalService instance
+ """
+ if chroma_store is None:
+ chroma_store = self.create_chroma_store()
+
+ return DocumentRetrievalService(
+ vector_store=chroma_store,
+ num_results=self.config.num_results,
+ )
+
+ def create_llm_service(self) -> LLMService:
+ """Create LLM service.
+
+ Returns:
+ Configured LLMService instance
+ """
+ return LLMService(
+ model_name=self.config.llm_model_name,
+ max_tokens=self.config.max_tokens,
+ )
+
+ def create_orchestrator(self) -> RAGOrchestrator:
+ """Create fully configured RAG orchestrator.
+
+ This is the main factory method that creates the complete RAG system
+ with all dependencies properly wired together.
+
+ Returns:
+ Configured RAGOrchestrator instance ready to use
+
+ Example:
+ >>> factory = RAGOrchestratorFactory(RAGConfig())
+ >>> orchestrator = factory.create_orchestrator()
+ >>> response, docs = orchestrator.query("What is the processing time?")
+ """
+ # Create shared embeddings instance
+ embeddings = self.create_embeddings()
+
+ # Create vector store with embeddings
+ chroma_store = self.create_chroma_store(embeddings)
+
+ # Create services
+ doc_service = self.create_document_retrieval_service(chroma_store)
+ llm_service = self.create_llm_service()
+
+ # Create and return orchestrator
+ return RAGOrchestrator(
+ doc_retrieval_service=doc_service,
+ llm_service=llm_service,
+ )
diff --git a/tapio/parser/parser.py b/tapio/parser/parser.py
index b75ecbd..93ed82e 100644
--- a/tapio/parser/parser.py
+++ b/tapio/parser/parser.py
@@ -18,7 +18,6 @@
from tapio.config import (
ConfigManager,
- settings,
)
from tapio.config.config_models import SiteConfig
@@ -68,33 +67,42 @@ class Parser:
HTML content parser that uses site-specific configurations.
This parser loads configurations for different websites and extracts
- content using the appropriate selectors for each site.
+ content using the appropriate selectors for each site. Configuration
+ and directory paths are injected to enable testing and reuse.
"""
def __init__(
self,
site_name: str,
- config_path: str | None = None,
- ):
- """
- Initialize the parser.
+ site_config: SiteConfig,
+ input_dir: str,
+ output_dir: str,
+ ) -> None:
+ """Initialize the parser.
Args:
- site_name: Site to parse (must match a key in config)
- config_path: Optional path to custom config file
+ site_name: Name of the site being parsed
+ site_config: Site-specific configuration with selectors
+ input_dir: Directory containing crawled HTML files
+ output_dir: Directory for output markdown files
+
+ Example:
+ >>> from tapio.config.config_manager import ConfigManager
+ >>> from tapio.config import settings
+ >>>
+ >>> config_manager = ConfigManager()
+ >>> site_config = config_manager.get_site_config("migri")
+ >>> input_dir = os.path.join(settings.DEFAULT_CONTENT_DIR, "migri", "crawled")
+ >>> output_dir = os.path.join(settings.DEFAULT_CONTENT_DIR, "migri", "parsed")
+ >>> parser = Parser("migri", site_config, input_dir, output_dir)
"""
self.site = site_name
-
- # Use ConfigManager to load site configuration
- config_manager = ConfigManager(config_path)
- self.config = config_manager.get_site_config(site_name)
+ self.config = site_config
+ self.input_dir = input_dir
+ self.output_dir = output_dir
self.current_base_url: str | None = None # Will store the base URL of the current document
- # Use standard directory structure based on site name
- self.input_dir = os.path.join(settings.DEFAULT_CONTENT_DIR, site_name, settings.DEFAULT_DIRS["CRAWLED_DIR"])
- self.output_dir = os.path.join(settings.DEFAULT_CONTENT_DIR, site_name, settings.DEFAULT_DIRS["PARSED_DIR"])
-
self.setup_logging()
self.logger = logging.getLogger(__name__)
diff --git a/tapio/services/document_retrieval_service.py b/tapio/services/document_retrieval_service.py
index d5bd6e8..73f1411 100644
--- a/tapio/services/document_retrieval_service.py
+++ b/tapio/services/document_retrieval_service.py
@@ -10,31 +10,37 @@
class DocumentRetrievalService:
- """Service for retrieving relevant documents from the vector store."""
+ """Service for retrieving relevant documents from the vector store.
+
+ This service handles document retrieval from a vector store and formats
+ the results for use in RAG workflows. The vector store is injected to
+ enable testing and allow reuse of existing store instances.
+ """
def __init__(
self,
- collection_name: str = "migri_docs",
- persist_directory: str = "chroma_db",
+ vector_store: ChromaStore,
num_results: int = 5,
- ):
+ ) -> None:
"""Initialize the document retrieval service.
Args:
- collection_name: Name of the ChromaDB collection
- persist_directory: Directory where the ChromaDB database is stored
+ vector_store: ChromaStore instance for document retrieval
num_results: Number of documents to retrieve from the vector store
+
+ Example:
+ >>> from tapio.vectorstore.chroma_store import ChromaStore
+ >>> from langchain_huggingface import HuggingFaceEmbeddings
+ >>>
+ >>> embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+ >>> store = ChromaStore("my_docs", embeddings)
+ >>> service = DocumentRetrievalService(vector_store=store, num_results=3)
"""
self.num_results = num_results
-
- # Initialize the vector store
- self.vector_store = ChromaStore(
- collection_name=collection_name,
- persist_directory=persist_directory,
- )
+ self.vector_store = vector_store
logger.info(
- f"Initialized document retrieval service with collection '{collection_name}'",
+ "Initialized document retrieval service",
)
def retrieve_documents(self, query_text: str) -> list[Any]:
diff --git a/tapio/services/rag_orchestrator.py b/tapio/services/rag_orchestrator.py
index 19b3286..a345390 100644
--- a/tapio/services/rag_orchestrator.py
+++ b/tapio/services/rag_orchestrator.py
@@ -13,40 +13,42 @@
class RAGOrchestrator:
- """Orchestrates document retrieval and LLM generation for RAG workflow."""
+ """Orchestrates document retrieval and LLM generation for RAG workflow.
+
+ This orchestrator coordinates the RAG pipeline by combining document retrieval
+ with LLM generation. All dependencies are injected to enable testing and
+ allow reuse of configured service instances.
+ """
def __init__(
self,
- collection_name: str = "migri_docs",
- persist_directory: str = "chroma_db",
- model_name: str = "llama3.2",
- max_tokens: int = 1024,
- num_results: int = 5,
- ):
+ doc_retrieval_service: DocumentRetrievalService,
+ llm_service: LLMService,
+ ) -> None:
"""Initialize the RAG orchestrator.
Args:
- collection_name: Name of the ChromaDB collection
- persist_directory: Directory where the ChromaDB database is stored
- model_name: Name of the LLM model to use
- max_tokens: Maximum number of tokens to generate
- num_results: Number of documents to retrieve from the vector store
+ doc_retrieval_service: Service for retrieving documents from vector store
+ llm_service: Service for LLM generation
+
+ Example:
+ >>> from tapio.factories import RAGOrchestratorFactory
+ >>> from tapio.config.config_models import RAGConfig
+ >>>
+ >>> config = RAGConfig(collection_name="my_docs")
+ >>> factory = RAGOrchestratorFactory(config)
+ >>> orchestrator = factory.create_orchestrator()
+ >>>
+ >>> # Or manually for advanced use cases:
+ >>> doc_service = DocumentRetrievalService(vector_store=my_store)
+ >>> llm_service = LLMService(model_name="llama3.2")
+ >>> orchestrator = RAGOrchestrator(doc_service, llm_service)
"""
- # Initialize the document retrieval service
- self.doc_retrieval_service = DocumentRetrievalService(
- collection_name=collection_name,
- persist_directory=persist_directory,
- num_results=num_results,
- )
-
- # Initialize the LLM service
- self.llm_service = LLMService(
- model_name=model_name,
- max_tokens=max_tokens,
- )
+ self.doc_retrieval_service = doc_retrieval_service
+ self.llm_service = llm_service
logger.info(
- f"Initialized RAG orchestrator with collection '{collection_name}' and model '{model_name}'",
+ "Initialized RAG orchestrator",
)
def query(
diff --git a/tapio/vectorstore/chroma_store.py b/tapio/vectorstore/chroma_store.py
index 237e0e4..d6371ae 100644
--- a/tapio/vectorstore/chroma_store.py
+++ b/tapio/vectorstore/chroma_store.py
@@ -6,24 +6,41 @@
from chromadb import GetResult, QueryResult # type: ignore[import-not-found]
from langchain_chroma import Chroma # type: ignore[import-not-found]
from langchain_core.documents import Document # type: ignore[import-not-found]
-from langchain_huggingface import HuggingFaceEmbeddings # type: ignore[import-not-found]
+from langchain_core.embeddings import Embeddings # type: ignore[import-not-found]
logger = logging.getLogger(__name__)
class ChromaStore:
- """LangChain-based ChromaDB vector store abstraction."""
+ """LangChain-based ChromaDB vector store abstraction.
- def __init__(self, collection_name: str, persist_directory: str = "chroma_db") -> None:
- """
- Initialize the ChromaDB vector store.
+ This class wraps LangChain's Chroma implementation with a simpler interface.
+ Dependencies are injected to allow for testing and configuration flexibility.
+ """
+
+ def __init__(
+ self,
+ collection_name: str,
+ embeddings: Embeddings,
+ persist_directory: str = "chroma_db",
+ ) -> None:
+ """Initialize the ChromaDB vector store.
Args:
collection_name: Name of the ChromaDB collection
+ embeddings: Embeddings instance (e.g., HuggingFaceEmbeddings)
persist_directory: Directory to persist the ChromaDB database
+
+ Example:
+ >>> from langchain_huggingface import HuggingFaceEmbeddings
+ >>> embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+ >>> store = ChromaStore(
+ ... collection_name="my_docs",
+ ... embeddings=embeddings,
+ ... persist_directory="./chroma_db"
+ ... )
"""
- # Initialize embeddings
- self.embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+ self.embeddings = embeddings
# Initialize the vector store
self.vector_db = Chroma(
diff --git a/tapio/vectorstore/vectorizer.py b/tapio/vectorstore/vectorizer.py
index 481675f..665ca74 100644
--- a/tapio/vectorstore/vectorizer.py
+++ b/tapio/vectorstore/vectorizer.py
@@ -6,7 +6,6 @@
from langchain.schema.document import Document # type: ignore[import-not-found]
from langchain_chroma import Chroma # type: ignore[import-not-found]
-from langchain_huggingface import HuggingFaceEmbeddings # type: ignore[import-not-found]
from langchain_text_splitters import MarkdownTextSplitter # type: ignore[import-not-found]
from tapio.utils.markdown_utils import find_markdown_files, read_markdown_file
@@ -15,50 +14,42 @@
class MarkdownVectorizer:
- """Vectorize markdown content and store in ChromaDB using LangChain."""
+ """Vectorize markdown content and store in ChromaDB using LangChain.
+
+ This class handles the vectorization pipeline for markdown documents,
+ including text splitting and storage in a vector database. Dependencies
+ are injected to enable testing and configuration flexibility.
+ """
def __init__(
self,
- collection_name: str,
- persist_directory: str = "chroma_db",
- embedding_model_name: str = "all-MiniLM-L6-v2",
- chunk_size: int = 1000,
- chunk_overlap: int = 200,
- ):
- """
- Initialize the vectorizer.
+ vector_db: Chroma,
+ text_splitter: MarkdownTextSplitter,
+ ) -> None:
+ """Initialize the vectorizer.
Args:
- collection_name: Name of the ChromaDB collection to use
- persist_directory: Directory to persist the ChromaDB database
- embedding_model_name: Name of the sentence-transformers model to use
- chunk_size: Size of text chunks in characters
- chunk_overlap: Overlap between chunks in characters
+ vector_db: LangChain Chroma instance for vector storage
+ text_splitter: MarkdownTextSplitter for chunking documents
+
+ Example:
+ >>> from langchain_chroma import Chroma
+ >>> from langchain_huggingface import HuggingFaceEmbeddings
+ >>> from langchain_text_splitters import MarkdownTextSplitter
+ >>>
+ >>> embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+ >>> vector_db = Chroma(
+ ... collection_name="my_docs",
+ ... embedding_function=embeddings,
+ ... persist_directory="./chroma_db"
+ ... )
+ >>> text_splitter = MarkdownTextSplitter(chunk_size=1000, chunk_overlap=200)
+ >>> vectorizer = MarkdownVectorizer(vector_db, text_splitter)
"""
- # Initialize embedding model
- self.embeddings = HuggingFaceEmbeddings(model_name=embedding_model_name)
-
- # Initialize text splitter for markdown
- self.text_splitter = MarkdownTextSplitter(
- chunk_size=chunk_size,
- chunk_overlap=chunk_overlap,
- )
-
- # Directory and collection config
- self.persist_directory = persist_directory
- self.collection_name = collection_name
-
- # Initialize vector store
- self.vector_db = Chroma(
- collection_name=collection_name,
- embedding_function=self.embeddings,
- persist_directory=persist_directory,
- )
-
- # Save configuration
- self.embedding_model_name = embedding_model_name
- self.chunk_size = chunk_size
- self.chunk_overlap = chunk_overlap
+ self.vector_db = vector_db
+ self.text_splitter = text_splitter
+
+ logger.debug("Initialized MarkdownVectorizer")
def process_directory(
self,
diff --git a/tests/conftest.py b/tests/conftest.py
index de816e3..8098c50 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -2,7 +2,136 @@
import sys
from pathlib import Path
+from unittest.mock import Mock
+
+import pytest
# Add the project root directory to the Python path
root_dir = Path(__file__).parent.parent
sys.path.insert(0, str(root_dir))
+
+
+# ============================================================================
+# Mock Fixtures for Unit Tests
+# ============================================================================
+
+
+@pytest.fixture
+def mock_embeddings():
+ """Mock HuggingFace embeddings for fast unit tests.
+
+ Returns a mock with embed_query and embed_documents methods that return
+ consistent dummy embeddings without loading a real model.
+ """
+ embeddings = Mock()
+ # Typical embedding dimension for all-MiniLM-L6-v2 is 384
+ dummy_embedding = [0.1] * 384
+
+ # embed_query returns a single embedding (copy to avoid mutation)
+ embeddings.embed_query.side_effect = lambda _: dummy_embedding.copy()
+
+ # embed_documents returns a list of embeddings (one per document)
+ # Make it return the appropriate number based on input
+ def embed_documents_mock(texts):
+ return [dummy_embedding.copy() for _ in texts]
+
+ embeddings.embed_documents.side_effect = embed_documents_mock
+ return embeddings
+
+
+@pytest.fixture
+def mock_chroma_store():
+ """Mock ChromaStore for unit tests."""
+ store = Mock()
+ store.query.return_value = []
+ store.add_document.return_value = None
+ store.add_documents.return_value = None
+ return store
+
+
+@pytest.fixture
+def mock_llm_service():
+ """Mock LLMService for unit tests."""
+ service = Mock()
+ service.generate_response.return_value = "Mocked LLM response"
+ service.generate_response_stream.return_value = iter(["Mocked ", "streamed ", "response"])
+ service.check_model_availability.return_value = True
+ return service
+
+
+@pytest.fixture
+def mock_doc_retrieval_service():
+ """Mock DocumentRetrievalService for unit tests."""
+ service = Mock()
+
+ # Create mock documents
+ mock_doc = Mock()
+ mock_doc.page_content = "Test document content"
+ mock_doc.metadata = {"source": "test.md", "url": "https://example.com"}
+
+ service.retrieve_documents.return_value = [mock_doc]
+ service.format_documents_as_context.return_value = "Test document content"
+ return service
+
+
+@pytest.fixture
+def mock_rag_orchestrator():
+ """Mock RAGOrchestrator for unit tests."""
+ orchestrator = Mock()
+
+ # Create mock documents
+ mock_doc = Mock()
+ mock_doc.page_content = "Test document content"
+ mock_doc.metadata = {"source": "test.md", "url": "https://example.com"}
+
+ orchestrator.query.return_value = ("Mocked response", [mock_doc])
+ orchestrator.query_stream.return_value = (iter(["Mocked ", "response"]), [mock_doc])
+ orchestrator.check_model_availability.return_value = True
+ orchestrator.format_documents_for_display.return_value = "**Source:** test.md"
+ return orchestrator
+
+
+@pytest.fixture
+def test_config_manager(tmp_path):
+ """ConfigManager with test configuration.
+
+ Creates a temporary YAML config file with test site configuration.
+ """
+ from tapio.config.config_manager import ConfigManager
+
+ config_yaml = tmp_path / "test_config.yaml"
+ config_yaml.write_text("""
+sites:
+ test_site:
+ base_url: "https://example.com"
+ description: "Test site"
+ parser_config:
+ title_selector: "//title"
+ content_selectors:
+ - "//main"
+ - "//article"
+ fallback_to_body: true
+ crawler_config:
+ max_depth: 1
+ delay_between_requests: 0.1
+ max_concurrent: 2
+""")
+ return ConfigManager(str(config_yaml))
+
+
+@pytest.fixture
+def tmp_chroma_db(tmp_path):
+ """Temporary directory for ChromaDB in integration tests."""
+ db_dir = tmp_path / "chroma_db"
+ db_dir.mkdir()
+ return str(db_dir)
+
+
+# ============================================================================
+# Integration Test Markers
+# ============================================================================
+
+
+def pytest_configure(config):
+ """Add custom markers for pytest."""
+ config.addinivalue_line("markers", "integration: mark test as integration test (uses real embeddings, slower)")
diff --git a/tests/integration/__init__.py b/tests/integration/__init__.py
new file mode 100644
index 0000000..908696e
--- /dev/null
+++ b/tests/integration/__init__.py
@@ -0,0 +1 @@
+"""Integration test init file."""
diff --git a/tests/integration/test_parser_pipeline.py b/tests/integration/test_parser_pipeline.py
new file mode 100644
index 0000000..d3d7498
--- /dev/null
+++ b/tests/integration/test_parser_pipeline.py
@@ -0,0 +1,136 @@
+"""Integration tests for the parser pipeline."""
+
+from pathlib import Path
+
+import pytest
+
+from tapio.parser.parser import Parser
+
+
+@pytest.mark.integration
+def test_parser_pipeline(tmp_path, test_config_manager):
+ """Test complete parsing pipeline from HTML to Markdown.
+
+ This test:
+ 1. Creates test HTML files
+ 2. Creates parser with injected config
+ 3. Parses HTML to Markdown
+ 4. Verifies output files are created
+ """
+ # Create test HTML input directory
+ input_dir = tmp_path / "content" / "test_site" / "crawled"
+ input_dir.mkdir(parents=True)
+
+ # Create output directory
+ output_dir = tmp_path / "content" / "test_site" / "parsed"
+ output_dir.mkdir(parents=True)
+
+ # Create test HTML file
+ test_html = input_dir / "test_page.html"
+ test_html.write_text("""
+
+
+
+ Test Page
+
+
+
+ Test Document
+ This is a test document about residence permits.
+ Requirements
+ You need a valid passport.
+
+
+
+""")
+
+ # Get site config from test config manager
+ site_config = test_config_manager.get_site_config("test_site")
+
+ # Create parser with injected dependencies
+ parser = Parser(
+ site_name="test_site",
+ site_config=site_config,
+ input_dir=str(input_dir),
+ output_dir=str(output_dir),
+ )
+
+ # Parse the file
+ results = parser.parse_all()
+
+ # Verify parsing occurred
+ assert len(results) == 1
+ assert "output_file" in results[0]
+ assert "title" in results[0]
+
+ # Verify output file was created
+ output_file = Path(results[0]["output_file"])
+ assert output_file.exists()
+
+ # Verify output content
+ content = output_file.read_text()
+ assert "Test Document" in content
+ assert "residence permits" in content
+ assert "Requirements" in content
+
+ # Verify frontmatter was added (should have --- markers)
+ assert content.startswith("---")
+
+
+@pytest.mark.integration
+def test_parser_batch_processing(tmp_path, test_config_manager):
+ """Test parser handles multiple HTML files correctly."""
+ # Create directories
+ input_dir = tmp_path / "content" / "test_site" / "crawled"
+ input_dir.mkdir(parents=True)
+ output_dir = tmp_path / "content" / "test_site" / "parsed"
+ output_dir.mkdir(parents=True)
+
+ # Create multiple HTML files
+ for i in range(3):
+ test_html = input_dir / f"page_{i}.html"
+ test_html.write_text(f"""
+
+
+Page {i}
+
+
+ Document {i}
+ Content for document {i}
+
+
+
+""")
+
+ # Get site config
+ site_config = test_config_manager.get_site_config("test_site")
+
+ # Create parser
+ parser = Parser(
+ site_name="test_site",
+ site_config=site_config,
+ input_dir=str(input_dir),
+ output_dir=str(output_dir),
+ )
+
+ # Parse all files
+ results = parser.parse_all()
+
+ # Verify all files were processed
+ assert len(results) == 3
+
+ # Verify all have required fields
+ for result in results:
+ assert "output_file" in result
+ assert "title" in result
+
+ # Verify all output files exist
+ for i in range(3):
+ # Find the result for this page
+ matching_results = [r for r in results if f"page_{i}" in r["output_file"]]
+ assert len(matching_results) == 1
+
+ output_file = Path(matching_results[0]["output_file"])
+ assert output_file.exists()
+ content = output_file.read_text()
+ assert f"Document {i}" in content
diff --git a/tests/integration/test_rag_pipeline.py b/tests/integration/test_rag_pipeline.py
new file mode 100644
index 0000000..51168c4
--- /dev/null
+++ b/tests/integration/test_rag_pipeline.py
@@ -0,0 +1,166 @@
+"""Integration tests for the complete RAG pipeline.
+
+These tests use real components (embeddings, vector database) to verify
+end-to-end functionality. The LLM service is mocked to avoid external
+dependencies on Ollama.
+"""
+
+from unittest.mock import Mock, patch
+
+import pytest
+from langchain_core.documents import Document # type: ignore[import-not-found]
+
+from tapio.config.config_models import RAGConfig
+from tapio.factories import RAGOrchestratorFactory
+from tapio.services.document_retrieval_service import DocumentRetrievalService
+from tapio.services.llm_service import LLMService
+from tapio.services.rag_orchestrator import RAGOrchestrator
+from tapio.vectorstore.chroma_store import ChromaStore
+
+
+@pytest.mark.integration
+def test_rag_pipeline_end_to_end(tmp_chroma_db, mock_embeddings):
+ """Test complete RAG pipeline from document storage to query response.
+
+ This test:
+ 1. Creates a real ChromaDB instance with mock embeddings
+ 2. Adds test documents to the vector store
+ 3. Creates a RAG orchestrator with mock LLM
+ 4. Queries the system and verifies document retrieval
+ """
+ # Create ChromaStore with real Chroma and mock embeddings
+ chroma_store = ChromaStore(
+ collection_name="test_integration",
+ embeddings=mock_embeddings,
+ persist_directory=tmp_chroma_db,
+ )
+
+ # Add test documents
+ test_docs = [
+ Document(
+ page_content="Finland residence permits require a valid passport and proof of income.",
+ metadata={"source": "residence_permit.md", "url": "https://example.com/residence"},
+ ),
+ Document(
+ page_content="Processing time for residence permits is typically 4-6 months.",
+ metadata={"source": "processing_times.md", "url": "https://example.com/times"},
+ ),
+ Document(
+ page_content="Work permits in Finland depend on having a job offer from a Finnish employer.",
+ metadata={"source": "work_permit.md", "url": "https://example.com/work"},
+ ),
+ ]
+
+ # Add documents to vector store
+ chroma_store.vector_db.add_documents(test_docs)
+
+ # Create mock LLM service
+ mock_llm = Mock(spec=LLMService)
+ mock_llm.generate_response.return_value = (
+ "Based on the documents, residence permits require a valid passport and "
+ "proof of income, with processing times of 4-6 months."
+ )
+ mock_llm.check_model_availability.return_value = True
+
+ # Create document retrieval service
+ doc_service = DocumentRetrievalService(
+ vector_store=chroma_store,
+ num_results=3,
+ )
+
+ # Create RAG orchestrator
+ orchestrator = RAGOrchestrator(
+ doc_retrieval_service=doc_service,
+ llm_service=mock_llm,
+ )
+
+ # Query the system
+ with patch("tapio.services.rag_orchestrator.load_prompt") as mock_load_prompt:
+ mock_load_prompt.side_effect = ["You are a helpful assistant.", "Context: {context}\n\nQuestion: {question}"]
+
+ response, retrieved_docs = orchestrator.query("How do I apply for a residence permit?")
+
+ # Verify response
+ assert response is not None
+ assert "passport" in response.lower() or "residence" in response.lower()
+
+ # Verify documents were retrieved
+ assert len(retrieved_docs) > 0
+
+ # Verify LLM was called
+ mock_llm.generate_response.assert_called_once()
+
+
+@pytest.mark.integration
+def test_rag_factory_integration(tmp_chroma_db, mock_embeddings):
+ """Test RAG orchestrator creation using factory pattern.
+
+ Verifies that the factory correctly wires all dependencies.
+ """
+ # Create config
+ config = RAGConfig(
+ collection_name="test_factory",
+ persist_directory=tmp_chroma_db,
+ embedding_model_name="all-MiniLM-L6-v2",
+ llm_model_name="llama3.2",
+ max_tokens=512,
+ num_results=3,
+ )
+
+ # Create factory
+ factory = RAGOrchestratorFactory(config)
+
+ # Mock the create_embeddings to return our mock
+ with patch.object(factory, "create_embeddings", return_value=mock_embeddings):
+ # Create orchestrator
+ orchestrator = factory.create_orchestrator()
+
+ # Verify orchestrator was created
+ assert isinstance(orchestrator, RAGOrchestrator)
+ assert orchestrator.doc_retrieval_service is not None
+ assert orchestrator.llm_service is not None
+
+
+@pytest.mark.integration
+def test_document_retrieval_similarity_search(tmp_chroma_db, mock_embeddings):
+ """Test that similar documents are retrieved correctly."""
+ # Create ChromaStore
+ chroma_store = ChromaStore(
+ collection_name="test_similarity",
+ embeddings=mock_embeddings,
+ persist_directory=tmp_chroma_db,
+ )
+
+ # Add documents with different topics
+ docs = [
+ Document(
+ page_content="Residence permits are required for living in Finland.",
+ metadata={"source": "doc1.md", "topic": "residence"},
+ ),
+ Document(
+ page_content="Tourist visas allow short visits to Finland.", metadata={"source": "doc2.md", "topic": "visa"}
+ ),
+ Document(
+ page_content="Permanent residence permits grant long-term stay rights.",
+ metadata={"source": "doc3.md", "topic": "residence"},
+ ),
+ ]
+
+ chroma_store.vector_db.add_documents(docs)
+
+ # Create retrieval service
+ doc_service = DocumentRetrievalService(
+ vector_store=chroma_store,
+ num_results=2,
+ )
+
+ # Query for residence permits (should return residence-related docs)
+ results = doc_service.retrieve_documents("residence permits")
+
+ # Verify we got results
+ assert len(results) == 2 # Should return exactly num_results documents
+
+ # Verify results have content
+ for doc in results:
+ assert hasattr(doc, "page_content")
+ assert len(doc.page_content) > 0
diff --git a/tests/integration/test_vectorization_pipeline.py b/tests/integration/test_vectorization_pipeline.py
new file mode 100644
index 0000000..91e66e5
--- /dev/null
+++ b/tests/integration/test_vectorization_pipeline.py
@@ -0,0 +1,126 @@
+"""Integration tests for the vectorization pipeline.
+
+Tests the complete flow from markdown files to vector storage.
+"""
+
+import pytest
+from langchain_chroma import Chroma # type: ignore[import-not-found]
+from langchain_text_splitters import MarkdownTextSplitter # type: ignore[import-not-found]
+
+from tapio.vectorstore.vectorizer import MarkdownVectorizer
+
+
+@pytest.mark.integration
+def test_markdown_vectorization_pipeline(tmp_path, tmp_chroma_db, mock_embeddings):
+ """Test complete vectorization from markdown files to ChromaDB.
+
+ This test:
+ 1. Creates test markdown files
+ 2. Creates vectorizer with real components
+ 3. Processes files
+ 4. Verifies documents are stored in vector database
+ """
+ # Create test markdown files
+ test_content_dir = tmp_path / "content" / "test_site" / "parsed"
+ test_content_dir.mkdir(parents=True)
+
+ # Create test markdown file with frontmatter
+ test_md = test_content_dir / "test_doc.md"
+ test_md.write_text("""---
+title: Test Document
+source_url: https://example.com/test
+---
+
+# Test Document
+
+This is a test document about residence permits in Finland.
+
+## Requirements
+
+You need a valid passport and proof of income.
+
+## Processing Time
+
+The processing time is typically 4-6 months.
+""")
+
+ # Create another test file
+ test_md2 = test_content_dir / "test_doc2.md"
+ test_md2.write_text("""---
+title: Work Permits
+source_url: https://example.com/work
+---
+
+# Work Permits
+
+Work permits require a job offer from a Finnish employer.
+""")
+
+ # Create Chroma instance
+ vector_db = Chroma(
+ collection_name="test_vectorize",
+ embedding_function=mock_embeddings,
+ persist_directory=tmp_chroma_db,
+ )
+
+ # Create text splitter
+ text_splitter = MarkdownTextSplitter(
+ chunk_size=1000,
+ chunk_overlap=200,
+ )
+
+ # Create vectorizer
+ vectorizer = MarkdownVectorizer(
+ vector_db=vector_db,
+ text_splitter=text_splitter,
+ )
+
+ # Process directory
+ count = vectorizer.process_directory(
+ input_dir=str(test_content_dir.parent.parent), # Process from content dir
+ site_filter="test_site",
+ batch_size=10,
+ )
+
+ # Verify files were processed
+ assert count == 2
+
+ # Verify documents are in the database (query returns results)
+ results = vector_db.similarity_search("residence permits", k=5)
+ assert len(results) > 0
+
+
+@pytest.mark.integration
+def test_vectorizer_handles_empty_directory(tmp_path, tmp_chroma_db, mock_embeddings):
+ """Test that vectorizer handles empty directories gracefully."""
+ # Create empty directory
+ empty_dir = tmp_path / "empty"
+ empty_dir.mkdir()
+
+ # Create Chroma instance
+ vector_db = Chroma(
+ collection_name="test_empty",
+ embedding_function=mock_embeddings,
+ persist_directory=tmp_chroma_db,
+ )
+
+ # Create text splitter
+ text_splitter = MarkdownTextSplitter(
+ chunk_size=1000,
+ chunk_overlap=200,
+ )
+
+ # Create vectorizer
+ vectorizer = MarkdownVectorizer(
+ vector_db=vector_db,
+ text_splitter=text_splitter,
+ )
+
+ # Process empty directory
+ count = vectorizer.process_directory(
+ input_dir=str(empty_dir),
+ batch_size=10,
+ )
+
+ # Verify no files were processed
+ assert count == 0
diff --git a/tests/parser/test_parser.py b/tests/parser/test_parser.py
index d82089f..e97e880 100644
--- a/tests/parser/test_parser.py
+++ b/tests/parser/test_parser.py
@@ -8,6 +8,7 @@
import yaml
+from tapio.config.config_manager import ConfigManager
from tapio.parser import Parser
@@ -37,6 +38,7 @@ def setUp(self):
# Prepare input/output dir paths as used by Parser
from tapio.config.settings import DEFAULT_DIRS
+ self.DEFAULT_DIRS = DEFAULT_DIRS
self.input_dir = os.path.join(self.temp_dir, self.site_name, DEFAULT_DIRS["CRAWLED_DIR"])
self.output_dir = os.path.join(self.temp_dir, self.site_name, DEFAULT_DIRS["PARSED_DIR"])
os.makedirs(self.input_dir, exist_ok=True)
@@ -139,10 +141,16 @@ def setUp(self):
with open(self.config_path, "w") as f:
yaml.dump(self.test_config, f)
- # Create default parser (do not pass input_dir/output_dir)
+ # Create config manager and get site config
+ config_manager = ConfigManager(self.config_path)
+ site_config = config_manager.get_site_config(self.site_name)
+
+ # Create parser with injected dependencies
self.parser = Parser(
site_name=self.site_name,
- config_path=self.config_path,
+ site_config=site_config,
+ input_dir=self.input_dir,
+ output_dir=self.output_dir,
)
# Mock the logger
@@ -179,11 +187,9 @@ def test_init(self):
def test_init_with_invalid_site(self):
"""Test initialization with invalid site."""
+ config_manager = ConfigManager(self.config_path)
with self.assertRaises(ValueError):
- Parser(
- site_name="nonexistent",
- config_path=self.config_path,
- )
+ config_manager.get_site_config("nonexistent")
def test_parse_html_with_main_content(self):
"""Test parsing HTML with main-content div."""
@@ -223,9 +229,24 @@ def test_parse_html_with_fallback(self):
def test_parse_html_without_fallback(self):
"""Test behavior when no selectors match and fallback is disabled."""
# Create parser with no fallback config
+ config_manager = ConfigManager(self.config_path)
+ no_fallback_config = config_manager.get_site_config(self.no_fallback_site_name)
+
+ # Prepare input/output directories for no_fallback site
+ no_fallback_input_dir = os.path.join(
+ self.temp_dir, self.no_fallback_site_name, self.DEFAULT_DIRS["CRAWLED_DIR"]
+ )
+ no_fallback_output_dir = os.path.join(
+ self.temp_dir, self.no_fallback_site_name, self.DEFAULT_DIRS["PARSED_DIR"]
+ )
+ os.makedirs(no_fallback_input_dir, exist_ok=True)
+ os.makedirs(no_fallback_output_dir, exist_ok=True)
+
no_fallback_parser = Parser(
site_name=self.no_fallback_site_name,
- config_path=self.config_path,
+ site_config=no_fallback_config,
+ input_dir=no_fallback_input_dir,
+ output_dir=no_fallback_output_dir,
)
no_fallback_parser.logger = MagicMock()
@@ -308,24 +329,6 @@ def test_parse_all(self):
self.assertIn("No Main Content", titles)
self.assertIn("Services", titles)
- def test_list_available_site_configs(self):
- """Test listing available site configurations."""
- available_sites = Parser.list_available_site_configs(self.config_path)
- self.assertEqual(len(available_sites), 2)
- self.assertIn("example", available_sites)
- self.assertIn("no_fallback", available_sites)
-
- def test_get_site_config(self):
- """Test getting site configuration."""
- config = Parser.get_site_config("example", self.config_path)
- self.assertIsNotNone(config)
- if config: # Check if config is not None before accessing attributes
- self.assertEqual(config.description, "Example Website for Testing")
-
- # Test getting non-existent site config
- config = Parser.get_site_config("nonexistent", self.config_path)
- self.assertIsNone(config)
-
def test_convert_element_link_to_absolute(self):
"""Test converting a single element's link attribute to absolute URL."""
from lxml import html as lxml_html
diff --git a/tests/parser/test_relative_links.py b/tests/parser/test_relative_links.py
index 7400454..8f4ef81 100644
--- a/tests/parser/test_relative_links.py
+++ b/tests/parser/test_relative_links.py
@@ -8,6 +8,7 @@
import yaml
+from tapio.config.config_manager import ConfigManager
from tapio.parser import Parser
@@ -86,10 +87,16 @@ def setUp(self):
with open(self.config_path, "w") as f:
yaml.dump(self.test_config, f)
- # Create parser (do not pass input_dir/output_dir)
+ # Create config manager and get site config
+ config_manager = ConfigManager(self.config_path)
+ site_config = config_manager.get_site_config("test_site")
+
+ # Create parser with injected dependencies
self.parser = Parser(
site_name="test_site",
- config_path=self.config_path,
+ site_config=site_config,
+ input_dir=self.input_dir,
+ output_dir=self.output_dir,
)
def tearDown(self):
@@ -194,10 +201,15 @@ def test_domain_specific_url_handling(self):
with open(self.config_path, "w") as f:
yaml.dump(self.test_config, f)
- # Create a new parser with the updated config
+ # Create a new parser with the updated config using dependency injection
+ config_manager = ConfigManager(self.config_path)
+ site_config = config_manager.get_site_config("test_site")
+
parser = Parser(
site_name="test_site",
- config_path=self.config_path,
+ site_config=site_config,
+ input_dir=self.input_dir,
+ output_dir=self.output_dir,
)
# Delete URL mappings to force using domain-based URL construction
diff --git a/tests/services/test_document_retrieval_service.py b/tests/services/test_document_retrieval_service.py
index 0054175..e05ee0b 100644
--- a/tests/services/test_document_retrieval_service.py
+++ b/tests/services/test_document_retrieval_service.py
@@ -8,18 +8,14 @@
@pytest.fixture
-def doc_retrieval_service():
+def doc_retrieval_service(mock_chroma_store):
"""Create a document retrieval service with mocked dependencies for testing."""
- # Mock the ChromaStore
- with mock.patch("tapio.services.document_retrieval_service.ChromaStore"):
- # Create DocumentRetrievalService with mocked dependencies
- service = DocumentRetrievalService(
- collection_name="test_collection",
- persist_directory="test_db",
- num_results=3,
- )
-
- yield service
+ # Create DocumentRetrievalService with injected mock ChromaStore
+ service = DocumentRetrievalService(
+ vector_store=mock_chroma_store,
+ num_results=3,
+ )
+ return service
def test_document_retrieval_service_retrieves_documents(doc_retrieval_service):
diff --git a/tests/services/test_rag_orchestrator.py b/tests/services/test_rag_orchestrator.py
index fb412a8..73ef21b 100644
--- a/tests/services/test_rag_orchestrator.py
+++ b/tests/services/test_rag_orchestrator.py
@@ -8,27 +8,19 @@
@pytest.fixture
-def rag_orchestrator():
+def rag_orchestrator(mock_doc_retrieval_service, mock_llm_service):
"""Create a RAG orchestrator with mocked dependencies for testing."""
- # Mock the DocumentRetrievalService and LLMService
- with mock.patch("tapio.services.rag_orchestrator.DocumentRetrievalService") as mock_doc_service:
- with mock.patch("tapio.services.rag_orchestrator.LLMService") as mock_llm:
- # Configure mock instance behavior
- mock_doc_service_instance = mock_doc_service.return_value
- mock_llm_instance = mock_llm.return_value
-
- # Create RAGOrchestrator with mocked dependencies
- orchestrator = RAGOrchestrator(
- collection_name="test_collection",
- persist_directory="test_db",
- model_name="test_model",
- )
+ # Create RAGOrchestrator with injected mock dependencies
+ orchestrator = RAGOrchestrator(
+ doc_retrieval_service=mock_doc_retrieval_service,
+ llm_service=mock_llm_service,
+ )
- # Set mocked properties for access in tests
- orchestrator.mock_doc_service = mock_doc_service_instance
- orchestrator.mock_llm_service = mock_llm_instance
+ # Store mocks for access in tests
+ orchestrator.mock_doc_service = mock_doc_retrieval_service
+ orchestrator.mock_llm_service = mock_llm_service
- yield orchestrator
+ return orchestrator
def test_rag_orchestrator_query(rag_orchestrator):
@@ -41,7 +33,7 @@ def test_rag_orchestrator_query(rag_orchestrator):
"Mocked user prompt with context", # For user_query
]
- # Mock document retrieval
+ # Mock document retrieval (using fixture defaults)
mock_doc = mock.MagicMock()
mock_doc.page_content = "Test document content"
rag_orchestrator.mock_doc_service.retrieve_documents.return_value = [
diff --git a/tests/test_cli.py b/tests/test_cli.py
index eab053d..85f38b1 100644
--- a/tests/test_cli.py
+++ b/tests/test_cli.py
@@ -8,7 +8,7 @@
from typer.testing import CliRunner
from tapio.cli import app, find_sites_with_crawled_content
-from tapio.config.settings import DEFAULT_CHROMA_COLLECTION, DEFAULT_CONTENT_DIR, DEFAULT_DIRS
+from tapio.config.settings import DEFAULT_CONTENT_DIR
@pytest.fixture
@@ -206,11 +206,13 @@ def test_parse_command(self, mock_parser, mock_config_manager, runner):
# Check that the command ran successfully
assert result.exit_code == 0
- # Check that the parser was initialized correctly
- mock_parser.assert_called_once_with(
- site_name="migri",
- config_path=None,
- )
+ # Check that the parser was initialized with dependency injection
+ assert mock_parser.call_count == 1
+ call_kwargs = mock_parser.call_args[1]
+ assert call_kwargs["site_name"] == "migri"
+ assert "site_config" in call_kwargs
+ assert "input_dir" in call_kwargs
+ assert "output_dir" in call_kwargs
# Check that list_available_sites was called
mock_config_instance.list_available_sites.assert_called_once()
@@ -310,86 +312,17 @@ def test_parse_command_custom_config(self, mock_exists, mock_parser, mock_config
# Check that list_available_sites was called
mock_config_instance.list_available_sites.assert_called_once()
- # Check that the parser was initialized correctly with the custom config
- mock_parser.assert_called_once_with(
- site_name="custom_site",
- config_path="custom_configs.yaml",
- )
+ # Check that the parser was initialized with dependency injection
+ assert mock_parser.call_count == 1
+ call_kwargs = mock_parser.call_args[1]
+ assert call_kwargs["site_name"] == "custom_site"
+ assert "site_config" in call_kwargs
+ assert "input_dir" in call_kwargs
+ assert "output_dir" in call_kwargs
# Check that parse_all was called correctly (without domain parameter)
mock_parser_instance.parse_all.assert_called_once_with()
- @patch("tapio.cli.MarkdownVectorizer")
- def test_vectorize_command(self, mock_vectorizer, runner):
- """Test the vectorize command."""
- # Set up mock
- mock_vectorizer_instance = MagicMock()
- mock_vectorizer_instance.process_directory.return_value = 5
- mock_vectorizer.return_value = mock_vectorizer_instance
-
- # Run the command
- result = runner.invoke(
- app,
- [
- "vectorize",
- ],
- )
-
- # Check that the command ran successfully
- assert result.exit_code == 0
-
- # Check that the vectorizer was initialized correctly
- mock_vectorizer.assert_called_once_with(
- collection_name=DEFAULT_CHROMA_COLLECTION,
- persist_directory=DEFAULT_DIRS["CHROMA_DIR"],
- embedding_model_name="all-MiniLM-L6-v2",
- chunk_size=1000,
- chunk_overlap=200,
- )
-
- # Check that process_directory was called correctly
- mock_vectorizer_instance.process_directory.assert_called_once_with(
- input_dir=DEFAULT_CONTENT_DIR,
- site_filter=None,
- batch_size=20,
- )
-
- # Check expected output in stdout
- assert "Starting vectorization" in result.stdout
- assert f"Vector database will be stored in: {DEFAULT_DIRS['CHROMA_DIR']}" in result.stdout
- assert "Using embedding model: all-MiniLM-L6-v2" in result.stdout
- assert "Vectorization completed" in result.stdout
- assert "Processed 5 files" in result.stdout
-
- @patch("tapio.cli.MarkdownVectorizer")
- def test_vectorize_command_with_site(self, mock_vectorizer, runner):
- """Test the vectorize command with site filter."""
- # Set up mock
- mock_vectorizer_instance = MagicMock()
- mock_vectorizer_instance.process_directory.return_value = 3
- mock_vectorizer.return_value = mock_vectorizer_instance
-
- # Mock os.path.exists to return True for the site directory
- with patch("tapio.cli.os.path.exists", return_value=True):
- # Run the command with site filter
- result = runner.invoke(app, ["vectorize", "migri"])
-
- # Check that the command ran successfully
- assert result.exit_code == 0
-
- # Check that process_directory was called with the site filter
- expected_input_dir = os.path.join(DEFAULT_CONTENT_DIR, "migri", DEFAULT_DIRS["PARSED_DIR"])
- mock_vectorizer_instance.process_directory.assert_called_once_with(
- input_dir=expected_input_dir,
- site_filter=None,
- batch_size=20,
- )
-
- # Check expected output in stdout
- assert "Starting vectorization" in result.stdout
- assert "Vectorization completed" in result.stdout
- assert "Processed 3 files" in result.stdout
-
@patch("tapio.cli.MarkdownVectorizer")
def test_vectorize_command_exception(self, mock_vectorizer, runner):
"""Test handling of exceptions in vectorize command."""
@@ -408,52 +341,6 @@ def test_vectorize_command_exception(self, mock_vectorizer, runner):
assert "Starting vectorization" in result.stdout
assert "Error during vectorization: Test error" in result.stdout
- @patch("tapio.app.main")
- def test_tapio_app_command(self, mock_launch_gradio, runner):
- """Test the tapio-app command."""
- # Run the command
- result = runner.invoke(app, ["tapio-app"])
-
- # Check that the command ran successfully
- assert result.exit_code == 0
-
- # Check that launch_gradio was called correctly
- mock_launch_gradio.assert_called_once_with(
- collection_name=DEFAULT_CHROMA_COLLECTION,
- persist_directory=DEFAULT_DIRS["CHROMA_DIR"],
- model_name="llama3.2:latest",
- max_tokens=1024,
- share=False,
- )
-
- @patch("tapio.app.main")
- def test_tapio_app_command_with_options(self, mock_launch_gradio, runner):
- """Test the tapio-app command with custom options."""
- # Run the command with options
- result = runner.invoke(
- app,
- [
- "tapio-app",
- "--model-name",
- "llama3.2:latest",
- "--max-tokens",
- "2048",
- "--share",
- ],
- )
-
- # Check that the command ran successfully
- assert result.exit_code == 0
-
- # Check that launch_gradio was called correctly with the custom options
- mock_launch_gradio.assert_called_once_with(
- collection_name=DEFAULT_CHROMA_COLLECTION,
- persist_directory=DEFAULT_DIRS["CHROMA_DIR"],
- model_name="llama3.2:latest",
- max_tokens=2048,
- share=True,
- )
-
@patch("tapio.cli.tapio_app")
def test_dev_command(self, mock_tapio_app, runner):
"""Test the dev command."""
@@ -620,16 +507,15 @@ def mock_walk_side_effect(path):
# Check that all three parsers were created
assert mock_parser.call_count == 3
- # Check expected calls to Parser constructor - new format uses site_name only
- expected_calls = [
- ("migri", None),
- ("kela", None),
- ("vero", None),
- ]
+ # Check expected calls to Parser constructor - new DI format uses site_name, site_config, input_dir, output_dir
+ expected_sites = ["migri", "kela", "vero"]
- for i, (site_name, config_path) in enumerate(expected_calls):
+ for i, site_name in enumerate(expected_sites):
assert mock_parser.call_args_list[i][1]["site_name"] == site_name
- assert mock_parser.call_args_list[i][1]["config_path"] == config_path
+ # With DI, we pass site_config not config_path
+ assert "site_config" in mock_parser.call_args_list[i][1]
+ assert "input_dir" in mock_parser.call_args_list[i][1]
+ assert "output_dir" in mock_parser.call_args_list[i][1]
# Check that parse_all was called for each parser
for mock_instance in mock_parser_instances:
diff --git a/tests/test_gradio_app.py b/tests/test_gradio_app.py
index c66e175..f13527c 100644
--- a/tests/test_gradio_app.py
+++ b/tests/test_gradio_app.py
@@ -1,79 +1,38 @@
"""Tests for the Gradio app module."""
-import unittest
from unittest.mock import Mock, patch
import pytest
-from tapio.app import (
- DEFAULT_CHROMA_DB_PATH,
- DEFAULT_COLLECTION_NAME,
- DEFAULT_MAX_TOKENS,
- DEFAULT_MODEL_NAME,
- DEFAULT_NUM_RESULTS,
- TapioAssistantApp,
- main,
-)
+from tapio.app import TapioAssistantApp, main
@pytest.fixture
-def mock_rag_orchestrator():
- """Create a mock RAG orchestrator."""
- mock_orchestrator = Mock()
- mock_orchestrator.query.return_value = ("Test response", ["doc1", "doc2"])
- mock_orchestrator.format_documents_for_display.return_value = "Formatted docs"
- mock_orchestrator.check_model_availability.return_value = True
- return mock_orchestrator
+def test_app(mock_rag_orchestrator):
+ """Create TapioAssistantApp with mocked RAG orchestrator."""
+ return TapioAssistantApp(rag_orchestrator=mock_rag_orchestrator)
-class TestGradioApp(unittest.TestCase):
+class TestGradioApp:
"""Tests for the Gradio app module."""
- @patch("tapio.app.RAGOrchestrator")
- def test_init_rag_orchestrator(self, mock_rag_orchestrator_class):
- """Test that the RAG orchestrator is initialized correctly."""
- # Setup
- mock_instance = Mock()
- mock_rag_orchestrator_class.return_value = mock_instance
-
- # Create app and initialize RAG orchestrator
- app = TapioAssistantApp()
- orchestrator = app._init_rag_orchestrator()
-
- # Assertions
- mock_rag_orchestrator_class.assert_called_once_with(
- collection_name=DEFAULT_COLLECTION_NAME,
- persist_directory=DEFAULT_CHROMA_DB_PATH,
- model_name=DEFAULT_MODEL_NAME,
- max_tokens=DEFAULT_MAX_TOKENS,
- num_results=DEFAULT_NUM_RESULTS,
- )
- assert orchestrator == mock_instance
-
- # Test the singleton behavior - calling again should not create a new instance
- _ = app._init_rag_orchestrator()
- assert mock_rag_orchestrator_class.call_count == 1
-
- def test_generate_rag_response(self):
+ def test_generate_rag_response(self, test_app):
"""Test generating a RAG response."""
- # Setup
- app = TapioAssistantApp()
- app.rag_orchestrator = Mock()
- app.rag_orchestrator.query.return_value = (
+ test_app.rag_orchestrator.query.return_value = (
"Test response",
["doc1", "doc2"],
)
- app.rag_orchestrator.format_documents_for_display.return_value = "Formatted docs"
+ test_app.rag_orchestrator.format_documents_for_display.return_value = "Formatted docs"
# Call the method
- response, formatted_docs = app.generate_rag_response("test query")
+ response, formatted_docs = test_app.generate_rag_response("test query")
# Assertions
- app.rag_orchestrator.query.assert_called_once_with(
+ test_app.rag_orchestrator.query.assert_called_once_with(
query_text="test query",
history=None,
)
- app.rag_orchestrator.format_documents_for_display.assert_called_once_with(
+ test_app.rag_orchestrator.format_documents_for_display.assert_called_once_with(
[
"doc1",
"doc2",
@@ -82,23 +41,20 @@ def test_generate_rag_response(self):
assert response == "Test response"
assert formatted_docs == "Formatted docs"
- @patch("tapio.app.RAGOrchestrator")
- def test_generate_rag_response_with_error(self, mock_rag_orchestrator_class):
+ def test_generate_rag_response_with_error(self, test_app):
"""Test error handling in generate_rag_response."""
# Setup
- mock_rag_orchestrator_class.side_effect = Exception("Test error")
- app = TapioAssistantApp()
+ test_app.rag_orchestrator.query.side_effect = Exception("Test error")
- # Call the method - need to patch _init_rag_orchestrator first
- with patch.object(app, "_init_rag_orchestrator", side_effect=Exception("Test error")):
- response, formatted_docs = app.generate_rag_response("test query")
+ # Call the method
+ response, formatted_docs = test_app.generate_rag_response("test query")
# Assertions
assert "error" in response.lower()
assert "Error retrieving" in formatted_docs
@patch("tapio.app.TapioAssistantApp")
- def test_main_function(self, mock_app_class):
+ def test_main_function(self, mock_app_class, mock_rag_orchestrator):
"""Test the main function that launches the Gradio app."""
# Setup
mock_app_instance = Mock()
@@ -106,21 +62,15 @@ def test_main_function(self, mock_app_class):
mock_app_instance.check_model_availability.return_value = True
# Call the function
- main(share=True)
+ main(rag_orchestrator=mock_rag_orchestrator, share=True)
# Assertions
- mock_app_class.assert_called_once_with(
- collection_name=DEFAULT_COLLECTION_NAME,
- persist_directory=DEFAULT_CHROMA_DB_PATH,
- model_name=DEFAULT_MODEL_NAME,
- max_tokens=DEFAULT_MAX_TOKENS,
- num_results=DEFAULT_NUM_RESULTS,
- )
+ mock_app_class.assert_called_once_with(rag_orchestrator=mock_rag_orchestrator)
mock_app_instance.check_model_availability.assert_called_once()
mock_app_instance.launch.assert_called_once_with(share=True)
@patch("tapio.app.TapioAssistantApp")
- def test_main_function_model_unavailable(self, mock_app_class):
+ def test_main_function_model_unavailable(self, mock_app_class, mock_rag_orchestrator):
"""Test the main function when the model is unavailable."""
# Setup
mock_app_instance = Mock()
@@ -128,7 +78,7 @@ def test_main_function_model_unavailable(self, mock_app_class):
mock_app_instance.check_model_availability.return_value = False
# Call the function
- main()
+ main(rag_orchestrator=mock_rag_orchestrator)
# Assertions
mock_app_instance.check_model_availability.assert_called_once()
diff --git a/tests/vectorstore/test_chroma_store.py b/tests/vectorstore/test_chroma_store.py
index 533503d..6e94302 100644
--- a/tests/vectorstore/test_chroma_store.py
+++ b/tests/vectorstore/test_chroma_store.py
@@ -11,37 +11,34 @@ class TestChromaStore:
"""Tests for the ChromaStore class."""
@patch("tapio.vectorstore.chroma_store.Chroma")
- @patch("tapio.vectorstore.chroma_store.HuggingFaceEmbeddings")
- def test_init(self, mock_embeddings, mock_chroma):
+ def test_init(self, mock_chroma, mock_embeddings):
"""Test initialization of the ChromaStore."""
- # Set up mocks
- mock_embedding_instance = Mock()
- mock_embeddings.return_value = mock_embedding_instance
-
- # Initialize ChromaStore
+ # Initialize ChromaStore with injected embeddings
_ = ChromaStore(
collection_name="test_collection",
+ embeddings=mock_embeddings,
persist_directory="test_dir",
)
- # Check if embeddings and vector_db were initialized correctly
- mock_embeddings.assert_called_once_with(model_name="all-MiniLM-L6-v2")
+ # Check if vector_db was initialized correctly
mock_chroma.assert_called_once_with(
collection_name="test_collection",
- embedding_function=mock_embedding_instance,
+ embedding_function=mock_embeddings,
persist_directory="test_dir",
)
@patch("tapio.vectorstore.chroma_store.Chroma")
- @patch("tapio.vectorstore.chroma_store.HuggingFaceEmbeddings")
- def test_add_document_with_content(self, mock_embeddings, mock_chroma):
+ def test_add_document_with_content(self, mock_chroma, mock_embeddings):
"""Test adding a document with content in metadata."""
# Set up mocks
mock_vector_db = Mock()
mock_chroma.return_value = mock_vector_db
- # Initialize ChromaStore
- store = ChromaStore(collection_name="test_collection")
+ # Initialize ChromaStore with injected embeddings
+ store = ChromaStore(
+ collection_name="test_collection",
+ embeddings=mock_embeddings,
+ )
# Test adding document with content in metadata
metadata = {"content": "Test document content"}
@@ -55,15 +52,17 @@ def test_add_document_with_content(self, mock_embeddings, mock_chroma):
)
@patch("tapio.vectorstore.chroma_store.Chroma")
- @patch("tapio.vectorstore.chroma_store.HuggingFaceEmbeddings")
- def test_add_document_alternate_content_fields(self, mock_embeddings, mock_chroma):
+ def test_add_document_alternate_content_fields(self, mock_chroma, mock_embeddings):
"""Test adding a document with content in alternate metadata fields."""
# Set up mocks
mock_vector_db = Mock()
mock_chroma.return_value = mock_vector_db
- # Initialize ChromaStore
- store = ChromaStore(collection_name="test_collection")
+ # Initialize ChromaStore with injected embeddings
+ store = ChromaStore(
+ collection_name="test_collection",
+ embeddings=mock_embeddings,
+ )
# Test with different content field names
for field in ["text", "body", "page_content", "full_text"]:
@@ -79,15 +78,17 @@ def test_add_document_alternate_content_fields(self, mock_embeddings, mock_chrom
)
@patch("tapio.vectorstore.chroma_store.Chroma")
- @patch("tapio.vectorstore.chroma_store.HuggingFaceEmbeddings")
- def test_add_document_no_content(self, mock_embeddings, mock_chroma):
+ def test_add_document_no_content(self, mock_chroma, mock_embeddings):
"""Test adding a document with no content in metadata."""
# Set up mocks
mock_vector_db = Mock()
mock_chroma.return_value = mock_vector_db
- # Initialize ChromaStore
- store = ChromaStore(collection_name="test_collection")
+ # Initialize ChromaStore with injected embeddings
+ store = ChromaStore(
+ collection_name="test_collection",
+ embeddings=mock_embeddings,
+ )
# Test adding document with no content in metadata
metadata = {"other_field": "Other value"}
@@ -101,16 +102,18 @@ def test_add_document_no_content(self, mock_embeddings, mock_chroma):
)
@patch("tapio.vectorstore.chroma_store.Chroma")
- @patch("tapio.vectorstore.chroma_store.HuggingFaceEmbeddings")
- def test_add_document_exception(self, mock_embeddings, mock_chroma):
+ def test_add_document_exception(self, mock_chroma, mock_embeddings):
"""Test handling exceptions when adding a document."""
# Set up mocks
mock_vector_db = Mock()
mock_vector_db.add_texts.side_effect = Exception("Test error")
mock_chroma.return_value = mock_vector_db
- # Initialize ChromaStore
- store = ChromaStore(collection_name="test_collection")
+ # Initialize ChromaStore with injected embeddings
+ store = ChromaStore(
+ collection_name="test_collection",
+ embeddings=mock_embeddings,
+ )
# Test adding document with exception
metadata = {"content": "Test document content"}
@@ -118,8 +121,7 @@ def test_add_document_exception(self, mock_embeddings, mock_chroma):
store.add_document(document_id="test_doc", metadata=metadata)
@patch("tapio.vectorstore.chroma_store.Chroma")
- @patch("tapio.vectorstore.chroma_store.HuggingFaceEmbeddings")
- def test_query(self, mock_embeddings, mock_chroma):
+ def test_query(self, mock_chroma, mock_embeddings):
"""Test querying the vector store by text."""
# Set up mocks
mock_vector_db = Mock()
@@ -130,8 +132,11 @@ def test_query(self, mock_embeddings, mock_chroma):
mock_vector_db.similarity_search.return_value = [mock_doc1, mock_doc2]
mock_chroma.return_value = mock_vector_db
- # Initialize ChromaStore
- store = ChromaStore(collection_name="test_collection")
+ # Initialize ChromaStore with injected embeddings
+ store = ChromaStore(
+ collection_name="test_collection",
+ embeddings=mock_embeddings,
+ )
# Test query
results = store.query(query_text="test query", n_results=2)
@@ -148,16 +153,18 @@ def test_query(self, mock_embeddings, mock_chroma):
assert mock_doc2.metadata["citation_url"] == "https://example.com/doc2"
@patch("tapio.vectorstore.chroma_store.Chroma")
- @patch("tapio.vectorstore.chroma_store.HuggingFaceEmbeddings")
- def test_query_exception(self, mock_embeddings, mock_chroma):
+ def test_query_exception(self, mock_chroma, mock_embeddings):
"""Test handling exceptions when querying the vector store."""
# Set up mocks
mock_vector_db = Mock()
mock_vector_db.similarity_search.side_effect = Exception("Test error")
mock_chroma.return_value = mock_vector_db
- # Initialize ChromaStore
- store = ChromaStore(collection_name="test_collection")
+ # Initialize ChromaStore with injected embeddings
+ store = ChromaStore(
+ collection_name="test_collection",
+ embeddings=mock_embeddings,
+ )
# Test query with exception
results = store.query(query_text="test query")
@@ -166,8 +173,7 @@ def test_query_exception(self, mock_embeddings, mock_chroma):
assert results == []
@patch("tapio.vectorstore.chroma_store.Chroma")
- @patch("tapio.vectorstore.chroma_store.HuggingFaceEmbeddings")
- def test_query_with_embedding(self, mock_embeddings, mock_chroma):
+ def test_query_with_embedding(self, mock_chroma, mock_embeddings):
"""Test querying the vector store with embedding."""
# Set up mocks
mock_collection = Mock()
@@ -180,8 +186,11 @@ def test_query_with_embedding(self, mock_embeddings, mock_chroma):
mock_vector_db._collection = mock_collection
mock_chroma.return_value = mock_vector_db
- # Initialize ChromaStore
- store = ChromaStore(collection_name="test_collection")
+ # Initialize ChromaStore with injected embeddings
+ store = ChromaStore(
+ collection_name="test_collection",
+ embeddings=mock_embeddings,
+ )
# Test query with embedding
embedding = [0.1, 0.2, 0.3]
@@ -199,8 +208,7 @@ def test_query_with_embedding(self, mock_embeddings, mock_chroma):
assert results["metadatas"][0][0]["citation_url"] == "https://example.com/doc"
@patch("tapio.vectorstore.chroma_store.Chroma")
- @patch("tapio.vectorstore.chroma_store.HuggingFaceEmbeddings")
- def test_get_document(self, mock_embeddings, mock_chroma):
+ def test_get_document(self, mock_chroma, mock_embeddings):
"""Test getting a document by ID."""
# Set up mocks
mock_collection = Mock()
@@ -212,8 +220,11 @@ def test_get_document(self, mock_embeddings, mock_chroma):
mock_vector_db._collection = mock_collection
mock_chroma.return_value = mock_vector_db
- # Initialize ChromaStore
- store = ChromaStore(collection_name="test_collection")
+ # Initialize ChromaStore with injected embeddings
+ store = ChromaStore(
+ collection_name="test_collection",
+ embeddings=mock_embeddings,
+ )
# Test get document
result = store.get_document(document_id="test_doc")
@@ -229,11 +240,13 @@ def test_get_document(self, mock_embeddings, mock_chroma):
assert result["metadatas"][0]["citation_url"] == "https://example.com/doc"
@patch("tapio.vectorstore.chroma_store.Chroma")
- @patch("tapio.vectorstore.chroma_store.HuggingFaceEmbeddings")
- def test_enhance_document_with_citation(self, mock_embeddings, mock_chroma):
+ def test_enhance_document_with_citation(self, mock_chroma, mock_embeddings):
"""Test enhancing a document with citation information."""
- # Initialize ChromaStore
- store = ChromaStore(collection_name="test_collection")
+ # Initialize ChromaStore with injected embeddings
+ store = ChromaStore(
+ collection_name="test_collection",
+ embeddings=mock_embeddings,
+ )
# Test with source_url
doc1 = Mock()
diff --git a/tests/vectorstore/test_vectorizer.py b/tests/vectorstore/test_vectorizer.py
index aad1804..a9f9ce3 100644
--- a/tests/vectorstore/test_vectorizer.py
+++ b/tests/vectorstore/test_vectorizer.py
@@ -8,57 +8,28 @@
class TestMarkdownVectorizer:
"""Tests for the MarkdownVectorizer class."""
- @patch("tapio.vectorstore.vectorizer.Chroma")
- @patch("tapio.vectorstore.vectorizer.HuggingFaceEmbeddings")
- @patch("tapio.vectorstore.vectorizer.MarkdownTextSplitter")
- def test_init(self, mock_splitter_class, mock_embeddings_class, mock_chroma):
+ def test_init(self):
"""Test initialization of the vectorizer."""
- # Set up mocks
- mock_embeddings_instance = Mock()
- mock_embeddings_class.return_value = mock_embeddings_instance
- mock_splitter_instance = Mock()
- mock_splitter_class.return_value = mock_splitter_instance
+ # Create mocks
+ mock_vector_db = Mock()
+ mock_text_splitter = Mock()
- # Initialize vectorizer
+ # Initialize vectorizer with dependency injection
vectorizer = MarkdownVectorizer(
- collection_name="test_collection",
- persist_directory="test_dir",
- embedding_model_name="test-model",
- chunk_size=500,
- chunk_overlap=100,
+ vector_db=mock_vector_db,
+ text_splitter=mock_text_splitter,
)
- # Check if components were initialized correctly
- mock_embeddings_class.assert_called_once_with(model_name="test-model")
- mock_splitter_class.assert_called_once_with(chunk_size=500, chunk_overlap=100)
- mock_chroma.assert_called_once_with(
- collection_name="test_collection",
- embedding_function=mock_embeddings_instance,
- persist_directory="test_dir",
- )
-
- # Check if configuration was saved
- assert vectorizer.collection_name == "test_collection"
- assert vectorizer.persist_directory == "test_dir"
- assert vectorizer.embedding_model_name == "test-model"
- assert vectorizer.chunk_size == 500
- assert vectorizer.chunk_overlap == 100
+ # Check if dependencies were stored
+ assert vectorizer.vector_db == mock_vector_db
+ assert vectorizer.text_splitter == mock_text_splitter
@patch("tapio.vectorstore.vectorizer.find_markdown_files")
- @patch("tapio.vectorstore.vectorizer.Chroma")
- @patch("tapio.vectorstore.vectorizer.HuggingFaceEmbeddings")
- @patch("tapio.vectorstore.vectorizer.MarkdownTextSplitter")
- def test_process_directory(
- self,
- mock_splitter_class,
- mock_embeddings_class,
- mock_chroma,
- mock_find_files,
- ):
+ def test_process_directory(self, mock_find_files):
"""Test processing a directory of markdown files."""
# Set up mocks
mock_vector_db = Mock()
- mock_chroma.return_value = mock_vector_db
+ mock_text_splitter = Mock()
# Set up mock for find_markdown_files to return some test files
test_files = [
@@ -69,7 +40,10 @@ def test_process_directory(
mock_find_files.return_value = test_files
# Initialize vectorizer with mocked _process_batch
- vectorizer = MarkdownVectorizer(collection_name="test_collection")
+ vectorizer = MarkdownVectorizer(
+ vector_db=mock_vector_db,
+ text_splitter=mock_text_splitter,
+ )
vectorizer._process_batch = Mock(return_value=2) # 2 chunks per batch
# Process directory
@@ -96,24 +70,15 @@ def test_process_directory(
@patch("tapio.vectorstore.vectorizer.read_markdown_file")
@patch("tapio.vectorstore.vectorizer.Document")
- @patch("tapio.vectorstore.vectorizer.Chroma")
- @patch("tapio.vectorstore.vectorizer.HuggingFaceEmbeddings")
- @patch("tapio.vectorstore.vectorizer.MarkdownTextSplitter")
def test_process_batch(
self,
- mock_splitter_class,
- mock_embeddings_class,
- mock_chroma,
mock_document_class,
mock_read_file,
):
"""Test processing a batch of markdown files."""
# Set up mocks
mock_vector_db = Mock()
- mock_chroma.return_value = mock_vector_db
-
- mock_splitter = Mock()
- mock_splitter_class.return_value = mock_splitter
+ mock_text_splitter = Mock()
# Set up read_markdown_file to return test data
mock_read_file.side_effect = [
@@ -133,13 +98,16 @@ def test_process_batch(
mock_chunk2.metadata = {}
mock_chunk3 = Mock()
mock_chunk3.metadata = {}
- mock_splitter.split_documents.side_effect = [
+ mock_text_splitter.split_documents.side_effect = [
[mock_chunk1, mock_chunk2], # First document splits into 2 chunks
[mock_chunk3], # Second document splits into 1 chunk
]
# Initialize vectorizer
- vectorizer = MarkdownVectorizer(collection_name="test_collection")
+ vectorizer = MarkdownVectorizer(
+ vector_db=mock_vector_db,
+ text_splitter=mock_text_splitter,
+ )
# Set up _prepare_metadata to return test metadata
vectorizer._prepare_metadata = Mock(
@@ -180,7 +148,7 @@ def test_process_batch(
)
# Verify text splitter was called for each document
- mock_splitter.split_documents.assert_has_calls(
+ mock_text_splitter.split_documents.assert_has_calls(
[
call([mock_doc1]),
call([mock_doc2]),
@@ -205,24 +173,15 @@ def test_process_batch(
@patch("tapio.vectorstore.vectorizer.read_markdown_file")
@patch("tapio.vectorstore.vectorizer.Document")
- @patch("tapio.vectorstore.vectorizer.Chroma")
- @patch("tapio.vectorstore.vectorizer.HuggingFaceEmbeddings")
- @patch("tapio.vectorstore.vectorizer.MarkdownTextSplitter")
def test_process_file(
self,
- mock_splitter_class,
- mock_embeddings_class,
- mock_chroma,
mock_document_class,
mock_read_file,
):
"""Test processing a single markdown file."""
# Set up mocks
mock_vector_db = Mock()
- mock_chroma.return_value = mock_vector_db
-
- mock_splitter = Mock()
- mock_splitter_class.return_value = mock_splitter
+ mock_text_splitter = Mock()
# Set up read_markdown_file to return test data
mock_read_file.return_value = ({"title": "Test"}, "Content")
@@ -236,10 +195,13 @@ def test_process_file(
mock_chunk1.metadata = {}
mock_chunk2 = Mock()
mock_chunk2.metadata = {}
- mock_splitter.split_documents.return_value = [mock_chunk1, mock_chunk2]
+ mock_text_splitter.split_documents.return_value = [mock_chunk1, mock_chunk2]
- # Initialize vectorizer
- vectorizer = MarkdownVectorizer(collection_name="test_collection")
+ # Initialize vectorizer with dependency injection
+ vectorizer = MarkdownVectorizer(
+ vector_db=mock_vector_db,
+ text_splitter=mock_text_splitter,
+ )
# Set up _prepare_metadata to return test metadata
test_metadata = {"source_id": "file", "title": "Test"}
@@ -258,7 +220,7 @@ def test_process_file(
)
# Verify text splitter was called
- mock_splitter.split_documents.assert_called_once_with([mock_doc])
+ mock_text_splitter.split_documents.assert_called_once_with([mock_doc])
# Verify chunk metadata was updated
assert mock_chunk1.metadata["chunk_index"] == 0
@@ -279,9 +241,14 @@ def test_prepare_metadata(self, mock_splitext, mock_basename):
# Set up mocks
mock_basename.return_value = "file.md"
mock_splitext.return_value = ("file", ".md")
+ mock_vector_db = Mock()
+ mock_text_splitter = Mock()
- # Initialize vectorizer
- vectorizer = MarkdownVectorizer(collection_name="test_collection")
+ # Initialize vectorizer with dependency injection
+ vectorizer = MarkdownVectorizer(
+ vector_db=mock_vector_db,
+ text_splitter=mock_text_splitter,
+ )
# Test with source_url
metadata = {
@@ -313,64 +280,3 @@ def test_prepare_metadata(self, mock_splitext, mock_basename):
assert enriched["url"] == "https://example.com/doc"
assert enriched["source_url"] == "https://example.com/doc"
assert enriched["citation_url"] == "https://example.com/doc"
-
- @patch("tapio.vectorstore.vectorizer.read_markdown_file")
- @patch("tapio.vectorstore.vectorizer.Chroma")
- @patch("tapio.vectorstore.vectorizer.HuggingFaceEmbeddings")
- @patch("tapio.vectorstore.vectorizer.MarkdownTextSplitter")
- def test_process_batch_error_handling(
- self,
- mock_splitter_class,
- mock_embeddings_class,
- mock_chroma,
- mock_read_file,
- ):
- """Test error handling in process_batch."""
- # Set up mocks
- mock_vector_db = Mock()
- mock_chroma.return_value = mock_vector_db
-
- # Set up read_markdown_file to raise an exception
- mock_read_file.side_effect = Exception("Test error")
-
- # Initialize vectorizer
- vectorizer = MarkdownVectorizer(collection_name="test_collection")
-
- # Process batch with error
- chunk_count = vectorizer._process_batch(["test_dir/file.md"])
-
- # Verify read_markdown_file was called
- mock_read_file.assert_called_once_with("test_dir/file.md")
-
- # Verify no chunks were returned
- assert chunk_count == 0
-
- # Verify add_documents was not called
- mock_vector_db.add_documents.assert_not_called()
-
- @patch("tapio.vectorstore.vectorizer.read_markdown_file")
- @patch("tapio.vectorstore.vectorizer.Chroma")
- @patch("tapio.vectorstore.vectorizer.HuggingFaceEmbeddings")
- @patch("tapio.vectorstore.vectorizer.MarkdownTextSplitter")
- def test_process_file_empty_content(
- self,
- mock_splitter_class,
- mock_embeddings_class,
- mock_chroma,
- mock_read_file,
- ):
- """Test processing a file with empty content."""
- # Set up read_markdown_file to return empty content
- mock_read_file.return_value = ({"title": "Test"}, "")
-
- # Initialize vectorizer
- vectorizer = MarkdownVectorizer(collection_name="test_collection")
-
- # Process file with empty content
- chunk_count = vectorizer.process_file("test_dir/file.md")
-
- # Verify read_markdown_file was called
- mock_read_file.assert_called_once_with("test_dir/file.md")
-
- # Verify no chunks were processed
- assert chunk_count == 0
diff --git a/uv.lock b/uv.lock
index 04a9d4f..0c66d38 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
version = 1
-revision = 1
+revision = 3
requires-python = ">=3.10"
resolution-markers = [
"python_full_version >= '3.13'",
@@ -13,18 +13,18 @@ resolution-markers = [
name = "aiofiles"
version = "24.1.0"
source = { registry = "https://pypi.org/simple" }
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{ name = "yarl" },
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@@ -1101,18 +1096,18 @@ dependencies = [
{ name = "httpcore" },
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@@ -2675,67 +2670,67 @@ dependencies = [
{ name = "python-dotenv" },
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