├── .gitignore ├── ai_agent_tutorials ├── ai_legal_agent_team │ ├── local_ai_legal_agent_team │ │ ├── README.md │ │ └── requirements.txt │ ├── requirements.txt │ └── README.md ├── multi_agent_researcher │ ├── requirements.txt │ ├── research_agent_llama3.py │ ├── research_agent.py │ └── README.md ├── ai_game_design_agent_team │ ├── requirements.txt │ └── README.md ├── ai_customer_support_agent │ ├── requirements.txt │ └── README.md ├── ai_investment_agent │ ├── requirements.txt │ ├── investment_agent.py │ └── README.md ├── ai_meeting_agent │ ├── requirements.txt │ └── README.md ├── ai_system_architect_r1 │ ├── requirements.txt │ └── README.md ├── ai_3dpygame_r1 │ ├── requirements.txt │ └── README.md ├── ai_travel_agent │ ├── requirements.txt │ └── README.MD ├── ai_coding_agent_o3-mini │ ├── requirements.txt │ └── README.md ├── ai_services_agency │ ├── requirements.txt │ └── README.md ├── multimodal_ai_agent │ ├── requirements.txt │ ├── README.md │ ├── multimodal_reasoning_agent.py │ └── mutimodal_agent.py ├── ai_health_fitness_agent │ ├── requirements.txt │ └── README.md ├── ai_personal_finance_agent │ ├── requirements.txt │ └── README.md ├── ai_real_estate_agent │ ├── requirements.txt │ └── README.md ├── local_news_agent_openai_swarm │ ├── .env │ ├── requirements.txt │ └── README.md ├── ai_chess_agent │ ├── requirements.txt │ └── README.md ├── xai_finance_agent │ ├── requirements.txt │ ├── xai_finance_agent.py │ └── README.md ├── ai_tic_tac_toe_agent │ ├── requirements.txt │ └── README.md ├── ai_movie_production_agent │ ├── requirements.txt │ └── README.md ├── ai_aqi_analysis_agent │ ├── requirements.txt │ └── README.md ├── ai_finance_agent_team │ ├── requirements.txt │ ├── finance_agent_team.py │ └── README.md ├── ai_journalist_agent │ ├── requirements.txt │ └── README.md ├── ai_data_analysis_agent │ ├── requirements.txt │ └── README.md ├── ai_lead_generation_agent │ ├── requirements.txt │ └── README.md ├── ai_medical_imaging_agent │ ├── requirements.txt │ └── README.md ├── ai_competitor_intelligence_agent_team │ ├── requirements.txt │ └── README.md ├── ai_startup_trend_analysis_agent │ ├── requirements.txt │ └── README.md ├── multimodal_design_agent_team │ ├── requirements.txt │ └── README.md ├── ai_data_visualisation_agent │ ├── requirements.txt │ └── README.md ├── ai_meme_generator_agent_browseruse │ ├── requirements.txt │ └── README.md ├── ai_teaching_agent_team │ ├── requirements.txt │ └── README.md ├── ai_recruitment_agent_team │ └── requirements.txt ├── ai_reasoning_agent │ ├── local_ai_reasoning_agent.py │ └── reasoning_agent.py ├── gemini_multimodal_agent_demo │ └── multimodal_ai_agent.py └── ai_tic_tac_toe_game │ └── README.md ├── advanced_tools_frameworks ├── local_chatgpt_clone │ ├── requirements.txt │ ├── README.md │ └── chatgpt_clone_llama3.py ├── multillm_chat_playground │ ├── requirements.txt │ └── multillm_playground.py ├── local_llama3.1_tool_use │ ├── requirements.txt │ └── README.md ├── mixture_of_agents │ └── requirements.txt ├── llm_router_app │ ├── requirements.txt │ ├── llm_router.py │ └── README.md ├── web_scrapping_ai_agent │ ├── requirements.txt │ ├── ai_scrapper.py │ ├── local_ai_scrapper.py │ └── README.md ├── gemini_multimodal_chatbot │ ├── requirements.txt │ ├── README.md │ └── gemini_multimodal_chatbot.py ├── web_search_ai_assistant │ ├── requirements.txt │ ├── claude_websearch.py │ ├── gpt4_websearch.py │ └── README.md └── cursor_ai_experiments │ ├── requirements.txt │ ├── ai_web_scrapper.py │ └── chatgpt_clone_llama3.py ├── chat_with_X_tutorials ├── chat_with_substack │ ├── requirements.txt │ ├── README.md │ └── chat_substack.py ├── chat_with_github │ ├── requirements.txt │ ├── chat_github.py │ ├── README.md │ └── chat_github_llama3.py ├── chat_with_gmail │ ├── requirements.txt │ ├── README.md │ └── chat_gmail.py ├── chat_with_pdf │ ├── requirements.txt │ ├── README.md │ ├── chat_pdf.py │ ├── chat_pdf_llama3.py │ └── chat_pdf_llama3.2.py ├── chat_with_research_papers │ ├── requirements.txt │ ├── chat_arxiv_llama3.py │ ├── README.md │ └── chat_arxiv.py └── chat_with_youtube_videos │ ├── requirements.txt │ ├── README.md │ └── chat_youtube.py ├── llm_apps_with_memory_tutorials ├── llama3_stateful_chat │ ├── requirements.txt │ └── local_llama3_chat.py ├── ai_arxiv_agent_memory │ ├── requirements.txt │ ├── README.md │ └── ai_arxiv_agent_memory.py ├── ai_travel_agent_memory │ ├── requirements.txt │ └── README.md ├── llm_app_personalized_memory │ ├── requirements.txt │ ├── README.md │ └── llm_app_memory.py ├── multi_llm_memory │ ├── requirements.txt │ └── README.md └── local_chatgpt_with_memory │ ├── requirements.txt │ └── README.md ├── rag_tutorials ├── rag-as-a-service │ ├── requirements.txt │ └── README.md ├── local_rag_agent │ ├── requirements.txt │ ├── README.md │ └── local_rag_agent.py ├── llama3.1_local_rag │ ├── requirements.txt │ ├── README.md │ └── llama3.1_local_rag.py ├── agentic_rag │ ├── requirements.txt │ ├── rag_agent.py │ └── README.md ├── gemini_agentic_rag │ ├── requirements.txt │ └── README.md ├── autonomous_rag │ ├── requirements.txt │ └── README.md ├── deepseek_local_rag_agent │ ├── requirements.txt │ └── README.md ├── rag_chain │ ├── requirements.txt │ └── README.md ├── ai_blog_search │ └── requirements.txt ├── hybrid_search_rag │ ├── requirements.txt │ └── README.md ├── rag_database_routing │ ├── requirements.txt │ └── README.md ├── local_hybrid_search_rag │ └── requirements.txt ├── rag_agent_cohere │ ├── requirements.txt │ └── README.md └── corrective_rag │ ├── requirements.txt │ └── README.md ├── docs └── banner │ └── unwind_black.png └── llm_finetuning_tutorials └── llama3.2_finetuning ├── requirements.txt ├── finetune_llama3.2.py └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_legal_agent_team/local_ai_legal_agent_team/README.md: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /advanced_tools_frameworks/local_chatgpt_clone/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | openai -------------------------------------------------------------------------------- /chat_with_X_tutorials/chat_with_substack/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | embedchain -------------------------------------------------------------------------------- /ai_agent_tutorials/multi_agent_researcher/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | agno 3 | openai -------------------------------------------------------------------------------- /chat_with_X_tutorials/chat_with_github/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | embedchain[github] -------------------------------------------------------------------------------- /chat_with_X_tutorials/chat_with_gmail/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | embedchain[gmail] -------------------------------------------------------------------------------- /advanced_tools_frameworks/multillm_chat_playground/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | litellm -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_game_design_agent_team/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit==1.41.1 2 | autogen -------------------------------------------------------------------------------- /llm_apps_with_memory_tutorials/llama3_stateful_chat/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | openai -------------------------------------------------------------------------------- /rag_tutorials/rag-as-a-service/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | anthropic 3 | requests 4 | -------------------------------------------------------------------------------- /advanced_tools_frameworks/local_llama3.1_tool_use/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | ollama 3 | agno -------------------------------------------------------------------------------- /advanced_tools_frameworks/mixture_of_agents/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | asyncio 3 | together -------------------------------------------------------------------------------- /chat_with_X_tutorials/chat_with_pdf/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | embedchain 3 | streamlit-chat -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_customer_support_agent/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | openai 3 | mem0ai==0.1.29 -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_investment_agent/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | agno 3 | openai 4 | yfinance 5 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_meeting_agent/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | crewai 3 | crewai-tools 4 | openai -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_system_architect_r1/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | openai 3 | anthropic 4 | agno -------------------------------------------------------------------------------- /advanced_tools_frameworks/llm_router_app/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | "routellm[serve,eval]" 3 | routellm -------------------------------------------------------------------------------- /advanced_tools_frameworks/web_scrapping_ai_agent/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | scrapegraphai 3 | playwright -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_3dpygame_r1/requirements.txt: -------------------------------------------------------------------------------- 1 | agno 2 | langchain-openai 3 | browser-use 4 | streamlit -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_travel_agent/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | agno 3 | openai 4 | google-search-results -------------------------------------------------------------------------------- /advanced_tools_frameworks/gemini_multimodal_chatbot/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | pillow 3 | google-generativeai -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_coding_agent_o3-mini/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | e2b-code-interpreter 3 | agno 4 | Pillow -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_services_agency/requirements.txt: -------------------------------------------------------------------------------- 1 | python-dotenv==1.0.1 2 | agency-swarm==0.4.1 3 | streamlit -------------------------------------------------------------------------------- /ai_agent_tutorials/multimodal_ai_agent/requirements.txt: -------------------------------------------------------------------------------- 1 | agno 2 | google-generativeai==0.8.3 3 | streamlit==1.40.2 -------------------------------------------------------------------------------- /llm_apps_with_memory_tutorials/ai_arxiv_agent_memory/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | openai 3 | mem0ai 4 | multion -------------------------------------------------------------------------------- /llm_apps_with_memory_tutorials/ai_travel_agent_memory/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | openai 3 | mem0ai==0.1.29 -------------------------------------------------------------------------------- /advanced_tools_frameworks/web_search_ai_assistant/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | openai 3 | agno 4 | duckduckgo-search -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_health_fitness_agent/requirements.txt: -------------------------------------------------------------------------------- 1 | google-generativeai==0.8.3 2 | streamlit==1.40.2 3 | agno -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_personal_finance_agent/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | agno 3 | openai 4 | google-search-results -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_real_estate_agent/requirements.txt: -------------------------------------------------------------------------------- 1 | agno 2 | firecrawl-py==1.9.0 3 | pydantic 4 | streamlit 5 | -------------------------------------------------------------------------------- /chat_with_X_tutorials/chat_with_research_papers/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | agno 3 | arxiv 4 | openai 5 | pypdf -------------------------------------------------------------------------------- /llm_apps_with_memory_tutorials/llm_app_personalized_memory/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | openai 3 | mem0ai==0.1.29 -------------------------------------------------------------------------------- /llm_apps_with_memory_tutorials/multi_llm_memory/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | openai 3 | mem0ai==0.1.29 4 | litellm -------------------------------------------------------------------------------- /ai_agent_tutorials/local_news_agent_openai_swarm/.env: -------------------------------------------------------------------------------- 1 | OPENAI_BASE_URL=http://localhost:11434/v1 2 | OPENAI_API_KEY=fake-key -------------------------------------------------------------------------------- /docs/banner/unwind_black.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/D3villl/shubhamsaboo-llm-apps/HEAD/docs/banner/unwind_black.png -------------------------------------------------------------------------------- /llm_finetuning_tutorials/llama3.2_finetuning/requirements.txt: -------------------------------------------------------------------------------- 1 | torch 2 | unsloth 3 | transformers 4 | datasets 5 | trl -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_chess_agent/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | chess==1.11.1 3 | autogen==0.6.1 4 | cairosvg 5 | pillow 6 | -------------------------------------------------------------------------------- /ai_agent_tutorials/xai_finance_agent/requirements.txt: -------------------------------------------------------------------------------- 1 | agno 2 | duckduckgo-search 3 | yfinance 4 | fastapi[standard] 5 | openai -------------------------------------------------------------------------------- /llm_apps_with_memory_tutorials/local_chatgpt_with_memory/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | openai 3 | mem0ai==0.1.29 4 | litellm -------------------------------------------------------------------------------- /rag_tutorials/local_rag_agent/requirements.txt: -------------------------------------------------------------------------------- 1 | agno 2 | qdrant-client 3 | ollama 4 | pypdf 5 | openai 6 | fastapi 7 | uvicorn -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_tic_tac_toe_agent/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit==1.41.1 2 | agno 3 | openai==1.58.1 4 | google-generativeai==0.8.3 -------------------------------------------------------------------------------- /chat_with_X_tutorials/chat_with_youtube_videos/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | embedchain[youtube] 3 | youtube-transcript-api==0.6.3 -------------------------------------------------------------------------------- /rag_tutorials/llama3.1_local_rag/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | ollama 3 | langchain 4 | langchain_community 5 | langchain_ollama 6 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_movie_production_agent/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | agno 3 | anthropic 4 | google-search-results 5 | lxml_html_clean -------------------------------------------------------------------------------- /ai_agent_tutorials/local_news_agent_openai_swarm/requirements.txt: -------------------------------------------------------------------------------- 1 | git+https://github.com/openai/swarm.git 2 | streamlit 3 | duckduckgo-search -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_aqi_analysis_agent/requirements.txt: -------------------------------------------------------------------------------- 1 | agno 2 | openai 3 | firecrawl-py==1.9.0 4 | gradio==5.9.1 5 | pydantic 6 | dataclasses 7 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_finance_agent_team/requirements.txt: -------------------------------------------------------------------------------- 1 | openai 2 | agno 3 | duckduckgo-search 4 | yfinance 5 | fastapi[standard] 6 | sqlalchemy -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_journalist_agent/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | agno 3 | openai 4 | google-search-results 5 | newspaper4k 6 | lxml_html_clean -------------------------------------------------------------------------------- /rag_tutorials/agentic_rag/requirements.txt: -------------------------------------------------------------------------------- 1 | agno 2 | openai 3 | lancedb 4 | tantivy 5 | pypdf 6 | sqlalchemy 7 | pgvector 8 | psycopg[binary] 9 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_legal_agent_team/local_ai_legal_agent_team/requirements.txt: -------------------------------------------------------------------------------- 1 | agno 2 | streamlit==1.40.2 3 | qdrant-client==1.12.1 4 | ollama==0.4.4 5 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_data_analysis_agent/requirements.txt: -------------------------------------------------------------------------------- 1 | phidata 2 | streamlit==1.41.1 3 | openai==1.58.1 4 | duckdb==1.1.3 5 | pandas 6 | numpy==1.26.4 7 | agno -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_lead_generation_agent/requirements.txt: -------------------------------------------------------------------------------- 1 | firecrawl-py==1.9.0 2 | agno 3 | composio-phidata 4 | composio==0.1.1 5 | pydantic==2.10.5 6 | streamlit -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_medical_imaging_agent/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit==1.40.2 2 | agno 3 | Pillow==10.0.0 4 | duckduckgo-search==6.4.1 5 | google-generativeai==0.8.3 -------------------------------------------------------------------------------- /advanced_tools_frameworks/cursor_ai_experiments/requirements.txt: -------------------------------------------------------------------------------- 1 | scrapegraphai 2 | playwright 3 | langchain-community 4 | streamlit-chat 5 | streamlit 6 | crewai 7 | ollama -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_competitor_intelligence_agent_team/requirements.txt: -------------------------------------------------------------------------------- 1 | exa-py==1.7.1 2 | firecrawl-py==1.9.0 3 | duckduckgo-search==7.2.1 4 | agno 5 | streamlit==1.41.1 -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_legal_agent_team/requirements.txt: -------------------------------------------------------------------------------- 1 | agno 2 | streamlit==1.40.2 3 | qdrant-client==1.12.1 4 | openai 5 | pypdf 6 | duckduckgo-search 7 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_startup_trend_analysis_agent/requirements.txt: -------------------------------------------------------------------------------- 1 | agno 2 | streamlit==1.40.2 3 | duckduckgo_search==6.3.7 4 | newspaper4k==0.9.3.1 5 | lxml_html_clean==0.4.1 -------------------------------------------------------------------------------- /rag_tutorials/gemini_agentic_rag/requirements.txt: -------------------------------------------------------------------------------- 1 | agno 2 | exa==0.5.26 3 | qdrant-client==1.12.1 4 | langchain-qdrant==0.2.0 5 | langchain-community==0.3.13 6 | streamlit==1.41.1 -------------------------------------------------------------------------------- /ai_agent_tutorials/multimodal_design_agent_team/requirements.txt: -------------------------------------------------------------------------------- 1 | google-generativeai==0.8.3 2 | streamlit==1.41.1 3 | agno 4 | Pillow==11.0.0 5 | duckduckgo-search==6.3.7 6 | 7 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_data_visualisation_agent/requirements.txt: -------------------------------------------------------------------------------- 1 | together==1.3.10 2 | e2b-code-interpreter==1.0.3 3 | e2b==1.0.5 4 | Pillow==10.4.0 5 | streamlit 6 | pandas 7 | matplotlib 8 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_meme_generator_agent_browseruse/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | browser-use==0.1.26 3 | playwright==1.49.1 4 | langchain-openai 5 | langchain-anthropic 6 | asyncio 7 | -------------------------------------------------------------------------------- /rag_tutorials/autonomous_rag/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | agno 3 | openai 4 | psycopg-binary 5 | pgvector 6 | requests 7 | sqlalchemy 8 | pypdf 9 | duckduckgo-search 10 | nest_asyncio 11 | -------------------------------------------------------------------------------- /rag_tutorials/deepseek_local_rag_agent/requirements.txt: -------------------------------------------------------------------------------- 1 | agno 2 | exa==0.5.26 3 | qdrant-client==1.12.1 4 | langchain-qdrant==0.2.0 5 | langchain-community==0.3.13 6 | streamlit==1.41.1 7 | ollama 8 | -------------------------------------------------------------------------------- /rag_tutorials/rag_chain/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | langchain-google-genai 3 | langchain-chroma 4 | langchain-community 5 | langchain-core 6 | chromadb 7 | sentence-transformers 8 | PyPDF2 9 | python-dotenv 10 | -------------------------------------------------------------------------------- /rag_tutorials/ai_blog_search/requirements.txt: -------------------------------------------------------------------------------- 1 | langchain 2 | langgraph 3 | langchainhub 4 | langchain-community 5 | langchain-google-genai 6 | langchain-qdrant 7 | langchain-text-splitters 8 | tiktoken 9 | beautifulsoup4 10 | python-dotenv -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_teaching_agent_team/requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit==1.41.1 2 | openai==1.58.1 3 | duckduckgo-search==6.4.1 4 | typing-extensions>=4.5.0 5 | agno 6 | composio-phidata==0.6.9 7 | composio_core 8 | composio==0.1.1 9 | google-search-results==2.4.2 -------------------------------------------------------------------------------- /rag_tutorials/hybrid_search_rag/requirements.txt: -------------------------------------------------------------------------------- 1 | raglite==0.2.1 2 | pydantic==2.10.1 3 | sqlalchemy>=2.0.0 4 | psycopg2-binary>=2.9.9 5 | openai>=1.0.0 6 | cohere>=4.37 7 | pypdf>=3.0.0 8 | python-dotenv>=1.0.0 9 | rerankers==0.6.0 10 | spacy>=3.7.0 11 | streamlit 12 | anthropic 13 | -------------------------------------------------------------------------------- /rag_tutorials/rag_database_routing/requirements.txt: -------------------------------------------------------------------------------- 1 | langchain==0.3.12 2 | langchain-community==0.3.12 3 | langchain-core==0.3.28 4 | qdrant-client==1.12.1 5 | streamlit>=1.29.0 6 | pypdf>=4.0.0 7 | sentence-transformers>=2.2.2 8 | phidata==2.7.3 9 | langchain-openai==0.2.14 10 | langgraph==0.2.53 11 | duckduckgo-search==6.4.1 -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_recruitment_agent_team/requirements.txt: -------------------------------------------------------------------------------- 1 | # Core dependencies 2 | phidata 3 | agno 4 | streamlit==1.40.2 5 | PyPDF2==3.0.1 6 | streamlit-pdf-viewer==0.0.19 7 | requests==2.32.3 8 | pytz==2023.4 9 | typing-extensions>=4.9.0 10 | 11 | # Optional but recommended 12 | black>=24.1.1 # for code formatting 13 | python-dateutil>=2.8.2 # for date parsing 14 | -------------------------------------------------------------------------------- /rag_tutorials/local_hybrid_search_rag/requirements.txt: -------------------------------------------------------------------------------- 1 | raglite==0.2.1 2 | llama-cpp-python>=0.2.56 3 | sentence-transformers>=2.5.1 4 | pydantic==2.10.1 5 | sqlalchemy>=2.0.0 6 | psycopg2-binary>=2.9.9 7 | pypdf>=3.0.0 8 | python-dotenv>=1.0.0 9 | rerankers==0.6.0 10 | spacy>=3.7.0 11 | streamlit>=1.31.0 12 | flashrank==0.2.9 13 | numpy>=1.24.0 14 | pandas>=2.0.0 15 | tqdm>=4.66.0 16 | -------------------------------------------------------------------------------- /rag_tutorials/rag_agent_cohere/requirements.txt: -------------------------------------------------------------------------------- 1 | langchain==0.3.12 2 | langchain-community==0.3.12 3 | langchain-core==0.3.25 4 | langchain-cohere==0.3.2 5 | langchain-qdrant==0.2.0 6 | cohere==5.11.4 7 | qdrant-client==1.12.1 8 | duckduckgo-search==6.4.1 9 | streamlit==1.40.2 10 | tenacity==9.0.0 11 | typing-extensions==4.12.2 12 | pydantic==2.9.2 13 | pydantic-core==2.23.4 14 | langgraph==0.2.53 -------------------------------------------------------------------------------- /rag_tutorials/corrective_rag/requirements.txt: -------------------------------------------------------------------------------- 1 | # Core dependencies 2 | langchain==0.3.12 3 | langgraph==0.2.53 4 | qdrant-client==1.12.1 5 | langchain-openai==0.2.14 6 | langchain-anthropic==0.3.0 7 | tavily-python==0.5.0 8 | langchain-community==0.3.12 9 | langchain-core==0.3.28 10 | streamlit==1.41.1 11 | tenacity==8.5.0 12 | anthropic>=0.7.0 13 | openai>=1.12.0 14 | tiktoken>=0.6.0 15 | pydantic>=2.0.0 16 | numpy>=1.24.0 17 | PyYAML>=6.0.0 18 | nest-asyncio>=1.5.0 19 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_reasoning_agent/local_ai_reasoning_agent.py: -------------------------------------------------------------------------------- 1 | from agno.agent import Agent 2 | from agno.models.ollama import Ollama 3 | from agno.playground import Playground, serve_playground_app 4 | 5 | reasoning_agent = Agent(name="Reasoning Agent", model=Ollama(id="qwq:32b"), markdown=True) 6 | 7 | # UI for Reasoning agent 8 | app = Playground(agents=[reasoning_agent]).get_app() 9 | 10 | # Run the Playground app 11 | if __name__ == "__main__": 12 | serve_playground_app("local_ai_reasoning_agent:app", reload=True) -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_reasoning_agent/reasoning_agent.py: -------------------------------------------------------------------------------- 1 | from agno.agent import Agent 2 | from agno.models.openai import OpenAIChat 3 | from rich.console import Console 4 | 5 | regular_agent = Agent(model=OpenAIChat(id="gpt-4o-mini"), markdown=True) 6 | console = Console() 7 | reasoning_agent = Agent( 8 | model=OpenAIChat(id="gpt-4o"), 9 | reasoning=True, 10 | markdown=True, 11 | structured_outputs=True, 12 | ) 13 | 14 | task = "How many 'r' are in the word 'supercalifragilisticexpialidocious'?" 15 | 16 | console.rule("[bold green]Regular Agent[/bold green]") 17 | regular_agent.print_response(task, stream=True) 18 | console.rule("[bold yellow]Reasoning Agent[/bold yellow]") 19 | reasoning_agent.print_response(task, stream=True, show_full_reasoning=True) -------------------------------------------------------------------------------- /chat_with_X_tutorials/chat_with_research_papers/chat_arxiv_llama3.py: -------------------------------------------------------------------------------- 1 | # Import the required libraries 2 | import streamlit as st 3 | from agno.agent import Agent 4 | from agno.models.ollama import Ollama 5 | from agno.tools.arxiv import ArxivTools 6 | 7 | # Set up the Streamlit app 8 | st.title("Chat with Research Papers 🔎🤖") 9 | st.caption("This app allows you to chat with arXiv research papers using Llama-3 running locally.") 10 | 11 | # Create an instance of the Assistant 12 | assistant = Agent( 13 | model=Ollama( 14 | id="llama3.1:8b") , tools=[ArxivTools()], show_tool_calls=True 15 | ) 16 | 17 | # Get the search query from the user 18 | query= st.text_input("Enter the Search Query", type="default") 19 | 20 | if query: 21 | # Search the web using the AI Assistant 22 | response = assistant.run(query, stream=False) 23 | st.write(response.content) -------------------------------------------------------------------------------- /ai_agent_tutorials/xai_finance_agent/xai_finance_agent.py: -------------------------------------------------------------------------------- 1 | # import necessary python libraries 2 | from agno.agent import Agent 3 | from agno.models.xai import xAI 4 | from agno.tools.yfinance import YFinanceTools 5 | from agno.tools.duckduckgo import DuckDuckGoTools 6 | from agno.playground import Playground, serve_playground_app 7 | 8 | # create the AI finance agent 9 | agent = Agent( 10 | name="xAI Finance Agent", 11 | model = xAI(id="grok-beta"), 12 | tools=[DuckDuckGoTools(), YFinanceTools(stock_price=True, analyst_recommendations=True, stock_fundamentals=True)], 13 | instructions = ["Always use tables to display financial/numerical data. For text data use bullet points and small paragrpahs."], 14 | show_tool_calls = True, 15 | markdown = True, 16 | ) 17 | 18 | # UI for finance agent 19 | app = Playground(agents=[agent]).get_app() 20 | 21 | if __name__ == "__main__": 22 | serve_playground_app("xai_finance_agent:app", reload=True) -------------------------------------------------------------------------------- /chat_with_X_tutorials/chat_with_substack/README.md: -------------------------------------------------------------------------------- 1 | ## 📝 Chat with Substack Newsletter 2 | Streamlit app that allows you to chat with a Substack newsletter using OpenAI's API and the Embedchain library. This app leverages GPT-4 to provide accurate answers to questions based on the content of the specified Substack newsletter. 3 | 4 | ## Features 5 | - Input a Substack blog URL 6 | - Ask questions about the content of the Substack newsletter 7 | - Get accurate answers using OpenAI's API and Embedchain 8 | 9 | ### How to get Started? 10 | 11 | 1. Clone the GitHub repository 12 | 13 | ```bash 14 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 15 | cd awesome-llm-apps/chat_with_X_tutorials/chat_with_substack 16 | ``` 17 | 2. Install the required dependencies: 18 | 19 | ```bash 20 | pip install -r requirements.txt 21 | ``` 22 | 3. Get your OpenAI API Key 23 | 24 | - Sign up for an [OpenAI account](https://platform.openai.com/) (or the LLM provider of your choice) and obtain your API key. 25 | 26 | 4. Run the Streamlit App 27 | ```bash 28 | streamlit run chat_substack.py 29 | ``` -------------------------------------------------------------------------------- /chat_with_X_tutorials/chat_with_research_papers/README.md: -------------------------------------------------------------------------------- 1 | ## 🔎 Chat with Arxiv Research Papers 2 | This Streamlit app enables you to engage in interactive conversations with arXiv, a vast repository of scholarly articles, using GPT-4o. With this RAG application, you can easily access and explore the wealth of knowledge contained within arXiv. 3 | 4 | ### Features 5 | - Engage in conversational interactions with arXiv 6 | - Access and explore a vast collection of research papers 7 | - Utilize OpenAI GPT-4o for intelligent responses 8 | 9 | ### How to get Started? 10 | 11 | 1. Clone the GitHub repository 12 | 13 | ```bash 14 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 15 | cd awesome-llm-apps/chat_with_X_tutorials/chat_with_research_papers 16 | ``` 17 | 2. Install the required dependencies: 18 | 19 | ```bash 20 | pip install -r requirements.txt 21 | ``` 22 | 3. Get your OpenAI API Key 23 | 24 | - Sign up for an [OpenAI account](https://platform.openai.com/) (or the LLM provider of your choice) and obtain your API key. 25 | 26 | 4. Run the Streamlit App 27 | ```bash 28 | streamlit run chat_arxiv.py 29 | ``` -------------------------------------------------------------------------------- /chat_with_X_tutorials/chat_with_youtube_videos/README.md: -------------------------------------------------------------------------------- 1 | ## 📽️ Chat with YouTube Videos 2 | 3 | LLM app with RAG to chat with YouTube Videos with OpenAI's gpt-4o, mem0/embedchain as memory and the youtube-transcript-api. The app uses Retrieval Augmented Generation (RAG) to provide accurate answers to questions based on the content of the uploaded video. 4 | 5 | ### Features 6 | 7 | - Input a YouTube video URL 8 | - Ask questions about the content of the video 9 | - Get accurate answers using RAG and the selected LLM 10 | 11 | ### How to get Started? 12 | 13 | 1. Clone the GitHub repository 14 | 15 | ```bash 16 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 17 | cd awesome-llm-apps/chat_with_X_tutorials/chat_with_youtube_videos 18 | ``` 19 | 2. Install the required dependencies: 20 | 21 | ```bash 22 | pip install -r requirements.txt 23 | ``` 24 | 3. Get your OpenAI API Key 25 | 26 | - Sign up for an [OpenAI account](https://platform.openai.com/) (or the LLM provider of your choice) and obtain your API key. 27 | 28 | 4. Run the Streamlit App 29 | ```bash 30 | streamlit run chat_youtube.py 31 | ``` 32 | -------------------------------------------------------------------------------- /chat_with_X_tutorials/chat_with_pdf/README.md: -------------------------------------------------------------------------------- 1 | ## 📄 Chat with PDF 2 | 3 | LLM app with RAG to chat with PDF in just 30 lines of Python Code. The app uses Retrieval Augmented Generation (RAG) to provide accurate answers to questions based on the content of the uploaded PDF. 4 | 5 | ### Features 6 | 7 | - Upload a PDF document 8 | - Ask questions about the content of the PDF 9 | - Get accurate answers using RAG and the selected LLM 10 | 11 | ### How to get Started? 12 | 13 | 1. Clone the GitHub repository 14 | 15 | ```bash 16 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 17 | cd awesome-llm-apps/chat_with_X_tutorials/chat_with_pdf 18 | ``` 19 | 2. Install the required dependencies 20 | 21 | ```bash 22 | pip install -r requirements.txt 23 | ``` 24 | 3. Get your OpenAI API Key 25 | 26 | - Sign up for an [OpenAI account](https://platform.openai.com/) (or the LLM provider of your choice) and obtain your API key. 27 | 28 | 4. Run the Streamlit App 29 | ```bash 30 | streamlit run chat_pdf.py 31 | ``` 32 | ### Interactive Application Demo 33 | https://github.com/Shubhamsaboo/awesome-llm-apps/assets/31396011/12bdfc11-c877-4fc7-9e70-63f21d2eb977 34 | 35 | -------------------------------------------------------------------------------- /advanced_tools_frameworks/web_search_ai_assistant/claude_websearch.py: -------------------------------------------------------------------------------- 1 | # Import the required libraries 2 | import streamlit as st 3 | from agno.agent import Agent 4 | from agno.tools.duckduckgo import DuckDuckGoTools 5 | from agno.models.anthropic import Claude 6 | 7 | # Set up the Streamlit app 8 | st.title("Claude Sonnet + AI Web Search 🤖") 9 | st.caption("This app allows you to search the web using Claude Sonnet 3.5") 10 | 11 | # Get Anthropic API key from user 12 | anthropic_api_key = st.text_input("Anthropic's Claude API Key", type="password") 13 | 14 | # If Anthropic API key is provided, create an instance of Assistant 15 | if anthropic_api_key: 16 | assistant = Agent( 17 | model=Claude( 18 | id="claude-3-5-sonnet-20240620", 19 | max_tokens=1024, 20 | temperature=0.3, 21 | api_key=anthropic_api_key) , tools=[DuckDuckGoTools()], show_tool_calls=True 22 | ) 23 | # Get the search query from the user 24 | query= st.text_input("Enter the Search Query", type="default") 25 | 26 | if query: 27 | # Search the web using the AI Assistant 28 | response = assistant.run(query, stream=False) 29 | st.write(response.content) -------------------------------------------------------------------------------- /chat_with_X_tutorials/chat_with_research_papers/chat_arxiv.py: -------------------------------------------------------------------------------- 1 | # Import the required libraries 2 | import streamlit as st 3 | from agno.agent import Agent 4 | from agno.models.openai import OpenAIChat 5 | from agno.tools.arxiv import ArxivTools 6 | 7 | # Set up the Streamlit app 8 | st.title("Chat with Research Papers 🔎🤖") 9 | st.caption("This app allows you to chat with arXiv research papers using OpenAI GPT-4o model.") 10 | 11 | # Get OpenAI API key from user 12 | openai_access_token = st.text_input("OpenAI API Key", type="password") 13 | 14 | # If OpenAI API key is provided, create an instance of Assistant 15 | if openai_access_token: 16 | # Create an instance of the Assistant 17 | assistant = Agent( 18 | model=OpenAIChat( 19 | id="gpt-4o", 20 | max_tokens=1024, 21 | temperature=0.9, 22 | api_key=openai_access_token) , tools=[ArxivTools()] 23 | ) 24 | 25 | # Get the search query from the user 26 | query= st.text_input("Enter the Search Query", type="default") 27 | 28 | if query: 29 | # Search the web using the AI Assistant 30 | response = assistant.run(query, stream=False) 31 | st.write(response.content) -------------------------------------------------------------------------------- /rag_tutorials/agentic_rag/rag_agent.py: -------------------------------------------------------------------------------- 1 | from agno.agent import Agent 2 | from agno.models.openai import OpenAIChat 3 | from agno.knowledge.pdf_url import PDFUrlKnowledgeBase 4 | from agno.vectordb.lancedb import LanceDb, SearchType 5 | from agno.playground import Playground, serve_playground_app 6 | from agno.tools.duckduckgo import DuckDuckGoTools 7 | 8 | db_uri = "tmp/lancedb" 9 | # Create a knowledge base from a PDF 10 | knowledge_base = PDFUrlKnowledgeBase( 11 | urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"], 12 | # Use LanceDB as the vector database 13 | vector_db=LanceDb(table_name="recipes", uri=db_uri, search_type=SearchType.vector), 14 | ) 15 | # Load the knowledge base: Comment out after first run 16 | knowledge_base.load(upsert=True) 17 | 18 | rag_agent = Agent( 19 | model=OpenAIChat(id="gpt-4o"), 20 | agent_id="rag-agent", 21 | knowledge=knowledge_base, # Add the knowledge base to the agent 22 | tools=[DuckDuckGoTools()], 23 | show_tool_calls=True, 24 | markdown=True, 25 | ) 26 | 27 | app = Playground(agents=[rag_agent]).get_app() 28 | 29 | if __name__ == "__main__": 30 | serve_playground_app("rag_agent:app", reload=True) -------------------------------------------------------------------------------- /advanced_tools_frameworks/web_search_ai_assistant/gpt4_websearch.py: -------------------------------------------------------------------------------- 1 | # Import the required libraries 2 | import streamlit as st 3 | from agno.agent import Agent 4 | from agno.tools.duckduckgo import DuckDuckGoTools 5 | from agno.models.openai import OpenAIChat 6 | 7 | # Set up the Streamlit app 8 | st.title("AI Web Search Assistant 🤖") 9 | st.caption("This app allows you to search the web using GPT-4o") 10 | 11 | # Get OpenAI API key from user 12 | openai_access_token = st.text_input("OpenAI API Key", type="password") 13 | 14 | # If OpenAI API key is provided, create an instance of Assistant 15 | if openai_access_token: 16 | # Create an instance of the Assistant 17 | assistant = Agent( 18 | model=OpenAIChat( 19 | id="gpt-4o", 20 | max_tokens=1024, 21 | temperature=0.9, 22 | api_key=openai_access_token) , tools=[DuckDuckGoTools()], show_tool_calls=True 23 | ) 24 | 25 | # Get the search query from the user 26 | query= st.text_input("Enter the Search Query", type="default") 27 | 28 | if query: 29 | # Search the web using the AI Assistant 30 | response = assistant.run(query, stream=False) 31 | st.write(response.content) -------------------------------------------------------------------------------- /rag_tutorials/rag-as-a-service/README.md: -------------------------------------------------------------------------------- 1 | ## 🖇️ RAG-as-a-Service with Claude 3.5 Sonnet 2 | Build and deploy a production-ready Retrieval-Augmented Generation (RAG) service using Claude 3.5 Sonnet and Ragie.ai. This implementation allows you to create a document querying system with a user-friendly Streamlit interface in less than 50 lines of Python code. 3 | 4 | ### Features 5 | - Production-ready RAG pipeline 6 | - Integration with Claude 3.5 Sonnet for response generation 7 | - Document upload from URLs 8 | - Real-time document querying 9 | - Support for both fast and accurate document processing modes 10 | 11 | ### How to get Started? 12 | 13 | 1. Clone the GitHub repository 14 | ```bash 15 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 16 | cd awesome-llm-apps/rag_tutorials/rag-as-a-service 17 | ``` 18 | 19 | 2. Install the required dependencies: 20 | 21 | ```bash 22 | pip install -r requirements.txt 23 | ``` 24 | 25 | 3. Get your Anthropic API and Ragie API Key 26 | 27 | - Sign up for an [Anthropic account](https://console.anthropic.com/) and get your API key 28 | - Sign up for an [Ragie account](https://www.ragie.ai/) and get your API key 29 | 30 | 4. Run the Streamlit app 31 | ```bash 32 | streamlit run rag_app.py 33 | ``` -------------------------------------------------------------------------------- /ai_agent_tutorials/multimodal_ai_agent/README.md: -------------------------------------------------------------------------------- 1 | ## 🧬 Multimodal AI Agent 2 | 3 | A Streamlit application that combines video analysis and web search capabilities using Google's Gemini 2.0 model. This agent can analyze uploaded videos and answer questions by combining visual understanding with web-search. 4 | 5 | ### Features 6 | 7 | - Video analysis using Gemini 2.0 Flash 8 | - Web research integration via DuckDuckGo 9 | - Support for multiple video formats (MP4, MOV, AVI) 10 | - Real-time video processing 11 | - Combined visual and textual analysis 12 | 13 | ### How to get Started? 14 | 15 | 1. Clone the GitHub repository 16 | 17 | ```bash 18 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 19 | cd ai_agent_tutorials/multimodal_ai_agent 20 | ``` 21 | 2. Install the required dependencies: 22 | 23 | ```bash 24 | pip install -r requirements.txt 25 | ``` 26 | 3. Get your Google Gemini API Key 27 | 28 | - Sign up for an [Google AI Studio account](https://aistudio.google.com/apikey) and obtain your API key. 29 | 30 | 4. Set up your Gemini API Key as the environment variable 31 | 32 | ```bash 33 | GOOGLE_API_KEY=your_api_key_here 34 | ``` 35 | 36 | 5. Run the Streamlit App 37 | ```bash 38 | streamlit run multimodal_agent.py 39 | ``` 40 | -------------------------------------------------------------------------------- /advanced_tools_frameworks/gemini_multimodal_chatbot/README.md: -------------------------------------------------------------------------------- 1 | ## ⚡️ Multimodal Chatbot with Gemini Flash 2 | This repository contains a Streamlit application that demonstrates a multimodal chatbot using Google's Gemini Flash model. The chatbot allows users to interact with the model using both image and text inputs, providing lightning-fast results. 3 | 4 | ## Features 5 | - Multimodal input: Users can upload images and enter text queries to interact with the chatbot. 6 | - Gemini Flash model: The chatbot leverages Google's powerful Gemini Flash model for generating responses. 7 | - Chat history: The application maintains a chat history, displaying the conversation between the user and the chatbot. 8 | 9 | ### How to get Started? 10 | 11 | 1. Clone the GitHub repository 12 | 13 | ```bash 14 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 15 | cd awesome-llm-apps/advanced_tools_frameworks/gemini_multimodal_chatbot 16 | ``` 17 | 2. Install the required dependencies: 18 | 19 | ```bash 20 | pip install -r requirements.txt 21 | ``` 22 | 3. Get your Google Studio API Key 23 | 24 | - Sign up for an [Google AI Studio](https://aistudio.google.com/app/apikey) and obtain your API key. 25 | 26 | 4. Run the Streamlit App 27 | ```bash 28 | streamlit run gemini_multimodal_chatbot.py 29 | ``` -------------------------------------------------------------------------------- /chat_with_X_tutorials/chat_with_pdf/chat_pdf.py: -------------------------------------------------------------------------------- 1 | import os 2 | import tempfile 3 | import streamlit as st 4 | from embedchain import App 5 | 6 | def embedchain_bot(db_path, api_key): 7 | return App.from_config( 8 | config={ 9 | "llm": {"provider": "openai", "config": {"api_key": api_key}}, 10 | "vectordb": {"provider": "chroma", "config": {"dir": db_path}}, 11 | "embedder": {"provider": "openai", "config": {"api_key": api_key}}, 12 | } 13 | ) 14 | 15 | st.title("Chat with PDF") 16 | 17 | openai_access_token = st.text_input("OpenAI API Key", type="password") 18 | 19 | if openai_access_token: 20 | db_path = tempfile.mkdtemp() 21 | app = embedchain_bot(db_path, openai_access_token) 22 | 23 | pdf_file = st.file_uploader("Upload a PDF file", type="pdf") 24 | 25 | if pdf_file: 26 | with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as f: 27 | f.write(pdf_file.getvalue()) 28 | app.add(f.name, data_type="pdf_file") 29 | os.remove(f.name) 30 | st.success(f"Added {pdf_file.name} to knowledge base!") 31 | 32 | prompt = st.text_input("Ask a question about the PDF") 33 | 34 | if prompt: 35 | answer = app.chat(prompt) 36 | st.write(answer) 37 | 38 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_investment_agent/investment_agent.py: -------------------------------------------------------------------------------- 1 | # Import the required libraries 2 | import streamlit as st 3 | from agno.agent import Assistant 4 | from agno.models.openai import OpenAIChat 5 | from agno.tools.yfinance import YFinanceTools 6 | 7 | # Set up the Streamlit app 8 | st.title("AI Investment Agent 📈🤖") 9 | st.caption("This app allows you to compare the performance of two stocks and generate detailed reports.") 10 | 11 | # Get OpenAI API key from user 12 | openai_api_key = st.text_input("OpenAI API Key", type="password") 13 | 14 | if openai_api_key: 15 | # Create an instance of the Assistant 16 | assistant = Assistant( 17 | llm=OpenAIChat(model="gpt-4o", api_key=openai_api_key), 18 | tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True, company_news=True)], 19 | show_tool_calls=True, 20 | ) 21 | 22 | # Input fields for the stocks to compare 23 | stock1 = st.text_input("Enter the first stock symbol") 24 | stock2 = st.text_input("Enter the second stock symbol") 25 | 26 | if stock1 and stock2: 27 | # Get the response from the assistant 28 | query = f"Compare {stock1} to {stock2}. Use every tool you have." 29 | response = assistant.run(query, stream=False) 30 | st.write(response.content) -------------------------------------------------------------------------------- /llm_apps_with_memory_tutorials/multi_llm_memory/README.md: -------------------------------------------------------------------------------- 1 | ## 🧠 Multi-LLM App with Shared Memory 2 | 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. 3 | 4 | ### Features 5 | 6 | - Support for multiple LLMs: 7 | - OpenAI's GPT-4o 8 | - Anthropic's Claude 3.5 Sonnet 9 | 10 | - Persistent memory using Qdrant vector store 11 | - User-specific conversation history 12 | - Memory retrieval for contextual responses 13 | - User-friendly interface with LLM selection 14 | 15 | ### How to get Started? 16 | 17 | 1. Clone the GitHub repository 18 | ```bash 19 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 20 | cd awesome-llm-apps/llm_apps_with_memory_tutorials/multi_llm_memory 21 | ``` 22 | 23 | 2. Install the required dependencies: 24 | 25 | ```bash 26 | pip install -r requirements.txt 27 | ``` 28 | 29 | 3. Ensure Qdrant is running: 30 | The app expects Qdrant to be running on localhost:6333. Adjust the configuration in the code if your setup is different. 31 | 32 | ```bash 33 | docker pull qdrant/qdrant 34 | docker run -p 6333:6333 qdrant/qdrant 35 | ``` 36 | 37 | 4. Run the Streamlit App 38 | ```bash 39 | streamlit run multi_llm_memory.py 40 | ``` -------------------------------------------------------------------------------- /advanced_tools_frameworks/web_scrapping_ai_agent/ai_scrapper.py: -------------------------------------------------------------------------------- 1 | # Import the required libraries 2 | import streamlit as st 3 | from scrapegraphai.graphs import SmartScraperGraph 4 | 5 | # Set up the Streamlit app 6 | st.title("Web Scrapping AI Agent 🕵️‍♂️") 7 | st.caption("This app allows you to scrape a website using OpenAI API") 8 | 9 | # Get OpenAI API key from user 10 | openai_access_token = st.text_input("OpenAI API Key", type="password") 11 | 12 | if openai_access_token: 13 | model = st.radio( 14 | "Select the model", 15 | ["gpt-3.5-turbo", "gpt-4"], 16 | index=0, 17 | ) 18 | graph_config = { 19 | "llm": { 20 | "api_key": openai_access_token, 21 | "model": model, 22 | }, 23 | } 24 | # Get the URL of the website to scrape 25 | url = st.text_input("Enter the URL of the website you want to scrape") 26 | # Get the user prompt 27 | user_prompt = st.text_input("What you want the AI agent to scrae from the website?") 28 | 29 | # Create a SmartScraperGraph object 30 | smart_scraper_graph = SmartScraperGraph( 31 | prompt=user_prompt, 32 | source=url, 33 | config=graph_config 34 | ) 35 | # Scrape the website 36 | if st.button("Scrape"): 37 | result = smart_scraper_graph.run() 38 | st.write(result) -------------------------------------------------------------------------------- /ai_agent_tutorials/multi_agent_researcher/research_agent_llama3.py: -------------------------------------------------------------------------------- 1 | # Import the required libraries 2 | import streamlit as st 3 | from agno.agent import Agent 4 | from agno.tools.hackernews import HackerNews 5 | from agno.models.ollama import Ollama 6 | 7 | # Set up the Streamlit app 8 | st.title("Multi-Agent AI Researcher using Llama-3 🔍🤖") 9 | st.caption("This app allows you to research top stories and users on HackerNews and write blogs, reports and social posts.") 10 | 11 | # Create instances of the Assistant 12 | story_researcher = Agent( 13 | name="HackerNews Story Researcher", 14 | role="Researches hackernews stories and users.", 15 | tools=[HackerNews()], 16 | model=Ollama(id="llama3.2", max_tokens=1024) 17 | ) 18 | 19 | user_researcher = Agent( 20 | name="HackerNews User Researcher", 21 | role="Reads articles from URLs.", 22 | tools=[HackerNews()], 23 | model=Ollama(id="llama3.2", max_tokens=1024) 24 | ) 25 | 26 | hn_assistant = Agent( 27 | name="Hackernews Team", 28 | team=[story_researcher, user_researcher], 29 | model=Ollama(id="llama3.2", max_tokens=1024) 30 | ) 31 | 32 | # Input field for the report query 33 | query = st.text_input("Enter your report query") 34 | 35 | if query: 36 | # Get the response from the assistant 37 | response = hn_assistant.run(query, stream=False) 38 | st.write(response.content) -------------------------------------------------------------------------------- /advanced_tools_frameworks/web_scrapping_ai_agent/local_ai_scrapper.py: -------------------------------------------------------------------------------- 1 | # Import the required libraries 2 | import streamlit as st 3 | from scrapegraphai.graphs import SmartScraperGraph 4 | 5 | # Set up the Streamlit app 6 | st.title("Web Scrapping AI Agent 🕵️‍♂️") 7 | st.caption("This app allows you to scrape a website using Llama 3.2") 8 | 9 | # Set up the configuration for the SmartScraperGraph 10 | graph_config = { 11 | "llm": { 12 | "model": "ollama/llama3.2", 13 | "temperature": 0, 14 | "format": "json", # Ollama needs the format to be specified explicitly 15 | "base_url": "http://localhost:11434", # set Ollama URL 16 | }, 17 | "embeddings": { 18 | "model": "ollama/nomic-embed-text", 19 | "base_url": "http://localhost:11434", # set Ollama URL 20 | }, 21 | "verbose": True, 22 | } 23 | # Get the URL of the website to scrape 24 | url = st.text_input("Enter the URL of the website you want to scrape") 25 | # Get the user prompt 26 | user_prompt = st.text_input("What you want the AI agent to scrae from the website?") 27 | 28 | # Create a SmartScraperGraph object 29 | smart_scraper_graph = SmartScraperGraph( 30 | prompt=user_prompt, 31 | source=url, 32 | config=graph_config 33 | ) 34 | # Scrape the website 35 | if st.button("Scrape"): 36 | result = smart_scraper_graph.run() 37 | st.write(result) -------------------------------------------------------------------------------- /llm_apps_with_memory_tutorials/llm_app_personalized_memory/README.md: -------------------------------------------------------------------------------- 1 | ## 🧠 LLM App with Memory 2 | This Streamlit app is an AI-powered chatbot that uses OpenAI's GPT-4o model with a persistent memory feature. It allows users to have conversations with the AI while maintaining context across multiple interactions. 3 | 4 | ### Features 5 | 6 | - Utilizes OpenAI's GPT-4o model for generating responses 7 | - Implements persistent memory using Mem0 and Qdrant vector store 8 | - Allows users to view their conversation history 9 | - Provides a user-friendly interface with Streamlit 10 | 11 | 12 | ### How to get Started? 13 | 14 | 1. Clone the GitHub repository 15 | ```bash 16 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 17 | cd awesome-llm-apps/llm_apps_with_memory_tutorials/llm_app_personalized_memory 18 | ``` 19 | 20 | 2. Install the required dependencies: 21 | 22 | ```bash 23 | pip install -r requirements.txt 24 | ``` 25 | 26 | 3. Ensure Qdrant is running: 27 | The app expects Qdrant to be running on localhost:6333. Adjust the configuration in the code if your setup is different. 28 | 29 | ```bash 30 | docker pull qdrant/qdrant 31 | 32 | docker run -p 6333:6333 -p 6334:6334 \ 33 | -v $(pwd)/qdrant_storage:/qdrant/storage:z \ 34 | qdrant/qdrant 35 | ``` 36 | 37 | 4. Run the Streamlit App 38 | ```bash 39 | streamlit run llm_app_memory.py 40 | ``` 41 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_personal_finance_agent/README.md: -------------------------------------------------------------------------------- 1 | ## 💰 AI Personal Finance Planner 2 | This Streamlit app is an AI-powered personal finance planner that generates personalized financial plans using OpenAI GPT-4o. It automates the process of researching, planning, and creating tailored budgets, investment strategies, and savings goals, empowering you to take control of your financial future with ease. 3 | 4 | ### Features 5 | - Set your financial goals and provide details about your current financial situation 6 | - Use GPT-4o to generate intelligent and personalized financial advice 7 | - Receive customized budgets, investment plans, and savings strategies 8 | 9 | ### How to get Started? 10 | 11 | 1. Clone the GitHub repository 12 | 13 | ```bash 14 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 15 | cd awesome-llm-apps/ai_agent_tutorials/ai_personal_finance_agent 16 | ``` 17 | 2. Install the required dependencies: 18 | 19 | ```bash 20 | pip install -r requirements.txt 21 | ``` 22 | 3. Get your OpenAI API Key 23 | 24 | - Sign up for an [OpenAI account](https://platform.openai.com/) (or the LLM provider of your choice) and obtain your API key. 25 | 26 | 4. Get your SerpAPI Key 27 | 28 | - Sign up for an [SerpAPI account](https://serpapi.com/) and obtain your API key. 29 | 30 | 5. Run the Streamlit App 31 | ```bash 32 | streamlit run finance_agent.py 33 | ``` -------------------------------------------------------------------------------- /ai_agent_tutorials/gemini_multimodal_agent_demo/multimodal_ai_agent.py: -------------------------------------------------------------------------------- 1 | from agno.agent import Agent 2 | from agno.models.google import Gemini 3 | from agno.tools.duckduckgo import DuckDuckGoTools 4 | from google.generativeai import upload_file, get_file 5 | import time 6 | 7 | # 1. Initialize the Multimodal Agent 8 | agent = Agent(model=Gemini(id="gemini-2.0-flash-exp"), tools=[DuckDuckGoTools()], markdown=True) 9 | 10 | # 2. Image Input 11 | image_url = "https://example.com/sample_image.jpg" 12 | 13 | # 3. Audio Input 14 | audio_file = "sample_audio.mp3" 15 | 16 | # 4. Video Input 17 | video_file = upload_file("sample_video.mp4") 18 | while video_file.state.name == "PROCESSING": 19 | time.sleep(2) 20 | video_file = get_file(video_file.name) 21 | 22 | # 5. Multimodal Query 23 | query = """ 24 | Combine insights from the inputs: 25 | 1. **Image**: Describe the scene and its significance. 26 | 2. **Audio**: Extract key messages that relate to the visual. 27 | 3. **Video**: Look at the video input and provide insights that connect with the image and audio context. 28 | 4. **Web Search**: Find the latest updates or events linking all these topics. 29 | Summarize the overall theme or story these inputs convey. 30 | """ 31 | 32 | # 6. Multimodal Agent generates unified response 33 | agent.print_response(query, images=[image_url], audio=audio_file, videos=[video_file], stream=True) -------------------------------------------------------------------------------- /advanced_tools_frameworks/local_chatgpt_clone/README.md: -------------------------------------------------------------------------------- 1 | ## 🦙💬 ChatGPT Clone using Llama-3 2 | This project demonstrates how to build a ChatGPT clone using the Llama-3 model running locally on your computer. The application is built using Python and Streamlit, providing a user-friendly interface for interacting with the language model. Best of all, it's 100% free and doesn't require an internet connection! 3 | 4 | ### Features 5 | - Runs locally on your computer without the need for an internet connection and completely free to use. 6 | - Utilizes the Llama-3 instruct model for generating responses 7 | - Provides a chat-like interface for seamless interaction 8 | 9 | ### How to get Started? 10 | 11 | 1. Clone the GitHub repository 12 | 13 | ```bash 14 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 15 | cd awesome-llm-apps/advanced_tools_frameworks/local_chatgpt_clone 16 | ``` 17 | 2. Install the required dependencies: 18 | 19 | ```bash 20 | pip install -r requirements.txt 21 | ``` 22 | 3. Download and install the [LM Studio desktop app](https://lmstudio.ai/). Download the Llama-3 instruct model. 23 | 24 | 4. Expose the Llama-3 model as an OpenAI API by starting the server in LM Studio. Watch this [video walkthrough](https://x.com/Saboo_Shubham_/status/1783715814790549683). 25 | 26 | 5. Run the Streamlit App 27 | ```bash 28 | streamlit run chatgpt_clone_llama3.py 29 | ``` 30 | 31 | -------------------------------------------------------------------------------- /rag_tutorials/local_rag_agent/README.md: -------------------------------------------------------------------------------- 1 | ## 🦙 Local RAG Agent with Llama 3.2 2 | This application implements a Retrieval-Augmented Generation (RAG) system using Llama 3.2 via Ollama, with Qdrant as the vector database. 3 | 4 | 5 | ### Features 6 | - Fully local RAG implementation 7 | - Powered by Llama 3.2 through Ollama 8 | - Vector search using Qdrant 9 | - Interactive playground interface 10 | - No external API dependencies 11 | 12 | ### How to get Started? 13 | 14 | 1. Clone the GitHub repository 15 | ```bash 16 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 17 | ``` 18 | 19 | 2. Install the required dependencies: 20 | 21 | ```bash 22 | cd awesome-llm-apps/rag_tutorials/local_rag_agent 23 | pip install -r requirements.txt 24 | ``` 25 | 26 | 3. Install and start [Qdrant](https://qdrant.tech/) vector database locally 27 | 28 | ```bash 29 | docker pull qdrant/qdrant 30 | docker run -p 6333:6333 qdrant/qdrant 31 | ``` 32 | 33 | 4. Install [Ollama](https://ollama.com/download) and pull Llama 3.2 for LLM and OpenHermes as the embedder for OllamaEmbedder 34 | ```bash 35 | ollama pull llama3.2 36 | ollama pull openhermes 37 | ``` 38 | 39 | 4. Run the AI RAG Agent 40 | ```bash 41 | python local_rag_agent.py 42 | ``` 43 | 44 | 5. Open your web browser and navigate to the URL provided in the console output to interact with the RAG agent through the playground interface. 45 | 46 | 47 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_customer_support_agent/README.md: -------------------------------------------------------------------------------- 1 | ## 🛒 AI Customer Support Agent with Memory 2 | This Streamlit app implements an AI-powered customer support agent for synthetic data generated using GPT-4o. The agent uses OpenAI's GPT-4o model and maintains a memory of past interactions using the Mem0 library with Qdrant as the vector store. 3 | 4 | ### Features 5 | 6 | - Chat interface for interacting with the AI customer support agent 7 | - Persistent memory of customer interactions and profiles 8 | - Synthetic data generation for testing and demonstration 9 | - Utilizes OpenAI's GPT-4o model for intelligent responses 10 | 11 | ### How to get Started? 12 | 13 | 1. Clone the GitHub repository 14 | ```bash 15 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 16 | cd awesome-llm-apps/ai_agent_tutorials/ai_customer_support_agent 17 | ``` 18 | 19 | 2. Install the required dependencies: 20 | 21 | ```bash 22 | pip install -r requirements.txt 23 | ``` 24 | 25 | 3. Ensure Qdrant is running: 26 | The app expects Qdrant to be running on localhost:6333. Adjust the configuration in the code if your setup is different. 27 | 28 | ```bash 29 | docker pull qdrant/qdrant 30 | 31 | docker run -p 6333:6333 -p 6334:6334 \ 32 | -v "$(pwd)/qdrant_storage:/qdrant/storage:z" \ 33 | qdrant/qdrant 34 | ``` 35 | 36 | 4. Run the Streamlit App 37 | ```bash 38 | streamlit run customer_support_agent.py 39 | ``` 40 | -------------------------------------------------------------------------------- /rag_tutorials/llama3.1_local_rag/README.md: -------------------------------------------------------------------------------- 1 | ## 💻 Local Lllama-3.1 with RAG 2 | Streamlit app that allows you to chat with any webpage using local Llama-3.1 and Retrieval Augmented Generation (RAG). This app runs entirely on your computer, making it 100% free and without the need for an internet connection. 3 | 4 | 5 | ### Features 6 | - Input a webpage URL 7 | - Ask questions about the content of the webpage 8 | - Get accurate answers using RAG and the Llama-3.1 model running locally on your computer 9 | 10 | ### How to get Started? 11 | 12 | 1. Clone the GitHub repository 13 | 14 | ```bash 15 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 16 | cd awesome-llm-apps/rag_tutorials/llama3.1_local_rag 17 | ``` 18 | 2. Install the required dependencies: 19 | 20 | ```bash 21 | pip install -r requirements.txt 22 | ``` 23 | 3. Run the Streamlit App 24 | ```bash 25 | streamlit run llama3.1_local_rag.py 26 | ``` 27 | 28 | ### How it Works? 29 | 30 | - The app loads the webpage data using WebBaseLoader and splits it into chunks using RecursiveCharacterTextSplitter. 31 | - It creates Ollama embeddings and a vector store using Chroma. 32 | - The app sets up a RAG (Retrieval-Augmented Generation) chain, which retrieves relevant documents based on the user's question. 33 | - The Llama-3.1 model is called to generate an answer using the retrieved context. 34 | - The app displays the answer to the user's question. 35 | 36 | -------------------------------------------------------------------------------- /ai_agent_tutorials/xai_finance_agent/README.md: -------------------------------------------------------------------------------- 1 | ## 📊 AI Finance Agent with xAI Grok 2 | This application creates a financial analysis agent powered by xAI's Grok model, combining real-time stock data with web search capabilities. It provides structured financial insights through an interactive playground interface. 3 | 4 | ### Features 5 | 6 | - Powered by xAI's Grok-beta model 7 | - Real-time stock data analysis via YFinance 8 | - Web search capabilities through DuckDuckGo 9 | - Formatted output with tables for financial data 10 | - Interactive playground interface 11 | 12 | ### How to get Started? 13 | 14 | 1. Clone the GitHub repository 15 | ```bash 16 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 17 | cd awesome-llm-apps/ai_agent_tutorials/xai_finance_agent 18 | ``` 19 | 20 | 2. Install the required dependencies: 21 | 22 | ```bash 23 | cd awesome-llm-apps/ai_agent_tutorials/xai_finance_agent 24 | pip install -r requirements.txt 25 | ``` 26 | 27 | 3. Get your OpenAI API Key 28 | 29 | - Sign up for an [xAI API account](https://console.x.ai/) 30 | - Set your XAI_API_KEY environment variable. 31 | ```bash 32 | export XAI_API_KEY='your-api-key-here' 33 | ``` 34 | 35 | 4. Run the team of AI Agents 36 | ```bash 37 | python xai_finance_agent.py 38 | ``` 39 | 40 | 5. Open your web browser and navigate to the URL provided in the console output to interact with the AI financial agent through the playground interface. 41 | -------------------------------------------------------------------------------- /chat_with_X_tutorials/chat_with_github/chat_github.py: -------------------------------------------------------------------------------- 1 | # Import the required libraries 2 | from embedchain.pipeline import Pipeline as App 3 | from embedchain.loaders.github import GithubLoader 4 | import streamlit as st 5 | import os 6 | 7 | loader = GithubLoader( 8 | config={ 9 | "token":"Your GitHub Token", 10 | } 11 | ) 12 | 13 | # Create Streamlit app 14 | st.title("Chat with GitHub Repository 💬") 15 | st.caption("This app allows you to chat with a GitHub Repo using OpenAI API") 16 | 17 | # Get OpenAI API key from user 18 | openai_access_token = st.text_input("OpenAI API Key", type="password") 19 | 20 | # If OpenAI API key is provided, create an instance of App 21 | if openai_access_token: 22 | os.environ["OPENAI_API_KEY"] = openai_access_token 23 | # Create an instance of Embedchain App 24 | app = App() 25 | # Get the GitHub repo from the user 26 | git_repo = st.text_input("Enter the GitHub Repo", type="default") 27 | if git_repo: 28 | # Add the repo to the knowledge base 29 | app.add("repo:" + git_repo + " " + "type:repo", data_type="github", loader=loader) 30 | st.success(f"Added {git_repo} to knowledge base!") 31 | # Ask a question about the Github Repo 32 | prompt = st.text_input("Ask any question about the GitHub Repo") 33 | # Chat with the GitHub Repo 34 | if prompt: 35 | answer = app.chat(prompt) 36 | st.write(answer) -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_chess_agent/README.md: -------------------------------------------------------------------------------- 1 | # ♜ Agent White vs Agent Black: Chess Game 2 | 3 | An advanced Chess game system where two AI agents play chess against each other using Autogen in a streamlit app. It is built with robust move validation and game state management. 4 | 5 | ## Features 6 | 7 | ### Multi-Agent Architecture 8 | - Player White: OpenAI-powered strategic decision maker 9 | - Player Black: OpenAI-powered tactical opponent 10 | - Board Proxy: Validation agent for move legality and game state 11 | 12 | ### Safety & Validation 13 | - Robust move verification system 14 | - Illegal move prevention 15 | - Real-time board state monitoring 16 | - Secure game progression control 17 | 18 | ### Strategic Gameplay 19 | - AI-powered position evaluation 20 | - Deep tactical analysis 21 | - Dynamic strategy adaptation 22 | - Complete chess ruleset implementation 23 | 24 | 25 | ### How to get Started? 26 | 27 | 1. Clone the GitHub repository 28 | 29 | ```bash 30 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 31 | cd ai_agent_tutorials/ai_chess_game 32 | ``` 33 | 2. Install the required dependencies: 34 | 35 | ```bash 36 | pip install -r requirements.txt 37 | ``` 38 | 3. Get your OpenAI API Key 39 | 40 | - Sign up for an [OpenAI account](https://platform.openai.com/) (or the LLM provider of your choice) and obtain your API key. 41 | 42 | 4. Run the Streamlit App 43 | ```bash 44 | streamlit run ai_chess_agent.py 45 | ``` 46 | 47 | -------------------------------------------------------------------------------- /rag_tutorials/local_rag_agent/local_rag_agent.py: -------------------------------------------------------------------------------- 1 | # Import necessary libraries 2 | from agno.agent import Agent 3 | from agno.models.ollama import Ollama 4 | from agno.knowledge.pdf_url import PDFUrlKnowledgeBase 5 | from agno.vectordb.qdrant import Qdrant 6 | from agno.embedder.ollama import OllamaEmbedder 7 | from agno.playground import Playground, serve_playground_app 8 | 9 | # Define the collection name for the vector database 10 | collection_name = "thai-recipe-index" 11 | 12 | # Set up Qdrant as the vector database with the embedder 13 | vector_db = Qdrant( 14 | collection=collection_name, 15 | url="http://localhost:6333/", 16 | embedder=OllamaEmbedder() 17 | ) 18 | 19 | # Define the knowledge base with the specified PDF URL 20 | knowledge_base = PDFUrlKnowledgeBase( 21 | urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"], 22 | vector_db=vector_db, 23 | ) 24 | 25 | # Load the knowledge base, comment out after the first run to avoid reloading 26 | knowledge_base.load(recreate=True, upsert=True) 27 | 28 | # Create the Agent using Ollama's llama3.2 model and the knowledge base 29 | agent = Agent( 30 | name="Local RAG Agent", 31 | model=Ollama(id="llama3.2"), 32 | knowledge=knowledge_base, 33 | ) 34 | 35 | # UI for RAG agent 36 | app = Playground(agents=[agent]).get_app() 37 | 38 | # Run the Playground app 39 | if __name__ == "__main__": 40 | serve_playground_app("local_rag_agent:app", reload=True) 41 | -------------------------------------------------------------------------------- /llm_apps_with_memory_tutorials/ai_travel_agent_memory/README.md: -------------------------------------------------------------------------------- 1 | ## 🧳 AI Travel Agent with Memory 2 | This Streamlit app implements an AI-powered travel assistant that remembers user preferences and past interactions. It utilizes OpenAI's GPT-4o for generating responses and Mem0 with Qdrant for maintaining conversation history. 3 | 4 | ### Features 5 | - Chat-based interface for interacting with an AI travel assistant 6 | - Persistent memory of user preferences and past conversations 7 | - Utilizes OpenAI's GPT-4o model for intelligent responses 8 | - Implements memory storage and retrieval using Mem0 and Qdrant 9 | - User-specific conversation history and memory viewing 10 | 11 | ### How to get Started? 12 | 13 | 1. Clone the GitHub repository 14 | ```bash 15 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 16 | cd awesome-llm-apps/llm_apps_with_memory_tutorials/ai_travel_agent_memory 17 | ``` 18 | 19 | 2. Install the required dependencies: 20 | 21 | ```bash 22 | pip install -r requirements.txt 23 | ``` 24 | 25 | 3. Ensure Qdrant is running: 26 | The app expects Qdrant to be running on localhost:6333. Adjust the configuration in the code if your setup is different. 27 | 28 | ```bash 29 | docker pull qdrant/qdrant 30 | 31 | docker run -p 6333:6333 -p 6334:6334 \ 32 | -v $(pwd)/qdrant_storage:/qdrant/storage:z \ 33 | qdrant/qdrant 34 | ``` 35 | 36 | 4. Run the Streamlit App 37 | ```bash 38 | streamlit run travel_agent_memory.py 39 | ``` 40 | -------------------------------------------------------------------------------- /llm_apps_with_memory_tutorials/local_chatgpt_with_memory/README.md: -------------------------------------------------------------------------------- 1 | ## 🧠 Local ChatGPT using Llama 3.1 with Personal Memory 2 | 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. 3 | 4 | ### Features 5 | - Fully local implementation with no external API dependencies 6 | - Powered by Llama 3.1 via Ollama 7 | - Personal memory space for each user 8 | - Local embedding generation using Nomic Embed 9 | - Vector storage with Qdrant 10 | 11 | ### How to get Started? 12 | 13 | 1. Clone the GitHub repository 14 | ```bash 15 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 16 | cd awesome-llm-apps/llm_apps_with_memory_tutorials/local_chatgpt_with_memory 17 | ``` 18 | 19 | 2. Install the required dependencies: 20 | 21 | ```bash 22 | cd awesome-llm-apps/rag_tutorials/local_rag_agent 23 | pip install -r requirements.txt 24 | ``` 25 | 26 | 3. Install and start [Qdrant](https://qdrant.tech/documentation/guides/installation/) vector database locally 27 | 28 | ```bash 29 | docker pull qdrant/qdrant 30 | docker run -p 6333:6333 qdrant/qdrant 31 | ``` 32 | 33 | 4. Install [Ollama](https://ollama.com/download) and pull Llama 3.1 34 | ```bash 35 | ollama pull llama3.1 36 | ``` 37 | 38 | 5. Run the Streamlit App 39 | ```bash 40 | streamlit run local_chatgpt_memory.py 41 | ``` -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_finance_agent_team/finance_agent_team.py: -------------------------------------------------------------------------------- 1 | from agno.agent import Agent 2 | from agno.models.openai import OpenAIChat 3 | from agno.storage.agent.sqlite import SqliteAgentStorage 4 | from agno.tools.duckduckgo import DuckDuckGoTools 5 | from agno.tools.yfinance import YFinanceTools 6 | from agno.playground import Playground, serve_playground_app 7 | 8 | web_agent = Agent( 9 | name="Web Agent", 10 | role="Search the web for information", 11 | model=OpenAIChat(id="gpt-4o"), 12 | tools=[DuckDuckGoTools()], 13 | storage=SqliteAgentStorage(table_name="web_agent", db_file="agents.db"), 14 | add_history_to_messages=True, 15 | markdown=True, 16 | ) 17 | 18 | finance_agent = Agent( 19 | name="Finance Agent", 20 | role="Get financial data", 21 | model=OpenAIChat(id="gpt-4o"), 22 | tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True, company_news=True)], 23 | instructions=["Always use tables to display data"], 24 | storage=SqliteAgentStorage(table_name="finance_agent", db_file="agents.db"), 25 | add_history_to_messages=True, 26 | markdown=True, 27 | ) 28 | 29 | agent_team = Agent( 30 | team=[web_agent, finance_agent], 31 | name="Agent Team (Web+Finance)", 32 | model=OpenAIChat(id="gpt-4o"), 33 | show_tool_calls=True, 34 | markdown=True, 35 | ) 36 | 37 | app = Playground(agents=[agent_team]).get_app() 38 | 39 | if __name__ == "__main__": 40 | serve_playground_app("finance_agent_team:app", reload=True) -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_meeting_agent/README.md: -------------------------------------------------------------------------------- 1 | ## 📝 AI Meeting Preparation Agent 2 | This Streamlit application leverages multiple AI agents to create comprehensive meeting preparation materials. It uses OpenAI's GPT-4, Anthropic's Claude, and the Serper API for web searches to generate context analysis, industry insights, meeting strategies, and executive briefings. 3 | 4 | ### Features 5 | 6 | - Multi-agent AI system for thorough meeting preparation 7 | - Utilizes OpenAI's GPT-4 and Anthropic's Claude models 8 | - Web search capability using Serper API 9 | - Generates detailed context analysis, industry insights, meeting strategies, and executive briefings 10 | 11 | ### How to get Started? 12 | 13 | 1. Clone the GitHub repository 14 | 15 | ```bash 16 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 17 | cd awesome-llm-apps/ai_agent_tutorials/ai_meeting_agent 18 | ``` 19 | 2. Install the required dependencies: 20 | 21 | ```bash 22 | pip install -r requirements.txt 23 | ``` 24 | 3. Get your Anthropic API Key 25 | 26 | - Sign up for an [Anthropic account](https://console.anthropic.com) (or the LLM provider of your choice) and obtain your API key. 27 | 28 | 4. Get your SerpAPI Key 29 | 30 | - Sign up for an [Serper API account](https://serper.dev/) and obtain your API key. 31 | 32 | 5. Get your OpenAI API Key 33 | 34 | - Sign up for an [OpenAI account](https://platform.openai.com/) (or the LLM provider of your choice) and obtain your API key. 35 | 36 | 6. Run the Streamlit App 37 | ```bash 38 | streamlit run meeting_agent.py 39 | ``` -------------------------------------------------------------------------------- /llm_apps_with_memory_tutorials/ai_arxiv_agent_memory/README.md: -------------------------------------------------------------------------------- 1 | ## 📚 AI Research Agent with Memory 2 | This Streamlit app implements an AI-powered research assistant that helps users search for academic papers on arXiv while maintaining a memory of user interests and past interactions. It utilizes OpenAI's GPT-4o-mini model for processing search results, MultiOn for web browsing, and Mem0 with Qdrant for maintaining user context. 3 | 4 | ### Features 5 | 6 | - Search interface for querying arXiv papers 7 | - AI-powered processing of search results for improved readability 8 | - Persistent memory of user interests and past searches 9 | - Utilizes OpenAI's GPT-4o-mini model for intelligent processing 10 | - Implements memory storage and retrieval using Mem0 and Qdrant 11 | 12 | ### How to get Started? 13 | 14 | 1. Clone the GitHub repository 15 | ```bash 16 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 17 | cd awesome-llm-apps/llm_apps_with_memory_tutorials/ai_arxiv_agent_memory 18 | ``` 19 | 20 | 2. Install the required dependencies: 21 | 22 | ```bash 23 | pip install -r requirements.txt 24 | ``` 25 | 26 | 3. Ensure Qdrant is running: 27 | The app expects Qdrant to be running on localhost:6333. Adjust the configuration in the code if your setup is different. 28 | 29 | ```bash 30 | docker pull qdrant/qdrant 31 | 32 | docker run -p 6333:6333 -p 6334:6334 \ 33 | -v $(pwd)/qdrant_storage:/qdrant/storage:z \ 34 | qdrant/qdrant 35 | ``` 36 | 37 | 4. Run the Streamlit App 38 | ```bash 39 | streamlit run ai_arxiv_agent_memory.py 40 | ``` 41 | -------------------------------------------------------------------------------- /advanced_tools_frameworks/local_chatgpt_clone/chatgpt_clone_llama3.py: -------------------------------------------------------------------------------- 1 | import streamlit as st 2 | from openai import OpenAI 3 | 4 | # Set up the Streamlit App 5 | st.title("ChatGPT Clone using Llama-3 🦙") 6 | st.caption("Chat with locally hosted Llama-3 using the LM Studio 💯") 7 | 8 | # Point to the local server setup using LM Studio 9 | client = OpenAI(base_url="http://localhost:1234/v1", api_key="lm-studio") 10 | 11 | # Initialize the chat history 12 | if "messages" not in st.session_state: 13 | st.session_state.messages = [] 14 | 15 | # Display the chat history 16 | for message in st.session_state.messages: 17 | with st.chat_message(message["role"]): 18 | st.markdown(message["content"]) 19 | 20 | # Accept user input 21 | if prompt := st.chat_input("What is up?"): 22 | # Add user message to chat history 23 | st.session_state.messages.append({"role": "user", "content": prompt}) 24 | # Display user message in chat message container 25 | with st.chat_message("user"): 26 | st.markdown(prompt) 27 | # Generate response 28 | response = client.chat.completions.create( 29 | model="lmstudio-community/Meta-Llama-3-8B-Instruct-GGUF", 30 | messages=st.session_state.messages, temperature=0.7 31 | ) 32 | # Add assistant response to chat history 33 | st.session_state.messages.append({"role": "assistant", "content": response.choices[0].message.content}) 34 | # Display assistant response in chat message container 35 | with st.chat_message("assistant"): 36 | st.markdown(response.choices[0].message.content) 37 | -------------------------------------------------------------------------------- /chat_with_X_tutorials/chat_with_gmail/README.md: -------------------------------------------------------------------------------- 1 | ## 📨 Chat with Gmail Inbox 2 | 3 | LLM app with RAG to chat with Gmail in just 30 lines of Python Code. The app uses Retrieval Augmented Generation (RAG) to provide accurate answers to questions based on the content of your Gmail Inbox. 4 | 5 | ### Features 6 | 7 | - Connect to your Gmail Inbox 8 | - Ask questions about the content of your emails 9 | - Get accurate answers using RAG and the selected LLM 10 | 11 | ### Installation 12 | 13 | 1. Clone the repository 14 | 15 | ```bash 16 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 17 | cd awesome-llm-apps/chat_with_X_tutorials/chat_with_gmail 18 | ``` 19 | 2. Install the required dependencies 20 | 21 | ```bash 22 | pip install -r requirements.txt 23 | ``` 24 | 25 | 3. Set up your Google Cloud project and enable the Gmail API: 26 | 27 | - Go to the [Google Cloud Console](https://console.cloud.google.com/) and create a new project. 28 | - Navigate to "APIs & Services > OAuth consent screen" and configure the OAuth consent screen. 29 | - Publish the OAuth consent screen by providing the necessary app information. 30 | - Enable the Gmail API and create OAuth client ID credentials. 31 | - Download the credentials in JSON format and save them as `credentials.json` in your working directory. 32 | 33 | 4. Get your OpenAI API Key 34 | 35 | - Sign up for an [OpenAI account](https://platform.openai.com/) (or the LLM provider of your choice) and obtain your API key. 36 | 37 | 4. Run the Streamlit App 38 | 39 | ```bash 40 | streamlit run chat_gmail.py 41 | ``` 42 | 43 | 44 | -------------------------------------------------------------------------------- /ai_agent_tutorials/local_news_agent_openai_swarm/README.md: -------------------------------------------------------------------------------- 1 | ## 📰 Multi-agent AI news assistant 2 | This Streamlit application implements a sophisticated news processing pipeline using multiple specialized AI agents to search, synthesize, and summarize news articles. It leverages the Llama 3.2 model via Ollama and DuckDuckGo search to provide comprehensive news analysis. 3 | 4 | 5 | ### Features 6 | - Multi-agent architecture with specialized roles: 7 | - News Searcher: Finds recent news articles 8 | - News Synthesizer: Analyzes and combines information 9 | - News Summarizer: Creates concise, professional summaries 10 | 11 | - Real-time news search using DuckDuckGo 12 | - AP/Reuters-style summary generation 13 | - User-friendly Streamlit interface 14 | 15 | 16 | ### How to get Started? 17 | 18 | 1. Clone the GitHub repository 19 | ```bash 20 | git clone https://github.com/your-username/ai-news-processor.git 21 | cd awesome-llm-apps/ai_agent_tutorials/local_news_agent_openai_swarm 22 | ``` 23 | 24 | 2. Install the required dependencies: 25 | 26 | ```bash 27 | pip install -r requirements.txt 28 | ``` 29 | 30 | 3. Pull and Run Llama 3.2 using Ollama: 31 | 32 | ```bash 33 | # Pull the model 34 | ollama pull llama3.2 35 | 36 | # Verify installation 37 | ollama list 38 | 39 | # Run the model (optional test) 40 | ollama run llama3.2 41 | ``` 42 | 43 | 4. Create a .env file with your configurations: 44 | ```bash 45 | OPENAI_BASE_URL=http://localhost:11434/v1 46 | OPENAI_API_KEY=fake-key 47 | ``` 48 | 5. Run the Streamlit app 49 | ```bash 50 | streamlit run news_agent.py 51 | ``` -------------------------------------------------------------------------------- /ai_agent_tutorials/multi_agent_researcher/research_agent.py: -------------------------------------------------------------------------------- 1 | # Import the required libraries 2 | import streamlit as st 3 | from agno.agent import Agent 4 | from agno.tools.hackernews import HackerNewsTools 5 | from agno.models.openai import OpenAIChat 6 | 7 | # Set up the Streamlit app 8 | st.title("Multi-Agent AI Researcher 🔍🤖") 9 | st.caption("This app allows you to research top stories and users on HackerNews and write blogs, reports and social posts.") 10 | 11 | # Get OpenAI API key from user 12 | openai_api_key = st.text_input("OpenAI API Key", type="password") 13 | 14 | if openai_api_key: 15 | # Create instances of the Assistant 16 | story_researcher = Agent( 17 | name="HackerNews Story Researcher", 18 | role="Researches hackernews stories and users.", 19 | tools=[HackerNewsTools()], 20 | ) 21 | 22 | user_researcher = Agent( 23 | name="HackerNews User Researcher", 24 | role="Reads articles from URLs.", 25 | tools=[HackerNewsTools()], 26 | ) 27 | 28 | hn_assistant = Agent( 29 | name="Hackernews Team", 30 | team=[story_researcher, user_researcher], 31 | model=OpenAIChat( 32 | id="gpt-4o", 33 | max_tokens=1024, 34 | temperature=0.5, 35 | api_key=openai_api_key 36 | ) 37 | ) 38 | 39 | # Input field for the report query 40 | query = st.text_input("Enter your report query") 41 | 42 | if query: 43 | # Get the response from the assistant 44 | response = hn_assistant.run(query, stream=False) 45 | st.write(response.content) -------------------------------------------------------------------------------- /rag_tutorials/autonomous_rag/README.md: -------------------------------------------------------------------------------- 1 | ## 🤖 AutoRAG: Autonomous RAG with GPT-4o and Vector Database 2 | 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. 3 | Features 4 | 5 | ### Freatures 6 | - Chat interface for interacting with the AI assistant 7 | - PDF document upload and processing 8 | - Knowledge base integration using PostgreSQL and Pgvector 9 | - Web search capability using DuckDuckGo 10 | - Persistent storage of assistant data and conversations 11 | 12 | ### How to get Started? 13 | 14 | 1. Clone the GitHub repository 15 | ```bash 16 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 17 | cd awesome-llm-apps/rag_tutorials/autonomous_rag 18 | ``` 19 | 20 | 2. Install the required dependencies: 21 | 22 | ```bash 23 | pip install -r requirements.txt 24 | ``` 25 | 26 | 3. Ensure PgVector Database is running: 27 | The app expects PgVector to be running on [localhost:5532](http://localhost:5532/). Adjust the configuration in the code if your setup is different. 28 | 29 | ```bash 30 | docker run -d \ 31 | -e POSTGRES_DB=ai \ 32 | -e POSTGRES_USER=ai \ 33 | -e POSTGRES_PASSWORD=ai \ 34 | -e PGDATA=/var/lib/postgresql/data/pgdata \ 35 | -v pgvolume:/var/lib/postgresql/data \ 36 | -p 5532:5432 \ 37 | --name pgvector \ 38 | phidata/pgvector:16 39 | ``` 40 | 41 | 4. Run the Streamlit App 42 | ```bash 43 | streamlit run autorag.py 44 | ``` 45 | -------------------------------------------------------------------------------- /chat_with_X_tutorials/chat_with_gmail/chat_gmail.py: -------------------------------------------------------------------------------- 1 | import tempfile 2 | import streamlit as st 3 | from embedchain import App 4 | 5 | # Define the embedchain_bot function 6 | def embedchain_bot(db_path, api_key): 7 | return App.from_config( 8 | config={ 9 | "llm": {"provider": "openai", "config": {"model": "gpt-4-turbo", "temperature": 0.5, "api_key": api_key}}, 10 | "vectordb": {"provider": "chroma", "config": {"dir": db_path}}, 11 | "embedder": {"provider": "openai", "config": {"api_key": api_key}}, 12 | } 13 | ) 14 | 15 | # Create Streamlit app 16 | st.title("Chat with your Gmail Inbox 📧") 17 | st.caption("This app allows you to chat with your Gmail inbox using OpenAI API") 18 | 19 | # Get the OpenAI API key from the user 20 | openai_access_token = st.text_input("Enter your OpenAI API Key", type="password") 21 | 22 | # Set the Gmail filter statically 23 | gmail_filter = "to: me label:inbox" 24 | 25 | # Add the Gmail data to the knowledge base if the OpenAI API key is provided 26 | if openai_access_token: 27 | # Create a temporary directory to store the database 28 | db_path = tempfile.mkdtemp() 29 | # Create an instance of Embedchain App 30 | app = embedchain_bot(db_path, openai_access_token) 31 | app.add(gmail_filter, data_type="gmail") 32 | st.success(f"Added emails from Inbox to the knowledge base!") 33 | 34 | # Ask a question about the emails 35 | prompt = st.text_input("Ask any question about your emails") 36 | 37 | # Chat with the emails 38 | if prompt: 39 | answer = app.query(prompt) 40 | st.write(answer) -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_finance_agent_team/README.md: -------------------------------------------------------------------------------- 1 | ## 💲 AI Finance Agent Team with Web Access 2 | This script demonstrates how to build a team of AI agents that work together as a financial analyst using GPT-4o in just 20 lines of Python code. The system combines web search capabilities with financial data analysis tools to provide comprehensive financial insights. 3 | 4 | ### Features 5 | - Multi-agent system with specialized roles: 6 | - Web Agent for general internet research 7 | - Finance Agent for detailed financial analysis 8 | - Team Agent for coordinating between agents 9 | - Real-time financial data access through YFinance 10 | - Web search capabilities using DuckDuckGo 11 | - Persistent storage of agent interactions using SQLite 12 | 13 | ### How to get Started? 14 | 15 | 1. Clone the GitHub repository 16 | ```bash 17 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 18 | cd awesome-llm-apps/ai_agent_tutorials/ai_finance_agent_team 19 | ``` 20 | 21 | 2. Install the required dependencies: 22 | 23 | ```bash 24 | pip install -r requirements.txt 25 | ``` 26 | 27 | 3. Get your OpenAI API Key 28 | 29 | - Sign up for an [OpenAI account](https://platform.openai.com/) (or the LLM provider of your choice) and obtain your API key. 30 | - Set your OpenAI API key as an environment variable: 31 | ```bash 32 | export OPENAI_API_KEY='your-api-key-here' 33 | ``` 34 | 35 | 4. Run the team of AI Agents 36 | ```bash 37 | python3 finance_agent_team.py 38 | ``` 39 | 40 | 5. Open your web browser and navigate to the URL provided in the console output to interact with the team of AI agents through the playground interface. 41 | -------------------------------------------------------------------------------- /advanced_tools_frameworks/cursor_ai_experiments/ai_web_scrapper.py: -------------------------------------------------------------------------------- 1 | import streamlit as st 2 | from scrapegraphai.graphs import SmartScraperGraph 3 | 4 | # Streamlit app title 5 | st.title("AI Web Scraper") 6 | 7 | # Input fields for user prompt and source URL 8 | prompt = st.text_input("Enter the information you want to extract:") 9 | source_url = st.text_input("Enter the source URL:") 10 | 11 | # Input field for OpenAI API key 12 | api_key = st.text_input("Enter your OpenAI API key:", type="password") 13 | 14 | # Configuration for the scraping pipeline 15 | graph_config = { 16 | "llm": { 17 | "api_key": api_key, 18 | "model": "openai/gpt-4o-mini", 19 | }, 20 | "verbose": True, 21 | "headless": False, 22 | } 23 | 24 | # Button to start the scraping process 25 | if st.button("Scrape"): 26 | if prompt and source_url and api_key: 27 | # Create the SmartScraperGraph instance 28 | smart_scraper_graph = SmartScraperGraph( 29 | prompt=prompt, 30 | source=source_url, 31 | config=graph_config 32 | ) 33 | 34 | # Run the pipeline 35 | result = smart_scraper_graph.run() 36 | 37 | # Display the result 38 | st.write(result) 39 | else: 40 | st.error("Please provide all the required inputs.") 41 | 42 | # Instructions for the user 43 | st.markdown(""" 44 | ### Instructions 45 | 1. Enter the information you want to extract in the first input box. 46 | 2. Enter the source URL from which you want to extract the information. 47 | 3. Enter your OpenAI API key. 48 | 4. Click on the "Scrape" button to start the scraping process. 49 | """) -------------------------------------------------------------------------------- /advanced_tools_frameworks/cursor_ai_experiments/chatgpt_clone_llama3.py: -------------------------------------------------------------------------------- 1 | import streamlit as st 2 | from ollama import Client 3 | 4 | # Initialize Ollama client 5 | client = Client() 6 | 7 | # Set up Streamlit page 8 | st.set_page_config(page_title="Local ChatGPT Clone", page_icon="🤖", layout="wide") 9 | st.title("🤖 Local ChatGPT Clone") 10 | 11 | # Initialize chat history 12 | if "messages" not in st.session_state: 13 | st.session_state.messages = [] 14 | 15 | # Display chat messages 16 | for message in st.session_state.messages: 17 | with st.chat_message(message["role"]): 18 | st.markdown(message["content"]) 19 | 20 | # User input 21 | if prompt := st.chat_input("What's on your mind?"): 22 | st.session_state.messages.append({"role": "user", "content": prompt}) 23 | with st.chat_message("user"): 24 | st.markdown(prompt) 25 | 26 | # Generate AI response 27 | with st.chat_message("assistant"): 28 | message_placeholder = st.empty() 29 | full_response = "" 30 | for response in client.chat(model="llama3.1:latest", messages=st.session_state.messages, stream=True): 31 | full_response += response['message']['content'] 32 | message_placeholder.markdown(full_response + "▌") 33 | message_placeholder.markdown(full_response) 34 | st.session_state.messages.append({"role": "assistant", "content": full_response}) 35 | 36 | # Add a sidebar with information 37 | st.sidebar.title("About") 38 | st.sidebar.info("This is a local ChatGPT clone using Ollama's llama3.1:latest model and Streamlit.") 39 | st.sidebar.markdown("---") 40 | st.sidebar.markdown("Made with ❤️ by Your Name") -------------------------------------------------------------------------------- /chat_with_X_tutorials/chat_with_pdf/chat_pdf_llama3.py: -------------------------------------------------------------------------------- 1 | # Import necessary libraries 2 | import os 3 | import tempfile 4 | import streamlit as st 5 | from embedchain import App 6 | 7 | # Define the embedchain_bot function 8 | def embedchain_bot(db_path): 9 | return App.from_config( 10 | config={ 11 | "llm": {"provider": "ollama", "config": {"model": "llama3:instruct", "max_tokens": 250, "temperature": 0.5, "stream": True, "base_url": 'http://localhost:11434'}}, 12 | "vectordb": {"provider": "chroma", "config": {"dir": db_path}}, 13 | "embedder": {"provider": "ollama", "config": {"model": "llama3:instruct", "base_url": 'http://localhost:11434'}}, 14 | } 15 | ) 16 | 17 | st.title("Chat with PDF") 18 | st.caption("This app allows you to chat with a PDF using Llama3 running locally wiht Ollama!") 19 | 20 | # Create a temporary directory to store the PDF file 21 | db_path = tempfile.mkdtemp() 22 | # Create an instance of the embedchain App 23 | app = embedchain_bot(db_path) 24 | 25 | # Upload a PDF file 26 | pdf_file = st.file_uploader("Upload a PDF file", type="pdf") 27 | 28 | # If a PDF file is uploaded, add it to the knowledge base 29 | if pdf_file: 30 | with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as f: 31 | f.write(pdf_file.getvalue()) 32 | app.add(f.name, data_type="pdf_file") 33 | os.remove(f.name) 34 | st.success(f"Added {pdf_file.name} to knowledge base!") 35 | 36 | # Ask a question about the PDF 37 | prompt = st.text_input("Ask a question about the PDF") 38 | # Display the answer 39 | if prompt: 40 | answer = app.chat(prompt) 41 | st.write(answer) -------------------------------------------------------------------------------- /chat_with_X_tutorials/chat_with_substack/chat_substack.py: -------------------------------------------------------------------------------- 1 | import streamlit as st 2 | from embedchain import App 3 | import tempfile 4 | 5 | # Define the embedchain_bot function 6 | def embedchain_bot(db_path, api_key): 7 | return App.from_config( 8 | config={ 9 | "llm": {"provider": "openai", "config": {"model": "gpt-4-turbo", "temperature": 0.5, "api_key": api_key}}, 10 | "vectordb": {"provider": "chroma", "config": {"dir": db_path}}, 11 | "embedder": {"provider": "openai", "config": {"api_key": api_key}}, 12 | } 13 | ) 14 | 15 | st.title("Chat with Substack Newsletter 📝") 16 | st.caption("This app allows you to chat with Substack newsletter using OpenAI API") 17 | 18 | # Get OpenAI API key from user 19 | openai_access_token = st.text_input("OpenAI API Key", type="password") 20 | 21 | if openai_access_token: 22 | # Create a temporary directory to store the database 23 | db_path = tempfile.mkdtemp() 24 | # Create an instance of Embedchain App 25 | app = embedchain_bot(db_path, openai_access_token) 26 | 27 | # Get the Substack blog URL from the user 28 | substack_url = st.text_input("Enter Substack Newsletter URL", type="default") 29 | 30 | if substack_url: 31 | # Add the Substack blog to the knowledge base 32 | app.add(substack_url, data_type='substack') 33 | st.success(f"Added {substack_url} to knowledge base!") 34 | 35 | # Ask a question about the Substack blog 36 | query = st.text_input("Ask any question about the substack newsletter!") 37 | 38 | # Query the Substack blog 39 | if query: 40 | result = app.query(query) 41 | st.write(result) 42 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_travel_agent/README.MD: -------------------------------------------------------------------------------- 1 | ## 🛫 AI Travel Agent 2 | This Streamlit app is an AI-powered travel Agent that generates personalized travel itineraries using OpenAI GPT-4o. It automates the process of researching, planning, and organizing your dream vacation, allowing you to explore exciting destinations with ease. 3 | 4 | ### Features 5 | - Research and discover exciting travel destinations, activities, and accommodations 6 | - Customize your itinerary based on the number of days you want to travel 7 | - Utilize the power of GPT-4o to generate intelligent and personalized travel plans 8 | 9 | ### How to get Started? 10 | 11 | 1. Clone the GitHub repository 12 | 13 | ```bash 14 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 15 | cd awesome-llm-apps/ai_agent_tutorials/ai_travel_agent 16 | ``` 17 | 2. Install the required dependencies: 18 | 19 | ```bash 20 | pip install -r requirements.txt 21 | ``` 22 | 3. Get your OpenAI API Key 23 | 24 | - Sign up for an [OpenAI account](https://platform.openai.com/) (or the LLM provider of your choice) and obtain your API key. 25 | 26 | 4. Get your SerpAPI Key 27 | 28 | - Sign up for an [SerpAPI account](https://serpapi.com/) and obtain your API key. 29 | 30 | 5. Run the Streamlit App 31 | ```bash 32 | streamlit run travel_agent.py 33 | ``` 34 | 35 | ### How it Works? 36 | 37 | The AI Travel Agent has two main components: 38 | - Researcher: Responsible for generating search terms based on the user's destination and travel duration, and searching the web for relevant activities and accommodations using SerpAPI. 39 | - Planner: Takes the research results and user preferences to generate a personalized draft itinerary that includes suggested activiti -------------------------------------------------------------------------------- /llm_apps_with_memory_tutorials/llama3_stateful_chat/local_llama3_chat.py: -------------------------------------------------------------------------------- 1 | import streamlit as st 2 | from openai import OpenAI 3 | 4 | # Set up the Streamlit App 5 | st.title("Local ChatGPT with Memory 🦙") 6 | st.caption("Chat with locally hosted memory-enabled Llama-3 using the LM Studio 💯") 7 | 8 | # Point to the local server setup using LM Studio 9 | client = OpenAI(base_url="http://localhost:1234/v1", api_key="lm-studio") 10 | 11 | # Initialize the chat history 12 | if "messages" not in st.session_state: 13 | st.session_state.messages = [] 14 | 15 | # Display the chat history 16 | for message in st.session_state.messages: 17 | with st.chat_message(message["role"]): 18 | st.markdown(message["content"]) 19 | 20 | # Accept user input 21 | if prompt := st.chat_input("What is up?"): 22 | st.session_state.messages.append({"role": "system", "content": "When the input starts with /add, don't follow up with a prompt."}) 23 | # Add user message to chat history 24 | st.session_state.messages.append({"role": "user", "content": prompt}) 25 | # Display user message in chat message container 26 | with st.chat_message("user"): 27 | st.markdown(prompt) 28 | # Generate response 29 | response = client.chat.completions.create( 30 | model="lmstudio-community/Meta-Llama-3-8B-Instruct-GGUF", 31 | messages=st.session_state.messages, temperature=0.7 32 | ) 33 | # Add assistant response to chat history 34 | st.session_state.messages.append({"role": "assistant", "content": response.choices[0].message.content}) 35 | # Display assistant response in chat message container 36 | with st.chat_message("assistant"): 37 | st.markdown(response.choices[0].message.content) 38 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_meme_generator_agent_browseruse/README.md: -------------------------------------------------------------------------------- 1 | # 🥸 AI Meme Generator Agent - Browser Use 2 | 3 | The AI Meme Generator Agent is a powerful browser automation tool that creates memes using AI agents. This app combines multi-LLM capabilities with automated browser interactions to generate memes based on text prompts through direct website manipulation. 4 | 5 | ## Features 6 | 7 | - **Multi-LLM Support** 8 | - Claude 3.5 Sonnet (Anthropic) 9 | - GPT-4o (OpenAI) 10 | - Deepseek v3 (Deepseek) 11 | - Automatic model switching with API key validation 12 | 13 | - **Browser Automation**: 14 | - Direct interaction with imgflip.com meme templates 15 | - Automated search for relevant meme formats 16 | - Dynamic text insertion for top/bottom captions 17 | - Image link extraction from generated memes 18 | 19 | - **Smart Generation Workflow**: 20 | - Action verb extraction from prompts 21 | - Metaphorical template matching 22 | - Multi-step quality validation 23 | - Automatic retry mechanism for failed generations 24 | 25 | - **User-Friendly Interface**: 26 | - Model configuration sidebar 27 | - API key management 28 | - Direct meme preview with clickable links 29 | - Responsive error handling 30 | 31 | 32 | API keys required: 33 | - **Anthropic** (for Claude) 34 | - **Deepseek** 35 | - **OpenAI** (for GPT-4o) 36 | 37 | ## How to Run 38 | 39 | 1. **Clone the Repository**: 40 | ```bash 41 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 42 | cd ai_agent_tutorials/ai_meme_generator_browseruse 43 | ``` 44 | 2. **Install the dependencies**: 45 | ```bash 46 | pip install -r requirements.txt 47 | ``` 48 | 3. **Run the Streamlit app**: 49 | ```bash 50 | streamlit run ai_meme_generator.py 51 | ``` -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_data_visualisation_agent/README.md: -------------------------------------------------------------------------------- 1 | # 📊 AI Data Visualization Agent 2 | A Streamlit application that acts as your personal data visualization expert, powered by LLMs. Simply upload your dataset and ask questions in natural language - the AI agent will analyze your data, generate appropriate visualizations, and provide insights through a combination of charts, statistics, and explanations. 3 | 4 | ## Features 5 | #### Natural Language Data Analysis 6 | - Ask questions about your data in plain English 7 | - Get instant visualizations and statistical analysis 8 | - Receive explanations of findings and insights 9 | - Interactive follow-up questioning 10 | 11 | #### Intelligent Visualization Selection 12 | - Automatic choice of appropriate chart types 13 | - Dynamic visualization generation 14 | - Statistical visualization support 15 | - Custom plot formatting and styling 16 | 17 | #### Multi-Model AI Support 18 | - Meta-Llama 3.1 405B for complex analysis 19 | - DeepSeek V3 for detailed insights 20 | - Qwen 2.5 7B for quick analysis 21 | - Meta-Llama 3.3 70B for advanced queries 22 | 23 | ## How to Run 24 | 25 | Follow the steps below to set up and run the application: 26 | - Before anything else, Please get a free Together AI API Key here: https://api.together.ai/signin 27 | - Get a free E2B API Key here: https://e2b.dev/ ; https://e2b.dev/docs/legacy/getting-started/api-key 28 | 29 | 1. **Clone the Repository** 30 | ```bash 31 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 32 | cd ai_agent_tutorials/ai_data_visualisation_agent 33 | ``` 34 | 2. **Install the dependencies** 35 | ```bash 36 | pip install -r requirements.txt 37 | ``` 38 | 3. **Run the Streamlit app** 39 | ```bash 40 | streamlit run ai_data_visualisation_agent.py 41 | ``` -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_journalist_agent/README.md: -------------------------------------------------------------------------------- 1 | ## 🗞️ AI Journalist Agent 2 | This Streamlit app is an AI-powered journalist agent that generates high-quality articles using OpenAI GPT-4o. It automates the process of researching, writing, and editing articles, allowing you to create compelling content on any topic with ease. 3 | 4 | ### Features 5 | - Searches the web for relevant information on a given topic 6 | - Writes well-structured, informative, and engaging articles 7 | - Edits and refines the generated content to meet the high standards of the New York Times 8 | 9 | ### How to get Started? 10 | 11 | 1. Clone the GitHub repository 12 | 13 | ```bash 14 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 15 | cd awesome-llm-apps/ai_agent_tutorials/ai_journalist_agent 16 | ``` 17 | 2. Install the required dependencies: 18 | 19 | ```bash 20 | pip install -r requirements.txt 21 | ``` 22 | 3. Get your OpenAI API Key 23 | 24 | - Sign up for an [OpenAI account](https://platform.openai.com/) (or the LLM provider of your choice) and obtain your API key. 25 | 26 | 4. Get your SerpAPI Key 27 | 28 | - Sign up for an [SerpAPI account](https://serpapi.com/) and obtain your API key. 29 | 30 | 5. Run the Streamlit App 31 | ```bash 32 | streamlit run journalist_agent.py 33 | ``` 34 | 35 | ### How it Works? 36 | 37 | The AI Journalist Agent utilizes three main components: 38 | - Searcher: Responsible for generating search terms based on the given topic and searching the web for relevant URLs using the SerpAPI. 39 | - Writer: Retrieves the text from the provided URLs using the NewspaperToolkit and writes a high-quality article based on the extracted information. 40 | - Editor: Coordinates the workflow between the Searcher and Writer, and performs final editing and refinement of the generated article. 41 | 42 | -------------------------------------------------------------------------------- /advanced_tools_frameworks/local_llama3.1_tool_use/README.md: -------------------------------------------------------------------------------- 1 | ## 🦙 Local Llama3 Tool Use 2 | This Streamlit app demonstrates function calling with the local Llama3 model using Ollama. It allows users to interact with an AI assistant that can access specific tools based on user selection. 3 | 4 | ### Features 5 | - Utilizes local Llama3 model via Ollama as LLM 6 | - Integrates YFinance for stock data retrieval and SerpAPI for web search capabilities 7 | - Dynamic tool selection through a user-friendly sidebar 8 | - Real-time chat interface with the AI assistant 9 | 10 | ### How to get Started? 11 | 12 | 1. Clone the GitHub repository 13 | 14 | ```bash 15 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 16 | cd awesome-llm-apps/advanced_tools_frameworks/local_llama3.1_tool_use 17 | ``` 18 | 2. Install the required dependencies: 19 | 20 | ```bash 21 | pip install -r requirements.txt 22 | ``` 23 | 24 | 3. Get your OpenAI API Key 25 | 26 | - Set up your SerpAPI API key: Export your SerpAPI API key as an environment variable. 27 | ```bash 28 | export SERPAPI_API_KEY=your_api_key_here 29 | ``` 30 | 31 | 4. Run the Streamlit App 32 | ```bash 33 | streamlit run llama3_tool_use.py 34 | ``` 35 | 36 | ## How it Works? 37 | 38 | 1. **Tool Selection:** Users can select which tools (YFinance and/or SerpAPI) they want the assistant to use via checkboxes in the sidebar. 39 | 40 | 2. **Assistant Initialization:** The app initializes or updates the assistant based on the selected tools. 41 | 42 | 3. **Chat Interface:** Users can ask questions through a chat input, and the assistant responds using the enabled tools. 43 | 44 | 4. **Real-time Response:** The assistant's response is displayed in real-time, with a typing indicator. 45 | 46 | 5. **Tool Usage Display:** The app shows which tools are currently enabled in the sidebar. -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_movie_production_agent/README.md: -------------------------------------------------------------------------------- 1 | ## 🎬 AI Movie Production Agent 2 | This Streamlit app is an AI-powered movie production assistant that helps bring your movie ideas to life using Claude 3.5 Sonnet model. It automates the process of script writing and casting, allowing you to create compelling movie concepts with ease. 3 | 4 | ### Features 5 | - Generates script outlines based on your movie idea, genre, and target audience 6 | - Suggests suitable actors for main roles, considering their past performances and current availability 7 | - Provides a concise movie concept overview 8 | 9 | ### How to get Started? 10 | 11 | 1. Clone the GitHub repository 12 | 13 | ```bash 14 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 15 | cd awesome-llm-apps/ai_agent_tutorials/ai_movie_production_agent 16 | ``` 17 | 2. Install the required dependencies: 18 | 19 | ```bash 20 | pip install -r requirements.txt 21 | ``` 22 | 3. Get your Anthropic API Key 23 | 24 | - Sign up for an [Anthropic account](https://console.anthropic.com) (or the LLM provider of your choice) and obtain your API key. 25 | 26 | 4. Get your SerpAPI Key 27 | 28 | - Sign up for an [SerpAPI account](https://serpapi.com/) and obtain your API key. 29 | 30 | 5. Run the Streamlit App 31 | ```bash 32 | streamlit run movie_production_agent.py 33 | ``` 34 | 35 | ### How it Works? 36 | 37 | The AI Movie Production Agent utilizes three main components: 38 | - **ScriptWriter**: Develops a compelling script outline with character descriptions and key plot points based on the given movie idea and genre. 39 | - **CastingDirector**: Suggests suitable actors for the main roles, considering their past performances and current availability. 40 | - **MovieProducer**: Oversees the entire process, coordinating between the ScriptWriter and CastingDirector, and providing a concise movie concept overview. -------------------------------------------------------------------------------- /advanced_tools_frameworks/web_scrapping_ai_agent/README.md: -------------------------------------------------------------------------------- 1 | ## 💻 Web Scrapping AI Agent 2 | This Streamlit app allows you to scrape a website using OpenAI API and the scrapegraphai library. Simply provide your OpenAI API key, enter the URL of the website you want to scrape, and specify what you want the AI agent to extract from the website. 3 | 4 | ### Features 5 | - Scrape any website by providing the URL 6 | - Utilize OpenAI's LLMs (GPT-3.5-turbo or GPT-4) for intelligent scraping 7 | - Customize the scraping task by specifying what you want the AI agent to extract 8 | 9 | ### How to get Started? 10 | 11 | 1. Clone the GitHub repository 12 | 13 | ```bash 14 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 15 | cd awesome-llm-apps/advanced_tools_frameworks/web_scrapping_ai_agent 16 | ``` 17 | 2. Install the required dependencies: 18 | 19 | ```bash 20 | pip install -r requirements.txt 21 | ``` 22 | 3. Get your OpenAI API Key 23 | 24 | - Sign up for an [OpenAI account](https://platform.openai.com/) (or the LLM provider of your choice) and obtain your API key. 25 | 26 | 4. Run the Streamlit App 27 | ```bash 28 | streamlit run ai_scrapper.py 29 | ``` 30 | 31 | ### How it Works? 32 | 33 | - The app prompts you to enter your OpenAI API key, which is used to authenticate and access the OpenAI language models. 34 | - You can select the desired language model (GPT-3.5-turbo or GPT-4) for the scraping task. 35 | - Enter the URL of the website you want to scrape in the provided text input field. 36 | - Specify what you want the AI agent to extract from the website by entering a user prompt. 37 | - The app creates a SmartScraperGraph object using the provided URL, user prompt, and OpenAI configuration. 38 | - The SmartScraperGraph object scrapes the website and extracts the requested information using the specified language model. 39 | - The scraped results are displayed in the app for you to view -------------------------------------------------------------------------------- /llm_finetuning_tutorials/llama3.2_finetuning/finetune_llama3.2.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from unsloth import FastLanguageModel 3 | from datasets import load_dataset 4 | from trl import SFTTrainer 5 | from transformers import TrainingArguments 6 | from unsloth.chat_templates import get_chat_template, standardize_sharegpt 7 | 8 | # Load model and tokenizer 9 | model, tokenizer = FastLanguageModel.from_pretrained( 10 | model_name="unsloth/Llama-3.2-3B-Instruct", 11 | max_seq_length=2048, load_in_4bit=True, 12 | ) 13 | 14 | # Add LoRA adapters 15 | model = FastLanguageModel.get_peft_model( 16 | model, r=16, 17 | target_modules=[ 18 | "q_proj", "k_proj", "v_proj", "o_proj", 19 | "gate_proj", "up_proj", "down_proj" 20 | ], 21 | ) 22 | 23 | # Set up chat template and prepare dataset 24 | tokenizer = get_chat_template(tokenizer, chat_template="llama-3.1") 25 | dataset = load_dataset("mlabonne/FineTome-100k", split="train") 26 | dataset = standardize_sharegpt(dataset) 27 | dataset = dataset.map( 28 | lambda examples: { 29 | "text": [ 30 | tokenizer.apply_chat_template(convo, tokenize=False) 31 | for convo in examples["conversations"] 32 | ] 33 | }, 34 | batched=True 35 | ) 36 | 37 | # Set up trainer 38 | trainer = SFTTrainer( 39 | model=model, 40 | train_dataset=dataset, 41 | dataset_text_field="text", 42 | max_seq_length=2048, 43 | args=TrainingArguments( 44 | per_device_train_batch_size=2, 45 | gradient_accumulation_steps=4, 46 | warmup_steps=5, 47 | max_steps=60, 48 | learning_rate=2e-4, 49 | fp16=not torch.cuda.is_bf16_supported(), 50 | bf16=torch.cuda.is_bf16_supported(), 51 | logging_steps=1, 52 | output_dir="outputs", 53 | ), 54 | ) 55 | 56 | # Train the model 57 | trainer.train() 58 | 59 | # Save the finetuned model 60 | model.save_pretrained("finetuned_model") -------------------------------------------------------------------------------- /advanced_tools_frameworks/llm_router_app/llm_router.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | os.environ["OPENAI_API_KEY"] = "your_openai_api_key" 4 | os.environ['TOGETHERAI_API_KEY'] = "your_togetherai_api_key" 5 | 6 | import streamlit as st 7 | from routellm.controller import Controller 8 | 9 | # Initialize RouteLLM client 10 | client = Controller( 11 | routers=["mf"], 12 | strong_model="gpt-4o-mini", 13 | weak_model="together_ai/meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo", 14 | ) 15 | 16 | # Set up Streamlit app 17 | st.title("RouteLLM Chat App") 18 | 19 | # Initialize chat history 20 | if "messages" not in st.session_state: 21 | st.session_state.messages = [] 22 | 23 | # Display chat messages 24 | for message in st.session_state.messages: 25 | with st.chat_message(message["role"]): 26 | st.markdown(message["content"]) 27 | if "model" in message: 28 | st.caption(f"Model used: {message['model']}") 29 | 30 | # Chat input 31 | if prompt := st.chat_input("What is your message?"): 32 | # Add user message to chat history 33 | st.session_state.messages.append({"role": "user", "content": prompt}) 34 | with st.chat_message("user"): 35 | st.markdown(prompt) 36 | 37 | # Get RouteLLM response 38 | with st.chat_message("assistant"): 39 | message_placeholder = st.empty() 40 | response = client.chat.completions.create( 41 | model="router-mf-0.11593", 42 | messages=[{"role": "user", "content": prompt}] 43 | ) 44 | message_content = response['choices'][0]['message']['content'] 45 | model_name = response['model'] 46 | 47 | # Display assistant's response 48 | message_placeholder.markdown(message_content) 49 | st.caption(f"Model used: {model_name}") 50 | 51 | # Add assistant's response to chat history 52 | st.session_state.messages.append({"role": "assistant", "content": message_content, "model": model_name}) -------------------------------------------------------------------------------- /advanced_tools_frameworks/web_search_ai_assistant/README.md: -------------------------------------------------------------------------------- 1 | ## 🎯 Generative AI Search Assistant 2 | This Streamlit app combines the power of search engines and LLMs to provide you with pinpointed answers to your queries. By leveraging OpenAI's GPT-4o and the DuckDuckGo search engine, this AI search assistant delivers accurate and concise responses to your questions. 3 | 4 | ### Features 5 | - Get pinpointed answers to your queries 6 | - Utilize DuckDuckGo search engine for web searching 7 | - Use OpenAI GPT-4o for intelligent answer generation 8 | 9 | ### How to get Started? 10 | 11 | 1. Clone the GitHub repository 12 | 13 | ```bash 14 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 15 | cd awesome-llm-apps/advanced_tools_frameworks/web_search_ai_assistant 16 | ``` 17 | 2. Install the required dependencies: 18 | 19 | ```bash 20 | pip install -r requirements.txt 21 | ``` 22 | 3. Get your OpenAI API Key 23 | 24 | - Sign up for an [OpenAI account](https://platform.openai.com/) (or the LLM provider of your choice) and obtain your API key. 25 | 26 | 4. Run the Streamlit App 27 | ```bash 28 | streamlit run ai_websearch.py 29 | ``` 30 | 31 | ### How It Works? 32 | 33 | - Upon running the app, you will be prompted to enter your OpenAI API key. This key is used to authenticate and access the OpenAI language models. 34 | 35 | - Once you provide a valid API key, an instance of the Assistant class is created. This assistant utilizes the GPT-4 language model from OpenAI and the DuckDuckGo search engine tool. 36 | 37 | - Enter your search query in the provided text input field. 38 | 39 | - The assistant will perform the following steps: 40 | - Conduct a web search using DuckDuckGo based on your query 41 | - Analyze the search results and extract relevant information 42 | - Generate a concise and targeted answer using the GPT-4 language model 43 | 44 | - The pinpointed answer will be displayed in the app, providing you with the information you need. -------------------------------------------------------------------------------- /rag_tutorials/agentic_rag/README.md: -------------------------------------------------------------------------------- 1 | ## 🗃️ AI RAG Agent with Web Access 2 | This script demonstrates how to build a Retrieval-Augmented Generation (RAG) agent with web access using GPT-4o in just 15 lines of Python code. The agent uses a PDF knowledge base and has the ability to search the web using DuckDuckGo. 3 | 4 | ### Features 5 | 6 | - Creates a RAG agent using GPT-4o 7 | - Incorporates a PDF-based knowledge base 8 | - Uses LanceDB as the vector database for efficient similarity search 9 | - Includes web search capability through DuckDuckGo 10 | - Provides a playground interface for easy interaction 11 | 12 | ### How to get Started? 13 | 14 | 1. Clone the GitHub repository 15 | ```bash 16 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 17 | cd awesome-llm-apps/rag_tutorials/agentic_rag 18 | ``` 19 | 20 | 2. Install the required dependencies: 21 | 22 | ```bash 23 | pip install -r requirements.txt 24 | ``` 25 | 26 | 3. Get your OpenAI API Key 27 | 28 | - Sign up for an [OpenAI account](https://platform.openai.com/) (or the LLM provider of your choice) and obtain your API key. 29 | - Set your OpenAI API key as an environment variable: 30 | ```bash 31 | export OPENAI_API_KEY='your-api-key-here' 32 | ``` 33 | 34 | 4. Run the AI RAG Agent 35 | ```bash 36 | python3 rag_agent.py 37 | ``` 38 | 5. Open your web browser and navigate to the URL provided in the console output to interact with the RAG agent through the playground interface. 39 | 40 | ### How it works? 41 | 42 | 1. **Knowledge Base Creation:** The script creates a knowledge base from a PDF file hosted online. 43 | 2. **Vector Database Setup:** LanceDB is used as the vector database for efficient similarity search within the knowledge base. 44 | 3. **Agent Configuration:** An AI agent is created using GPT-4o as the underlying model, with the PDF knowledge base and DuckDuckGo search tool. 45 | 4. **Playground Setup:** A playground interface is set up for easy interaction with the RAG agent. 46 | 47 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_data_analysis_agent/README.md: -------------------------------------------------------------------------------- 1 | # 📊 AI Data Analysis Agent 2 | 3 | An AI data analysis Agent built using the Agno Agent framework and Openai's gpt-4o model. This agent helps users analyze their data - csv, excel files through natural language queries, powered by OpenAI's language models and DuckDB for efficient data processing - making data analysis accessible to users regardless of their SQL expertise. 4 | 5 | ## Features 6 | 7 | - 📤 **File Upload Support**: 8 | - Upload CSV and Excel files 9 | - Automatic data type detection and schema inference 10 | - Support for multiple file formats 11 | 12 | - 💬 **Natural Language Queries**: 13 | - Convert natural language questions into SQL queries 14 | - Get instant answers about your data 15 | - No SQL knowledge required 16 | 17 | - 🔍 **Advanced Analysis**: 18 | - Perform complex data aggregations 19 | - Filter and sort data 20 | - Generate statistical summaries 21 | - Create data visualizations 22 | 23 | - 🎯 **Interactive UI**: 24 | - User-friendly Streamlit interface 25 | - Real-time query processing 26 | - Clear result presentation 27 | 28 | ## How to Run 29 | 30 | 1. **Setup Environment** 31 | ```bash 32 | # Clone the repository 33 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 34 | cd ai_agent_tutorials/ai_data_analysis_agent 35 | 36 | # Install dependencies 37 | pip install -r requirements.txt 38 | ``` 39 | 40 | 2. **Configure API Keys** 41 | - Get OpenAI API key from [OpenAI Platform](https://platform.openai.com) 42 | 43 | 3. **Run the Application** 44 | ```bash 45 | streamlit run ai_data_analyst.py 46 | ``` 47 | 48 | ## Usage 49 | 50 | 1. Launch the application using the command above 51 | 2. Provide your OpenAI API key in the sidebar of Streamlit 52 | 3. Upload your CSV or Excel file through the Streamlit interface 53 | 4. Ask questions about your data in natural language 54 | 5. View the results and generated visualizations 55 | 56 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_health_fitness_agent/README.md: -------------------------------------------------------------------------------- 1 | # AI Health & Fitness Planner Agent 🏋️‍♂️ 2 | 3 | The **AI Health & Fitness Planner** is a personalized health and fitness Agent powered by Agno AI Agent framework. This app generates tailored dietary and fitness plans based on user inputs such as age, weight, height, activity level, dietary preferences, and fitness goals. 4 | 5 | ## Features 6 | 7 | - **Health Agent and Fitness Agent** 8 | - The app has two phidata agents that are specialists in giving Diet advice and Fitness/workout advice respectively. 9 | 10 | - **Personalized Dietary Plans**: 11 | - Generates detailed meal plans (breakfast, lunch, dinner, and snacks). 12 | - Includes important considerations like hydration, electrolytes, and fiber intake. 13 | - Supports various dietary preferences like Keto, Vegetarian, Low Carb, etc. 14 | 15 | - **Personalized Fitness Plans**: 16 | - Provides customized exercise routines based on fitness goals. 17 | - Covers warm-ups, main workouts, and cool-downs. 18 | - Includes actionable fitness tips and progress tracking advice. 19 | 20 | - **Interactive Q&A**: Allows users to ask follow-up questions about their plans. 21 | 22 | 23 | ## Requirements 24 | 25 | The application requires the following Python libraries: 26 | 27 | - `agno` 28 | - `google-generativeai` 29 | - `streamlit` 30 | 31 | Ensure these dependencies are installed via the `requirements.txt` file according to their mentioned versions 32 | 33 | ## How to Run 34 | 35 | Follow the steps below to set up and run the application: 36 | Before anything else, Please get a free Gemini API Key provided by Google AI here: https://aistudio.google.com/apikey 37 | 38 | 1. **Clone the Repository**: 39 | ```bash 40 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 41 | cd awesome-llm-apps/ai_agent_tutorials/ai_health_fitness_agent 42 | ``` 43 | 44 | 2. **Install the dependencies** 45 | ```bash 46 | pip install -r requirements.txt 47 | ``` 48 | 3. **Run the Streamlit app** 49 | ```bash 50 | streamlit run health_agent.py 51 | ``` 52 | 53 | 54 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_lead_generation_agent/README.md: -------------------------------------------------------------------------------- 1 | ## 🎯 AI Lead Generation Agent - Powered by Firecrawl's Extract Endpoint 2 | 3 | The AI Lead Generation Agent automates the process of finding and qualifying potential leads from Quora. It uses Firecrawl's search and the new Extract endpoint to identify relevant user profiles, extract valuable information, and organize it into a structured format in Google Sheets. This agent helps sales and marketing teams efficiently build targeted lead lists while saving hours of manual research. 4 | 5 | ### Features 6 | - **Targeted Search**: Uses Firecrawl's search endpoint to find relevant Quora URLs based on your search criteria 7 | - **Intelligent Extraction**: Leverages Firecrawl's new Extract endpoint to pull user information from Quora profiles 8 | - **Automated Processing**: Formats extracted user information into a clean, structured format 9 | - **Google Sheets Integration**: Automatically creates and populates Google Sheets with lead information 10 | - **Customizable Criteria**: Allows you to define specific search parameters to find your ideal leads for your niche 11 | 12 | ### How to Get Started 13 | 1. **Clone the repository**: 14 | ```bash 15 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 16 | cd ai_agent_tutorials/ai_lead_generation_agent 17 | ``` 18 | 3. **Install the required packages**: 19 | ```bash 20 | pip install -r requirements.txt 21 | ``` 22 | 4. **Important thing to do in composio**: 23 | - in the terminal, run this command: `composio add googlesheets` 24 | - In your compposio dashboard, create a new google sheet intergation and make sure it is active in the active integrations/connections tab 25 | 26 | 5. **Set up your API keys**: 27 | - Get your Firecrawl API key from [Firecrawl's website](https://www.firecrawl.dev/app/api-keys) 28 | - Get your Composio API key from [Composio's website](https://composio.ai) 29 | - Get your OpenAI API key from [OpenAI's website](https://platform.openai.com/api-keys) 30 | 31 | 6. **Run the application**: 32 | ```bash 33 | streamlit run ai_lead_generation_agent.py 34 | ``` 35 | 36 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_investment_agent/README.md: -------------------------------------------------------------------------------- 1 | ## 📈 AI Investment Agent 2 | This Streamlit app is an AI-powered investment agent built with Agno's AI Agent framework that compares the performance of two stocks and generates detailed reports. By using GPT-4o with Yahoo Finance data, this app provides valuable insights to help you make informed investment decisions. 3 | 4 | ### Features 5 | - Compare the performance of two stocks 6 | - Retrieve comprehensive company information 7 | - Get the latest company news and analyst recommendations 8 | - Get the latest company news and analyst recommendations 9 | 10 | ### How to get Started? 11 | 12 | 1. Clone the GitHub repository 13 | 14 | ```bash 15 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 16 | cd awesome-llm-apps/ai_agent_tutorials/ai_investment_agent 17 | ``` 18 | 2. Install the required dependencies: 19 | 20 | ```bash 21 | pip install -r requirements.txt 22 | ``` 23 | 3. Get your OpenAI API Key 24 | 25 | - Sign up for an [OpenAI account](https://platform.openai.com/) (or the LLM provider of your choice) and obtain your API key. 26 | 27 | 4. Run the Streamlit App 28 | ```bash 29 | streamlit run investment_agent.py 30 | ``` 31 | 32 | ### How it Works? 33 | 34 | - Upon running the app, you will be prompted to enter your OpenAI API key. This key is used to authenticate and access the OpenAI language model. 35 | - Once you provide a valid API key, an instance of the Assistant class is created. This assistant utilizes the GPT-4o language model from OpenAI and the YFinanceTools for accessing stock data. 36 | - Enter the stock symbols of the two companies you want to compare in the provided text input fields. 37 | - The assistant will perform the following steps: 38 | - Retrieve real-time stock prices and historical data using YFinanceTools 39 | - Fetch the latest company news and analyst recommendations 40 | - Gather comprehensive company information 41 | - Generate a detailed comparison report using the GPT-4 language model 42 | - The generated report will be displayed in the app, providing you with valuable insights and analysis to guide your investment decisions. -------------------------------------------------------------------------------- /rag_tutorials/rag_agent_cohere/README.md: -------------------------------------------------------------------------------- 1 | # RAG Agent with Cohere ⌘R 2 | 3 | A RAG Agentic system built with Cohere's new model Command-r7b-12-2024, Qdrant for vector storage, Langchain for RAG and LangGraph for orchestration. This application allows users to upload documents, ask questions about them, and get AI-powered responses with fallback to web search when needed. 4 | 5 | ## Features 6 | 7 | - **Document Processing** 8 | - PDF document upload and processing 9 | - Automatic text chunking and embedding 10 | - Vector storage in Qdrant cloud 11 | 12 | - **Intelligent Querying** 13 | - RAG-based document retrieval 14 | - Similarity search with threshold filtering 15 | - Automatic fallback to web search when no relevant documents found 16 | - Source attribution for answers 17 | 18 | - **Advanced Capabilities** 19 | - DuckDuckGo web search integration 20 | - LangGraph agent for web research 21 | - Context-aware response generation 22 | - Long answer summarization 23 | 24 | - **Model Specific Features** 25 | - Command-r7b-12-2024 model for Chat and RAG 26 | - cohere embed-english-v3.0 model for embeddings 27 | - create_react_agent function from langgraph 28 | - DuckDuckGoSearchRun tool for web search 29 | 30 | ## Prerequisites 31 | 32 | ### 1. Cohere API Key 33 | 1. Go to [Cohere Platform](https://dashboard.cohere.ai/api-keys) 34 | 2. Sign up or log in to your account 35 | 3. Navigate to API Keys section 36 | 4. Create a new API key 37 | 38 | ### 2. Qdrant Cloud Setup 39 | 1. Visit [Qdrant Cloud](https://cloud.qdrant.io/) 40 | 2. Create an account or sign in 41 | 3. Create a new cluster 42 | 4. Get your credentials: 43 | - Qdrant API Key: Found in API Keys section 44 | - Qdrant URL: Your cluster URL (format: `https://xxx-xxx.aws.cloud.qdrant.io`) 45 | 46 | 47 | ## How to Run 48 | 49 | 1. Clone the repository: 50 | ```bash 51 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 52 | cd rag_tutorials/rag_agent_cohere 53 | ``` 54 | 55 | 2. Install dependencies: 56 | ```bash 57 | pip install -r requirements.txt 58 | ``` 59 | 60 | ```bash 61 | streamlit run rag_agent_cohere.py 62 | ``` 63 | 64 | 65 | -------------------------------------------------------------------------------- /chat_with_X_tutorials/chat_with_github/README.md: -------------------------------------------------------------------------------- 1 | ## 💬 Chat with GitHub Repo 2 | 3 | LLM app with RAG to chat with GitHub Repo in just 30 lines of Python Code. The app uses Retrieval Augmented Generation (RAG) to provide accurate answers to questions based on the content of the specified GitHub repository. 4 | 5 | ### Features 6 | 7 | - Provide the name of GitHub Repository as input 8 | - Ask questions about the content of the GitHub repository 9 | - Get accurate answers using OpenAI's API and Embedchain 10 | 11 | ### How to get Started? 12 | 13 | 1. Clone the GitHub repository 14 | 15 | ```bash 16 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 17 | cd awesome-llm-apps/chat_with_X_tutorials/chat_with_github 18 | ``` 19 | 2. Install the required dependencies: 20 | 21 | ```bash 22 | pip install -r requirements.txt 23 | ``` 24 | 3. Get your OpenAI API Key 25 | 26 | - Sign up for an [OpenAI account](https://platform.openai.com/) (or the LLM provider of your choice) and obtain your API key. 27 | 28 | 4. Get your GitHub Access Token 29 | 30 | - Create a [personal access token](https://docs.github.com/en/enterprise-server@3.6/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens#creating-a-personal-access-token) with the necessary permissions to access the desired GitHub repository. 31 | 32 | 4. Run the Streamlit App 33 | ```bash 34 | streamlit run chat_github.py 35 | ``` 36 | 37 | ### How it Works? 38 | 39 | - The app prompts the user to enter their OpenAI API key, which is used to authenticate requests to the OpenAI API. 40 | 41 | - It initializes an instance of the Embedchain App class and a GithubLoader with the provided GitHub Access Token. 42 | 43 | - The user is prompted to enter a GitHub repository URL, which is then added to the Embedchain app's knowledge base using the GithubLoader. 44 | 45 | - The user can ask questions about the GitHub repository using the text input. 46 | 47 | - When a question is asked, the app uses the chat method of the Embedchain app to generate an answer based on the content of the GitHub repository. 48 | 49 | - The app displays the generated answer to the user. 50 | -------------------------------------------------------------------------------- /ai_agent_tutorials/multi_agent_researcher/README.md: -------------------------------------------------------------------------------- 1 | ## 📰 Multi-Agent AI Researcher 2 | This Streamlit app empowers you to research top stories and users on HackerNews using a team of AI assistants with GPT-4o. 3 | 4 | ### Features 5 | - Research top stories and users on HackerNews 6 | - Utilize a team of AI assistants specialized in story and user research 7 | - Generate blog posts, reports, and social media content based on your research queries 8 | 9 | ### How to get Started? 10 | 11 | 1. Clone the GitHub repository 12 | 13 | ```bash 14 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 15 | cd awesome-llm-apps/ai_agent_tutorials/multi_agent_researcher 16 | ``` 17 | 2. Install the required dependencies: 18 | 19 | ```bash 20 | pip install -r requirements.txt 21 | ``` 22 | 3. Get your OpenAI API Key 23 | 24 | - Sign up for an [OpenAI account](https://platform.openai.com/) (or the LLM provider of your choice) and obtain your API key. 25 | 26 | 4. Run the Streamlit App 27 | ```bash 28 | streamlit run research_agent.py 29 | ``` 30 | 31 | ### How it works? 32 | 33 | - Upon running the app, you will be prompted to enter your OpenAI API key. This key is used to authenticate and access the OpenAI language models. 34 | - Once you provide a valid API key, three instances of the Assistant class are created: 35 | - **story_researcher**: Specializes in researching HackerNews stories. 36 | - **user_researcher**: Focuses on researching HackerNews users and reading articles from URLs. 37 | - **hn_assistant**: A team assistant that coordinates the research efforts of the story and user researchers. 38 | 39 | - Enter your research query in the provided text input field. This could be a topic, keyword, or specific question related to HackerNews stories or users. 40 | - The hn_assistant will orchestrate the research process by delegating tasks to the story_researcher and user_researcher based on your query. 41 | - The AI assistants will gather relevant information from HackerNews using the provided tools and generate a comprehensive response using the GPT-4 language model. 42 | - The generated content, which could be a blog post, report, or social media post, will be displayed in the app for you to review and use. 43 | 44 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_3dpygame_r1/README.md: -------------------------------------------------------------------------------- 1 | # 🎮 AI 3D PyGame Visualizer with DeepSeek R1 2 | This Project demonstrates R1's code capabilities with a PyGame code generator and visualizer with browser use. The system uses DeepSeek for reasoning, OpenAI for code extraction, and browser automation agents to visualize the code on Trinket.io. 3 | 4 | ### Features 5 | 6 | - Generates PyGame code from natural language descriptions 7 | - Uses DeepSeek Reasoner for code logic and explanation 8 | - Extracts clean code using OpenAI GPT-4o 9 | - Automates code visualization on Trinket.io using browser agents 10 | - Provides a streamlined Streamlit interface 11 | - Multi-agent system for handling different tasks (navigation, coding, execution, viewing) 12 | 13 | ### How to get Started? 14 | 15 | 1. Clone the GitHub repository 16 | ```bash 17 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 18 | cd awesome-llm-apps/ai_agent_tutorials/ai_3dpygame_r1 19 | ``` 20 | 21 | 2. Install the required dependencies: 22 | ```bash 23 | pip install -r requirements.txt 24 | ``` 25 | 26 | 3. Get your API Keys 27 | - Sign up for [DeepSeek](https://platform.deepseek.com/) and obtain your API key 28 | - Sign up for [OpenAI](https://platform.openai.com/) and obtain your API key 29 | 30 | 4. Run the AI PyGame Visualizer 31 | ```bash 32 | streamlit run ai_3dpygame_r1.py 33 | ``` 34 | 35 | 5. Browser use automatically opens your web browser and navigate to the URL provided in the console output to interact with the PyGame generator. 36 | 37 | ### How it works? 38 | 39 | 1. **Query Processing:** User enters a natural language description of the desired PyGame visualization. 40 | 2. **Code Generation:** 41 | - DeepSeek Reasoner analyzes the query and provides detailed reasoning with code 42 | - OpenAI agent extracts clean, executable code from the reasoning 43 | 3. **Visualization:** 44 | - Browser agents automate the process of running code on Trinket.io 45 | - Multiple specialized agents handle different tasks: 46 | - Navigation to Trinket.io 47 | - Code input 48 | - Execution 49 | - Visualization viewing 50 | 4. **User Interface:** Streamlit provides an intuitive interface for entering queries, viewing code, and managing the visualization process. 51 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_startup_trend_analysis_agent/README.md: -------------------------------------------------------------------------------- 1 | ## 📈 AI Startup Trend Analysis Agent 2 | The AI Startup Trend Analysis Agent is tool for budding entrepreneurs that generates actionable insights by identifying nascent trends, potential market gaps, and growth opportunities in specific sectors. Entrepreneurs can use these data-driven insights to validate ideas, spot market opportunities, and make informed decisions about their startup ventures. It combines Newspaper4k and DuckDuckGo to scan and analyze startup-focused articles and market data. Using Claude 3.5 Sonnet, it processes this information to extract emerging patterns and enable entrepreneurs to identify promising startup opportunities. 3 | 4 | 5 | ### Features 6 | - **User Prompt**: Entrepreneurs can input specific startup sectors or technologies of interest for research. 7 | - **News Collection**: This agent gathers recent startup news, funding rounds, and market analyses using DuckDuckGo. 8 | - **Summary Generation**: Concise summaries of verified information are generated using Newspaper4k. 9 | - **Trend Analysis**: The system identifies emerging patterns in startup funding, technology adoption, and market opportunities across analyzed stories. 10 | - **Streamlit UI**: The application features a user-friendly interface built with Streamlit for easy interaction. 11 | 12 | ### How to Get Started 13 | 1. **Clone the repository**: 14 | ```bash 15 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 16 | cd awesome-llm-apps/ai_agent_tutorials/ai_startup_trend_analysis_agent 17 | ``` 18 | 19 | 2. **Create and activate a virtual environment**: 20 | ```bash 21 | # For macOS/Linux 22 | python -m venv venv 23 | source venv/bin/activate 24 | 25 | # For Windows 26 | python -m venv venv 27 | .\venv\Scripts\activate 28 | ``` 29 | 30 | 3. **Install the required packages**: 31 | ```bash 32 | pip install -r requirements.txt 33 | ``` 34 | 35 | 4. **Run the application**: 36 | ```bash 37 | streamlit run startup_trends_agent.py 38 | ``` 39 | ### Important Note 40 | - The system specifically uses Claude's API for advanced language processing. You can obtain your Anthropic API key from [Anthropic's website](https://www.anthropic.com/api). 41 | 42 | 43 | -------------------------------------------------------------------------------- /advanced_tools_frameworks/llm_router_app/README.md: -------------------------------------------------------------------------------- 1 | ## 📡 RouteLLM Chat App 2 | 3 | > Note: This project is inspired by the opensource [RouteLLM library](https://github.com/lm-sys/RouteLLM/tree/main), which provides intelligent routing between different language models. 4 | 5 | This Streamlit application demonstrates the use of RouteLLM, a system that intelligently routes queries between different language models based on the complexity of the task. It provides a chat interface where users can interact with AI models, and the app automatically selects the most appropriate model for each query. 6 | 7 | ### Features 8 | - Chat interface for interacting with AI models 9 | - Automatic model selection using RouteLLM 10 | - Utilizes both GPT-4 and Meta-Llama 3.1 models 11 | - Displays chat history with model information 12 | 13 | ### How to get Started? 14 | 15 | 1. Clone the GitHub repository 16 | 17 | ```bash 18 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 19 | cd awesome-llm-apps/advanced_tools_frameworks/llm_router_app 20 | ``` 21 | 2. Install the required dependencies: 22 | 23 | ```bash 24 | pip install -r requirements.txt 25 | ``` 26 | 3. Set up your API keys: 27 | 28 | ```bash 29 | os.environ["OPENAI_API_KEY"] = "your_openai_api_key" 30 | os.environ['TOGETHERAI_API_KEY'] = "your_togetherai_api_key" 31 | ``` 32 | Note: In a production environment, it's recommended to use environment variables or a secure configuration management system instead of hardcoding API keys. 33 | 34 | 4. Run the Streamlit App 35 | ```bash 36 | streamlit run llm_router.py 37 | ``` 38 | 39 | ### How it Works? 40 | 41 | 1. RouteLLM Initialization: The app initializes the RouteLLM controller with two models: 42 | - Strong model: GPT-4 (mini) 43 | - Weak model: Meta-Llama 3.1 70B Instruct Turbo 44 | 45 | 2. Chat Interface: Users can input messages through a chat interface. 46 | 47 | 3. Model Selection: RouteLLM automatically selects the appropriate model based on the complexity of the user's query. 48 | 49 | 4. Response Generation: The selected model generates a response to the user's input. 50 | 51 | 5. Display: The app displays the response along with information about which model was used. 52 | 53 | 6. History: The chat history is maintained and displayed, including model information for each response. -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_medical_imaging_agent/README.md: -------------------------------------------------------------------------------- 1 | # 🩻 Medical Imaging Diagnosis Agent 2 | 3 | A Medical Imaging Diagnosis Agent build on agno powered by Gemini 2.0 Flash that provides AI-assisted analysis of medical images of various scans. The agent acts as a medical imaging diagnosis expert to analyze various types of medical images and videos, providing detailed diagnostic insights and explanations. 4 | 5 | ## Features 6 | 7 | - **Comprehensive Image Analysis** 8 | - Image Type Identification (X-ray, MRI, CT scan, ultrasound) 9 | - Anatomical Region Detection 10 | - Key Findings and Observations 11 | - Potential Abnormalities Detection 12 | - Image Quality Assessment 13 | - Research and Reference 14 | 15 | ## How to Run 16 | 17 | 1. **Setup Environment** 18 | ```bash 19 | # Clone the repository 20 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 21 | cd ai_agent_tutorials/ai_medical_imaging_agent 22 | 23 | # Install dependencies 24 | pip install -r requirements.txt 25 | ``` 26 | 27 | 2. **Configure API Keys** 28 | - Get Google API key from [Google AI Studio](https://aistudio.google.com) 29 | 30 | 3. **Run the Application** 31 | ```bash 32 | streamlit run ai_medical_imaging.py 33 | ``` 34 | 35 | ## Analysis Components 36 | 37 | - **Image Type and Region** 38 | - Identifies imaging modality 39 | - Specifies anatomical region 40 | 41 | - **Key Findings** 42 | - Systematic listing of observations 43 | - Detailed appearance descriptions 44 | - Abnormality highlighting 45 | 46 | - **Diagnostic Assessment** 47 | - Potential diagnoses ranking 48 | - Differential diagnoses 49 | - Severity assessment 50 | 51 | - **Patient-Friendly Explanations** 52 | - Simplified terminology 53 | - Detailed first-principles explanations 54 | - Visual reference points 55 | 56 | ## Notes 57 | 58 | - Uses Gemini 2.0 Flash for analysis 59 | - Requires stable internet connection 60 | - Free API usage costs - 1,500 free requests per day by google! 61 | - For educational and development purposes only 62 | - Not a replacement for professional medical diagnosis 63 | 64 | ## Disclaimer 65 | 66 | This tool is for educational and informational purposes only. All analyses should be reviewed by qualified healthcare professionals. Do not make medical decisions based solely on this analysis. -------------------------------------------------------------------------------- /ai_agent_tutorials/multimodal_ai_agent/multimodal_reasoning_agent.py: -------------------------------------------------------------------------------- 1 | import streamlit as st 2 | from agno.agent import Agent 3 | from agno.models.google import Gemini 4 | import tempfile 5 | import os 6 | 7 | def main(): 8 | # Set up the reasoning agent 9 | agent = Agent( 10 | model=Gemini(id="gemini-2.0-flash-thinking-exp-1219"), 11 | markdown=True 12 | ) 13 | 14 | # Streamlit app title 15 | st.title("Multimodal Reasoning AI Agent 🧠") 16 | 17 | # Instruction 18 | st.write( 19 | "Upload an image and provide a reasoning-based task for the AI Agent. " 20 | "The AI Agent will analyze the image and respond based on your input." 21 | ) 22 | 23 | # File uploader for image 24 | uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"]) 25 | 26 | if uploaded_file is not None: 27 | try: 28 | # Save uploaded file to temporary file 29 | with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as tmp_file: 30 | tmp_file.write(uploaded_file.getvalue()) 31 | temp_path = tmp_file.name 32 | 33 | # Display the uploaded image 34 | st.image(uploaded_file, caption="Uploaded Image", use_container_width=True) 35 | 36 | # Input for dynamic task 37 | task_input = st.text_area( 38 | "Enter your task/question for the AI Agent:" 39 | ) 40 | 41 | # Button to process the image and task 42 | if st.button("Analyze Image") and task_input: 43 | with st.spinner("AI is thinking... 🤖"): 44 | try: 45 | # Call the agent with the dynamic task and image path 46 | response = agent.run(task_input, images=[temp_path]) 47 | 48 | # Display the response from the model 49 | st.markdown("### AI Response:") 50 | st.markdown(response.content) 51 | except Exception as e: 52 | st.error(f"An error occurred during analysis: {str(e)}") 53 | finally: 54 | # Clean up temp file 55 | if os.path.exists(temp_path): 56 | os.unlink(temp_path) 57 | 58 | except Exception as e: 59 | st.error(f"An error occurred while processing the image: {str(e)}") 60 | 61 | if __name__ == "__main__": 62 | main() -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_coding_agent_o3-mini/README.md: -------------------------------------------------------------------------------- 1 | # 💻 Multimodal AI Coding Agent Team with o3-mini and Gemini 2 | An AI Powered Streamlit application that serves as your personal coding assistant, powered by multiple Agents built on the new o3-mini model. You can also upload an image of a coding problem or describe it in text, and the AI agent will analyze, generate an optimal solution, and execute it in a sandbox environment. 3 | 4 | ## Features 5 | #### Multi-Modal Problem Input 6 | - Upload images of coding problems (supports PNG, JPG, JPEG) 7 | - Type problems in natural language 8 | - Automatic problem extraction from images 9 | - Interactive problem processing 10 | 11 | #### Intelligent Code Generation 12 | - Optimal solution generation with best time/space complexity 13 | - Clean, documented Python code output 14 | - Type hints and proper documentation 15 | - Edge case handling 16 | 17 | #### Secure Code Execution 18 | - Sandboxed code execution environment 19 | - Real-time execution results 20 | - Error handling and explanations 21 | - 30-second execution timeout protection 22 | 23 | #### Multi-Agent Architecture 24 | - Vision Agent (Gemini-2.0-flash) for image processing 25 | - Coding Agent (OpenAI- o3-mini) for solution generation 26 | - Execution Agent (OpenAI) for code running and result analysis 27 | - E2B Sandbox for secure code execution 28 | 29 | ## How to Run 30 | 31 | Follow the steps below to set up and run the application: 32 | - Get an OpenAI API key from: https://platform.openai.com/ 33 | - Get a Google (Gemini) API key from: https://makersuite.google.com/app/apikey 34 | - Get an E2B API key from: https://e2b.dev/docs/getting-started/api-key 35 | 36 | 1. **Clone the Repository** 37 | ```bash 38 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 39 | cd ai_agent_tutorials/ai_coding_agent_o3-mini 40 | ``` 41 | 42 | 2. **Install the dependencies** 43 | ```bash 44 | pip install -r requirements.txt 45 | ``` 46 | 47 | 3. **Run the Streamlit app** 48 | ```bash 49 | streamlit run ai_coding_agent_o3.py 50 | ``` 51 | 52 | 4. **Configure API Keys** 53 | - Enter your API keys in the sidebar 54 | - All three keys (OpenAI, Gemini, E2B) are required for full functionality 55 | 56 | ## Usage 57 | 1. Upload an image of a coding problem OR type your problem description 58 | 2. Click "Generate & Execute Solution" 59 | 3. View the generated solution with full documentation 60 | 4. See execution results and any generated files 61 | 5. Review any error messages or execution timeouts 62 | -------------------------------------------------------------------------------- /advanced_tools_frameworks/gemini_multimodal_chatbot/gemini_multimodal_chatbot.py: -------------------------------------------------------------------------------- 1 | import os 2 | import streamlit as st 3 | import google.generativeai as genai 4 | from PIL import Image 5 | 6 | # Set up the Streamlit App 7 | st.set_page_config(page_title="Multimodal Chatbot with Gemini Flash", layout="wide") 8 | st.title("Multimodal Chatbot with Gemini Flash ⚡️") 9 | st.caption("Chat with Google's Gemini Flash model using image and text input to get lightning fast results. 🌟") 10 | 11 | # Get OpenAI API key from user 12 | api_key = st.text_input("Enter Google API Key", type="password") 13 | 14 | # Set up the Gemini model 15 | genai.configure(api_key=api_key) 16 | model = genai.GenerativeModel(model_name="gemini-1.5-flash-latest") 17 | 18 | if api_key: 19 | # Initialize the chat history 20 | if "messages" not in st.session_state: 21 | st.session_state.messages = [] 22 | 23 | # Sidebar for image upload 24 | with st.sidebar: 25 | st.title("Chat with Images") 26 | uploaded_file = st.file_uploader("Upload an image...", type=["jpg", "jpeg", "png"]) 27 | 28 | if uploaded_file: 29 | image = Image.open(uploaded_file) 30 | st.image(image, caption='Uploaded Image', use_column_width=True) 31 | 32 | # Main layout 33 | chat_placeholder = st.container() 34 | 35 | with chat_placeholder: 36 | # Display the chat history 37 | for message in st.session_state.messages: 38 | with st.chat_message(message["role"]): 39 | st.markdown(message["content"]) 40 | 41 | # User input area at the bottom 42 | prompt = st.chat_input("What do you want to know?") 43 | 44 | if prompt: 45 | inputs = [prompt] 46 | 47 | # Add user message to chat history 48 | st.session_state.messages.append({"role": "user", "content": prompt}) 49 | # Display user message in chat message container 50 | with chat_placeholder: 51 | with st.chat_message("user"): 52 | st.markdown(prompt) 53 | 54 | if uploaded_file: 55 | inputs.append(image) 56 | 57 | with st.spinner('Generating response...'): 58 | # Generate response 59 | response = model.generate_content(inputs) 60 | 61 | # Display assistant response in chat message container 62 | with chat_placeholder: 63 | with st.chat_message("assistant"): 64 | st.markdown(response.text) 65 | 66 | if uploaded_file and not prompt: 67 | st.warning("Please enter a text query to accompany the image.") -------------------------------------------------------------------------------- /chat_with_X_tutorials/chat_with_github/chat_github_llama3.py: -------------------------------------------------------------------------------- 1 | # Import the required libraries 2 | import tempfile 3 | from embedchain import App 4 | from embedchain.loaders.github import GithubLoader 5 | import streamlit as st 6 | import os 7 | 8 | GITHUB_TOKEN = os.getenv("Your GitHub Token") 9 | 10 | def get_loader(): 11 | loader = GithubLoader( 12 | config={ 13 | "token": GITHUB_TOKEN 14 | } 15 | ) 16 | return loader 17 | 18 | if "loader" not in st.session_state: 19 | st.session_state['loader'] = get_loader() 20 | 21 | loader = st.session_state.loader 22 | 23 | # Define the embedchain_bot function 24 | def embedchain_bot(db_path): 25 | return App.from_config( 26 | config={ 27 | "llm": {"provider": "ollama", "config": {"model": "llama3:instruct", "max_tokens": 250, "temperature": 0.5, "stream": True, "base_url": 'http://localhost:11434'}}, 28 | "vectordb": {"provider": "chroma", "config": {"dir": db_path}}, 29 | "embedder": {"provider": "ollama", "config": {"model": "llama3:instruct", "base_url": 'http://localhost:11434'}}, 30 | } 31 | ) 32 | 33 | def load_repo(git_repo): 34 | global app 35 | # Add the repo to the knowledge base 36 | print(f"Adding {git_repo} to knowledge base!") 37 | app.add("repo:" + git_repo + " " + "type:repo", data_type="github", loader=loader) 38 | st.success(f"Added {git_repo} to knowledge base!") 39 | 40 | 41 | def make_db_path(): 42 | ret = tempfile.mkdtemp(suffix="chroma") 43 | print(f"Created Chroma DB at {ret}") 44 | return ret 45 | 46 | # Create Streamlit app 47 | st.title("Chat with GitHub Repository 💬") 48 | st.caption("This app allows you to chat with a GitHub Repo using Llama-3 running with Ollama") 49 | 50 | # Initialize the Embedchain App 51 | if "app" not in st.session_state: 52 | st.session_state['app'] = embedchain_bot(make_db_path()) 53 | 54 | app = st.session_state.app 55 | 56 | # Get the GitHub repo from the user 57 | git_repo = st.text_input("Enter the GitHub Repo", type="default") 58 | 59 | if git_repo and ("repos" not in st.session_state or git_repo not in st.session_state.repos): 60 | if "repos" not in st.session_state: 61 | st.session_state["repos"] = [git_repo] 62 | else: 63 | st.session_state.repos.append(git_repo) 64 | load_repo(git_repo) 65 | 66 | 67 | # Ask a question about the Github Repo 68 | prompt = st.text_input("Ask any question about the GitHub Repo") 69 | # Chat with the GitHub Repo 70 | if prompt: 71 | answer = st.session_state.app.chat(prompt) 72 | st.write(answer) -------------------------------------------------------------------------------- /rag_tutorials/corrective_rag/README.md: -------------------------------------------------------------------------------- 1 | # 🔄 Corrective RAG Agent 2 | A sophisticated Retrieval-Augmented Generation (RAG) system that implements a corrective multi-stage workflow using LangGraph. This system combines document retrieval, relevance grading, query transformation, and web search to provide comprehensive and accurate responses. 3 | 4 | ## Features 5 | 6 | - **Smart Document Retrieval**: Uses Qdrant vector store for efficient document retrieval 7 | - **Document Relevance Grading**: Employs Claude 3.5 sonnet to assess document relevance 8 | - **Query Transformation**: Improves search results by optimizing queries when needed 9 | - **Web Search Fallback**: Uses Tavily API for web search when local documents aren't sufficient 10 | - **Multi-Model Approach**: Combines OpenAI embeddings and Claude 3.5 sonnet for different tasks 11 | - **Interactive UI**: Built with Streamlit for easy document upload and querying 12 | 13 | ## How to Run? 14 | 15 | 1. **Clone the Repository**: 16 | ```bash 17 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 18 | cd rag_tutorials/corrective_rag 19 | ``` 20 | 21 | 2. **Install Dependencies**: 22 | ```bash 23 | pip install -r requirements.txt 24 | ``` 25 | 26 | 3. **Set Up API Keys**: 27 | You'll need to obtain the following API keys: 28 | - [OpenAI API key](https://platform.openai.com/api-keys) (for embeddings) 29 | - [Anthropic API key](https://console.anthropic.com/settings/keys) (for Claude 3.5 sonnet as LLM) 30 | - [Tavily API key](https://app.tavily.com/home) (for web search) 31 | - Qdrant Cloud Setup 32 | 1. Visit [Qdrant Cloud](https://cloud.qdrant.io/) 33 | 2. Create an account or sign in 34 | 3. Create a new cluster 35 | 4. Get your credentials: 36 | - Qdrant API Key: Found in API Keys section 37 | - Qdrant URL: Your cluster URL (format: `https://xxx-xxx.aws.cloud.qdrant.io`) 38 | 39 | 4. **Run the Application**: 40 | ```bash 41 | streamlit run corrective_rag.py 42 | ``` 43 | 44 | 5. **Use the Application**: 45 | - Upload documents or provide URLs 46 | - Enter your questions in the query box 47 | - View the step-by-step Corrective RAG process 48 | - Get comprehensive answers 49 | 50 | ## Tech Stack 51 | 52 | - **LangChain**: For RAG orchestration and chains 53 | - **LangGraph**: For workflow management 54 | - **Qdrant**: Vector database for document storage 55 | - **Claude 3.5 sonnet**: Main language model for analysis and generation 56 | - **OpenAI**: For document embeddings 57 | - **Tavily**: For web search capabilities 58 | - **Streamlit**: For the user interface 59 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_tic_tac_toe_agent/README.md: -------------------------------------------------------------------------------- 1 | # 🎮 Agent X vs Agent O: Tic-Tac-Toe Game 2 | 3 | An interactive Tic-Tac-Toe game where two AI agents powered by different language models compete against each other built on Agno Agent Framework and Streamlit as UI. Watch as GPT-4O battles against either DeepSeek V3 or Google's Gemini 1.5 Flash in this classic game. 4 | 5 | ## Features 6 | 7 | ### Multi-Agent System 8 | - Player X: OpenAI's Gemini Flash 2.0 9 | - Player O: DeepSeek v3 10 | - Judge: GPT-4o for game outcome validation 11 | 12 | ### Interactive Interface 13 | - Real-time game board visualization 14 | - Move-by-move analysis 15 | - Agent response tracking 16 | - Clear game status updates 17 | 18 | ### Strategic Gameplay 19 | - AI-powered move decisions 20 | - Winning strategy implementation 21 | - Opponent move blocking 22 | - Victory condition monitoring 23 | 24 | ## Prerequisites 25 | - Python 3.8+ 26 | - OpenAI API key 27 | - Either DeepSeek API key or Google API key (for Player O) 28 | 29 | ## How to Run 30 | 31 | 1. **Setup Environment** 32 | ```bash 33 | # Clone the repository 34 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 35 | cd ai_agent_tutorials/ai_tic_tac_toe_agent 36 | 37 | # Install dependencies 38 | pip install -r requirements.txt 39 | ``` 40 | 41 | 2. **Configure API Keys** 42 | - Get OpenAI API key from [OpenAI Platform](https://platform.openai.com) 43 | - Get Google API key from [Google AI Studio](https://aistudio.google.com) (if using Gemini) 44 | - Get DeepSeek API key from DeepSeek platform [Deepseek platform](https://www.deepseek.com) (if using DeepSeek v3 model) 45 | 46 | 4. **Run the Application** 47 | ```bash 48 | streamlit run ai_tic_tac_toe_agent.py 49 | ``` 50 | 51 | 5. **Using the Interface** 52 | - Enter your API keys in the sidebar 53 | - Click "Start Game" to begin 54 | - Watch as the AI agents battle it out! 55 | - Monitor the game progress and final results 56 | 57 | ## Game Components 58 | 59 | - **Game Board** 60 | - 3x3 interactive grid 61 | - Real-time move visualization 62 | - Clear symbol placement (X/O) 63 | 64 | - **AI Agent Players** 65 | - Player X: Strategic offensive moves 66 | - Player O: Defensive countermoves since this agent started later 67 | - AI Judge: Game outcome validation 68 | 69 | - **Game Flow** 70 | - Alternating turns between AIs 71 | - Move validation and error handling 72 | - Winner determination 73 | - Draw detection 74 | 75 | ## Disclaimer ⚠️ 76 | 77 | This is a demonstration project showcasing AI capabilities in game playing. API costs will apply based on your usage of the OpenAI, DeepSeek, or Google APIs. 78 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_competitor_intelligence_agent_team/README.md: -------------------------------------------------------------------------------- 1 | # 🧲 AI Competitor Intelligence Agent Team 2 | 3 | The AI Competitor Intelligence Agent Team is a powerful competitor analysis tool powered by Firecrawl and Agno's AI Agent framework. This app helps businesses analyze their competitors by extracting structured data from competitor websites and generating actionable insights using AI. 4 | 5 | ## Features 6 | 7 | - **Multi-Agent System** 8 | - **Firecrawl Agent**: Specializes in crawling and summarizing competitor websites 9 | - **Analysis Agent**: Generates detailed competitive analysis reports 10 | - **Comparison Agent**: Creates structured comparisons between competitors 11 | 12 | - **Competitor Discovery**: 13 | - Finds similar companies using URL matching with Exa AI 14 | - Discovers competitors based on business descriptions 15 | - Automatically extracts relevant competitor URLs 16 | 17 | - **Comprehensive Analysis**: 18 | - Provides structured analysis reports with: 19 | - Market gaps and opportunities 20 | - Competitor weaknesses 21 | - Recommended features 22 | - Pricing strategies 23 | - Growth opportunities 24 | - Actionable recommendations 25 | 26 | - **Interactive Analysis**: Users can input either their company URL or description for analysis 27 | 28 | ## Requirements 29 | 30 | The application requires the following Python libraries: 31 | 32 | - `agno` 33 | - `exa-py` 34 | - `streamlit` 35 | - `pandas` 36 | - `firecrawl-py` 37 | 38 | You'll also need API keys for: 39 | - OpenAI 40 | - Firecrawl 41 | - Exa 42 | 43 | ## How to Run 44 | 45 | Follow these steps to set up and run the application: 46 | 47 | 1. **Clone the Repository**: 48 | ```bash 49 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 50 | cd ai_agent_tutorials/ai_competitors_analysis_team 51 | ``` 52 | 53 | 2. **Install the dependencies**: 54 | ```bash 55 | pip install -r requirements.txt 56 | ``` 57 | 58 | 3. **Set up your API keys**: 59 | - Get an OpenAI API key from: https://platform.openai.com/api-keys 60 | - Get a Firecrawl API key from: [Firecrawl website](https://www.firecrawl.dev/app/api-keys) 61 | - Get an Exa API key from: [Exa website](https://dashboard.exa.ai/api-keys) 62 | 63 | 4. **Run the Streamlit app**: 64 | ```bash 65 | streamlit run ai_competitor_analyser.py 66 | ``` 67 | 68 | ## Usage 69 | 70 | 1. Enter your API keys in the sidebar 71 | 2. Input either: 72 | - Your company's website URL 73 | - A description of your company 74 | 3. Click "Analyze Competitors" to generate: 75 | - Competitor comparison table 76 | - Detailed analysis report 77 | - Strategic recommendations 78 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_legal_agent_team/README.md: -------------------------------------------------------------------------------- 1 | # 👨‍⚖️ AI Legal Agent Team 2 | 3 | A Streamlit application that simulates a full-service legal team using multiple AI agents to analyze legal documents and provide comprehensive legal insights. Each agent represents a different legal specialist role, from research and contract analysis to strategic planning, working together to provide thorough legal analysis and recommendations. 4 | 5 | ## Features 6 | 7 | - **Specialized Legal AI Agent Team** 8 | - **Legal Researcher**: Equipped with DuckDuckGo search tool to find and cite relevant legal cases and precedents. Provides detailed research summaries with sources and references specific sections from uploaded documents. 9 | 10 | - **Contract Analyst**: Specializes in thorough contract review, identifying key terms, obligations, and potential issues. References specific clauses from documents for detailed analysis. 11 | 12 | - **Legal Strategist**: Focuses on developing comprehensive legal strategies, providing actionable recommendations while considering both risks and opportunities. 13 | 14 | - **Team Lead**: Coordinates analysis between team members, ensures comprehensive responses, properly sourced recommendations, and references to specific document parts. Acts as an Agent Team coordinator for all three agents. 15 | 16 | - **Document Analysis Types** 17 | - Contract Review - Done by Contract Analyst 18 | - Legal Research - Done by Legal Researcher 19 | - Risk Assessment - Done by Legal Strategist, Contract Analyst 20 | - Compliance Check - Done by Legal Strategist, Legal Researcher, Contract Analyst 21 | - Custom Queries - Done by Agent Team - Legal Researcher, Legal Strategist, Contract Analyst 22 | 23 | ## How to Run 24 | 25 | 1. **Setup Environment** 26 | ```bash 27 | # Clone the repository 28 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 29 | cd awesome-llm-apps/ai_agent_tutorials/ai_legal_agent_team 30 | 31 | # Install dependencies 32 | pip install -r requirements.txt 33 | ``` 34 | 35 | 2. **Configure API Keys** 36 | - Get OpenAI API key from [OpenAI Platform](https://platform.openai.com) 37 | - Get Qdrant API key and URL from [Qdrant Cloud](https://cloud.qdrant.io) 38 | 39 | 3. **Run the Application** 40 | ```bash 41 | streamlit run legal_agent_team.py 42 | ``` 43 | 4. **Use the Interface** 44 | - Enter API credentials 45 | - Upload a legal document (PDF) 46 | - Select analysis type 47 | - Add custom queries if needed 48 | - View analysis results 49 | 50 | ## Notes 51 | 52 | - Supports PDF documents only 53 | - Uses GPT-4o for analysis 54 | - Uses text-embedding-3-small for embeddings 55 | - Requires stable internet connection 56 | - API usage costs apply 57 | -------------------------------------------------------------------------------- /llm_apps_with_memory_tutorials/llm_app_personalized_memory/llm_app_memory.py: -------------------------------------------------------------------------------- 1 | import os 2 | import streamlit as st 3 | from mem0 import Memory 4 | from openai import OpenAI 5 | 6 | st.title("LLM App with Memory 🧠") 7 | st.caption("LLM App with personalized memory layer that remembers ever user's choice and interests") 8 | 9 | openai_api_key = st.text_input("Enter OpenAI API Key", type="password") 10 | os.environ["OPENAI_API_KEY"] = openai_api_key 11 | 12 | if openai_api_key: 13 | # Initialize OpenAI client 14 | client = OpenAI(api_key=openai_api_key) 15 | 16 | # Initialize Mem0 with Qdrant 17 | config = { 18 | "vector_store": { 19 | "provider": "qdrant", 20 | "config": { 21 | "collection_name": "llm_app_memory", 22 | "host": "localhost", 23 | "port": 6333, 24 | } 25 | }, 26 | } 27 | 28 | memory = Memory.from_config(config) 29 | 30 | user_id = st.text_input("Enter your Username") 31 | 32 | prompt = st.text_input("Ask ChatGPT") 33 | 34 | if st.button('Chat with LLM'): 35 | with st.spinner('Searching...'): 36 | relevant_memories = memory.search(query=prompt, user_id=user_id) 37 | # Prepare context with relevant memories 38 | context = "Relevant past information:\n" 39 | 40 | for mem in relevant_memories: 41 | context += f"- {mem['text']}\n" 42 | 43 | # Prepare the full prompt 44 | full_prompt = f"{context}\nHuman: {prompt}\nAI:" 45 | 46 | # Get response from GPT-4 47 | response = client.chat.completions.create( 48 | model="gpt-4o", 49 | messages=[ 50 | {"role": "system", "content": "You are a helpful assistant with access to past conversations."}, 51 | {"role": "user", "content": full_prompt} 52 | ] 53 | ) 54 | 55 | answer = response.choices[0].message.content 56 | 57 | st.write("Answer: ", answer) 58 | 59 | # Add AI response to memory 60 | memory.add(answer, user_id=user_id) 61 | 62 | 63 | # Sidebar option to show memory 64 | st.sidebar.title("Memory Info") 65 | if st.button("View My Memory"): 66 | memories = memory.get_all(user_id=user_id) 67 | if memories and "results" in memories: 68 | st.write(f"Memory history for **{user_id}**:") 69 | for mem in memories["results"]: 70 | if "memory" in mem: 71 | st.write(f"- {mem['memory']}") 72 | else: 73 | st.sidebar.info("No learning history found for this user ID.") -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_real_estate_agent/README.md: -------------------------------------------------------------------------------- 1 | ## 🏠 AI Real Estate Agent - Powered by Firecrawl's Extract Endpoint 2 | 3 | The AI Real Estate Agent automates property search and market analysis using Firecrawl's Extract endpoint and Agno AI Agent's insights. It helps users find properties matching their criteria while providing detailed location trends and investment recommendations. This agent streamlines the property search process by combining data from multiple real estate websites and offering intelligent analysis. 4 | 5 | ### Features 6 | - **Smart Property Search**: Uses Firecrawl's Extract endpoint to find properties across multiple real estate websites 7 | - **Multi-Source Integration**: Aggregates data from 99acres, Housing.com, Square Yards, Nobroker, and MagicBricks 8 | - **Location Analysis**: Provides detailed price trends and investment insights for different localities 9 | - **AI-Powered Recommendations**: Uses GPT models to analyze properties and provide structured recommendations 10 | - **User-Friendly Interface**: Clean Streamlit UI for easy property search and results viewing 11 | - **Customizable Search**: Filter by city, property type, category, and budget 12 | 13 | ### How to Get Started 14 | 1. **Clone the repository**: 15 | ```bash 16 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 17 | cd ai_agent_tutorials/ai_real_estate_agent 18 | ``` 19 | 20 | 2. **Install the required packages**: 21 | ```bash 22 | pip install -r requirements.txt 23 | ``` 24 | 25 | 3. **Set up your API keys**: 26 | - Get your Firecrawl API key from [Firecrawl's website](https://www.firecrawl.dev/app/api-keys) 27 | - Get your OpenAI API key from [OpenAI's website](https://platform.openai.com/api-keys) 28 | 29 | 4. **Run the application**: 30 | ```bash 31 | streamlit run ai_real_estate_agent.py 32 | ``` 33 | 34 | ### Using the Agent 35 | 1. **Enter API Keys**: 36 | - Input your Firecrawl and OpenAI API keys in the sidebar 37 | - Keys are securely stored in the session state 38 | 39 | 2. **Set Search Criteria**: 40 | - Enter the city name 41 | - Select property category (Residential/Commercial) 42 | - Choose property type (Flat/Individual House) 43 | - Set maximum budget in Crores 44 | 45 | 3. **View Results**: 46 | - Property recommendations with detailed analysis 47 | - Location trends with investment insights 48 | - Expandable sections for easy reading 49 | 50 | ### Features in Detail 51 | - **Property Finding**: 52 | - Searches across multiple real estate websites 53 | - Returns 3-6 properties matching criteria 54 | - Provides detailed property information and analysis 55 | 56 | - **Location Analysis**: 57 | - Price trends for different localities 58 | - Rental yield analysis 59 | - Investment potential assessment 60 | - Top performing areas identification 61 | 62 | -------------------------------------------------------------------------------- /ai_agent_tutorials/multimodal_design_agent_team/README.md: -------------------------------------------------------------------------------- 1 | # Multimodal AI Design Agent Team 2 | 3 | A Streamlit application that provides comprehensive design analysis using a team of specialized AI agents powered by Google's Gemini model. 4 | 5 | This application leverages multiple specialized AI agents to provide comprehensive analysis of UI/UX designs of your product and your competitors, combining visual understanding, user experience evaluation, and market research insights. 6 | 7 | ## Features 8 | 9 | - **Specialized Legal AI Agent Team** 10 | 11 | - 🎨 **Visual Design Agent**: Evaluates design elements, patterns, color schemes, typography, and visual hierarchy 12 | - 🔄 **UX Analysis Agent**: Assesses user flows, interaction patterns, usability, and accessibility 13 | - 📊 **Market Analysis Agent**: Provides market insights, competitor analysis, and positioning recommendations 14 | 15 | - **Multiple Analysis Types**: Choose from Visual Design, UX, and Market Analysis 16 | - **Comparative Analysis**: Upload competitor designs for comparative insights 17 | - **Customizable Focus Areas**: Select specific aspects for detailed analysis 18 | - **Context-Aware**: Provide additional context for more relevant insights 19 | - **Real-time Processing**: Get instant analysis with progress indicators 20 | - **Structured Output**: Receive well-organized, actionable insights 21 | 22 | ## How to Run 23 | 24 | 1. **Setup Environment** 25 | ```bash 26 | # Clone the repository 27 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 28 | cd awesome-llm-apps/ai_agent_tutorials/multimodal_design_agent_team 29 | 30 | # Create and activate virtual environment (optional) 31 | python -m venv venv 32 | source venv/bin/activate # On Windows: venv\Scripts\activate 33 | 34 | # Install dependencies 35 | pip install -r requirements.txt 36 | ``` 37 | 38 | 2. **Get API Key** 39 | - Visit [Google AI Studio](https://aistudio.google.com/apikey) 40 | - Generate an API key 41 | 42 | 3. **Run the Application** 43 | ```bash 44 | streamlit run design_agent_team.py 45 | ``` 46 | 47 | 4. **Use the Application** 48 | - Enter your Gemini API key in the sidebar 49 | - Upload design files (supported formats: JPG, JPEG, PNG) 50 | - Select analysis types and focus areas 51 | - Add context if needed 52 | - Click "Run Analysis" to get insights 53 | 54 | 55 | ## Technical Stack 56 | 57 | - **Frontend**: Streamlit 58 | - **AI Model**: Google Gemini 2.0 59 | - **Image Processing**: Pillow 60 | - **Market Research**: DuckDuckGo Search API 61 | - **Framework**: Phidata for agent orchestration 62 | 63 | ## Tips for Best Results 64 | 65 | - Upload clear, high-resolution images 66 | - Include multiple views/screens for better context 67 | - Add competitor designs for comparative analysis 68 | - Provide specific context about your target audience 69 | 70 | -------------------------------------------------------------------------------- /rag_tutorials/deepseek_local_rag_agent/README.md: -------------------------------------------------------------------------------- 1 | # 🐋 Deepseek Local RAG Reasoning Agent 2 | 3 | A powerful reasoning agent that combines local Deepseek models with RAG capabilities. Built using Deepseek (via Ollama), Snowflake for embeddings, Qdrant for vector storage, and Agno for agent orchestration, this application offers both simple local chat and advanced RAG-enhanced interactions with comprehensive document processing and web search capabilities. 4 | 5 | ## Features 6 | 7 | - **Dual Operation Modes** 8 | - Local Chat Mode: Direct interaction with Deepseek locally 9 | - RAG Mode: Enhanced reasoning with document context and web search integration - llama3.2 10 | 11 | - **Document Processing** (RAG Mode) 12 | - PDF document upload and processing 13 | - Web page content extraction 14 | - Automatic text chunking and embedding 15 | - Vector storage in Qdrant cloud 16 | 17 | - **Intelligent Querying** (RAG Mode) 18 | - RAG-based document retrieval 19 | - Similarity search with threshold filtering 20 | - Automatic fallback to web search 21 | - Source attribution for answers 22 | 23 | - **Advanced Capabilities** 24 | - Exa AI web search integration 25 | - Custom domain filtering for web search 26 | - Context-aware response generation 27 | - Chat history management 28 | - Thinking process visualization 29 | 30 | - **Model Specific Features** 31 | - Flexible model selection: 32 | - Deepseek r1 1.5b (lighter, suitable for most laptops) 33 | - Deepseek r1 7b (more capable, requires better hardware) 34 | - Snowflake Arctic Embedding model (SOTA) for vector embeddings 35 | - Agno Agent framework for orchestration 36 | - Streamlit-based interactive interface 37 | 38 | ## Prerequisites 39 | 40 | ### 1. Ollama Setup 41 | 1. Install [Ollama](https://ollama.ai) 42 | 2. Pull the Deepseek r1 model(s): 43 | ```bash 44 | # For the lighter model 45 | ollama pull deepseek-r1:1.5b 46 | 47 | # For the more capable model (if your hardware supports it) 48 | ollama pull deepseek-r1:7b 49 | 50 | ollama pull snowflake-arctic-embed 51 | ollama pull llama3.2 52 | ``` 53 | 54 | ### 2. Qdrant Cloud Setup (for RAG Mode) 55 | 1. Visit [Qdrant Cloud](https://cloud.qdrant.io/) 56 | 2. Create an account or sign in 57 | 3. Create a new cluster 58 | 4. Get your credentials: 59 | - Qdrant API Key: Found in API Keys section 60 | - Qdrant URL: Your cluster URL (format: `https://xxx-xxx.cloud.qdrant.io`) 61 | 62 | ### 3. Exa AI API Key (Optional) 63 | 1. Visit [Exa AI](https://exa.ai) 64 | 2. Sign up for an account 65 | 3. Generate an API key for web search capabilities 66 | 67 | ## How to Run 68 | 69 | 1. Clone the repository: 70 | ```bash 71 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 72 | cd rag_tutorials/deepseek_local_rag_agent 73 | ``` 74 | 75 | 2. Install dependencies: 76 | ```bash 77 | pip install -r requirements.txt 78 | ``` 79 | 80 | 3. Run the application: 81 | ```bash 82 | streamlit run deepseek_rag_agent.py 83 | ``` 84 | 85 | -------------------------------------------------------------------------------- /chat_with_X_tutorials/chat_with_pdf/chat_pdf_llama3.2.py: -------------------------------------------------------------------------------- 1 | # Import necessary libraries 2 | import os 3 | import tempfile 4 | import streamlit as st 5 | from embedchain import App 6 | import base64 7 | from streamlit_chat import message 8 | 9 | # Define the embedchain_bot function 10 | def embedchain_bot(db_path): 11 | return App.from_config( 12 | config={ 13 | "llm": {"provider": "ollama", "config": {"model": "llama3.2:latest", "max_tokens": 250, "temperature": 0.5, "stream": True, "base_url": 'http://localhost:11434'}}, 14 | "vectordb": {"provider": "chroma", "config": {"dir": db_path}}, 15 | "embedder": {"provider": "ollama", "config": {"model": "llama3.2:latest", "base_url": 'http://localhost:11434'}}, 16 | } 17 | ) 18 | 19 | # Add a function to display PDF 20 | def display_pdf(file): 21 | base64_pdf = base64.b64encode(file.read()).decode('utf-8') 22 | pdf_display = f'' 23 | st.markdown(pdf_display, unsafe_allow_html=True) 24 | 25 | st.title("Chat with PDF using Llama 3.2") 26 | st.caption("This app allows you to chat with a PDF using Llama 3.2 running locally with Ollama!") 27 | 28 | # Define the database path 29 | db_path = tempfile.mkdtemp() 30 | 31 | # Create a session state to store the app instance and chat history 32 | if 'app' not in st.session_state: 33 | st.session_state.app = embedchain_bot(db_path) 34 | if 'messages' not in st.session_state: 35 | st.session_state.messages = [] 36 | 37 | # Sidebar for PDF upload and preview 38 | with st.sidebar: 39 | st.header("PDF Upload") 40 | pdf_file = st.file_uploader("Upload a PDF file", type="pdf") 41 | 42 | if pdf_file: 43 | st.subheader("PDF Preview") 44 | display_pdf(pdf_file) 45 | 46 | if st.button("Add to Knowledge Base"): 47 | with st.spinner("Adding PDF to knowledge base..."): 48 | with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as f: 49 | f.write(pdf_file.getvalue()) 50 | st.session_state.app.add(f.name, data_type="pdf_file") 51 | os.remove(f.name) 52 | st.success(f"Added {pdf_file.name} to knowledge base!") 53 | 54 | # Chat interface 55 | for i, msg in enumerate(st.session_state.messages): 56 | message(msg["content"], is_user=msg["role"] == "user", key=str(i)) 57 | 58 | if prompt := st.chat_input("Ask a question about the PDF"): 59 | st.session_state.messages.append({"role": "user", "content": prompt}) 60 | message(prompt, is_user=True) 61 | 62 | with st.spinner("Thinking..."): 63 | response = st.session_state.app.chat(prompt) 64 | st.session_state.messages.append({"role": "assistant", "content": response}) 65 | message(response) 66 | 67 | # Clear chat history button 68 | if st.button("Clear Chat History"): 69 | st.session_state.messages = [] -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_tic_tac_toe_game/README.md: -------------------------------------------------------------------------------- 1 | # Agent Tic Tac Toe 2 | 3 | This example shows how to build an interactive Tic Tac Toe game where AI agents compete against each other. The application showcases how to: 4 | - Coordinate multiple AI agents in a turn-based game 5 | - Use different language models for different players 6 | - Create an interactive web interface with Streamlit 7 | - Handle game state and move validation 8 | - Display real-time game progress and move history 9 | 10 | ## Features 11 | - Multiple AI models support (GPT-4, Claude, Gemini, etc.) 12 | - Real-time game visualization 13 | - Move history tracking with board states 14 | - Interactive player selection 15 | - Game state management 16 | - Move validation and coordination 17 | 18 | ### 1. Create a virtual environment 19 | 20 | ```shell 21 | python3 -m venv .venv 22 | source .venv/bin/activate 23 | ``` 24 | 25 | ### 2. Install dependencies 26 | 27 | ```shell 28 | pip install -r ai_agent_tutorials/ai_tic_tac_toe_game/requirements.txt 29 | ``` 30 | 31 | ### 3. Export API Keys 32 | 33 | The game supports multiple AI models. Export the API keys for the models you want to use: 34 | 35 | ```shell 36 | # Required for OpenAI models 37 | export OPENAI_API_KEY=*** 38 | 39 | # Optional - for additional models 40 | export ANTHROPIC_API_KEY=*** # For Claude models 41 | export GOOGLE_API_KEY=*** # For Gemini models 42 | export GROQ_API_KEY=*** # For Groq models 43 | ``` 44 | 45 | ### 4. Run the Game 46 | 47 | ```shell 48 | streamlit run ai_agent_tutorials/ai_tic_tac_toe_game/app.py 49 | ``` 50 | 51 | - Open [localhost:8501](http://localhost:8501) to view the game interface 52 | 53 | ## How It Works 54 | 55 | The game consists of three agents: 56 | 57 | 1. **Master Agent (Referee)** 58 | - Coordinates the game 59 | - Validates moves 60 | - Maintains game state 61 | - Determines game outcome 62 | 63 | 2. **Two Player Agents** 64 | - Make strategic moves 65 | - Analyze board state 66 | - Follow game rules 67 | - Respond to opponent moves 68 | 69 | ## Available Models 70 | 71 | The game supports various AI models: 72 | - GPT-4o (OpenAI) 73 | - GPT-o3-mini (OpenAI) 74 | - Gemini (Google) 75 | - Llama 3 (Groq) 76 | - Claude (Anthropic) 77 | 78 | ## Game Features 79 | 80 | 1. **Interactive Board** 81 | - Real-time updates 82 | - Visual move tracking 83 | - Clear game status display 84 | 85 | 2. **Move History** 86 | - Detailed move tracking 87 | - Board state visualization 88 | - Player action timeline 89 | 90 | 3. **Game Controls** 91 | - Start/Pause game 92 | - Reset board 93 | - Select AI models 94 | - View game history 95 | 96 | 4. **Performance Analysis** 97 | - Move timing 98 | - Strategy tracking 99 | - Game statistics 100 | 101 | ## Support 102 | 103 | Join our [Discord community](https://agno.link/discord) for help and discussions. 104 | -------------------------------------------------------------------------------- /rag_tutorials/gemini_agentic_rag/README.md: -------------------------------------------------------------------------------- 1 | # 🤔 Agentic RAG with Gemini Flash Thinking 2 | 3 | A RAG Agentic system built with the new Gemini 2.0 Flash Thinking model and gemini-exp-1206, Qdrant for vector storage, and Agno (phidata prev) for agent orchestration. This application features intelligent query rewriting, document processing, and web search fallback capabilities to provide comprehensive AI-powered responses. 4 | 5 | ## Features 6 | 7 | - **Document Processing** 8 | - PDF document upload and processing 9 | - Web page content extraction 10 | - Automatic text chunking and embedding 11 | - Vector storage in Qdrant cloud 12 | 13 | - **Intelligent Querying** 14 | - Query rewriting for better retrieval 15 | - RAG-based document retrieval 16 | - Similarity search with threshold filtering 17 | - Automatic fallback to web search 18 | - Source attribution for answers 19 | 20 | - **Advanced Capabilities** 21 | - Exa AI web search integration 22 | - Custom domain filtering for web search 23 | - Context-aware response generation 24 | - Chat history management 25 | - Query reformulation agent 26 | 27 | - **Model Specific Features** 28 | - Gemini Thinking 2.0 Flash for chat and reasoning 29 | - Gemini Embedding model for vector embeddings 30 | - Agno Agent framework for orchestration 31 | - Streamlit-based interactive interface 32 | 33 | ## Prerequisites 34 | 35 | ### 1. Google API Key 36 | 1. Go to [Google AI Studio](https://aistudio.google.com/apikey) 37 | 2. Sign up or log in to your account 38 | 3. Create a new API key 39 | 40 | ### 2. Qdrant Cloud Setup 41 | 1. Visit [Qdrant Cloud](https://cloud.qdrant.io/) 42 | 2. Create an account or sign in 43 | 3. Create a new cluster 44 | 4. Get your credentials: 45 | - Qdrant API Key: Found in API Keys section 46 | - Qdrant URL: Your cluster URL (format: `https://xxx-xxx.cloud.qdrant.io`) 47 | 48 | ### 3. Exa AI API Key (Optional) 49 | 1. Visit [Exa AI](https://exa.ai) 50 | 2. Sign up for an account 51 | 3. Generate an API key for web search capabilities 52 | 53 | ## How to Run 54 | 55 | 1. Clone the repository: 56 | ```bash 57 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 58 | cd rag_tutorials/gemini_agentic_rag 59 | ``` 60 | 61 | 2. Install dependencies: 62 | ```bash 63 | pip install -r requirements.txt 64 | ``` 65 | 66 | 3. Run the application: 67 | ```bash 68 | streamlit run agentic_rag_gemini.py 69 | ``` 70 | 71 | ## Usage 72 | 73 | 1. Configure API keys in the sidebar: 74 | - Enter your Google API key 75 | - Add Qdrant credentials 76 | - (Optional) Add Exa AI key for web search 77 | 78 | 2. Upload documents: 79 | - Use the file uploader for PDFs 80 | - Enter URLs for web content 81 | 82 | 3. Ask questions: 83 | - Type your query in the chat interface 84 | - View rewritten queries and sources 85 | - See web search results when relevant 86 | 87 | 4. Manage your session: 88 | - Clear chat history as needed 89 | - Configure web search domains 90 | - Monitor processed documents 91 | -------------------------------------------------------------------------------- /ai_agent_tutorials/multimodal_ai_agent/mutimodal_agent.py: -------------------------------------------------------------------------------- 1 | import streamlit as st 2 | from agno.agent import Agent 3 | from agno.models.google import Gemini 4 | from agno.tools.duckduckgo import DuckDuckGoTools 5 | from google.generativeai import upload_file, get_file 6 | import time 7 | from pathlib import Path 8 | import tempfile 9 | 10 | st.set_page_config( 11 | page_title="Multimodal AI Agent", 12 | page_icon="🧬", 13 | layout="wide" 14 | ) 15 | 16 | st.title("Multimodal AI Agent 🧬") 17 | 18 | # Initialize single agent with both capabilities 19 | @st.cache_resource 20 | def initialize_agent(): 21 | return Agent( 22 | name="Multimodal Analyst", 23 | model=Gemini(id="gemini-2.0-flash-exp"), 24 | tools=[DuckDuckGoTools()], 25 | markdown=True, 26 | ) 27 | 28 | agent = initialize_agent() 29 | 30 | # File uploader 31 | uploaded_file = st.file_uploader("Upload a video file", type=['mp4', 'mov', 'avi']) 32 | 33 | if uploaded_file: 34 | with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as tmp_file: 35 | tmp_file.write(uploaded_file.read()) 36 | video_path = tmp_file.name 37 | 38 | st.video(video_path) 39 | 40 | user_prompt = st.text_area( 41 | "What would you like to know?", 42 | placeholder="Ask any question related to the video - the AI Agent will analyze it and search the web if needed", 43 | help="You can ask questions about the video content and get relevant information from the web" 44 | ) 45 | 46 | if st.button("Analyze & Research"): 47 | if not user_prompt: 48 | st.warning("Please enter your question.") 49 | else: 50 | try: 51 | with st.spinner("Processing video and researching..."): 52 | video_file = upload_file(video_path) 53 | while video_file.state.name == "PROCESSING": 54 | time.sleep(2) 55 | video_file = get_file(video_file.name) 56 | 57 | prompt = f""" 58 | First analyze this video and then answer the following question using both 59 | the video analysis and web research: {user_prompt} 60 | 61 | Provide a comprehensive response focusing on practical, actionable information. 62 | """ 63 | 64 | result = agent.run(prompt, videos=[video_file]) 65 | 66 | st.subheader("Result") 67 | st.markdown(result.content) 68 | 69 | except Exception as e: 70 | st.error(f"An error occurred: {str(e)}") 71 | finally: 72 | Path(video_path).unlink(missing_ok=True) 73 | else: 74 | st.info("Please upload a video to begin analysis.") 75 | 76 | st.markdown(""" 77 | 82 | """, unsafe_allow_html=True) -------------------------------------------------------------------------------- /rag_tutorials/rag_chain/README.md: -------------------------------------------------------------------------------- 1 | # PharmaQuery 2 | 3 | ## Overview 4 | PharmaQuery is an advanced Pharmaceutical Insight Retrieval System designed to help users gain meaningful insights from research papers and documents in the pharmaceutical domain. 5 | 6 | ## Demo 7 | https://github.com/user-attachments/assets/c12ee305-86fe-4f71-9219-57c7f438f291 8 | 9 | ## Features 10 | - **Natural Language Querying**: Ask complex questions about the pharmaceutical industry and get concise, accurate answers. 11 | - **Custom Database**: Upload your own research documents to enhance the retrieval system's knowledge base. 12 | - **Similarity Search**: Retrieves the most relevant documents for your query using AI embeddings. 13 | - **Streamlit Interface**: User-friendly interface for queries and document uploads. 14 | 15 | ## Technologies Used 16 | - **Programming Language**: [Python 3.10+](https://www.python.org/downloads/release/python-31011/) 17 | - **Framework**: [LangChain](https://www.langchain.com/) 18 | - **Database**: [ChromaDB](https://www.trychroma.com/) 19 | - **Models**: 20 | - Embeddings: [Google Gemini API (embedding-001)](https://ai.google.dev/gemini-api/docs/embeddings) 21 | - Chat: [Google Gemini API (gemini-1.5-pro)](https://ai.google.dev/gemini-api/docs/models/gemini#gemini-1.5-pro) 22 | - **PDF Processing**: [PyPDFLoader](https://python.langchain.com/docs/integrations/document_loaders/pypdfloader/) 23 | - **Document Splitter**: [SentenceTransformersTokenTextSplitter](https://python.langchain.com/api_reference/text_splitters/sentence_transformers/langchain_text_splitters.sentence_transformers.SentenceTransformersTokenTextSplitter.html) 24 | 25 | ## Requirements 26 | 1. **Install Dependencies**: 27 | ```bash 28 | pip install -r requirements.txt 29 | ``` 30 | 31 | 2. **Run the Application**: 32 | ```bash 33 | streamlit run app.py 34 | ``` 35 | 36 | 3. **Use the Application**: 37 | - Paste your Google API Key in the sidebar. 38 | - Enter your query in the main interface. 39 | - Optionally, upload research papers in the sidebar to enhance the database. 40 | 41 | ## :mailbox: Connect With Me 42 | handshake gif 43 | 44 |

45 | codewithcharan 46 | __mr.__.unique 47 | codewithcharan 48 |

-------------------------------------------------------------------------------- /ai_agent_tutorials/ai_aqi_analysis_agent/README.md: -------------------------------------------------------------------------------- 1 | # 🌍 AQI Analysis Agent 2 | 3 | The AQI Analysis Agent is a powerful air quality monitoring and health recommendation tool powered by Firecrawl and Agno's AI Agent framework. This app helps users make informed decisions about outdoor activities by analyzing real-time air quality data and providing personalized health recommendations. 4 | 5 | ## Features 6 | 7 | - **Multi-Agent System** 8 | - **AQI Analyzer**: Fetches and processes real-time air quality data 9 | - **Health Recommendation Agent**: Generates personalized health advice 10 | 11 | - **Air Quality Metrics**: 12 | - Overall Air Quality Index (AQI) 13 | - Particulate Matter (PM2.5 and PM10) 14 | - Carbon Monoxide (CO) levels 15 | - Temperature 16 | - Humidity 17 | - Wind Speed 18 | 19 | - **Comprehensive Analysis**: 20 | - Real-time data visualization 21 | - Health impact assessment 22 | - Activity safety recommendations 23 | - Best time suggestions for outdoor activities 24 | - Weather condition correlations 25 | 26 | - **Interactive Features**: 27 | - Location-based analysis 28 | - Medical condition considerations 29 | - Activity-specific recommendations 30 | - Downloadable reports 31 | - Example queries for quick testing 32 | 33 | ## How to Run 34 | 35 | Follow these steps to set up and run the application: 36 | 37 | 1. **Clone the Repository**: 38 | ```bash 39 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 40 | cd ai_agent_tutorials/ai_aqi_analysis_agent 41 | ``` 42 | 43 | 2. **Install the dependencies**: 44 | ```bash 45 | pip install -r requirements.txt 46 | ``` 47 | 48 | 3. **Set up your API keys**: 49 | - Get an OpenAI API key from: https://platform.openai.com/api-keys 50 | - Get a Firecrawl API key from: [Firecrawl website](https://www.firecrawl.dev/app/api-keys) 51 | 52 | 4. **Run the Gradio app**: 53 | ```bash 54 | python ai_aqi_analysis_agent.py 55 | ``` 56 | 57 | 5. **Access the Web Interface**: 58 | - The terminal will display two URLs: 59 | - Local URL: `http://127.0.0.1:7860` (for local access) 60 | - Public URL: `https://xxx-xxx-xxx.gradio.live` (for temporary public access) 61 | - Click on either URL to open the web interface in your browser 62 | 63 | ## Usage 64 | 65 | 1. Enter your API keys in the API Configuration section 66 | 2. Input location details: 67 | - City name 68 | - State (optional for Union Territories/US cities) 69 | - Country 70 | 3. Provide personal information: 71 | - Medical conditions (optional) 72 | - Planned outdoor activity 73 | 4. Click "Analyze & Get Recommendations" to receive: 74 | - Current air quality data 75 | - Health impact analysis 76 | - Activity safety recommendations 77 | 5. Try the example queries for quick testing 78 | 79 | ## Note 80 | 81 | The air quality data is fetched using Firecrawl's web scraping capabilities. Due to caching and rate limiting, the data might not always match real-time values on the website. For the most accurate real-time data, consider checking the source website directly. 82 | -------------------------------------------------------------------------------- /llm_apps_with_memory_tutorials/ai_arxiv_agent_memory/ai_arxiv_agent_memory.py: -------------------------------------------------------------------------------- 1 | import streamlit as st 2 | import os 3 | from mem0 import Memory 4 | from multion.client import MultiOn 5 | from openai import OpenAI 6 | 7 | st.title("AI Research Agent with Memory 📚") 8 | 9 | api_keys = {k: st.text_input(f"{k.capitalize()} API Key", type="password") for k in ['openai', 'multion']} 10 | 11 | if all(api_keys.values()): 12 | os.environ['OPENAI_API_KEY'] = api_keys['openai'] 13 | # Initialize Mem0 with Qdrant 14 | config = { 15 | "vector_store": { 16 | "provider": "qdrant", 17 | "config": { 18 | "model": "gpt-4o-mini", 19 | "host": "localhost", 20 | "port": 6333, 21 | } 22 | }, 23 | } 24 | memory, multion, openai_client = Memory.from_config(config), MultiOn(api_key=api_keys['multion']), OpenAI(api_key=api_keys['openai']) 25 | 26 | user_id = st.sidebar.text_input("Enter your Username") 27 | #user_interests = st.text_area("Research interests and background") 28 | 29 | search_query = st.text_input("Research paper search query") 30 | 31 | def process_with_gpt4(result): 32 | """Processes an arXiv search result to produce a structured markdown output. 33 | 34 | This function takes a search result from arXiv and generates a markdown-formatted 35 | table containing details about each paper. The table includes columns for the 36 | paper's title, authors, a brief abstract, and a link to the paper on arXiv. 37 | 38 | Args: 39 | result (str): The raw search result from arXiv, typically in a text format. 40 | 41 | Returns: 42 | str: A markdown-formatted string containing a table with paper details.""" 43 | prompt = f""" 44 | Based on the following arXiv search result, provide a proper structured output in markdown that is readable by the users. 45 | Each paper should have a title, authors, abstract, and link. 46 | Search Result: {result} 47 | Output Format: Table with the following columns: [{{"title": "Paper Title", "authors": "Author Names", "abstract": "Brief abstract", "link": "arXiv link"}}, ...] 48 | """ 49 | response = openai_client.chat.completions.create(model="gpt-4o-mini", messages=[{"role": "user", "content": prompt}], temperature=0.2) 50 | return response.choices[0].message.content 51 | 52 | if st.button('Search for Papers'): 53 | with st.spinner('Searching and Processing...'): 54 | relevant_memories = memory.search(search_query, user_id=user_id, limit=3) 55 | prompt = f"Search for arXiv papers: {search_query}\nUser background: {' '.join(mem['text'] for mem in relevant_memories)}" 56 | result = process_with_gpt4(multion.browse(cmd=prompt, url="https://arxiv.org/")) 57 | st.markdown(result) 58 | 59 | if st.sidebar.button("View Memory"): 60 | st.sidebar.write("\n".join([f"- {mem['text']}" for mem in memory.get_all(user_id=user_id)])) 61 | 62 | else: 63 | st.warning("Please enter your API keys to use this app.") -------------------------------------------------------------------------------- /llm_finetuning_tutorials/llama3.2_finetuning/README.md: -------------------------------------------------------------------------------- 1 | ## 🦙 Finetune Llama 3.2 in 30 Lines of Python 2 | 3 | This script demonstrates how to finetune the Llama 3.2 model using the [Unsloth](https://unsloth.ai/) library, which makes the process easy and fast. You can run this example to finetune Llama 3.1 1B and 3B models for free in Google Colab. 4 | 5 | ### Features 6 | 7 | - Finetunes Llama 3.2 model using the Unsloth library 8 | - Implements Low-Rank Adaptation (LoRA) for efficient finetuning 9 | - Uses the FineTome-100k dataset for training 10 | - Configurable for different model sizes (1B and 3B) 11 | 12 | ### Installation 13 | 14 | 1. Clone the repository: 15 | 16 | ```bash 17 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 18 | cd awesome-llm-apps/llm_finetuning_tutorials/llama3.2_finetuning 19 | ``` 20 | 21 | 2. Install the required dependencies: 22 | 23 | ```bash 24 | pip install -r requirements.txt 25 | ``` 26 | 27 | ## Usage 28 | 29 | 1. Open the script in Google Colab or your preferred Python environment. 30 | 31 | 2. Run the script to start the finetuning process: 32 | 33 | ```bash 34 | # Run the entire script 35 | python finetune_llama3.2.py 36 | ``` 37 | 38 | 3. The finetuned model will be saved in the "finetuned_model" directory. 39 | 40 | ## How it Works 41 | 42 | 1. **Model Loading**: The script loads the Llama 3.2 3B Instruct model using Unsloth's FastLanguageModel. 43 | 44 | 2. **LoRA Setup**: Low-Rank Adaptation is applied to specific layers of the model for efficient finetuning. 45 | 46 | 3. **Data Preparation**: The FineTome-100k dataset is loaded and preprocessed using a chat template. 47 | 48 | 4. **Training Configuration**: The script sets up the SFTTrainer with specific training arguments. 49 | 50 | 5. **Finetuning**: The model is finetuned on the prepared dataset. 51 | 52 | 6. **Model Saving**: The finetuned model is saved to disk. 53 | 54 | ## Configuration 55 | 56 | You can modify the following parameters in the script: 57 | 58 | - `model_name`: Change to "unsloth/Llama-3.1-1B-Instruct" for the 1B model 59 | - `max_seq_length`: Adjust the maximum sequence length 60 | - `r`: LoRA rank 61 | - Training hyperparameters in `TrainingArguments` 62 | 63 | ## Customization 64 | 65 | - To use a different dataset, replace the `load_dataset` function call with your desired dataset. 66 | - Adjust the `target_modules` in the LoRA setup to finetune different layers of the model. 67 | - Modify the chat template in `get_chat_template` if you're using a different conversational format. 68 | 69 | ## Running on Google Colab 70 | 71 | 1. Open a new Google Colab notebook. 72 | 2. Copy the entire script into a code cell. 73 | 3. Add a cell at the beginning to install the required libraries: 74 | 75 | ``` 76 | !pip install torch transformers datasets trl unsloth 77 | ``` 78 | 79 | 4. Run the cells to start the finetuning process. 80 | 81 | ## Notes 82 | 83 | - This script is optimized for running on Google Colab's free tier, which provides access to GPUs. 84 | - The finetuning process may take some time, depending on the model size and the available computational resources. 85 | - Make sure you have enough storage space in your Colab instance to save the finetuned model. -------------------------------------------------------------------------------- /chat_with_X_tutorials/chat_with_youtube_videos/chat_youtube.py: -------------------------------------------------------------------------------- 1 | import tempfile 2 | import streamlit as st 3 | from embedchain import App 4 | from youtube_transcript_api import YouTubeTranscriptApi 5 | from typing import Tuple 6 | 7 | def embedchain_bot(db_path: str, api_key: str) -> App: 8 | return App.from_config( 9 | config={ 10 | "llm": {"provider": "openai", "config": {"model": "gpt-4", "temperature": 0.5, "api_key": api_key}}, 11 | "vectordb": {"provider": "chroma", "config": {"dir": db_path}}, 12 | "embedder": {"provider": "openai", "config": {"api_key": api_key}}, 13 | } 14 | ) 15 | 16 | def extract_video_id(video_url: str) -> str: 17 | if "youtube.com/watch?v=" in video_url: 18 | return video_url.split("v=")[-1].split("&")[0] 19 | elif "youtube.com/shorts/" in video_url: 20 | return video_url.split("/shorts/")[-1].split("?")[0] 21 | else: 22 | raise ValueError("Invalid YouTube URL") 23 | 24 | def fetch_video_data(video_url: str) -> Tuple[str, str]: 25 | try: 26 | video_id = extract_video_id(video_url) 27 | transcript = YouTubeTranscriptApi.get_transcript(video_id) 28 | transcript_text = " ".join([entry["text"] for entry in transcript]) 29 | return "Unknown", transcript_text # Title is set to "Unknown" since we're not fetching it 30 | except Exception as e: 31 | st.error(f"Error fetching transcript: {e}") 32 | return "Unknown", "No transcript available for this video." 33 | 34 | # Create Streamlit app 35 | st.title("Chat with YouTube Video 📺") 36 | st.caption("This app allows you to chat with a YouTube video using OpenAI API") 37 | 38 | # Get OpenAI API key from user 39 | openai_access_token = st.text_input("OpenAI API Key", type="password") 40 | 41 | # If OpenAI API key is provided, create an instance of App 42 | if openai_access_token: 43 | # Create a temporary directory to store the database 44 | db_path = tempfile.mkdtemp() 45 | # Create an instance of Embedchain App 46 | app = embedchain_bot(db_path, openai_access_token) 47 | # Get the YouTube video URL from the user 48 | video_url = st.text_input("Enter YouTube Video URL", type="default") 49 | # Add the video to the knowledge base 50 | if video_url: 51 | try: 52 | title, transcript = fetch_video_data(video_url) 53 | if transcript != "No transcript available for this video.": 54 | app.add(transcript, data_type="text", metadata={"title": title, "url": video_url}) 55 | st.success(f"Added video '{title}' to knowledge base!") 56 | else: 57 | st.warning(f"No transcript available for video '{title}'. Cannot add to knowledge base.") 58 | except Exception as e: 59 | st.error(f"Error adding video: {e}") 60 | # Ask a question about the video 61 | prompt = st.text_input("Ask any question about the YouTube Video") 62 | # Chat with the video 63 | if prompt: 64 | try: 65 | answer = app.chat(prompt) 66 | st.write(answer) 67 | except Exception as e: 68 | st.error(f"Error chatting with the video: {e}") -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_system_architect_r1/README.md: -------------------------------------------------------------------------------- 1 | # 🤖 AI System Architect Advisor with R1 2 | 3 | An Agno agentic system that provides expert software architecture analysis and recommendations using a dual-model approach combining DeepSeek R1's Reasoning and Claude. The system provides detailed technical analysis, implementation roadmaps, and architectural decisions for complex software systems. 4 | 5 | ## Features 6 | 7 | - **Dual AI Model Architecture** 8 | - **DeepSeek Reasoner**: Provides initial technical analysis and structured reasoning about architecture patterns, tools, and implementation strategies 9 | - **Claude-3.5**: Generates detailed explanations, implementation roadmaps, and technical specifications based on DeepSeek's analysis 10 | 11 | - **Comprehensive Analysis Components** 12 | - Architecture Pattern Selection 13 | - Infrastructure Resource Planning 14 | - Security Measures and Compliance 15 | - Database Architecture 16 | - Performance Requirements 17 | - Cost Estimation 18 | - Risk Assessment 19 | 20 | - **Analysis Types** 21 | - Real-time Event Processing Systems 22 | - Healthcare Data Platforms 23 | - Financial Trading Platforms 24 | - Multi-tenant SaaS Solutions 25 | - Digital Content Delivery Networks 26 | - Supply Chain Management Systems 27 | 28 | ## How to Run 29 | 30 | 1. **Setup Environment** 31 | ```bash 32 | # Clone the repository 33 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 34 | cd awesome-llm-apps/ai_agent_tutorials/ai_system_architect_r1 35 | 36 | # Install dependencies 37 | pip install -r requirements.txt 38 | ``` 39 | 40 | 2. **Configure API Keys** 41 | - Get DeepSeek API key from DeepSeek platform 42 | - Get Anthropic API key from [Anthropic Platform](https://www.anthropic.com) 43 | 44 | 3. **Run the Application** 45 | ```bash 46 | streamlit run ai_system_architect_r1.py 47 | ``` 48 | 49 | 4. **Use the Interface** 50 | - Enter API credentials in sidebar 51 | - Structure your prompt with: 52 | - Project Context 53 | - Requirements 54 | - Constraints 55 | - Scale 56 | - Security/Compliance needs 57 | - View detailed analysis results 58 | 59 | ## Example Test Prompts: 60 | 61 | ### 1. Financial Trading Platform 62 | "We need to build a high-frequency trading platform that processes market data streams, executes trades with sub-millisecond latency, maintains audit trails, and handles complex risk calculations. The system needs to be globally distributed, handle 100,000 transactions per second, and have robust disaster recovery capabilities." 63 | ### 2. Multi-tenant SaaS Platform 64 | "Design a multi-tenant SaaS platform for enterprise resource planning that needs to support customization per tenant, handle different data residency requirements, support offline capabilities, and maintain performance isolation between tenants. The system should scale to 10,000 concurrent users and support custom integrations." 65 | 66 | ## Notes 67 | 68 | - Requires both DeepSeek and Anthropic API keys 69 | - Provides real-time analysis with detailed explanations 70 | - Supports chat-based interaction 71 | - Includes clear reasoning for all architectural decisions 72 | - API usage costs apply 73 | 74 | 75 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_services_agency/README.md: -------------------------------------------------------------------------------- 1 | # AI Services Agency 👨‍💼 2 | 3 | An AI application that simulates a full-service digital agency using multiple AI agents to analyze and plan software projects. Each agent represents a different role in the project lifecycle, from strategic planning to technical implementation. 4 | 5 | ## Demo: 6 | 7 | https://github.com/user-attachments/assets/a0befa3a-f4c3-400d-9790-4b9e37254405 8 | 9 | ## Features 10 | 11 | ### Five specialized AI agents 12 | 13 | - **CEO Agent**: Strategic leader and final decision maker 14 | - Analyzes startup ideas using structured evaluation 15 | - Makes strategic decisions across product, technical, marketing, and financial domains 16 | - Uses AnalyzeStartupTool and MakeStrategicDecision tools 17 | 18 | - **CTO Agent**: Technical architecture and feasibility expert 19 | - Evaluates technical requirements and feasibility 20 | - Provides architecture decisions 21 | - Uses QueryTechnicalRequirements and EvaluateTechnicalFeasibility tools 22 | 23 | - **Product Manager Agent**: Product strategy specialist 24 | - Defines product strategy and roadmap 25 | - Coordinates between technical and marketing teams 26 | - Focuses on product-market fit 27 | 28 | - **Developer Agent**: Technical implementation expert 29 | - Provides detailed technical implementation guidance 30 | - Suggests optimal tech stack and cloud solutions 31 | - Estimates development costs and timelines 32 | 33 | - **Client Success Agent**: Marketing strategy leader 34 | - Develops go-to-market strategies 35 | - Plans customer acquisition approaches 36 | - Coordinates with product team 37 | 38 | ### Custom Tools 39 | 40 | The agency uses specialized tools built with OpenAI Schema for structured analysis: 41 | - **Analysis Tools**: AnalyzeProjectRequirements for market evaluation and analysis of startup idea 42 | - **Technical Tools**: CreateTechnicalSpecification for technical assessment 43 | 44 | ### 🔄 Asynchronous Communication 45 | 46 | The agency operates in async mode, enabling: 47 | - Parallel processing of analyses from different agents 48 | - Efficient multi-agent collaboration 49 | - Real-time communication between agents 50 | - Non-blocking operations for better performance 51 | 52 | ### 🔗 Agent Communication Flows 53 | - CEO ↔️ All Agents (Strategic Oversight) 54 | - CTO ↔️ Developer (Technical Implementation) 55 | - Product Manager ↔️ Marketing Manager (Go-to-Market Strategy) 56 | - Product Manager ↔️ Developer (Feature Implementation) 57 | - (and more!) 58 | 59 | ## How to Run 60 | 61 | Follow the steps below to set up and run the application: 62 | Before anything else, Please get your OpenAI API Key here: https://platform.openai.com/api-keys 63 | 64 | 1. **Clone the Repository**: 65 | ```bash 66 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 67 | cd awesome-llm-apps/ai_agent_tutorials/ai_services_agency 68 | ``` 69 | 70 | 2. **Install the dependencies**: 71 | ```bash 72 | pip install -r requirements.txt 73 | ``` 74 | 75 | 3. **Run the Streamlit app**: 76 | ```bash 77 | streamlit run ai_services_agency/agency.py 78 | ``` 79 | 80 | 4. **Enter your OpenAI API Key** in the sidebar when prompted and start analyzing your startup idea! 81 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_teaching_agent_team/README.md: -------------------------------------------------------------------------------- 1 | # 👨‍🏫 AI Teaching Agent Team 2 | 3 | A Streamlit application that brings together a team of specialized AI teaching agents who collaborate like a professional teaching faculty. Each agent acts as a specialized educator: a curriculum designer, learning path expert, resource librarian, and practice instructor - working together to create a complete educational experience through Google Docs. 4 | 5 | ## 🪄 Meet your AI Teaching Agent Team 6 | 7 | #### 🧠 Professor Agent 8 | - Creates fundamental knowledge base in Google Docs 9 | - Organizes content with proper headings and sections 10 | - Includes detailed explanations and examples 11 | - Output: Comprehensive knowledge base document with table of contents 12 | 13 | #### 🗺️ Academic Advisor Agent 14 | - Designs learning path in a structured Google Doc 15 | - Creates progressive milestone markers 16 | - Includes time estimates and prerequisites 17 | - Output: Visual roadmap document with clear progression paths 18 | 19 | #### 📚 Research Librarian Agent 20 | - Compiles resources in an organized Google Doc 21 | - Includes links to academic papers and tutorials 22 | - Adds descriptions and difficulty levels 23 | - Output: Categorized resource list with quality ratings 24 | 25 | #### ✍️ Teaching Assistant Agent 26 | - Develops exercises in an interactive Google Doc 27 | - Creates structured practice sections 28 | - Includes solution guides 29 | - Output: Complete practice workbook with answers 30 | 31 | 32 | ## How to Run 33 | 34 | 1. Clone the repository 35 | ```bash 36 | # Clone the repository 37 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 38 | cd ai_agent_tutorials/ai_personal_learning_agent 39 | 40 | # Install dependencies 41 | pip install -r requirements.txt 42 | ``` 43 | 44 | ## Configuration - IMPORTANT STEP 45 | 46 | 1. Get your OpenAI API Key 47 | - Create an account on [OpenAI Platform](https://platform.openai.com/) 48 | - Navigate to API Keys section 49 | - Create a new API key 50 | 51 | 2. Get your Composio API Key 52 | - Create an account on [Composio Platform](https://composio.ai/) 53 | - **IMPORTANT** - For you to use the app, you need to make new connection ID with google docs and composio.Follow the below two steps to do so: 54 | - composio add googledocs (IN THE TERMINAL) 55 | - Create a new connection 56 | - Select OAUTH2 57 | - Select Google Account and Done. 58 | - On the composio account website, go to apps, select google docs tool, and [click create integration](https://app.composio.dev/app/googledocs) (violet button) and click Try connecting default’s googldocs button and we are done. 59 | 60 | 3. Sign up and get the [SerpAPI Key](https://serpapi.com/) 61 | 62 | ## How to Use? 63 | 64 | 1. Start the Streamlit app 65 | ```bash 66 | streamlit run teaching_agent_team.py 67 | ``` 68 | 69 | 2. Use the application 70 | - Enter your OpenAI API key in the sidebar (if not set in environment) 71 | - Enter your Composio API key in the sidebar 72 | - Type a topic you want to learn about (e.g., "Python Programming", "Machine Learning") 73 | - Click "Generate Learning Plan" 74 | - Wait for the agents to generate your personalized learning plan 75 | - View the results and terminal output in the interface 76 | -------------------------------------------------------------------------------- /ai_agent_tutorials/ai_game_design_agent_team/README.md: -------------------------------------------------------------------------------- 1 | # AI Game Design Agent Team 🎮 2 | 3 | The AI Game Design Agent Team is a collaborative game design system powered by [AG2](https://github.com/ag2ai/ag2?tab=readme-ov-file)(formerly AutoGen)'s AI Agent framework. This app generates comprehensive game concepts through the coordination of multiple specialized AI agents, each focusing on different aspects of game design based on user inputs such as game type, target audience, art style, and technical requirements. This is built on AG2's new swarm feature run through initiate_chat() method. 4 | 5 | ## Features 6 | 7 | - **Specialized Game Design Agent Team** 8 | - 🎭 **Story Agent**: Specializes in narrative design and world-building, including character development, plot arcs, dialogue writing, and lore creation 9 | - 🎮 **Gameplay Agent**: Focuses on game mechanics and systems design, including player progression, combat systems, resource management, and balancing 10 | - 🎨 **Visuals Agent**: Handles art direction and audio design, covering UI/UX, character/environment art style, sound effects, and music composition 11 | - ⚙️ **Tech Agent**: Provides technical architecture and implementation guidance, including engine selection, optimization strategies, networking requirements, and development roadmap 12 | - 🎯 **Task Agent**: Coordinates between all specialized agents and ensures cohesive integration of different game aspects 13 | 14 | - **Comprehensive Game Design Outputs**: 15 | - Detailed narrative and world-building elements 16 | - Core gameplay mechanics and systems 17 | - Visual and audio direction 18 | - Technical specifications and requirements 19 | - Development timeline and budget considerations 20 | - Coherent game design from the team. 21 | 22 | - **Customizable Input Parameters**: 23 | - Game type and target audience 24 | - Art style and visual preferences 25 | - Platform requirements 26 | - Development constraints (time, budget) 27 | - Core mechanics and gameplay features 28 | 29 | - **Interactive Results**: 30 | - Quick show of game design ideas from each agent 31 | - Detailed results are presented in expandable sections for easy navigation and reference 32 | 33 | ## How to Run 34 | 35 | Follow these steps to set up and run the application: 36 | 37 | 1. **Clone the Repository**: 38 | ```bash 39 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 40 | cd ai_agent_tutorials/ai_game_design_team 41 | ``` 42 | 43 | 2. **Install Dependencies**: 44 | ```bash 45 | pip install -r requirements.txt 46 | ``` 47 | 48 | 3. **Set Up OpenAI API Key**: 49 | - Obtain an OpenAI API key from [OpenAI's platform](https://platform.openai.com) 50 | - You'll input this key in the app's sidebar when running 51 | 52 | 4. **Run the Streamlit App**: 53 | ```bash 54 | streamlit run ai_game_design_agent_team/game_design_agent_team.py 55 | ``` 56 | 57 | ## Usage 58 | 59 | 1. Enter your OpenAI API key in the sidebar 60 | 2. Fill in the game details: 61 | - Background vibe and setting 62 | - Game type and target audience 63 | - Visual style preferences 64 | - Technical requirements 65 | - Development constraints 66 | 3. Click "Generate Game Concept" to receive comprehensive design documentation from all agents 67 | 4. Review the outputs in the expandable sections for each aspect of game design 68 | -------------------------------------------------------------------------------- /rag_tutorials/hybrid_search_rag/README.md: -------------------------------------------------------------------------------- 1 | # 👀 RAG App with Hybrid Search 2 | 3 | A powerful document Q&A application that leverages Hybrid Search (RAG) and Claude's advanced language capabilities to provide comprehensive answers. Built with RAGLite for robust document processing and retrieval, and Streamlit for an intuitive chat interface, this system seamlessly combines document-specific knowledge with Claude's general intelligence to deliver accurate and contextual responses. 4 | 5 | ## Features 6 | 7 | - **Hybrid Search Question Answering** 8 | - RAG-based answers for document-specific queries 9 | - Fallback to Claude for general knowledge questions 10 | 11 | - **Document Processing**: 12 | - PDF document upload and processing 13 | - Automatic text chunking and embedding 14 | - Hybrid search combining semantic and keyword matching 15 | - Reranking for better context selection 16 | 17 | - **Multi-Model Integration**: 18 | - Claude for text generation - tested with Claude 3 Opus 19 | - OpenAI for embeddings - tested with text-embedding-3-large 20 | - Cohere for reranking - tested with Cohere 3.5 reranker 21 | 22 | ## Prerequisites 23 | 24 | You'll need the following API keys and database setup: 25 | 26 | 1. **Database**: Create a free PostgreSQL database at [Neon](https://neon.tech): 27 | - Sign up/Login at Neon 28 | - Create a new project 29 | - Copy the connection string (looks like: `postgresql://user:pass@ep-xyz.region.aws.neon.tech/dbname`) 30 | 31 | 2. **API Keys**: 32 | - [OpenAI API key](https://platform.openai.com/api-keys) for embeddings 33 | - [Anthropic API key](https://console.anthropic.com/settings/keys) for Claude 34 | - [Cohere API key](https://dashboard.cohere.com/api-keys) for reranking 35 | 36 | ## How to get Started? 37 | 38 | 1. **Clone the Repository**: 39 | ```bash 40 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 41 | cd awesome-llm-apps/rag_tutorials/hybrid_search_rag 42 | ``` 43 | 44 | 2. **Install Dependencies**: 45 | ```bash 46 | pip install -r requirements.txt 47 | ``` 48 | 49 | 3. **Install spaCy Model**: 50 | ```bash 51 | pip install https://github.com/explosion/spacy-models/releases/download/xx_sent_ud_sm-3.7.0/xx_sent_ud_sm-3.7.0-py3-none-any.whl 52 | ``` 53 | 54 | 4. **Run the Application**: 55 | ```bash 56 | streamlit run main.py 57 | ``` 58 | 59 | ## Usage 60 | 61 | 1. Start the application 62 | 2. Enter your API keys in the sidebar: 63 | - OpenAI API key 64 | - Anthropic API key 65 | - Cohere API key 66 | - Database URL (optional, defaults to SQLite) 67 | 3. Click "Save Configuration" 68 | 4. Upload PDF documents 69 | 5. Start asking questions! 70 | - Document-specific questions will use RAG 71 | - General questions will use Claude directly 72 | 73 | ## Database Options 74 | 75 | The application supports multiple database backends: 76 | 77 | - **PostgreSQL** (Recommended): 78 | - Create a free serverless PostgreSQL database at [Neon](https://neon.tech) 79 | - Get instant provisioning and scale-to-zero capability 80 | - Connection string format: `postgresql://user:pass@ep-xyz.region.aws.neon.tech/dbname` 81 | 82 | - **MySQL**: 83 | ``` 84 | mysql://user:pass@host:port/db 85 | ``` 86 | - **SQLite** (Local development): 87 | ``` 88 | sqlite:///path/to/db.sqlite 89 | ``` 90 | 91 | ## Contributing 92 | 93 | Contributions are welcome! Please feel free to submit a Pull Request. 94 | -------------------------------------------------------------------------------- /rag_tutorials/rag_database_routing/README.md: -------------------------------------------------------------------------------- 1 | # 📠 RAG Agent with Database Routing 2 | 3 | A Streamlit application that demonstrates an advanced implementation of RAG Agent with intelligent query routing. The system combines multiple specialized databases with smart fallback mechanisms to ensure reliable and accurate responses to user queries. 4 | 5 | ## Features 6 | 7 | - **Document Upload**: Users can upload multiple PDF documents related to a particular company. These documents are processed and stored in one of the three databases: Product Information, Customer Support & FAQ, or Financial Information. 8 | 9 | - **Natural Language Querying**: Users can ask questions in natural language. The system automatically routes the query to the most relevant database using a phidata agent as the router. 10 | 11 | - **RAG Orchestration**: Utilizes Langchain for orchestrating the retrieval augmented generation process, ensuring that the most relevant information is retrieved and presented to the user. 12 | 13 | - **Fallback Mechanism**: If no relevant documents are found in the databases, a LangGraph agent with a DuckDuckGo search tool is used to perform web research and provide an answer. 14 | 15 | ## How to Run? 16 | 17 | 1. **Clone the Repository**: 18 | ```bash 19 | git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 20 | cd rag_tutorials/rag_database_routing 21 | ``` 22 | 23 | 2. **Install Dependencies**: 24 | ```bash 25 | pip install -r requirements.txt 26 | ``` 27 | 28 | 3. **Run the Application**: 29 | ```bash 30 | streamlit run rag_database_routing.py 31 | ``` 32 | 33 | 4. **Get OpenAI API Key**: Obtain an OpenAI API key and set it in the application. This is required for initializing the language models used in the application. 34 | 35 | 5. **Setup Qdrant Cloud** 36 | - Visit [Qdrant Cloud](https://cloud.qdrant.io/) 37 | - Create an account or sign in 38 | - Create a new cluster 39 | - Get your credentials: 40 | - Qdrant API Key: Found in API Keys section 41 | - Qdrant URL: Your cluster URL (format: https://xxx-xxx.aws.cloud.qdrant.io) 42 | 43 | 5. **Upload Documents**: Use the document upload section to add PDF documents to the desired database. 44 | 45 | 6. **Ask Questions**: Enter your questions in the query section. The application will route your question to the appropriate database and provide an answer. 46 | 47 | ## Technologies Used 48 | 49 | - **Langchain**: For RAG orchestration, ensuring efficient retrieval and generation of information. 50 | - **Phidata Agent**: Used as the router agent to determine the most relevant database for a given query. 51 | - **LangGraph Agent**: Acts as a fallback mechanism, utilizing DuckDuckGo for web research when necessary. 52 | - **Streamlit**: Provides a user-friendly interface for document upload and querying. 53 | - **Qdrant**: Used for managing the databases, storing and retrieving document embeddings efficiently. 54 | 55 | ## How It Works? 56 | 57 | **1. Query Routing** 58 | The system uses a three-stage routing approach: 59 | - Vector similarity search across all databases 60 | - LLM-based routing for ambiguous queries 61 | - Web search fallback for unknown topics 62 | 63 | **2. Document Processing** 64 | - Automatic text extraction from PDFs 65 | - Smart text chunking with overlap 66 | - Vector embedding generation 67 | - Efficient database storage 68 | 69 | **3. Answer Generation** 70 | - Context-aware retrieval 71 | - Smart document combination 72 | - Confidence-based responses 73 | - Web research integration -------------------------------------------------------------------------------- /rag_tutorials/llama3.1_local_rag/llama3.1_local_rag.py: -------------------------------------------------------------------------------- 1 | import streamlit as st 2 | from langchain.text_splitter import RecursiveCharacterTextSplitter 3 | from langchain_community.document_loaders import WebBaseLoader 4 | from langchain_community.vectorstores import Chroma 5 | from langchain_ollama import OllamaEmbeddings 6 | from langchain_ollama import ChatOllama 7 | 8 | st.title("Chat with Webpage 🌐") 9 | st.caption("This app allows you to chat with a webpage using local llama3 and RAG") 10 | 11 | # Get the webpage URL from the user 12 | webpage_url = st.text_input("Enter Webpage URL", type="default") 13 | # Connect to Ollama 14 | ollama_endpoint = "http://127.0.0.1:11434" 15 | ollama_model = "llama3.1" 16 | ollama = ChatOllama(model=ollama_model, base_url=ollama_endpoint) 17 | 18 | if webpage_url: 19 | # 1. Load the data 20 | loader = WebBaseLoader(webpage_url) 21 | docs = loader.load() 22 | text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=10) 23 | splits = text_splitter.split_documents(docs) 24 | 25 | # 2. Create Ollama embeddings and vector store 26 | embeddings = OllamaEmbeddings(model=ollama_model, base_url=ollama_endpoint) 27 | vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings) 28 | 29 | # 3. Call Ollama Llama3 model 30 | def ollama_llm(question, context): 31 | """Generates a response to a question using the Ollama Llama3 model. 32 | 33 | This function takes a question and its context, formats them into a prompt, 34 | and invokes the Ollama Llama3 model to generate a response. 35 | 36 | Args: 37 | question (str): The question to be answered by the model. 38 | context (str): The context or additional information related to the question. 39 | 40 | Returns: 41 | str: The response generated by the Ollama Llama3 model, stripped of leading and trailing whitespace.""" 42 | formatted_prompt = f"Question: {question}\n\nContext: {context}" 43 | response = ollama.invoke([('human', formatted_prompt)]) 44 | return response.content.strip() 45 | 46 | # 4. RAG Setup 47 | retriever = vectorstore.as_retriever() 48 | 49 | def combine_docs(docs): 50 | """Combines the content of multiple document objects into a single string. 51 | 52 | Args: 53 | docs (list): A list of document objects, each having a 'page_content' attribute. 54 | 55 | Returns: 56 | str: A string consisting of the combined 'page_content' of all document objects, 57 | separated by two newline characters.""" 58 | return "\n\n".join(doc.page_content for doc in docs) 59 | 60 | def rag_chain(question): 61 | """Processes a question to retrieve and format relevant documents, and generates a response using a language model. 62 | 63 | Args: 64 | question (str): The question or query that needs to be answered. 65 | 66 | Returns: 67 | str: The response generated by the language model based on the retrieved and formatted documents.""" 68 | retrieved_docs = retriever.invoke(question) 69 | formatted_context = combine_docs(retrieved_docs) 70 | return ollama_llm(question, formatted_context) 71 | 72 | st.success(f"Loaded {webpage_url} successfully!") 73 | 74 | # Ask a question about the webpage 75 | prompt = st.text_input("Ask any question about the webpage") 76 | 77 | # Chat with the webpage 78 | if prompt: 79 | result = rag_chain(prompt) 80 | st.write(result) 81 | -------------------------------------------------------------------------------- /advanced_tools_frameworks/multillm_chat_playground/multillm_playground.py: -------------------------------------------------------------------------------- 1 | import streamlit as st 2 | from litellm import completion 3 | 4 | # Set up the Streamlit app 5 | st.title("Multi-LLM Chat Playground") 6 | 7 | # Get API keys from the user 8 | openai_api_key = st.text_input("Enter your OpenAI API Key:", type="password") 9 | anthropic_api_key = st.text_input("Enter your Anthropic API Key:", type="password") 10 | cohere_api_key = st.text_input("Enter your Cohere API Key:", type="password") 11 | 12 | # Check if all API keys are provided 13 | if openai_api_key and anthropic_api_key and cohere_api_key: 14 | 15 | # Create a text input for user messages 16 | user_input = st.text_input("Enter your message:") 17 | 18 | if st.button("Send to All LLMs"): 19 | if user_input: 20 | messages = [{"role": "user", "content": user_input}] 21 | 22 | # Create three columns for side-by-side display 23 | col1, col2, col3 = st.columns(3) 24 | 25 | # GPT-4o response 26 | with col1: 27 | st.subheader("GPT-4o") 28 | try: 29 | gpt_response = completion(model="gpt-4o", messages=messages, api_key=openai_api_key) 30 | st.write(gpt_response.choices[0].message.content) 31 | except Exception as e: 32 | st.error(f"Error with GPT-4o: {str(e)}") 33 | 34 | # Claude-3-sonnet response 35 | with col2: 36 | st.subheader("Claude 3.5 Sonnet") 37 | try: 38 | claude_response = completion(model="claude-3-5-sonnet-20240620", messages=messages, api_key=anthropic_api_key) 39 | st.write(claude_response.choices[0].message.content) 40 | except Exception as e: 41 | st.error(f"Error with Claude 3.5 Sonnet: {str(e)}") 42 | 43 | # Cohere response 44 | with col3: 45 | st.subheader("Cohere") 46 | try: 47 | cohere_response = completion(model="command-r-plus", messages=messages, api_key=cohere_api_key) 48 | st.write(cohere_response.choices[0].message.content) 49 | except Exception as e: 50 | st.error(f"Error with Cohere: {str(e)}") 51 | 52 | # Compare responses 53 | st.subheader("Response Comparison") 54 | st.write("You can see how the three models responded differently to the same input.") 55 | st.write("This demonstrates the ability to use multiple LLMs in a single application.") 56 | else: 57 | st.warning("Please enter a message.") 58 | else: 59 | st.warning("Please enter all API keys to use the chat.") 60 | 61 | # Add some information about the app 62 | st.sidebar.title("About this app") 63 | 64 | st.sidebar.write( 65 | "This app demonstrates the use of multiple Language Models (LLMs) " 66 | "in a single application using the LiteLLM library." 67 | ) 68 | 69 | st.sidebar.subheader("Key features:") 70 | st.sidebar.markdown( 71 | """ 72 | - Utilizes three different LLMs: 73 | - OpenAI's GPT-4o 74 | - Anthropic's Claude 3.5 Sonnet 75 | - Cohere's Command R Plus 76 | - Sends the same user input to all models 77 | - Displays responses side-by-side for easy comparison 78 | - Showcases the ability to use multiple LLMs in one application 79 | """ 80 | ) 81 | 82 | st.sidebar.write( 83 | "Try it out to see how different AI models respond to the same prompt!" 84 | ) 85 | --------------------------------------------------------------------------------