Developer Guide Template

[Guide Title - Be Specific]

Overview

What you’ll build: [Describe the end result]

Use cases: [When would someone use this?]

Time to complete: [Realistic estimate]

Prerequisites

Required knowledge:

[e.g., Python 3.9+]
[e.g., Understanding of async/await patterns]
[e.g., Familiarity with REST APIs]

Required accounts/tools:

[e.g., OpenAI API key]
[e.g., Pinecone account (free tier works)]
[e.g., Git and a code editor]

Optional but helpful:

[e.g., Docker knowledge for containerization]
[e.g., Experience with LangChain]

Architecture Overview

[Component 1] → [Component 2] → [Component 3]
     ↓               ↓               ↓
[Data Flow Description]

Key components:

[Component 1]: [What it does]
[Component 2]: [What it does]
[Component 3]: [What it does]

Environment Setup

Install Dependencies

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install packages
pip install langchain openai pinecone-client tiktoken

Configuration

Create a .env file:

OPENAI_API_KEY=your-openai-key-here
PINECONE_API_KEY=your-pinecone-key-here
PINECONE_ENVIRONMENT=your-environment

Security note: Never commit .env files to version control. Add to .gitignore:

.env
venv/
__pycache__/

Implementation

Step 1: [First Major Step]

Goal: [What this step accomplishes]

# Import necessary modules
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Pinecone
import os
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Initialize embeddings
embeddings = OpenAIEmbeddings(
    model="text-embedding-3-small",
    openai_api_key=os.getenv("OPENAI_API_KEY")
)

Why this works: [Explain the technical reasoning]

Common issues:

Problem: [Common error]
- Solution: [How to fix it]

Step 2: [Second Major Step]

Goal: [What this step accomplishes]

import pinecone

# Initialize Pinecone
pinecone.init(
    api_key=os.getenv("PINECONE_API_KEY"),
    environment=os.getenv("PINECONE_ENVIRONMENT")
)

# Create or connect to index
index_name = "my-rag-index"
if index_name not in pinecone.list_indexes():
    pinecone.create_index(
        name=index_name,
        dimension=1536,  # OpenAI embedding dimension
        metric="cosine"
    )

# Initialize vector store
vectorstore = Pinecone.from_existing_index(
    index_name=index_name,
    embedding=embeddings
)

Performance considerations:

[e.g., Cosine similarity is optimal for OpenAI embeddings]
[e.g., Dimension must match embedding model output]

Step 3: [Continue with remaining steps]

[Add as many steps as needed to complete the implementation]

Testing

Unit Tests

import pytest
from your_module import your_function

def test_embedding_generation():
    """Test that embeddings are generated correctly"""
    text = "Test document"
    result = your_function(text)
    assert len(result) == 1536  # OpenAI embedding dimension
    assert all(isinstance(x, float) for x in result)

def test_retrieval():
    """Test that similar documents are retrieved"""
    query = "test query"
    results = vectorstore.similarity_search(query, k=3)
    assert len(results) <= 3
    assert all(hasattr(doc, 'page_content') for doc in results)

Integration Testing

def test_full_pipeline():
    """Test the complete RAG pipeline"""
    # Add documents
    docs = ["Document 1", "Document 2", "Document 3"]
    vectorstore.add_texts(docs)

    # Query
    query = "test query"
    results = rag_chain.run(query)

    # Verify
    assert results is not None
    assert len(results) > 0

Run tests:

pytest tests/ -v

Optimization

Performance Tuning

Embedding optimization:

Use text-embedding-3-small for cost/performance balance
Batch embeddings for large document sets
Cache frequently used embeddings

Vector store optimization:

Set appropriate k value for retrieval (3-5 typically optimal)
Use metadata filtering to narrow search space
Consider approximate nearest neighbor (ANN) for large datasets

Cost Optimization

Estimated costs (as of 2025):

Embeddings: ~$0.02 per 1M tokens
LLM calls: Varies by model ($0.50-$15 per 1M tokens)
Vector storage: ~$0.10 per 100k vectors/month

Cost-saving strategies:

# Cache embeddings
from langchain.cache import InMemoryCache
langchain.llm_cache = InMemoryCache()

# Use cheaper models for simple queries
from langchain.llms import OpenAI
llm = OpenAI(model="gpt-3.5-turbo")  # vs gpt-4

Deployment

Docker Container

# Dockerfile
FROM python:3.11-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

CMD ["python", "main.py"]

Build and run:

docker build -t my-rag-app .
docker run --env-file .env my-rag-app

Production Considerations

Monitoring:

Track API latency and error rates
Monitor token usage for cost control
Set up alerts for API failures

Security:

Rotate API keys regularly
Use environment variables, never hardcode keys
Implement rate limiting
Validate and sanitize user inputs

Scalability:

Use async operations for concurrent requests
Implement caching layer (Redis/Memcached)
Consider serverless deployment (AWS Lambda, Cloud Run)

Troubleshooting

Common Errors

Error: RateLimitError: Rate limit exceeded

# Solution: Implement exponential backoff
import time
from openai.error import RateLimitError

def call_with_retry(func, max_retries=3):
    for attempt in range(max_retries):
        try:
            return func()
        except RateLimitError:
            if attempt < max_retries - 1:
                time.sleep(2 ** attempt)
            else:
                raise

Error: InvalidDimensionError: Vector dimension mismatch

Cause: Embedding model output doesn’t match index dimension
Solution: Verify index dimension matches model (1536 for text-embedding-3-small)

Error: No results returned from similarity search

Cause: No documents in vector store or query too specific
Solution: Verify documents were added; try broader query terms

Next Steps

Enhancements to consider:

Add conversation memory for multi-turn interactions
Implement semantic caching to reduce API calls
Add metadata filtering for more precise retrieval
Create a web interface with Streamlit or FastAPI
Add observability with LangSmith or similar

Related guides:

[Link to related developer guide]
[Link to another relevant guide]

Additional Resources

Official documentation:

Example repositories:

[Link to GitHub repo with complete code]
[Link to related example]

Community:

Found an issue with this guide? Open an issue or submit a PR!