Add SQLite database support for conversation history

- Introduce SQLite database to store conversation history
- Implement functions to save and load conversations from the database
- Replace previous file-based history storage with database-backed solution
- Add new command to list and resume previous conversations
- Update chat logic to generate topics for saved conversations
- Add database initialization and management functions
- Modify command line interface to support resuming conversations
This commit is contained in:
Hayden Johnson 2025-05-17 10:18:50 -07:00
parent 892fdc317e
commit 780b755837

View file

@ -3,8 +3,8 @@
import argparse
import os
import sys
import datetime
import sqlite3
import warnings
import json
from ollama import Client
@ -19,6 +19,10 @@ from prompt_toolkit.key_binding import KeyBindings
from prompt_toolkit import PromptSession
default_assistant = "qwen3:14b"
default_topic_llm = "qwen3:1.7b"
warnings.filterwarnings("ignore")
class AIAssistant:
def __init__(self, server="http://localhost:11434", model="qwen3:14b"):
@ -76,14 +80,32 @@ class AIAssistant:
else:
self.history = [self.system_prompt()]
def _stringify_history(self):
s = ""
for item in self.history:
s += item["role"] + ":\n" + item["content"]
return s
def _strip_thinking_tags(self, text: str):
start_idx = text.find("<think>")
if (start_idx < 0):
return text
end_idx = text.find("</think>") + len("</think>")
stripped_text = text[end_idx:]
stripped_text = stripped_text.strip()
return stripped_text
def save_history(self):
"""Save the current conversation to the database with a generated topic."""
# Only save if this is the first user message
if len(self.history) == 3:
# Generate a topic using the AI
system_prompt = self.system_prompt()
user_prompt = "Generate a concise, descriptive topic for this conversation based on the following content:\n"
topic = self.client.chat(model=self.model, messages=[system_prompt, {"role": "user", "content": user_prompt}], stream=False)['message']['content'].strip()
user_prompt = "/no_think Generate a concise, 5 word descriptive topic for this conversation based on the following content. Do not use markdown, just plaintext. KEEP IT TO 5 WORDS OR LESS.:\n\n"
user_prompt += self._stringify_history()
topic = self.client.chat(model=default_topic_llm, messages=[system_prompt, {"role": "user", "content": user_prompt}], stream=False)['message']['content'].strip()
topic = self._strip_thinking_tags(topic)
self._save_to_db(topic)
else:
# For subsequent messages, we can update the topic in the future
@ -93,6 +115,7 @@ class AIAssistant:
"""Load a conversation from the database by ID. If no ID, start a new one."""
if conversation_id:
self._load_from_db(conversation_id)
print(self._stringify_history())
else:
self.history = [self.system_prompt()]
@ -223,15 +246,25 @@ class InputHandler:
except:
return
def follow_up(self, args, query):
if type(args.follow_up) is str:
second_input = args.follow_up
else:
second_input = self.improved_input()
query += f'\n\nUser Question:\n{second_input}'
return query
def handle_piped_input(self, args):
all_input = sys.stdin.read()
query = f'Use the following context to answer the question. There will be no follow up questions from the user so make sure your answer is complete:\n{all_input}\n'
if args.copy:
query += 'Answer the question using a codeblock for any code or shell scripts\n'
if args.follow_up:
second_input = self.improved_input()
query += f'\n{second_input}'
query = self.follow_up(args, query)
result = self.assistant.chat(query, stream=False)
result = self.assistant._strip_thinking_tags(result)
print(result)
blocks = self.extract_code_block(result)
if args.copy and len(blocks):
self.copy_string_to_clipboard(blocks[0])
@ -256,6 +289,14 @@ Description:\n
self.assistant.history = [self.assistant.system_prompt()]
self.arg_shell(args)
exit()
if args.follow_up:
query = "Provide a complete answer to the user's question, there will be no follow up questions from the user.\n\n"
query = self.follow_up(args, query)
result = self.assistant.chat(query, stream=False)
result = self.assistant._strip_thinking_tags(result)
print(result)
exit()
print("\033[91massistant\033[0m: Type your message (press Ctrl+D to send):")
while True:
@ -313,7 +354,8 @@ class CommandParser:
'/clear': self.handle_clear,
'/clipboard': None,
'/exit': self.handle_exit,
'/copy': self.handle_copy
'/copy': self.handle_copy,
'/list': self.handle_list
}
def parse_commands(self, text):
@ -368,6 +410,36 @@ class CommandParser:
self.input_handler.copy_string_to_clipboard(block)
return True
def handle_list(self):
assistant = self.assistant
db_path = assistant.db_path
# Connect to the database and fetch saved conversations
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
cursor.execute("SELECT id, topic FROM conversations")
rows = cursor.fetchall()
conn.close()
if not rows:
print("No saved conversations.")
return
# Display saved conversations
print("\nSaved conversations:")
for row in rows:
print(f"ID: {row[0]}, Topic: {row[1]}")
# Prompt user to select an ID
choice = input("\nEnter conversation ID to load (or 'q' to quit): ").strip()
if choice.lower() == 'q':
return
try:
conv_id = int(choice)
assistant.load_history(conversation_id=conv_id)
print(f"\nLoaded conversation with ID {conv_id}.")
except ValueError:
print("Invalid ID. Please enter a number.")
except Exception as e:
print(f"Error loading conversation: {e}")
def handle_exit(self):
sys.exit(0)