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modules/api/anthropic.py
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oobabooga
API: Add thinking block signature field, fix error codes, clean up logging
23 мар 2026, 17:06
23 мар 2026, 17:06
02f18a1
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import json import time from modules import shared def convert_request(body: dict) -> dict: """Transform Anthropic Messages API body into the dict that chat_completions_common expects.""" messages = [] # System message system = body.get('system') if system: if isinstance(system, list): # List of content blocks like [{"type":"text","text":"..."}] text_parts = [block.get('text', '') for block in system if isinstance(block, dict) and block.get('type') == 'text'] system_text = '\n'.join(text_parts) else: system_text = str(system) if system_text: messages.append({"role": "system", "content": system_text}) # Convert messages for msg in body.get('messages', []): role = msg.get('role') content = msg.get('content') if isinstance(content, str): messages.append({"role": role, "content": content}) continue if not isinstance(content, list): messages.append({"role": role, "content": str(content) if content else ""}) continue if role == 'assistant': # Split into text content, tool_calls, and skip thinking blocks text_parts = [] tool_calls = [] for block in content: btype = block.get('type') if btype == 'text': text_parts.append(block.get('text', '')) elif btype == 'tool_use': tool_calls.append({ "id": block.get('id', ''), "type": "function", "function": { "name": block.get('name', ''), "arguments": json.dumps(block.get('input', {})) } }) elif btype == 'thinking': pass # Strip thinking blocks assistant_msg = {"role": "assistant", "content": '\n'.join(text_parts) if text_parts else ""} if tool_calls: assistant_msg["tool_calls"] = tool_calls messages.append(assistant_msg) elif role == 'user': # Handle tool_result blocks and regular content regular_parts = [] for block in content: btype = block.get('type') if btype == 'tool_result': # Emit any accumulated regular content first if regular_parts: if len(regular_parts) == 1 and regular_parts[0].get('type') == 'text': messages.append({"role": "user", "content": regular_parts[0]['text']}) else: messages.append({"role": "user", "content": regular_parts}) regular_parts = [] # Convert tool_result to OpenAI tool message tool_content = block.get('content', '') if isinstance(tool_content, list): tool_content = '\n'.join( b.get('text', '') for b in tool_content if isinstance(b, dict) and b.get('type') == 'text' ) messages.append({ "role": "tool", "tool_call_id": block.get('tool_use_id', ''), "content": str(tool_content) }) elif btype == 'text': regular_parts.append({"type": "text", "text": block.get('text', '')}) elif btype == 'image': source = block.get('source', {}) if source.get('type') == 'base64': media_type = source.get('media_type', 'image/png') data = source.get('data', '') regular_parts.append({ "type": "image_url", "image_url": {"url": f"data:{media_type};base64,{data}"} }) elif btype == 'thinking': pass # Strip thinking blocks if regular_parts: if len(regular_parts) == 1 and regular_parts[0].get('type') == 'text': messages.append({"role": "user", "content": regular_parts[0]['text']}) else: messages.append({"role": "user", "content": regular_parts}) else: messages.append({"role": role, "content": str(content)}) # Start with all fields from the original body (includes GenerationOptions defaults) result = dict(body) # Remove Anthropic-specific fields that don't map directly for key in ('system', 'stop_sequences', 'tools', 'tool_choice', 'thinking', 'metadata'): result.pop(key, None) # Set converted fields result['messages'] = messages result['max_tokens'] = body.get('max_tokens', 4096) result['stream'] = body.get('stream', False) result['mode'] = 'instruct' # Ensure ChatCompletionRequestParams defaults are present result.setdefault('continue_', False) result.setdefault('instruction_template', None) result.setdefault('instruction_template_str', None) result.setdefault('character', None) result.setdefault('bot_name', None) result.setdefault('context', None) result.setdefault('greeting', None) result.setdefault('user_name', None) result.setdefault('user_bio', None) result.setdefault('chat_template_str', None) result.setdefault('chat_instruct_command', 'Continue the chat dialogue below. Write a single reply for the character "<|character|>".\n\n<|prompt|>') result.setdefault('frequency_penalty', None) result.setdefault('presence_penalty', None) result.setdefault('logit_bias', None) result.setdefault('logprobs', None) result.setdefault('top_logprobs', None) result.setdefault('n', 1) result.setdefault('model', None) result.setdefault('functions', None) result.setdefault('function_call', None) result.setdefault('stream_options', None) result.setdefault('user', None) result.setdefault('stop', None) result.setdefault('tool_choice', None) # Always request usage in streaming so the usage-only chunk triggers # the deferred message_delta/message_stop with accurate output_tokens if body.get('stream', False): result['stream_options'] = {'include_usage': True} # Map stop_sequences -> stop if body.get('stop_sequences'): result['stop'] = body['stop_sequences'] # Tools if body.get('tools'): result['tools'] = [ { "type": "function", "function": { "name": t.get('name', ''), "description": t.get('description', ''), "parameters": t.get('input_schema', {"type": "object", "properties": {}}) } } for t in body['tools'] ] # Tool choice tc = body.get('tool_choice') if tc and isinstance(tc, dict): tc_type = tc.get('type') if tc_type == 'auto': result['tool_choice'] = 'auto' elif tc_type == 'any': result['tool_choice'] = 'required' elif tc_type == 'tool': result['tool_choice'] = {"type": "function", "function": {"name": tc.get('name', '')}} elif tc_type == 'none': result['tool_choice'] = 'none' else: result.setdefault('tool_choice', None) # Thinking thinking = body.get('thinking') if thinking and isinstance(thinking, dict) and thinking.get('type') in ('enabled', 'adaptive'): result['enable_thinking'] = True return result _FINISH_REASON_MAP = { "stop": "end_turn", "length": "max_tokens", "tool_calls": "tool_use", } def build_response(openai_resp: dict, model: str) -> dict: """Transform OpenAI chat completion response dict into Anthropic Messages format.""" resp_id = openai_resp.get('id', 'msg_unknown') if resp_id.startswith('chatcmpl-'): resp_id = 'msg_' + resp_id[9:] choice = openai_resp.get('choices', [{}])[0] message = choice.get('message', {}) content = [] # Reasoning/thinking content reasoning = message.get('reasoning_content') if reasoning: content.append({"type": "thinking", "thinking": reasoning, "signature": ""}) # Text content text = message.get('content') if text: content.append({"type": "text", "text": text}) # Tool calls tool_calls = message.get('tool_calls') if tool_calls: for tc in tool_calls: func = tc.get('function', {}) try: input_data = json.loads(func.get('arguments', '{}')) except (json.JSONDecodeError, TypeError): input_data = {} content.append({ "type": "tool_use", "id": tc.get('id', ''), "name": func.get('name', ''), "input": input_data }) finish_reason = choice.get('finish_reason', 'stop') stop_reason = _FINISH_REASON_MAP.get(finish_reason, 'end_turn') usage = openai_resp.get('usage', {}) return { "id": resp_id, "type": "message", "role": "assistant", "content": content, "model": model, "stop_reason": stop_reason, "stop_sequence": None, "usage": { "input_tokens": usage.get('prompt_tokens', 0), "output_tokens": usage.get('completion_tokens', 0), } } class StreamConverter: """Stateful converter: processes one OpenAI chunk at a time, yields Anthropic SSE events. When include_usage is enabled in the OpenAI request, the final chunk with finish_reason has usage=None, followed by a separate usage-only chunk (choices=[], usage={...}). We defer emitting message_delta and message_stop until we receive that usage chunk so output_tokens is accurate. """ def __init__(self, model: str): self.model = model self.msg_id = "msg_%d" % int(time.time() * 1000000000) self.block_index = 0 self.in_thinking = False self.in_text = False self.input_tokens = 0 self.output_tokens = 0 self.tool_calls_accum = {} self.stop_reason = "end_turn" self._pending_finish = False # True after we've seen finish_reason def process_chunk(self, chunk: dict) -> list[dict]: """Process a single OpenAI streaming chunk; return list of Anthropic SSE event dicts.""" events = [] choices = chunk.get('choices', []) usage = chunk.get('usage') if usage: self.input_tokens = usage.get('prompt_tokens', self.input_tokens) self.output_tokens = usage.get('completion_tokens', self.output_tokens) # Usage-only chunk (choices=[]) arrives after the finish chunk if not choices: if self._pending_finish: events.extend(self.finish()) return events choice = choices[0] delta = choice.get('delta', {}) finish_reason = choice.get('finish_reason') # First chunk with role if 'role' in delta: events.append({ "event": "message_start", "data": json.dumps({ "type": "message_start", "message": { "id": self.msg_id, "type": "message", "role": "assistant", "content": [], "model": self.model, "stop_reason": None, "stop_sequence": None, "usage": {"input_tokens": self.input_tokens, "output_tokens": 0} } }) }) events.append({"event": "ping", "data": json.dumps({"type": "ping"})}) return events # Reasoning content reasoning_content = delta.get('reasoning_content') if reasoning_content: if not self.in_thinking: self.in_thinking = True events.append({ "event": "content_block_start", "data": json.dumps({ "type": "content_block_start", "index": self.block_index, "content_block": {"type": "thinking", "thinking": "", "signature": ""} }) }) events.append({ "event": "content_block_delta", "data": json.dumps({ "type": "content_block_delta", "index": self.block_index, "delta": {"type": "thinking_delta", "thinking": reasoning_content} }) }) return events # Text content text_content = delta.get('content') if text_content: if self.in_thinking: events.append({ "event": "content_block_stop", "data": json.dumps({"type": "content_block_stop", "index": self.block_index}) }) self.in_thinking = False self.block_index += 1 if not self.in_text: self.in_text = True events.append({ "event": "content_block_start", "data": json.dumps({ "type": "content_block_start", "index": self.block_index, "content_block": {"type": "text", "text": ""} }) }) events.append({ "event": "content_block_delta", "data": json.dumps({ "type": "content_block_delta", "index": self.block_index, "delta": {"type": "text_delta", "text": text_content} }) }) return events # Tool calls in delta chunk_tool_calls = delta.get('tool_calls') if chunk_tool_calls: for tc in chunk_tool_calls: tc_id = tc.get('id', '') tc_idx = tc.get('index', 0) func = tc.get('function', {}) if tc_id: self.tool_calls_accum[tc_idx] = { "id": tc_id, "name": func.get('name', ''), "arguments": func.get('arguments', '') } elif tc_idx in self.tool_calls_accum: self.tool_calls_accum[tc_idx]["arguments"] += func.get('arguments', '') # Final chunk — close open content blocks, defer message_delta/stop for usage if finish_reason is not None: self.stop_reason = _FINISH_REASON_MAP.get(finish_reason, 'end_turn') if self.in_thinking: events.append({ "event": "content_block_stop", "data": json.dumps({"type": "content_block_stop", "index": self.block_index}) }) self.in_thinking = False self.block_index += 1 if self.in_text: events.append({ "event": "content_block_stop", "data": json.dumps({"type": "content_block_stop", "index": self.block_index}) }) self.in_text = False self.block_index += 1 for tc_idx in sorted(self.tool_calls_accum.keys()): tc = self.tool_calls_accum[tc_idx] arguments_str = tc["arguments"] or "{}" events.append({ "event": "content_block_start", "data": json.dumps({ "type": "content_block_start", "index": self.block_index, "content_block": { "type": "tool_use", "id": tc["id"], "name": tc["name"], "input": {} } }) }) # Emit the full input as a single input_json_delta so SDK # clients that reconstruct from deltas get the correct data events.append({ "event": "content_block_delta", "data": json.dumps({ "type": "content_block_delta", "index": self.block_index, "delta": { "type": "input_json_delta", "partial_json": arguments_str } }) }) events.append({ "event": "content_block_stop", "data": json.dumps({"type": "content_block_stop", "index": self.block_index}) }) self.block_index += 1 # Defer message_delta/stop — usage chunk may follow self._pending_finish = True return events def finish(self) -> list[dict]: """Emit deferred message_delta and message_stop. Safe to call multiple times.""" if not self._pending_finish: return [] self._pending_finish = False return [ { "event": "message_delta", "data": json.dumps({ "type": "message_delta", "delta": {"stop_reason": self.stop_reason, "stop_sequence": None}, "usage": {"input_tokens": self.input_tokens, "output_tokens": self.output_tokens} }) }, { "event": "message_stop", "data": json.dumps({"type": "message_stop"}) } ]