Building a Private AI Research Assistant with Llama

Imagine a research assistant that doesn't just search the web, but actually reads, dedupes, and synthesizes academic papers from global databases, all while running privately on your own machine. In this post, I will show you how to build a Local AI Research Agent using Llama 3.2 and the PydanticAI framework. If you want to jump straight into the code, you can follow along or run the project right now using this Google Colab Notebook.

The Stack: Why These Tools?

To build an effective research assistant, you need three things: a brain, a librarian, and a bridge.

1. The Brain (Llama 3.2 via Ollama): Using llama3.2:3b, we get a highly capable model that runs locally on modest hardware (or a free Colab T4 GPU).
2. The Librarians (OpenAlex & Semantic Scholar): Instead of hallucinating facts, our agent fetches real metadata and abstracts from the two largest open academic databases in the world.
3. The Bridge (PydanticAI): This is the secret sauce. It enforces Structured Outputs, ensuring the LLM speaks in clean JSON that our application can actually render into a report.

Setting Up the Local Environment

Running Ollama in a Colab environment requires a bit of "plumbing." We have to install the server and run it in the background before we can pull our model.

# 1. Install the missing dependency (zstd) and pciutils (for GPU detection)
!sudo apt-get update
!sudo apt-get install -y zstd pciutils

# 2. Install Ollama
!curl -fsSL https://ollama.com/install.sh | sh

# 3. Launch Ollama Server in the background
import subprocess
import time

# Start the server
process = subprocess.Popen(['ollama', 'serve'])

# Give it 10 seconds to fully initialize
time.sleep(10)

# 4. Pull your model
!ollama pull llama3.2

Defining "Success" with Schemas

One of the best features of this setup is using Pydantic to define exactly what a research report should look like. We don't want the AI to chat; we want it to extract specific data.

class PaperAnalysis(BaseModel):
    title: str
    year: Optional[int] = None
    key_points: List[str] = Field(default_factory=list)
    why_relevant: List[str] = Field(default_factory=list)

class ResearchReport(BaseModel):
    query: str
    papers: List[PaperAnalysis]

The Retrieval Logic

The agent is only as good as the papers it reads. The script uses aiohttp to search OpenAlex (great for metadata) and Semantic Scholar (great for abstracts) concurrently.

The coolest bit of logic here? Deduplication. By normalizing titles and comparing DOIs, we ensure that if both APIs find the same paper, you only see it once.

The Orchestrator

The final "Main" function takes your query, hits the APIs, cleans the data, and feeds the abstracts to Llama 3.2. The model then analyzes the text and decides why each paper is relevant to your specific research goal.

The Result

When you run a query like "On-device LLM reasoning for IoT DDoS detection",  list of research papers relevant to the topic.

Why Local Research Matters

By running this setup, you gain three major advantages:

• Privacy: Your research queries and specific areas of interest stay on your machine.
• Zero Cost: You aren't paying per-token for a commercial LLM.
• Structure: Because we used PydanticAI, this data is ready to be saved to a database or exported to a Zotero library.

The future of research isn't just "searching", it's building your own tools to synthesize the world's knowledge.