diff --git a/pyproject.toml b/pyproject.toml index 48a5553f..beb1235b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,6 +26,7 @@ dependencies = [ "redis>=6.2.0", "requests>=2.32.4", "rich>=14.0.0", + "sentence-transformers>=5.1.1", "setuptools>=80.9.0", "stockstats>=0.6.5", "tqdm>=4.67.1", diff --git a/tradingagents/agents/utils/memory.py b/tradingagents/agents/utils/memory.py index 69b8ab8c..1ced293d 100644 --- a/tradingagents/agents/utils/memory.py +++ b/tradingagents/agents/utils/memory.py @@ -1,25 +1,81 @@ import chromadb from chromadb.config import Settings -from openai import OpenAI +import os class FinancialSituationMemory: def __init__(self, name, config): - if config["backend_url"] == "http://localhost:11434/v1": - self.embedding = "nomic-embed-text" + # Use local embeddings for all providers - no external API dependency + self.use_local_embeddings = config.get("use_local_embeddings", True) + + if self.use_local_embeddings: + try: + from sentence_transformers import SentenceTransformer + # Use a good general-purpose model for financial text + self.embedding_model = SentenceTransformer('all-MiniLM-L6-v2') + self.embedding_type = "local" + print(f"✅ Using local embeddings with sentence-transformers") + except ImportError: + print("⚠️ sentence-transformers not found. Install with: pip install sentence-transformers") + print("Falling back to ChromaDB's default embeddings...") + self.embedding_model = None + self.embedding_type = "chromadb_default" else: - self.embedding = "text-embedding-3-small" - self.client = OpenAI(base_url=config["backend_url"]) + # Legacy API-based embeddings (kept for backward compatibility) + from openai import OpenAI + if config["backend_url"] == "http://localhost:11434/v1": + self.embedding = "nomic-embed-text" + self.client = OpenAI(base_url=config["backend_url"]) + else: + self.embedding = "text-embedding-3-small" + if "openrouter.ai" in config["backend_url"]: + openai_api_key = os.getenv("OPENAI_API_KEY") + if not openai_api_key: + raise ValueError("❌ OPENAI_API_KEY required for API-based embeddings with OpenRouter") + self.client = OpenAI(api_key=openai_api_key) + else: + api_key = None + if config.get("llm_provider") == "openai": + api_key = os.getenv("OPENAI_API_KEY") + self.client = OpenAI(base_url=config["backend_url"], api_key=api_key) + self.embedding_type = "api" + self.chroma_client = chromadb.Client(Settings(allow_reset=True)) - self.situation_collection = self.chroma_client.create_collection(name=name) + + # Create collection with or without custom embedding function + if self.embedding_type == "chromadb_default": + # Let ChromaDB handle embeddings with its default function + self.situation_collection = self.chroma_client.create_collection(name=name) + else: + # We'll handle embeddings ourselves + self.situation_collection = self.chroma_client.create_collection( + name=name, + metadata={"hnsw:space": "cosine"} # Use cosine similarity + ) 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 using local or API-based models""" + try: + if self.embedding_type == "local": + # Use local sentence-transformers model + embedding = self.embedding_model.encode(text, convert_to_tensor=False) + return embedding.tolist() if hasattr(embedding, 'tolist') else embedding + + elif self.embedding_type == "chromadb_default": + # ChromaDB will handle embeddings automatically, return None + return None + + else: # API-based embeddings + response = self.client.embeddings.create( + model=self.embedding, input=text + ) + if hasattr(response, 'data') and len(response.data) > 0: + return response.data[0].embedding + else: + raise ValueError(f"Unexpected response format from embeddings API: {type(response)}") + + except Exception as e: + raise RuntimeError(f"Failed to get embedding for text: {str(e)}") def add_situations(self, situations_and_advice): """Add financial situations and their corresponding advice. Parameter is a list of tuples (situation, rec)""" @@ -35,24 +91,46 @@ class FinancialSituationMemory: situations.append(situation) advice.append(recommendation) ids.append(str(offset + i)) - embeddings.append(self.get_embedding(situation)) + + # Only compute embeddings if not using ChromaDB's default + if self.embedding_type != "chromadb_default": + embeddings.append(self.get_embedding(situation)) - self.situation_collection.add( - documents=situations, - metadatas=[{"recommendation": rec} for rec in advice], - embeddings=embeddings, - ids=ids, - ) + # Add to collection with or without custom embeddings + if self.embedding_type == "chromadb_default": + # Let ChromaDB compute embeddings automatically + self.situation_collection.add( + documents=situations, + metadatas=[{"recommendation": rec} for rec in advice], + ids=ids, + ) + else: + # Use our custom embeddings + self.situation_collection.add( + documents=situations, + metadatas=[{"recommendation": rec} for rec in advice], + embeddings=embeddings, + ids=ids, + ) def get_memories(self, current_situation, n_matches=1): - """Find matching recommendations using OpenAI embeddings""" - query_embedding = self.get_embedding(current_situation) - - results = self.situation_collection.query( - query_embeddings=[query_embedding], - n_results=n_matches, - include=["metadatas", "documents", "distances"], - ) + """Find matching recommendations using local or API-based embeddings""" + + if self.embedding_type == "chromadb_default": + # Use ChromaDB's built-in embeddings - query with text directly + results = self.situation_collection.query( + query_texts=[current_situation], + n_results=n_matches, + include=["metadatas", "documents", "distances"], + ) + else: + # Use our custom embeddings + query_embedding = self.get_embedding(current_situation) + results = self.situation_collection.query( + query_embeddings=[query_embedding], + n_results=n_matches, + include=["metadatas", "documents", "distances"], + ) matched_results = 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