Dev 101: How to build True AGI (instead of Narrow AI)

⚠️ A Reality Check

Even with all of the info from below, you’d still be building what’s called a strong narrow AI or an agentic assistant — not true AGI yet. AGI (as per today’s standards) would mean human-like general reasoning across any domain, adaptable without task-specific training. That’s still an active research frontier.

Building a “true AGI–style” personal assistant today means combining state-of-the-art LLMs with robust retrieval, memory, planning, tool-use, multi-modal I/O, and safety layers—rather than waiting for an elusive singularity. The core is a Retrieval-Augmented Generation (RAG) loop over a high-performance vector database, grounded by continuous memory and learning, orchestrated by an agentic planning loop (e.g., AutoGPT), and connected to real-world APIs for actions. 

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What Generative AI Code Does Well:

• Basic information retrieval: It looks into a pre-defined dictionary (simulated database) for known answers.

• Fallback to LLM (GPT): If no direct match is found, it uses APIs (like text-davinci-003) to generate an answer.

• Simple decision logic: Chooses between database lookup and LLM generation.
  
  

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1. Core: Retrieval-Augmented Generation (RAG)

1.1 Why RAG?

RAG grounds LLM outputs in actual documents, drastically reducing hallucinations and keeping information fresh & the assistant always grounds its answers in updated facts. WIRED.

1.2 Best Practices

• Chunking & Embeddings: Split knowledge base into fixed-size chunks, embed with an LLM embedding model Stack Overflow Blog.

• Multi-Modal  Input/Output Retrieval: Extend retrieval to images and tables for richer context arXiv. 

• Automated Evaluation: Monitor retrieval accuracy, citation safety, and response completeness Kapa AI.

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2. High-Performance Vector Database

An AGI would learn and update its knowledge continuously from conversations, external data, or APIs.

2.1 Choosing the Right Store

• Cloud vs Self-hosted:  Pinecone or FAISS to store and retrieve semantically relevant information, Qdrant/Chroma for open-source control RedditOpenAI Community.

• Performance: Ensure millisecond-level similarity search at scale Medium.

2.2 Integration Tips

• Use batched inserts/queries and HNSW indices.

• Monitor index health and re-index when updating large corpora.

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3. Memory: Short-Term & Long-Term

• Short-Term (Session): Keep recent dialogue turns for context windows arXiv.

• Long-Term: Store user preferences, project details, and facts in a rolling knowledge base with periodic indexing arXiv using embeddings stored over time and retrieved when relevant.

• Context: True assistants remember context over time (e.g., user’s preferences, past interactions)

• Retrieval: Use a secondary RAG pipeline over memory to inject relevant past info.

• Learning: Each conversation teaches the assistant new facts or preferences. This could involve retraining on logs, or fine-tuning on user-specific data.

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4. Agentic Planning Loop

4.1 AutoGPT-Style Loop

1. Summarize State: Condense current context + goals.

2. LLM Action Proposal: GPT proposes next step (e.g., “Schedule meeting”).

3. Execute Tools: Call calendar API, send email, etc. Not just answering, but doing:

   • Schedule meetings (via Google Calendar API)

   • Send emails (via SMTP or Gmail API)

   • Set reminders or control smart devices (via APIs like Alexa, Home Assistant, etc.)

4. Observe & Store Results: Log outcome to memory; repeat MediumMaarten Grootendorst.

4.2 Multi-Step Task Chaining

• Implement failure handling (retries, clarifications).

• Set sub-goals and track completion.

• Use a planning algorithm (like BabyAGI or AutoGPT patterns) so it can chain multiple steps: "Book me a ticket, notify my boss, and update my calendar" (a chain of tasks).

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5. Tooling & API Integrations

• LangChain Agents: Simplifies hooking LLMs to tools (search, SQL, Python REPL) AutoGPT Documentation.

• Zapier / IFTTT Connectors: Rapidly expose new services.

• Custom Plugins: Build domain-specific functions and register via OpenAI plugin spec.

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6. Multi-Modal Interaction

• Support not just text but also voice (via speech recognition APIs), images (via vision models), and possibly even video.

• Voice In/Out: Whisper for transcription; ElevenLabs or native TTS for responses.

• Vision: Use CLIP or Vision-LLMs to interpret images.

• Haptics/Devices: Integrate IoT protocols for home/office control.

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7. Safety, Ethics & Monitoring

• Hallucination Detection: Cross-verify claims via additional RAG lookups WIRED

• Privacy Filters: Mask PII and enforce user consent.

• Human-in-Loop: Escalate high-risk requests for manual review.

• AGI must not misuse user data, so privacy and ethical design are mandatory.

• Implement user consent, privacy filters, and data encryption.

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Next Steps & Continuous Improvement

1. Scale corpus: Ingest company docs, emails, and user manuals.

2. Fine-tune embeddings: Train on your domain for sharper retrieval.

3. Expand multimodality: Plug in Whisper and Vision-LLMs.

4. Deploy & Monitor: Use telemetry to track performance, hallucination rates, and user feedback.

5. Iterate: Continuously refine modules, add new tools, and update memory.
   
   
   

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🔥 Modern Tech Stack Ideas

Use the following to evolve your code toward a more powerful assistant, with an upgraded architecture:

Component	Recommended Tools
LLM Backend	OpenAI GPT-4, Claude, Mistral, or Llama 3
Vector Search	Pinecone, Weaviate, FAISS
Memory Management	LlamaIndex (formerly GPT Index), LangChain Memory
Task Automation	LangChain Agents, BabyAGI, AutoGPT frameworks
Voice Interface	Whisper (for input), ElevenLabs (for output)
APIs for Actions	Google APIs, Zapier, or custom REST APIs
Database	PostgreSQL + Vector DB

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Prototype Skeleton (Python)

python
from langchain.llms import OpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.memory import ConversationBufferMemory
from langchain.agents import initialize_agent, Tool

# 1. Initialize LLM + embeddings
llm = OpenAI(model_name="gpt-4", temperature=0.7)                              
embeddings = OpenAIEmbeddings()

# 2. Vector DB (Chroma for demo)
vectordb = Chroma(persist_directory="db/", embedding_function=embeddings)    

# 3. RetrievalQA chain
qa_chain = RetrievalQA.from_chain_type(
    llm=llm, 
    chain_type="stuff", 
    retriever=vectordb.as_retriever()
)

# 4. Memory
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

# 5. Define Tools (e.g., calendar, email)
tools = [
    Tool(name="qa", func=qa_chain.run, description="Answer general questions"),
    # Tool(name="calendar", func=call_calendar_api, description="Manage calendar"),
    # Tool(name="email", func=send_email_api, description="Send emails"),
]

# 6. Agent
agent = initialize_agent(
    tools, 
    llm, 
    memory=memory, 
    agent="zero-shot-react-description", 
    verbose=True
)

# 7. Run agent
def ask_assistant(query: str):
    return agent.run(query)

# Example
print(ask_assistant("Schedule a meeting with Bob next Tuesday at 3pm."))

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