Master - Githubissues

ajitesh123 commented 4 months ago

PR Type

Enhancement, Dependencies

Description

Added support for multiple LLMs (OpenAI, Google, Anthropic, Groq) in the application.
Refactored the code to use a more modular approach for integrating different LLMs.
Updated the Streamlit UI to include options for selecting LLM type and model size.
Introduced a new module (llm.py) for handling different LLMs with specific classes for each LLM.
Updated requirements.txt to include new dependencies for Anthropic, Groq, and Google Generative AI.

Changes walkthrough 📝

Relevant files

Enhancement

app.py `Add support for multiple LLMs and update UI` src/app.py Added support for multiple LLMs (OpenAI, Google, Anthropic, Groq). Refactored code to use a more modular approach for LLM integration. Updated Streamlit UI to include options for selecting LLM type and model size.	+46/-35
llm.py `Implement module for handling multiple LLMs` src/llm.py Introduced a new module for handling different LLMs. Implemented classes for OpenAI, Google, Anthropic, and Groq LLMs. Added environment setup and model mapping for each LLM.	+156/-0

Dependencies

requirements.txt

Update dependencies for new LLMs

requirements.txt

Added new dependencies for Anthropic, Groq, and Google Generative AI.

+3/-0

💡 PR-Agent usage: Comment /help on the PR to get a list of all available PR-Agent tools and their descriptions

Summary by CodeRabbit

New Features
- Introduced support for new language models from Anthropic, Groq, and Google.
- Added functionality for generating performance reviews and self-reviews using various language models.
- Launched a FastAPI-based API for handling review generation requests.
- Added Vercel configuration for React project deployment.
Enhancements
- Updated text generation logic to utilize newly supported language models.
- Improved flexibility in model size selection for review generation.
- Enhanced the Streamlit UI for better user input handling and review generation.
Refactor
- Replaced dependency on langchain.chat_models with custom language model classes.

coderabbitai[bot] commented 4 months ago

Walkthrough

The recent updates introduce a slew of new dependencies and overhaul existing application logic to support multiple language models. Primarily, custom language model classes replace previous dependencies, enhancing flexibility and expandability. Additionally, several new modules and routes for API functionalities and a FastAPI-based interface are included to enrich the system's ability to generate performance reviews and self-reviews utilizing various LLMs.

Changes

File Path	Change Summary
`requirements.txt`	Added dependencies: `anthropic==0.25.8`, `groq==0.5.0`, and `google-generativeai`.
`src/app.py`	Replaced `langchain.chat_models`, introduced custom LLM classes, refactored functions.
`src/llm.py`	New module defining custom language model classes for OpenAI, Anthropic, Groq, and Google APIs.
`app_fastapi.py`	New file introducing FastAPI functionality with endpoints for generating reviews.
`perf-ui/vercel.json`	New Vercel configuration file for React project deployment.
`review.py`, `self_review.py`	New files introducing review and self-review generation logic, parsing, and LLM interaction.
`app.py`	Introduced performance review generation using various LLMs via a Streamlit UI.

Sequence Diagram(s)

sequenceDiagram
    participant User
    participant UI as UI (app.py)
    participant Server as Server (app_fastapi.py)
    participant LLM as Language Models (src/llm.py)

    User->>UI: Input review details
    UI->>Server: Send review request
    Server->>Server: Validate request, choose LLM
    Server->>LLM: Request text generation with prompt
    LLM-->>Server: Return generated text
    Server-->>UI: Send generated review
    UI-->>User: Display generated review

Poem

In fields of green where rabbits play, Code changes come our way, New models speak, and reviews they sing, With Groq, Anthropic on Spring's wing, FastAPI routes and Streamlit's glow, Our application's set to grow! 🌸

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codiumai-pr-agent-pro[bot] commented 4 months ago

PR Reviewer Guide 🔍

⏱️ Estimated effort to review [1-5]	4
🧪 Relevant tests	No
🔒 Security concerns	No
⚡ Key issues to review	Error Handling: The new `create_llm_instance` function in `app.py` does not handle the case where an invalid LLM type might be passed other than through a direct exception. It would be beneficial to have a more graceful handling or user feedback mechanism.
	Dependency Management: The PR introduces new dependencies (`anthropic`, `groq`, `google-generativeai`) which are added to `requirements.txt`. Ensure that these libraries are compatible with the existing system and do not introduce conflicts.
	Code Duplication: The `generate_text` and `stream_text` methods in the LLM classes in `llm.py` have repeated code for setting up model configurations. Consider refactoring to reduce duplication and improve maintainability.