Of course! A "Python Answer Engine" can mean a few different things, from a simple script that answers specific questions to a complex system powered by Large Language Models (LLMs).
Let's break it down into three levels of increasing complexity:
- Level 1: The Simple Script - A Q&A system based on a pre-defined dictionary of questions and answers.
- Level 2: The Knowledge Graph Engine - A system that can answer questions by querying a structured knowledge base.
- Level 3: The AI-Powered Engine - A modern system that uses an LLM to understand and answer almost any question.
Level 1: The Simple Script (Rule-Based)
This is the most basic approach. You define a set of questions and their corresponding answers. The engine simply matches the user's input to your predefined questions.
How it works:
- Store questions and answers in a dictionary.
- Use a function to compare the user's input to the keys in the dictionary.
- Return the corresponding value if a match is found.
Code Example:
# A simple in-memory database of questions and answers
qa_database = {
"what is the capital of france?": "The capital of France is Paris.",
"who wrote 'romeo and juliet'?": "William Shakespeare wrote 'Romeo and Juliet'.",
"what is the largest planet in our solar system?": "Jupiter is the largest planet in our solar system.",
"how do you say 'hello' in spanish?": "'Hello' in Spanish is 'Hola'.",
}
def simple_answer_engine(user_question):
"""
Finds an answer in the qa_database based on a user's question.
This is a very basic, case-sensitive, and exact-match approach.
"""
# Normalize the question for better matching (e.g., lower case, remove punctuation)
normalized_question = user_question.lower().strip('!?.')
# Directly look up the answer
answer = qa_database.get(normalized_question)
if answer:
return answer
else:
# Provide a default response if no answer is found
return "Sorry, I don't have an answer to that question. Try asking: 'What is the capital of France?'"
# --- Let's use the engine ---
if __name__ == "__main__":
while True:
question = input("Ask me a question (or type 'quit' to exit): ")
if question.lower() == 'quit':
break
answer = simple_answer_engine(question)
print(f"Answer: {answer}\n")
Pros:
- Extremely simple to build and understand.
- Fast and reliable for its specific set of questions.
- No external dependencies.
Cons:
- Brittle: It only works for the exact questions it knows.
- Not scalable: Adding new questions requires changing the code.
- No understanding of context or synonyms.
Level 2: The Knowledge Graph Engine (Semantic Search)
This is a more powerful approach. Instead of simple string matching, we use a Knowledge Graph—a network of "entities" (things) and "relationships" between them. To answer a question, the engine traverses this graph.
How it works:
- Build a Graph: Represent your knowledge as nodes (entities) and edges (relationships). For example:
(Paris)--is_capital_of-->(France). - Parse the Question: Convert the user's natural language question into a query that the graph can understand.
- Query the Graph: Execute the query to find the path or node that answers the question.
Example using networkx library:
import networkx as nx
# 1. Build a simple knowledge graph
G = nx.DiGraph()
# Add entities (nodes)
G.add_node("Paris", type="City")
G.add_node("France", type="Country")
G.add_node("William Shakespeare", type="Person")
G.add_node("Romeo and Juliet", type="Play")
G.add_node("Jupiter", type="Planet")
G.add_node("Solar System", type="System")
# Add relationships (edges)
G.add_edge("Paris", "France", relation="is_capital_of")
G.add_edge("William Shakespeare", "Romeo and Juliet", relation="authored")
G.add_edge("Jupiter", "Solar System", relation="is_in")
def kg_answer_engine(question):
"""
A simplified knowledge graph engine.
It looks for specific patterns in the question to decide which edge to traverse.
"""
question = question.lower()
if "capital" in question and "france" in question:
# Find the city that has an 'is_capital_of' relationship to France
for source, target, data in G.out_edges("France", data=True):
if data.get('relation') == 'is_capital_of':
return f"The capital of France is {source}."
elif "wrote" in question or "author" in question and "romeo and juliet" in question:
# Find the person who 'authored' Romeo and Juliet
for source, target, data in G.in_edges("Romeo and Juliet", data=True):
if data.get('relation') == 'authored':
return f"{source} wrote 'Romeo and Juliet'."
elif "largest planet" in question and "solar system" in question:
# Find the planet in the solar system (this logic is simplified)
for node in G.nodes():
if G.nodes[node].get('type') == 'Planet':
return f"{node} is a planet in our solar system." # A simplified answer
return "Sorry, I can't answer that based on my knowledge graph."
# --- Let's use the engine ---
if __name__ == "__main__":
print(kg_answer_engine("What is the capital of France?"))
print(kg_answer_engine("Who wrote Romeo and Juliet?"))
print(kg_answer_engine("What is the largest planet?"))
Pros:
- More robust than simple matching. Can handle synonyms and rephrased questions if the query logic is good.
- Data is structured and interconnected, leading to more insightful answers.
- Can be extended with more complex graph databases (like Neo4j) for much larger datasets.
Cons:
- Building and maintaining the knowledge graph is a significant effort.
- The query parser is still brittle and requires manual tuning for new question types.
Level 3: The AI-Powered Engine (Using LLMs)
This is the state-of-the-art approach. Instead of hard-coding rules or data, we use a powerful Large Language Model (like GPT-4, Llama, or an open-source alternative) that has been trained on a massive amount of text from the internet.
How it works:
- Prompt Engineering: We design a "prompt" that instructs the LLM on how to behave. This is the most critical part.
- Context Augmentation (Retrieval-Augmented Generation - RAG): To make the LLM more accurate and reduce "hallucinations," we don't just ask it a question. We first search a specific, trusted knowledge base (like your company's documents or a database) for relevant information and include that in the prompt. This is called RAG.
- API Call: We send the final prompt to the LLM API (e.g., OpenAI, Anthropic, or a local model via
Ollama). - Response: The LLM generates a natural language answer based on the instructions and context provided.
Code Example using OpenAI's API and a simple "retriever":
First, install the library: pip install openai
import openai
import os
# It's best practice to use environment variables for your API key
# os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY_HERE"
# openai.api_key = os.environ.get("OPENAI_API_KEY")
# For demonstration, if you don't have a key, you can simulate a response
# This part will be commented out in a real scenario
# client = openai.OpenAI()
# --- A Simple "Retriever" (our knowledge base) ---
# In a real app, this would be a vector database search.
def retrieve_context(question):
"""Simulates finding relevant documents in a knowledge base."""
# This is a very simple keyword-based lookup.
# A real system would use embeddings and semantic search.
knowledge_base = {
"paris": "Paris is the capital and most populous city of France.",
"france": "France is a country in Western Europe with several overseas regions and territories.",
"shakespeare": "William Shakespeare was an English playwright, poet and actor, widely regarded as the greatest writer in the English language.",
"jupiter": "Jupiter is the fifth planet from the Sun and the largest in the Solar System. It is a gas giant."
}
# Find the key with the most overlap with the question
best_match = None
max_overlap = 0
for key, text in knowledge_base.items():
if key in question.lower():
if len(key) > max_overlap:
max_overlap = len(key)
best_match = text
return
