Build an agentic RAG app from scratch with Claude Code. Learn hybrid search, reranking, text-to-SQL, and subagents. Use React, FastAPI, and Supabase. Ideal for operations teams automating data workflows.
git clone https://github.com/theaiautomators/claude-code-agentic-rag-masterclass.gitBuild an agentic RAG app from scratch with Claude Code. Learn hybrid search, reranking, text-to-SQL, and subagents. Use React, FastAPI, and Supabase. Ideal for operations teams automating data workflows.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/theaiautomators/claude-code-agentic-rag-masterclassCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
I want to build an agentic RAG app using Claude Code. My app will help [COMPANY] in the [INDUSTRY] sector automate [DATA] workflows. Guide me through setting up hybrid search, reranking, text-to-SQL, and subagents using React, FastAPI, and Supabase. Provide step-by-step instructions with code snippets.
# Building an Agentic RAG App for [COMPANY] in the [INDUSTRY] Sector
## Step 1: Setting Up the Project
1. **Initialize the Project**:
```bash
mkdir agentic-rag-app
cd agentic-rag-app
npm init -y
npm install react fastapi supabase
```
2. **Set Up Supabase**:
- Create a new project in Supabase.
- Note your project URL and API key.
## Step 2: Implementing Hybrid Search
1. **Integrate Claude Code**:
```python
from claude_code import ClaudeCode
claude = ClaudeCode(api_key='your_api_key')
```
2. **Hybrid Search Function**:
```python
def hybrid_search(query, documents):
# Implement hybrid search logic here
return reranked_results
```
## Step 3: Adding Reranking and Text-to-SQL
1. **Reranking Logic**:
```python
def rerank_results(results):
# Implement reranking logic here
return reranked_results
```
2. **Text-to-SQL Conversion**:
```python
def text_to_sql(query):
# Implement text-to-SQL logic here
return sql_query
```
## Step 4: Creating Subagents
1. **Define Subagents**:
```python
class Subagent:
def __init__(self, task):
self.task = task
def execute(self):
# Implement subagent logic here
return result
```
2. **Integrate Subagents**:
```python
subagent = Subagent(task='data_analysis')
result = subagent.execute()
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