Algorithm to extract conversation thread and send to OpenAI API for embeddings:
import sqlite3
from openai import OpenAIAPI
def fetch_conversation(conversation_id):
# Create a DB connection
conn = sqlite3.connect('chatgpt_conversation_db.db')
cursor = conn.cursor()
# SQL query to retrieve a conversation based on id
query = """SELECT responses.prompt, responses.response
FROM responses
WHERE responses.conversation_id = ?"""
# Execute the query
cursor.execute(query, (conversation_id,))
# Fetch results
conversation = cursor.fetchall()
conn.close()
return conversation
def send_to_openai_api(conversation):
convo_text = "
".join([f"User: {c[0]}
ChatGPT: {c[1]}" for c in conversation])
openai_api = OpenAIAPI("your-api-key")
embeddings = openai_api.encode(convo_text)
return embeddings
Potential usages for embeddings and chat DB:
Conversation Classification: We can use the embeddings to train machine learning models that classify the conversations by their content or sentiment.
Topic Modeling: The embeddings can be used to conduct topic modeling to understand the main topics discussed during the conversation.
Information Retrieval: The chat database could be utilized to build a retrieval-based chatbot that fetches relevant information based on context.
User Behavior Understanding: Analyzing chat logs can help in understanding user behavior, preferences, and interaction patterns.
Approaches to enrich the database:
Adding metadata: Information like user demographics, time of conversation, etc., can add value to the analyses.
Adding conversation context: Adding data about the context of the conversation can provide help in retrieving and understanding the conversation better.
Creating topic system:
We can introduce a table "topics" with columns for "topic_id" and "topic_name". We add a "topic_id" column to "conversations" table. We then use a topic modeling algorithm like LDA (Latent Dirichlet Allocation) on conversation text to find main topics and link conversations to these topics.
CREATE TABLE [topics] (
[id] INTEGER PRIMARY KEY,
[name] TEXT
);
ALTER TABLE [conversations]
ADD COLUMN [topic_id] INTEGER REFERENCES [topics]([id]);
Then to retrieve chats based on their topic, we can query:
SELECT *
FROM conversations, responses, topics
WHERE conversations.id = responses.conversation_id
AND conversations.topic_id = topics.id
AND topics.name = ?;
Algorithm to extract conversation thread and send to OpenAI API for embeddings:
Potential usages for embeddings and chat DB:
Conversation Classification: We can use the embeddings to train machine learning models that classify the conversations by their content or sentiment.
Topic Modeling: The embeddings can be used to conduct topic modeling to understand the main topics discussed during the conversation.
Information Retrieval: The chat database could be utilized to build a retrieval-based chatbot that fetches relevant information based on context.
User Behavior Understanding: Analyzing chat logs can help in understanding user behavior, preferences, and interaction patterns.
Approaches to enrich the database:
Adding metadata: Information like user demographics, time of conversation, etc., can add value to the analyses.
Adding conversation context: Adding data about the context of the conversation can provide help in retrieving and understanding the conversation better.
Creating topic system:
We can introduce a table "topics" with columns for "topic_id" and "topic_name". We add a "topic_id" column to "conversations" table. We then use a topic modeling algorithm like LDA (Latent Dirichlet Allocation) on conversation text to find main topics and link conversations to these topics.
Then to retrieve chats based on their topic, we can query: