diff --git a/.gitignore b/.gitignore index 05885ca..b7c04b4 100755 --- a/.gitignore +++ b/.gitignore @@ -95,4 +95,8 @@ tests/__pytest_cache__/ tests/__pycache__/ paru-bin/ +CLAUDE.md +CLAUDE.local.md +.claude/*.local.* +.claude/local/* benchmarks/data/ diff --git a/README.md b/README.md index bf8282f..6b35801 100755 --- a/README.md +++ b/README.md @@ -220,7 +220,8 @@ ollama pull llama3.2:1b -### ā­ Flexible Configuration + +## ā­ Flexible Configuration LEANN provides flexible parameters for embedding models, search strategies, and data processing to fit your specific needs. @@ -626,6 +627,33 @@ Options: +## šŸš€ Advanced Features + +### šŸŽÆ Metadata Filtering + +LEANN supports a simple metadata filtering system to enable sophisticated use cases like document filtering by date/type, code search by file extension, and content management based on custom criteria. + +```python +# Add metadata during indexing +builder.add_text( + "def authenticate_user(token): ...", + metadata={"file_extension": ".py", "lines_of_code": 25} +) + +# Search with filters +results = searcher.search( + query="authentication function", + metadata_filters={ + "file_extension": {"==": ".py"}, + "lines_of_code": {"<": 100} + } +) +``` + +**Supported operators**: `==`, `!=`, `<`, `<=`, `>`, `>=`, `in`, `not_in`, `contains`, `starts_with`, `ends_with`, `is_true`, `is_false` + +šŸ“– **[Complete Metadata filtering guide ā†’](docs/metadata_filtering.md)** + ## šŸ—ļø Architecture & How It Works

diff --git a/docs/metadata_filtering.md b/docs/metadata_filtering.md new file mode 100644 index 0000000..1a267e1 --- /dev/null +++ b/docs/metadata_filtering.md @@ -0,0 +1,300 @@ +# LEANN Metadata Filtering Usage Guide + +## Overview + +Leann possesses metadata filtering capabilities that allow you to filter search results based on arbitrary metadata fields set during chunking. This feature enables use cases like spoiler-free book search, document filtering by date/type, code search by file type, and potentially much more. + +## Basic Usage + +### Adding Metadata to Your Documents + +When building your index, add metadata to each text chunk: + +```python +from leann.api import LeannBuilder + +builder = LeannBuilder("hnsw") + +# Add text with metadata +builder.add_text( + text="Chapter 1: Alice falls down the rabbit hole", + metadata={ + "chapter": 1, + "character": "Alice", + "themes": ["adventure", "curiosity"], + "word_count": 150 + } +) + +builder.build_index("alice_in_wonderland_index") +``` + +### Searching with Metadata Filters + +Use the `metadata_filters` parameter in search calls: + +```python +from leann.api import LeannSearcher + +searcher = LeannSearcher("alice_in_wonderland_index") + +# Search with filters +results = searcher.search( + query="What happens to Alice?", + top_k=10, + metadata_filters={ + "chapter": {"<=": 5}, # Only chapters 1-5 + "spoiler_level": {"!=": "high"} # No high spoilers + } +) +``` + +## Filter Syntax + +### Basic Structure + +```python +metadata_filters = { + "field_name": {"operator": value}, + "another_field": {"operator": value} +} +``` + +### Supported Operators + +#### Comparison Operators +- `"=="`: Equal to +- `"!="`: Not equal to +- `"<"`: Less than +- `"<="`: Less than or equal +- `">"`: Greater than +- `">="`: Greater than or equal + +```python +# Examples +{"chapter": {"==": 1}} # Exactly chapter 1 +{"page": {">": 100}} # Pages after 100 +{"rating": {">=": 4.0}} # Rating 4.0 or higher +{"word_count": {"<": 500}} # Short passages +``` + +#### Membership Operators +- `"in"`: Value is in list +- `"not_in"`: Value is not in list + +```python +# Examples +{"character": {"in": ["Alice", "Bob"]}} # Alice OR Bob +{"genre": {"not_in": ["horror", "thriller"]}} # Exclude genres +{"tags": {"in": ["fiction", "adventure"]}} # Any of these tags +``` + +#### String Operators +- `"contains"`: String contains substring +- `"starts_with"`: String starts with prefix +- `"ends_with"`: String ends with suffix + +```python +# Examples +{"title": {"contains": "alice"}} # Title contains "alice" +{"filename": {"ends_with": ".py"}} # Python files +{"author": {"starts_with": "Dr."}} # Authors with "Dr." prefix +``` + +#### Boolean Operators +- `"is_true"`: Field is truthy +- `"is_false"`: Field is falsy + +```python +# Examples +{"is_published": {"is_true": True}} # Published content +{"is_draft": {"is_false": False}} # Not drafts +``` + +### Multiple Operators on Same Field + +You can apply multiple operators to the same field (AND logic): + +```python +metadata_filters = { + "word_count": { + ">=": 100, # At least 100 words + "<=": 500 # At most 500 words + } +} +``` + +### Compound Filters + +Multiple fields are combined with AND logic: + +```python +metadata_filters = { + "chapter": {"<=": 10}, # Up to chapter 10 + "character": {"==": "Alice"}, # About Alice + "spoiler_level": {"!=": "high"} # No major spoilers +} +``` + +## Use Case Examples + +### 1. Spoiler-Free Book Search + +```python +# Reader has only read up to chapter 5 +def search_spoiler_free(query, max_chapter): + return searcher.search( + query=query, + metadata_filters={ + "chapter": {"<=": max_chapter}, + "spoiler_level": {"in": ["none", "low"]} + } + ) + +results = search_spoiler_free("What happens to Alice?", max_chapter=5) +``` + +### 2. Document Management by Date + +```python +# Find recent documents +recent_docs = searcher.search( + query="project updates", + metadata_filters={ + "date": {">=": "2024-01-01"}, + "document_type": {"==": "report"} + } +) +``` + +### 3. Code Search by File Type + +```python +# Search only Python files +python_code = searcher.search( + query="authentication function", + metadata_filters={ + "file_extension": {"==": ".py"}, + "lines_of_code": {"<": 100} + } +) +``` + +### 4. Content Filtering by Audience + +```python +# Age-appropriate content +family_content = searcher.search( + query="adventure stories", + metadata_filters={ + "age_rating": {"in": ["G", "PG"]}, + "content_warnings": {"not_in": ["violence", "adult_themes"]} + } +) +``` + +### 5. Multi-Book Series Management + +```python +# Search across first 3 books only +early_series = searcher.search( + query="character development", + metadata_filters={ + "series": {"==": "Harry Potter"}, + "book_number": {"<=": 3} + } +) +``` + +## Running the Example + +You can see metadata filtering in action with our spoiler-free book RAG example: + +```bash +# Don't forget to set up the environment +uv venv +source .venv/bin/activate + +# Set your OpenAI API key (required for embeddings, but you can update the example locally and use ollama instead) +export OPENAI_API_KEY="your-api-key-here" + +# Run the spoiler-free book RAG example +uv run examples/spoiler_free_book_rag.py +``` + +This example demonstrates: +- Building an index with metadata (chapter numbers, characters, themes, locations) +- Searching with filters to avoid spoilers (e.g., only show results up to chapter 5) +- Different scenarios for readers at various points in the book + +The example uses Alice's Adventures in Wonderland as sample data and shows how you can search for information without revealing plot points from later chapters. + +## Advanced Patterns + +### Custom Chunking with metadata + +```python +def chunk_book_with_metadata(book_text, book_info): + chunks = [] + + for chapter_num, chapter_text in parse_chapters(book_text): + # Extract entities, themes, etc. + characters = extract_characters(chapter_text) + themes = classify_themes(chapter_text) + spoiler_level = assess_spoiler_level(chapter_text, chapter_num) + + # Create chunks with rich metadata + for paragraph in split_paragraphs(chapter_text): + chunks.append({ + "text": paragraph, + "metadata": { + "book_title": book_info["title"], + "chapter": chapter_num, + "characters": characters, + "themes": themes, + "spoiler_level": spoiler_level, + "word_count": len(paragraph.split()), + "reading_level": calculate_reading_level(paragraph) + } + }) + + return chunks +``` + +## Performance Considerations + +### Efficient Filtering Strategies + +1. **Post-search filtering**: Applies filters after vector search, which should be efficient for typical result sets (10-100 results). + +2. **Metadata design**: Keep metadata fields simple and avoid deeply nested structures. + +### Best Practices + +1. **Consistent metadata schema**: Use consistent field names and value types across your documents. + +2. **Reasonable metadata size**: Keep metadata reasonably sized to avoid storage overhead. + +3. **Type consistency**: Use consistent data types for the same fields (e.g., always integers for chapter numbers). + +4. **Index multiple granularities**: Consider chunking at different levels (paragraph, section, chapter) with appropriate metadata. + +### Adding Metadata to Existing Indices + +To add metadata filtering to existing indices, you'll need to rebuild them with metadata: + +```python +# Read existing passages and add metadata +def add_metadata_to_existing_chunks(chunks): + for chunk in chunks: + # Extract or assign metadata based on content + chunk["metadata"] = extract_metadata(chunk["text"]) + return chunks + +# Rebuild index with metadata +enhanced_chunks = add_metadata_to_existing_chunks(existing_chunks) +builder = LeannBuilder("hnsw") +for chunk in enhanced_chunks: + builder.add_text(chunk["text"], chunk["metadata"]) +builder.build_index("enhanced_index") +``` diff --git a/examples/spoiler_free_book_rag.py b/examples/spoiler_free_book_rag.py new file mode 100644 index 0000000..7f02fd4 --- /dev/null +++ b/examples/spoiler_free_book_rag.py @@ -0,0 +1,250 @@ +#!/usr/bin/env python3 +""" +Spoiler-Free Book RAG Example using LEANN Metadata Filtering + +This example demonstrates how to use LEANN's metadata filtering to create +a spoiler-free book RAG system where users can search for information +up to a specific chapter they've read. + +Usage: + python spoiler_free_book_rag.py +""" + +import os +import sys +from typing import Any, Optional + +# Add LEANN to path (adjust path as needed) +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../packages/leann-core/src")) + +from leann.api import LeannBuilder, LeannSearcher + + +def chunk_book_with_metadata(book_title: str = "Sample Book") -> list[dict[str, Any]]: + """ + Create sample book chunks with metadata for demonstration. + + In a real implementation, this would parse actual book files (epub, txt, etc.) + and extract chapter boundaries, character mentions, etc. + + Args: + book_title: Title of the book + + Returns: + List of chunk dictionaries with text and metadata + """ + # Sample book chunks with metadata + # In practice, you'd use proper text processing libraries + + sample_chunks = [ + { + "text": "Alice was beginning to get very tired of sitting by her sister on the bank, and of having nothing to do.", + "metadata": { + "book": book_title, + "chapter": 1, + "page": 1, + "characters": ["Alice", "Sister"], + "themes": ["boredom", "curiosity"], + "location": "riverbank", + }, + }, + { + "text": "So she was considering in her own mind (as well as she could, for the hot day made her feel very sleepy and stupid), whether the pleasure of making a daisy-chain would be worth the trouble of getting up and picking the daisies, when suddenly a White Rabbit with pink eyes ran close by her.", + "metadata": { + "book": book_title, + "chapter": 1, + "page": 2, + "characters": ["Alice", "White Rabbit"], + "themes": ["decision", "surprise", "magic"], + "location": "riverbank", + }, + }, + { + "text": "Alice found herself falling down a very deep well. Either the well was very deep, or she fell very slowly, for she had plenty of time as she fell to look about her and to wonder what was going to happen next.", + "metadata": { + "book": book_title, + "chapter": 2, + "page": 15, + "characters": ["Alice"], + "themes": ["falling", "wonder", "transformation"], + "location": "rabbit hole", + }, + }, + { + "text": "Alice meets the Cheshire Cat, who tells her that everyone in Wonderland is mad, including Alice herself.", + "metadata": { + "book": book_title, + "chapter": 6, + "page": 85, + "characters": ["Alice", "Cheshire Cat"], + "themes": ["madness", "philosophy", "identity"], + "location": "Duchess's house", + }, + }, + { + "text": "At the Queen's croquet ground, Alice witnesses the absurd trial that reveals the arbitrary nature of Wonderland's justice system.", + "metadata": { + "book": book_title, + "chapter": 8, + "page": 120, + "characters": ["Alice", "Queen of Hearts", "King of Hearts"], + "themes": ["justice", "absurdity", "authority"], + "location": "Queen's court", + }, + }, + { + "text": "Alice realizes that Wonderland was all a dream, even the Rabbit, as she wakes up on the riverbank next to her sister.", + "metadata": { + "book": book_title, + "chapter": 12, + "page": 180, + "characters": ["Alice", "Sister", "Rabbit"], + "themes": ["revelation", "reality", "growth"], + "location": "riverbank", + }, + }, + ] + + return sample_chunks + + +def build_spoiler_free_index(book_chunks: list[dict[str, Any]], index_name: str) -> str: + """ + Build a LEANN index with book chunks that include spoiler metadata. + + Args: + book_chunks: List of book chunks with metadata + index_name: Name for the index + + Returns: + Path to the built index + """ + print(f"šŸ“š Building spoiler-free book index: {index_name}") + + # Initialize LEANN builder + builder = LeannBuilder( + backend_name="hnsw", embedding_model="text-embedding-3-small", embedding_mode="openai" + ) + + # Add each chunk with its metadata + for chunk in book_chunks: + builder.add_text(text=chunk["text"], metadata=chunk["metadata"]) + + # Build the index + index_path = f"{index_name}_book_index" + builder.build_index(index_path) + + print(f"āœ… Index built successfully: {index_path}") + return index_path + + +def spoiler_free_search( + index_path: str, + query: str, + max_chapter: int, + character_filter: Optional[list[str]] = None, +) -> list[dict[str, Any]]: + """ + Perform a spoiler-free search on the book index. + + Args: + index_path: Path to the LEANN index + query: Search query + max_chapter: Maximum chapter number to include + character_filter: Optional list of characters to focus on + + Returns: + List of search results safe for the reader + """ + print(f"šŸ” Searching: '{query}' (up to chapter {max_chapter})") + + searcher = LeannSearcher(index_path) + + metadata_filters = {"chapter": {"<=": max_chapter}} + + if character_filter: + metadata_filters["characters"] = {"contains": character_filter[0]} + + results = searcher.search(query=query, top_k=10, metadata_filters=metadata_filters) + + return results + + +def demo_spoiler_free_rag(): + """ + Demonstrate the spoiler-free book RAG system. + """ + print("šŸŽ­ Spoiler-Free Book RAG Demo") + print("=" * 40) + + # Step 1: Prepare book data + book_title = "Alice's Adventures in Wonderland" + book_chunks = chunk_book_with_metadata(book_title) + + print(f"šŸ“– Loaded {len(book_chunks)} chunks from '{book_title}'") + + # Step 2: Build the index (in practice, this would be done once) + try: + index_path = build_spoiler_free_index(book_chunks, "alice_wonderland") + except Exception as e: + print(f"āŒ Failed to build index (likely missing dependencies): {e}") + print( + "šŸ’” This demo shows the filtering logic - actual indexing requires LEANN dependencies" + ) + return + + # Step 3: Demonstrate various spoiler-free searches + search_scenarios = [ + { + "description": "Reader who has only read Chapter 1", + "query": "What can you tell me about the rabbit?", + "max_chapter": 1, + }, + { + "description": "Reader who has read up to Chapter 5", + "query": "Tell me about Alice's adventures", + "max_chapter": 5, + }, + { + "description": "Reader who has read most of the book", + "query": "What does the Cheshire Cat represent?", + "max_chapter": 10, + }, + { + "description": "Reader who has read the whole book", + "query": "What can you tell me about the rabbit?", + "max_chapter": 12, + }, + ] + + for scenario in search_scenarios: + print(f"\nšŸ“š Scenario: {scenario['description']}") + print(f" Query: {scenario['query']}") + + try: + results = spoiler_free_search( + index_path=index_path, + query=scenario["query"], + max_chapter=scenario["max_chapter"], + ) + + print(f" šŸ“„ Found {len(results)} results:") + for i, result in enumerate(results[:3], 1): # Show top 3 + chapter = result.metadata.get("chapter", "?") + location = result.metadata.get("location", "?") + print(f" {i}. Chapter {chapter} ({location}): {result.text[:80]}...") + + except Exception as e: + print(f" āŒ Search failed: {e}") + + +if __name__ == "__main__": + print("šŸ“š LEANN Spoiler-Free Book RAG Example") + print("=====================================") + + try: + demo_spoiler_free_rag() + except ImportError as e: + print(f"āŒ Cannot run demo due to missing dependencies: {e}") + except Exception as e: + print(f"āŒ Error running demo: {e}") diff --git a/packages/leann-core/src/leann/api.py b/packages/leann-core/src/leann/api.py index 47f1d5a..28b49da 100644 --- a/packages/leann-core/src/leann/api.py +++ b/packages/leann-core/src/leann/api.py @@ -10,7 +10,7 @@ import time import warnings from dataclasses import dataclass, field from pathlib import Path -from typing import Any, Literal, Optional +from typing import Any, Literal, Optional, Union import numpy as np @@ -18,6 +18,7 @@ from leann.interface import LeannBackendSearcherInterface from .chat import get_llm from .interface import LeannBackendFactoryInterface +from .metadata_filter import MetadataFilterEngine from .registry import BACKEND_REGISTRY logger = logging.getLogger(__name__) @@ -125,6 +126,7 @@ class PassageManager: # footprint on very large corpora (e.g., 60M+ passages). Instead, keep # per-shard maps and do a lightweight per-shard lookup on demand. self._total_count: int = 0 + self.filter_engine = MetadataFilterEngine() # Initialize filter engine # Derive index base name for standard sibling fallbacks, e.g., .passages.* index_name_base = None @@ -212,6 +214,56 @@ class PassageManager: continue raise KeyError(f"Passage ID not found: {passage_id}") + def filter_search_results( + self, + search_results: list[SearchResult], + metadata_filters: Optional[dict[str, dict[str, Union[str, int, float, bool, list]]]], + ) -> list[SearchResult]: + """ + Apply metadata filters to search results. + + Args: + search_results: List of SearchResult objects + metadata_filters: Filter specifications to apply + + Returns: + Filtered list of SearchResult objects + """ + if not metadata_filters: + return search_results + + logger.debug(f"Applying metadata filters to {len(search_results)} results") + + # Convert SearchResult objects to dictionaries for the filter engine + result_dicts = [] + for result in search_results: + result_dicts.append( + { + "id": result.id, + "score": result.score, + "text": result.text, + "metadata": result.metadata, + } + ) + + # Apply filters using the filter engine + filtered_dicts = self.filter_engine.apply_filters(result_dicts, metadata_filters) + + # Convert back to SearchResult objects + filtered_results = [] + for result_dict in filtered_dicts: + filtered_results.append( + SearchResult( + id=result_dict["id"], + score=result_dict["score"], + text=result_dict["text"], + metadata=result_dict["metadata"], + ) + ) + + logger.debug(f"Filtered results: {len(filtered_results)} remaining") + return filtered_results + def __len__(self) -> int: return self._total_count @@ -599,12 +651,38 @@ class LeannSearcher: recompute_embeddings: bool = True, pruning_strategy: Literal["global", "local", "proportional"] = "global", expected_zmq_port: int = 5557, + metadata_filters: Optional[dict[str, dict[str, Union[str, int, float, bool, list]]]] = None, batch_size: int = 0, **kwargs, ) -> list[SearchResult]: + """ + Search for nearest neighbors with optional metadata filtering. + + Args: + query: Text query to search for + top_k: Number of nearest neighbors to return + complexity: Search complexity/candidate list size, higher = more accurate but slower + beam_width: Number of parallel search paths/IO requests per iteration + prune_ratio: Ratio of neighbors to prune via approximate distance (0.0-1.0) + recompute_embeddings: Whether to fetch fresh embeddings from server vs use stored codes + pruning_strategy: Candidate selection strategy - "global" (default), "local", or "proportional" + expected_zmq_port: ZMQ port for embedding server communication + metadata_filters: Optional filters to apply to search results based on metadata. + Format: {"field_name": {"operator": value}} + Supported operators: + - Comparison: "==", "!=", "<", "<=", ">", ">=" + - Membership: "in", "not_in" + - String: "contains", "starts_with", "ends_with" + Example: {"chapter": {"<=": 5}, "tags": {"in": ["fiction", "drama"]}} + **kwargs: Backend-specific parameters + + Returns: + List of SearchResult objects with text, metadata, and similarity scores + """ logger.info("šŸ” LeannSearcher.search() called:") logger.info(f" Query: '{query}'") logger.info(f" Top_k: {top_k}") + logger.info(f" Metadata filters: {metadata_filters}") logger.info(f" Additional kwargs: {kwargs}") # Smart top_k detection and adjustment @@ -704,6 +782,13 @@ class LeannSearcher: f" {RED}āœ—{RESET} [{i + 1:2d}] ID: '{string_id}' -> {RED}ERROR: Passage not found!{RESET}" ) + # Apply metadata filters if specified + if metadata_filters: + logger.info(f" šŸ” Applying metadata filters: {metadata_filters}") + enriched_results = self.passage_manager.filter_search_results( + enriched_results, metadata_filters + ) + # Define color codes outside the loop for final message GREEN = "\033[92m" RESET = "\033[0m" @@ -766,6 +851,7 @@ class LeannChat: pruning_strategy: Literal["global", "local", "proportional"] = "global", llm_kwargs: Optional[dict[str, Any]] = None, expected_zmq_port: int = 5557, + metadata_filters: Optional[dict[str, dict[str, Union[str, int, float, bool, list]]]] = None, batch_size: int = 0, **search_kwargs, ): @@ -781,6 +867,7 @@ class LeannChat: recompute_embeddings=recompute_embeddings, pruning_strategy=pruning_strategy, expected_zmq_port=expected_zmq_port, + metadata_filters=metadata_filters, batch_size=batch_size, **search_kwargs, ) diff --git a/packages/leann-core/src/leann/metadata_filter.py b/packages/leann-core/src/leann/metadata_filter.py new file mode 100644 index 0000000..1bf4ac1 --- /dev/null +++ b/packages/leann-core/src/leann/metadata_filter.py @@ -0,0 +1,240 @@ +""" +Metadata filtering engine for LEANN search results. + +This module provides generic metadata filtering capabilities that can be applied +to search results from any LEANN backend. The filtering supports various +operators for different data types including numbers, strings, booleans, and lists. +""" + +import logging +from typing import Any, Union + +logger = logging.getLogger(__name__) + +# Type alias for filter specifications +FilterValue = Union[str, int, float, bool, list] +FilterSpec = dict[str, FilterValue] +MetadataFilters = dict[str, FilterSpec] + + +class MetadataFilterEngine: + """ + Engine for evaluating metadata filters against search results. + + Supports various operators for filtering based on metadata fields: + - Comparison: ==, !=, <, <=, >, >= + - Membership: in, not_in + - String operations: contains, starts_with, ends_with + - Boolean operations: is_true, is_false + """ + + def __init__(self): + """Initialize the filter engine with supported operators.""" + self.operators = { + "==": self._equals, + "!=": self._not_equals, + "<": self._less_than, + "<=": self._less_than_or_equal, + ">": self._greater_than, + ">=": self._greater_than_or_equal, + "in": self._in, + "not_in": self._not_in, + "contains": self._contains, + "starts_with": self._starts_with, + "ends_with": self._ends_with, + "is_true": self._is_true, + "is_false": self._is_false, + } + + def apply_filters( + self, search_results: list[dict[str, Any]], metadata_filters: MetadataFilters + ) -> list[dict[str, Any]]: + """ + Apply metadata filters to a list of search results. + + Args: + search_results: List of result dictionaries, each containing 'metadata' field + metadata_filters: Dictionary of filter specifications + Format: {"field_name": {"operator": value}} + + Returns: + Filtered list of search results + """ + if not metadata_filters: + return search_results + + logger.debug(f"Applying filters: {metadata_filters}") + logger.debug(f"Input results count: {len(search_results)}") + + filtered_results = [] + for result in search_results: + if self._evaluate_filters(result, metadata_filters): + filtered_results.append(result) + + logger.debug(f"Filtered results count: {len(filtered_results)}") + return filtered_results + + def _evaluate_filters(self, result: dict[str, Any], filters: MetadataFilters) -> bool: + """ + Evaluate all filters against a single search result. + + All filters must pass (AND logic) for the result to be included. + + Args: + result: Full search result dictionary (including metadata, text, etc.) + filters: Filter specifications to evaluate + + Returns: + True if all filters pass, False otherwise + """ + for field_name, filter_spec in filters.items(): + if not self._evaluate_field_filter(result, field_name, filter_spec): + return False + return True + + def _evaluate_field_filter( + self, result: dict[str, Any], field_name: str, filter_spec: FilterSpec + ) -> bool: + """ + Evaluate a single field filter against a search result. + + Args: + result: Full search result dictionary + field_name: Name of the field to filter on + filter_spec: Filter specification for this field + + Returns: + True if the filter passes, False otherwise + """ + # First check top-level fields, then check metadata + field_value = result.get(field_name) + if field_value is None: + # Try to get from metadata if not found at top level + metadata = result.get("metadata", {}) + field_value = metadata.get(field_name) + + # Handle missing fields - they fail all filters except existence checks + if field_value is None: + logger.debug(f"Field '{field_name}' not found in result or metadata") + return False + + # Evaluate each operator in the filter spec + for operator, expected_value in filter_spec.items(): + if operator not in self.operators: + logger.warning(f"Unsupported operator: {operator}") + return False + + try: + if not self.operators[operator](field_value, expected_value): + logger.debug( + f"Filter failed: {field_name} {operator} {expected_value} " + f"(actual: {field_value})" + ) + return False + except Exception as e: + logger.warning( + f"Error evaluating filter {field_name} {operator} {expected_value}: {e}" + ) + return False + + return True + + # Comparison operators + def _equals(self, field_value: Any, expected_value: Any) -> bool: + """Check if field value equals expected value.""" + return field_value == expected_value + + def _not_equals(self, field_value: Any, expected_value: Any) -> bool: + """Check if field value does not equal expected value.""" + return field_value != expected_value + + def _less_than(self, field_value: Any, expected_value: Any) -> bool: + """Check if field value is less than expected value.""" + return self._numeric_compare(field_value, expected_value, lambda a, b: a < b) + + def _less_than_or_equal(self, field_value: Any, expected_value: Any) -> bool: + """Check if field value is less than or equal to expected value.""" + return self._numeric_compare(field_value, expected_value, lambda a, b: a <= b) + + def _greater_than(self, field_value: Any, expected_value: Any) -> bool: + """Check if field value is greater than expected value.""" + return self._numeric_compare(field_value, expected_value, lambda a, b: a > b) + + def _greater_than_or_equal(self, field_value: Any, expected_value: Any) -> bool: + """Check if field value is greater than or equal to expected value.""" + return self._numeric_compare(field_value, expected_value, lambda a, b: a >= b) + + # Membership operators + def _in(self, field_value: Any, expected_value: Any) -> bool: + """Check if field value is in the expected list/collection.""" + if not isinstance(expected_value, (list, tuple, set)): + raise ValueError("'in' operator requires a list, tuple, or set") + return field_value in expected_value + + def _not_in(self, field_value: Any, expected_value: Any) -> bool: + """Check if field value is not in the expected list/collection.""" + if not isinstance(expected_value, (list, tuple, set)): + raise ValueError("'not_in' operator requires a list, tuple, or set") + return field_value not in expected_value + + # String operators + def _contains(self, field_value: Any, expected_value: Any) -> bool: + """Check if field value contains the expected substring.""" + field_str = str(field_value) + expected_str = str(expected_value) + return expected_str in field_str + + def _starts_with(self, field_value: Any, expected_value: Any) -> bool: + """Check if field value starts with the expected prefix.""" + field_str = str(field_value) + expected_str = str(expected_value) + return field_str.startswith(expected_str) + + def _ends_with(self, field_value: Any, expected_value: Any) -> bool: + """Check if field value ends with the expected suffix.""" + field_str = str(field_value) + expected_str = str(expected_value) + return field_str.endswith(expected_str) + + # Boolean operators + def _is_true(self, field_value: Any, expected_value: Any) -> bool: + """Check if field value is truthy.""" + return bool(field_value) + + def _is_false(self, field_value: Any, expected_value: Any) -> bool: + """Check if field value is falsy.""" + return not bool(field_value) + + # Helper methods + def _numeric_compare(self, field_value: Any, expected_value: Any, compare_func) -> bool: + """ + Helper for numeric comparisons with type coercion. + + Args: + field_value: Value from metadata + expected_value: Value to compare against + compare_func: Comparison function to apply + + Returns: + Result of comparison + """ + try: + # Try to convert both values to numbers for comparison + if isinstance(field_value, str) and isinstance(expected_value, str): + # String comparison if both are strings + return compare_func(field_value, expected_value) + + # Numeric comparison - attempt to convert to float + field_num = ( + float(field_value) if not isinstance(field_value, (int, float)) else field_value + ) + expected_num = ( + float(expected_value) + if not isinstance(expected_value, (int, float)) + else expected_value + ) + + return compare_func(field_num, expected_num) + except (ValueError, TypeError): + # Fall back to string comparison if numeric conversion fails + return compare_func(str(field_value), str(expected_value)) diff --git a/tests/test_metadata_filtering.py b/tests/test_metadata_filtering.py new file mode 100644 index 0000000..cc6003c --- /dev/null +++ b/tests/test_metadata_filtering.py @@ -0,0 +1,365 @@ +""" +Comprehensive tests for metadata filtering functionality. + +This module tests the MetadataFilterEngine class and its integration +with the LEANN search system. +""" + +import os + +# Import the modules we're testing +import sys +from unittest.mock import Mock, patch + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../packages/leann-core/src")) + +from leann.api import PassageManager, SearchResult +from leann.metadata_filter import MetadataFilterEngine + + +class TestMetadataFilterEngine: + """Test suite for the MetadataFilterEngine class.""" + + def setup_method(self): + """Setup test fixtures.""" + self.engine = MetadataFilterEngine() + + # Sample search results for testing + self.sample_results = [ + { + "id": "doc1", + "score": 0.95, + "text": "This is chapter 1 content", + "metadata": { + "chapter": 1, + "character": "Alice", + "tags": ["adventure", "fantasy"], + "word_count": 150, + "is_published": True, + "genre": "fiction", + }, + }, + { + "id": "doc2", + "score": 0.87, + "text": "This is chapter 3 content", + "metadata": { + "chapter": 3, + "character": "Bob", + "tags": ["mystery", "thriller"], + "word_count": 250, + "is_published": True, + "genre": "fiction", + }, + }, + { + "id": "doc3", + "score": 0.82, + "text": "This is chapter 5 content", + "metadata": { + "chapter": 5, + "character": "Alice", + "tags": ["romance", "drama"], + "word_count": 300, + "is_published": False, + "genre": "non-fiction", + }, + }, + { + "id": "doc4", + "score": 0.78, + "text": "This is chapter 10 content", + "metadata": { + "chapter": 10, + "character": "Charlie", + "tags": ["action", "adventure"], + "word_count": 400, + "is_published": True, + "genre": "fiction", + }, + }, + ] + + def test_engine_initialization(self): + """Test that the filter engine initializes correctly.""" + assert self.engine is not None + assert len(self.engine.operators) > 0 + assert "==" in self.engine.operators + assert "contains" in self.engine.operators + assert "in" in self.engine.operators + + def test_direct_instantiation(self): + """Test direct instantiation of the engine.""" + engine = MetadataFilterEngine() + assert isinstance(engine, MetadataFilterEngine) + + def test_no_filters_returns_all_results(self): + """Test that passing None or empty filters returns all results.""" + # Test with None + result = self.engine.apply_filters(self.sample_results, None) + assert len(result) == len(self.sample_results) + + # Test with empty dict + result = self.engine.apply_filters(self.sample_results, {}) + assert len(result) == len(self.sample_results) + + # Test comparison operators + def test_equals_filter(self): + """Test equals (==) filter.""" + filters = {"chapter": {"==": 1}} + result = self.engine.apply_filters(self.sample_results, filters) + assert len(result) == 1 + assert result[0]["id"] == "doc1" + + def test_not_equals_filter(self): + """Test not equals (!=) filter.""" + filters = {"genre": {"!=": "fiction"}} + result = self.engine.apply_filters(self.sample_results, filters) + assert len(result) == 1 + assert result[0]["metadata"]["genre"] == "non-fiction" + + def test_less_than_filter(self): + """Test less than (<) filter.""" + filters = {"chapter": {"<": 5}} + result = self.engine.apply_filters(self.sample_results, filters) + assert len(result) == 2 + chapters = [r["metadata"]["chapter"] for r in result] + assert all(ch < 5 for ch in chapters) + + def test_less_than_or_equal_filter(self): + """Test less than or equal (<=) filter.""" + filters = {"chapter": {"<=": 5}} + result = self.engine.apply_filters(self.sample_results, filters) + assert len(result) == 3 + chapters = [r["metadata"]["chapter"] for r in result] + assert all(ch <= 5 for ch in chapters) + + def test_greater_than_filter(self): + """Test greater than (>) filter.""" + filters = {"word_count": {">": 200}} + result = self.engine.apply_filters(self.sample_results, filters) + assert len(result) == 3 # Documents with word_count 250, 300, 400 + word_counts = [r["metadata"]["word_count"] for r in result] + assert all(wc > 200 for wc in word_counts) + + def test_greater_than_or_equal_filter(self): + """Test greater than or equal (>=) filter.""" + filters = {"word_count": {">=": 250}} + result = self.engine.apply_filters(self.sample_results, filters) + assert len(result) == 3 + word_counts = [r["metadata"]["word_count"] for r in result] + assert all(wc >= 250 for wc in word_counts) + + # Test membership operators + def test_in_filter(self): + """Test in filter.""" + filters = {"character": {"in": ["Alice", "Bob"]}} + result = self.engine.apply_filters(self.sample_results, filters) + assert len(result) == 3 + characters = [r["metadata"]["character"] for r in result] + assert all(ch in ["Alice", "Bob"] for ch in characters) + + def test_not_in_filter(self): + """Test not_in filter.""" + filters = {"character": {"not_in": ["Alice", "Bob"]}} + result = self.engine.apply_filters(self.sample_results, filters) + assert len(result) == 1 + assert result[0]["metadata"]["character"] == "Charlie" + + # Test string operators + def test_contains_filter(self): + """Test contains filter.""" + filters = {"genre": {"contains": "fiction"}} + result = self.engine.apply_filters(self.sample_results, filters) + assert len(result) == 4 # Both "fiction" and "non-fiction" + + def test_starts_with_filter(self): + """Test starts_with filter.""" + filters = {"genre": {"starts_with": "non"}} + result = self.engine.apply_filters(self.sample_results, filters) + assert len(result) == 1 + assert result[0]["metadata"]["genre"] == "non-fiction" + + def test_ends_with_filter(self): + """Test ends_with filter.""" + filters = {"text": {"ends_with": "content"}} + result = self.engine.apply_filters(self.sample_results, filters) + assert len(result) == 4 # All sample texts end with "content" + + # Test boolean operators + def test_is_true_filter(self): + """Test is_true filter.""" + filters = {"is_published": {"is_true": True}} + result = self.engine.apply_filters(self.sample_results, filters) + assert len(result) == 3 + assert all(r["metadata"]["is_published"] for r in result) + + def test_is_false_filter(self): + """Test is_false filter.""" + filters = {"is_published": {"is_false": False}} + result = self.engine.apply_filters(self.sample_results, filters) + assert len(result) == 1 + assert not result[0]["metadata"]["is_published"] + + # Test compound filters (AND logic) + def test_compound_filters(self): + """Test multiple filters applied together (AND logic).""" + filters = {"genre": {"==": "fiction"}, "chapter": {"<=": 5}} + result = self.engine.apply_filters(self.sample_results, filters) + assert len(result) == 2 + for r in result: + assert r["metadata"]["genre"] == "fiction" + assert r["metadata"]["chapter"] <= 5 + + def test_multiple_operators_same_field(self): + """Test multiple operators on the same field.""" + filters = {"word_count": {">=": 200, "<=": 350}} + result = self.engine.apply_filters(self.sample_results, filters) + assert len(result) == 2 + for r in result: + wc = r["metadata"]["word_count"] + assert 200 <= wc <= 350 + + # Test edge cases + def test_missing_field_fails_filter(self): + """Test that missing metadata fields fail filters.""" + filters = {"nonexistent_field": {"==": "value"}} + result = self.engine.apply_filters(self.sample_results, filters) + assert len(result) == 0 + + def test_invalid_operator(self): + """Test that invalid operators are handled gracefully.""" + filters = {"chapter": {"invalid_op": 1}} + result = self.engine.apply_filters(self.sample_results, filters) + assert len(result) == 0 # Should filter out all results + + def test_type_coercion_numeric(self): + """Test numeric type coercion in comparisons.""" + # Add a result with string chapter number + test_results = [ + *self.sample_results, + { + "id": "doc5", + "score": 0.75, + "text": "String chapter test", + "metadata": {"chapter": "2", "genre": "test"}, + }, + ] + + filters = {"chapter": {"<": 3}} + result = self.engine.apply_filters(test_results, filters) + # Should include doc1 (chapter=1) and doc5 (chapter="2") + assert len(result) == 2 + ids = [r["id"] for r in result] + assert "doc1" in ids + assert "doc5" in ids + + def test_list_membership_with_nested_tags(self): + """Test membership operations with list metadata.""" + # Note: This tests the metadata structure, not list field filtering + # For list field filtering, we'd need to modify the test data + filters = {"character": {"in": ["Alice"]}} + result = self.engine.apply_filters(self.sample_results, filters) + assert len(result) == 2 + assert all(r["metadata"]["character"] == "Alice" for r in result) + + def test_empty_results_list(self): + """Test filtering on empty results list.""" + filters = {"chapter": {"==": 1}} + result = self.engine.apply_filters([], filters) + assert len(result) == 0 + + +class TestPassageManagerFiltering: + """Test suite for PassageManager filtering integration.""" + + def setup_method(self): + """Setup test fixtures.""" + # Mock the passage manager without actual file I/O + self.passage_manager = Mock(spec=PassageManager) + self.passage_manager.filter_engine = MetadataFilterEngine() + + # Sample SearchResult objects + self.search_results = [ + SearchResult( + id="doc1", + score=0.95, + text="Chapter 1 content", + metadata={"chapter": 1, "character": "Alice"}, + ), + SearchResult( + id="doc2", + score=0.87, + text="Chapter 5 content", + metadata={"chapter": 5, "character": "Bob"}, + ), + SearchResult( + id="doc3", + score=0.82, + text="Chapter 10 content", + metadata={"chapter": 10, "character": "Alice"}, + ), + ] + + def test_search_result_filtering(self): + """Test filtering SearchResult objects.""" + # Create a real PassageManager instance just for the filtering method + # We'll mock the file operations + with patch("builtins.open"), patch("json.loads"), patch("pickle.load"): + pm = PassageManager([{"type": "jsonl", "path": "test.jsonl"}]) + + filters = {"chapter": {"<=": 5}} + result = pm.filter_search_results(self.search_results, filters) + + assert len(result) == 2 + chapters = [r.metadata["chapter"] for r in result] + assert all(ch <= 5 for ch in chapters) + + def test_filter_search_results_no_filters(self): + """Test that None filters return all results.""" + with patch("builtins.open"), patch("json.loads"), patch("pickle.load"): + pm = PassageManager([{"type": "jsonl", "path": "test.jsonl"}]) + + result = pm.filter_search_results(self.search_results, None) + assert len(result) == len(self.search_results) + + def test_filter_maintains_search_result_type(self): + """Test that filtering returns SearchResult objects.""" + with patch("builtins.open"), patch("json.loads"), patch("pickle.load"): + pm = PassageManager([{"type": "jsonl", "path": "test.jsonl"}]) + + filters = {"character": {"==": "Alice"}} + result = pm.filter_search_results(self.search_results, filters) + + assert len(result) == 2 + for r in result: + assert isinstance(r, SearchResult) + assert r.metadata["character"] == "Alice" + + +# Integration tests would go here, but they require actual LEANN backend setup +# These would test the full pipeline from LeannSearcher.search() with metadata_filters + +if __name__ == "__main__": + # Run basic smoke tests + engine = MetadataFilterEngine() + + sample_data = [ + { + "id": "test1", + "score": 0.9, + "text": "Test content", + "metadata": {"chapter": 1, "published": True}, + } + ] + + # Test basic filtering + result = engine.apply_filters(sample_data, {"chapter": {"==": 1}}) + assert len(result) == 1 + print("āœ… Basic filtering test passed") + + result = engine.apply_filters(sample_data, {"chapter": {"==": 2}}) + assert len(result) == 0 + 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