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from llama_index.core.agent.workflow import FunctionAgent
from llama_index.core.tools import FunctionTool
from llama_index.core import VectorStoreIndex, Document
from llama_index.core.node_parser import SentenceWindowNodeParser, HierarchicalNodeParser
from llama_index.core.postprocessor import SentenceTransformerRerank
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.readers.file import PDFReader, DocxReader, CSVReader, ImageReader
import os
from typing import List, Dict, Any
from llama_index.tools.arxiv import ArxivToolSpec
from llama_index.tools.duckduckgo import DuckDuckGoSearchToolSpec
import re
from llama_index.core.agent.workflow import ReActAgent
import wandb
from llama_index.callbacks.wandb import WandbCallbackHandler
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.callbacks.llama_debug import LlamaDebugHandler
from llama_index.core import Settings
from transformers import AutoModelForCausalLM, AutoTokenizer
from llama_index.llms.huggingface import HuggingFaceLLM
import requests
import logging
from llama_index.core.workflow import Context
from llama_index.core.agent.workflow import AgentStream
from llama_index.readers.web import TrafilaturaWebReader
from llama_index.readers.youtube_transcript import YoutubeTranscriptReader
wandb_callback = WandbCallbackHandler(run_args={"project": "gaia-llamaindex-agents"})
llama_debug = LlamaDebugHandler(print_trace_on_end=True)
# Comprehensive callback manager
callback_manager = CallbackManager([
wandb_callback, # For W&B tracking
llama_debug # For general debugging
])
logging.basicConfig(level=logging.INFO)
logging.getLogger("llama_index.core.agent").setLevel(logging.DEBUG)
logging.getLogger("llama_index.llms").setLevel(logging.DEBUG)
model_id = "Qwen/Qwen2.5-7B-Instruct"
proj_llm = HuggingFaceLLM(
model_name=model_id,
tokenizer_name=model_id,
device_map="auto", # will use GPU if available
model_kwargs={"torch_dtype": "auto"},
generate_kwargs={"temperature": 0.1, "top_p": 0.3} # More focused
)
embed_model = HuggingFaceEmbedding("BAAI/bge-small-en-v1.5")
wandb.init(project="gaia-llamaindex-agents") # Choisis ton nom de projet
wandb_callback = WandbCallbackHandler(run_args={"project": "gaia-llamaindex-agents"})
llama_debug = LlamaDebugHandler(print_trace_on_end=True)
callback_manager = CallbackManager([wandb_callback, llama_debug])
Settings.llm = proj_llm
Settings.embed_model = embed_model
Settings.callback_manager = callback_manager
class EnhancedRAGQueryEngine:
def __init__(self, task_context: str = ""):
self.task_context = task_context
self.embed_model = embed_model
self.reranker = SentenceTransformerRerank(model="cross-encoder/ms-marco-MiniLM-L-2-v2", top_n=5)
self.readers = {
'.pdf': PDFReader(),
'.docx': DocxReader(),
'.doc': DocxReader(),
'.csv': CSVReader(),
'.txt': lambda file_path: [Document(text=open(file_path, 'r', encoding='utf-8').read())],
'.jpg': ImageReader(),
'.jpeg': ImageReader(),
'.png': ImageReader(),
'web': TrafilaturaWebReader(),
'youtube': YoutubeTranscriptReader()
}
self.sentence_window_parser = SentenceWindowNodeParser.from_defaults(
window_size=3,
window_metadata_key="window",
original_text_metadata_key="original_text"
)
self.hierarchical_parser = HierarchicalNodeParser.from_defaults(
chunk_sizes=[2048, 512, 128]
)
def load_and_process_documents(self, file_paths: List[str]) -> List[Document]:
documents = []
for file_path in file_paths:
file_ext = os.path.splitext(file_path)[1].lower()
try:
if file_ext in self.readers:
reader = self.readers[file_ext]
if callable(reader):
docs = reader(file_path)
else:
docs = reader.load_data(file=file_path)
# Ensure docs is a list
if not isinstance(docs, list):
docs = [docs]
# Add metadata to all documents
for doc in docs:
if hasattr(doc, 'metadata'):
doc.metadata.update({
"file_path": file_path,
"file_type": file_ext[1:],
"task_context": self.task_context
})
documents.extend(docs)
except Exception as e:
# Fallback to text reading
try:
with open(file_path, 'r', encoding='utf-8') as f:
content = f.read()
documents.append(Document(
text=content,
metadata={"file_path": file_path, "file_type": "text", "error": str(e)}
))
except Exception as fallback_error:
print(f"Failed to process {file_path}: {e}, Fallback error: {fallback_error}")
return documents
def create_advanced_index(self, documents: List[Document], use_hierarchical: bool = False) -> VectorStoreIndex:
if use_hierarchical or len(documents) > 10:
nodes = self.hierarchical_parser.get_nodes_from_documents(documents)
else:
nodes = self.sentence_window_parser.get_nodes_from_documents(documents)
index = VectorStoreIndex(
nodes,
embed_model=self.embed_model
)
return index
def create_context_aware_query_engine(self, index: VectorStoreIndex):
retriever = VectorIndexRetriever(
index=index,
similarity_top_k=10
)
query_engine = RetrieverQueryEngine(
retriever=retriever,
node_postprocessors=[self.reranker],
llm=proj_llm
)
return query_engine
class HybridWebRAGTool:
def __init__(self, rag_engine: EnhancedRAGQueryEngine):
self.duckduckgo_tool = DuckDuckGoSearchToolSpec().to_tool_list()[0]
self.rag_engine = rag_engine
def is_youtube_url(self, url: str) -> bool:
"""Check if URL is a YouTube video"""
return 'youtube.com/watch' in url or 'youtu.be/' in url
def search_and_analyze(self, query: str, max_results: int = 3) -> str:
"""Search web and analyze content with RAG, including YouTube support"""
try:
# Step 1: Get URLs from DuckDuckGo
search_results = self.duckduckgo_tool.call(query=query, max_results=max_results)
if isinstance(search_results, list):
urls = [r.get('href', '') for r in search_results if r.get('href')]
else:
return f"Search failed: {search_results}"
if not urls:
return "No URLs found in search results"
# Step 2: Process URLs based on type
web_documents = []
youtube_urls = []
regular_urls = []
# Separate YouTube URLs from regular web URLs
for url in urls:
if self.is_youtube_url(url):
youtube_urls.append(url)
else:
regular_urls.append(url)
# Process YouTube videos
if youtube_urls:
try:
youtube_docs = self.rag_engine.readers['youtube'].load_data(youtube_urls)
if isinstance(youtube_docs, list):
web_documents.extend(youtube_docs)
else:
web_documents.append(youtube_docs)
except Exception as e:
print(f"Failed to load YouTube videos: {e}")
# Process regular web pages
for url in regular_urls:
try:
docs = self.rag_engine.readers['web'].load_data([url])
if isinstance(docs, list):
web_documents.extend(docs)
else:
web_documents.append(docs)
except Exception as e:
print(f"Failed to load {url}: {e}")
continue
if not web_documents:
return "No content could be extracted from URLs"
# Step 3: Create temporary index and query
temp_index = self.rag_engine.create_advanced_index(web_documents)
# Step 4: Query the indexed content
query_engine = self.rag_engine.create_context_aware_query_engine(temp_index)
response = query_engine.query(query)
# Add source information
source_info = []
if youtube_urls:
source_info.append(f"YouTube videos: {len(youtube_urls)}")
if regular_urls:
source_info.append(f"Web pages: {len(regular_urls)}")
return f"{str(response)}\n\nSources analyzed: {', '.join(source_info)}"
except Exception as e:
return f"Error in hybrid search: {str(e)}"
# Create the research tool function
def research_tool_function(query: str) -> str:
"""Combines DuckDuckGo search with RAG analysis of web content and YouTube videos"""
try:
rag_engine = EnhancedRAGQueryEngine()
hybrid_tool = HybridWebRAGTool(rag_engine)
return hybrid_tool.search_and_analyze(query)
except Exception as e:
return f"Research tool error: {str(e)}"
# Create the research tool for your agent
research_tool = FunctionTool.from_defaults(
fn=research_tool_function,
name="research_tool",
description="""Advanced research tool that combines web search with RAG analysis, supporting both web pages and YouTube videos, with context-aware processing.
**When to Use:**
- Questions requiring external knowledge beyond training data
- Current or recent information (post-training cutoff)
- Scientific research requiring academic sources
- Factual verification of specific claims
- Any question where search results could provide the exact answer
- Research involving video content and tutorials
- Complex queries needing synthesis of multiple sources
**Advantages:**
- Full content analysis from both web and video sources
- Automatic content type detection and processing
- Semantic search within retrieved content
- Reranking for relevance across all source types
- Comprehensive synthesis of multimedia information"""
)
def comprehensive_rag_analysis(file_paths: List[str], query: str, task_context: str = "") -> str:
try:
rag_engine = EnhancedRAGQueryEngine(task_context)
documents = rag_engine.load_and_process_documents(file_paths)
if not documents:
return "No documents could be processed successfully."
total_text_length = sum(len(doc.text) for doc in documents)
use_hierarchical = total_text_length > 50000 or len(documents) > 5
index = rag_engine.create_advanced_index(documents, use_hierarchical)
query_engine = rag_engine.create_context_aware_query_engine(index)
enhanced_query = f"""
Task Context: {task_context}
Original Query: {query}
Please analyze the provided documents and answer the query with precise, factual information.
"""
response = query_engine.query(enhanced_query)
result = f"**RAG Analysis Results:**\n\n"
result += f"**Documents Processed:** {len(documents)}\n"
result += f"**Answer:**\n{response.response}\n\n"
return result
except Exception as e:
return f"RAG analysis failed: {str(e)}"
def cross_document_analysis(file_paths: List[str], query: str, task_context: str = "") -> str:
try:
rag_engine = EnhancedRAGQueryEngine(task_context)
all_documents = []
document_groups = {}
for file_path in file_paths:
docs = rag_engine.load_and_process_documents([file_path])
doc_key = os.path.basename(file_path)
document_groups[doc_key] = docs
for doc in docs:
doc.metadata.update({
"document_group": doc_key,
"total_documents": len(file_paths)
})
all_documents.extend(docs)
index = rag_engine.create_advanced_index(all_documents, use_hierarchical=True)
query_engine = rag_engine.create_context_aware_query_engine(index)
response = query_engine.query(f"Task: {task_context}\nQuery: {query}")
result = f"**Cross-Document Analysis:**\n"
result += f"**Documents:** {list(document_groups.keys())}\n"
result += f"**Answer:**\n{response.response}\n"
return result
except Exception as e:
return f"Cross-document analysis failed: {str(e)}"
# Create tools
enhanced_rag_tool = FunctionTool.from_defaults(
fn=comprehensive_rag_analysis,
name="Enhanced RAG Analysis",
description="Comprehensive document analysis using advanced RAG with context-aware processing"
)
cross_document_tool = FunctionTool.from_defaults(
fn=cross_document_analysis,
name="Cross-Document Analysis",
description="Advanced analysis across multiple documents with cross-referencing capabilities"
)
# Analysis Agent
analysis_agent = FunctionAgent(
name="AnalysisAgent",
description="Advanced multimodal analysis using enhanced RAG and cross-document capabilities",
system_prompt="""
You are an advanced analysis specialist with access to:
- Enhanced RAG with hybrid search and reranking
- Multi-format document processing (PDF, Word, CSV, images, text)
- Cross-document analysis and synthesis
- Context-aware query processing
Your capabilities:
1. Process multiple file types simultaneously
2. Perform semantic search across document collections
3. Cross-reference information between documents
4. Extract precise information with source attribution
5. Handle both text and visual content analysis
Always consider the task context and provide precise, well-sourced answers.
""",
llm=proj_llm,
tools=[enhanced_rag_tool, cross_document_tool],
max_steps=5,
verbose = True
)
class IntelligentSourceRouter:
def __init__(self):
self.arxiv_tool = ArxivToolSpec().to_tool_list()[0]
self.duckduckgo_tool = DuckDuckGoSearchToolSpec().to_tool_list()[0]
def route_and_search(self, query: str) -> str:
"""Simple routing between academic and general search - returns URLs only"""
# Quick intent detection
intent_prompt = f"""
Is this question about scientific research or general information?
Question: "{query}"
Answer "arxiv" for scientific/academic topics, "web" for everything else.
"""
response = proj_llm.complete(intent_prompt)
source = "arxiv" if "arxiv" in response.text.lower() else "web"
try:
if source == "arxiv":
# ArXiv results typically contain URLs in the response text
arxiv_result = self.arxiv_tool.call(query=query)
# Extract URLs from ArXiv response (you may need to parse this based on actual format)
return str(arxiv_result) # ArXiv tool should return URLs
else:
result = self.duckduckgo_tool.call(query=query)
if isinstance(result, list):
# Extract only URLs from search results
urls = [r.get('href', '') for r in result if r.get('href')]
return "\n".join(urls)
return str(result)
except Exception as e:
return f"Search failed: {str(e)}"
# Simple research function
def research_tool_function(query: str) -> str:
"""Returns URLs for queries using intelligent source routing"""
router = IntelligentSourceRouter()
return router.route_and_search(query)
# Clean tool definition
research_tool = FunctionTool.from_defaults(
fn=research_tool_function,
name="research_tool",
description="""Intelligent URL finder that routes between academic (ArXiv) and general (web) search sources to return relevant URLs.
**When to Use:**
- Questions requiring external knowledge beyond training data
- Current or recent information (post-training cutoff)
- Scientific research requiring academic sources
- Factual verification of specific claims
- Any question where you need URLs to relevant sources
Simply provide your question and get URLs to visit for further reading."""
)
def execute_python_code(code: str) -> str:
try:
safe_globals = {
"__builtins__": {
"len": len, "str": str, "int": int, "float": float,
"list": list, "dict": dict, "sum": sum, "max": max, "min": min,
"round": round, "abs": abs, "sorted": sorted, "enumerate": enumerate,
"range": range, "zip": zip, "map": map, "filter": filter,
"any": any, "all": all, "type": type, "isinstance": isinstance,
"print": print, "open": open, "bool": bool, "set": set, "tuple": tuple
},
# Core Python modules
"math": __import__("math"),
"datetime": __import__("datetime"),
"re": __import__("re"),
"os": __import__("os"),
"sys": __import__("sys"),
"json": __import__("json"),
"csv": __import__("csv"),
"random": __import__("random"),
"itertools": __import__("itertools"),
"collections": __import__("collections"),
"functools": __import__("functools"),
# Data Science and Numerical Computing
"numpy": __import__("numpy"),
"np": __import__("numpy"),
"pandas": __import__("pandas"),
"pd": __import__("pandas"),
"scipy": __import__("scipy"),
# Visualization
"matplotlib": __import__("matplotlib"),
"plt": __import__("matplotlib.pyplot"),
"seaborn": __import__("seaborn"),
"sns": __import__("seaborn"),
"plotly": __import__("plotly"),
# Machine Learning
"sklearn": __import__("sklearn"),
"xgboost": __import__("xgboost"),
"lightgbm": __import__("lightgbm"),
# Statistics
"statistics": __import__("statistics"),
"statsmodels": __import__("statsmodels"),
# Image Processing
"PIL": __import__("PIL"),
"cv2": __import__("cv2"),
"skimage": __import__("skimage"),
# Network and Web
"requests": __import__("requests"),
"urllib": __import__("urllib"),
# Text Processing
"nltk": __import__("nltk"),
"spacy": __import__("spacy"),
# Time Series
"pytz": __import__("pytz"),
# Utilities
"tqdm": __import__("tqdm"),
"pickle": __import__("pickle"),
"gzip": __import__("gzip"),
"base64": __import__("base64"),
"hashlib": __import__("hashlib"),
"uuid": __import__("uuid"),
# Scientific Computing
"sympy": __import__("sympy"),
"networkx": __import__("networkx"),
# Database
"sqlite3": __import__("sqlite3"),
# Parallel Processing
"multiprocessing": __import__("multiprocessing"),
"threading": __import__("threading"),
"concurrent": __import__("concurrent"),
}
exec_locals = {}
exec(code, safe_globals, exec_locals)
if 'result' in exec_locals:
return str(exec_locals['result'])
else:
return "Code executed successfully"
except Exception as e:
return f"Code execution failed: {str(e)}"
code_execution_tool = FunctionTool.from_defaults(
fn=execute_python_code,
name="Python Code Execution",
description="Execute Python code safely for calculations and data processing"
)
# Code Agent as ReActAgent with explicit code generation
code_agent = ReActAgent(
name="CodeAgent",
description="Advanced calculations, data processing using code generation and execution",
system_prompt="""
You are a coding specialist. For EVERY computational task:
1. THINK: Analyze what calculation/processing is needed
2. GENERATE CODE: Write Python code to solve the problem
3. EXECUTE: Use the Python Code Execution tool to run your code
4. OBSERVE: Check the results
5. REPEAT if needed
ALWAYS write code for:
- Mathematical calculations
- Data processing
- Numerical analysis
- Text processing
- Any computational task
Example workflow:
Question: "What is 15 * 23 + 7?"
Thought: I need to calculate 15 * 23 + 7
Action: Python Code Execution
Action Input: {"code": "result = 15 * 23 + 7\nprint(f'The answer is: {result}')"}
Store your final answer in a variable called 'result'.
""",
llm=proj_llm,
tools=[code_execution_tool],
max_steps=5,
verbose=True,
callback_manager=callback_manager,
)
def analysis_function(query: str, files=None):
ctx = Context(analysis_agent)
return analysis_agent.run(query, ctx=ctx)
def code_function(query: str):
ctx = Context(code_agent)
return code_agent.run(query, ctx=ctx)
analysis_tool = FunctionTool.from_defaults(
fn=analysis_function,
name="AnalysisAgent",
description="""Advanced multimodal document analysis specialist. Use this tool at least when you need to:
**Document Processing:**
- Analyze PDF, Word, CSV, or image files provided with the question
- Extract specific information from tables, charts, or structured documents
- Cross-reference information across multiple documents
- Perform semantic search within document collections
**Content Analysis:**
- Summarize long documents or extract key facts
- Find specific data points, numbers, or text within files
- Analyze visual content in images (charts, graphs, diagrams)
- Compare information between different document sources
**When to use:** Questions involving file attachments, document analysis, data extraction from PDFs/images, or when you need to process structured/unstructured content.
**Input format:** Provide the query and mention any relevant files or context."""
)
code_tool = FunctionTool.from_defaults(
fn=code_function,
name="CodeAgent",
description="""Advanced computational specialist using ReAct reasoning. Use this tool at least when you need:
**Core Capabilities:**
- **Autonomous Code Generation**: Writes Python code from scratch to solve computational problems
- **Multi-step Problem Solving**: Breaks complex tasks into manageable coding steps
- **Self-debugging**: Identifies and fixes errors through iterative refinement
- **Library Integration**: Leverages numpy, pandas, matplotlib, scipy, sklearn, and other scientific libraries
- **Result Verification**: Validates outputs and adjusts approach as needed
**When to Use:**
- Mathematical calculations requiring step-by-step computation
- Data analysis and statistical processing
- Algorithm implementation, optimization and execution
- Numerical simulations and modeling
- Text processing and pattern analysis
- Complex logical operations requiring code verification
**Unique Advantage**: Unlike simple calculation tools, this agent can autonomously write, execute, debug, and refine code until achieving the correct solution, making it ideal for complex computational tasks that require adaptive problem-solving.
**Input Format**: Describe the computational task clearly, including any data, constraints, or specific requirements."""
)
class EnhancedGAIAAgent:
def __init__(self):
print("Initializing Enhanced GAIA Agent...")
# Vérification du token HuggingFace
hf_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
if not hf_token:
raise ValueError("HUGGINGFACEHUB_API_TOKEN environment variable is required")
# Agent coordinateur principal qui utilise les agents spécialisés comme tools
self.coordinator = ReActAgent(
name="GAIACoordinator",
description="Main GAIA coordinator that uses specialized capabilities as intelligent tools",
system_prompt="""
You are the main GAIA coordinator using ReAct reasoning methodology.
You have access to THREE specialist tools:
**1. analysis_tool** - Advanced multimodal document analysis specialist
- Use for: PDF, Word, CSV, image file analysis
- When to use: Questions with file attachments, document analysis, data extraction
**2. research_tool** - Intelligent research specialist with automatic routing
- Use for: External knowledge, current events, scientific papers
- When to use: Questions requiring external knowledge, factual verification, current information
**3. code_tool** - Advanced computational specialist using ReAct reasoning
- Use for: Mathematical calculations, data processing, logical operations
- Capabilities: Generates and executes Python, handles complex computations, step-by-step problem solving
- When to use: Precise calculations, data manipulation, mathematical problem solving
**4. code_execution_tool** - Use only to execute .py file
CRITICAL: Your final answer must be EXACT and CONCISE as required by GAIA format : NO explanations, NO additional text, ONLY the precise answer
""",
llm=proj_llm,
tools=[analysis_tool, research_tool, code_tool, code_execution_tool],
max_steps=10,
verbose = True,
callback_manager=callback_manager,
)
async def format_gaia_answer(self, raw_response: str, original_question: str) -> str:
"""
Post-process the agent response to extract the exact GAIA format answer
"""
format_prompt = f"""Extract the exact answer from the response below. Follow GAIA formatting rules strictly.
Examples:
Question: "How many research papers were published by the university between 2010 and 2020?"
Response: "Based on my analysis of the data, I found that the university published 156 research papers between 2010 and 2020."
Answer: 156
Question: "What is the last name of the software engineer mentioned in the report?"
Response: "After reviewing the document, the software engineer mentioned is Dr. Martinez who developed the system."
Answer: Martinez
Question: "List the programming languages from this job description, alphabetized:"
Response: "The job description mentions several programming languages including Python, Java, C++, and JavaScript. When alphabetized, these are: C++, Java, JavaScript, Python"
Answer: C++, Java, JavaScript, Python
Question: "Give only the first name of the developer who created the framework."
Response: "The framework was created by Sarah Johnson, a senior developer at the company."
Answer: Sarah
Question: "Give the ISO country code as your answer."
Response: "The country in question is France, which has the ISO code FRA."
Answer: FRA
Question: "Provide your response in standard notation."
Response: "The calculated value is 314 million, which in standard notation is 3.14e+8"
Answer: 3.14e+8
Now extract the exact answer:
Question: {original_question}
Response: {raw_response}
Answer:"""
try:
formatting_response = proj_llm.complete(format_prompt)
answer = str(formatting_response).strip()
# Extract just the answer after "Answer:"
if "Answer:" in answer:
answer = answer.split("Answer:")[-1].strip()
return answer
except Exception as e:
print(f"Error in formatting: {e}")
return self._extract_fallback_answer(raw_response)
def download_gaia_file(self, task_id: str, api_url: str = "https://agents-course-unit4-scoring.hf.space") -> str:
"""Download file associated with task_id"""
try:
response = requests.get(f"{api_url}/files/{task_id}", timeout=30)
response.raise_for_status()
# Save file locally
filename = f"task_{task_id}_file"
with open(filename, 'wb') as f:
f.write(response.content)
return filename
except Exception as e:
print(f"Failed to download file for task {task_id}: {e}")
return None
async def solve_gaia_question(self, question_data: Dict[str, Any]) -> str:
question = question_data.get("Question", "")
task_id = question_data.get("task_id", "")
# Try to download file
try:
file_path = self.download_gaia_file(task_id)
except Exception as e:
print(f"Failed to download file for task {task_id}: {e}")
file_path = None
context_prompt = f"""
GAIA Task ID: {task_id}
Question: {question}
{'File downloaded: ' + file_path if file_path else 'No additional files referenced'}
Additionnal instructions to system prompt :
1. If a file is available, use the analysis_tool (except for .py files).
2. If a link is in the question, use the research_tool.
"""
try:
ctx = Context(self.coordinator)
# Use streaming to see step-by-step reasoning
print("=== AGENT REASONING STEPS ===")
handler = self.coordinator.run(ctx=ctx, user_msg=context_prompt)
full_response = ""
async for event in handler.stream_events():
if isinstance(event, AgentStream):
print(event.delta, end="", flush=True)
full_response += event.delta
# Get the final response
raw_response = await handler
print("\n=== END REASONING ===")
# Post-process to extract exact GAIA format
formatted_answer = await self.format_gaia_answer(str(raw_response), question)
print(f"Formatted answer: {formatted_answer}")
return formatted_answer
except Exception as e:
error_msg = f"Error processing question: {str(e)}"
print(error_msg)
return error_msg |