Feat/fastapi - Githubissues

ajitesh123 commented 1 week ago

PR Type

Enhancement, Documentation

Description

Added functionality to generate performance reviews using different LLMs (OpenAI, Google, Anthropic, Groq) with a Streamlit UI.
Implemented FastAPI endpoints for generating performance reviews and self-reviews, including CORS middleware.
Defined abstract base class for LLMs and implemented specific classes for each LLM provider.
Created Pydantic models and functions for generating and parsing review and self-review prompts.
Removed old implementation of performance review generation.
Updated project title formatting in README.
Added Vercel configuration for deployment.

Changes walkthrough 📝

Relevant files

Enhancement

app.py `Add performance review generation with multiple LLMs and Streamlit UI.` app.py Added functionality to generate performance reviews using different LLMs. Implemented Streamlit UI for user inputs and review generation. Included options for LLM type, model size, and API key input.	+88/-0
app_fastapi.py `Implement FastAPI endpoints for review generation.` app_fastapi.py Created FastAPI endpoints for generating performance reviews. Implemented CORS middleware for cross-origin requests. Added endpoints for both review and self-review generation.	+52/-0
llm.py `Add LLM classes for various providers with text generation methods.` llm.py Defined abstract base class for LLMs. Implemented specific classes for OpenAI, Google, Anthropic, and Groq LLMs. Included methods for text generation and streaming.	+156/-0
review.py `Add review request model and review generation functions.` review.py Created Pydantic model for review request. Implemented functions to generate and parse performance review prompts. Integrated LLM instances for review generation.	+95/-0
self_review.py `Add self-review request model and self-review generation functions.` self_review.py Created Pydantic model for self-review request. Implemented functions to generate and parse self-review prompts. Integrated LLM instances for self-review generation.	+81/-0
app.py `Remove old performance review generation implementation.` src/app.py Removed old implementation of performance review generation. Deprecated single LLM (OpenAI) usage in favor of multiple LLMs.	+0/-77

Documentation

README.md

Update project title formatting in README.

README.md - Updated project title formatting.

+1/-1

Configuration changes

vercel.json

Add Vercel configuration for deployment.

perf-ui/vercel.json - Added Vercel configuration for deployment.

+8/-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 functionality for generating performance reviews using various language models via a Streamlit UI.
- Added a FastAPI-based API for generating performance and self-reviews.
- Implemented support for multiple language model providers, including OpenAI, Google, Anthropic, and Groq.
- Deployed configuration for Vercel with specific build commands and routing rules.
Documentation
- Updated the header level for better readability in the README file.

coderabbitai[bot] commented 1 week ago

[!WARNING]

Review failed

The pull request is closed.

Walkthrough

The update introduces functionality for generating performance reviews and self-reviews using multiple language models (LLMs) through two main interfaces: a Streamlit app (app.py) and a FastAPI-based API (app_fastapi.py). Additional supporting files (llm.py, review.py, self_review.py) handle the core logic for interacting with LLMs and generating reviews. Configuration for deployment on Vercel is also included.

Changes

File	Summary
`README.md`	Updated header level for the "Performance Review AI" title.
`app.py`	Introduced Streamlit UI functionality for generating performance reviews using LLMs.
`app_fastapi.py`	Introduced FastAPI-based API for generating performance and self-reviews.
`llm.py`	Defined multiple classes for handling different LLM providers (OpenAI, Google, etc.).
`review.py`	Added functions and classes for generating and parsing performance reviews.
`self_review.py`	Added functions and classes for generating and parsing self-reviews.
`perf-ui/vercel.json`	Added Vercel configuration for the deployment of the performance review UI.

Sequence Diagrams

Performance Review Generation via the Streamlit UI

sequenceDiagram
    participant User
    participant StreamlitApp as Streamlit App
    participant LLM as Language Model
    participant ReviewModule as Review Module

    User->>StreamlitApp: Input roles, questions, and review
    StreamlitApp->>ReviewModule: Generate review prompt
    ReviewModule->>LLM: Get completion from LLM
    LLM-->>ReviewModule: Return generated review
    ReviewModule-->>StreamlitApp: Display review result
    StreamlitApp-->>User: Show generated review

Performance Review Generation via FastAPI

sequenceDiagram
    participant Client
    participant FastAPI as FastAPI
    participant LLM as Language Model
    participant ReviewModule as Review Module

    Client->>FastAPI: POST /generate_review with request body
    FastAPI->>ReviewModule: Process request
    ReviewModule->>LLM: Get completion from LLM
    LLM-->>ReviewModule: Return generated review
    ReviewModule-->>FastAPI: Send review response
    FastAPI-->>Client: Return generated review

Poem

In the land of code and AI's song,
We weave the tales, where reviews belong.
From Streamlit’s touch to FastAPI's might,
In Vercel’s cloud, they take their flight.
Oh, what a world where bytes do dance,
To code’s own rhythm, not by chance. ✨🐇

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codiumai-pr-agent-pro[bot] commented 1 week ago

PR Reviewer Guide 🔍

⏱️ Estimated effort to review [1-5]	4
🧪 Relevant tests	No
🔒 Security concerns	No
⚡ Key issues to review	Error Handling: The current implementation in `app_fastapi.py` uses a broad exception handling strategy which might obscure the underlying errors. It's recommended to handle specific exceptions to provide more detailed error messages and for better error resolution.
	Input Validation: There is a lack of input validation in the endpoints defined in `app_fastapi.py`. Proper validation of inputs is crucial to prevent injection attacks and to ensure that the inputs meet the expected format.
	Dependency Management: The code imports various external libraries and uses environment variables which suggests that managing dependencies and configurations might be complex. Ensure that all dependencies are properly documented and managed.