112 lines
4.2 KiB
Python
112 lines
4.2 KiB
Python
import chromadb
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from chromadb.config import Settings
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from openai import OpenAI
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class FinancialSituationMemory:
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def __init__(self, name, config):
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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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else:
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self.embedding = "text-embedding-3-small"
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self.client = OpenAI(base_url=config["backend_url"])
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self.chroma_client = chromadb.Client(Settings(allow_reset=True))
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self.situation_collection = self.chroma_client.create_collection(name=name)
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def get_embedding(self, text):
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"""Get OpenAI embedding for a text"""
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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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return response.data[0].embedding
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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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embeddings.append(self.get_embedding(situation))
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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 OpenAI 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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# (
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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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