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# Defaults
import spaces
import logging
import gradio as gr
from huggingface_hub import hf_hub_download
from llama_cpp import Llama
# Locals
from llama_cpp_agent.providers import LlamaCppPythonProvider
from content import css, PLACEHOLDER
from utils import CitingSources
# Agents
from llama_cpp_agent import LlamaCppAgent
from llama_cpp_agent.chat_history import BasicChatHistory
from llama_cpp_agent.chat_history.messages import Roles
from llama_cpp_agent.llm_output_settings import (
LlmStructuredOutputSettings,
LlmStructuredOutputType,
)
# Tools
from llama_cpp_agent.tools import WebSearchTool
from llama_cpp_agent.prompt_templates import web_search_system_prompt, research_system_prompt
examples = [
["Latest uplifting news"],
["Latest news site:bloomberg.com"],
["Where I can find best hotel in Galapagos, Ecuador intitle:hotel"],
["filetype:pdf intitle:python"]
]
hf_hub_download(
repo_id="bartowski/Mistral-7B-Instruct-v0.3-GGUF",
filename="Mistral-7B-Instruct-v0.3-Q6_K.gguf",
local_dir="./models"
)
hf_hub_download(
repo_id="bartowski/Meta-Llama-3-8B-Instruct-GGUF",
filename="Meta-Llama-3-8B-Instruct-Q6_K.gguf",
local_dir="./models"
)
def get_context_by_model(model_name):
model_context_limits = {
"Mistral-7B-Instruct-v0.3-Q6_K.gguf": 32768,
"Meta-Llama-3-8B-Instruct-Q6_K.gguf": 8192
}
return model_context_limits.get(model_name, None)
def get_messages_formatter_type(model_name):
from llama_cpp_agent import MessagesFormatterType
if "Meta" in model_name or "aya" in model_name:
return MessagesFormatterType.LLAMA_3
elif "Mistral" in model_name:
return MessagesFormatterType.MISTRAL
elif "Einstein-v6-7B" in model_name or "dolphin" in model_name:
return MessagesFormatterType.CHATML
elif "Phi" in model_name:
return MessagesFormatterType.PHI_3
else:
return MessagesFormatterType.CHATML
## Run inference
@spaces.GPU(duration=120)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
top_k,
repetition_penalty,
model,
):
chat_template = get_messages_formatter_type(model)
llm = Llama(
model_path=f"models/{model}",
flash_attn=True,
n_threads=40,
n_gpu_layers=81,
n_batch=1024,
n_ctx=get_context_by_model(model),
)
provider = LlamaCppPythonProvider(llm)
logging.info(f"Loaded chat examples: {chat_template}")
search_tool = WebSearchTool(
llm_provider=provider,
message_formatter_type=chat_template,
max_tokens_search_results=12000,
max_tokens_per_summary=2048,
)
web_search_agent = LlamaCppAgent(
provider,
system_prompt=web_search_system_prompt,
predefined_messages_formatter_type=chat_template,
debug_output=True,
)
answer_agent = LlamaCppAgent(
provider,
system_prompt=research_system_prompt,
predefined_messages_formatter_type=chat_template,
debug_output=True,
)
settings = provider.get_provider_default_settings()
settings.stream = False
settings.temperature = temperature
settings.top_k = top_k
settings.top_p = top_p
settings.max_tokens = max_tokens
settings.repeat_penalty = repetition_penalty
output_settings = LlmStructuredOutputSettings.from_functions(
[search_tool.get_tool()]
)
messages = BasicChatHistory()
for msn in history:
user = {"role": Roles.user, "content": msn[0]}
assistant = {"role": Roles.assistant, "content": msn[1]}
messages.add_message(user)
messages.add_message(assistant)
result = web_search_agent.get_chat_response(
message,
llm_sampling_settings=settings,
structured_output_settings=output_settings,
add_message_to_chat_history=False,
add_response_to_chat_history=False,
print_output=False,
)
outputs = ""
settings.stream = True
response_text = answer_agent.get_chat_response(
f"Write a detailed and complete research document that fulfills the following user request: '{message}', based on the information from the web below.\n\n" + result[0]["return_value"],
role=Roles.tool,
llm_sampling_settings=settings,
chat_history=messages,
returns_streaming_generator=True,
print_output=False,
)
for text in response_text:
outputs += text
yield outputs
output_settings = LlmStructuredOutputSettings.from_pydantic_models(
[CitingSources], LlmStructuredOutputType.object_instance
)
citing_sources = answer_agent.get_chat_response(
"Cite the sources you used in your response.",
role=Roles.tool,
llm_sampling_settings=settings,
chat_history=messages,
returns_streaming_generator=False,
structured_output_settings=output_settings,
print_output=False,
)
outputs += "\n\nSources:\n"
outputs += "\n".join(citing_sources.sources)
yield outputs
# Begin Gradio UI
main = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(
value=web_search_system_prompt,
label="System message",
interactive=True,
),
gr.Slider(minimum=1, maximum=4096, value=2048, step=1, label="Max tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p",
),
gr.Slider(
minimum=0,
maximum=100,
value=40,
step=1,
label="Top-k",
),
gr.Slider(
minimum=0.0,
maximum=2.0,
value=1.1,
step=0.1,
label="Repetition penalty",
),
],
theme=gr.themes.Soft(
primary_hue="violet",
secondary_hue="violet",
neutral_hue="gray",
font=[gr.themes.GoogleFont("Exo"), "ui-sans-serif", "system-ui", "sans-serif"],
).set(
body_background_fill_dark="#111111",
block_background_fill_dark="#111111",
block_border_width="1px",
block_title_background_fill_dark="#1e1c26",
input_background_fill_dark="#292733",
button_secondary_background_fill_dark="#24212b",
border_color_primary_dark="#343140",
background_fill_secondary_dark="#111111",
color_accent_soft_dark="transparent",
),
css=css,
retry_btn="Retry",
undo_btn="Undo",
clear_btn="Clear",
submit_btn="Send",
examples=(examples),
analytics_enabled=False,
description="Llama-cpp-agent: Chat Web Search Agent",
chatbot=gr.Chatbot(scale=1, placeholder=PLACEHOLDER),
)
if __name__ == "__main__":
main.launch()
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