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from fireworks import LLM
from pydantic import BaseModel
import asyncio
import json
import time
from typing import Dict, Any, List
from gradio import ChatMessage
MODELS = {
"small": "accounts/fireworks/models/qwen3-235b-a22b-instruct-2507",
"large": "accounts/fireworks/models/kimi-k2-instruct"
}
TODAY = time.strftime("%Y-%m-%d")
semaphore = asyncio.Semaphore(10)
def get_llm(model: str, api_key: str) -> LLM:
return LLM(model=MODELS[model], api_key=api_key, deployment_type="serverless")
async def get_llm_completion(llm: LLM, prompt_text: str, output_class: BaseModel = None) -> str:
if output_class:
return llm.chat.completions.create(
messages=[
{
"role": "user",
"content": prompt_text
},
],
temperature=0.1,
response_format={
"type": "json_object",
"schema": output_class.model_json_schema(),
},
)
return llm.chat.completions.create(
messages=[
{
"role": "user",
"content": prompt_text
},
],
temperature=0.1
)
async def get_streaming_completion(llm: LLM, prompt_text: str, system_prompt: str = None):
"""
Get streaming completion from LLM for real-time responses
:param llm: The LLM instance
:param prompt_text: The user's input message
:param system_prompt: Optional system prompt for context
:return: Generator yielding response chunks
"""
messages = []
if system_prompt:
messages.append({
"role": "system",
"content": system_prompt
})
messages.append({
"role": "user",
"content": prompt_text
})
try:
response = llm.chat.completions.create(
messages=messages,
temperature=0.2,
stream=True,
max_tokens=1000
)
for chunk in response:
if chunk.choices[0].delta.content:
yield chunk.choices[0].delta.content
except Exception as e:
yield f"Error generating response: {str(e)}"
async def run_multi_llm_completions(llm: LLM, prompts: list[str], output_class: BaseModel) -> list[str]:
"""
Run multiple LLM completions in parallel
:param llm:
:param prompts:
:param output_class:
:return:
"""
async with semaphore:
if output_class:
print(f"Running LLM with structured outputs")
tasks = [
asyncio.create_task(
get_llm_completion(llm=llm, prompt_text=prompt, output_class=output_class)
) for prompt in prompts
]
else:
print(f"Running LLM with non-structured outputs")
tasks = [
asyncio.create_task(
get_llm_completion(llm=llm, prompt_text=prompt)
) for prompt in prompts
]
return await asyncio.gather(*tasks)
def get_orchestrator_decision(user_query: str, api_key: str, prompt_library: Dict[str, str]) -> Dict[str, Any]:
"""Use orchestrator LLM to decide which tools to use"""
try:
orchestrator_prompt = prompt_library.get('fed_orchestrator', '')
formatted_prompt = orchestrator_prompt.format(user_query=user_query, date=TODAY)
llm = get_llm("large", api_key)
response = llm.chat.completions.create(
messages=[
{"role": "system", "content": "You are a tool orchestrator. Always respond with valid JSON."},
{"role": "user", "content": formatted_prompt}
],
temperature=0.1,
max_tokens=500
)
# Parse JSON response
result = json.loads(response.choices[0].message.content)
return {"success": True, "decision": result}
except Exception as e:
print(f"Error in orchestrator: {e}")
# Fallback to simple logic
return {
"success": False,
"decision": {
"tools_needed": [{"function": "get_latest_meeting", "parameters": {}, "reasoning": "Fallback to latest meeting"}],
"query_analysis": f"Error occurred, using fallback for: {user_query}"
}
}
def execute_fed_tools(tools_decision: Dict[str, Any], fed_tools: Dict[str, callable]) -> List[Dict[str, Any]]:
"""Execute the tools determined by the orchestrator"""
results = []
for tool in tools_decision.get("tools_needed", []):
function_name = tool.get("function", "")
parameters = tool.get("parameters", {})
reasoning = tool.get("reasoning", "")
start_time = time.time()
try:
# Execute the appropriate function
if function_name in fed_tools:
tool_func = fed_tools[function_name]
result = tool_func(**parameters)
else:
result = {"success": False, "error": f"Unknown function: {function_name}"}
execution_time = time.time() - start_time
results.append({
"function": function_name,
"parameters": parameters,
"reasoning": reasoning,
"result": result,
"execution_time": execution_time,
"success": result.get("success", False)
})
except Exception as e:
execution_time = time.time() - start_time
results.append({
"function": function_name,
"parameters": parameters,
"reasoning": reasoning,
"result": {"success": False, "error": str(e)},
"execution_time": execution_time,
"success": False
})
return results
def stream_fed_agent_response(
message: str,
api_key: str,
prompt_library: Dict[str, str],
fed_tools: Dict[str, callable]
):
"""Main orchestrator function that coordinates tools and generates responses with ChatMessage objects"""
if not message.strip():
yield [ChatMessage(role="assistant", content="Please enter a question about Federal Reserve policy or FOMC meetings.")]
return
if not api_key.strip():
yield [ChatMessage(role="assistant", content="❌ Please set your FIREWORKS_API_KEY environment variable.")]
return
messages = []
try:
# Step 1: Use orchestrator to determine tools needed
messages.append(ChatMessage(
role="assistant",
content="Analyzing your query...",
metadata={"title": "🧠 Planning", "status": "pending"}
))
yield messages
orchestrator_result = get_orchestrator_decision(message, api_key, prompt_library)
tools_decision = orchestrator_result["decision"]
# Update planning message
messages[0] = ChatMessage(
role="assistant",
content=f"Query Analysis: {tools_decision.get('query_analysis', 'Analyzing Fed data requirements')}\n\nTools needed: {len(tools_decision.get('tools_needed', []))}",
metadata={"title": "🧠 Planning", "status": "done"}
)
yield messages
# Step 2: Execute the determined tools
if tools_decision.get("tools_needed"):
for i, tool in enumerate(tools_decision["tools_needed"]):
tool_msg = ChatMessage(
role="assistant",
content=f"Executing: {tool['function']}({', '.join([f'{k}={v}' for k, v in tool['parameters'].items()])})\n\nReasoning: {tool['reasoning']}",
metadata={"title": f"🔧 Tool {i+1}: {tool['function']}", "status": "pending"}
)
messages.append(tool_msg)
yield messages
# Execute all tools
tool_results = execute_fed_tools(tools_decision, fed_tools)
# Update tool messages with results
for i, (tool_result, tool_msg) in enumerate(zip(tool_results, messages[1:])):
execution_time = tool_result["execution_time"]
success_status = "✅" if tool_result["success"] else "❌"
messages[i+1] = ChatMessage(
role="assistant",
content=f"{success_status} {tool_result['function']} completed\n\nExecution time: {execution_time:.2f}s\n\nResult summary: {str(tool_result['result'])[:200]}...",
metadata={"title": f"🔧 Tool {i+1}: {tool_result['function']}", "status": "done", "duration": execution_time}
)
yield messages
# Step 3: Use results to generate final response
combined_context = ""
for result in tool_results:
if result["success"]:
combined_context += f"\n\nFrom {result['function']}: {json.dumps(result['result'], indent=2)}"
# Generate Fed Savant response using tool results
system_prompt_template = prompt_library.get('fed_savant_chat', '')
system_prompt = system_prompt_template.format(
fed_data_context=combined_context,
user_question=message,
date=TODAY
)
# Initialize LLM and get streaming response
llm = get_llm("large", api_key)
final_response = ""
for chunk in llm.chat.completions.create(
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": message}
],
temperature=0.2,
stream=True,
max_tokens=1000
):
if chunk.choices[0].delta.content:
final_response += chunk.choices[0].delta.content
# Update messages list with current response
if len(messages) > len(tool_results):
messages[-1] = ChatMessage(role="assistant", content=final_response)
else:
messages.append(ChatMessage(role="assistant", content=final_response))
yield messages
else:
# No tools needed, direct response
messages.append(ChatMessage(role="assistant", content="No specific tools required. Providing general Fed information."))
yield messages
except Exception as e:
messages.append(ChatMessage(role="assistant", content=f"Error generating response: {str(e)}"))
yield messages
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