Dev madina

[madina1203]

PR Type

enhancement, documentation, tests

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Description

• Added support for multiple LLMs per agent.

• Introduced Docker support for containerized deployment.

• Enhanced documentation with setup and usage instructions.

• Implemented automated cleanup for files and database entries.

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Changes walkthrough 📝

	Relevant files
Enhancement	19 files agents_factory.py Add dynamic LLM instance selection for agents                        +25/-2    agent.py Support LLM instance parameter in agent creation                  +3/-2      agent.py Add LLM instance parameter to entry agent                                +4/-3      agent.py Include LLM instance parameter in SPARQL agent                      +6/-6      tool_merge_result.py Adjust default parameter for file path                                      +1/-1      tool_sparql.py Set default value for entities parameter                                  +1/-1      agent.py Add LLM instance parameter to supervisor agent                      +2/-2      agent.py Support LLM instance parameter in validator agent                +4/-2      evaluation.py Update evaluation logic and configuration                                +54/-43  RdfGraphCustom.py Add authentication support for SPARQL store                            +3/-2      llm_handler.py Implement LLM creation with configuration support                +47/-0    main.py Refactor main workflow and add API key handling                    +191/-197 langraph_workflow.py Refactor workflow creation and agent management                    +151/-107 cleanup_database.py Implement database cleanup for old checkpoints                      +18/-0    cleanup_files.py Add script for cleaning up old files                                          +44/-0    postgres_database.py Implement PostgreSQL checkpoint saver                                        +95/-0    postgres_tool_database.py Add PostgreSQL tool database manager                                          +76/-0    streamlit_app.py Develop Streamlit app for MetaboT                                                +488/-0  streamlit_utils.py Add utility functions for Streamlit app                                    +306/-0
Documentation	3 files prompt.py Fix typo in entry agent prompt                                                      +2/-2      README.md Update README with Docker and LLM instructions                      +39/-9    README.md Add README for Streamlit web app                                                  +114/-0
Formatting	1 files test_db_connection.py Format code for database connection test                                  +7/-7
Tests	1 files installation_test.py Add installation test for quick setup verification              +12/-0
Configuration changes	4 files Dockerfile Create Dockerfile for containerized deployment                      +10/-0    params.ini Update LLM configuration parameters                                            +18/-9    params_alternative.ini Add alternative configuration for LLMs                                      +55/-0    docker-compose.yml Add Docker Compose configuration                                                  +8/-0
Dependencies	1 files environment.yml Update environment dependencies                                                    +2/-1
Additional files	1 files __init__.py [link]

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⏱️ Estimated effort to review: 4 🔵🔵🔵🔵⚪

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⚡ Recommended focus areas for review Documentation The newly added create_agent function does not include a Google DocString. Please add a detailed docstring following the Google DocString format that documents all parameters (including the new llm_instance parameter), expected types, and return value. def create_agent(llms, graph, openai_key, llm_instance=None) -> AgentExecutor: logger.info("Creating agent with tools...") directory = os.path.dirname(__file__) Consistent Docstring Usage The create_agent function was updated with a new llm_instance parameter but is missing a Google-style docstring. Ensure that all agent creation functions across modules (such as in entry, sparql, and validator) follow consistent documentation practices. def create_agent(llms, session_id, llm_instance=None) -> AgentExecutor: directory = os.path.dirname(__file__) module_prefix = get_module_prefix(__name__)

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