chore: updated to work with latest version

ai_agent_tutorials/ai_customer_support_agent/customer_support_agent.py

@@ -34,8 +34,10 @@ if openai_api_key:
         def handle_query(self, query, user_id=None):
             relevant_memories = self.memory.search(query=query, user_id=user_id)
             context = "Relevant past information:\n"
-            for mem in relevant_memories:
-                context += f"- {mem['text']}\n"
+            if relevant_memories and "results" in relevant_memories:
+                for memory in relevant_memories["results"]:
+                    if "memory" in memory:
+                        context += f"- {memory['memory']}\n"
 
             full_prompt = f"{context}\nCustomer: {query}\nSupport Agent:"
 
@@ -126,8 +128,10 @@ if openai_api_key:
             memories = support_agent.get_memories(user_id=customer_id)
             if memories:
                 st.sidebar.write(f"Memory for customer **{customer_id}**:")
-                for mem in memories:
-                    st.sidebar.write(f"- {mem['text']}")
+                if memories and "results" in memories:
+                    for memory in memories["results"]:
+                        if "memory" in memory:
+                            st.write(f"- {memory['memory']}")
             else:
                 st.sidebar.info("No memory found for this customer ID.")
         else:

ai_agent_tutorials/ai_customer_support_agent/requirements.txt

@@ -1,3 +1,3 @@
 streamlit 
 openai
-mem0ai
+mem0ai==0.1.29

ai_agent_tutorials/legal_ai_agent/legal_agent.py

@@ -1,36 +0,0 @@
-from phi.agent import Agent
-from phi.model.openai import OpenAIChat
-from phi.knowledge.pdf import PDFKnowledgeBase, PDFReader
-from phi.vectordb.lancedb import LanceDb, SearchType
-from phi.playground import Playground, serve_playground_app
-from phi.tools.duckduckgo import DuckDuckGo
-
-# Set up configurations
-DB_URI = "tmp/legal_docs_db"
-
-# Create a knowledge base for legal documents
-knowledge_base = PDFKnowledgeBase(
-    path="tmp/legal_docs",
-    vector_db=LanceDb(
-        table_name="legal_documents",
-        uri=DB_URI,
-        search_type=SearchType.vector
-    ),
-    reader=PDFReader(chunk=True),
-    num_documents=5
-)
-
-# Create the agent
-agent = Agent(
-    model=OpenAIChat(id="gpt-4"),
-    agent_id="legal-analysis-agent",
-    knowledge=knowledge_base,
-    tools=[DuckDuckGo()],
-    show_tool_calls=True,
-    markdown=True,
-)
-
-app = Playground(agents=[agent]).get_app()
-
-if __name__ == "__main__":
-    serve_playground_app("legal_agent:app", reload=True)

ai_agent_tutorials/legal_ai_agent/requirements.txt

@@ -1,7 +0,0 @@
-streamlit
-phidata
-openai
-lancedb
-tantivy
-pypdf
-duckduckgo-search

llm_apps_with_memory_tutorials/ai_travel_agent_memory/requirements.txt

@@ -1,3 +1,3 @@
 streamlit 
 openai
-mem0ai
+mem0ai==0.1.29

llm_apps_with_memory_tutorials/ai_travel_agent_memory/travel_agent_memory.py

@@ -34,17 +34,19 @@ if openai_api_key:
         st.session_state.messages = []
         st.session_state.previous_user_id = user_id
 
-    if st.sidebar.button("View Memory Info"):
-        if user_id:
-            memories = memory.get_all(user_id=user_id)
-            if memories:
-                st.sidebar.write(f"Memory for user **{user_id}**:")
-                for mem in memories:
-                    st.sidebar.write(f"- {mem['text']}")
-            else:
-                st.sidebar.info("No memory found for this user ID.")
+    # Sidebar option to show memory
+    st.sidebar.title("Memory Info")
+    if st.button("View My Memory"):
+        memories = memory.get_all(user_id=user_id)
+        if memories and "results" in memories:
+            st.write(f"Memory history for **{user_id}**:")
+            for mem in memories["results"]:
+                if "memory" in mem:
+                    st.write(f"- {mem['memory']}")
         else:
-            st.sidebar.error("Please enter a username to view memory info.")
+            st.sidebar.info("No learning history found for this user ID.")
+    else:
+        st.sidebar.error("Please enter a username to view memory info.")
 
     # Initialize the chat history
     if "messages" not in st.session_state:
@@ -67,8 +69,10 @@ if openai_api_key:
         # Retrieve relevant memories
         relevant_memories = memory.search(query=prompt, user_id=user_id)
         context = "Relevant past information:\n"
-        for mem in relevant_memories:
-            context += f"- {mem['text']}\n"
+        if relevant_memories and "results" in relevant_memories:
+            for memory in relevant_memories["results"]:
+                if "memory" in memory:
+                    context += f"- {memory['memory']}\n"
 
         # Prepare the full prompt
         full_prompt = f"{context}\nHuman: {prompt}\nAI:"

llm_apps_with_memory_tutorials/llama3_stateful_chat/local_llama3_chat.py

@@ -0,0 +1,37 @@
+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)

llm_apps_with_memory_tutorials/llama3_stateful_chat/requirements.txt

@@ -0,0 +1,2 @@
+streamlit 
+openai

llm_apps_with_memory_tutorials/llm_app_personalized_memory/llm_app_memory.py

@@ -1,3 +1,4 @@
+import os
 import streamlit as st
 from mem0 import Memory
 from openai import OpenAI
@@ -6,6 +7,7 @@ st.title("LLM App with Memory 🧠")
 st.caption("LLM App with personalized memory layer that remembers ever user's choice and interests")
 
 openai_api_key = st.text_input("Enter OpenAI API Key", type="password")
+os.environ["OPENAI_API_KEY"] = openai_api_key
 
 if openai_api_key:
     # Initialize OpenAI client
@@ -16,6 +18,7 @@ if openai_api_key:
         "vector_store": {
             "provider": "qdrant",
             "config": {
+                "collection_name": "llm_app_memory",
                 "host": "localhost",
                 "port": 6333,
             }
@@ -59,11 +62,12 @@ if openai_api_key:
 
     # Sidebar option to show memory
     st.sidebar.title("Memory Info")
-    if st.sidebar.button("View Memory Info"):
-        memories = memory.get_all(user_id=user_id)
-        if memories:
-            st.sidebar.write(f"You are viewing memory for user **{user_id}**")
-            for mem in memories:
-                st.sidebar.write(f"- {mem['text']}")
-        else:
-            st.sidebar.info("No learning history found for this user ID.")
+    if st.button("View My Memory"):
+            memories = memory.get_all(user_id=user_id)
+            if memories and "results" in memories:
+                st.write(f"Memory history for **{user_id}**:")
+                for mem in memories["results"]:
+                    if "memory" in mem:
+                        st.write(f"- {mem['memory']}")
+            else:
+                st.sidebar.info("No learning history found for this user ID.")

llm_apps_with_memory_tutorials/llm_app_personalized_memory/requirements.txt

@@ -1,4 +1,3 @@
 streamlit 
 openai
-mem0ai
-litellm
+mem0ai==0.1.29

llm_apps_with_memory_tutorials/local_chatgpt_with_memory/README.md

@@ -0,0 +1,40 @@
+## 🧠 Local ChatGPT using Llama 3.1 with Personal Memory
+This Streamlit application implements a fully local ChatGPT-like experience using Llama 3.1, featuring personalized memory storage for each user. All components, including the language model, embeddings, and vector store, run locally without requiring external API keys.
+
+### Features
+- Fully local implementation with no external API dependencies
+- Powered by Llama 3.1 via Ollama
+- Personal memory space for each user
+- Local embedding generation using Nomic Embed
+- Vector storage with Qdrant
+
+### How to get Started?
+
+1. Clone the GitHub repository
+```bash
+git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
+```
+
+2. Install the required dependencies:
+
+```bash
+cd rag_tutorials/local_rag_agent
+pip install -r requirements.txt
+```
+
+3. Install and start [Qdrant](https://qdrant.tech/) vector database locally
+
+```bash
+docker pull qdrant/qdrant
+docker run -p 6333:6333 qdrant/qdrant
+```
+
+4. Install [Ollama](https://ollama.com/download) and pull Llama 3.2
+```bash
+ollama pull llama3.1
+```
+
+5. Run the Streamlit App
+```bash
+streamlit run local_chatgpt_memory.py
+```

llm_apps_with_memory_tutorials/local_chatgpt_with_memory/local_chatgpt_memory.py

@@ -1,37 +1,137 @@
 import streamlit as st
-from openai import OpenAI
+from mem0 import Memory
+from litellm import completion
 
-# 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 💯")
+# Configuration for Memory
+config = {
+    "vector_store": {
+        "provider": "qdrant",
+        "config": {
+            "collection_name": "local-chatgpt-memory",
+            "host": "localhost",
+            "port": 6333,
+            "embedding_model_dims": 768,
+        },
+    },
+    "llm": {
+        "provider": "ollama",
+        "config": {
+            "model": "llama3.1:latest",
+            "temperature": 0,
+            "max_tokens": 8000,
+            "ollama_base_url": "http://localhost:11434",  # Ensure this URL is correct
+        },
+    },
+    "embedder": {
+        "provider": "ollama",
+        "config": {
+            "model": "nomic-embed-text:latest",
+            # Alternatively, you can use "snowflake-arctic-embed:latest"
+            "ollama_base_url": "http://localhost:11434",
+        },
+    },
+    "version": "v1.1"
+}
 
-# Point to the local server setup using LM Studio
-client = OpenAI(base_url="http://localhost:1234/v1", api_key="lm-studio")
+st.title("Local ChatGPT using Llama 3.1 with Personal Memory 🧠")
+st.caption("Each user gets their own personalized memory space!")
 
-# Initialize the chat history
+# Initialize session state for chat history and previous user ID
 if "messages" not in st.session_state:
     st.session_state.messages = []
+if "previous_user_id" not in st.session_state:
+    st.session_state.previous_user_id = None
 
-# Display the chat history
-for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.markdown(message["content"])
+# Sidebar for user authentication
+with st.sidebar:
+    st.title("User Settings")
+    user_id = st.text_input("Enter your Username", key="user_id")
+    
+    # Check if user ID has changed
+    if user_id != st.session_state.previous_user_id:
+        st.session_state.messages = []  # Clear chat history
+        st.session_state.previous_user_id = user_id  # Update previous user ID
+    
+    if user_id:
+        st.success(f"Logged in as: {user_id}")
+        
+        # Initialize Memory with the configuration
+        m = Memory.from_config(config)
+        
+        # Memory viewing section
+        st.header("Memory Context")
+        if st.button("View My Memory"):
+            memories = m.get_all(user_id=user_id)
+            if memories and "results" in memories:
+                st.write(f"Memory history for **{user_id}**:")
+                for memory in memories["results"]:
+                    if "memory" in memory:
+                        st.write(f"- {memory['memory']}")
 
-# 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)
+# Main chat interface
+if user_id:  # Only show chat interface if user is "logged in"
+    # Display chat history
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    # User input
+    if prompt := st.chat_input("What is your message?"):
+        # Add user message to chat history
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        
+        # Display user message
+        with st.chat_message("user"):
+            st.markdown(prompt)
+
+        # Add to memory
+        m.add(prompt, user_id=user_id)
+        
+        # Get context from memory
+        memories = m.get_all(user_id=user_id)
+        context = ""
+        if memories and "results" in memories:
+            for memory in memories["results"]:
+                if "memory" in memory:
+                    context += f"- {memory['memory']}\n"
+
+        # Generate assistant response
+        with st.chat_message("assistant"):
+            message_placeholder = st.empty()
+            full_response = ""
+            
+            # Stream the response
+            try:
+                response = completion(
+                    model="ollama/llama3.1:latest",
+                    messages=[
+                        {"role": "system", "content": "You are a helpful assistant with access to past conversations. Use the context provided to give personalized responses."},
+                        {"role": "user", "content": f"Context from previous conversations with {user_id}: {context}\nCurrent message: {prompt}"}
+                    ],
+                    api_base="http://localhost:11434",
+                    stream=True
+                )
+                
+                # Process streaming response
+                for chunk in response:
+                    if hasattr(chunk, 'choices') and len(chunk.choices) > 0:
+                        content = chunk.choices[0].delta.get('content', '')
+                        if content:
+                            full_response += content
+                            message_placeholder.markdown(full_response + "▌")
+                
+                # Final update
+                message_placeholder.markdown(full_response)
+            except Exception as e:
+                st.error(f"Error generating response: {str(e)}")
+                full_response = "I apologize, but I encountered an error generating the response."
+                message_placeholder.markdown(full_response)
+
+        # Add assistant response to chat history
+        st.session_state.messages.append({"role": "assistant", "content": full_response})
+        
+        # Add response to memory
+        m.add(f"Assistant: {full_response}", user_id=user_id)
+
+else:
+    st.info("👈 Please enter your username in the sidebar to start chatting!")

llm_apps_with_memory_tutorials/local_chatgpt_with_memory/requirements.txt

@@ -1,2 +1,4 @@
 streamlit 
-openai
+openai
+mem0ai==0.1.29
+litellm

llm_apps_with_memory_tutorials/multi_llm_memory/README.md

@@ -0,0 +1,39 @@
+## 🧠 Multi-LLM App with Shared Memory
+This Streamlit application demonstrates a multi-LLM system with a shared memory layer, allowing users to interact with different language models while maintaining conversation history and context across sessions.
+
+### Features
+
+- Support for multiple LLMs:
+    - OpenAI's GPT-4o
+    - Anthropic's Claude 3.5 Sonnet
+
+- Persistent memory using Qdrant vector store
+- User-specific conversation history
+- Memory retrieval for contextual responses
+- User-friendly interface with LLM selection
+
+### How to get Started?
+
+1. Clone the GitHub repository
+```bash
+git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
+```
+
+2. Install the required dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+3. Ensure Qdrant is running:
+The app expects Qdrant to be running on localhost:6333. Adjust the configuration in the code if your setup is different.
+
+```bash
+docker pull qdrant/qdrant
+docker run -p 6333:6333 qdrant/qdrant
+```
+
+4. Run the Streamlit App
+```bash
+streamlit run multi_llm_memory.py
+```

llm_apps_with_memory_tutorials/multi_llm_memory/multi_llm_memory.py

@@ -4,7 +4,7 @@ from openai import OpenAI
 import os
 from litellm import completion
 
-st.title("LLM App with Shared Memory 🧠")
+st.title("Multi-LLM App with Shared Memory 🧠")
 st.caption("LLM App with a personalized memory layer that remembers each user's choices and interests across multiple users and LLMs")
 
 openai_api_key = st.text_input("Enter OpenAI API Key", type="password")
@@ -50,9 +50,10 @@ if openai_api_key and anthropic_api_key:
         with st.spinner('Searching...'):
             relevant_memories = memory.search(query=prompt, user_id=user_id)
             context = "Relevant past information:\n"
-
-            for mem in relevant_memories:
-                context += f"- {mem['text']}\n"
+            if relevant_memories and "results" in relevant_memories:
+                for memory in relevant_memories["results"]:
+                    if "memory" in memory:
+                        context += f"- {memory['memory']}\n"
                 
             full_prompt = f"{context}\nHuman: {prompt}\nAI:"
 
@@ -76,12 +77,14 @@ if openai_api_key and anthropic_api_key:
 
             memory.add(answer, user_id=user_id)
 
+    # Sidebar option to show memory
     st.sidebar.title("Memory Info")
-    if st.sidebar.button("View Memory Info"):
-        memories = memory.get_all(user_id=user_id)
-        if memories:
-            st.sidebar.write(f"You are viewing memory for user **{user_id}**")
-            for mem in memories:
-                st.sidebar.write(f"- {mem['text']}")
-        else:
-            st.sidebar.info("No learning history found for this user ID.")
+    if st.button("View My Memory"):
+            memories = memory.get_all(user_id=user_id)
+            if memories and "results" in memories:
+                st.write(f"Memory history for **{user_id}**:")
+                for mem in memories["results"]:
+                    if "memory" in mem:
+                        st.write(f"- {mem['memory']}")
+            else:
+                st.sidebar.info("No learning history found for this user ID.")

llm_apps_with_memory_tutorials/multi_llm_memory/requirements.txt

@@ -0,0 +1,4 @@
+streamlit 
+openai
+mem0ai==0.1.29
+litellm

rag_tutorials/local_rag_agent/README.md

@@ -33,7 +33,6 @@ docker run -p 6333:6333 qdrant/qdrant
 4. Install [Ollama](https://ollama.com/download) and pull Llama 3.2
 ```bash
 ollama pull llama3.2
-
 ```
 
 4. Run the AI RAG Agent