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from typing import Any, Dict, List, Optional
import anthropic
class AIGenerator:
"""Handles interactions with Anthropic's Claude API for generating responses"""
# Static system prompt to avoid rebuilding on each call
SYSTEM_PROMPT = """ You are an AI assistant specialized in course materials and educational content with access to search tools for course information.
Available Tools:
- **Content Search Tool**: Use for questions about specific course content or detailed educational materials
- **Course Outline Tool**: Use for questions about course structure, lesson lists, or course overviews
Tool Usage Guidelines:
- Use content search for detailed questions about specific topics or lessons
- Use course outline tool for questions about course structure, lesson titles, or complete course overviews
- **You can make up to 2 rounds of tool calls to gather comprehensive information**
- Use multiple rounds for complex queries that require information gathering then refinement
- Synthesize tool results into accurate, fact-based responses
- If tools yield no results, state this clearly without offering alternatives
Course Outline Responses:
When using the course outline tool, always include:
- Course title
- Course link (if available)
- Complete lesson list with lesson numbers and titles
- Present information in a clear, structured format
Response Protocol:
- **General knowledge questions**: Answer using existing knowledge without searching
- **Course-specific questions**: Use appropriate tool first, then answer
- **No meta-commentary**:
- Provide direct answers only — no reasoning process, search explanations, or question-type analysis
- Do not mention "based on the search results" or "using the tool"
All responses must be:
1. **Brief, Concise and focused** - Get to the point quickly
2. **Educational** - Maintain instructional value
3. **Clear** - Use accessible language
4. **Example-supported** - Include relevant examples when they aid understanding
Provide only the direct answer to what was asked.
"""
def __init__(self, api_key: str, model: str):
self.client = anthropic.Anthropic(api_key=api_key)
self.model = model
# Pre-build base API parameters
self.base_params = {"model": self.model, "temperature": 0, "max_tokens": 800}
def generate_response(
self,
query: str,
conversation_history: Optional[str] = None,
tools: Optional[List] = None,
tool_manager=None,
) -> str:
"""
Generate AI response with optional tool usage and conversation context.
Supports up to 2 sequential rounds of tool calling.
Args:
query: The user's question or request
conversation_history: Previous messages for context
tools: Available tools the AI can use
tool_manager: Manager to execute tools
Returns:
Generated response as string
"""
# Build system content efficiently - avoid string ops when possible
system_content = (
f"{self.SYSTEM_PROMPT}\n\nPrevious conversation:\n{conversation_history}"
if conversation_history
else self.SYSTEM_PROMPT
)
# Start with initial messages
messages = [{"role": "user", "content": query}]
# Execute up to 2 rounds of tool calling
for round_num in range(2):
# Prepare API call parameters
api_params = {
**self.base_params,
"messages": messages,
"system": system_content,
}
# Add tools if available
if tools:
api_params["tools"] = tools
api_params["tool_choice"] = {"type": "auto"}
# Get response from Claude
response = self.client.messages.create(**api_params)
# Handle tool execution if needed
if response.stop_reason == "tool_use" and tool_manager:
messages, should_continue = self._handle_tool_execution(
response, messages, tool_manager
)
if not should_continue:
break
else:
# No tool use, return direct response
return response.content[0].text
# After max rounds, make final call without tools to get response
final_params = {
**self.base_params,
"messages": messages,
"system": system_content,
}
final_response = self.client.messages.create(**final_params)
return final_response.content[0].text
def _handle_tool_execution(self, initial_response, messages: List, tool_manager):
"""
Handle execution of tool calls and update message history.
Args:
initial_response: The response containing tool use requests
messages: Current message history
tool_manager: Manager to execute tools
Returns:
Tuple of (updated_messages, should_continue)
"""
# Add AI's tool use response
messages.append({"role": "assistant", "content": initial_response.content})
# Execute all tool calls and collect results
tool_results = []
for content_block in initial_response.content:
if content_block.type == "tool_use":
try:
tool_result = tool_manager.execute_tool(
content_block.name, **content_block.input
)
tool_results.append(
{
"type": "tool_result",
"tool_use_id": content_block.id,
"content": tool_result,
}
)
except Exception as e:
# Tool execution failed, stop rounds
tool_results.append(
{
"type": "tool_result",
"tool_use_id": content_block.id,
"content": f"Error: Tool execution failed - {str(e)}",
}
)
# Add tool results and signal to stop
if tool_results:
messages.append({"role": "user", "content": tool_results})
return messages, False
# Add tool results as single message
if tool_results:
messages.append({"role": "user", "content": tool_results})
# Continue with next round
return messages, True