mirror of
https://github.com/farcasclaudiu/TradingAgents.git
synced 2026-06-22 09:01:24 +03:00
maxer137
99789f9cd1
This aims to offer alternative OpenAI capable api's. This offers people to experiment with running the application locally
114 lines
4.2 KiB
Python
114 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["openai_backend"] == "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-ada-002"
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self.client = OpenAI(base_url=config["openai_backend"])
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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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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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