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import json
from rich import print as rich_print
from rich.panel import Panel
from rich.console import Console
from rich.pretty import Pretty
from rich.markdown import Markdown
from rich.json import JSON
from typing import TypedDict, Sequence, Annotated
from langchain_core.messages import BaseMessage, HumanMessage, AIMessage, SystemMessage
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.agents import create_tool_calling_agent, AgentExecutor
from openai import RateLimitError
import time
def print_conversation(messages):
console = Console(width=200, soft_wrap=True)
for msg in messages:
role = msg.get("role", "unknown").capitalize()
content = msg.get("content", "")
try:
if isinstance(content, str):
content = json.loads(content)
elif isinstance(content, dict) and 'output' in content.keys():
if isinstance(content['output'], HumanMessage):
content['output'] = content['output'].content
elif isinstance(content, HumanMessage):
content = content.content
rendered_content = JSON.from_data(content)
except (json.JSONDecodeError, TypeError):
try:
rendered_content = Markdown(content.strip())
except AttributeError:
# from gemini
try:
rendered_content = {
'query': content.get('query', 'QueryKeyNotFound').content[0]['text'],
'output': content.get('output', 'OutputKeyNotFound'),
}
rendered_content = JSON.from_data(rendered_content)
except Exception as e:
print(f"Failed to render content for role: {role}. Content: {content}")
print("Error:", e)
border_style_color = "red"
if "Assistant" in role:
border_style_color = "magenta"
elif "User" in role:
border_style_color = "green"
elif "System" in role:
border_style_color = "blue"
elif "Tool" in role:
border_style_color = "yellow"
elif "Token" in role:
border_style_color = "white"
panel = Panel(
rendered_content,
title=f"[bold blue]{role}[/]",
border_style=border_style_color,
expand=True
)
console.print(panel)
def generate_final_answer(qa: dict[str, str]) -> str:
"""Invokes gpt-4o-mini to extract generate a final answer based on the content query, response, and metadata"""
final_answer_llm = ChatOpenAI(model="gpt-4o-mini", temperature=0, max_retries=5)
system_prompt = (
"You will be given a JSON object containing a user's query, a response from an AI assistant, and optional metadata. "
"Your task is to extract and return a final answer to the query as a plain string, strictly suitable for exact match evaluation. "
"Do NOT answer the query yourself. Use the response as the source of truth. "
"Use the query only as context to interpret the response and extract a final, normalized answer. "
"Your output must be:\n"
"- A **single plain string** with **no prefixes, labels, or explanations**.\n"
"- Suitable for exact string comparison.\n"
"- Clean and deterministic: no variation in formatting, casing, or punctuation."
"Special rules:\n"
"- If the response shows inability to process attached media (images, audio, video), return: **'File not found'**.\n"
"- If the response is a list of search results aggregate the information before constructing an answer"
"- If the query is quantitative (How many...?), **aggregate the results of the tool(s) call(s) and return the numeric answer** only.\n"
"- If the query is unanswerable from the response, return: **'No answer found: <brief reason>'**."
"Examples:\n"
"- Query: 'What’s in the attached image?'\n"
" Response: 'I'm unable to view images directly...'\n"
" Output: 'File not found'\n\n"
"- Query: 'What’s the total population of X'\n"
" Response: '{title: demographics of X, content: 1. City A: 2M, 2. City B: 3M, title: history of X, content: currently there are Y number of inhabitants in X...'\n"
" Output: '5000000'\n"
"Strictly follow these rules. Some final answers will require more analysis if the provided response. "
"You can reason to get to the answer but always consider the response as the base_knowledge (keep coherence)."
"Return only the final string answer. Do not include any other content."
)
system_message = SystemMessage(content=system_prompt)
if isinstance(qa['response']['query'], HumanMessage):
qa['response'] = qa['response']['output']
messages = [
system_message,
HumanMessage(content=f'Generate the final answer for the following query:\n\n{json.dumps(qa)}')
]
response = final_answer_llm.invoke(messages)
return response.content
class ToolAgent:
"""Basic custom class from an agent prompted for tool-use pattern"""
def __init__(self, tools: list, model='gpt-4o', backstory:str="", streaming=False):
self.name = "GAIA Tool-Use Agent"
self.tools = tools
self.llm = ChatOpenAI(model=model, temperature=0, streaming=streaming, max_retries=5)
self.executor = None
self.backstory = backstory if backstory else "You are a helpful assistant that can use tools to answer questions. Your name is Gaia."
def initialize(self, custom_tools_nm="tools"):
"""Binds tools, creates and compiles graph"""
chatgpt_with_tools = self.llm.bind_tools(self.tools)
prompt_template = ChatPromptTemplate.from_messages(
[
("system", self.backstory),
MessagesPlaceholder(variable_name="history", optional=True),
("human", "{query}"),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
)
agent = create_tool_calling_agent(self.llm, self.tools, prompt_template)
self.executor = AgentExecutor(
agent=agent,
tools=self.tools,
early_stopping_method='force',
max_iterations=10
)
def chat(self, query:str, metadata):
"""Perform a single step in the conversation with the tool agent executor."""
if metadata is None:
metadata = {}
with_attachments = False
query_message = HumanMessage(content=query)
if "image_path" in metadata:
# Create a HumanMessage with image content
query_message = HumanMessage(
content=[
{"type": "text", "text": query},
{"type": "text", "text": f"image_path: {metadata['image_path']}"},
]
)
with_attachments = True
if "file_path" in metadata:
# Create a HumanMessage with image content
query_message = HumanMessage(
content=[
{"type": "text", "text": query},
{"type": "text", "text": f"file_path: {metadata['file_path']}"},
]
)
with_attachments = True
user_message = {'role': 'user', 'content': query if not with_attachments else query_message}
print_conversation([user_message])
response = self.executor.invoke({
"query": query if not with_attachments else query_message,
})
response_message = {'role': 'assistant', 'content': response}
print_conversation([response_message])
final_answer = generate_final_answer({
'query': query,
'response': response,
})
final_answer_message = {'role': 'Final Answer', 'content': final_answer}
print_conversation([final_answer_message])
return final_answer
def invoke(self, q_data):
"""Invoke the executor input data"""
query = q_data.get("query", "")
metadata = q_data.get("metadata", None)
try:
response = self.chat(query, metadata)
time.sleep(3)
except RateLimitError:
response = 'Rate limit error encountered. Retrying after a short pause...'
error_message = {'role': 'Rate-limit-hit', 'content': response}
print_conversation([error_message])
time.sleep(5)
try:
response = self.chat(query, metadata)
except RateLimitError:
response = 'Rate limit error encountered again. Skipping this query.'
error_message = {'role': 'Rate-limit-hit', 'content': response}
print_conversation([error_message])
print()
return response
def __call__(self, q_data):
"""Call the invoke method from the agent executor."""
return self.invoke(q_data) |