Added new demo

autonomous_rag/README.md

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+### 🤖 AutoRAG: Autonomous RAG with GPT-4o and Vector Database
+This Streamlit application implements an Autonomous Retrieval-Augmented Generation (RAG) system using OpenAI's GPT-4o model and PgVector database. It allows users to upload PDF documents, add them to a knowledge base, and query the AI assistant with context from both the knowledge base and web searches.
+Features
+
+### Freatures 
+- Chat interface for interacting with the AI assistant
+- PDF document upload and processing
+- Knowledge base integration using PostgreSQL and Pgvector
+- Web search capability using DuckDuckGo
+- Persistent storage of assistant data and conversations
+
+### 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 PgVector Database is running:
+The app expects PgVector to be running on [localhost:6333](http://localhost:5532/). Adjust the configuration in the code if your setup is different.
+
+```bash
+docker run -d \
+  -e POSTGRES_DB=ai \
+  -e POSTGRES_USER=ai \
+  -e POSTGRES_PASSWORD=ai \
+  -e PGDATA=/var/lib/postgresql/data/pgdata \
+  -v pgvolume:/var/lib/postgresql/data \
+  -p 5532:5432 \
+  --name pgvector \
+  phidata/pgvector:16
+```
+
+4. Run the Streamlit App
+```bash
+streamlit run autorag.py
+```

autonomous_rag/autorag.py

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+import streamlit as st
+import nest_asyncio
+from io import BytesIO
+from phi.assistant import Assistant
+from phi.document.reader.pdf import PDFReader
+from phi.llm.openai import OpenAIChat
+from phi.knowledge import AssistantKnowledge
+from phi.tools.duckduckgo import DuckDuckGo
+from phi.embedder.openai import OpenAIEmbedder
+from phi.vectordb.pgvector import PgVector2
+from phi.storage.assistant.postgres import PgAssistantStorage
+
+# Apply nest_asyncio to allow nested event loops, required for running async functions in Streamlit
+nest_asyncio.apply()
+
+# Database connection string for PostgreSQL
+DB_URL = "postgresql+psycopg://ai:ai@localhost:5532/ai"
+
+# Function to set up the Assistant, utilizing caching for resource efficiency
+@st.cache_resource
+def setup_assistant(api_key: str) -> Assistant:
+    llm = OpenAIChat(model="gpt-4o-mini", api_key=api_key)
+    # Set up the Assistant with storage, knowledge base, and tools
+    return Assistant(
+        name="auto_rag_assistant",  # Name of the Assistant
+        llm=llm,  # Language model to be used
+        storage=PgAssistantStorage(table_name="auto_rag_storage", db_url=DB_URL),  
+        knowledge_base=AssistantKnowledge(
+            vector_db=PgVector2(
+                db_url=DB_URL,  
+                collection="auto_rag_docs",  
+                embedder=OpenAIEmbedder(model="text-embedding-ada-002", dimensions=1536, api_key=api_key),  
+            ),
+            num_documents=3,  
+        ),
+        tools=[DuckDuckGo()],  # Additional tool for web search via DuckDuckGo
+        instructions=[
+            "Search your knowledge base first.",  
+            "If not found, search the internet.",  
+            "Provide clear and concise answers.",  
+        ],
+        show_tool_calls=True,  
+        search_knowledge=True,  
+        read_chat_history=True,  
+        markdown=True,  
+        debug_mode=True,  
+    )
+
+# Function to add a PDF document to the knowledge base
+def add_document(assistant: Assistant, file: BytesIO):
+    reader = PDFReader()
+    docs = reader.read(file)
+    if docs:
+        assistant.knowledge_base.load_documents(docs, upsert=True)
+        st.success("Document added to the knowledge base.")
+    else:
+        st.error("Failed to read the document.")
+
+# Function to query the Assistant and return a response
+def query_assistant(assistant: Assistant, question: str) -> str:
+    return "".join([delta for delta in assistant.run(question)])
+
+# Main function to handle Streamlit app layout and interactions
+def main():
+    st.set_page_config(page_title="AutoRAG", layout="wide")
+    st.title("🤖 Auto-RAG: Autonomous RAG with GPT-4o")
+
+    api_key = st.sidebar.text_input("Enter your OpenAI API Key 🔑", type="password")
+    
+    if not api_key:
+        st.sidebar.warning("Enter your OpenAI API Key to proceed.")
+        st.stop()
+
+    assistant = setup_assistant(api_key)
+    
+    uploaded_file = st.sidebar.file_uploader("📄 Upload PDF", type=["pdf"])
+    
+    if uploaded_file and st.sidebar.button("🛠️ Add to Knowledge Base"):
+        add_document(assistant, BytesIO(uploaded_file.read()))
+
+    question = st.text_input("💬 Ask Your Question:")
+    
+    # When the user submits a question, query the assistant for an answer
+    if st.button("🔍 Get Answer"):
+        # Ensure the question is not empty
+        if question.strip():
+            with st.spinner("🤔 Thinking..."):
+                # Query the assistant and display the response
+                answer = query_assistant(assistant, question)
+                st.write("📝 **Response:**", answer)
+        else:
+            # Show an error if the question input is empty
+            st.error("Please enter a question.")
+
+# Entry point of the application
+if __name__ == "__main__":
+    main()

autonomous_rag/requirements.txt

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+streamlit 
+phidata
+openai
+psycopg-binary
+pgvector
+requests
+sqlalchemy
+pypdf
+duckduckgo-search