From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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99 lines
3.3 KiB
99 lines
3.3 KiB
import os |
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import sys |
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import logging |
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import json |
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from typing import List, Optional |
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from twilio.rest import Client |
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from dotenv import load_dotenv |
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import chromadb |
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from agents.planning_agent import PlanningAgent |
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from agents.deals import Opportunity |
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from sklearn.manifold import TSNE |
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import numpy as np |
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# Colors for logging |
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BG_BLUE = '\033[44m' |
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WHITE = '\033[37m' |
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RESET = '\033[0m' |
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# Colors for plot |
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CATEGORIES = ['Appliances', 'Automotive', 'Cell_Phones_and_Accessories', 'Electronics','Musical_Instruments', 'Office_Products', 'Tools_and_Home_Improvement', 'Toys_and_Games'] |
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COLORS = ['red', 'blue', 'brown', 'orange', 'yellow', 'green' , 'purple', 'cyan'] |
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def init_logging(): |
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root = logging.getLogger() |
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root.setLevel(logging.INFO) |
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handler = logging.StreamHandler(sys.stdout) |
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handler.setLevel(logging.INFO) |
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formatter = logging.Formatter( |
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"[%(asctime)s] [Agents] [%(levelname)s] %(message)s", |
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datefmt="%Y-%m-%d %H:%M:%S %z", |
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) |
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handler.setFormatter(formatter) |
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root.addHandler(handler) |
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class DealAgentFramework: |
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DB = "products_vectorstore" |
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MEMORY_FILENAME = "memory.json" |
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def __init__(self): |
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init_logging() |
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load_dotenv() |
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client = chromadb.PersistentClient(path=self.DB) |
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self.memory = self.read_memory() |
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self.collection = client.get_or_create_collection('products') |
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self.planner = None |
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def init_agents_as_needed(self): |
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if not self.planner: |
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self.log("Initializing Agent Framework") |
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self.planner = PlanningAgent(self.collection) |
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self.log("Agent Framework is ready") |
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def read_memory(self) -> List[Opportunity]: |
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if os.path.exists(self.MEMORY_FILENAME): |
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with open(self.MEMORY_FILENAME, "r") as file: |
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data = json.load(file) |
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opportunities = [Opportunity(**item) for item in data] |
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return opportunities |
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return [] |
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def write_memory(self) -> None: |
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data = [opportunity.dict() for opportunity in self.memory] |
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with open(self.MEMORY_FILENAME, "w") as file: |
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json.dump(data, file, indent=2) |
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def log(self, message: str): |
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text = BG_BLUE + WHITE + "[Agent Framework] " + message + RESET |
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logging.info(text) |
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def run(self) -> List[Opportunity]: |
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self.init_agents_as_needed() |
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logging.info("Kicking off Planning Agent") |
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result = self.planner.plan(memory=self.memory) |
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logging.info(f"Planning Agent has completed and returned: {result}") |
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if result: |
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self.memory.append(result) |
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self.write_memory() |
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return self.memory |
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@classmethod |
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def get_plot_data(cls, max_datapoints=10000): |
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client = chromadb.PersistentClient(path=cls.DB) |
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collection = client.get_or_create_collection('products') |
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result = collection.get(include=['embeddings', 'documents', 'metadatas'], limit=max_datapoints) |
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vectors = np.array(result['embeddings']) |
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documents = result['documents'] |
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categories = [metadata['category'] for metadata in result['metadatas']] |
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colors = [COLORS[CATEGORIES.index(c)] for c in categories] |
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tsne = TSNE(n_components=3, random_state=42, n_jobs=-1) |
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reduced_vectors = tsne.fit_transform(vectors) |
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return documents, reduced_vectors, colors |
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if __name__=="__main__": |
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DealAgentFramework().run() |
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