From d40c0dd000d230caac438f5b3384d3c45d5046a2 Mon Sep 17 00:00:00 2001
From: Ivan Lee <84584280+ivanleekk@users.noreply.github.com>
Date: Wed, 20 Aug 2025 00:56:12 +0800
Subject: [PATCH] fix: llamacpp

---
 .python-version                      |  2 +-
 main.py                              |  4 +--
 tradingagents/agents/utils/memory.py | 47 +++++++++++++++++++++++-----
 tradingagents/default_config.py      |  2 +-
 tradingagents/graph/trading_graph.py |  4 ++-
 5 files changed, 47 insertions(+), 12 deletions(-)

diff --git a/.python-version b/.python-version
index fdcfcfdf..7c7a975f 100644
--- a/.python-version
+++ b/.python-version
@@ -1 +1 @@
-3.12
\ No newline at end of file
+3.10
\ No newline at end of file
diff --git a/main.py b/main.py
index 057781cd..84c90366 100644
--- a/main.py
+++ b/main.py
@@ -8,7 +8,7 @@ load_dotenv()
 # Create a custom config
 config = DEFAULT_CONFIG.copy()
 config["llm_provider"] = "llamacpp"  # Use a different model
-config["backend_url"] = "http://localhost:8000/v1"  # Use a different backend
+config["backend_url"] = "http://localhost:8080/v1"  # Use a different backend
 config["deep_think_llm"] = (
     "models/Qwen3-4B-Thinking-2507-UD-Q8_K_XL.gguf"  # Use a different model
 )
@@ -22,7 +22,7 @@ config["online_tools"] = True  # Increase debate rounds
 ta = TradingAgentsGraph(debug=True, config=config)
 
 # forward propagate
-_, decision = ta.propagate("SPY", "2024-07-09")
+_, decision = ta.propagate("AAPL", "2025-08-18")
 print(decision)
 
 # Memorize mistakes and reflect
diff --git a/tradingagents/agents/utils/memory.py b/tradingagents/agents/utils/memory.py
index 69b8ab8c..3751ee4f 100644
--- a/tradingagents/agents/utils/memory.py
+++ b/tradingagents/agents/utils/memory.py
@@ -5,8 +5,38 @@ from openai import OpenAI
 
 class FinancialSituationMemory:
     def __init__(self, name, config):
-        if config["backend_url"] == "http://localhost:11434/v1":
+        if config["llm_provider"] == "ollama":
             self.embedding = "nomic-embed-text"
+        elif config["llm_provider"] == "llamacpp":
+            try:
+                from langchain_community.embeddings.llamacpp import LlamaCppEmbeddings
+            except Exception:
+                LlamaCppEmbeddings = None
+            # path to your local gguf/ggml model file (absolute recommended)
+            self.embedding = config.get(
+                "embedding_model_path", "models/nomic-embed-text-v2-moe.f32.gguf"
+            )
+            if LlamaCppEmbeddings is None:
+                raise RuntimeError(
+                    "LlamaCppEmbeddings not available; install llama-cpp-python and langchain_community."
+                )
+            # instantiate the LlamaCpp embeddings wrapper
+            self.embeddings_model = LlamaCppEmbeddings(
+                model_path=self.embedding,
+                n_ctx=2048,
+                n_parts=-1,
+                seed=0,
+                f16_kv=True,
+                logits_all=False,
+                vocab_only=False,
+                use_mlock=False,
+                n_threads=4,
+                n_batch=512,
+                n_gpu_layers=0,
+                verbose=False,
+                device="cpu",
+            )
+            self.client = None
         else:
             self.embedding = "text-embedding-3-small"
         self.client = OpenAI(base_url=config["backend_url"])
@@ -14,12 +44,15 @@ class FinancialSituationMemory:
         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 (provider-dependent)"""
+        if self.embeddings_model is not None:
+            # LlamaCppEmbeddings implements embed_documents(list[str]) -> list[list[float]]
+            emb = self.embeddings_model.embed_documents([text])
+            return emb[0]
+        else:
+            # OpenAI/ollama path using OpenAI client
+            response = self.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)"""
diff --git a/tradingagents/default_config.py b/tradingagents/default_config.py
index 921f25d4..a21dd8fb 100644
--- a/tradingagents/default_config.py
+++ b/tradingagents/default_config.py
@@ -12,7 +12,7 @@ DEFAULT_CONFIG = {
     "llm_provider": "ollama",
     "deep_think_llm": "llama3.2",
     "quick_think_llm": "llama3.2",
-    "backend_url": "http://localhost:11434/v1",
+    "backend_url": "http://localhost:11434",
     # Debate and discussion settings
     "max_debate_rounds": 1,
     "max_risk_discuss_rounds": 1,
diff --git a/tradingagents/graph/trading_graph.py b/tradingagents/graph/trading_graph.py
index b3b00a72..38fef3f5 100644
--- a/tradingagents/graph/trading_graph.py
+++ b/tradingagents/graph/trading_graph.py
@@ -77,7 +77,9 @@ class TradingAgentsGraph:
             )
         elif self.config["llm_provider"].lower() == "ollama":
             self.deep_thinking_llm = ChatOllama(model=self.config["deep_think_llm"])
-            self.quick_thinking_llm = ChatOllama(model=self.config["quick_think_llm"])
+            self.quick_thinking_llm = ChatOllama(
+                model=self.config["quick_think_llm"],
+            )
         elif self.config["llm_provider"].lower() == "anthropic":
             self.deep_thinking_llm = ChatAnthropic(
                 model=self.config["deep_think_llm"], base_url=self.config["backend_url"]