| from llama_index.core.tools import FunctionTool |
| from src.setup.utils import retry |
| from src.controller.customlogger import logging |
| from src.llm.source_llm import LLMCall |
|
|
|
|
| class ChatBotFunctionTools: |
| def __init__(self, llm_type="google"): |
| self.generator = LLMCall(llm_type).get_llm() |
|
|
| @retry(max_retries=5, delay=1) |
| def machine_learning_concept(self, query): |
| logging.info(f"Tool Call: machine_learning_concept('{query}')") |
| prompt = ( |
| "You are a Machine Learning teacher.\n" |
| "Explain the following ML concept in:\n" |
| "The explaination should include geometrical or mathematical intuition" |
| "Try to keep answer crisp and compact" |
| f"User Query: {query}" |
| ) |
| return self.generator.complete(prompt) |
|
|
| @retry(max_retries=5, delay=1) |
| def math_concept(self, query): |
| logging.info(f"Tool Call: math_concept('{query}')") |
| prompt = ( |
| "You are a Math teacher.\n" |
| "Explain the following mathematics behind the Machine Learning algorithm in details with each step by step :\n" |
| "Try to keep answer crisp and compact" |
| f"User Query: {query}" |
| ) |
| return self.generator.complete(prompt) |
|
|
| @retry(max_retries=5, delay=1) |
| def deep_learning_architecture(self, arch): |
| logging.info(f"Tool Call: deep_learning_architecture('{arch}')") |
| prompt = f"Explain the {arch} neural network architecture with diagram description" |
| return self.generator.complete(prompt) |
|
|
| @retry(max_retries=5, delay=1) |
| def visualize_algorithm(self, algo): |
| logging.info(f"Tool Call: visualize_algorithm('{algo}')") |
| prompt = f"Create visualization code that demonstrates how {algo} works" |
| return self.generator.complete(prompt) |
|
|
| @retry(max_retries=5, delay=1) |
| def concept_combiner(self, concepts): |
| logging.info(f"Tool Call: concept_combiner('{concepts}')") |
| prompt = f"Explain the relationship between these concepts: {', '.join(concepts)}" |
| return self.generator.complete(prompt) |
|
|
| @retry(max_retries=5, delay=1) |
| def code_generator(self, query): |
| logging.info(f"Tool Call: copilot('{query}')") |
| prompt = ( |
| "You are a Python Expert.\n" |
| "Give the python source code as asked like copilot or help to debug a particular code block:\n" |
| f"{query}\n" |
| "Keep it compact and dont give theory until you are asked. Explain code blocks only." |
| "Strictly folllow the instructions") |
| return self.generator.complete(prompt) |
|
|
| @retry(max_retries=5, delay=1) |
| def llm_query(self, concepts): |
| logging.info(f"Tool Call: llm_query('{concepts}')") |
| prompt = f"You are an Expert to Answer the following question respond to the best of your knowledge: {', '.join(concepts)}" |
| return self.generator.complete(prompt) |
|
|
| @retry(max_retries=5, delay=1) |
| def get_tools(self): |
| return { |
| "ml_concept": FunctionTool.from_defaults(fn=self.machine_learning_concept), |
| "dl_architecture": FunctionTool.from_defaults(fn=self.deep_learning_architecture), |
| "algo_visualizer": FunctionTool.from_defaults(fn=self.visualize_algorithm), |
| "concept_combiner": FunctionTool.from_defaults(fn=self.concept_combiner), |
| "math_concept": FunctionTool.from_defaults(fn=self.math_concept), |
| "llm_query": FunctionTool.from_defaults(fn=self.llm_query), |
| "code_generator": FunctionTool.from_defaults(fn=self.code_generator) |
| } |
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|
