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Refa: update Anthropic models. (#6445)
### What problem does this PR solve? #6421 ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue)
This commit is contained in:
Kevin Hu
GitHub
parent
e4c8d703b5
commit
85eb3775d6
@ -3169,34 +3169,28 @@
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"status": "1",
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"llm": [
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{
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"llm_name": "claude-3-5-sonnet-20240620",
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"tags": "LLM,CHAT,200k",
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"llm_name": "claude-3-7-sonnet-20250219",
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"tags": "LLM,IMAGE2TEXT,200k",
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"max_tokens": 204800,
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"model_type": "chat"
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"model_type": "image2text"
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},
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{
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"llm_name": "claude-3-5-sonnet-20241022",
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"tags": "LLM,CHAT,200k",
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"tags": "LLM,IMAGE2TEXT,200k",
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"max_tokens": 204800,
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"model_type": "chat"
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},
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{
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"llm_name": "claude-3-opus-20240229",
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"tags": "LLM,CHAT,200k",
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"max_tokens": 204800,
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"model_type": "chat"
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},
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{
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"llm_name": "claude-3-sonnet-20240229",
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"tags": "LLM,CHAT,200k",
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"tags": "LLM,IMAGE2TEXT,200k",
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"max_tokens": 204800,
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"model_type": "chat"
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},
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{
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"llm_name": "claude-3-haiku-20240307",
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"tags": "LLM,CHAT,200k",
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"tags": "LLM,IMAGE2TEXT,200k",
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"max_tokens": 204800,
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"model_type": "chat"
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"model_type": "image2text"
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},
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{
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"llm_name": "claude-2.1",
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@ -3209,12 +3203,6 @@
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"tags": "LLM,CHAT,100k",
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"max_tokens": 102400,
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"model_type": "chat"
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},
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{
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"llm_name": "claude-3-5-sonnet-20241022",
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"tags": "LLM,CHAT,200k",
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"max_tokens": 102400,
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"model_type": "chat"
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}
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]
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},
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@ -106,6 +106,7 @@ from .cv_model import (
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TogetherAICV,
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YiCV,
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HunyuanCV,
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AnthropicCV
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)
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from .rerank_model import (
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@ -198,6 +199,7 @@ CvModel = {
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"TogetherAI": TogetherAICV,
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"01.AI": YiCV,
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"Tencent Hunyuan": HunyuanCV,
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"Anthropic": AnthropicCV,
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}
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ChatModel = {
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@ -1443,6 +1443,9 @@ class AnthropicChat(Base):
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del gen_conf["presence_penalty"]
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if "frequency_penalty" in gen_conf:
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del gen_conf["frequency_penalty"]
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gen_conf["max_tokens"] = 8196
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if "haiku" in self.model_name or "opus" in self.model_name:
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gen_conf["max_tokens"] = 4096
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ans = ""
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try:
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@ -1474,6 +1477,9 @@ class AnthropicChat(Base):
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del gen_conf["presence_penalty"]
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if "frequency_penalty" in gen_conf:
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del gen_conf["frequency_penalty"]
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gen_conf["max_tokens"] = 8196
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if "haiku" in self.model_name or "opus" in self.model_name:
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gen_conf["max_tokens"] = 4096
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ans = ""
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total_tokens = 0
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@ -1481,12 +1487,18 @@ class AnthropicChat(Base):
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response = self.client.messages.create(
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model=self.model_name,
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messages=history,
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system=self.system,
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system=system,
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stream=True,
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**gen_conf,
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)
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for res in response:
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if res.type == 'content_block_delta':
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if res.delta.type == "thinking_delta" and res.delta.thinking:
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if ans.find("<think>") < 0:
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ans += "<think>"
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ans = ans.replace("</think>", "")
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ans += res.delta.thinking + "</think>"
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else:
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text = res.delta.text
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ans += text
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total_tokens += num_tokens_from_string(text)
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@ -31,6 +31,7 @@ from api.utils import get_uuid
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from api.utils.file_utils import get_project_base_directory
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from rag.nlp import is_english
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from rag.prompts import vision_llm_describe_prompt
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from rag.utils import num_tokens_from_string
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class Base(ABC):
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@ -899,3 +900,125 @@ class HunyuanCV(Base):
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],
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}
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]
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class AnthropicCV(Base):
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def __init__(self, key, model_name, base_url=None):
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import anthropic
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self.client = anthropic.Anthropic(api_key=key)
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self.model_name = model_name
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self.system = ""
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self.max_tokens = 8192
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if "haiku" in self.model_name or "opus" in self.model_name:
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self.max_tokens = 4096
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def prompt(self, b64, prompt):
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return [
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{
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"role": "user",
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"content": [
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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": "image/jpeg",
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"data": b64,
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},
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},
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{
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"type": "text",
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"text": prompt
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}
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],
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}
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]
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def describe(self, image):
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b64 = self.image2base64(image)
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prompt = self.prompt(b64,
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"请用中文详细描述一下图中的内容，比如时间，地点，人物，事情，人物心情等，如果有数据请提取出数据。" if self.lang.lower() == "chinese" else
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"Please describe the content of this picture, like where, when, who, what happen. If it has number data, please extract them out."
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)
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response = self.client.messages.create(
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model=self.model_name,
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max_tokens=self.max_tokens,
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messages=prompt
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)
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return response["content"][0]["text"].strip(), response["usage"]["input_tokens"]+response["usage"]["output_tokens"]
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def describe_with_prompt(self, image, prompt=None):
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b64 = self.image2base64(image)
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prompt = self.prompt(b64, prompt if prompt else vision_llm_describe_prompt())
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response = self.client.messages.create(
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model=self.model_name,
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max_tokens=self.max_tokens,
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messages=prompt
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)
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return response["content"][0]["text"].strip(), response["usage"]["input_tokens"]+response["usage"]["output_tokens"]
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def chat(self, system, history, gen_conf):
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if "presence_penalty" in gen_conf:
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del gen_conf["presence_penalty"]
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if "frequency_penalty" in gen_conf:
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del gen_conf["frequency_penalty"]
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gen_conf["max_tokens"] = self.max_tokens
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ans = ""
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try:
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response = self.client.messages.create(
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model=self.model_name,
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messages=history,
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system=system,
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stream=False,
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**gen_conf,
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).to_dict()
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ans = response["content"][0]["text"]
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if response["stop_reason"] == "max_tokens":
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ans += (
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"...\nFor the content length reason, it stopped, continue?"
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if is_english([ans])
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else "······\n由于长度的原因，回答被截断了，要继续吗？"
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)
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return (
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ans,
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response["usage"]["input_tokens"] + response["usage"]["output_tokens"],
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)
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except Exception as e:
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return ans + "\n**ERROR**: " + str(e), 0
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def chat_streamly(self, system, history, gen_conf):
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if "presence_penalty" in gen_conf:
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del gen_conf["presence_penalty"]
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if "frequency_penalty" in gen_conf:
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del gen_conf["frequency_penalty"]
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gen_conf["max_tokens"] = self.max_tokens
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ans = ""
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total_tokens = 0
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try:
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response = self.client.messages.create(
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model=self.model_name,
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messages=history,
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system=system,
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stream=True,
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**gen_conf,
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)
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for res in response:
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if res.type == 'content_block_delta':
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if res.delta.type == "thinking_delta" and res.delta.thinking:
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if ans.find("<think>") < 0:
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ans += "<think>"
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ans = ans.replace("</think>", "")
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ans += res.delta.thinking + "</think>"
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else:
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text = res.delta.text
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ans += text
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total_tokens += num_tokens_from_string(text)
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yield ans
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except Exception as e:
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yield ans + "\n**ERROR**: " + str(e)
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yield total_tokens
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