support xai

cli/utils.py

@@ -148,10 +148,14 @@ def select_shallow_thinking_agent(provider) -> str:
             ("Meta: Llama 4 Scout", "meta-llama/llama-4-scout:free"),
             ("Meta: Llama 3.3 8B Instruct - A lightweight and ultra-fast variant of Llama 3.3 70B", "meta-llama/llama-3.3-8b-instruct:free"),
             ("google/gemini-2.0-flash-exp:free - Gemini Flash 2.0 offers a significantly faster time to first token", "google/gemini-2.0-flash-exp:free"),
+            ("qwen/qwen3-coder:free - Qwen3-Coder-480B-A35B-Instruct is a Mixture-of-Experts (MoE) code generation model", "qwen/qwen3-coder:free"),
         ],
         "ollama": [
             ("llama3.1 local", "llama3.1"),
             ("llama3.2 local", "llama3.2"),
+        ],
+        "xai": [
+            ("grok-4-fast-non-reasoning", "grok-4-fast-non-reasoning")
         ]
     }
 
@@ -209,11 +213,14 @@ def select_deep_thinking_agent(provider) -> str:
         ],
         "openrouter": [
             ("DeepSeek V3 - a 685B-parameter, mixture-of-experts model", "deepseek/deepseek-chat-v3-0324:free"),
-            ("Deepseek - latest iteration of the flagship chat model family from the DeepSeek team.", "deepseek/deepseek-chat-v3-0324:free"),
+            ("Deepseek - latest iteration of the flagship chat model family from the DeepSeek team.", "deepseek/deepseek-chat-v3-0324:free")
         ],
         "ollama": [
             ("llama3.1 local", "llama3.1"),
             ("qwen3", "qwen3"),
+        ],
+        "xai": [
+            ("grok-4-fast-reasoning", "grok-4-fast-reasoning")
         ]
     }
     
@@ -243,11 +250,12 @@ def select_llm_provider() -> tuple[str, str]:
     """Select the OpenAI api url using interactive selection."""
     # Define OpenAI api options with their corresponding endpoints
     BASE_URLS = [
+        ("XAI", "https://api.x.ai/v1"),       
         ("OpenAI", "https://api.openai.com/v1"),
         ("Anthropic", "https://api.anthropic.com/"),
         ("Google", "https://generativelanguage.googleapis.com/v1"),
         ("Openrouter", "https://openrouter.ai/api/v1"),
-        ("Ollama", "http://localhost:11434/v1"),        
+        ("Ollama", "http://localhost:11434/v1"), 
     ]
     
     choice = questionary.select(

requirements.txt

@@ -24,3 +24,5 @@ rich
 questionary
 langchain_anthropic
 langchain-google-genai
+langchain-xai
+sentence-transformers

tradingagents/agents/utils/memory.py

@@ -1,25 +1,44 @@
 import chromadb
 from chromadb.config import Settings
 from openai import OpenAI
+from sentence_transformers import SentenceTransformer
 
 
 class FinancialSituationMemory:
     def __init__(self, name, config):
-        if config["backend_url"] == "http://localhost:11434/v1":
+        self.client = OpenAI(base_url=config["backend_url"], api_key=config.get("api_key"))
+        
+        # 根據 backend 決定 embedding 策略
+        if config["llm_provider"] == "ollama":
             self.embedding = "nomic-embed-text"
+            self.embedding_client = OpenAI(base_url="http://localhost:11434/v1")
+            self.use_local_embedding = False
+        elif config["llm_provider"] == "xai":
+            # Grok - 使用本地 Hugging Face 模型
+            self.embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
+            self.use_local_embedding = True
         else:
             self.embedding = "text-embedding-3-small"
-        self.client = OpenAI(base_url=config["backend_url"])
+            self.embedding_client = self.client
+            self.use_local_embedding = False
+        
         self.chroma_client = chromadb.Client(Settings(allow_reset=True))
         self.situation_collection = self.chroma_client.create_collection(name=name)
 
+
     def get_embedding(self, text):
-        """Get OpenAI embedding for a text"""
-        
-        response = self.client.embeddings.create(
-            model=self.embedding, input=text
-        )
-        return response.data[0].embedding
+        """Get embedding for a text"""
+        if self.use_local_embedding:
+            # 使用本地 Hugging Face 模型
+            return self.embedding_model.encode(text).tolist()
+        else:
+            # 使用 API
+            response = self.embedding_client.embeddings.create(
+                model=self.embedding, 
+                input=text
+            )
+            return response.data[0].embedding
+
 
     def add_situations(self, situations_and_advice):
         """Add financial situations and their corresponding advice. Parameter is a list of tuples (situation, rec)"""

tradingagents/graph/trading_graph.py

@@ -9,6 +9,7 @@ from typing import Dict, Any, Tuple, List, Optional
 from langchain_openai import ChatOpenAI
 from langchain_anthropic import ChatAnthropic
 from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_xai import ChatXAI
 
 from langgraph.prebuilt import ToolNode
 
@@ -62,6 +63,47 @@ class TradingAgentsGraph:
         self.debug = debug
         self.config = config or DEFAULT_CONFIG
 
+        # get the language
+        self.output_language = self.config.get("output_language", "en")
+        self.language_instruction = self.config.get("language_system_prompts", {}).get(
+            self.output_language, ""
+        )
+
+        self._setup_agents()
+        self._setup_graph()
+
+        def _setup_agents(self):
+            """初始化所有 agents，並注入語言指示"""
+            # 為每個 agent 加入語言指示
+            if self.language_instruction:
+                self._inject_language_to_agents()
+        
+        def _inject_language_to_agents(self):
+            """將語言指示注入所有 agents"""
+            # 這個方法會在每個 agent 的 system message 前加入語言指示
+            self.language_system_message = SystemMessage(content=self.language_instruction)
+        
+        def _create_agent_with_language(self, agent_name, agent_prompt, llm):
+            """創建帶有語言指示的 agent"""
+            from langchain.agents import AgentExecutor, create_openai_functions_agent
+            from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+            
+            # 構建包含語言指示的 prompt
+            if self.language_instruction:
+                system_prompt = f"{agent_prompt}{self.language_instruction}"
+            else:
+                system_prompt = agent_prompt
+            
+            prompt = ChatPromptTemplate.from_messages([
+                ("system", system_prompt),
+                MessagesPlaceholder(variable_name="chat_history", optional=True),
+                ("human", "{input}"),
+                MessagesPlaceholder(variable_name="agent_scratchpad"),
+            ])
+            
+            agent = create_openai_functions_agent(llm, tools=[], prompt=prompt)
+            return AgentExecutor(agent=agent, tools=[], verbose=self.debug)
+    
         # Update the interface's config
         set_config(self.config)
 
@@ -81,6 +123,9 @@ class TradingAgentsGraph:
         elif self.config["llm_provider"].lower() == "google":
             self.deep_thinking_llm = ChatGoogleGenerativeAI(model=self.config["deep_think_llm"])
             self.quick_thinking_llm = ChatGoogleGenerativeAI(model=self.config["quick_think_llm"])
+        elif self.config["llm_provider"].lower() == "xai":
+            self.deep_thinking_llm = ChatXAI(model=self.config["deep_think_llm"])
+            self.quick_thinking_llm = ChatXAI(model=self.config["quick_think_llm"])
         else:
             raise ValueError(f"Unsupported LLM provider: {self.config['llm_provider']}")