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fix MiniMax api error (#1567)
### What problem does this PR solve? #1353 ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) --------- Co-authored-by: Zhedong Cen <cenzhedong2@126.com>
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fe5dd5b70a
commit
9ae57eb370
@ -558,7 +558,7 @@ class TenantLLM(DataBaseModel):
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null=True,
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help_text="LLM name",
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default="")
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api_key = CharField(max_length=255, null=True, help_text="API KEY")
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api_key = CharField(max_length=1024, null=True, help_text="API KEY")
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api_base = CharField(max_length=255, null=True, help_text="API Base")
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used_tokens = IntegerField(default=0)
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@ -885,3 +885,9 @@ def migrate_db():
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)
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except Exception as e:
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pass
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try:
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migrate(
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migrator.alter_column_type('tenant_llm', 'api_key', CharField(max_length=1024, null=True, help_text="API KEY"))
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)
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except Exception as e:
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pass
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@ -433,37 +433,37 @@
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]
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},
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{
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"name": "Minimax",
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"name": "MiniMax",
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"logo": "",
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"tags": "LLM,TEXT EMBEDDING",
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"status": "1",
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"llm": [
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{
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"llm_name": "abab6.5",
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"llm_name": "abab6.5-chat",
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"tags": "LLM,CHAT,8k",
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"max_tokens": 8192,
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"model_type": "chat"
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},
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{
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"llm_name": "abab6.5s",
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"llm_name": "abab6.5s-chat",
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"tags": "LLM,CHAT,245k",
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"max_tokens": 245760,
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"model_type": "chat"
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},
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{
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"llm_name": "abab6.5t",
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"llm_name": "abab6.5t-chat",
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"tags": "LLM,CHAT,8k",
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"max_tokens": 8192,
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"model_type": "chat"
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},
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{
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"llm_name": "abab6.5g",
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"llm_name": "abab6.5g-chat",
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"tags": "LLM,CHAT,8k",
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"max_tokens": 8192,
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"model_type": "chat"
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},
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{
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"llm_name": "abab5.5s",
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"llm_name": "abab5.5s-chat",
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"tags": "LLM,CHAT,8k",
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"max_tokens": 8192,
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"model_type": "chat"
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@ -24,7 +24,8 @@ from volcengine.maas.v2 import MaasService
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from rag.nlp import is_english
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from rag.utils import num_tokens_from_string
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from groq import Groq
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import json
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import requests
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class Base(ABC):
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def __init__(self, key, model_name, base_url):
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@ -475,11 +476,83 @@ class VolcEngineChat(Base):
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class MiniMaxChat(Base):
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def __init__(self, key, model_name="abab6.5s-chat",
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base_url="https://api.minimax.chat/v1/text/chatcompletion_v2"):
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def __init__(
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self,
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key,
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model_name,
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base_url="https://api.minimax.chat/v1/text/chatcompletion_v2",
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):
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if not base_url:
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base_url="https://api.minimax.chat/v1/text/chatcompletion_v2"
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super().__init__(key, model_name, base_url)
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base_url = "https://api.minimax.chat/v1/text/chatcompletion_v2"
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self.base_url = base_url
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self.model_name = model_name
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self.api_key = key
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def chat(self, system, history, gen_conf):
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if system:
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history.insert(0, {"role": "system", "content": system})
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for k in list(gen_conf.keys()):
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if k not in ["temperature", "top_p", "max_tokens"]:
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del gen_conf[k]
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json",
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}
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payload = json.dumps(
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{"model": self.model_name, "messages": history, **gen_conf}
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)
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try:
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response = requests.request(
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"POST", url=self.base_url, headers=headers, data=payload
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)
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print(response, flush=True)
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response = response.json()
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ans = response["choices"][0]["message"]["content"].strip()
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if response["choices"][0]["finish_reason"] == "length":
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ans += "...\nFor the content length reason, it stopped, continue?" if is_english(
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[ans]) else "······\n由于长度的原因,回答被截断了,要继续吗?"
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return ans, response["usage"]["total_tokens"]
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except Exception as e:
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return "**ERROR**: " + str(e), 0
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def chat_streamly(self, system, history, gen_conf):
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if system:
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history.insert(0, {"role": "system", "content": system})
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ans = ""
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total_tokens = 0
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try:
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json",
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}
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payload = json.dumps(
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{
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"model": self.model_name,
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"messages": history,
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"stream": True,
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**gen_conf,
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}
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)
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response = requests.request(
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"POST",
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url=self.base_url,
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headers=headers,
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data=payload,
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)
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for resp in response.text.split("\n\n")[:-1]:
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resp = json.loads(resp[6:])
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if "delta" in resp["choices"][0]:
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text = resp["choices"][0]["delta"]["content"]
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else:
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continue
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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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class MistralChat(Base):
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@ -748,4 +821,3 @@ class OpenRouterChat(Base):
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self.base_url = "https://openrouter.ai/api/v1"
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self.client = OpenAI(base_url=self.base_url, api_key=key)
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self.model_name = model_name
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