236 lines
No EOL
9.2 KiB
Python
236 lines
No EOL
9.2 KiB
Python
import logging
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import httpx
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from typing import Dict, Any, List, Union
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from fastapi import HTTPException
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from modules.configuration import APP_CONFIG
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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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"apiUrl": APP_CONFIG.get('Connector_AiAnthropic_API_URL'),
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"modelName": APP_CONFIG.get('Connector_AiAnthropic_MODEL_NAME'),
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"temperature": float(APP_CONFIG.get('Connector_AiAnthropic_TEMPERATURE')),
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"maxTokens": int(APP_CONFIG.get('Connector_AiAnthropic_MAX_TOKENS'))
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}
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class ChatService:
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"""Connector for communication with the Anthropic API."""
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def __init__(self):
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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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self.apiUrl = self.config["apiUrl"]
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self.modelName = self.config["modelName"]
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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(f"Anthropic Connector initialized with model: {self.modelName}")
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async def callApi(self, messages: List[Dict[str, Any]], temperature: float = None, maxTokens: int = None) -> Dict[str, Any]:
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"""
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Calls the Anthropic API with the given messages.
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Args:
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messages: List of messages in OpenAI format (role, content)
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temperature: Temperature for response generation (0.0-1.0)
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maxTokens: Maximum number of tokens in the response
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Returns:
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The response converted to OpenAI format
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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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# Convert OpenAI format to Anthropic format
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formattedMessages = self._convertToAnthropicFormat(messages)
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# Use parameters from configuration if none were overridden
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if temperature is None:
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temperature = self.config.get("temperature", 0.2)
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if maxTokens is None:
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maxTokens = self.config.get("maxTokens", 2000)
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# Create Anthropic API payload
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payload = {
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"model": self.modelName,
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"messages": formattedMessages,
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"temperature": temperature,
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"max_tokens": maxTokens
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}
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response = await self.httpClient.post(
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self.apiUrl,
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json=payload
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)
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if response.status_code != 200:
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logger.error(f"Anthropic API error: {response.status_code} - {response.text}")
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raise HTTPException(status_code=500, detail="Error communicating with Anthropic API")
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# Convert response from Anthropic format to OpenAI format
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anthropicResponse = response.json()
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openaiFormattedResponse = self._convertToOpenaiFormat(anthropicResponse)
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return openaiFormattedResponse
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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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def _convertToAnthropicFormat(self, openaiMessages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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"""
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Converts messages from OpenAI format to Anthropic format.
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OpenAI uses:
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[{"role": "system", "content": "..."},
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{"role": "user", "content": "..."},
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{"role": "assistant", "content": "..."}]
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Anthropic uses:
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[{"role": "user", "content": "..."},
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{"role": "assistant", "content": "..."}]
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Note: Anthropic has no direct system message equivalent,
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so we add system messages to the first user message.
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"""
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anthropicMessages = []
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systemContent = ""
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# First extract all system messages
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for msg in openaiMessages:
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if msg.get("role") == "system":
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systemContent += msg.get("content", "") + "\n\n"
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# Convert the remaining messages
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for msg in openaiMessages:
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role = msg.get("role")
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content = msg.get("content", "")
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# Skip system messages (already extracted)
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if role == "system":
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continue
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# For the first user message: prepend system content if available
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if role == "user" and systemContent and not any(m.get("role") == "user" for m in anthropicMessages):
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if isinstance(content, str):
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content = systemContent + content
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elif isinstance(content, list):
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# If content is an array (for multimodal messages)
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textParts = []
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for part in content:
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if part.get("type") == "text":
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textParts.append(part)
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if textParts:
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textParts[0]["text"] = systemContent + textParts[0].get("text", "")
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# Anthropic only supports "user" and "assistant" roles
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if role not in ["user", "assistant"]:
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role = "user"
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anthropicMessages.append({"role": role, "content": content})
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return anthropicMessages
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def _convertToOpenaiFormat(self, anthropicResponse: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Converts a response from Anthropic format to OpenAI format.
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"""
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# Extract content from Anthropic 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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# Create OpenAI-formatted response
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return {
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"id": anthropicResponse.get("id", ""),
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"object": "chat.completion",
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"created": anthropicResponse.get("created", 0),
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"model": anthropicResponse.get("model", self.modelName),
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"choices": [
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{
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"message": {
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"role": "assistant",
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"content": content
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},
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"index": 0,
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"finish_reason": "stop"
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}
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]
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}
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async def analyzeImage(self, imageData: Union[str, bytes], mimeType: str = None, prompt: str = "Describe this image") -> str:
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"""
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Analyzes an image using Anthropic's vision capabilities.
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Args:
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imageData: Either a file path (str) or image data (bytes)
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mimeType: The MIME type of the image (optional, only for binary data)
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prompt: The prompt for analysis
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Returns:
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The analysis response as text
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"""
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try:
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# Distinguish between file path and binary data
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if isinstance(imageData, str):
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# It's a file path - import filehandling only when needed
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from modules import agentserviceFilemanager as fileHandler
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base64Data, autoMimeType = fileHandler.encodeFileToBase64(imageData)
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mimeType = mimeType or autoMimeType
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else:
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# It's binary data
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import base64
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base64Data = base64.b64encode(imageData).decode('utf-8')
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# MIME type must be specified for binary data
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if not mimeType:
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# Fallback to generic image type
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mimeType = "image/png"
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# Prepare the payload for the Vision API
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": prompt},
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:{mimeType};base64,{base64Data}"
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}
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}
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]
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}
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]
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# Use the existing callApi function with the Vision model
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response = await self.callApi(messages)
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# Extract and return content
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return response["choices"][0]["message"]["content"]
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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 f"[Error during image analysis: {str(e)}]" |