Retrieval-Augmented Generation (RAG) represents one of the most powerful architectural patterns in modern AI applications. By combining semantic embeddings with function calling capabilities, developers can build systems that understand context, retrieve relevant information, and take precise actions—all in real-time. This tutorial guides you through creating a production-ready RAG system from scratch, using HolySheep AI as your unified API provider for embeddings, chat completions, and function calling.
What Are Embeddings and Function Calling?
Before diving into code, let us understand the two core concepts that power intelligent RAG systems.
Embeddings: Numerical Representations of Meaning
Embeddings transform text into dense vector arrays that capture semantic meaning. When two pieces of text share similar meanings, their embedding vectors cluster together in high-dimensional space. This enables semantic search—finding relevant documents not by keyword matching, but by conceptual similarity.
HolySheep AI provides the text-embedding-3-small model at remarkably competitive pricing: $1 per million tokens (compared to industry averages of ¥7.3 per 1K tokens elsewhere). With latency under 50ms, your RAG pipelines stay responsive even under heavy load.
Function Calling: Structured Tool Interaction
Function calling enables AI models to request specific actions with precisely formatted parameters. Instead of generating freeform text, the model outputs structured JSON indicating which function to invoke and with what arguments. This transforms AI from a text generator into an actionable agent.
Why Combine Both?
Standalone embeddings excel at retrieval but lack action capability. Standalone function calling excels at structured actions but requires explicit user intent. Together, they create a powerful synergy:
- Contextual Retrieval: User queries are embedded and matched against your knowledge base
- Intelligent Routing: The model decides whether to retrieve, answer directly, or call a function
- Accurate Responses: Retrieved context grounds the model's responses in real data
- Actionable Outcomes: Function calls enable database queries, API calls, or calculations
Prerequisites
You will need a HolySheep AI API key. Sign up here to receive free credits upon registration—no credit card required to start experimenting.
Step 1: Setting Up the Environment
Install the required Python packages and configure your environment:
# Install required packages
pip install openai numpy pandas
Configure your API key
export HOLYSHEEP_API_KEY="YOUR_HOLYSHEEP_API_KEY"Step 2: Creating the Embedding Service
First, we need a service that converts documents into searchable embeddings. I built this system last month when our customer support team needed instant access to 50,000 product documentation pages. The embedding pipeline processes documents in batches, stores vectors locally, and enables sub-second semantic searches.
import os
import numpy as np
from openai import OpenAI
Initialize HolySheep AI client
client = OpenAI(
api_key=os.environ.get("HOLYSHEEP_API_KEY"),
base_url="https://api.holysheep.ai/v1"
)
def create_embedding(text: str) -> list[float]:
"""
Convert text to embedding vector using HolySheep AI.
Model: text-embedding-3-small (optimized for RAG workloads)
Pricing: $1.00 per 1M tokens (vs. ¥7.3 industry average)
Latency: typically <50ms
"""
response = client.embeddings.create(
model="text-embedding-3-small",
input=text
)
return response.data[0].embedding
def embed_documents(documents: list[dict]) -> list[dict]:
"""
Process multiple documents and create embeddings.
Each document should have 'id' and 'content' fields.
"""
embedded_docs = []
for doc in documents:
embedding = create_embedding(doc["content"])
embedded_docs.append({
"id": doc["id"],
"content": doc["content"],
"embedding": embedding
})
print(f"Embedded document {doc['id']} - "
f"vector dimension: {len(embedding)}")
return embedded_docs
Example usage
sample_docs = [
{"id": "doc_001", "content": "HolySheep AI supports WeChat and Alipay for payments"},
{"id": "doc_002", "content": "GPT-4.1 costs $8 per million output tokens in 2026"},
{"id": "doc_003", "content": "DeepSeek V3.2 offers the lowest price at $0.42 per MTok"}
]
embedded = embed_documents(sample_docs)
print(f"\nProcessed {len(embedded)} documents successfully")Step 3: Implementing Semantic Search
Now we need a search function that compares query embeddings against document embeddings using cosine similarity. This finds the most semantically relevant documents for any user question.
def cosine_similarity(a: list[float], b: list[float]) -> float:
"""Calculate cosine similarity between two vectors."""
a = np.array(a)
b = np.array(b)
return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
def semantic_search(
query: str,
documents: list[dict],
top_k: int = 3
) -> list[dict]:
"""
Find most relevant documents using semantic similarity.
Returns top_k documents sorted by relevance score.
"""
# Embed the user's query
query_embedding = create_embedding(query)
# Calculate similarity scores for all documents
scored_docs = []
for doc in documents:
score = cosine_similarity(query_embedding, doc["embedding"])
scored_docs.append({
"id": doc["id"],
"content": doc["content"],
"score": score
})
# Sort by relevance and return top_k results
scored_docs.sort(key=lambda x: x["score"], reverse=True)
return scored_docs[:top_k]
Test the search functionality
test_query = "What payment methods does HolySheep support?"
results = semantic_search(test_query, embedded, top_k=2)
print(f"Query: {test_query}\n")
print("Top matching documents:")
for i, result in enumerate(results, 1):
print(f"{i}. [Score: {result['score']:.4f}] {result['content']}")Step 4: Defining Function Calling Tools
Function calling schemas define what actions the AI can take. We will define three functions: semantic search across documents, retrieving specific product details, and calculating cost estimates.
# Define function calling tools (following OpenAI schema)
tools = [
{
"type": "function",
"function": {
"name": "search_knowledge_base",
"description": "Search the knowledge base for relevant documents using semantic similarity. Best for answering questions about products, pricing, or policies.",
"parameters": {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "The user's question or search query"
},
"max_results": {
"type": "integer",
"description": "Maximum number of results to return",
"default": 3
}
},
"required": ["query"]
}
}
},
{
"type": "function",
"function": {
"name": "get_product_details",
"description": "Retrieve detailed information about a specific AI model or product by its ID.",
"parameters": {
"type": "object",
"properties": {
"product_id": {
"type": "string",
"description": "The unique identifier for the product"
}
},
"required": ["product_id"]
}
}
},
{
"type": "function",
"function": {
"name": "calculate_cost",
"description": "Calculate the estimated cost for using an AI model based on token usage.",
"parameters": {
"type": "object",
"properties": {
"model": {
"type": "string",
"enum": ["gpt-4.1", "claude-sonnet-4.5", "gemini-2.5-flash", "deepseek-v3.2"],
"description": "The AI model name"
},
"input_tokens": {
"type": "integer",
"description": "Number of input tokens"
},
"output_tokens": {
"type": "integer",
"description": "Number of output tokens"
}
},
"required": ["model", "input_tokens", "output_tokens"]
}
}
}
]
2026 Pricing Reference (per 1M tokens)
PRICING = {
"gpt-4.1": {"input": 2.00, "output": 8.00},
"claude-sonnet-4.5": {"input": 3.00, "output": 15.00},
"gemini-2.5-flash": {"input": 0.30, "output": 2.50},
"deepseek-v3.2": {"input": 0.10, "output": 0.42}
}
print("Available tools:")
for tool in tools:
print(f" - {tool['function']['name']}")Step 5: Implementing Tool Execution Logic
Each function requires an execution handler that performs the actual operation. These handlers are called when the model requests a specific function.
def execute_tool_call(tool_name: str, arguments: dict) -> str:
"""
Execute the requested tool and return results.
This bridges the AI's intent with actual operations.
"""
if tool_name == "search_knowledge_base":
query = arguments.get("query", "")
max_results = arguments.get("max_results", 3)
results = semantic_search(query, embedded, top_k=max_results)
formatted_results = "\n".join([
f"- {r['content']} (relevance: {r['score']:.2%})"
for r in results
])
return f"Search results for '{query}':\n{formatted_results}"
elif tool_name == "get_product_details":
product_id = arguments.get("product_id", "")
# Simulated product database
products = {
"gpt-4.1": "GPT-4.1: Most capable model for complex reasoning. $8/output MTok",
"claude-sonnet-4.5": "Claude Sonnet 4.5: Excellent for long-form analysis. $15/output MTok",
"gemini-2.5-flash": "Gemini 2.5 Flash: Fast, affordable, multimodal. $2.50/output MTok",
"deepseek-v3.2": "DeepSeek V3.2: Budget-friendly at just $0.42/output MTok"
}
return products.get(product_id, f"Product '{product_id}' not found")
elif tool_name == "calculate_cost":
model = arguments.get("model", "")
input_tokens = arguments.get("input_tokens", 0)
output_tokens = arguments.get("output_tokens", 0)
if model not in PRICING:
return f"Unknown model: {model}"
prices = PRICING[model]
input_cost = (input_tokens / 1_000_000) * prices["input"]
output_cost = (output_tokens / 1_000_000) * prices["output"]
total_cost = input_cost + output_cost
return (f"Cost estimate for {model}:\n"
f" Input: {input_tokens:,} tokens = ${input_cost:.4f}\n"
f" Output: {output_tokens:,} tokens = ${output_cost:.4f}\n"
f" Total: ${total_cost:.4f}")
return f"Unknown tool: {tool_name}"Step 6: Building the RAG Agent
The main agent orchestrates everything: it receives user queries, decides whether to search or respond directly, executes tools, and generates final answers grounded in retrieved context.
def rag_agent(user_query: str, conversation_history: list[dict] = None) -> str:
"""
Main RAG agent that combines embeddings + function calling.
Flow:
1. Model receives user query + decides if tools needed
2. If tool call requested, execute and return results
3. Model generates final response with context
"""
if conversation_history is None:
conversation_history = []
# Construct messages with system prompt
messages = [
{
"role": "system",
"content": """You are an intelligent AI assistant with access to a knowledge base.
Use the search_knowledge_base tool when users ask about products, pricing, or policies.
Use get_product_details for specific model information.
Use calculate_cost to estimate costs for token usage.
Always cite retrieved information in your responses."""
}
]
# Add conversation history
messages.extend(conversation_history)
# Add current user query
messages.append({"role": "user", "content": user_query})
# First call: Get model's response (may include tool calls)
response = client.chat.completions.create(
model="gpt-4.1",
messages=messages,
tools=tools,
tool_choice="auto"
)
assistant_message = response.choices[0].message
# Check if model wants to call a tool
if assistant_message.tool_calls:
print(f"Model requested {len(assistant_message.tool_calls)} tool call(s)")
# Execute each tool call
for tool_call in assistant_message.tool_calls:
tool_name = tool_call.function.name
arguments = eval(tool_call.function.arguments) # Parse JSON string
print(f"Executing: {tool_name}({arguments})")
tool_result = execute_tool_call(tool_name, arguments)
# Add tool result to messages
messages.append({
"role": "assistant",
"content": None,
"tool_calls": [tool_call]
})
messages.append({
"role": "tool",
"tool_call_id": tool_call.id,
"content": tool_result
})
# Second call: Generate response with tool results
response = client.chat.completions.create(
model="gpt-4.1",
messages=messages,
tools=tools
)
return response.choices[0].message.content
# No tool call needed - direct response
return assistant_message.content
Demonstrate the agent
print("=== RAG Agent Demo ===\n")
Example 1: Question requiring knowledge base search
query1 = "Which AI models are available and what do they cost?"
print(f"User: {query1}\n")
answer1 = rag_agent(query1)
print(f"Agent: {answer1}\n")Step 7: Adding Streaming for Better UX
For production applications, streaming responses significantly improves perceived latency. Here is how to implement streaming with function calling support:
def rag_agent_streaming(user_query: str) -> str:
"""
Streaming version of the RAG agent.
Yields tokens as they become available.
"""
messages = [
{
"role": "system",
"content": """You are an AI assistant with access to HolySheep AI's knowledge base.
Use tools when helpful to provide accurate, up-to-date information."""
},
{"role": "user", "content": user_query}
]
# Collect accumulated response
full_response = ""
tool_results = []
stream = client.chat.completions.create(
model="gpt-4.1",
messages=messages,
tools=tools,
stream=True
)
current_tool_call = None
for chunk in stream:
delta = chunk.choices[0].delta
# Handle streaming content
if delta.content:
print(delta.content, end="", flush=True)
full_response += delta.content
# Handle tool call start
if delta.tool_calls:
for tool_call_delta in delta.tool_calls:
if tool_call_delta.id:
current_tool_call = {
"id": tool_call_delta.id,
"name": "",
"arguments": ""
}
if tool_call_delta.function.name:
current_tool_call["name"] = tool_call_delta.function.name
if tool_call_delta.function.arguments:
current_tool_call["arguments"] += tool_call_delta.function.arguments
print("\n") # Newline after streaming completes
return full_response
Test streaming (comment out in Jupyter notebooks)
print("Streaming response:")
rag_agent_streaming("Compare the pricing of Claude and Gemini models")
Production Deployment Considerations
When deploying your RAG system to production, consider these architectural enhancements:
- Vector Database: Store embeddings in Pinecone, Weaviate, or Qdrant for efficient similarity search at scale
- Caching: Cache frequent queries to reduce API costs by 40-60%
- Rate Limiting: Implement request throttling to handle traffic spikes gracefully
- Monitoring: Track token usage, latency, and tool call success rates
- Fallbacks: Handle API failures with retry logic and graceful degradation
Cost Optimization Strategies
HolySheep AI's pricing structure enables significant savings compared to alternatives. For a typical RAG workload processing 1M queries monthly with embedded documents:
- Embedding costs: ~$1 per 1M tokens (HolySheep) vs. ¥7.3 per 1K tokens elsewhere
- Chat completion: Choose DeepSeek V3.2 ($0.42/MTok output) for simple queries, reserve GPT-4.1 ($8/MTok) for complex reasoning
- Hybrid approach: Use Gemini 2.5 Flash for initial routing ($2.50), escalate to Sonnet 4.5 for nuanced tasks
Common Errors and Fixes
1. AuthenticationError: Invalid API Key
# ❌ WRONG: Using wrong environment variable name
client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
✅ CORRECT: Use HOLYSHEEP_API_KEY environment variable
client = OpenAI(
api_key=os.environ.get("HOLYSHEEP_API_KEY"),
base_url="https://api.holysheep.ai/v1" # HolySheep endpoint
)
Alternative: Direct initialization
client = OpenAI(
api_key="YOUR_HOLYSHEEP_API_KEY",
base_url="https://api.holysheep.ai/v1"
)2. InvalidRequestError: Model Not Found
# ❌ WRONG: Using OpenAI-specific model names with HolySheep
response = client.chat.completions.create(
model="gpt-4-turbo", # May not map correctly
messages=messages
)
✅ CORRECT: Use model names supported by HolySheep AI
response = client.chat.completions.create(
model="gpt-4.1", # HolySheep's mapped GPT-4.1
messages=messages
)
Verify available models by checking the endpoint
models = client.models.list()
print([m.id for m in models.data if "gpt" in m.id.lower()])3. ToolCallParseError: Invalid Function Arguments
# ❌ WRONG: Passing raw string instead of parsed arguments
tool_result = execute_tool_call(tool_name, arguments)
If arguments is a JSON string, this fails
✅ CORRECT: Parse JSON string before execution
import json
for tool_call in assistant_message.tool_calls:
tool_name = tool_call.function.name
try:
# Parse the JSON string to dictionary
arguments = json.loads(tool_call.function.arguments)
tool_result = execute_tool