Added new demo

ai_travel_agent/local_travel_agent.py

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+from textwrap import dedent
+from phi.assistant import Assistant
+from phi.tools.serpapi_tools import SerpApiTools
+import streamlit as st
+from phi.llm.ollama import Ollama
+
+# Set up the Streamlit app
+st.title("AI Travel Planner using Llama-3 ✈️")
+st.caption("Plan your next adventure with AI Travel Planner by researching and planning a personalized itinerary on autopilot using local Llama-3")
+
+# Get SerpAPI key from the user
+serp_api_key = st.text_input("Enter Serp API Key for Search functionality", type="password")
+
+if serp_api_key:
+    researcher = Assistant(
+        name="Researcher",
+        role="Searches for travel destinations, activities, and accommodations based on user preferences",
+        llm=Ollama(model="llama3:instruct", max_tokens=1024),
+        description=dedent(
+            """\
+        You are a world-class travel researcher. Given a travel destination and the number of days the user wants to travel for,
+        generate a list of search terms for finding relevant travel activities and accommodations.
+        Then search the web for each term, analyze the results, and return the 10 most relevant results.
+        """
+        ),
+        instructions=[
+            "Given a travel destination and the number of days the user wants to travel for, first generate a list of 3 search terms related to that destination and the number of days.",
+            "For each search term, `search_google` and analyze the results."
+            "From the results of all searches, return the 10 most relevant results to the user's preferences.",
+            "Remember: the quality of the results is important.",
+        ],
+        tools=[SerpApiTools(api_key=serp_api_key)],
+        add_datetime_to_instructions=True,
+    )
+    planner = Assistant(
+        name="Planner",
+        role="Generates a draft itinerary based on user preferences and research results",
+        llm=Ollama(model="llama3:instruct", max_tokens=1024),
+        description=dedent(
+            """\
+        You are a senior travel planner. Given a travel destination, the number of days the user wants to travel for, and a list of research results,
+        your goal is to generate a draft itinerary that meets the user's needs and preferences.
+        """
+        ),
+        instructions=[
+            "Given a travel destination, the number of days the user wants to travel for, and a list of research results, generate a draft itinerary that includes suggested activities and accommodations.",
+            "Ensure the itinerary is well-structured, informative, and engaging.",
+            "Ensure you provide a nuanced and balanced itinerary, quoting facts where possible.",
+            "Remember: the quality of the itinerary is important.",
+            "Focus on clarity, coherence, and overall quality.",
+            "Never make up facts or plagiarize. Always provide proper attribution.",
+        ],
+        add_datetime_to_instructions=True,
+        add_chat_history_to_prompt=True,
+        num_history_messages=3,
+    )
+
+    # Input fields for the user's destination and the number of days they want to travel for
+    destination = st.text_input("Where do you want to go?")
+    num_days = st.number_input("How many days do you want to travel for?", min_value=1, max_value=30, value=7)
+
+    if st.button("Generate Itinerary"):
+        with st.spinner("Processing..."):
+            # Get the response from the assistant
+            response = planner.run(f"{destination} for {num_days} days", stream=False)
+            st.write(response)

local_chatgpt_with_memory/local_chatgpt_memory.py

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+import streamlit as st
+from openai import OpenAI
+
+# Set up the Streamlit App
+st.title("Local ChatGPT with Memory 🦙")
+st.caption("Chat with locally hosted memory-enabled Llama-3 using the LM Studio 💯")
+
+# Point to the local server setup using LM Studio
+client = OpenAI(base_url="http://localhost:1234/v1", api_key="lm-studio")
+
+# Initialize the chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display the chat history
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Accept user input
+if prompt := st.chat_input("What is up?"):
+    st.session_state.messages.append({"role": "system", "content": "When the input starts with /add, don't follow up with a prompt."})
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    # Display user message in chat message container
+    with st.chat_message("user"):
+        st.markdown(prompt)
+    # Generate response
+    response = client.chat.completions.create(
+        model="lmstudio-community/Meta-Llama-3-8B-Instruct-GGUF",
+        messages=st.session_state.messages, temperature=0.7
+    )
+    # Add assistant response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": response.choices[0].message.content})
+    # Display assistant response in chat message container
+    with st.chat_message("assistant"):
+        st.markdown(response.choices[0].message.content)

local_chatgpt_with_memory/requirements.txt

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+streamlit 
+openai