emrgnt-cmplxty
1138 lines
44 KiB
Python
1138 lines
44 KiB
Python
"""
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Unit tests for citation extraction and propagation in the R2RStreamingAgent.
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These tests focus specifically on citation-related functionality:
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- Citation extraction from text
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- Citation tracking during streaming
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- Citation event emission
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- Citation formatting and propagation
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- Citation edge cases and validation
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"""
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import pytest
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import asyncio
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import json
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import re
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from unittest.mock import MagicMock, patch, AsyncMock
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from typing import Dict, List, Tuple, Any, AsyncGenerator
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import pytest_asyncio
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from core.base import Message, LLMChatCompletion, LLMChatCompletionChunk, GenerationConfig
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from core.utils import CitationTracker, extract_citations, extract_citation_spans
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from core.agent.base import R2RStreamingAgent
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# Import mock classes from conftest
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from conftest import (
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MockDatabaseProvider,
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MockLLMProvider,
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MockR2RStreamingAgent,
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MockSearchResultsCollector,
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collect_stream_output
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)
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class MockLLMProvider:
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"""Mock LLM provider for testing."""
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def __init__(self, response_content=None, citations=None):
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self.response_content = response_content or "This is a response"
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self.citations = citations or []
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async def aget_completion(self, messages, generation_config):
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"""Mock synchronous completion."""
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content = self.response_content
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for citation in self.citations:
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content += f" [{citation}]"
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mock_response = MagicMock(spec=LLMChatCompletion)
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mock_response.choices = [MagicMock()]
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mock_response.choices[0].message = MagicMock()
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mock_response.choices[0].message.content = content
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mock_response.choices[0].finish_reason = "stop"
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return mock_response
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async def aget_completion_stream(self, messages, generation_config):
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"""Mock streaming completion."""
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content = self.response_content
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for citation in self.citations:
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content += f" [{citation}]"
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# Simulate streaming by yielding one character at a time
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for i in range(len(content)):
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chunk = MagicMock(spec=LLMChatCompletionChunk)
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chunk.choices = [MagicMock()]
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chunk.choices[0].delta = MagicMock()
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chunk.choices[0].delta.content = content[i]
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chunk.choices[0].finish_reason = None
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yield chunk
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# Final chunk with finish_reason="stop"
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final_chunk = MagicMock(spec=LLMChatCompletionChunk)
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final_chunk.choices = [MagicMock()]
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final_chunk.choices[0].delta = MagicMock()
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final_chunk.choices[0].delta.content = ""
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final_chunk.choices[0].finish_reason = "stop"
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yield final_chunk
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class MockPromptsHandler:
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"""Mock prompts handler for testing."""
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async def get_cached_prompt(self, prompt_key, inputs=None, *args, **kwargs):
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"""Return a mock system prompt."""
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return "You are a helpful assistant that provides well-sourced information."
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class MockDatabaseProvider:
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"""Mock database provider for testing."""
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def __init__(self):
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# Add a prompts_handler attribute to prevent AttributeError
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self.prompts_handler = MockPromptsHandler()
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async def acreate_conversation(self, *args, **kwargs):
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return {"id": "conv_12345"}
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async def aupdate_conversation(self, *args, **kwargs):
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return True
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async def acreate_message(self, *args, **kwargs):
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return {"id": "msg_12345"}
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class MockSearchResultsCollector:
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"""Mock search results collector for testing."""
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def __init__(self, results=None):
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self.results = results or {}
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def find_by_short_id(self, short_id):
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return self.results.get(short_id, {
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"document_id": f"doc_{short_id}",
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"text": f"This is document text for {short_id}",
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"metadata": {"source": f"source_{short_id}"}
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})
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# Create a concrete implementation of R2RStreamingAgent for testing
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class MockR2RStreamingAgent(R2RStreamingAgent):
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"""Mock streaming agent for testing that implements the abstract method."""
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# Regex pattern for citations, copied from the actual agent
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BRACKET_PATTERN = re.compile(r"\[([^\]]+)\]")
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SHORT_ID_PATTERN = re.compile(r"[A-Za-z0-9]{7,8}")
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def _register_tools(self):
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"""Implement the abstract method with a no-op version."""
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pass
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async def _setup(self, system_instruction=None, *args, **kwargs):
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"""Override _setup to simplify initialization and avoid external dependencies."""
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# Use a simple system message instead of fetching from database
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system_content = system_instruction or "You are a helpful assistant that provides well-sourced information."
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# Add system message to conversation
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await self.conversation.add_message(
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Message(role="system", content=system_content)
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)
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def _format_sse_event(self, event_type, data):
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"""Format an SSE event manually."""
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return f"event: {event_type}\ndata: {json.dumps(data)}\n\n"
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async def arun(
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self,
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system_instruction: str = None,
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messages: list[Message] = None,
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*args,
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**kwargs,
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) -> AsyncGenerator[str, None]:
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"""
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Simplified version of arun that focuses on citation handling for testing.
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"""
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await self._setup(system_instruction)
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if messages:
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for m in messages:
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await self.conversation.add_message(m)
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# Initialize citation tracker
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citation_tracker = CitationTracker()
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citation_payloads = {}
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# Track streaming citations for final persistence
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self.streaming_citations = []
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# Get the LLM response with citations
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response_content = "This is a test response with citations"
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response_content += " [abc1234] [def5678]"
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# Yield an initial message event with the start of the text
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yield self._format_sse_event("message", {"content": response_content})
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# Manually extract and emit citation events
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# This is a simpler approach than the character-by-character approach
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citation_spans = extract_citation_spans(response_content)
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# Process the citations
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for cid, spans in citation_spans.items():
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for span in spans:
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# Check if the span is new and record it
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if citation_tracker.is_new_span(cid, span):
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# Look up the source document for this citation
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source_doc = self.search_results_collector.find_by_short_id(cid)
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# Create citation payload
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citation_payload = {
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"document_id": source_doc.get("document_id", f"doc_{cid}"),
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"text": source_doc.get("text", f"This is document text for {cid}"),
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"metadata": source_doc.get("metadata", {"source": f"source_{cid}"}),
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}
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# Store the payload by citation ID
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citation_payloads[cid] = citation_payload
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# Track for persistence
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self.streaming_citations.append({
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"id": cid,
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"span": {"start": span[0], "end": span[1]},
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"payload": citation_payload
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})
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# Emit citation event
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citation_event = {
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"id": cid,
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"object": "citation",
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"span": {"start": span[0], "end": span[1]},
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"payload": citation_payload
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}
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yield self._format_sse_event("citation", citation_event)
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# Add assistant message with citation metadata to conversation
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await self.conversation.add_message(
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Message(
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role="assistant",
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content=response_content,
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metadata={"citations": self.streaming_citations}
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)
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)
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# Prepare consolidated citations for final answer
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consolidated_citations = []
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# Group citations by ID with all their spans
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for cid, spans in citation_tracker.get_all_spans().items():
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if cid in citation_payloads:
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consolidated_citations.append({
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"id": cid,
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"object": "citation",
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"spans": [{"start": s[0], "end": s[1]} for s in spans],
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"payload": citation_payloads[cid]
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})
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# Create and emit final answer event
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final_evt_payload = {
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"id": "msg_final",
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"object": "agent.final_answer",
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"generated_answer": response_content,
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"citations": consolidated_citations
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}
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# Manually format the final answer event
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yield self._format_sse_event("agent.final_answer", final_evt_payload)
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# Signal the end of the SSE stream
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yield "event: done\ndata: {}\n\n"
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@pytest.fixture
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def mock_streaming_agent():
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"""Create a streaming agent with mocked dependencies."""
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# Create mock config
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config = MagicMock()
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config.stream = True
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config.max_iterations = 3
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# Create mock providers
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llm_provider = MockLLMProvider(
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response_content="This is a test response with citations",
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citations=["abc1234", "def5678"]
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)
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db_provider = MockDatabaseProvider()
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# Create agent with mocked dependencies using our concrete implementation
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agent = MockR2RStreamingAgent(
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database_provider=db_provider,
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llm_provider=llm_provider,
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config=config,
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rag_generation_config=GenerationConfig(model="test/model")
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)
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# Replace the search results collector with our mock
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agent.search_results_collector = MockSearchResultsCollector({
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"abc1234": {
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"document_id": "doc_abc1234",
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"text": "This is document text for abc1234",
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"metadata": {"source": "source_abc1234"}
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},
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"def5678": {
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"document_id": "doc_def5678",
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"text": "This is document text for def5678",
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"metadata": {"source": "source_def5678"}
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}
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})
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return agent
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async def collect_stream_output(stream):
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"""Collect all output from a stream into a list."""
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output = []
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async for event in stream:
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output.append(event)
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return output
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def test_extract_citations_from_response():
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"""Test that citations are extracted from LLM responses."""
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response_text = "This is a response with a citation [abc1234]."
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# Use the utility function directly
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citations = extract_citations(response_text)
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assert "abc1234" in citations, "Citation should be extracted from response"
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@pytest.mark.asyncio
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async def test_streaming_agent_citation_extraction(mock_streaming_agent):
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"""Test that streaming agent extracts citations from streamed content."""
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# Run the agent
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messages = [Message(role="user", content="Test query")]
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# We need to run this in a coroutine
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stream = mock_streaming_agent.arun(messages=messages)
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output = await collect_stream_output(stream)
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# Look for citation events in the output
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citation_events = [
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line for line in output
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if 'event: citation' in line
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]
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assert len(citation_events) > 0, "Citation events should be emitted"
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# Check citation IDs in events
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citation_abc = any('abc1234' in event for event in citation_events)
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citation_def = any('def5678' in event for event in citation_events)
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assert citation_abc, "Citation abc1234 should be found in stream output"
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assert citation_def, "Citation def5678 should be found in stream output"
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@pytest.mark.asyncio
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async def test_citation_tracker_during_streaming(mock_streaming_agent):
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"""Test that CitationTracker correctly tracks processed citations during streaming."""
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# We need to patch the is_new_span method to verify it's being used correctly
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# Use autospec=True to ensure the method signature is preserved
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with patch('core.utils.CitationTracker.is_new_span', autospec=True) as mock_is_new_span:
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# Configure the mock to return True so citations will be processed
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mock_is_new_span.return_value = True
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messages = [Message(role="user", content="Test query")]
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# Run the agent
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stream = mock_streaming_agent.arun(messages=messages)
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output = await collect_stream_output(stream)
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# Verify that CitationTracker.is_new_span method was called
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assert mock_is_new_span.call_count > 0, "is_new_span should be called to track citation spans"
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@pytest.mark.asyncio
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async def test_final_answer_includes_consolidated_citations(mock_streaming_agent):
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"""Test that the final answer includes consolidated citations."""
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messages = [Message(role="user", content="Test query")]
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# Run the agent
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stream = mock_streaming_agent.arun(messages=messages)
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output = await collect_stream_output(stream)
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# Look for final answer event in the output
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final_answer_events = [
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line for line in output
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if 'event: agent.final_answer' in line
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]
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assert len(final_answer_events) > 0, "Final answer event should be emitted"
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# Parse the event to check for citations
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for event in final_answer_events:
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data_part = event.split('data: ')[1] if 'data: ' in event else event
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try:
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data = json.loads(data_part)
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if 'citations' in data:
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assert len(data['citations']) > 0, "Final answer should include citations"
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citation_ids = [citation.get('id') for citation in data['citations']]
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assert 'abc1234' in citation_ids or 'def5678' in citation_ids, "Known citation IDs should be included"
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except json.JSONDecodeError:
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continue
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@pytest.mark.asyncio
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async def test_conversation_message_includes_citation_metadata(mock_streaming_agent):
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"""Test that conversation messages include citation metadata."""
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with patch.object(mock_streaming_agent.conversation, 'add_message', wraps=mock_streaming_agent.conversation.add_message) as mock_add_message:
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messages = [Message(role="user", content="Test query")]
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# Run the agent
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stream = mock_streaming_agent.arun(messages=messages)
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output = await collect_stream_output(stream)
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# Check that add_message was called with citation metadata
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citation_calls = 0
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for call in mock_add_message.call_args_list:
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args, kwargs = call
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if args and isinstance(args[0], Message):
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message = args[0]
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if message.role == 'assistant' and message.metadata and 'citations' in message.metadata:
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citation_calls += 1
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assert citation_calls > 0, "At least one assistant message should include citation metadata"
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@pytest.mark.asyncio
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async def test_multiple_citations_for_same_source(mock_streaming_agent):
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"""Test handling of multiple citations for the same source document."""
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# Create a custom citation tracker that we can control
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citation_tracker = CitationTracker()
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# Create a custom MockR2RStreamingAgent with our controlled citation tracker
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with patch('core.utils.CitationTracker', return_value=citation_tracker):
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custom_agent = mock_streaming_agent
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# Modify the arun method to include repeated citations for the same source
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original_arun = custom_agent.arun
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async def custom_arun(*args, **kwargs):
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"""Custom arun that includes repeated citations for the same source."""
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# Setup like the original
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await custom_agent._setup(kwargs.get('system_instruction'))
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messages = kwargs.get('messages', [])
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if messages:
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for m in messages:
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await custom_agent.conversation.add_message(m)
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|
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# Initialize payloads dict for tracking
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citation_payloads = {}
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|
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# Track streaming citations for final persistence
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custom_agent.streaming_citations = []
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# Create text with multiple citations to the same source
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response_content = "This text has multiple citations to the same source: [abc1234] and again here [abc1234]."
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|
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# Yield the message event
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yield custom_agent._format_sse_event("message", {"content": response_content})
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|
|
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# Manually extract and emit citation events
|
|
# This is a simpler approach than the character-by-character approach
|
|
citation_spans = extract_citation_spans(response_content)
|
|
|
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# Process the citations
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|
for cid, spans in citation_spans.items():
|
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for span in spans:
|
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# Mark as processed in the tracker
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citation_tracker.is_new_span(cid, span)
|
|
|
|
# Look up the source document for this citation
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source_doc = custom_agent.search_results_collector.find_by_short_id(cid)
|
|
|
|
# Create citation payload
|
|
citation_payload = {
|
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"document_id": source_doc.get("document_id", f"doc_{cid}"),
|
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"text": source_doc.get("text", f"This is document text for {cid}"),
|
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"metadata": source_doc.get("metadata", {"source": f"source_{cid}"}),
|
|
}
|
|
|
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# Store the payload
|
|
citation_payloads[cid] = citation_payload
|
|
|
|
# Track for persistence
|
|
custom_agent.streaming_citations.append({
|
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"id": cid,
|
|
"span": {"start": span[0], "end": span[1]},
|
|
"payload": citation_payload
|
|
})
|
|
|
|
# Emit citation event
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|
citation_event = {
|
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"id": cid,
|
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"object": "citation",
|
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"span": {"start": span[0], "end": span[1]},
|
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"payload": citation_payload
|
|
}
|
|
|
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yield custom_agent._format_sse_event("citation", citation_event)
|
|
|
|
# Add assistant message with citation metadata to conversation
|
|
await custom_agent.conversation.add_message(
|
|
Message(
|
|
role="assistant",
|
|
content=response_content,
|
|
metadata={"citations": custom_agent.streaming_citations}
|
|
)
|
|
)
|
|
|
|
# Prepare consolidated citations for final answer
|
|
consolidated_citations = []
|
|
|
|
# Group citations by ID with all their spans
|
|
for cid, spans in citation_tracker.get_all_spans().items():
|
|
if cid in citation_payloads:
|
|
consolidated_citations.append({
|
|
"id": cid,
|
|
"object": "citation",
|
|
"spans": [{"start": s[0], "end": s[1]} for s in spans],
|
|
"payload": citation_payloads[cid]
|
|
})
|
|
|
|
# Create and emit final answer event
|
|
final_evt_payload = {
|
|
"id": "msg_final",
|
|
"object": "agent.final_answer",
|
|
"generated_answer": response_content,
|
|
"citations": consolidated_citations
|
|
}
|
|
|
|
yield custom_agent._format_sse_event("agent.final_answer", final_evt_payload)
|
|
|
|
# Signal the end of the SSE stream
|
|
yield "event: done\ndata: {}\n\n"
|
|
|
|
# Apply the custom arun method
|
|
with patch.object(custom_agent, 'arun', custom_arun):
|
|
messages = [Message(role="user", content="Test query")]
|
|
|
|
# Run the agent with overlapping citations
|
|
stream = custom_agent.arun(messages=messages)
|
|
output = await collect_stream_output(stream)
|
|
|
|
# Count citation events for abc1234
|
|
citation_abc_events = [
|
|
line for line in output
|
|
if 'event: citation' in line and 'abc1234' in line
|
|
]
|
|
|
|
# There should be at least 2 citations for abc1234 (the original and our added one)
|
|
assert len(citation_abc_events) >= 2, "Should emit multiple citation events for the same source"
|
|
|
|
# Check the final answer to ensure spans were consolidated
|
|
final_answer_events = [
|
|
line for line in output
|
|
if 'event: agent.final_answer' in line
|
|
]
|
|
|
|
for event in final_answer_events:
|
|
data_part = event.split('data: ')[1] if 'data: ' in event else event
|
|
try:
|
|
data = json.loads(data_part)
|
|
if 'citations' in data:
|
|
# Find the citation for abc1234
|
|
abc_citation = next((citation for citation in data['citations'] if citation.get('id') == 'abc1234'), None)
|
|
if abc_citation:
|
|
# It should have multiple spans
|
|
assert abc_citation.get('spans') and len(abc_citation['spans']) >= 2, "Citation should have multiple spans consolidated"
|
|
except json.JSONDecodeError:
|
|
continue
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_citation_consolidation_logic(mock_streaming_agent):
|
|
"""Test that citation consolidation properly groups spans by citation ID."""
|
|
# Patch the get_all_spans method to return a controlled set of spans
|
|
citation_tracker = CitationTracker()
|
|
|
|
# Add spans for multiple citations
|
|
citation_tracker.is_new_span("abc1234", (10, 20))
|
|
citation_tracker.is_new_span("abc1234", (30, 40))
|
|
citation_tracker.is_new_span("def5678", (50, 60))
|
|
citation_tracker.is_new_span("ghi9012", (70, 80))
|
|
citation_tracker.is_new_span("ghi9012", (90, 100))
|
|
|
|
# Create a custom mock agent that uses our pre-populated citation tracker
|
|
with patch('core.utils.CitationTracker', return_value=citation_tracker):
|
|
# Create a fresh agent with our mocked citation tracker
|
|
new_agent = mock_streaming_agent
|
|
|
|
messages = [Message(role="user", content="Test query")]
|
|
|
|
# Run the agent
|
|
stream = new_agent.arun(messages=messages)
|
|
output = await collect_stream_output(stream)
|
|
|
|
# Look for the final answer event
|
|
final_answer_events = [
|
|
line for line in output
|
|
if 'event: agent.final_answer' in line
|
|
]
|
|
|
|
# Verify consolidation in final answer
|
|
for event in final_answer_events:
|
|
data_part = event.split('data: ')[1] if 'data: ' in event else event
|
|
try:
|
|
data = json.loads(data_part)
|
|
if 'citations' in data:
|
|
# There should be at least 2 citations (from our mock agent implementation)
|
|
assert len(data['citations']) >= 2, "Should include multiple citation objects"
|
|
|
|
# Check spans for each citation
|
|
for citation in data['citations']:
|
|
cid = citation.get('id')
|
|
if cid == 'abc1234':
|
|
# Spans should be consolidated for abc1234
|
|
spans = citation.get('spans', [])
|
|
assert len(spans) >= 1, f"Citation {cid} should have spans"
|
|
except json.JSONDecodeError:
|
|
continue
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_citation_event_format(mock_streaming_agent):
|
|
"""Test that citation events follow the expected format."""
|
|
messages = [Message(role="user", content="Test query")]
|
|
|
|
# Run the agent
|
|
stream = mock_streaming_agent.arun(messages=messages)
|
|
output = await collect_stream_output(stream)
|
|
|
|
# Extract citation events
|
|
citation_events = [
|
|
line for line in output
|
|
if 'event: citation' in line
|
|
]
|
|
|
|
assert len(citation_events) > 0, "Citation events should be emitted"
|
|
|
|
# Check the format of each citation event
|
|
for event in citation_events:
|
|
# Should have 'event: citation' and 'data: {...}'
|
|
assert 'event: citation' in event, "Event type should be 'citation'"
|
|
assert 'data: ' in event, "Event should have data payload"
|
|
|
|
# Parse the data payload
|
|
data_part = event.split('data: ')[1] if 'data: ' in event else event
|
|
try:
|
|
data = json.loads(data_part)
|
|
|
|
# Check required fields
|
|
assert 'id' in data, "Citation event should have an 'id'"
|
|
assert 'object' in data and data['object'] == 'citation', "Event object should be 'citation'"
|
|
assert 'span' in data, "Citation event should have a 'span'"
|
|
assert 'start' in data['span'] and 'end' in data['span'], "Span should have 'start' and 'end'"
|
|
assert 'payload' in data, "Citation event should have a 'payload'"
|
|
|
|
# Check payload fields
|
|
assert 'document_id' in data['payload'], "Payload should have 'document_id'"
|
|
assert 'text' in data['payload'], "Payload should have 'text'"
|
|
assert 'metadata' in data['payload'], "Payload should have 'metadata'"
|
|
|
|
except json.JSONDecodeError:
|
|
pytest.fail(f"Citation event data is not valid JSON: {data_part}")
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_final_answer_event_format(mock_streaming_agent):
|
|
"""Test that the final answer event follows the expected format."""
|
|
messages = [Message(role="user", content="Test query")]
|
|
|
|
# Run the agent
|
|
stream = mock_streaming_agent.arun(messages=messages)
|
|
output = await collect_stream_output(stream)
|
|
|
|
# Look for final answer event
|
|
final_answer_events = [
|
|
line for line in output
|
|
if 'event: agent.final_answer' in line
|
|
]
|
|
|
|
assert len(final_answer_events) > 0, "Final answer event should be emitted"
|
|
|
|
# Check the format of the final answer event
|
|
for event in final_answer_events:
|
|
assert 'event: agent.final_answer' in event, "Event type should be 'agent.final_answer'"
|
|
assert 'data: ' in event, "Event should have data payload"
|
|
|
|
# Parse the data payload
|
|
data_part = event.split('data: ')[1] if 'data: ' in event else event
|
|
try:
|
|
data = json.loads(data_part)
|
|
|
|
# Check required fields
|
|
assert 'id' in data, "Final answer event should have an 'id'"
|
|
assert 'object' in data and data['object'] == 'agent.final_answer', "Event object should be 'agent.final_answer'"
|
|
assert 'generated_answer' in data, "Final answer event should have a 'generated_answer'"
|
|
assert 'citations' in data, "Final answer event should have 'citations'"
|
|
|
|
# Check citation fields
|
|
for citation in data['citations']:
|
|
assert 'id' in citation, "Citation should have an 'id'"
|
|
assert 'object' in citation and citation['object'] == 'citation', "Citation object should be 'citation'"
|
|
assert 'spans' in citation, "Citation should have 'spans'"
|
|
assert 'payload' in citation, "Citation should have a 'payload'"
|
|
|
|
# Check spans format
|
|
for span in citation['spans']:
|
|
assert 'start' in span, "Span should have 'start'"
|
|
assert 'end' in span, "Span should have 'end'"
|
|
|
|
# Check payload fields
|
|
assert 'document_id' in citation['payload'], "Payload should have 'document_id'"
|
|
assert 'text' in citation['payload'], "Payload should have 'text'"
|
|
assert 'metadata' in citation['payload'], "Payload should have 'metadata'"
|
|
|
|
except json.JSONDecodeError:
|
|
pytest.fail(f"Final answer event data is not valid JSON: {data_part}")
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_overlapping_citation_handling():
|
|
"""Test that overlapping citations are handled correctly."""
|
|
# Create a custom agent configuration
|
|
config = MagicMock()
|
|
config.stream = True
|
|
config.max_iterations = 3
|
|
|
|
# Create providers
|
|
llm_provider = MockLLMProvider(
|
|
response_content="This is a test response with overlapping citations",
|
|
citations=["abc1234", "def5678"]
|
|
)
|
|
db_provider = MockDatabaseProvider()
|
|
|
|
# Create agent
|
|
agent = MockR2RStreamingAgent(
|
|
database_provider=db_provider,
|
|
llm_provider=llm_provider,
|
|
config=config,
|
|
rag_generation_config=GenerationConfig(model="test/model")
|
|
)
|
|
|
|
# Replace the search results collector with our mock
|
|
agent.search_results_collector = MockSearchResultsCollector({
|
|
"abc1234": {
|
|
"document_id": "doc_abc1234",
|
|
"text": "This is document text for abc1234",
|
|
"metadata": {"source": "source_abc1234"}
|
|
},
|
|
"def5678": {
|
|
"document_id": "doc_def5678",
|
|
"text": "This is document text for def5678",
|
|
"metadata": {"source": "source_def5678"}
|
|
}
|
|
})
|
|
|
|
# Modify the arun method for overlapping citations
|
|
original_arun = agent.arun
|
|
|
|
async def custom_arun(*args, **kwargs):
|
|
"""Custom arun that includes overlapping citations."""
|
|
# Setup like the original
|
|
await agent._setup(kwargs.get('system_instruction'))
|
|
|
|
messages = kwargs.get('messages', [])
|
|
if messages:
|
|
for m in messages:
|
|
await agent.conversation.add_message(m)
|
|
|
|
# Initialize citation tracker
|
|
citation_tracker = CitationTracker()
|
|
citation_payloads = {}
|
|
|
|
# Track streaming citations for final persistence
|
|
agent.streaming_citations = []
|
|
|
|
# Create text with overlapping citations (citation spans that overlap)
|
|
response_content = "This text has overlapping citations [abc1234] part of which [def5678] overlap."
|
|
|
|
# Yield the message event
|
|
yield agent._format_sse_event("message", {"content": response_content})
|
|
|
|
# Manually create overlapping citation spans
|
|
# For simplicity, we'll define the spans directly rather than using regex
|
|
citation_spans = {
|
|
"abc1234": [(30, 39)], # This span includes "[abc1234]"
|
|
"def5678": [(55, 64)] # This span includes "[def5678]"
|
|
}
|
|
|
|
# Process the citations
|
|
for cid, spans in citation_spans.items():
|
|
for span in spans:
|
|
# Mark as processed in the tracker
|
|
citation_tracker.is_new_span(cid, span)
|
|
|
|
# Look up the source document for this citation
|
|
source_doc = agent.search_results_collector.find_by_short_id(cid)
|
|
|
|
# Create citation payload
|
|
citation_payload = {
|
|
"document_id": source_doc.get("document_id", f"doc_{cid}"),
|
|
"text": source_doc.get("text", f"This is document text for {cid}"),
|
|
"metadata": source_doc.get("metadata", {"source": f"source_{cid}"}),
|
|
}
|
|
|
|
# Store the payload by citation ID
|
|
citation_payloads[cid] = citation_payload
|
|
|
|
# Track for persistence
|
|
agent.streaming_citations.append({
|
|
"id": cid,
|
|
"span": {"start": span[0], "end": span[1]},
|
|
"payload": citation_payload
|
|
})
|
|
|
|
# Emit citation event
|
|
citation_event = {
|
|
"id": cid,
|
|
"object": "citation",
|
|
"span": {"start": span[0], "end": span[1]},
|
|
"payload": citation_payload
|
|
}
|
|
|
|
yield agent._format_sse_event("citation", citation_event)
|
|
|
|
# Add assistant message with citation metadata to conversation
|
|
await agent.conversation.add_message(
|
|
Message(
|
|
role="assistant",
|
|
content=response_content,
|
|
metadata={"citations": agent.streaming_citations}
|
|
)
|
|
)
|
|
|
|
# Prepare consolidated citations for final answer
|
|
consolidated_citations = []
|
|
|
|
# Group citations by ID with all their spans
|
|
for cid, spans in citation_tracker.get_all_spans().items():
|
|
if cid in citation_payloads:
|
|
consolidated_citations.append({
|
|
"id": cid,
|
|
"object": "citation",
|
|
"spans": [{"start": s[0], "end": s[1]} for s in spans],
|
|
"payload": citation_payloads[cid]
|
|
})
|
|
|
|
# Create and emit final answer event
|
|
final_evt_payload = {
|
|
"id": "msg_final",
|
|
"object": "agent.final_answer",
|
|
"generated_answer": response_content,
|
|
"citations": consolidated_citations
|
|
}
|
|
|
|
# Emit final answer event
|
|
yield agent._format_sse_event("agent.final_answer", final_evt_payload)
|
|
|
|
# Signal the end of the SSE stream
|
|
yield "event: done\ndata: {}\n\n"
|
|
|
|
# Replace the arun method
|
|
with patch.object(agent, 'arun', custom_arun):
|
|
messages = [Message(role="user", content="Test query")]
|
|
|
|
# Run the agent with overlapping citations
|
|
stream = agent.arun(messages=messages)
|
|
output = await collect_stream_output(stream)
|
|
|
|
# Check that both citations were emitted
|
|
citation_abc = any('abc1234' in event for event in output if 'event: citation' in event)
|
|
citation_def = any('def5678' in event for event in output if 'event: citation' in event)
|
|
|
|
assert citation_abc, "Citation abc1234 should be emitted"
|
|
assert citation_def, "Citation def5678 should be emitted"
|
|
|
|
# Check the final answer for both citations
|
|
final_answer_events = [
|
|
line for line in output
|
|
if 'event: agent.final_answer' in line
|
|
]
|
|
|
|
for event in final_answer_events:
|
|
data_part = event.split('data: ')[1] if 'data: ' in event else event
|
|
try:
|
|
data = json.loads(data_part)
|
|
if 'citations' in data:
|
|
citation_ids = [citation.get('id') for citation in data['citations']]
|
|
assert 'abc1234' in citation_ids, "abc1234 should be in final answer citations"
|
|
assert 'def5678' in citation_ids, "def5678 should be in final answer citations"
|
|
except json.JSONDecodeError:
|
|
continue
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_robustness_against_citation_variations(mock_streaming_agent):
|
|
"""Test agent's robustness against different citation formats and variations."""
|
|
# Create a custom text with different citation variations
|
|
response_text = """
|
|
This text has different citation variations:
|
|
1. Standard citation: [abc1234]
|
|
2. Another citation: [def5678]
|
|
3. Adjacent citations: [abc1234][def5678]
|
|
4. Special characters around citation: ([abc1234]) or "[def5678]".
|
|
"""
|
|
|
|
# Use the extract_citations function directly to see what would be detected
|
|
citations = extract_citations(response_text)
|
|
|
|
# There should be at least two different citation IDs
|
|
unique_citations = set(citations)
|
|
assert len(unique_citations) >= 2, "Should extract at least two different citation IDs"
|
|
assert "abc1234" in unique_citations, "Should extract abc1234"
|
|
assert "def5678" in unique_citations, "Should extract def5678"
|
|
|
|
# Count occurrences of each citation
|
|
counts = {}
|
|
for cid in citations:
|
|
counts[cid] = counts.get(cid, 0) + 1
|
|
|
|
# Each citation should be found the correct number of times based on the text
|
|
assert counts.get("abc1234", 0) >= 2, "abc1234 should appear at least twice"
|
|
assert counts.get("def5678", 0) >= 2, "def5678 should appear at least twice"
|
|
|
|
|
|
class TestCitationEdgeCases:
|
|
"""
|
|
Test class for citation edge cases using parameterized tests to cover multiple scenarios.
|
|
"""
|
|
|
|
@pytest.mark.parametrize("test_case", [
|
|
# Test case 1: Empty text
|
|
{"text": "", "expected_citations": []},
|
|
|
|
# Test case 2: Text with no citations
|
|
{"text": "This text has no citations.", "expected_citations": []},
|
|
|
|
# Test case 3: Adjacent citations
|
|
{"text": "Adjacent citations [abc1234][def5678]", "expected_citations": ["abc1234", "def5678"]},
|
|
|
|
# Test case 4: Repeated citations
|
|
{"text": "Repeated [abc1234] citation [abc1234]", "expected_citations": ["abc1234", "abc1234"]},
|
|
|
|
# Test case 5: Citation at beginning
|
|
{"text": "[abc1234] at beginning", "expected_citations": ["abc1234"]},
|
|
|
|
# Test case 6: Citation at end
|
|
{"text": "At end [abc1234]", "expected_citations": ["abc1234"]},
|
|
|
|
# Test case 7: Mixed valid and invalid citations
|
|
{"text": "Valid [abc1234] and invalid [ab123] citations", "expected_citations": ["abc1234"]},
|
|
|
|
# Test case 8: Citations with punctuation
|
|
{"text": "Citations with punctuation: ([abc1234]), [def5678]!", "expected_citations": ["abc1234", "def5678"]}
|
|
])
|
|
def test_citation_extraction_cases(self, test_case):
|
|
"""Test citation extraction with various edge cases."""
|
|
text = test_case["text"]
|
|
expected = test_case["expected_citations"]
|
|
|
|
# Extract citations
|
|
actual = extract_citations(text)
|
|
|
|
# Check count
|
|
assert len(actual) == len(expected), f"Expected {len(expected)} citations, got {len(actual)}"
|
|
|
|
# Check content (allowing for different orders)
|
|
if expected:
|
|
for expected_citation in expected:
|
|
assert expected_citation in actual, f"Expected citation {expected_citation} not found"
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_citation_handling_with_empty_response():
|
|
"""Test how the agent handles responses with no citations."""
|
|
# Create a custom R2RStreamingAgent with no citations
|
|
|
|
# Custom agent class for testing empty citations
|
|
class EmptyResponseAgent(MockR2RStreamingAgent):
|
|
async def arun(
|
|
self,
|
|
system_instruction: str = None,
|
|
messages: list[Message] = None,
|
|
*args,
|
|
**kwargs,
|
|
) -> AsyncGenerator[str, None]:
|
|
"""Custom arun with no citations in the response."""
|
|
await self._setup(system_instruction)
|
|
|
|
if messages:
|
|
for m in messages:
|
|
await self.conversation.add_message(m)
|
|
|
|
# Initialize citation tracker
|
|
citation_tracker = CitationTracker()
|
|
|
|
# Empty response with no citations
|
|
response_content = "This is a response with no citations."
|
|
|
|
# Yield an initial message event with the start of the text
|
|
yield self._format_sse_event("message", {"content": response_content})
|
|
|
|
# No citation spans to extract
|
|
citation_spans = extract_citation_spans(response_content)
|
|
|
|
# Should be empty
|
|
assert len(citation_spans) == 0, "No citation spans should be found"
|
|
|
|
# Add assistant message to conversation (with no citation metadata)
|
|
await self.conversation.add_message(
|
|
Message(
|
|
role="assistant",
|
|
content=response_content,
|
|
metadata={"citations": []}
|
|
)
|
|
)
|
|
|
|
# Create and emit final answer event
|
|
final_evt_payload = {
|
|
"id": "msg_final",
|
|
"object": "agent.final_answer",
|
|
"generated_answer": response_content,
|
|
"citations": []
|
|
}
|
|
|
|
yield self._format_sse_event("agent.final_answer", final_evt_payload)
|
|
yield "event: done\ndata: {}\n\n"
|
|
|
|
# Create the agent with empty citation response
|
|
config = MagicMock()
|
|
config.stream = True
|
|
|
|
llm_provider = MockLLMProvider(
|
|
response_content="This is a response with no citations.",
|
|
citations=[]
|
|
)
|
|
|
|
db_provider = MockDatabaseProvider()
|
|
|
|
# Create the custom agent
|
|
agent = EmptyResponseAgent(
|
|
database_provider=db_provider,
|
|
llm_provider=llm_provider,
|
|
config=config,
|
|
rag_generation_config=GenerationConfig(model="test/model")
|
|
)
|
|
|
|
# Test a simple query
|
|
messages = [Message(role="user", content="Query with no citations")]
|
|
|
|
# Run the agent
|
|
stream = agent.arun(messages=messages)
|
|
output = await collect_stream_output(stream)
|
|
|
|
# Verify no citation events were emitted
|
|
citation_events = [line for line in output if 'event: citation' in line]
|
|
assert len(citation_events) == 0, "No citation events should be emitted"
|
|
|
|
# Parse the final answer event to check citations
|
|
final_answer_events = [line for line in output if 'event: agent.final_answer' in line]
|
|
assert len(final_answer_events) > 0, "Final answer event should be emitted"
|
|
|
|
data_part = final_answer_events[0].split('data: ')[1] if 'data: ' in final_answer_events[0] else ""
|
|
|
|
# Parse final answer data
|
|
try:
|
|
data = json.loads(data_part)
|
|
assert 'citations' in data, "Final answer event should include citations field"
|
|
assert len(data['citations']) == 0, "Citations list should be empty"
|
|
except json.JSONDecodeError:
|
|
assert False, "Final answer event data should be valid JSON"
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_citation_sanitization():
|
|
"""Test that citation IDs are properly sanitized before processing."""
|
|
# Since extract_citations uses a strict regex pattern [A-Za-z0-9]{7,8},
|
|
# we should test with valid citation formats
|
|
text = "Citation with surrounding text[abc1234]and [def5678]with no spaces."
|
|
|
|
# Extract citations
|
|
citations = extract_citations(text)
|
|
|
|
# Check if citations are properly extracted
|
|
assert "abc1234" in citations, "Citation abc1234 should be extracted"
|
|
assert "def5678" in citations, "Citation def5678 should be extracted"
|
|
|
|
# Test with spaces - these should NOT be extracted based on the implementation
|
|
text_with_spaces = "Citation with [abc1234 ] and [ def5678] spaces."
|
|
citations_with_spaces = extract_citations(text_with_spaces)
|
|
|
|
# The current implementation doesn't extract citations with spaces inside the brackets
|
|
assert len(citations_with_spaces) == 0 or "abc1234" not in citations_with_spaces, "Citations with spaces should not be extracted with current implementation"
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_citation_tracking_state_persistence():
|
|
"""Test that the CitationTracker correctly maintains state across multiple calls."""
|
|
tracker = CitationTracker()
|
|
|
|
# Record some initial spans
|
|
tracker.is_new_span("abc1234", (10, 18))
|
|
tracker.is_new_span("def5678", (30, 38))
|
|
|
|
# Check if spans are correctly stored
|
|
all_spans = tracker.get_all_spans()
|
|
assert "abc1234" in all_spans, "Citation abc1234 should be tracked"
|
|
assert "def5678" in all_spans, "Citation def5678 should be tracked"
|
|
assert all_spans["abc1234"] == [(10, 18)], "Span positions should match"
|
|
|
|
# Add another span for an existing citation
|
|
tracker.is_new_span("abc1234", (50, 58))
|
|
|
|
# Check if the new span was added
|
|
all_spans = tracker.get_all_spans()
|
|
assert len(all_spans["abc1234"]) == 2, "Citation abc1234 should have 2 spans"
|
|
assert (50, 58) in all_spans["abc1234"], "New span should be added"
|
|
|
|
def test_citation_span_uniqueness():
|
|
"""Test that CitationTracker correctly identifies duplicate spans."""
|
|
tracker = CitationTracker()
|
|
|
|
# Record a span
|
|
tracker.is_new_span("abc1234", (10, 18))
|
|
|
|
# Check if the same span is recognized as not new
|
|
assert not tracker.is_new_span("abc1234", (10, 18)), "Duplicate span should not be considered new"
|
|
|
|
# Check if different span for same citation is recognized as new
|
|
assert tracker.is_new_span("abc1234", (20, 28)), "Different span should be considered new"
|
|
|
|
# Check if same span for different citation is recognized as new
|
|
assert tracker.is_new_span("def5678", (10, 18)), "Same span for different citation should be considered new"
|
|
|
|
def test_citation_with_punctuation():
|
|
"""Test extraction of citations with surrounding punctuation."""
|
|
text = "Citations with punctuation: ([abc1234]), [def5678]!, and [ghi9012]."
|
|
|
|
# Extract citations
|
|
citations = extract_citations(text)
|
|
|
|
# Check if all citations are extracted correctly
|
|
assert "abc1234" in citations, "Citation abc1234 should be extracted"
|
|
assert "def5678" in citations, "Citation def5678 should be extracted"
|
|
assert "ghi9012" in citations, "Citation ghi9012 should be extracted"
|
|
|
|
def test_citation_extraction_with_invalid_formats():
|
|
"""Test that invalid citation formats are not extracted."""
|
|
text = "Invalid citation formats: [123], [abcdef], [abc123456789], and valid [abc1234]."
|
|
|
|
# Extract citations
|
|
citations = extract_citations(text)
|
|
|
|
# Check that only valid citations are extracted
|
|
assert len(citations) == 1, "Only one valid citation should be extracted"
|
|
assert "abc1234" in citations, "Only valid citation abc1234 should be extracted"
|
|
assert "123" not in citations, "Invalid citation [123] should not be extracted"
|
|
assert "abcdef" not in citations, "Invalid citation [abcdef] should not be extracted"
|
|
assert "abc123456789" not in citations, "Invalid citation [abc123456789] should not be extracted"
|