192 lines
8.2 KiB
Python
192 lines
8.2 KiB
Python
import chromadb
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from chromadb.config import Settings
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import os
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class FinancialSituationMemory:
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def __init__(self, name, config):
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# Use local embeddings for all providers - no external API dependency
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self.use_local_embeddings = config.get("use_local_embeddings", True)
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if self.use_local_embeddings:
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try:
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from sentence_transformers import SentenceTransformer
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# Use a good general-purpose model for financial text
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self.embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
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self.embedding_type = "local"
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print(f"✅ Using local embeddings with sentence-transformers")
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except ImportError:
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print("⚠️ sentence-transformers not found. Install with: pip install sentence-transformers")
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print("Falling back to ChromaDB's default embeddings...")
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self.embedding_model = None
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self.embedding_type = "chromadb_default"
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else:
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# Legacy API-based embeddings (kept for backward compatibility)
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from openai import OpenAI
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if config["backend_url"] == "http://localhost:11434/v1":
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self.embedding = "nomic-embed-text"
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self.client = OpenAI(base_url=config["backend_url"])
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else:
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self.embedding = "text-embedding-3-small"
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if "openrouter.ai" in config["backend_url"]:
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openai_api_key = os.getenv("OPENAI_API_KEY")
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if not openai_api_key:
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raise ValueError("❌ OPENAI_API_KEY required for API-based embeddings with OpenRouter")
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self.client = OpenAI(api_key=openai_api_key)
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else:
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api_key = None
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if config.get("llm_provider") == "openai":
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api_key = os.getenv("OPENAI_API_KEY")
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self.client = OpenAI(base_url=config["backend_url"], api_key=api_key)
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self.embedding_type = "api"
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self.chroma_client = chromadb.Client(Settings(allow_reset=True))
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# Create collection with or without custom embedding function
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if self.embedding_type == "chromadb_default":
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# Let ChromaDB handle embeddings with its default function
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self.situation_collection = self.chroma_client.create_collection(name=name)
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else:
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# We'll handle embeddings ourselves
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self.situation_collection = self.chroma_client.create_collection(
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name=name,
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metadata={"hnsw:space": "cosine"} # Use cosine similarity
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)
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def get_embedding(self, text):
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"""Get embedding for a text using local or API-based models"""
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try:
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if self.embedding_type == "local":
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# Use local sentence-transformers model
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embedding = self.embedding_model.encode(text, convert_to_tensor=False)
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return embedding.tolist() if hasattr(embedding, 'tolist') else embedding
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elif self.embedding_type == "chromadb_default":
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# ChromaDB will handle embeddings automatically, return None
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return None
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else: # API-based embeddings
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response = self.client.embeddings.create(
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model=self.embedding, input=text
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)
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if hasattr(response, 'data') and len(response.data) > 0:
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return response.data[0].embedding
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else:
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raise ValueError(f"Unexpected response format from embeddings API: {type(response)}")
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except Exception as e:
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raise RuntimeError(f"Failed to get embedding for text: {str(e)}")
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def add_situations(self, situations_and_advice):
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"""Add financial situations and their corresponding advice. Parameter is a list of tuples (situation, rec)"""
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situations = []
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advice = []
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ids = []
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embeddings = []
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offset = self.situation_collection.count()
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for i, (situation, recommendation) in enumerate(situations_and_advice):
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situations.append(situation)
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advice.append(recommendation)
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ids.append(str(offset + i))
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# Only compute embeddings if not using ChromaDB's default
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if self.embedding_type != "chromadb_default":
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embeddings.append(self.get_embedding(situation))
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# Add to collection with or without custom embeddings
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if self.embedding_type == "chromadb_default":
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# Let ChromaDB compute embeddings automatically
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self.situation_collection.add(
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documents=situations,
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metadatas=[{"recommendation": rec} for rec in advice],
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ids=ids,
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)
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else:
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# Use our custom embeddings
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self.situation_collection.add(
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documents=situations,
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metadatas=[{"recommendation": rec} for rec in advice],
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embeddings=embeddings,
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ids=ids,
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)
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def get_memories(self, current_situation, n_matches=1):
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"""Find matching recommendations using local or API-based embeddings"""
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if self.embedding_type == "chromadb_default":
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# Use ChromaDB's built-in embeddings - query with text directly
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results = self.situation_collection.query(
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query_texts=[current_situation],
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n_results=n_matches,
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include=["metadatas", "documents", "distances"],
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)
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else:
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# Use our custom embeddings
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query_embedding = self.get_embedding(current_situation)
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results = self.situation_collection.query(
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query_embeddings=[query_embedding],
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n_results=n_matches,
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include=["metadatas", "documents", "distances"],
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)
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matched_results = []
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for i in range(len(results["documents"][0])):
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matched_results.append(
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{
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"matched_situation": results["documents"][0][i],
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"recommendation": results["metadatas"][0][i]["recommendation"],
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"similarity_score": 1 - results["distances"][0][i],
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}
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)
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return matched_results
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if __name__ == "__main__":
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# Example usage
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matcher = FinancialSituationMemory()
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# Example data
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example_data = [
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(
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"High inflation rate with rising interest rates and declining consumer spending",
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"Consider defensive sectors like consumer staples and utilities. Review fixed-income portfolio duration.",
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),
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(
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"Tech sector showing high volatility with increasing institutional selling pressure",
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"Reduce exposure to high-growth tech stocks. Look for value opportunities in established tech companies with strong cash flows.",
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),
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(
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"Strong dollar affecting emerging markets with increasing forex volatility",
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"Hedge currency exposure in international positions. Consider reducing allocation to emerging market debt.",
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),
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(
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"Market showing signs of sector rotation with rising yields",
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"Rebalance portfolio to maintain target allocations. Consider increasing exposure to sectors benefiting from higher rates.",
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),
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]
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# Add the example situations and recommendations
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matcher.add_situations(example_data)
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# Example query
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current_situation = """
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Market showing increased volatility in tech sector, with institutional investors
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reducing positions and rising interest rates affecting growth stock valuations
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"""
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try:
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recommendations = matcher.get_memories(current_situation, n_matches=2)
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for i, rec in enumerate(recommendations, 1):
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print(f"\nMatch {i}:")
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print(f"Similarity Score: {rec['similarity_score']:.2f}")
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print(f"Matched Situation: {rec['matched_situation']}")
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print(f"Recommendation: {rec['recommendation']}")
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except Exception as e:
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print(f"Error during recommendation: {str(e)}")
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