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import chainlit as cl
from openai import AuthenticationError
from aicore.logger import _logger
from aicore.config import LlmConfig
from aicore.const import STREAM_END_TOKEN, STREAM_START_TOKEN, REASONING_START_TOKEN, REASONING_STOP_TOKEN
from aicore.llm import Llm
from ulid import ulid
import asyncio
import time
from utils import MODELS_PROVIDERS_MAP, PROVIDERS_API_KEYS, REASONER_PROVIDERS_MAP, check_openai_api_key, trim_messages
from settings import PROFILES_SETTINGS
import os
from aicore.config import LlmConfig
DEFAULT_REASONER_CONFIG = {
"openai": LlmConfig(
provider="openai",
api_key=os.getenv("OPENAI_API_KEY") or os.getenv("YOUR_SUPER_SECRET_OPENAI_API_KEY"),
model="deepseek-r1-distill-llama-70b",
temperature=0.5,
max_tokens=1024
),
"groq": LlmConfig(
provider="groq",
api_key=os.getenv("YOUR_SUPER_SECRET_GROQ_API_KEY"),
model="deepseek-r1-distill-llama-70b",
temperature=0.5,
max_tokens=1024
),
"gemini": LlmConfig(
provider="gemini",
api_key=os.getenv("GEMINI_API_KEY") or os.getenv("YOUR_SUPER_SECRET_GEMINI_API_KEY"),
model="deepseek-r1-distill-llama-70b",
temperature=0.5,
max_tokens=1024
),
}
DEFAULT_LLM_CONFIG = {
"ZeppFusion": LlmConfig(
provider="groq",
api_key=PROVIDERS_API_KEYS.get("groq"),
model="meta-llama/llama-4-scout-17b-16e-instruct",
temperature=0,
max_tokens=4196,
# reasoner=DEFAULT_REASONER_CONFIG
),
"OpenAi": LlmConfig(
provider="openai",
api_key=PROVIDERS_API_KEYS.get("openai", ""),
model="gpt-4o-mini",
temperature=0,
max_tokens=4196,
# reasoner=DEFAULT_REASONER_CONFIG
)
}
@cl.set_chat_profiles
async def chat_profile():
return [
cl.ChatProfile(
name="ZeppFusion",
markdown_description="Talk with the lastest Llm models! Powered by AiCore, check it on GitHub, link in Readme",
),
cl.ChatProfile(
name="OpenAi",
markdown_description="Talk with the lastest Llm models! Powered by AiCore, check it on GitHub, link in Readme",
)
]
@cl.on_settings_update
async def setup_agent(settings):
provider = MODELS_PROVIDERS_MAP.get(settings.get("Model"), "openai")
llm_config = LlmConfig(
provider=provider,
api_key=PROVIDERS_API_KEYS.get(provider) or settings.get("Api Key"),
model=settings.get("Model"),
temperature=settings.get("Temperature"),
max_tokens=settings.get("Max Tokens")
)
if settings.get("Use Reasoner"):
reasoner_provder = REASONER_PROVIDERS_MAP.get(settings.get("Reasoner Model"), "openai")
reasoner_config = LlmConfig(
provider=reasoner_provder,
api_key=PROVIDERS_API_KEYS.get(reasoner_provder) or settings.get("Reasoner Api Key"),
model=settings.get("Reasoner Model"),
temperature=settings.get("Reasoner Temperature"),
max_tokens=settings.get("Reasoner Max Tokens")
)
llm_config.reasoner = reasoner_config
llm = Llm.from_config(llm_config)
llm.session_id = ulid()
llm.system_prompt = settings.get("System Prompt")
if llm.reasoner:
llm.reasoner.system_prompt = settings.get("Reasoner System Prompt")
cl.user_session.set(
"llm", llm
)
@cl.on_chat_start
async def start_chat():
user_profile = cl.user_session.get("chat_profile")
cl.user_session.set("history", [])
llm_config = DEFAULT_LLM_CONFIG.get(user_profile)
llm = Llm.from_config(llm_config)
llm.session_id = ulid()
cl.user_session.set(
"llm", llm
)
settings = await cl.ChatSettings(
PROFILES_SETTINGS.get("messages",
[])
).send()
async def run_concurrent_tasks(llm, message):
asyncio.create_task(llm.acomplete(message))
asyncio.create_task(_logger.distribute())
# Stream logger output while LLM is running
while True:
async for chunk in _logger.get_session_logs(llm.session_id):
yield chunk # Yield each chunk directly
@cl.on_message
async def main(message: cl.Message):
llm = cl.user_session.get("llm")
if not llm.config.api_key:
while True:
api_key_msg = await cl.AskUserMessage(content="Please provide a valid api_key", timeout=10).send()
if api_key_msg:
api_key = api_key_msg.get("output")
valid = check_openai_api_key(api_key)
if valid:
await cl.Message(
content=f"Config updated with key.",
).send()
llm.config.api_key = api_key
cl.user_session.set("llm", llm)
break
start = time.time()
thinking=False
history = cl.user_session.get("history")
history.append(message.content)
history = trim_messages(history, llm.tokenizer)
model_id = None
try:
if llm.reasoner is not None or llm.config.model in REASONER_PROVIDERS_MAP:
# Streaming the thinking
async with cl.Step(name=f"{llm.reasoner.config.provider} - {llm.reasoner.config.model} to think", type="llm") as thinking_step:
msg = cl.Message(content="")
async for chunk in run_concurrent_tasks(
llm,
message=history
):
if chunk == STREAM_START_TOKEN:
continue
if chunk == REASONING_START_TOKEN:
thinking = True
continue
# chunk = " - *reasoning*\n```html\n"
if chunk == REASONING_STOP_TOKEN:
thinking = False
thought_for = round(time.time() - start)
thinking_step.name = f"{llm.reasoner.config.model} to think for {thought_for}s"
await thinking_step.update()
chunk = f"```{llm.config.model}```\n"
model_id = f"```{llm.config.model}```\n"
if chunk == STREAM_END_TOKEN:
break
if thinking:
await thinking_step.stream_token(chunk)
else:
await msg.stream_token(chunk)
else:
msg = cl.Message(content="")
async for chunk in run_concurrent_tasks(
llm,
message=history
):
if chunk == STREAM_START_TOKEN:
continue
if chunk == STREAM_END_TOKEN:
break
await msg.stream_token(chunk)
hst_msg = msg.content.replace(model_id, "") if model_id else msg.content
history.append(hst_msg)
await msg.send()
except Exception as e:
await cl.ErrorMessage("Internal Server Error").send()
print(e)
### TODO add future todos, include support for images and pdf upload for conversation |