345 lines
12 KiB
Python
345 lines
12 KiB
Python
from pathlib import Path
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import pytest
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from pydantic_ai import Agent
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from haiku.rag.agents.rlm.agent import create_rlm_agent
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from haiku.rag.agents.rlm.dependencies import RLMContext, RLMDeps
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from haiku.rag.agents.rlm.models import CodeExecution, RLMResult
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from haiku.rag.config import Config
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@pytest.fixture(scope="module")
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def vcr_cassette_dir():
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return str(Path(__file__).parent.parent.parent / "cassettes" / "test_rlm")
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class TestCreateRLMAgent:
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def test_creates_agent_with_correct_types(self):
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agent = create_rlm_agent(Config)
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assert isinstance(agent, Agent)
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assert agent.deps_type is RLMDeps
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assert agent.output_type is RLMResult
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def test_agent_has_execute_code_tool(self):
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agent = create_rlm_agent(Config)
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tool_names = list(agent._function_toolset.tools.keys())
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assert "execute_code" in tool_names
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class TestExecuteCodeTool:
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@pytest.mark.asyncio
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async def test_execute_code_returns_structured_result(self, empty_client):
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"""Test that execute_code tool produces structured CodeExecution output."""
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from haiku.rag.agents.rlm.agent import _get_or_create_repl
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context = RLMContext()
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deps = RLMDeps(
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client=empty_client,
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config=Config,
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context=context,
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)
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class MockCtx:
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def __init__(self, deps):
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self.deps = deps
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ctx = MockCtx(deps)
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repl = _get_or_create_repl(ctx)
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result = await repl.execute_async("print(1 + 1)")
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assert result.success
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assert "2" in result.stdout
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@pytest.mark.asyncio
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async def test_execute_code_tracks_executions_in_context(self, empty_client):
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"""Test that code executions are tracked as CodeExecution objects in RLMContext."""
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from haiku.rag.agents.rlm.agent import _get_or_create_repl
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context = RLMContext()
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deps = RLMDeps(
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client=empty_client,
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config=Config,
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context=context,
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)
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class MockCtx:
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def __init__(self, deps):
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self.deps = deps
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ctx = MockCtx(deps)
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repl = _get_or_create_repl(ctx)
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assert len(context.code_executions) == 0
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result = await repl.execute_async("x = 42")
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assert result.success
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@pytest.mark.asyncio
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async def test_code_execution_has_correct_fields(self, empty_client):
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"""Test that CodeExecution has all expected fields."""
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execution = CodeExecution(
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code="print('hello')",
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stdout="hello\n",
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stderr="",
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success=True,
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)
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assert execution.code == "print('hello')"
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assert execution.stdout == "hello\n"
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assert execution.stderr == ""
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assert execution.success is True
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@pytest.mark.asyncio
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async def test_code_execution_captures_errors(self, empty_client):
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"""Test that failed executions are properly captured."""
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from haiku.rag.agents.rlm.agent import _get_or_create_repl
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context = RLMContext()
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deps = RLMDeps(
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client=empty_client,
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config=Config,
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context=context,
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)
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class MockCtx:
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def __init__(self, deps):
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self.deps = deps
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ctx = MockCtx(deps)
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repl = _get_or_create_repl(ctx)
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result = await repl.execute_async("1/0")
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assert result.success is False
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assert "ZeroDivisionError" in result.stderr
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class TestClientRLMIntegration:
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"""Integration tests for client.rlm() method."""
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@pytest.mark.asyncio
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@pytest.mark.vcr()
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async def test_rlm_count_documents(self, allow_model_requests, temp_db_path):
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"""Test RLM agent can count documents.
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Agent program:
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docs = list_documents(limit=1000)
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print(len(docs))
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"""
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from haiku.rag.client import HaikuRAG
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async with HaikuRAG(temp_db_path, create=True) as client:
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await client.create_document("First document about cats.", title="Doc 1")
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await client.create_document("Second document about dogs.", title="Doc 2")
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await client.create_document("Third document about birds.", title="Doc 3")
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answer = await client.rlm("How many documents are in the database?")
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assert "3" in answer
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@pytest.mark.asyncio
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@pytest.mark.vcr()
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async def test_rlm_aggregation(self, allow_model_requests, temp_db_path):
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"""Test RLM agent can perform aggregation across documents.
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Agent program:
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import re
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revs = {}
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for d in ['Q1 Report', 'Q2 Report', 'Q3 Report']:
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content = get_document(d)
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if content:
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vals = re.findall(r'\\$([\\d,]+)', content)
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if vals:
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rev = sum(int(v.replace(',', '')) for v in vals)
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else:
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rev = None
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else:
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rev = None
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revs[d] = rev
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print(revs)
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"""
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from haiku.rag.client import HaikuRAG
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async with HaikuRAG(temp_db_path, create=True) as client:
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await client.create_document(
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"Sales report Q1: Revenue was $100,000.", title="Q1 Report"
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)
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await client.create_document(
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"Sales report Q2: Revenue was $150,000.", title="Q2 Report"
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)
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await client.create_document(
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"Sales report Q3: Revenue was $200,000.", title="Q3 Report"
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)
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answer = await client.rlm(
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"What is the total revenue across all quarterly reports?"
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)
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assert "450" in answer or "450,000" in answer
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@pytest.mark.asyncio
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@pytest.mark.vcr()
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async def test_rlm_with_filter(self, allow_model_requests, temp_db_path):
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"""Test RLM agent respects filter parameter.
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Agent program:
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docs = list_documents(limit=1000)
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print(len(docs))
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print(docs[:5])
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The filter is applied via context, so list_documents() only sees "Cats".
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"""
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from haiku.rag.client import HaikuRAG
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async with HaikuRAG(temp_db_path, create=True) as client:
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await client.create_document("Cat document.", title="Cats")
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await client.create_document("Dog document.", title="Dogs")
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await client.create_document("Bird document.", title="Birds")
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answer = await client.rlm(
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"How many documents are available?",
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filter="title = 'Cats'",
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)
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assert "1" in answer
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@pytest.mark.asyncio
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@pytest.mark.vcr()
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async def test_rlm_docling_document_structure(
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self, allow_model_requests, temp_db_path
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):
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"""Test RLM agent can analyze document structure using DoclingDocument.
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Agent program:
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docs = list_documents(limit=20)
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print(docs)
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doc = get_docling_document('<doc_id>')
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print(doc.name)
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print('tables:', len(doc.tables))
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print('pictures:', len(doc.pictures))
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"""
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from pathlib import Path
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from haiku.rag.client import HaikuRAG
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from haiku.rag.config import AppConfig
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pdf_path = Path("tests/data/doclaynet.pdf")
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config = AppConfig()
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config.processing.conversion_options.do_ocr = False
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async with HaikuRAG(temp_db_path, config=config, create=True) as client:
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await client.create_document_from_source(pdf_path)
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answer = await client.rlm(
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"How many tables are in the document? "
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"Also tell me how many pictures/figures it contains."
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)
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# The doclaynet.pdf has 1 table and 1 picture
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assert "1" in answer
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@pytest.mark.asyncio
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@pytest.mark.vcr()
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async def test_rlm_semantic_analysis_with_llm(
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self, allow_model_requests, temp_db_path
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):
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"""Test RLM agent can use llm() for semantic analysis combined with computation.
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Agent program:
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docs = list_documents(limit=100)
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print(len(docs))
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print([d['title'] for d in docs[:20]])
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sentiments = {}
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for title in ['Q1 Update', 'Q2 Update', 'Q3 Update']:
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content = get_document(title)
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if content:
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result = llm(f"Classify sentiment as positive/negative/mixed: {content}")
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sentiments[title] = result
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print(sentiments)
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"""
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from haiku.rag.client import HaikuRAG
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async with HaikuRAG(temp_db_path, create=True) as client:
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await client.create_document(
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"The new product launch exceeded expectations. Sales grew 40% "
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"and customer feedback has been overwhelmingly positive. "
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"Team morale is at an all-time high.",
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title="Q1 Update",
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)
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await client.create_document(
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"We faced significant challenges this quarter. Supply chain issues "
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"caused delays, and we missed our revenue target by 15%. "
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"Several key employees left the company.",
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title="Q2 Update",
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)
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await client.create_document(
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"Mixed results this quarter. While product quality improved, "
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"marketing campaigns underperformed. Revenue was flat compared "
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"to last year but customer retention increased.",
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title="Q3 Update",
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)
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answer = await client.rlm(
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"Analyze the sentiment of each quarterly update. "
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"How many quarters were positive, negative, and mixed?"
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)
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# Should identify: Q1=positive, Q2=negative, Q3=mixed
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assert "positive" in answer.lower()
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assert "negative" in answer.lower()
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@pytest.mark.asyncio
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@pytest.mark.vcr()
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async def test_rlm_search_and_extract(self, allow_model_requests, temp_db_path):
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"""Test RLM agent can use search() to find content and extract information.
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Agent program:
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results = search("document element types", limit=20)
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print(len(results))
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for r in results[:5]:
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print(r['document_title'], r['chunk_id'], r['score'])
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print(r['content'][:200])
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results = search("DocBank element types", limit=10)
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...
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"""
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from pathlib import Path
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from haiku.rag.client import HaikuRAG
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from haiku.rag.config import AppConfig
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pdf_path = Path("tests/data/doclaynet.pdf")
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config = AppConfig()
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config.processing.conversion_options.do_ocr = False
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async with HaikuRAG(temp_db_path, config=config, create=True) as client:
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await client.create_document_from_source(pdf_path)
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answer = await client.rlm(
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"Search for content about document element types or labels. "
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"What are all the different document element types mentioned? "
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"List them all."
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)
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# The doclaynet.pdf defines exactly 11 class labels for document elements
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# Normalize Unicode hyphens (U+2011 non-breaking hyphen) to regular hyphens
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answer_lower = answer.lower().replace("\u2011", "-")
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expected_labels = [
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"caption",
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"footnote",
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"formula",
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"list-item",
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"page-footer",
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"page-header",
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"picture",
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"section-header",
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"table",
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"text",
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"title",
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]
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for label in expected_labels:
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# Allow for hyphen or space variants
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assert (
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label in answer_lower or label.replace("-", " ") in answer_lower
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), f"Missing label: {label}"
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