145 lines
5.3 KiB
Python
145 lines
5.3 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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import os
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from google import genai
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class FinancialSituationMemory:
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def __init__(self, name, config):
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self.config = config
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self.backend_url = config["backend_url"]
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# Determine embedding configuration based on provider
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if self.backend_url == "http://localhost:11434/v1":
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# Ollama
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self.embedding_model = "nomic-embed-text"
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self.use_openai_api = True
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elif "openai.com" in self.backend_url:
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# OpenAI
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self.embedding_model = "text-embedding-3-small"
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self.use_openai_api = True
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elif "generativelanguage.googleapis.com" in self.backend_url:
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# Google Gemini API
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self.embedding_model = "gemini-embedding-exp-03-07" # Use Google's embedding model
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self.use_openai_api = False
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else:
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# Default to OpenAI-compatible
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self.embedding_model = "text-embedding-3-small"
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self.use_openai_api = True
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# Initialize clients
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if self.use_openai_api:
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self.client = OpenAI(base_url=self.backend_url)
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else:
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self.client = genai.Client()
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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 embedding for a text using the appropriate API"""
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if self.use_openai_api:
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# Use OpenAI-compatible API
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response = self.client.embeddings.create(
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model=self.embedding_model, input=text
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)
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return response.data[0].embedding
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else:
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response = self.client.models.embed_content(
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model=self.embedding_model,
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contents=text
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)
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return response.embeddings[0].values
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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 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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