359 lines
No EOL
15 KiB
Python
359 lines
No EOL
15 KiB
Python
import logging
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import httpx
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import os
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from typing import Dict, Any, List
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from fastapi import HTTPException
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from modules.shared.configuration import APP_CONFIG
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from modules.aicore.aicoreBase import BaseConnectorAi
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from modules.datamodels.datamodelAi import AiModel, PriorityEnum, ProcessingModeEnum, OperationTypeEnum, AiModelCall, AiModelResponse, createOperationTypeRatings
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# Configure logger
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logger = logging.getLogger(__name__)
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def loadConfigData():
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"""Load configuration data for Anthropic connector"""
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return {
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"apiKey": APP_CONFIG.get('Connector_AiAnthropic_API_SECRET'),
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}
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class AiAnthropic(BaseConnectorAi):
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"""Connector for communication with the Anthropic API."""
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def __init__(self):
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super().__init__()
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# Load configuration
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self.config = loadConfigData()
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self.apiKey = self.config["apiKey"]
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# HttpClient for API calls
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self.httpClient = httpx.AsyncClient(
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timeout=120.0, # Longer timeout for complex requests
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headers={
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"x-api-key": self.apiKey,
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"anthropic-version": "2023-06-01", # Anthropic API Version
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"Content-Type": "application/json"
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}
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)
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logger.info("Anthropic Connector initialized")
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def getConnectorType(self) -> str:
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"""Get the connector type identifier."""
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return "anthropic"
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def getModels(self) -> List[AiModel]:
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"""Get all available Anthropic models."""
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return [
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AiModel(
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name="claude-sonnet-4-5-20250929",
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displayName="Anthropic Claude Sonnet 4.5",
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connectorType="anthropic",
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apiUrl="https://api.anthropic.com/v1/messages",
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temperature=0.2,
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maxTokens=8192,
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contextLength=200000,
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costPer1kTokensInput=0.015,
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costPer1kTokensOutput=0.075,
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speedRating=6, # Slower due to high-quality processing
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qualityRating=10, # Best quality available
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# capabilities removed (not used in business logic)
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functionCall=self.callAiBasic,
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priority=PriorityEnum.QUALITY,
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processingMode=ProcessingModeEnum.DETAILED,
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operationTypes=createOperationTypeRatings(
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(OperationTypeEnum.PLAN, 9),
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(OperationTypeEnum.DATA_ANALYSE, 10),
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(OperationTypeEnum.DATA_GENERATE, 9),
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(OperationTypeEnum.DATA_EXTRACT, 8)
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),
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version="claude-sonnet-4-5-20250929",
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calculatePriceUsd=lambda processingTime, bytesSent, bytesReceived: (bytesSent / 4 / 1000) * 0.015 + (bytesReceived / 4 / 1000) * 0.075
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),
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AiModel(
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name="claude-sonnet-4-5-20250929",
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displayName="Anthropic Claude Sonnet 4.5 Vision",
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connectorType="anthropic",
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apiUrl="https://api.anthropic.com/v1/messages",
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temperature=0.2,
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maxTokens=8192,
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contextLength=200000,
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costPer1kTokensInput=0.015,
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costPer1kTokensOutput=0.075,
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speedRating=6,
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qualityRating=10,
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functionCall=self.callAiImage,
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priority=PriorityEnum.QUALITY,
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processingMode=ProcessingModeEnum.DETAILED,
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operationTypes=createOperationTypeRatings(
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(OperationTypeEnum.IMAGE_ANALYSE, 10)
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),
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version="claude-sonnet-4-5-20250929",
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calculatePriceUsd=lambda processingTime, bytesSent, bytesReceived: (bytesSent / 4 / 1000) * 0.015 + (bytesReceived / 4 / 1000) * 0.075
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)
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]
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async def callAiBasic(self, modelCall: AiModelCall) -> AiModelResponse:
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"""
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Calls the Anthropic API with the given messages using standardized pattern.
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Args:
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modelCall: AiModelCall with messages and options
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Returns:
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AiModelResponse with content and metadata
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Raises:
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HTTPException: For errors in API communication
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"""
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try:
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# Extract parameters from modelCall
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messages = modelCall.messages
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model = modelCall.model
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options = modelCall.options
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temperature = getattr(options, "temperature", None)
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if temperature is None:
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temperature = model.temperature
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maxTokens = model.maxTokens
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# Transform OpenAI-style messages to Anthropic format:
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# - Move any 'system' role content to top-level 'system'
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# - Keep only 'user'/'assistant' messages in the list
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system_contents: List[str] = []
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converted_messages: List[Dict[str, Any]] = []
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for m in messages:
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role = m.get("role")
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content = m.get("content", "")
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if role == "system":
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# Collect system content; Anthropic expects top-level 'system'
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if isinstance(content, list):
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# Join text parts if provided as blocks
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joined = "\n\n".join(
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[
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(part.get("text") if isinstance(part, dict) else str(part))
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for part in content
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]
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)
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system_contents.append(joined)
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else:
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system_contents.append(str(content))
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continue
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# For Anthropic, content can be a string; pass through strings, collapse blocks
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if isinstance(content, list):
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# Collapse to text if blocks are provided
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collapsed = "\n\n".join(
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[
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(part.get("text") if isinstance(part, dict) else str(part))
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for part in content
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]
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)
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converted_messages.append({"role": role, "content": collapsed})
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else:
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converted_messages.append({"role": role, "content": content})
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system_prompt = "\n\n".join([s for s in system_contents if s]) if system_contents else None
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# Create Anthropic API payload
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payload: Dict[str, Any] = {
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"model": model.name,
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"messages": converted_messages,
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"temperature": temperature,
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}
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# Anthropic requires max_tokens - use provided value or throw error
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if maxTokens is None:
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raise ValueError("maxTokens must be provided for Anthropic API calls")
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payload["max_tokens"] = maxTokens
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if system_prompt:
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payload["system"] = system_prompt
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response = await self.httpClient.post(
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model.apiUrl,
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json=payload
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)
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if response.status_code != 200:
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error_detail = f"Anthropic API error: {response.status_code} - {response.text}"
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logger.error(error_detail)
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# Provide more specific error messages based on status code
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if response.status_code == 529:
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error_message = "Anthropic API is currently overloaded. Please try again in a few minutes."
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elif response.status_code == 429:
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error_message = "Rate limit exceeded. Please wait before making another request."
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elif response.status_code == 401:
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error_message = "Invalid API key. Please check your Anthropic API configuration."
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elif response.status_code == 400:
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error_message = f"Invalid request to Anthropic API: {response.text}"
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else:
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error_message = f"Anthropic API error ({response.status_code}): {response.text}"
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raise HTTPException(status_code=500, detail=error_message)
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# Parse response
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anthropicResponse = response.json()
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# Extract content from response
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content = ""
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if "content" in anthropicResponse:
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if isinstance(anthropicResponse["content"], list):
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# Content is a list of parts (in newer API versions)
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for part in anthropicResponse["content"]:
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if part.get("type") == "text":
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content += part.get("text", "")
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else:
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# Direct content as string (in older API versions)
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content = anthropicResponse["content"]
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# Debug logging for empty responses
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if not content or content.strip() == "":
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logger.warning(f"Anthropic API returned empty content. Full response: {anthropicResponse}")
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content = "[Anthropic API returned empty response]"
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# Return standardized response
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return AiModelResponse(
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content=content,
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success=True,
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modelId=model.name,
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metadata={"response_id": anthropicResponse.get("id", "")}
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)
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except Exception as e:
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logger.error(f"Error calling Anthropic API: {str(e)}")
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raise HTTPException(status_code=500, detail=f"Error calling Anthropic API: {str(e)}")
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async def callAiImage(self, modelCall: AiModelCall) -> AiModelResponse:
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"""
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Analyzes an image using Anthropic's vision capabilities using standardized pattern.
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Args:
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modelCall: AiModelCall with messages and image data in options
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Returns:
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AiModelResponse with analysis content
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"""
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try:
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# Extract parameters from messages for Anthropic Vision API
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messages = modelCall.messages
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model = modelCall.model
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# Verify messages contain image data
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if not messages or not messages[0].get("content"):
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raise ValueError("No messages provided for image analysis")
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logger.info(f"callAiImage called with {len(messages)} message(s)...")
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# Extract text prompt and image data from messages
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# Messages format: [{"role": "user", "content": [{"type": "text", "text": "..."}, {"type": "image_url", "image_url": {"url": "data:..."}}]}]
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userContent = messages[0]["content"]
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if not isinstance(userContent, list):
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raise ValueError("Expected content to be a list for vision")
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textPrompt = ""
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imageUrl = None
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for contentItem in userContent:
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if contentItem.get("type") == "text":
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textPrompt = contentItem.get("text", "")
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elif contentItem.get("type") == "image_url":
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imageUrl = contentItem.get("image_url", {}).get("url", "")
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if not imageUrl or not imageUrl.startswith("data:"):
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raise ValueError("No image data found in messages")
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# Extract base64 data and mime type from data URL
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# Format: data:image/jpeg;base64,/9j/4AAQSkZ...
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parts = imageUrl.split(";base64,")
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if len(parts) != 2:
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raise ValueError("Invalid image data URL format")
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mimeType = parts[0].replace("data:", "")
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base64Data = parts[1]
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# Convert to Anthropic's vision format
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anthropicMessages = [{
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"role": "user",
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"content": [
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{"type": "text", "text": textPrompt},
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{
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"type": "image",
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"source": {
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"type": "base64",
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"media_type": mimeType,
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"data": base64Data
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}
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}
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]
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}]
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# Call Anthropic API directly for vision
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import time
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import base64
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startTime = time.time()
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# Prepare system prompt if available
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systemPrompt = None
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for msg in messages:
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if msg.get("role") == "system":
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systemContent = msg.get("content")
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if isinstance(systemContent, list):
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systemPrompt = "\n".join([item.get("text", "") for item in systemContent if item.get("type") == "text"])
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else:
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systemPrompt = systemContent
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break
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# Get parameters from model (consistent with callAiBasic)
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maxTokens = model.maxTokens if hasattr(model, 'maxTokens') else 8192
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temperature = model.temperature if hasattr(model, 'temperature') else 0.2
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# Prepare API payload
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payload = {
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"model": model.name, # Use standard model.name
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"max_tokens": maxTokens,
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"messages": anthropicMessages
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}
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if systemPrompt:
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payload["system"] = systemPrompt
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# Set temperature from model
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payload["temperature"] = temperature
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# Make API call with headers from httpClient (which includes anthropic-version)
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response = await self.httpClient.post(
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"https://api.anthropic.com/v1/messages",
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json=payload
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)
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if response.status_code != 200:
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errorText = response.text
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logger.error(f"Anthropic API error: {response.status_code} - {errorText}")
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raise HTTPException(status_code=response.status_code, detail=f"Anthropic API error: {errorText}")
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# Parse response
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result = response.json()
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content = result["content"][0]["text"] if result.get("content") else ""
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endTime = time.time()
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processingTime = endTime - startTime
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# Calculate cost
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inputTokens = result.get("usage", {}).get("input_tokens", 0)
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outputTokens = result.get("usage", {}).get("output_tokens", 0)
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# Return standardized response
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return AiModelResponse(
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content=content,
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success=True,
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modelId=model.name,
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processingTime=processingTime
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)
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except Exception as e:
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logger.error(f"Error during image analysis: {str(e)}", exc_info=True)
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return AiModelResponse(
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content="",
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success=False,
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error=f"Error during image analysis: {str(e)}"
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) |