Fix: anthropic llm issue. (#8633)

### What problem does this PR solve?

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

api/settings.py

 import rag.utils
 import rag.utils.es_conn
 import rag.utils.infinity_conn
-import rag.utils.opensearch_coon
+import rag.utils.opensearch_conn
 from api.constants import RAG_FLOW_SERVICE_NAME
 from api.utils import decrypt_database_config, get_base_config
 from api.utils.file_utils import get_project_base_directory

conf/llm_factories.json

                     "max_tokens": 204800,
                     "model_type": "image2text",
                     "is_tools": true
-                },
-                {
-                    "llm_name": "claude-2.1",
-                    "tags": "LLM,CHAT,200k",
-                    "max_tokens": 204800,
-                    "model_type": "chat"
-                },
-                {
-                    "llm_name": "claude-2.0",
-                    "tags": "LLM,CHAT,100k",
-                    "max_tokens": 102400,
-                    "model_type": "chat"
                 }
             ]
         },

rag/llm/chat_model.py

 
 class QWenChat(Base):
     def __init__(self, key, model_name=Generation.Models.qwen_turbo, base_url=None, **kwargs):
-        super().__init__(key, model_name, base_url="https://dashscope.aliyuncs.com/compatible-mode/v1", **kwargs)
+        if not base_url:
+            base_url = "https://dashscope.aliyuncs.com/compatible-mode/v1"
+        super().__init__(key, model_name, base_url=base_url, **kwargs)
         return
 
 
 
 
 class AnthropicChat(Base):
-    def __init__(self, key, model_name, base_url=None, **kwargs):
+    def __init__(self, key, model_name, base_url="https://api.anthropic.com/v1/", **kwargs):
+        if not base_url:
+            base_url = "https://api.anthropic.com/v1/"
         super().__init__(key, model_name, base_url=base_url, **kwargs)
 
-        import anthropic
-
-        self.client = anthropic.Anthropic(api_key=key)
-        self.model_name = model_name
-
-    def _clean_conf(self, gen_conf):
-        if "presence_penalty" in gen_conf:
-            del gen_conf["presence_penalty"]
-        if "frequency_penalty" in gen_conf:
-            del gen_conf["frequency_penalty"]
-        gen_conf["max_tokens"] = 8192
-        if "haiku" in self.model_name or "opus" in self.model_name:
-            gen_conf["max_tokens"] = 4096
-        return gen_conf
-
-    def _chat(self, history, gen_conf):
-        system = history[0]["content"] if history and history[0]["role"] == "system" else ""
-        response = self.client.messages.create(
-            model=self.model_name,
-            messages=[h for h in history if h["role"] != "system"],
-            system=system,
-            stream=False,
-            **gen_conf,
-        ).to_dict()
-        ans = response["content"][0]["text"]
-        if response["stop_reason"] == "max_tokens":
-            ans += "...\nFor the content length reason, it stopped, continue?" if is_english([ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？"
-        return (
-            ans,
-            response["usage"]["input_tokens"] + response["usage"]["output_tokens"],
-        )
-
-    def chat_streamly(self, system, history, gen_conf):
-        if "presence_penalty" in gen_conf:
-            del gen_conf["presence_penalty"]
-        if "frequency_penalty" in gen_conf:
-            del gen_conf["frequency_penalty"]
-        gen_conf["max_tokens"] = 8192
-        if "haiku" in self.model_name or "opus" in self.model_name:
-            gen_conf["max_tokens"] = 4096
-
-        ans = ""
-        total_tokens = 0
-        reasoning_start = False
-        try:
-            response = self.client.messages.create(
-                model=self.model_name,
-                messages=history,
-                system=system,
-                stream=True,
-                **gen_conf,
-            )
-            for res in response:
-                if res.type == "content_block_delta":
-                    if res.delta.type == "thinking_delta" and res.delta.thinking:
-                        ans = ""
-                        if not reasoning_start:
-                            reasoning_start = True
-                            ans = "<think>"
-                        ans += res.delta.thinking + "</think>"
-                    else:
-                        reasoning_start = False
-                        text = res.delta.text
-                        ans = text
-                        total_tokens += num_tokens_from_string(text)
-                    yield ans
-        except Exception as e:
-            yield ans + "\n**ERROR**: " + str(e)
-
-        yield total_tokens
-
 
 class GoogleChat(Base):
     def __init__(self, key, model_name, base_url=None, **kwargs):