Merge remote-tracking branch 'paikchu/dev' into test-merge

cli/utils.py

@@ -151,6 +151,9 @@ def select_shallow_thinking_agent(provider) -> str:
         ],
         "ollama": [
             ("llama3.2 local", "llama3.2"),
+        ],
+        "deepseek": [
+            ("DeepSeek V3 - a 685B-parameter, mixture-of-experts model", "deepseek-chat")
         ]
     }
 
@@ -212,6 +215,10 @@ def select_deep_thinking_agent(provider) -> str:
         ],
         "ollama": [
             ("qwen3", "qwen3"),
+        ],
+        "deepseek": [
+            ("DeepSeek V3 - a 685B-parameter, mixture-of-experts model", "deepseek-chat"),
+            ("DeepSeek-R1 - latest iteration of the flagship chat model family from the DeepSeek team.", "deepseek-reasoner"),
         ]
     }
     
@@ -245,7 +252,8 @@ def select_llm_provider() -> tuple[str, str]:
         ("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"),
+        ("DeepSeek", "https://api.deepseek.com/v1") 
     ]
     
     choice = questionary.select(

requirements.txt

@@ -22,3 +22,8 @@ redis
 chainlit
 rich
 questionary
+langchain_anthropic
+langchain_deepseek
+langchain_google_genai
+openai
+sentence-transformers

tradingagents/agents/utils/memory.py

@@ -1,7 +1,9 @@
+import os
+
 import chromadb
 from chromadb.config import Settings
 from openai import OpenAI
-
+from sentence_transformers import SentenceTransformer
 
 class FinancialSituationMemory:
     def __init__(self, name, config):
@@ -9,17 +11,22 @@ class FinancialSituationMemory:
             self.embedding = "nomic-embed-text"
         else:
             self.embedding = "text-embedding-3-small"
-            self.client = OpenAI()
         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"""
-        
+        if os.environ.get("OPENAI_API_KEY"):
-        response = self.client.embeddings.create(
+            client = OpenAI()
-            model=self.embedding, input=text
+            response = client.embeddings.create(
-        )
+                model=self.embedding, input=text
-        return response.data[0].embedding
+            )
+            return response.data[0].embedding
+        else:
+            model = SentenceTransformer("all-MiniLM-L6-v2")
+            embeddings = model.encode(text, convert_to_numpy=True)
+            return embeddings
+
 
     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

@@ -6,6 +6,7 @@ import json
 from datetime import date
 from typing import Dict, Any, Tuple, List, Optional
 
+from langchain_deepseek import ChatDeepSeek
 from langchain_openai import ChatOpenAI
 from langchain_anthropic import ChatAnthropic
 from langchain_google_genai import ChatGoogleGenerativeAI
@@ -67,6 +68,9 @@ 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() == 'deepseek':
+            self.deep_thinking_llm = ChatDeepSeek(model=self.config["deep_think_llm"])
+            self.quick_thinking_llm = ChatDeepSeek(model=self.config["quick_think_llm"])
         else:
             raise ValueError(f"Unsupported LLM provider: {self.config['llm_provider']}")