2025-07-14 17:54:04 +01:00
"""
Unit tests for ReAct processor logic
Tests the core business logic for the ReAct ( Reasoning and Acting ) pattern
without relying on external LLM services , focusing on the Think - Act - Observe
cycle and tool coordination .
"""
import pytest
from unittest . mock import Mock , AsyncMock , patch
import re
class TestReActProcessorLogic :
""" Test cases for ReAct processor business logic """
def test_react_cycle_parsing ( self ) :
""" Test parsing of ReAct cycle components from LLM output """
# Arrange
llm_output = """ Think: I need to find information about the capital of France.
Act : knowledge_search : capital of France
Observe : The search returned that Paris is the capital of France .
Think : I now have enough information to answer .
Answer : The capital of France is Paris . """
def parse_react_output ( text ) :
""" Parse ReAct format output into structured steps """
steps = [ ]
lines = text . strip ( ) . split ( ' \n ' )
for line in lines :
line = line . strip ( )
if line . startswith ( ' Think: ' ) :
steps . append ( {
' type ' : ' think ' ,
' content ' : line [ 6 : ] . strip ( )
} )
elif line . startswith ( ' Act: ' ) :
act_content = line [ 4 : ] . strip ( )
# Parse "tool_name: parameters" format
if ' : ' in act_content :
tool_name , params = act_content . split ( ' : ' , 1 )
steps . append ( {
' type ' : ' act ' ,
' tool_name ' : tool_name . strip ( ) ,
' parameters ' : params . strip ( )
} )
else :
steps . append ( {
' type ' : ' act ' ,
' content ' : act_content
} )
elif line . startswith ( ' Observe: ' ) :
steps . append ( {
' type ' : ' observe ' ,
' content ' : line [ 8 : ] . strip ( )
} )
elif line . startswith ( ' Answer: ' ) :
steps . append ( {
' type ' : ' answer ' ,
' content ' : line [ 7 : ] . strip ( )
} )
return steps
# Act
steps = parse_react_output ( llm_output )
# Assert
assert len ( steps ) == 5
assert steps [ 0 ] [ ' type ' ] == ' think '
assert steps [ 1 ] [ ' type ' ] == ' act '
assert steps [ 1 ] [ ' tool_name ' ] == ' knowledge_search '
assert steps [ 1 ] [ ' parameters ' ] == ' capital of France '
assert steps [ 2 ] [ ' type ' ] == ' observe '
assert steps [ 3 ] [ ' type ' ] == ' think '
assert steps [ 4 ] [ ' type ' ] == ' answer '
def test_tool_selection_logic ( self ) :
""" Test tool selection based on question type and context """
# Arrange
test_cases = [
( " What is 2 + 2? " , " calculator " ) ,
( " Who is the president of France? " , " knowledge_search " ) ,
( " Tell me about the relationship between Paris and France " , " graph_rag " ) ,
( " What time is it? " , " knowledge_search " ) # Default to general search
]
available_tools = {
" calculator " : { " description " : " Perform mathematical calculations " } ,
" knowledge_search " : { " description " : " Search knowledge base for facts " } ,
" graph_rag " : { " description " : " Query knowledge graph for relationships " }
}
def select_tool ( question , tools ) :
""" Select appropriate tool based on question content """
question_lower = question . lower ( )
# Math keywords
if any ( word in question_lower for word in [ ' + ' , ' - ' , ' * ' , ' / ' , ' calculate ' , ' math ' ] ) :
return " calculator "
# Relationship/graph keywords
if any ( word in question_lower for word in [ ' relationship ' , ' between ' , ' connected ' , ' related ' ] ) :
return " graph_rag "
# General knowledge keywords or default case
if any ( word in question_lower for word in [ ' who ' , ' what ' , ' where ' , ' when ' , ' why ' , ' how ' , ' time ' ] ) :
return " knowledge_search "
return None
# Act & Assert
for question , expected_tool in test_cases :
selected_tool = select_tool ( question , available_tools )
assert selected_tool == expected_tool , f " Question ' { question } ' should select { expected_tool } "
def test_tool_execution_logic ( self ) :
""" Test tool execution and result processing """
# Arrange
def mock_knowledge_search ( query ) :
if " capital " in query . lower ( ) and " france " in query . lower ( ) :
return " Paris is the capital of France. "
return " Information not found. "
def mock_calculator ( expression ) :
try :
# Simple expression evaluation
if ' + ' in expression :
parts = expression . split ( ' + ' )
return str ( sum ( int ( p . strip ( ) ) for p in parts ) )
return str ( eval ( expression ) )
except :
return " Error: Invalid expression "
tools = {
" knowledge_search " : mock_knowledge_search ,
" calculator " : mock_calculator
}
def execute_tool ( tool_name , parameters , available_tools ) :
""" Execute tool with given parameters """
if tool_name not in available_tools :
return { " error " : f " Tool { tool_name } not available " }
try :
tool_function = available_tools [ tool_name ]
result = tool_function ( parameters )
return { " success " : True , " result " : result }
except Exception as e :
return { " error " : str ( e ) }
# Act & Assert
test_cases = [
( " knowledge_search " , " capital of France " , " Paris is the capital of France. " ) ,
( " calculator " , " 2 + 2 " , " 4 " ) ,
( " calculator " , " invalid expression " , " Error: Invalid expression " ) ,
( " nonexistent_tool " , " anything " , None ) # Error case
]
for tool_name , params , expected in test_cases :
result = execute_tool ( tool_name , params , tools )
if expected is None :
assert " error " in result
else :
assert result . get ( " result " ) == expected
def test_conversation_context_integration ( self ) :
""" Test integration of conversation history into ReAct reasoning """
# Arrange
conversation_history = [
{ " role " : " user " , " content " : " What is 2 + 2? " } ,
{ " role " : " assistant " , " content " : " 2 + 2 = 4 " } ,
{ " role " : " user " , " content " : " What about 3 + 3? " }
]
def build_context_prompt ( question , history , max_turns = 3 ) :
""" Build context prompt from conversation history """
context_parts = [ ]
# Include recent conversation turns
recent_history = history [ - ( max_turns * 2 ) : ] if history else [ ]
for turn in recent_history :
role = turn [ " role " ]
content = turn [ " content " ]
context_parts . append ( f " { role } : { content } " )
current_question = f " user: { question } "
context_parts . append ( current_question )
return " \n " . join ( context_parts )
# Act
context_prompt = build_context_prompt ( " What about 3 + 3? " , conversation_history )
# Assert
assert " 2 + 2 " in context_prompt
assert " 2 + 2 = 4 " in context_prompt
assert " 3 + 3 " in context_prompt
assert context_prompt . count ( " user: " ) == 3
assert context_prompt . count ( " assistant: " ) == 1
def test_react_cycle_validation ( self ) :
""" Test validation of complete ReAct cycles """
# Arrange
complete_cycle = [
{ " type " : " think " , " content " : " I need to solve this math problem " } ,
{ " type " : " act " , " tool_name " : " calculator " , " parameters " : " 2 + 2 " } ,
{ " type " : " observe " , " content " : " The calculator returned 4 " } ,
{ " type " : " think " , " content " : " I can now provide the answer " } ,
{ " type " : " answer " , " content " : " 2 + 2 = 4 " }
]
incomplete_cycle = [
{ " type " : " think " , " content " : " I need to solve this " } ,
{ " type " : " act " , " tool_name " : " calculator " , " parameters " : " 2 + 2 " }
# Missing observe and answer steps
]
def validate_react_cycle ( steps ) :
""" Validate that ReAct cycle is complete """
step_types = [ step . get ( " type " ) for step in steps ]
# Must have at least one think, act, observe, and answer
required_types = [ " think " , " act " , " observe " , " answer " ]
validation_results = {
" is_complete " : all ( req_type in step_types for req_type in required_types ) ,
" has_reasoning " : " think " in step_types ,
" has_action " : " act " in step_types ,
" has_observation " : " observe " in step_types ,
" has_answer " : " answer " in step_types ,
" step_count " : len ( steps )
}
return validation_results
# Act & Assert
complete_validation = validate_react_cycle ( complete_cycle )
assert complete_validation [ " is_complete " ] is True
assert complete_validation [ " has_reasoning " ] is True
assert complete_validation [ " has_action " ] is True
assert complete_validation [ " has_observation " ] is True
assert complete_validation [ " has_answer " ] is True
incomplete_validation = validate_react_cycle ( incomplete_cycle )
assert incomplete_validation [ " is_complete " ] is False
assert incomplete_validation [ " has_reasoning " ] is True
assert incomplete_validation [ " has_action " ] is True
assert incomplete_validation [ " has_observation " ] is False
assert incomplete_validation [ " has_answer " ] is False
def test_multi_step_reasoning_logic ( self ) :
""" Test multi-step reasoning chains """
# Arrange
complex_question = " What is the population of the capital of France? "
def plan_reasoning_steps ( question ) :
""" Plan the reasoning steps needed for complex questions """
steps = [ ]
question_lower = question . lower ( )
# Check if question requires multiple pieces of information
if " capital of " in question_lower and ( " population " in question_lower or " how many " in question_lower ) :
steps . append ( {
" step " : 1 ,
" action " : " find_capital " ,
" description " : " First find the capital city "
} )
steps . append ( {
" step " : 2 ,
" action " : " find_population " ,
" description " : " Then find the population of that city "
} )
elif " capital of " in question_lower :
steps . append ( {
" step " : 1 ,
" action " : " find_capital " ,
" description " : " Find the capital city "
} )
elif " population " in question_lower :
steps . append ( {
" step " : 1 ,
" action " : " find_population " ,
" description " : " Find the population "
} )
else :
steps . append ( {
" step " : 1 ,
" action " : " general_search " ,
" description " : " Search for relevant information "
} )
return steps
# Act
reasoning_plan = plan_reasoning_steps ( complex_question )
# Assert
assert len ( reasoning_plan ) == 2
assert reasoning_plan [ 0 ] [ " action " ] == " find_capital "
assert reasoning_plan [ 1 ] [ " action " ] == " find_population "
assert all ( " step " in step for step in reasoning_plan )
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def test_multi_iteration_react_execution ( self ) :
""" Test complete multi-iteration ReACT cycle with sequential tool invocations
This test simulates a complex query that requires :
1. Tool #1: Search for initial information
2. Tool #2: Analyze/refine based on Tool #1's output
3. Tool #3: Generate final answer using accumulated context
Each iteration includes Think - > Act - > Observe phases with
observations feeding into subsequent thinking phases .
"""
# Arrange
question = " Find the GDP of the capital of Japan and compare it to Tokyo ' s population "
# Mock tools that build on each other's outputs
tool_invocation_log = [ ]
def mock_geo_search ( query ) :
""" Tool 1: Geographic information search """
tool_invocation_log . append ( ( " geo_search " , query ) )
if " capital " in query . lower ( ) and " japan " in query . lower ( ) :
return { " city " : " Tokyo " , " country " : " Japan " , " is_capital " : True }
return { " error " : " Location not found " }
def mock_economic_data ( query , context = None ) :
""" Tool 2: Economic data retrieval (uses context from Tool 1) """
tool_invocation_log . append ( ( " economic_data " , query , context ) )
if context and context . get ( " city " ) == " Tokyo " :
return { " city " : " Tokyo " , " gdp_trillion_yen " : 115.7 , " year " : 2023 }
return { " error " : " Economic data not available " }
def mock_demographic_data ( query , context = None ) :
""" Tool 3: Demographic data and comparison (uses context from Tools 1 & 2) """
tool_invocation_log . append ( ( " demographic_data " , query , context ) )
if context and context . get ( " city " ) == " Tokyo " :
population_millions = 14.0
gdp_from_context = context . get ( " gdp_trillion_yen " , 0 )
return {
" city " : " Tokyo " ,
" population_millions " : population_millions ,
" gdp_trillion_yen " : gdp_from_context ,
" gdp_per_capita_million_yen " : round ( gdp_from_context / population_millions , 2 ) if population_millions > 0 else 0
}
return { " error " : " Demographic data not available " }
# Execute multi-iteration ReACT cycle
def execute_multi_iteration_react ( question , tools ) :
""" Execute a complete multi-iteration ReACT cycle """
iterations = [ ]
context = { }
# Iteration 1: Initial geographic search
iteration_1 = {
" iteration " : 1 ,
" think " : " I need to first identify the capital of Japan to get its GDP " ,
" act " : { " tool " : " geo_search " , " query " : " capital of Japan " } ,
" observe " : None
}
result_1 = tools [ " geo_search " ] ( iteration_1 [ " act " ] [ " query " ] )
iteration_1 [ " observe " ] = f " Found that { result_1 [ ' city ' ] } is the capital of { result_1 [ ' country ' ] } "
context . update ( result_1 )
iterations . append ( iteration_1 )
# Iteration 2: Get economic data using context from iteration 1
iteration_2 = {
" iteration " : 2 ,
" think " : f " Now I know { context [ ' city ' ] } is the capital. I need to get its GDP data " ,
" act " : { " tool " : " economic_data " , " query " : f " GDP of { context [ ' city ' ] } " } ,
" observe " : None
}
result_2 = tools [ " economic_data " ] ( iteration_2 [ " act " ] [ " query " ] , context )
iteration_2 [ " observe " ] = f " Retrieved GDP data: { result_2 [ ' gdp_trillion_yen ' ] } trillion yen for { result_2 [ ' year ' ] } "
context . update ( result_2 )
iterations . append ( iteration_2 )
# Iteration 3: Get demographic data and compare using accumulated context
iteration_3 = {
" iteration " : 3 ,
" think " : f " I have the GDP ( { context [ ' gdp_trillion_yen ' ] } trillion yen). Now I need population data to compare " ,
" act " : { " tool " : " demographic_data " , " query " : f " population of { context [ ' city ' ] } " } ,
" observe " : None
}
result_3 = tools [ " demographic_data " ] ( iteration_3 [ " act " ] [ " query " ] , context )
iteration_3 [ " observe " ] = f " Population is { result_3 [ ' population_millions ' ] } million. GDP per capita is { result_3 [ ' gdp_per_capita_million_yen ' ] } million yen "
context . update ( result_3 )
iterations . append ( iteration_3 )
# Final answer synthesis
final_answer = {
" think " : " I now have all the information needed to answer the question " ,
" answer " : f " Tokyo, the capital of Japan, has a GDP of { context [ ' gdp_trillion_yen ' ] } trillion yen and a population of { context [ ' population_millions ' ] } million people, resulting in a GDP per capita of { context [ ' gdp_per_capita_million_yen ' ] } million yen. "
}
return {
" iterations " : iterations ,
" final_answer " : final_answer ,
" context " : context ,
" tool_invocations " : len ( tool_invocation_log )
}
tools = {
" geo_search " : mock_geo_search ,
" economic_data " : mock_economic_data ,
" demographic_data " : mock_demographic_data
}
# Act
result = execute_multi_iteration_react ( question , tools )
# Assert - Verify complete multi-iteration execution
assert len ( result [ " iterations " ] ) == 3 , " Should have exactly 3 iterations "
# Verify each iteration has complete Think-Act-Observe cycle
for i , iteration in enumerate ( result [ " iterations " ] , 1 ) :
assert iteration [ " iteration " ] == i
assert " think " in iteration and len ( iteration [ " think " ] ) > 0
assert " act " in iteration and " tool " in iteration [ " act " ]
assert " observe " in iteration and iteration [ " observe " ] is not None
# Verify sequential tool invocations
assert tool_invocation_log [ 0 ] [ 0 ] == " geo_search "
assert tool_invocation_log [ 1 ] [ 0 ] == " economic_data "
assert tool_invocation_log [ 2 ] [ 0 ] == " demographic_data "
# Verify context accumulation across iterations
assert " Tokyo " in tool_invocation_log [ 1 ] [ 1 ] , " Iteration 2 should use data from iteration 1 "
assert tool_invocation_log [ 2 ] [ 2 ] . get ( " gdp_trillion_yen " ) == 115.7 , " Iteration 3 should have accumulated GDP data "
# Verify observations feed into subsequent thinking
assert " Tokyo " in result [ " iterations " ] [ 1 ] [ " think " ] , " Iteration 2 thinking should reference observation from iteration 1 "
assert " 115.7 " in result [ " iterations " ] [ 2 ] [ " think " ] , " Iteration 3 thinking should reference GDP from iteration 2 "
# Verify final answer synthesis
assert " Tokyo " in result [ " final_answer " ] [ " answer " ]
assert " 115.7 " in result [ " final_answer " ] [ " answer " ]
assert " 14.0 " in result [ " final_answer " ] [ " answer " ]
assert " 8.26 " in result [ " final_answer " ] [ " answer " ] , " Should include calculated GDP per capita "
# Verify all 3 tools were invoked in sequence
assert result [ " tool_invocations " ] == 3
def test_multi_iteration_with_dynamic_tool_selection ( self ) :
""" Test multi-iteration ReACT with mocked LLM reasoning dynamically selecting tools
This test simulates how an LLM would dynamically choose tools based on :
1. The original question
2. Previous observations
3. Accumulated context
The mocked LLM reasoning adapts its tool selection based on what it has learned
in previous iterations , mimicking real agent behavior .
"""
# Arrange
question = " What are the main exports of the largest city in Brazil by population? "
# Track reasoning and tool selection
reasoning_log = [ ]
tool_invocation_log = [ ]
def mock_llm_reasoning ( question , history , available_tools ) :
""" Mock LLM that reasons about tool selection based on context """
# Analyze what we know from history
context = { }
for step in history :
if " observation " in step :
# Extract information from observations
obs = step [ " observation " ]
if " São Paulo " in obs :
context [ " city " ] = " São Paulo "
if " largest city " in obs :
context [ " is_largest " ] = True
if " million " in obs and " population " in obs :
context [ " has_population " ] = True
if " exports " in obs :
context [ " has_exports " ] = True
# Decide next action based on what we know
if not context . get ( " city " ) :
# Step 1: Need to find the largest city
reasoning = " I need to find the largest city in Brazil by population "
tool = " geo_search "
args = { " query " : " largest city Brazil population " }
elif not context . get ( " has_population " ) :
# Step 2: Confirm population data
reasoning = f " I found { context [ ' city ' ] } . Now I need to verify it ' s the largest by checking population "
tool = " demographic_data "
args = { " query " : f " population { context [ ' city ' ] } Brazil " }
elif not context . get ( " has_exports " ) :
# Step 3: Get export information
reasoning = f " Confirmed { context [ ' city ' ] } is the largest. Now I need export information "
tool = " economic_data "
args = { " query " : f " main exports { context [ ' city ' ] } Brazil " }
else :
# Final: Have all information
reasoning = " I have all the information needed to answer "
tool = " final_answer "
args = None
reasoning_log . append ( { " reasoning " : reasoning , " tool " : tool , " context " : context . copy ( ) } )
return reasoning , tool , args
def mock_geo_search ( query ) :
""" Mock geographic search tool """
tool_invocation_log . append ( ( " geo_search " , query ) )
if " largest city brazil " in query . lower ( ) :
return {
" result " : " São Paulo is the largest city in Brazil " ,
" details " : { " city " : " São Paulo " , " country " : " Brazil " , " rank " : 1 }
}
return { " error " : " No results found " }
def mock_demographic_data ( query ) :
""" Mock demographic data tool """
tool_invocation_log . append ( ( " demographic_data " , query ) )
if " são paulo " in query . lower ( ) :
return {
" result " : " São Paulo has a population of 12.4 million in the city proper, 22.8 million in the metro area " ,
" details " : { " city_population " : 12.4 , " metro_population " : 22.8 , " unit " : " million " }
}
return { " error " : " No demographic data found " }
def mock_economic_data ( query ) :
""" Mock economic data tool """
tool_invocation_log . append ( ( " economic_data " , query ) )
if " são paulo " in query . lower ( ) and " export " in query . lower ( ) :
return {
" result " : " São Paulo ' s main exports include aircraft, vehicles, machinery, coffee, and soybeans " ,
" details " : {
" top_exports " : [ " aircraft " , " vehicles " , " machinery " , " coffee " , " soybeans " ] ,
" export_value_billions_usd " : 65.2
}
}
return { " error " : " No economic data found " }
# Execute multi-iteration ReACT with dynamic tool selection
def execute_dynamic_react ( question , tools , llm_reasoner ) :
""" Execute ReACT with dynamic LLM-based tool selection """
iterations = [ ]
history = [ ]
available_tools = list ( tools . keys ( ) )
max_iterations = 4
for i in range ( max_iterations ) :
# LLM reasons about next action
reasoning , tool_name , args = llm_reasoner ( question , history , available_tools )
if tool_name == " final_answer " :
# Agent has decided it has enough information
final_answer = {
" reasoning " : reasoning ,
" answer " : " São Paulo, Brazil ' s largest city with 12.4 million people, " +
" has main exports including aircraft, vehicles, machinery, coffee, and soybeans. "
}
break
# Execute selected tool
iteration = {
" iteration " : i + 1 ,
" think " : reasoning ,
" act " : { " tool " : tool_name , " args " : args } ,
" observe " : None
}
# Get tool result
if tool_name in tools :
result = tools [ tool_name ] ( args [ " query " ] )
iteration [ " observe " ] = result . get ( " result " , " No information found " )
else :
iteration [ " observe " ] = f " Tool { tool_name } not available "
iterations . append ( iteration )
# Add to history for next iteration
history . append ( {
" thought " : reasoning ,
" action " : tool_name ,
" args " : args ,
" observation " : iteration [ " observe " ]
} )
return {
" iterations " : iterations ,
" final_answer " : final_answer if ' final_answer ' in locals ( ) else None ,
" reasoning_log " : reasoning_log ,
" tool_invocations " : len ( tool_invocation_log )
}
tools = {
" geo_search " : mock_geo_search ,
" demographic_data " : mock_demographic_data ,
" economic_data " : mock_economic_data
}
# Act
result = execute_dynamic_react ( question , tools , mock_llm_reasoning )
# Assert - Verify dynamic multi-iteration execution
assert len ( result [ " iterations " ] ) == 3 , " Should have 3 iterations before final answer "
# Verify reasoning adapts based on observations
assert len ( reasoning_log ) == 4 , " Should have 4 reasoning steps (3 tools + final) "
# Verify first iteration searches for largest city
assert reasoning_log [ 0 ] [ " tool " ] == " geo_search "
assert " largest city " in reasoning_log [ 0 ] [ " reasoning " ] . lower ( )
assert not reasoning_log [ 0 ] [ " context " ] . get ( " city " )
# Verify second iteration uses city name from first observation
assert reasoning_log [ 1 ] [ " tool " ] == " demographic_data "
assert " São Paulo " in reasoning_log [ 1 ] [ " reasoning " ]
assert reasoning_log [ 1 ] [ " context " ] [ " city " ] == " São Paulo "
# Verify third iteration builds on previous knowledge
assert reasoning_log [ 2 ] [ " tool " ] == " economic_data "
assert " export " in reasoning_log [ 2 ] [ " reasoning " ] . lower ( )
assert reasoning_log [ 2 ] [ " context " ] [ " has_population " ] is True
# Verify final reasoning has all information
assert reasoning_log [ 3 ] [ " tool " ] == " final_answer "
assert reasoning_log [ 3 ] [ " context " ] [ " has_exports " ] is True
# Verify tool invocation sequence
assert tool_invocation_log [ 0 ] [ 0 ] == " geo_search "
assert tool_invocation_log [ 1 ] [ 0 ] == " demographic_data "
assert tool_invocation_log [ 2 ] [ 0 ] == " economic_data "
# Verify observations influence subsequent tool selection
assert " São Paulo " in result [ " iterations " ] [ 1 ] [ " act " ] [ " args " ] [ " query " ]
assert " São Paulo " in result [ " iterations " ] [ 2 ] [ " act " ] [ " args " ] [ " query " ]
# Verify final answer synthesizes all gathered information
assert result [ " final_answer " ] is not None
assert " São Paulo " in result [ " final_answer " ] [ " answer " ]
assert " 12.4 million " in result [ " final_answer " ] [ " answer " ]
assert " aircraft " in result [ " final_answer " ] [ " answer " ]
assert " vehicles " in result [ " final_answer " ] [ " answer " ]
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def test_action_name_with_quotes_handling ( self ) :
""" Test that action names with quotes are properly stripped
This test verifies the fix for when LLMs output action names wrapped
in quotes , e . g . , Action : " get_bank_balance " instead of Action : get_bank_balance
"""
# Arrange
def parse_react_output ( text ) :
""" Parse ReAct format output into structured steps """
steps = [ ]
lines = text . strip ( ) . split ( ' \n ' )
thought = None
action = None
args = None
for line in lines :
line = line . strip ( )
if line . startswith ( ' Think: ' ) or line . startswith ( ' Thought: ' ) :
thought = line . split ( ' : ' , 1 ) [ 1 ] . strip ( )
elif line . startswith ( ' Action: ' ) :
action = line [ 7 : ] . strip ( )
# Strip quotes from action name - this is the fix being tested
while action and action [ 0 ] == ' " ' :
action = action [ 1 : ]
while action and action [ - 1 ] == ' " ' :
action = action [ : - 1 ]
elif line . startswith ( ' Args: ' ) :
# Simple args parsing for test
args_text = line [ 5 : ] . strip ( )
if args_text :
import json
try :
args = json . loads ( args_text )
except :
args = { " raw " : args_text }
return {
" thought " : thought ,
" action " : action ,
" args " : args
}
# Test cases with various quote patterns
test_cases = [
# Normal case without quotes
(
' Thought: I need to check the bank balance \n Action: get_bank_balance \n Args: { " account " : " 12345 " } ' ,
" get_bank_balance "
) ,
# Single quotes around action name
(
' Thought: I need to check the bank balance \n Action: " get_bank_balance " \n Args: { " account " : " 12345 " } ' ,
" get_bank_balance "
) ,
# Multiple quotes (nested)
(
' Thought: I need to check the bank balance \n Action: " " get_bank_balance " " \n Args: { " account " : " 12345 " } ' ,
" get_bank_balance "
) ,
# Action with underscores and quotes
(
' Thought: I need to search \n Action: " search_knowledge_base " \n Args: { " query " : " test " } ' ,
" search_knowledge_base "
) ,
# Action with hyphens and quotes
(
' Thought: I need to search \n Action: " search-knowledge-base " \n Args: { " query " : " test " } ' ,
" search-knowledge-base "
) ,
# Edge case: just quotes (should result in empty string)
(
' Thought: Error case \n Action: " " \n Args: {} ' ,
" "
) ,
# Mixed quotes at start and end
(
' Thought: Processing \n Action: " " " complex_tool " " " \n Args: {} ' ,
" complex_tool "
) ,
]
# Act & Assert
for llm_output , expected_action in test_cases :
result = parse_react_output ( llm_output )
assert result [ " action " ] == expected_action , \
f " Failed to parse action correctly from: { llm_output } \n Expected: { expected_action } , Got: { result [ ' action ' ] } "
# Test with actual tool matching
tools = {
" get_bank_balance " : { " description " : " Get bank balance " } ,
" search_knowledge_base " : { " description " : " Search knowledge " } ,
" complex_tool " : { " description " : " Complex operations " }
}
# Simulate tool lookup with quoted action names
quoted_actions = [
' " get_bank_balance " ' ,
' " " search_knowledge_base " " ' ,
' complex_tool ' , # without quotes
' " complex_tool " '
]
for quoted_action in quoted_actions :
# Strip quotes as the fix does
clean_action = quoted_action
while clean_action and clean_action [ 0 ] == ' " ' :
clean_action = clean_action [ 1 : ]
while clean_action and clean_action [ - 1 ] == ' " ' :
clean_action = clean_action [ : - 1 ]
# Verify the cleaned action exists in tools (except empty string case)
if clean_action :
assert clean_action in tools , \
f " Cleaned action ' { clean_action } ' from ' { quoted_action } ' should be in tools "
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def test_mcp_tool_arguments_support ( self ) :
""" Test that MCP tools can be configured with arguments and expose them correctly
This test verifies the MCP tool arguments feature where :
1. MCP tool configurations can specify arguments
2. Configuration parsing extracts arguments correctly
3. Arguments are structured properly for tool use
"""
# Define a simple Argument class for testing (mimics the real one)
class TestArgument :
def __init__ ( self , name , type , description ) :
self . name = name
self . type = type
self . description = description
# Define a mock McpToolImpl that mimics the new functionality
class MockMcpToolImpl :
def __init__ ( self , context , mcp_tool_id , arguments = None ) :
self . context = context
self . mcp_tool_id = mcp_tool_id
self . arguments = arguments or [ ]
def get_arguments ( self ) :
return self . arguments
# Test 1: MCP tool with arguments
test_arguments = [
TestArgument (
name = " account_id " ,
type = " string " ,
description = " Bank account identifier "
) ,
TestArgument (
name = " date " ,
type = " string " ,
description = " Date for balance query (optional, format: YYYY-MM-DD) "
)
]
context_mock = lambda service_name : None
mcp_tool_with_args = MockMcpToolImpl (
context = context_mock ,
mcp_tool_id = " get_bank_balance " ,
arguments = test_arguments
)
returned_args = mcp_tool_with_args . get_arguments ( )
# Verify arguments are stored and returned correctly
assert len ( returned_args ) == 2
assert returned_args [ 0 ] . name == " account_id "
assert returned_args [ 0 ] . type == " string "
assert returned_args [ 0 ] . description == " Bank account identifier "
assert returned_args [ 1 ] . name == " date "
assert returned_args [ 1 ] . type == " string "
assert " optional " in returned_args [ 1 ] . description . lower ( )
# Test 2: MCP tool without arguments (backward compatibility)
mcp_tool_no_args = MockMcpToolImpl (
context = context_mock ,
mcp_tool_id = " simple_tool "
)
returned_args_empty = mcp_tool_no_args . get_arguments ( )
assert len ( returned_args_empty ) == 0
assert returned_args_empty == [ ]
# Test 3: MCP tool with empty arguments list
mcp_tool_empty_args = MockMcpToolImpl (
context = context_mock ,
mcp_tool_id = " another_tool " ,
arguments = [ ]
)
returned_args_explicit_empty = mcp_tool_empty_args . get_arguments ( )
assert len ( returned_args_explicit_empty ) == 0
assert returned_args_explicit_empty == [ ]
# Test 4: Configuration parsing simulation
def simulate_config_parsing ( config_data ) :
""" Simulate how service.py parses MCP tool configuration """
config_args = config_data . get ( " arguments " , [ ] )
arguments = [
TestArgument (
name = arg . get ( " name " ) ,
type = arg . get ( " type " ) ,
description = arg . get ( " description " )
)
for arg in config_args
]
return arguments
# Test configuration with arguments
config_with_args = {
" type " : " mcp-tool " ,
" name " : " get_bank_balance " ,
" description " : " Get bank account balance " ,
" mcp-tool " : " get_bank_balance " ,
" arguments " : [
{
" name " : " account_id " ,
" type " : " string " ,
" description " : " Bank account identifier "
} ,
{
" name " : " date " ,
" type " : " string " ,
" description " : " Date for balance query (optional) "
}
]
}
parsed_args = simulate_config_parsing ( config_with_args )
assert len ( parsed_args ) == 2
assert parsed_args [ 0 ] . name == " account_id "
assert parsed_args [ 1 ] . name == " date "
# Test configuration without arguments
config_without_args = {
" type " : " mcp-tool " ,
" name " : " simple_tool " ,
" description " : " Simple MCP tool " ,
" mcp-tool " : " simple_tool "
}
parsed_args_empty = simulate_config_parsing ( config_without_args )
assert len ( parsed_args_empty ) == 0
# Test 5: Argument structure validation
def validate_argument_structure ( arg ) :
""" Validate that an argument has required fields """
required_fields = [ ' name ' , ' type ' , ' description ' ]
return all ( hasattr ( arg , field ) and getattr ( arg , field ) for field in required_fields )
# Validate all parsed arguments have proper structure
for arg in parsed_args :
assert validate_argument_structure ( arg ) , f " Argument { arg . name } missing required fields "
# Test 6: Prompt template integration simulation
def simulate_prompt_template_rendering ( tools ) :
""" Simulate how agent prompts include tool arguments """
tool_descriptions = [ ]
for tool in tools :
tool_desc = f " - ** { tool . name } **: { tool . description } "
# Add argument details if present
for arg in tool . arguments :
tool_desc + = f " \n - Required: ` \" { arg . name } \" ` ( { arg . type } ): { arg . description } "
tool_descriptions . append ( tool_desc )
return " \n " . join ( tool_descriptions )
# Create mock tools with our MCP tool
class MockTool :
def __init__ ( self , name , description , arguments ) :
self . name = name
self . description = description
self . arguments = arguments
mock_tools = [
MockTool ( " search " , " Search the web " , [ ] ) , # Tool without arguments
MockTool ( " get_bank_balance " , " Get bank account balance " , parsed_args ) # MCP tool with arguments
]
prompt_section = simulate_prompt_template_rendering ( mock_tools )
# Verify the prompt includes MCP tool arguments
assert " get_bank_balance " in prompt_section
assert " account_id " in prompt_section
assert " Bank account identifier " in prompt_section
assert " date " in prompt_section
assert " (string) " in prompt_section
assert " Required: " in prompt_section
# Verify tools without arguments still work
assert " search " in prompt_section
assert " Search the web " in prompt_section
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def test_error_handling_in_react_cycle ( self ) :
""" Test error handling during ReAct execution """
# Arrange
def execute_react_step_with_errors ( step_type , content , tools = None ) :
""" Execute ReAct step with potential error handling """
try :
if step_type == " think " :
# Thinking step - validate reasoning
if not content or len ( content . strip ( ) ) < 5 :
return { " error " : " Reasoning too brief " }
return { " success " : True , " content " : content }
elif step_type == " act " :
# Action step - validate tool exists and execute
if not tools or not content :
return { " error " : " No tools available or no action specified " }
# Parse tool and parameters
if " : " in content :
tool_name , params = content . split ( " : " , 1 )
tool_name = tool_name . strip ( )
params = params . strip ( )
if tool_name not in tools :
return { " error " : f " Tool { tool_name } not available " }
# Execute tool
result = tools [ tool_name ] ( params )
return { " success " : True , " tool_result " : result }
else :
return { " error " : " Invalid action format " }
elif step_type == " observe " :
# Observation step - validate observation
if not content :
return { " error " : " No observation provided " }
return { " success " : True , " content " : content }
else :
return { " error " : f " Unknown step type: { step_type } " }
except Exception as e :
return { " error " : f " Execution error: { str ( e ) } " }
# Test cases
mock_tools = {
" calculator " : lambda x : str ( eval ( x ) ) if x . replace ( ' + ' , ' ' ) . replace ( ' - ' , ' ' ) . replace ( ' * ' , ' ' ) . replace ( ' / ' , ' ' ) . replace ( ' ' , ' ' ) . isdigit ( ) else " Error "
}
test_cases = [
( " think " , " I need to calculate " , { " success " : True } ) ,
( " think " , " " , { " error " : True } ) , # Empty reasoning
( " act " , " calculator: 2 + 2 " , { " success " : True } ) ,
( " act " , " nonexistent: something " , { " error " : True } ) , # Tool doesn't exist
( " act " , " invalid format " , { " error " : True } ) , # Invalid format
( " observe " , " The result is 4 " , { " success " : True } ) ,
( " observe " , " " , { " error " : True } ) , # Empty observation
( " invalid_step " , " content " , { " error " : True } ) # Invalid step type
]
# Act & Assert
for step_type , content , expected in test_cases :
result = execute_react_step_with_errors ( step_type , content , mock_tools )
if expected . get ( " error " ) :
assert " error " in result , f " Expected error for step { step_type } : { content } "
else :
assert " success " in result , f " Expected success for step { step_type } : { content } "
def test_response_synthesis_logic ( self ) :
""" Test synthesis of final response from ReAct steps """
# Arrange
react_steps = [
{ " type " : " think " , " content " : " I need to find the capital of France " } ,
{ " type " : " act " , " tool_name " : " knowledge_search " , " tool_result " : " Paris is the capital of France " } ,
{ " type " : " observe " , " content " : " The search confirmed Paris is the capital " } ,
{ " type " : " think " , " content " : " I have the information needed to answer " }
]
def synthesize_response ( steps , original_question ) :
""" Synthesize final response from ReAct steps """
# Extract key information from steps
tool_results = [ ]
observations = [ ]
reasoning = [ ]
for step in steps :
if step [ " type " ] == " think " :
reasoning . append ( step [ " content " ] )
elif step [ " type " ] == " act " and " tool_result " in step :
tool_results . append ( step [ " tool_result " ] )
elif step [ " type " ] == " observe " :
observations . append ( step [ " content " ] )
# Build response based on available information
if tool_results :
# Use tool results as primary information source
primary_info = tool_results [ 0 ]
# Extract specific answer from tool result
if " capital " in original_question . lower ( ) and " Paris " in primary_info :
return " The capital of France is Paris. "
elif " + " in original_question and any ( char . isdigit ( ) for char in primary_info ) :
return f " The answer is { primary_info } . "
else :
return primary_info
else :
# Fallback to reasoning if no tool results
return " I need more information to answer this question. "
# Act
response = synthesize_response ( react_steps , " What is the capital of France? " )
# Assert
assert " Paris " in response
assert " capital of france " in response . lower ( )
assert len ( response ) > 10 # Should be a complete sentence
def test_tool_parameter_extraction ( self ) :
""" Test extraction and validation of tool parameters """
# Arrange
def extract_tool_parameters ( action_content , tool_schema ) :
""" Extract and validate parameters for tool execution """
# Parse action content for tool name and parameters
if " : " not in action_content :
return { " error " : " Invalid action format - missing tool parameters " }
tool_name , params_str = action_content . split ( " : " , 1 )
tool_name = tool_name . strip ( )
params_str = params_str . strip ( )
if tool_name not in tool_schema :
return { " error " : f " Unknown tool: { tool_name } " }
schema = tool_schema [ tool_name ]
required_params = schema . get ( " required_parameters " , [ ] )
# Simple parameter extraction (for more complex tools, this would be more sophisticated)
if len ( required_params ) == 1 and required_params [ 0 ] == " query " :
# Single query parameter
return { " tool_name " : tool_name , " parameters " : { " query " : params_str } }
elif len ( required_params ) == 1 and required_params [ 0 ] == " expression " :
# Single expression parameter
return { " tool_name " : tool_name , " parameters " : { " expression " : params_str } }
else :
# Multiple parameters would need more complex parsing
return { " tool_name " : tool_name , " parameters " : { " input " : params_str } }
tool_schema = {
" knowledge_search " : { " required_parameters " : [ " query " ] } ,
" calculator " : { " required_parameters " : [ " expression " ] } ,
" graph_rag " : { " required_parameters " : [ " query " ] }
}
test_cases = [
( " knowledge_search: capital of France " , " knowledge_search " , { " query " : " capital of France " } ) ,
( " calculator: 2 + 2 " , " calculator " , { " expression " : " 2 + 2 " } ) ,
( " invalid format " , None , None ) , # No colon
( " unknown_tool: something " , None , None ) # Unknown tool
]
# Act & Assert
for action_content , expected_tool , expected_params in test_cases :
result = extract_tool_parameters ( action_content , tool_schema )
if expected_tool is None :
assert " error " in result
else :
assert result [ " tool_name " ] == expected_tool
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assert result [ " parameters " ] == expected_params
