Duplicate from llamaindex/llama_agi_auto

Co-authored-by: Logan Markewich <cheesyFishes@users.noreply.huggingface.co>

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README.md

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+---
+title: Llama AGI Auto
+emoji: 🤖 🦙
+colorFrom: gray
+colorTo: red
+sdk: streamlit
+sdk_version: 1.19.0
+app_file: app.py
+pinned: false
+license: mit
+duplicated_from: llamaindex/llama_agi_auto
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+import streamlit as st
+from langchain.agents import load_tools
+from langchain.chat_models import ChatOpenAI
+from langchain.llms import OpenAI
+
+from llama_agi.execution_agent import ToolExecutionAgent
+from llama_agi.runners import AutoStreamlitAGIRunner
+from llama_agi.task_manager import LlamaTaskManager
+
+from llama_index import ServiceContext, LLMPredictor
+
+
+st.set_page_config(layout="wide")
+st.header("🤖 Llama AGI 🦙")
+st.markdown("This demo uses the [llama-agi](https://github.com/run-llama/llama-lab/tree/main/llama_agi) package to create an AutoGPT-like agent, powered by [LlamaIndex](https://github.com/jerryjliu/llama_index) and Langchain. The AGI has access to tools that search the web and record notes, as it works to achieve an objective. Use the setup tab to configure your LLM settings and initial objective+tasks. Then use the Launch tab to run the AGI. Kill the AGI by refreshing the page.")
+
+setup_tab, launch_tab = st.tabs(["Setup", "Launch"])
+
+with setup_tab:
+    if 'init' in st.session_state:
+        st.success("Initialized!")
+
+    st.subheader("LLM Setup")
+    col1, col2, col3 = st.columns(3)
+
+    with col1:
+        openai_api_key = st.text_input("Enter your OpenAI API key here", type="password")
+        llm_name = st.selectbox(
+            "Which LLM?", ["text-davinci-003", "gpt-3.5-turbo", "gpt-4"]
+        )
+
+    with col2:
+        google_api_key = st.text_input("Enter your Google API key here", type="password")
+        model_temperature = st.slider(
+            "LLM Temperature", min_value=0.0, max_value=1.0, step=0.1, value=0.0
+        )
+    
+    with col3:
+        google_cse_id = st.text_input("Enter your Google CSE ID key here", type="password")
+        max_tokens = st.slider(
+            "LLM Max Tokens", min_value=256, max_value=1024, step=8, value=512
+        )
+
+    st.subheader("AGI Setup")
+    objective = st.text_input("Objective:", value="Solve world hunger")
+    initial_task = st.text_input("Initial Task:", value="Create a list of tasks")
+    max_iterations = st.slider("Iterations until pause", value=1, min_value=1, max_value=10, step=1)
+
+    if st.button('Initialize?'):
+        os.environ['OPENAI_API_KEY'] = openai_api_key
+        os.environ['GOOGLE_API_KEY'] = google_api_key
+        os.environ['GOOGLE_CSE_ID'] = google_cse_id
+        if llm_name == "text-davinci-003":
+            llm = OpenAI(
+                temperature=model_temperature, model_name=llm_name, max_tokens=max_tokens
+            )
+        else:
+            llm= ChatOpenAI(
+                temperature=model_temperature, model_name=llm_name, max_tokens=max_tokens
+            )
+        
+        service_context = ServiceContext.from_defaults(
+            llm_predictor=LLMPredictor(llm=llm), chunk_size_limit=512
+        )
+
+        st.session_state['task_manager'] = LlamaTaskManager(
+            [initial_task], task_service_context=service_context
+        )
+
+        from llama_agi.tools import search_notes, record_note, search_webpage
+        tools = load_tools(["google-search-results-json"])
+        tools = tools + [search_notes, record_note, search_webpage]
+        st.session_state['execution_agent'] = ToolExecutionAgent(llm=llm, tools=tools)
+
+        st.session_state['initial_task'] = initial_task
+        st.session_state['objective'] = objective
+
+        st.session_state['init'] = True
+        st.experimental_rerun()
+
+with launch_tab:
+    st.subheader("AGI Status")
+    if st.button(f"Continue for {max_iterations} Steps"):
+        if st.session_state.get('init', False):
+            # launch the auto runner
+            with st.spinner("Running!"):
+                runner = AutoStreamlitAGIRunner(st.session_state['task_manager'], st.session_state['execution_agent'])
+                runner.run(st.session_state['objective'], st.session_state['initial_task'], 2, max_iterations=max_iterations)
+

requirements.txt

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+google-api-python-client
+llama-agi==0.1.1