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Merge pull request #124 from justinh-rahb/anthropic-0.1.2
Update Anthropic Manifold Pipeline to v0.1.2
This commit is contained in:
Timothy Jaeryang Baek
GitHub
commit
4907bd0692
@ -2,9 +2,9 @@
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title: Anthropic Manifold Pipeline
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author: justinh-rahb
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date: 2024-06-20
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version: 1.1
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version: 1.2
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license: MIT
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description: A pipeline for generating text using the Anthropic API.
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description: A pipeline for generating text and processing images using the Anthropic API.
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requirements: requests, anthropic
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environment_variables: ANTHROPIC_API_KEY
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"""
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@ -35,13 +35,11 @@ class Pipeline:
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self.client = Anthropic(api_key=self.valves.ANTHROPIC_API_KEY)
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def get_anthropic_models(self):
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# In the future, this could fetch models dynamically from Anthropic
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return [
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{"id": "claude-3-haiku-20240307", "name": "claude-3-haiku"},
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{"id": "claude-3-opus-20240229", "name": "claude-3-opus"},
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{"id": "claude-3-sonnet-20240229", "name": "claude-3-sonnet"},
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{"id": "claude-3-5-sonnet-20240620", "name": "claude-3.5-sonnet"},
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# Add other Anthropic models here as they become available
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]
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async def on_startup(self):
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@ -53,63 +51,94 @@ class Pipeline:
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pass
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async def on_valves_updated(self):
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# This function is called when the valves are updated.
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self.client = Anthropic(api_key=self.valves.ANTHROPIC_API_KEY)
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pass
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# Pipelines are the models that are available in the manifold.
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# It can be a list or a function that returns a list.
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def pipelines(self) -> List[dict]:
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return self.get_anthropic_models()
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def process_image(self, image_data):
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if image_data["image_url"]["url"].startswith("data:image"):
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mime_type, base64_data = image_data["image_url"]["url"].split(",", 1)
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media_type = mime_type.split(":")[1].split(";")[0]
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return {
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"type": "image",
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"source": {
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"type": "base64",
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"media_type": media_type,
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"data": base64_data,
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},
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}
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else:
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return {
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"type": "image",
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"source": {"type": "url", "url": image_data["image_url"]["url"]},
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}
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def pipe(
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self, user_message: str, model_id: str, messages: List[dict], body: dict
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) -> Union[str, Generator, Iterator]:
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try:
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if "user" in body:
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del body["user"]
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if "chat_id" in body:
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del body["chat_id"]
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if "title" in body:
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del body["title"]
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# Remove unnecessary keys
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for key in ['user', 'chat_id', 'title']:
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body.pop(key, None)
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system_message, messages = pop_system_message(messages)
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processed_messages = []
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image_count = 0
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total_image_size = 0
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for message in messages:
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processed_content = []
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if isinstance(message.get("content"), list):
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for item in message["content"]:
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if item["type"] == "text":
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processed_content.append({"type": "text", "text": item["text"]})
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elif item["type"] == "image_url":
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if image_count >= 5:
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raise ValueError("Maximum of 5 images per API call exceeded")
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processed_image = self.process_image(item)
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processed_content.append(processed_image)
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if processed_image["source"]["type"] == "base64":
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image_size = len(processed_image["source"]["data"]) * 3 / 4
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else:
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image_size = 0
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total_image_size += image_size
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if total_image_size > 100 * 1024 * 1024:
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raise ValueError("Total size of images exceeds 100 MB limit")
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image_count += 1
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else:
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processed_content = [{"type": "text", "text": message.get("content", "")}]
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processed_messages.append({"role": message["role"], "content": processed_content})
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# Prepare the payload
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payload = {
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"model": model_id,
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"messages": processed_messages,
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"max_tokens": body.get("max_tokens", 4096),
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"temperature": body.get("temperature", 0.8),
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"top_k": body.get("top_k", 40),
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"top_p": body.get("top_p", 0.9),
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"stop_sequences": body.get("stop", []),
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**({"system": str(system_message)} if system_message else {}),
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"stream": body.get("stream", False),
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}
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if body.get("stream", False):
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return self.stream_response(model_id, messages, body)
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return self.stream_response(model_id, payload)
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else:
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return self.get_completion(model_id, messages, body)
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return self.get_completion(model_id, payload)
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except (RateLimitError, APIStatusError, APIConnectionError) as e:
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return f"Error: {e}"
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def stream_response(
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self, model_id: str, messages: List[dict], body: dict
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) -> Generator:
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system_message, messages = pop_system_message(messages)
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max_tokens = (
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body.get("max_tokens") if body.get("max_tokens") is not None else 4096
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)
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temperature = (
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body.get("temperature") if body.get("temperature") is not None else 0.8
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)
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top_k = body.get("top_k") if body.get("top_k") is not None else 40
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top_p = body.get("top_p") if body.get("top_p") is not None else 0.9
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stop_sequences = body.get("stop") if body.get("stop") is not None else []
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stream = self.client.messages.create(
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**{
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"model": model_id,
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**(
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{"system": system_message} if system_message else {}
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), # Add system message if it exists (optional
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"messages": messages,
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"max_tokens": max_tokens,
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"temperature": temperature,
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"top_k": top_k,
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"top_p": top_p,
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"stop_sequences": stop_sequences,
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"stream": True,
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}
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)
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def stream_response(self, model_id: str, payload: dict) -> Generator:
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stream = self.client.messages.create(**payload)
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for chunk in stream:
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if chunk.type == "content_block_start":
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@ -117,31 +146,6 @@ class Pipeline:
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elif chunk.type == "content_block_delta":
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yield chunk.delta.text
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def get_completion(self, model_id: str, messages: List[dict], body: dict) -> str:
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system_message, messages = pop_system_message(messages)
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max_tokens = (
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body.get("max_tokens") if body.get("max_tokens") is not None else 4096
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)
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temperature = (
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body.get("temperature") if body.get("temperature") is not None else 0.8
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)
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top_k = body.get("top_k") if body.get("top_k") is not None else 40
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top_p = body.get("top_p") if body.get("top_p") is not None else 0.9
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stop_sequences = body.get("stop") if body.get("stop") is not None else []
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response = self.client.messages.create(
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**{
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"model": model_id,
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**(
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{"system": system_message} if system_message else {}
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), # Add system message if it exists (optional
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"messages": messages,
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"max_tokens": max_tokens,
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"temperature": temperature,
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"top_k": top_k,
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"top_p": top_p,
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"stop_sequences": stop_sequences,
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}
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)
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return response.content[0].text
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def get_completion(self, model_id: str, payload: dict) -> str:
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response = self.client.messages.create(**payload)
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return response.content[0].text
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