I'm building my multimodal system with modular AI models. I then realized that I need long-term memory to make this much more effective as a companion bot. So, I asked ChatGpt to solve this problem.
I want this approach to also have long-term memory, so if I ask it what did we do last month it could summarize that month. Is this possible?
ChatGPT said:
ChatGPT
Memory updated
Yes, it's possible to implement long-term memory in an AI system so that it can remember and summarize past interactions, even over longer periods like a month. To achieve this, we can build a system that saves conversation logs, extracts key events or topics, and stores them in a retrievable, structured format. When you ask, "What did we do last month?" the system would access this memory and summarize the important points.
Key Steps to Implement Long-Term Memory:
Memory Storage:
You need a persistent database to store the conversation history. This can be structured in a way that makes it easy to retrieve past conversations based on the timestamp or content.
You could use a simple database like SQLite or a cloud-based solution for more scalability (e.g., Firebase, AWS DynamoDB).
Summarization:
For each conversation, the system can store summaries or important keywords so that it can later condense a month’s worth of data into key points.
You can use NLP-based summarization models to extract concise summaries from each conversation.
Indexing by Time and Topic:
Index the data by date and topic. This way, if you ask about specific time frames ("last month"), the system can filter relevant conversations from that period.
Conversations could also be categorized (e.g., personal, philosophical, work) for better context retrieval.
Recall System:
When you ask a question about past events, like "What did we discuss last month?", the system can scan its memory for that time frame, retrieve key topics, and provide a high-level summary.
Example of How to Structure the System:
Step 1: Memory Logging
Whenever you have a conversation, the system can log it like this:
python
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import datetime
import sqlite3
class MemoryManager:
def __init__(self):
# Initialize SQLite database
self.conn = sqlite3.connect('conversations.db')
self.create_table()
def create_table(self):
# Create a table to store conversation logs
with self.conn:
self.conn.execute('''CREATE TABLE IF NOT EXISTS conversations
(id INTEGER PRIMARY KEY AUTOINCREMENT,
timestamp TEXT,
summary TEXT,
full_text TEXT)''')
def log_conversation(self, conversation_text, summary):
"""Log the conversation with a timestamp and summary."""
timestamp = datetime.datetime.now().isoformat()
with self.conn:
self.conn.execute("INSERT INTO conversations (timestamp, summary, full_text) VALUES (?, ?, ?)",
(timestamp, summary, conversation_text))
def retrieve_conversations(self, start_date, end_date):
"""Retrieve conversations within a specific time frame."""
with self.conn:
cur = self.conn.cursor()
cur.execute("SELECT timestamp, summary FROM conversations WHERE timestamp BETWEEN ? AND ?",
(start_date, end_date))
return cur.fetchall()
# Example usage
memory_manager = MemoryManager()
# Log conversation
conversation_text = "We talked about the new AI project and discussed ethical implications."
summary = "Discussion on AI project and ethics."
memory_manager.log_conversation(conversation_text, summary)
# Retrieve conversation from last month
start_date = '2023-09-01T00:00:00'
end_date = '2023-09-30T23:59:59'
logs = memory_manager.retrieve_conversations(start_date, end_date)
for log in logs:
print(log)
Step 2: Summarizing Conversations
You can use an NLP model like BART or GPT-based summarizers to generate summaries of each conversation as they are logged:
python
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from transformers import pipeline
# Initialize the summarization model
summarizer = pipeline("summarization")
def generate_summary(text):
"""Generate a summary of the conversation."""
summary = summarizer(text, max_length=50, min_length=25, do_sample=False)
return summary[0]['summary_text']
# Example usage
conversation = "Today we discussed our plans for launching the new product and how it might impact the market. ..."
summary = generate_summary(conversation)
print(summary) # This will be saved in the memory log.
Step 3: Retrieving and Summarizing a Time Frame
To answer questions like "What did we discuss last month?" you can retrieve and combine conversations from that time period, then summarize them.
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def summarize_monthly_conversations(memory_manager, month, year):
"""Summarize all conversations from a specific month."""
start_date = f'{year}-{month:02d}-01T00:00:00'
end_date = f'{year}-{month:02d}-30T23:59:59'
# Retrieve all conversations from the database
logs = memory_manager.retrieve_conversations(start_date, end_date)
# Combine summaries
combined_text = " ".join([log[1] for log in logs])
# Summarize the entire month
monthly_summary = generate_summary(combined_text)
return monthly_summary
# Example usage for summarizing September 2023
monthly_summary = summarize_monthly_conversations(memory_manager, 9, 2023)
print(monthly_summary)
Key Features of This System:
Long-Term Memory: All conversations are logged and indexed by time, allowing for easy retrieval of past interactions.
Automatic Summarization: Each conversation is automatically summarized when logged, allowing the system to quickly summarize larger time periods like weeks or months.
Contextual Understanding: By combining summaries from a time frame, the system can give meaningful overviews, answering queries like "What did we discuss last month?" in a concise manner.
Possible Expansions:
Topic-Based Search: Allow the system to search for specific topics (e.g., "What did we discuss about AI last month?").
User-Defined Tags: Enable the user to label or highlight important conversations for easier retrieval later.
Cloud-Based Storage: For scalability and availability across devices, you can store the memory in a cloud database like Firebase or DynamoDB.
This will give your system the ability to maintain a long-term, searchable memory and respond to queries about past interactions efficiently. Would you like to explore further customization of this memory system?
You said:
It's okay, but I just realized that the system should have a sense of its past in order to have a more realistic conversation. For example, if a User says, "I'm going to raise that fence another three feet, " the AI responds, "But remember you said last year that the city codes prevent you from raising that fence." Is this possible?
ChatGPT said:
ChatGPT
Memory updated
Yes, it's possible to give the AI system a sense of its past, so it can remember previous interactions and bring them up naturally in future conversations. This would make the system feel more realistic and responsive. To implement this, the AI needs to track conversation history in more detail, including facts, decisions, and constraints mentioned by the user, and use these inferences when generating responses.
Here’s how this can be done:
Key Components:
Memory Database:
Store key facts and statements from conversations in a memory database. For instance, if the user says something important like “city codes prevent raising the fence,” the system would store this information as a retrievable fact.
Contextual Fact Retrieval:
Whenever a new conversation happens, the AI analyzes the current context (e.g., user talking about fences) and retrieves relevant facts from past conversations. It then uses these facts in its responses.
Natural Recall Mechanism:
The AI should dynamically decide when to bring up past information, based on relevance. For example, if the topic of fences comes up again, the AI should remember the constraint the user mentioned last year.
How to Implement:
Step 1: Store Key Facts in Memory
The system should recognize and log important facts from user conversations. You can use NLP techniques to identify statements that involve user plans, constraints, or decisions.
python
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import sqlite3
import datetime
class MemoryWithFacts:
def __init__(self):
# Initialize SQLite database
self.conn = sqlite3.connect('facts_memory.db')
self.create_table()
def create_table(self):
with self.conn:
self.conn.execute('''CREATE TABLE IF NOT EXISTS facts
(id INTEGER PRIMARY KEY AUTOINCREMENT,
timestamp TEXT,
fact TEXT,
topic TEXT)''')
def store_fact(self, fact, topic):
"""Store an important fact related to a specific topic."""
timestamp = datetime.datetime.now().isoformat()
with self.conn:
self.conn.execute("INSERT INTO facts (timestamp, fact, topic) VALUES (?, ?, ?)",
(timestamp, fact, topic))
def retrieve_facts(self, topic):
"""Retrieve relevant facts related to a specific topic."""
with self.conn:
cur = self.conn.cursor()
cur.execute("SELECT fact FROM facts WHERE topic = ?", (topic,))
return cur.fetchall()
# Example usage
memory_manager = MemoryWithFacts()
# Store a fact about fences
memory_manager.store_fact("City codes prevent raising the fence", "fence")
# Retrieve facts about fences
fence_facts = memory_manager.retrieve_facts("fence")
for fact in fence_facts:
print(fact[0])
Step 2: Detect Important Facts During Conversations
You can use a rule-based system or NLP model to detect important user statements. For example, if a user mentions something like city codes, it could be flagged as a fact worth remembering:
python
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from transformers import pipeline
fact_classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
def detect_important_fact(conversation_text):
"""Detect important facts to store in memory."""
candidate_labels = ["constraint", "decision", "plan", "preference"]
result = fact_classifier(conversation_text, candidate_labels)
# If a certain label has high confidence, store the fact
if result['labels'][0] in ['constraint', 'decision']:
return conversation_text
# Example conversation
conversation = "The city codes prevent me from raising the fence."
fact = detect_important_fact(conversation)
if fact:
memory_manager.store_fact(fact, "fence")
Step 3: Context-Aware Fact Recall
When a new conversation happens, the system should automatically recall relevant past facts. If the user mentions "fence," the AI will retrieve facts related to that topic and bring them up naturally:
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def respond_with_context(conversation_text, memory_manager):
"""Respond to the user by recalling past facts if relevant."""
topic = "fence" # This can be inferred from the conversation context using NLP.
facts = memory_manager.retrieve_facts(topic)
if facts:
# Example response using the past fact
return f"But remember, you mentioned last year that the city codes prevent you from raising the fence."
else:
return "Tell me more about your plans for the fence."
# Example conversation
conversation = "I'm going to raise that fence another three feet."
response = respond_with_context(conversation, memory_manager)
print(response) # Expected output: "But remember, you mentioned last year that the city codes prevent you from raising the fence."
Step 4: Advanced Memory Features
Time-Based Recall: Facts could be tagged with timestamps, so the AI can mention when the user originally said something (e.g., “you mentioned last year”).
Topic Tracking: More advanced topic modeling could be used to automatically detect and track topics, so the user doesn’t need to explicitly mention a topic every time.
Contextual Matching: The system could also analyze partial matches between the current conversation and past facts. Even if the user doesn’t directly mention “fence,” the system could infer the relevance if the conversation context is similar.
Result
This system will give the AI the ability to naturally refer back to important user statements from previous conversations. It will enhance the AI's responsiveness by making it feel like it has a consistent memory, leading to more realistic and contextual interactions. Would you like to explore any other aspects of this memory system?
You said:
how can NLP infer the topic?
ChatGPT said:
ChatGPT
NLP can infer topics from text using a variety of techniques, from simple keyword extraction to more advanced machine learning methods. The goal is to identify the main subject or theme of a given conversation. Below are some methods that NLP uses to infer topics:
1. Keyword and Phrase Extraction
TF-IDF (Term Frequency-Inverse Document Frequency): This is a statistical technique that ranks words based on how frequently they appear in a document compared to other documents. Keywords that have a high TF-IDF score are considered more important.
Noun-Phrase Extraction: NLP models can identify noun phrases (e.g., "fence height," "city codes") as potential topics by using POS tagging (part-of-speech tagging), which classifies each word as a noun, verb, etc.
Example:
python
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from sklearn.feature_extraction.text import TfidfVectorizer
# Example set of conversations
conversations = ["I'm going to raise that fence another three feet.",
"City codes prevent me from changing the fence.",
"Let's talk about AI and how it impacts society."]
# Use TF-IDF to extract keywords
vectorizer = TfidfVectorizer(stop_words="english")
tfidf_matrix = vectorizer.fit_transform(conversations)
feature_names = vectorizer.get_feature_names_out()
# Show keywords from first conversation
dense = tfidf_matrix.todense()
keyword_list = dense[0].tolist()[0]
phrase_scores = [pair for pair in zip(range(0, len(keyword_list)), keyword_list) if pair[1] > 0]
keywords = [feature_names[index] for index, score in phrase_scores]
print(keywords) # This could return ['fence', 'three', 'feet']
2. Named Entity Recognition (NER)
NER is a method that identifies entities in text, such as people, organizations, locations, etc. If a specific named entity is consistently mentioned in the conversation, it can be inferred as the topic.
Example:
python
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from transformers import pipeline
ner = pipeline("ner")
conversation = "The city codes prevent me from raising the fence."
entities = ner(conversation)
print(entities) # Expected output: [{'entity': 'LOC', 'word': 'city'}, {'entity': 'MISC', 'word': 'codes'}]
Entity categories can then be linked to potential topics, such as:
LOC (Location) could point to discussions about a place.
MISC (Miscellaneous) could point to rules, regulations, etc.
3. Topic Modeling with LDA (Latent Dirichlet Allocation)
LDA is a popular unsupervised technique used to detect topics in text. It assumes that each document is a mixture of various topics, and it tries to infer the most probable topics that generated the text.
Example with gensim:
python
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from gensim import corpora
from gensim.models import LdaModel
# Example conversations
documents = ["I'm going to raise that fence another three feet.",
"City codes prevent me from changing the fence.",
"Let's talk about AI and its impact on society."]
# Preprocess the text
texts = [[word for word in document.lower().split()] for document in documents]
dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]
# Train LDA model
lda = LdaModel(corpus, num_topics=2, id2word=dictionary, passes=15)
topics = lda.print_topics(num_words=3)
print(topics)
LDA will output something like:
graphql
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[(0, '0.2*"fence" + 0.1*"codes" + 0.05*"three"'),
(1, '0.3*"ai" + 0.1*"impact" + 0.05*"society"')]
This suggests two topics: one about "fence" and "codes," and another about "AI" and "impact."
4. Zero-Shot Topic Classification
With zero-shot learning models, you don’t need to train the model on specific topics in advance. Instead, the model can infer the most likely topic based on predefined candidate topics provided at runtime.
Example using Hugging Face's zero-shot-classification:
python
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from transformers import pipeline
classifier = pipeline("zero-shot-classification")
conversation = "The city codes prevent me from raising the fence."
candidate_labels = ["construction", "legal issue", "personal development", "AI"]
result = classifier(conversation, candidate_labels)
print(result)
Expected output:
arduino
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{'labels': ['legal issue', 'construction', 'personal development', 'AI'],
'scores': [0.9, 0.7, 0.1, 0.05]}
Here, "legal issue" is the most likely topic, inferred from the discussion of "city codes."
5. BERT-Based Semantic Similarity Matching
Another approach is to use models like BERT to compute the semantic similarity between the current conversation and predefined topic categories. The model matches user statements to the closest topic based on meaning, even if exact words aren’t used.
Example:
python
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from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
# Example conversation and topic list
conversation = "The city codes prevent me from raising the fence."
candidate_topics = ["construction issues", "legal restrictions", "AI development", "personal growth"]
# Encode conversation and topics
conversation_embedding = model.encode(conversation, convert_to_tensor=True)
topic_embeddings = model.encode(candidate_topics, convert_to_tensor=True)
# Compute similarity
similarities = util.pytorch_cos_sim(conversation_embedding, topic_embeddings)
best_match = candidate_topics[similarities.argmax()]
print(best_match) # This should return 'legal restrictions' or 'construction issues' depending on the context.
6. Rule-Based Systems
For more structured and specific use cases, you can manually define rules to classify topics based on keywords or patterns.
Example:
python
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def rule_based_topic_detection(conversation_text):
if "fence" in conversation_text.lower():
return "construction"
elif "city codes" in conversation_text.lower():
return "legal issue"
else:
return "general"
# Example
conversation = "The city codes prevent me from raising the fence."
topic = rule_based_topic_detection(conversation)
print(topic) # Output: "legal issue"
Conclusion
Using a combination of these methods, NLP can infer the topic of a conversation with varying degrees of accuracy, depending on the complexity and type of conversation. For a flexible system, combining zero-shot learning, LDA, and keyword extraction might give the best results in most general cases, while more structured systems can use rule-based approaches for highly specific domains. Would you like to implement one of these approaches for your system?
