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AskImmigration: Navigate U.S. Immigration with an AI Assistant
**Authors:** Geoffrey Duncan Opiyo, Hillary Arinda, Justine Okumu, Deo Mugabe
---
---
# AI-Powered Guide for Your U.S. Legal Immigration Journey
## Introduction
Immigration rules change all the time. Forms and guides can be hard to follow. AskImmigration is a simple chat tool. It answers your U.S. immigration questions using real documents. It keeps answers clear and on point.
---
[🚀 GitHub Repository ↗](https://github.com/okumujustine/AskImmigrate)
## How It Works
AskImmigration relies on a vetted library of official immigration PDFs and JSON forms.
It breaks each document into small, searchable chunks, turns those into vectors, then—when you ask a question—finds the most relevant passages, builds a safe prompt, and returns a clear, document-backed answer.
For full setup and examples, see our [README](https://github.com/okumujustine/AskImmigrate/blob/main/README.md).
---
## System Overview
- **Chat Interface**: A simple Python CLI or web form.
- **Prompt Builder**: Merges role, tone, and rules into one prompt.
- **Vector Store**: Chroma holds all document embeddings.
- **AI Engine**: LangChain passes your question and context to Groq.
- **Logging**: All questions and answers go into Firestore.
---
## Key Features
✅ **Document-grounded answers**
Pulls directly from well curated PDFs and JSON files from trusted public sources, so every response is backed by real, cited sources.
✅ **Safety rules**
Built-in guardrails keep the assistant on topic and prevent it from sharing unsafe or off-scope information.
✅ **Multi-turn chat**
Remembers your previous questions and answers, letting you follow up without losing context.
✅ **Configurable prompts**
Adjust the assistant’s role, tone, and boundaries in a simple YAML file—no code changes needed.
✅ **Full audit trail**
Saves every question and answer in Firestore for easy review and compliance tracking.
---
## Tech Stack
| Part | Tech |
|---------------------|-----------------------------------|
| Frontend/CLI | React/Python |
| Prompt Config | YAML |
| Vector Search | Chroma |
| Embeddings | Hugging Face Sentence Transformers |
| LLM Engine | LangChain + Groq |
| Data Storage | Firestore |
| Utilities | `cli.py`, `load_data.py` |
---
### Why AskImmigration
- 🧭 **AskImmigration** helps you navigate the U.S. immigration process with clear, direct answers — no legal jargon or confusion.
- 📄 It uses official, up-to-date data from USCIS forms and government policies to ensure accuracy and reliability.
- Whether you're applying for a visa, adjusting your status, or planning for citizenship, AskImmigration is here to support you every step of the way.
---
### Potential Feature Enhancements
- 🌐 **Multilingual Support**
Allow users to interact in multiple languages (e.g. Spanish, Mandarin, Arabic) to make the tool accessible to a broader audience.
- ✍️ **Form Assistant**
Help users fill out common USCIS forms by guiding them section-by-section with plain-language explanations and example answers.
- 📄 **Document Uploader**
Let users upload their USCIS notices or forms. The assistant could analyze them and provide insights or next steps based on the content.
---
### Conclusion
AskImmigration makes U.S. immigration easier by reading real documents, following clear rules, and acting as a helpful guide—Just ask your questions. Get simple answers. Good luck on your journey.
|
Given the following content, create as much questions whose answer can be found within the content. Then, provide the answer to the questions. Ensure the answers are derived directly from the content. Format the questions and answers in the following JSON structure: {Question: '', Answer: ''}.
|
RAG-powered-AI-chatbot
--DIVIDER--
# RAG Turns General AI into Domain Experts by Connecting It to Specific Knowledge
## Project Goal
Project Type:
_Software Tool + Real-World Application_
This project demonstrates how Retrieval-Augmented Generation (RAG) can transform a general language model into a domain-specific expert by connecting it to a curated knowledge base.
The approach combines the flexibility of generative models with the accuracy of verified sources, enabling high-quality, relevant, and evidence-backed an
## Purpose of Publication
_To share a practical solution and teach others how to build a RAG pipeline using ChromaDB and LangChain._
Many researchers and engineers face the limitations of LLMs when they need accurate responses in niche areas. I show how easily you can integrate custom data into LLMs using a modern RAG stack, allowing for factual and source-grounded answers.
## Target Audience
- Researchers and academics looking to query their own literature.
- Students and educators building educational tools with personalized content
- AI/ML engineers developing products with RAG integration.
- Potential employers seeking talent with hands-on experience in LangChain,
ChromaDB, and generative AI.
- AI/ML engineers developing products with RAG integration.
- Potential employers seeking talent with hands-on experience in LangChain,
ChromaDB, and generative AI.
## What is this about?
This project shows a typical RAG pipeline in action:
1. Document chunking (pdf, html, txt, etc.)
2. Embedding creationusing OpenAIEmbeddings or others
3. Storing in ChromaDB— local or remote vector database
4. Retrieval + generation via LangChain or LlamaIndex
## Why does it matter?
- RAG overcomes LLM hallucinations by grounding answers in real, relevant documents.
- It enables the creation of _knowledge-aware systems_ for researchers, companies, educators, or even healthcare and defense sectors.
- It’s simple to implement using open-source tools and well-supported libraries.
## Can I trust it?
Yes. The code is based on proven libraries with active communities:
- [LangChain](https://www.langchain.com/)
- [ChromaDB](https://www.trychroma.com/)
- Integrations with \[OpenAI\], \[HuggingFace\], \[FAISS\], and other vector stores
Users can inspect the sources behind every answer, reducing the risk of misinformation.
## Can I use it?
Absolutely. The system is production-ready and includes:
- Error handling (e.g., no relevant context)
- Query quality monitoring (LangSmith, Weights & Biases)
- Multi-format support (txt, pdf, web, docx)
- Fast integration with Streamlit.
## Code Repository
--DIVIDER--requirement.txt--DIVIDER--```python
langchain
chromadb
google-generativeai
langchain-google-genai
sentence-transformers
streamlit
python-dotenv
```--DIVIDER-- ### Ingest Documents into ChromaDB
This script loads PDFs, splits them into chunks, creates embeddings using HuggingFace, and saves them in Chroma vector DB.
📂 ingest_documents.py--DIVIDER--
```python
from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma
import os
def ingest_documents(doc_dir="docs", persist_dir="db"):
if not os.path.exists(doc_dir):
raise FileNotFoundError(f"Document directory '{doc_dir}' not found. Please create it and add PDFs.")
loader = DirectoryLoader(doc_dir, glob="*.pdf", loader_cls=PyPDFLoader)
documents = loader.load()
splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
splits = splitter.split_documents(documents)
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
vectordb = Chroma.from_documents(splits, embedding=embeddings, persist_directory=persist_dir)
vectordb.persist()
print("Documents successfully ingested into vector store.")
if __name__ == "__main__":
ingest_documents()
```--DIVIDER--### Build the RAG Chain
The following script connects ChromaDB to an OpenAI LLM using LangChain. It retrieves top-k documents and uses the LLM to generate an answer.
🔗 langchain_pipeline.py--DIVIDER--
```python
from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.chains import RetrievalQA
import os
from dotenv import load_dotenv
load_dotenv()
def build_rag_chain(persist_dir="db"):
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
vectordb = Chroma(persist_directory=persist_dir, embedding_function=embeddings)
retriever = vectordb.as_retriever(
search_type="mmr", # Maximal Marginal Relevance
search_kwargs={"k": 3, "lambda_mult": 0.5}
)
llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0.2, google_api_key=os.getenv("GOOGLE_API_KEY"))
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
retriever=retriever,
return_source_documents=True
)
return qa_chain
```--DIVIDER--### Chatbot UI with Streamlit
Let users ask questions via a web interface powered by your RAG pipeline.
🖥️ app.py--DIVIDER--
```python
import streamlit as st
from rag_pipeline import build_rag_chain
st.set_page_config(page_title="RAG-powered Chatbot", layout="wide")
# Initialize chain only once
@st.cache_resource
def get_qa_chain():
return build_rag_chain()
qa_chain = get_qa_chain()
# Initialize session state
if "chat_history" not in st.session_state:
st.session_state.chat_history = []
# Streamlit UI
st.title("RAG-powered Chatbot")
st.markdown("Ask questions based on your custom documents.")
# Safely load the RAG chain
try:
qa_chain = build_rag_chain()
except Exception as e:
st.error(f"Failed to load RAG pipeline: {e}")
st.stop()
# UI input box
query = st.text_input("Ask a question:")
# If user has entered a question
if query:
with st.spinner("Thinking..."):
try:
result = qa_chain({"query": query})
st.markdown("### Answer")
st.success(result["result"])
# Show source documents
with st.expander("Source Documents"):
for i, doc in enumerate(result.get("source_documents", [])):
st.markdown(f"**Source {i+1}:** {doc.metadata.get('source', 'N/A')}")
st.code(doc.page_content[:500], language="markdown")
except Exception as e:
st.error(f"Failed to get a response: {e}")
```
--DIVIDER--## Results:--DIVIDER--
--DIVIDER--## Summary
This project showcases how to build a custom, local RAG chatbot that:
Loads PDFs
Creates vector embeddings
Uses Chroma DB for retrieval
Generates responses via LLM
Displays results in a user-friendly web app
|
Given the following content, create as much questions whose answer can be found within the content. Then, provide the answer to the questions. Ensure the answers are derived directly from the content. Format the questions and answers in the following JSON structure: {Question: '', Answer: ''}.
|
A simple RAG-powered assistant (ChatBot) that answers questions on the LangChain Documentation
## Publication Overview
As part of my learning journey in the Ready Tensor Flow program on Agentic AI, I built a Retrieval-Augmented Generation (RAG) powered chatbot that specializes in answering questions related to the LangChain documentation. Although this project is not itself an Agentic AI application, it forms a solid foundation for understanding retrieval workflows that underpin more advanced agentic systems.
What sets this project apart is its end-to-end implementation:
FastAPI backend that manages document retrieval and response generation.
Blazor WebAssembly (WASM) frontend, providing a clean UI for interacting with the chatbot.
## Why This Project?
The LangChain ecosystem is a powerful framework for building applications with LLMs, yet its documentation can be overwhelming to navigate manually. My aim was to build a domain-specific, context-aware chatbot that allows developers and enthusiasts to extract concise answers to specific questions from LangChain’s extensive documentation.
Building both a robust API and an intuitive UI ensured that the project mirrored real-world software engineering practices.
## Technical Stack
| Component | Technology |
| ------------------ | ------------------------------------------- |
| Backend Framework | FastAPI |
| Embedding Model | sentence-transformers/all-MiniLM-L12-v2 |
| Vector Store | ChromaDB |
| LLM | TinyLlama 1.1B Q4\_K\_M (via ctransformers) |
| Frontend Framework | Blazor WebAssembly (WASM) |
| Document Parser | BeautifulSoup & LXML |
| Deployment | Local (with Docker planned) |
## Backend Project Structure
LanggraphBotDocIngestor/
├── chroma_db/ # Persistent vector database
├── loaders/ # Document ingestion scripts
├── models/ # Contains quantized Llama model files
├── .env # Environment variables
├── memory.py # Session memory implementation
├── ingest.py # Data ingestion into ChromaDB
├── qa_api.py # Main FastAPI backend
├── requirements.txt # Dev dependencies
├── pinned-requirements.txt # Production dependencies
├── README.md # Project documentation
## How it works
### Load up LangChain documentation (Backend)
```python
from langchain_community.document_loaders import WebBaseLoader
from dotenv import load_dotenv
import os
load_dotenv()
USER_AGENT = os.getenv("USER_AGENT", "LanggraphBotDocIngestor/1.0 (youremailaddress@domain.com)")
HEADERS = {"User-Agent": USER_AGENT}
# This file contains logic to load and clean docs from the web (LangChain/LangGraph)
# 20 - 50 web pages from the LangChain documentation were used for this project. This is not an exhaustive list in the array
URLS = [
"https://python.langchain.com/docs/introduction/",
"https://python.langchain.com/docs/tutorials/",
"https://python.langchain.com/docs/how_to/",
"https://python.langchain.com/docs/concepts/"
]
def load_documents():
loader = WebBaseLoader(web_paths=URLS, header_template=HEADERS)
return loader.load()
```
### Document Ingestion
```python
import os
import shutil
from dotenv import load_dotenv
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain_huggingface import HuggingFaceEmbeddings
from loaders.langchain_docs import load_documents
from tqdm import tqdm
# Load .env variables
load_dotenv()
EMBEDDING_MODEL = "sentence-transformers/all-MiniLM-L12-v2"
def ingest_documents():
chroma_dir = "./chroma_db"
if os.path.exists(chroma_dir):
print("Cleaning up existing Chroma vectorstore...")
shutil.rmtree(chroma_dir)
print("Loading documents...")
raw_docs = load_documents()
print("Splitting documents into chunks...")
splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
chunks = splitter.split_documents(raw_docs)
print("Embedding and storing in Chroma (locally)...")
embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL)
vectorstore = Chroma.from_documents(
documents=chunks,
embedding=embeddings,
persist_directory=chroma_dir
)
vectorstore.persist()
print(f"Ingested {len(chunks)} chunks into vectorstore.")
if __name__ == "__main__":
ingest_documents()
```
The purpose of the above script is to scrape the documentation and store it in ChromaDB for semantic retrieval.
### Query Workflow (Backend)
```python
import os
import uuid
import traceback
from fastapi import FastAPI, HTTPException, Query
from fastapi.middleware.cors import CORSMiddleware
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_chroma import Chroma
from ctransformers import AutoModelForCausalLM
from langchain.llms.base import LLM
from memory import SessionMemory
from pydantic import BaseModel, PrivateAttr
from dotenv import load_dotenv
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
# python code
@app.post("/ask")
async def ask_question(
request: QueryRequest,
session_id: str = Query(default=None)
):
try:
if not session_id:
session_id = str(uuid.uuid4())
history_pairs = memory.get_session(session_id)
history_text = "\n".join([f"User: {q}\nBot: {a}" for q, a in history_pairs])
docs = retriever.get_relevant_documents(request.question)
context = "\n\n".join([doc.page_content for doc in docs])
if not context.strip() or len(context.strip()) < MIN_CONTEXT_LENGTH:
fallback_answer = (
"I specialize in answering questions about LangGraph and LangChain documentation. \n"
"That topic appears unrelated, so I can't provide a reliable answer."
)
return {"response": fallback_answer, "session_id": session_id}
result = llm_chain.invoke({
"history": history_text,
"context": context,
"question": request.question
})
raw_answer = result.get("text", "").strip() if isinstance(result, dict) else str(result).strip()
# More python code
```
This implementation's purpose is to embed the question/query, retrieve similar document chunks, and pass them to the language model for response generation.
### Session Management
```python
from typing import Dict, List, Tuple
from threading import Lock
class SessionMemory:
def __init__(self):
self.sessions: Dict[str, List[Tuple[str, str]]] = {}
self.lock = Lock()
def add_message(self, session_id:str, question: str, answer: str):
with self.lock:
if session_id not in self.sessions:
self.sessions[session_id] = []
self.sessions[session_id].append((question, answer))
def get_session(self, session_id: str) -> List[Tuple[str, str]]:
with self.lock:
return list(self.sessions.get(session_id, []))
def clear_session(self, session_id: str):
with self.lock:
self.sessions.pop(session_id, None)
```
This implementation defines methods to get and set a user's session to be used for chat history which only applies to the user's active browser session as it goes when the browser window is closed. This was done as an alternative to creating a user authentication setup.
```python
# Prompt Template
prompt_template = PromptTemplate(
input_variables=["history", "context", "question"],
template=(
"You are a helpful assistant specializing in LangGraph and LangChain documentation.\n"
"Example Q&A:\n"
"Q: What is LangChain?\n"
"A: LangChain is an open-source framework for developing applications powered by language models.\n"
"Q: What is LangGraph?\n"
"A: LangGraph extends LangChain to enable building applications as stateful graphs.\n\n"
"Now, using the following context:\n{context}\n\n"
"Conversation history:\n{history}\n\n"
"Q: {question}\nA:"
)
)
```
The above shows the prompt template which is sent to the LLM.
### Frontend Query (Blazor UI)
```
@code {
private string question = "";
private bool isLoading = false;
private string? sessionId;
// Chat History
private List<(string User, string Bot)> chatHistory = new();
// More C# code
private async Task SubmitQuestion()
{
if (string.IsNullOrWhiteSpace(question) || string.IsNullOrWhiteSpace(sessionId)) return;
var thisQuestion = question;
question = string.Empty;
isLoading = true;
try
{
var request = new QuestionRequest { Question = thisQuestion };
var client = HttpClientFactory.CreateClient("LangGraphDocsBotAPI");
// Send session_id as query string
var url = $"/ask?session_id={sessionId}";
var result = await client.PostAsJsonAsync(url, request);
if (result.IsSuccessStatusCode)
{
var response = await result.Content.ReadFromJsonAsync<QaResponse>();
var answer = response?.Response ?? "No response received.";
//add to history
chatHistory.Add((thisQuestion, answer));
}
else
{
var errorText = await result.Content.ReadAsStringAsync();
chatHistory.Add((thisQuestion, $"Error: {result.StatusCode}\n{errorText}"));
}
}
catch (Exception ex)
{
chatHistory.Add((thisQuestion, $"Exception: {ex.Message}"));
Console.WriteLine("Exception: " + ex);
}
finally
{
isLoading = false;
}
}
}
```
### Response Display (Blazor UI)
```
@page "/ask"
@using LangGraphDocsBot.Models
@inject IHttpClientFactory HttpClientFactory
@inject IJSRuntime JS
<div class="container mt-4" style="max-width: 800px;">
<h3 class="mb-4">Ask LangGraphDocsBot</h3>
@if (chatHistory.Any() || isLoading)
{
<div class="chat-box border p-3 rounded bg-light mb-3">
@foreach (var exchange in chatHistory)
{
<div class="d-flex justify-content-end mb-2">
<div class="p-2 bg-primary text-white rounded" style="max-width: 75%;">
@exchange.User
</div>
</div>
<div class="d-flex justify-content-start mb-2">
<div class="p-2 bg-white border rounded shadow-sm" style="max-width: 75%;">
@((MarkupString)Markdig.Markdown.ToHtml(exchange.Bot))
</div>
</div>
}
@if (isLoading)
{
<div class="d-flex align-items-center mb-2">
<span class="spinner-border spinner-border-sm me-2 text-primary" role="status"></span>
<em>LangGraphDocsBot is typing...</em>
</div>
}
</div>
}
<div class="input-group">
<input class="form-control" @bind="question" @bind:event="oninput" placeholder="Ask a question..." />
<button class="btn btn-primary" @onclick="SubmitQuestion" disabled="@string.IsNullOrWhiteSpace(question)">Ask</button>
</div>
</div>
```
This block provides a conversational interface to make querying documentation seamless and user-friendly.
### Sample payload (request and response)
```json
POST /ask
Content-Type: application/json
{
"question": "What is LangGraph?"
}
{
"response": "LangGraph extends LangChain to enable building applications as stateful graphs.",
"session_id": "b7c3901c-e137-4cc8-9453-52cf0795c7f2"
}
```
### Limitations
While the project achieves its primary goal, it comes with some notable limitations:
LLM Performance: TinyLlama is computationally efficient but occasionally generates vague or generic answers.
Contextual Memory: Session-based memory exists but lacks sophisticated dialogue management.
Frontend Simplicity: The UI is intentionally minimal for demonstration purposes and could use some improvements in persistent chat history; for now the chat history is limited to the user's browser session.
Limited Retrieval Scope: Only the LangChain documentation was ingested. Adding LangGraph source examples would make answers richer.
### Future Work
- Improve LLM Responses: Integrate more powerful APIs like GPT-4 or LLama3 via external services.
- Enhance UI: Add streaming responses, and modern UI/UX improvements.
- Full Agentic Capabilities: Future projects will build on this foundation by adding task planning, memory chains, and autonomous behaviors.
### Key Achievements
- Successfully combined RAG pipelines, semantic search, and quantized LLMs into an integrated solution.
- Developed an end-to-end prototype with both backend and frontend.
### Github Repository
- Python (Fast API Backend): [LangGraphBotDoctIngestor](https://github.com/ayorindeadunse/langgraph-project-bot-doc-ingestion-layer)
- Blazor UI (ASP.NET Core Frontend): [LangGraphDocsBot](https://github.com/ayorindeadunse/langgraph-docs-bot)
### References
[LangChain Documentation](https://python.langchain.com/docs/introduction/)
[LangGraph Documentation](https://python.langchain.com/docs/langgraph/)
[Blazor](https://learn.microsoft.com/en-us/aspnet/core/blazor/?view=aspnetcore-9.0)
[Chroma DB](https://www.trychroma.com/)
[ctransformers](https://github.com/marella/ctransformers)
[Tiny Llama](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)
Thank you for reading!
|
Given the following content, create as much questions whose answer can be found within the content. Then, provide the answer to the questions. Ensure the answers are derived directly from the content. Format the questions and answers in the following JSON structure: {Question: '', Answer: ''}.
|
Mini-RAG Chat: Local Document Retrieval and Answering via LangChain, FAISS, and Ollama
# Abstract
This publication introduces Mini-RAG Chat, a fully offline, lightweight document question-answering system using LangChain, FAISS for vector retrieval, and Ollama for local LLM inference. Users can upload PDFs, DOCX, or JSON files, which are chunked, embedded, and indexed locally. The system retrieves the most relevant context and formulates responses using a selected LLM (e.g., Mistral or LLaMA). Built with Streamlit, it features session memory, dynamic prompt templates, and real-time QA. It serves as a privacy-preserving assistant for enterprise, education, and research applications — with no internet or cloud dependencies. All processing is performed locally on the user’s machine.
--DIVIDER--# Methodology
Methodology
The Mini-RAG Chat system is designed using a modular pipeline that integrates document processing, semantic search, and local LLM-based generation. It follows the core Retrieval-Augmented Generation (RAG) architecture with the following components:
1. Document Ingestion
Accepts user-uploaded .pdf, .docx, and .json files.
Files are parsed and converted into plain text using the Unstructured library.
Text is split into overlapping chunks using RecursiveCharacterTextSplitter to preserve semantic context.
2. Embedding and Vector Store Construction
Each text chunk is embedded using HuggingFace Embeddings (all-MiniLM-L6-v2) or optionally via Ollama Embeddings if local-only setup is preferred.
Resulting dense vectors are stored in a local FAISS index.
Metadata such as source filename and position is attached to each vector for traceability.
3. Semantic Retrieval and Prompt Formulation
On each user query, the system retrieves the top k semantically similar chunks using FAISS.
These retrieved chunks are formatted into a custom prompt template.
The prompt is passed to a local LLM (e.g., mistral or llama2 via Ollama) to generate a grounded response.
4. Conversational Memory
A ConversationBufferMemory is attached to the chain to retain previous question-answer pairs.
This enables contextual follow-up questions and continuity across interactions.
The memory stores only the answer key to avoid ambiguity with source_documents.
5. User Interface
A lightweight Streamlit frontend enables user interaction.
Features include a chat box, document upload area, and persistent session state.
Chat history is rendered using st.chat_message, and sources can optionally be displayed for transparency.
This architecture ensures data privacy, modularity, and local-first inference, suitable for secure enterprise use and academic research without dependency on cloud APIs.--DIVIDER--# Results
|
Given the following content, create as much questions whose answer can be found within the content. Then, provide the answer to the questions. Ensure the answers are derived directly from the content. Format the questions and answers in the following JSON structure: {Question: '', Answer: ''}.
|
Ready Tensor Publication Explorer: Your Conversational Guide to AI/ML Knowledge
## TL;DR / Abstract
The Ready Tensor Publication Explorer is a cutting-edge RAG-powered chatbot built with LangChain and FAISS that transforms how users interact with AI/ML knowledge. This intelligent assistant allows researchers, students, and practitioners to ask natural language questions about a curated collection of Ready Tensor publications, making research and exploration remarkably efficient. Instead of manually searching through dozens of technical articles, users can simply ask questions and receive accurate, context-aware answers with full source attribution.
## Tool Overview
### The Problem We Solve
In today's rapidly evolving AI/ML landscape, practitioners face a critical challenge: **information overload**. With hundreds of research papers, tutorials, and technical articles published daily, finding specific information across many technical documents becomes increasingly difficult. Researchers and students often spend hours manually searching through publications, struggling to locate relevant insights buried within extensive documentation.
### Our Solution: Conversational AI Assistant
The Ready Tensor Publication Explorer addresses this challenge by providing an intelligent, conversational interface to AI/ML knowledge. Built using state-of-the-art Retrieval-Augmented Generation (RAG) technology, this tool acts as your personal research assistant, understanding natural language queries and providing precise, contextual answers from Ready Tensor's curated publication library.
### Target Audience
This tool is designed for:
- **Researchers** seeking quick access to specific methodologies and findings
- **Students** learning AI/ML concepts and looking for comprehensive explanations
- **AI Practitioners** implementing solutions and needing rapid technical reference
- **Technical Writers** researching topics for content creation
- **Educators** preparing course materials and examples
## Features & Benefits
### 🗣️ Natural Language Queries
**Feature**: Users can ask questions in plain English without needing technical search syntax.
**Benefits**:
- No learning curve - ask questions as you would to a human expert
- Intuitive interaction reduces time spent formulating search queries
- Accessible to users regardless of technical background
### 🎯 Context-Aware Answers
**Feature**: The RAG model provides answers based solely on the provided documents, ensuring accuracy and relevance.
**Benefits**:
- Eliminates hallucination by grounding responses in actual publication content
- Provides source attribution for every answer, ensuring credibility
- Maintains consistency with Ready Tensor's established knowledge base
### 💻 Interactive UI
**Feature**: A simple, web-based interface built with Streamlit for easy interaction.
**Benefits**:
- Zero installation complexity - runs in any web browser
- Clean, intuitive design requires no technical expertise
- Real-time responses with conversation history
- Mobile-friendly responsive design
### ⚡ Intelligent Document Processing
**Feature**: Advanced text chunking and semantic search capabilities.
**Benefits**:
- Processes 35+ publications into 1,200+ searchable chunks
- Maintains context across document boundaries
- Optimized for both speed and accuracy
### 📚 Comprehensive Source Attribution
**Feature**: Every answer includes references to specific publications and content sections.
**Benefits**:
- Enables users to dive deeper into original sources
- Maintains academic integrity and credibility
- Facilitates proper citation in research and writing
## Installation and Usage Instructions
### Quick Start
1. **Access the GitHub Repository**
Visit our GitHub repository for the complete source code and detailed setup instructions. https://github.com/YanCotta/rag_publication_explorer
2. **Clone and Setup**
```bash
git clone https://github.com/YanCotta/rag_publication_explorer
cd rag_publication_explorer
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt
```
3. **Configure API Access**
```bash
# Create .env file and add your OpenAI API key
echo "OPENAI_API_KEY='your-api-key-here'" > .env
```
4. **Launch the Application**
```bash
streamlit run app.py
```
5. **Start Exploring**
Open your browser to `http://localhost:8501` and begin asking questions about AI/ML topics covered in Ready Tensor publications.
### Sample Queries to Get Started
- "What is RAG and how does it work?"
- "What are the best practices for prompt engineering?"
- "How do I implement effective document chunking strategies?"
- "What evaluation metrics are recommended for ML models?"
## Technical Specs / How It Works
### Technology Stack
- **Python 3.8+**: Core programming language
- **LangChain**: Orchestration framework for RAG pipeline
- **OpenAI API**: Text embeddings (text-embedding-ada-002) and generation (GPT-3.5-turbo)
- **FAISS**: High-performance vector store for similarity search
- **Streamlit**: Web-based user interface framework
### RAG Process Flow
```
1. User Question (Natural Language)
↓
2. Question Embedding (OpenAI text-embedding-ada-002)
↓
3. Semantic Search (FAISS Vector Store)
↓
4. Context Retrieval (Top-K Relevant Documents)
↓
5. Prompt Construction (Question + Context)
↓
6. Answer Generation (OpenAI GPT-3.5-turbo)
↓
7. Response with Source Attribution
```
### Architecture Highlights
- **Document Processing**: RecursiveCharacterTextSplitter with 1000-character chunks and 200-character overlap
- **Vector Storage**: 1,536-dimensional embeddings stored in FAISS IndexFlatL2
- **Retrieval Strategy**: Semantic similarity search with configurable top-k results
- **Generation**: Temperature-controlled GPT-3.5-turbo with structured prompts
- **Caching**: Streamlit-based caching for optimal performance
### Performance Specifications
- **Document Coverage**: 35 Ready Tensor publications
- **Searchable Chunks**: 1,200+ optimally sized text segments
- **Response Time**: Sub-second retrieval, 2-5 second generation
- **Accuracy**: Grounded responses with full source traceability
## Limitations and Future Work
### Current Limitations
- **Knowledge Scope**: Limited to the provided JSON file of Ready Tensor publications
- **Complex Reasoning**: May struggle with highly complex multi-step reasoning tasks
- **Real-time Updates**: Cannot access information published after the knowledge base creation
- **Language Support**: Currently optimized for English-language queries
- **API Dependency**: Requires OpenAI API access for embeddings and generation
### Future Enhancement Roadmap
#### Immediate Improvements (Next Release)
- **Expanded Knowledge Base**: Integration of additional Ready Tensor content and external AI/ML resources
- **Advanced Memory**: Implementation of conversation memory for multi-turn contextual discussions
- **Enhanced UI**: Addition of query suggestions, advanced filtering, and export capabilities
#### Medium-term Goals
- **Hybrid Search**: Combination of semantic and keyword-based retrieval for improved accuracy
- **Multi-modal Support**: Processing of figures, tables, and code snippets from publications
- **Personalization**: User-specific knowledge preferences and query history
#### Long-term Vision
- **Real-time Updates**: Automatic integration of new publications as they're released
- **Collaborative Features**: Shared knowledge bases and team-based exploration tools
- **Advanced Analytics**: Usage patterns and knowledge gap identification
## Technical Asset Access Links
**The complete source code and associated files are available in our GitHub repository**: https://github.com/YanCotta/rag_publication_explorer
### Repository Contents
- Complete application source code (`app.py`)
- Modular architecture components (optional advanced usage)
- Comprehensive documentation and setup instructions
- Sample data and configuration files
- Validation and testing scripts
### Additional Resources
- **Documentation**: Comprehensive README with troubleshooting guide
- **API Reference**: Detailed documentation for programmatic usage
- **Video Tutorial**: Step-by-step setup and usage demonstration
## About Ready Tensor
This publication is part of Ready Tensor's commitment to making AI/ML knowledge accessible and actionable. The Ready Tensor Publication Explorer exemplifies our mission to bridge the gap between cutting-edge research and practical implementation, providing tools that empower the AI/ML community to build better solutions faster.
---
**Category**: Tool / App / Software
**Difficulty Level**: Intermediate
**Estimated Setup Time**: 15-30 minutes
**Prerequisites**: Basic Python knowledge, OpenAI API access
**License**: MIT License
|
Given the following content, create as much questions whose answer can be found within the content. Then, provide the answer to the questions. Ensure the answers are derived directly from the content. Format the questions and answers in the following JSON structure: {Question: '', Answer: ''}.
|
SmartMatch Resume Analyzer: Advanced NLP for Career Optimization
# SmartMatch Resume Analyzer: Advanced NLP for Career Optimization
## A Production-Ready Natural Language Processing Application
**Category**: Natural Language Processing (NLP)
**Author**: Bryan Thompson / Triepod AI
**Publication Type**: Implementation & Applications, Educational Resource
**Keywords**: `nlp`, `semantic-analysis`, `langchain`, `openai`, `resume-optimization`, `production-patterns`, 'AAIDC-M1'
---
## Executive Summary
SmartMatch Resume Analyzer represents a significant advancement in NLP-powered career technology, moving beyond traditional keyword matching to deliver semantic understanding of professional qualifications. This production-ready application demonstrates modern NLP patterns including LangChain integration, response normalization, and async processing while solving real-world career optimization challenges.
**Key Innovations:**
- **Semantic Analysis**: GPT-3.5-turbo powered understanding beyond surface-level keywords
- **Response Normalization**: Novel approach to handling LLM output variations in production
- **Performance Excellence**: Sub-3 second analysis (0.8s-2.6s measured in production)
- **Educational Value**: Complete Jupyter notebook tutorial with production patterns
---
## The Challenge: Traditional Resume Analysis Falls Short
In today's competitive job market, 75% of resumes are filtered out by Applicant Tracking Systems (ATS) before human review. Traditional resume optimization tools rely on simplistic keyword matching, missing the semantic relationships and contextual relevance that modern ATS systems and recruiters actually value.
**Current Limitations:**
- **Surface-Level Matching**: Missing contextual understanding of skills and experience
- **Static Analysis**: No adaptation to different job contexts or industries
- **Generic Feedback**: Lacking actionable, role-specific improvement suggestions
- **Poor User Experience**: Slow processing with unreliable results
---
## The Solution: AI-Powered Semantic Analysis
SmartMatch Resume Analyzer leverages advanced NLP techniques to understand not just what keywords are present, but their contextual relevance and professional impact.
### Technical Architecture
```
Resume Input → LangChain Document Processing → Parallel Extraction
→ Vector Similarity Analysis → GPT-3.5 Semantic Understanding
→ Response Normalization → Actionable Career Insights
```
**Core Technologies:**
- **LangChain**: Advanced document processing and LLM orchestration
- **OpenAI GPT-3.5-turbo**: State-of-the-art semantic understanding
- **FastAPI**: High-performance async backend with automatic documentation
- **Next.js 15**: Modern React frontend with TypeScript
- **Pydantic**: Type-safe validation ensuring robust API responses
### Production-Ready Innovation
#### 1. **Response Normalization System**
One of our key innovations addresses a critical production challenge: LLM output format variations. Our normalization system ensures consistent, reliable results:
```python
class ResponseNormalizer:
def normalize_llm_output(self, raw_response: str) -> AnalysisResult:
"""
Handles variations in LLM responses with automatic fallback
"""
try:
# Primary: Parse structured JSON response
return self.parse_structured_response(raw_response)
except JSONDecodeError:
# Secondary: Extract from natural language
return self.extract_from_text(raw_response)
except Exception:
# Tertiary: Rule-based fallback
return self.apply_rule_based_matching()
```
#### 2. **Async Processing Pipeline**
Optimized for concurrent operations without blocking:
```python
async def analyze_resume_job_pair(
resume: str,
job_description: str
) -> AnalysisResult:
# Parallel extraction for performance
tasks = [
extract_resume_keywords(resume),
extract_job_keywords(job_description),
generate_embeddings(resume),
generate_embeddings(job_description)
]
results = await asyncio.gather(*tasks)
# Semantic analysis with GPT-3.5
analysis = await perform_semantic_matching(results)
# Response normalization
return normalize_response(analysis)
```
---
## Real-World Impact: Measurable Results
### Performance Metrics
- **Analysis Speed**: 0.8s - 2.6s (average: 1.7s)
- **Accuracy**: 94% semantic matching precision
- **Reliability**: 99.9% uptime with automatic fallback systems
- **Scalability**: Handles documents up to 10,000 characters
### Example Analysis Output
**Input**: Software Engineer applying for Machine Learning Engineer role
```
📊 MATCH SCORE: 68%
✅ STRONG MATCHES (15 keywords):
• Python, Docker, AWS - Direct alignment
• Data processing, SQL - Transferable skills
• Team leadership - Soft skill match
⚠️ CRITICAL GAPS (12 keywords):
• Machine Learning frameworks (TensorFlow, PyTorch)
• ML concepts (Neural Networks, Deep Learning)
• MLOps practices and model deployment
💡 IMPROVEMENT SUGGESTIONS:
1. Add ML project: "Implemented customer churn prediction using
scikit-learn, achieving 87% accuracy"
2. Rephrase experience: "Optimized data pipelines" →
"Built ML data pipelines for model training"
3. Include relevant coursework or certifications
🎯 IMPACT PREDICTION: +34% interview likelihood with suggested changes
```
---
## Educational Value: Learn Modern NLP Patterns
### Comprehensive Tutorial Resources
1. **Interactive Jupyter Notebook** (`SmartMatch_AI_Analysis_Tutorial.ipynb`)
- Complete NLP pipeline walkthrough
- Production pattern demonstrations
- Performance optimization techniques
- Error handling best practices
2. **Production Patterns Demonstrated**
- LangChain document processing with chunking strategies
- Prompt engineering for consistent LLM responses
- Async/await patterns for scalable NLP applications
- Type safety with Pydantic validation
3. **Sample Implementation**
```python
# Example from tutorial showing semantic analysis
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
def semantic_similarity_analysis(resume_text, job_text):
# Create embeddings for semantic understanding
embeddings = OpenAIEmbeddings()
# Build vector store for similarity search
resume_vectors = FAISS.from_texts(
resume_chunks, embeddings
)
# Find semantically similar sections
similar_sections = resume_vectors.similarity_search(
job_requirements, k=5
)
return calculate_match_score(similar_sections)
```
---
## Open Source Contribution
### Repository Structure
```
smart-resume-analyzer/
├── backend/ # FastAPI NLP processing server
│ ├── analyzers/ # Core NLP analysis modules
│ ├── models/ # Pydantic validation models
│ └── utils/ # Response normalization
├── frontend/ # Next.js 15 React application
├── examples/ # Sample data and outputs
├── docs/ # Architecture documentation
└── SmartMatch_AI_Analysis_Tutorial.ipynb # Educational tutorial
```
### Getting Started
```bash
# Clone and setup (3 minutes)
git clone https://github.com/triepod-ai/smartmatch-resume-advisor
cd smart-resume-analyzer
./scripts/setup.sh
# Add OpenAI API key
echo "OPENAI_API_KEY=your_key" > backend/.env
# Launch application
./scripts/dev-start.sh
```
---
## Technical Deep Dive: Key Innovations
### 1. **Semantic Understanding Beyond Keywords**
Traditional keyword matching might miss that "built scalable data pipelines" is relevant to "ML data engineering." Our semantic analysis understands these connections:
```python
# Semantic relevance scoring
relevance_score = cosine_similarity(
embed("built scalable data pipelines"),
embed("ML data engineering experience")
) # Returns: 0.87 (high relevance)
```
### 2. **Production Response Normalization**
LLMs can return responses in various formats. Our normalization system handles this gracefully:
- **Structured JSON**: Primary expected format
- **Natural Language**: Secondary extraction using regex patterns
- **Fallback System**: Rule-based matching ensuring 100% reliability
### 3. **Performance Optimization**
- **Document Chunking**: Optimal 1000-character chunks for LLM processing
- **Parallel Processing**: Concurrent keyword and embedding extraction
- **Caching Strategy**: Results cached for repeated analyses
- **Resource Management**: Automatic cleanup and memory optimization
---
## Community Impact & Future Development
### For NLP Developers
- **Reference Implementation**: Production-ready LangChain + OpenAI integration
- **Best Practices**: Error handling, response normalization, async patterns
- **Educational Resource**: Complete tutorial for learning modern NLP
### For Career Professionals
- **Free Tool**: Open-source solution for resume optimization
- **Data Privacy**: All processing can be done locally
- **Continuous Improvement**: Community-driven enhancements
### Roadmap
- **Multi-language Support**: Extending beyond English resumes
- **Industry Specialization**: Custom models for specific sectors
- **Batch Processing**: Analyze multiple resumes simultaneously
- **API Integration**: RESTful API for third-party applications
---
## Conclusion
SmartMatch Resume Analyzer demonstrates how modern NLP techniques can solve real-world problems while serving as an educational resource for the developer community. By combining semantic analysis, production-ready patterns, and comprehensive documentation, this project advances both the state of career technology and NLP education.
**Try it yourself**: [GitHub Repository](https://github.com/triepod-ai/smartmatch-resume-advisor)
**Interactive Tutorial**: Included Jupyter notebook with complete walkthrough
**Live Demo**: Run locally in 3 minutes with our setup scripts
---
## Author Bio
Bryan Thompson is a software engineer specializing in AI/ML applications and production NLP systems. With experience in building scalable solutions at companies like Ford Motor Company, he focuses on bridging the gap between cutting-edge AI research and practical, production-ready applications.
**Contact**: [GitHub](https://github.com/triepod-ai) | [LinkedIn](https://www.linkedin.com/in/bryan-thompson-it/)
---
## Acknowledgments
Special thanks to the open-source NLP community, particularly the teams behind LangChain, OpenAI, and FastAPI for providing the robust foundations that make projects like this possible. Shout out to Victory.
---
**License**: MIT - Open for educational and commercial use
**Tags**: `nlp`, `natural-language-processing`, `semantic-analysis`, `langchain`, `openai`, `gpt-models`, `resume-optimization`, `career-technology`, `production-patterns`, `async-processing`, `fastapi`, `nextjs`
|
Given the following content, create as much questions whose answer can be found within the content. Then, provide the answer to the questions. Ensure the answers are derived directly from the content. Format the questions and answers in the following JSON structure: {Question: '', Answer: ''}.
|
Simple RAG Assistant
## TL;DR
This project implements a basic Retrieval-Augmented Generation (RAG) assistant. It uses a vector store to find relevant documents and an LLM to generate answers based on that context. The user can interact with it through a command-line interface, add new documents, and choose from different local or remote LLMs.
## Abstraction
This is a command-line RAG application that answers questions based on a provided set of documents. It is built with Python and leverages LangChain for interacting with LLMs and a vector store. The core components are:
- **Vector Store**: Uses ChromaDB to store and retrieve document embeddings. It employs a HuggingFace model for creating embeddings.
- **LLM Client**: Connects to both local (via Ollama) and remote (via OpenAI) language models.
- **Conversation Manager**: Handles the main chat loop, maintains conversation history, and orchestrates the RAG pipeline.
- **CLI**: A simple command-line interface for user interaction.
## Motivation
The goal of this project is to create a simple, easy-to-understand RAG assistant that can be extended and modified. It serves as a learning tool for understanding the core concepts of RAG and as a foundation for building more complex conversational AI applications. The motivation is to provide a clear and concise example of how to build a RAG system from scratch.
## Instruction for run
1. **Activate virtual environment**:
Before you begin, make sure to activate your Python virtual environment.
```bash
source venv/bin/activate
```
2. **Install dependencies**:
```bash
pip install -r requirements.txt
```
3. **Set up environment variables**:
Create a `.env` file in the root directory and add your OpenAI API key:
```
OPENAI_API_KEY="your-api-key"
```
4. **Add data**:
Place your JSON files with publications in the `data/` directory. The JSON file should be an array of objects, with each object having `id`, `title`, and `publication_description` keys.
5. **Run the application**:
```bash
python src/main.py
```
6. **Interact with the assistant**:
- The application will first ask you if you want to manage the vector store or start a conversation.
- If you choose to manage the vector store, you can add new publications, check the number of documents, or clear the store.
- If you choose to start a conversation, you'll be prompted to select an LLM, and then you can start asking questions.
## Methodology
The application follows a standard RAG pipeline:
1. **Data Ingestion**: The user can add publications from JSON files. The text from these publications is chunked into smaller pieces.
2. **Embedding**: A HuggingFace sentence-transformer model (`all-MiniLM-L6-v2`) is used to create vector embeddings for each text chunk.
3. **Vector Storage**: The embeddings and the corresponding text chunks are stored in a persistent ChromaDB collection.
4. **Retrieval**: When the user asks a question, the same embedding model is used to create an embedding for the query. The application then queries the vector store to find the most similar text chunks (based on cosine similarity).
5. **Generation**: The retrieved text chunks are used as context for a language model. The application uses a prompt template to combine the context and the user's question, and then sends it to the selected LLM (either local or OpenAI) to generate an answer.
6. **Conversation Memory**: The conversation history is stored in a JSON file to maintain context throughout the session.
7. **Dependency Injection**: The application uses a dependency injection framework to manage and inject dependencies, such as the vector store, into different components. This promotes a modular and testable architecture.
```mermaid
graph TD
subgraph "User"
A["<br/>User<br/>"]
end
subgraph "RAG Assistant"
B["CLI"]
C["Conversation Manager"]
D["Vector Store<br/>(ChromaDB)"]
E["LLM<br/>(OpenAI/Local)"]
end
subgraph "Data"
F["<br/>JSON<br/>Documents"]
end
A -- "Asks question" --> B
B -- " " --> C
C -- "Retrieves context" --> D
C -- "Sends prompt + context" --> E
E -- "Generates answer" --> C
C -- " " --> B
B -- "Displays answer" --> A
F -- "Data Ingestion" --> D
```
## Conclusions
This project is a simple demonstration of a RAG assistant. Working with it revealed a few key insights:
- Using "dumber" local models is highly beneficial for development. It forces more careful prompt engineering and tuning of the retrieval process, as the model is less likely to generate correct answers without accurate context.
- The current implementation always queries the vector store. A valuable future improvement would be to introduce a decision-making agent. This agent could determine whether a user's query requires a database search or if it can be answered directly, making the assistant "smarter" and more efficient.
|
Given the following content, create as much questions whose answer can be found within the content. Then, provide the answer to the questions. Ensure the answers are derived directly from the content. Format the questions and answers in the following JSON structure: {Question: '', Answer: ''}.
|
Aether: A Retrieval-Augmented Generation Assistant for Ready Tensor Publications
--DIVIDER--
## Abstract
This publication introduces Aether, a conversational AI assistant designed to provide users with a seamless and intuitive way to access and understand information from the "Ready Tensor" collection of publications. Aether leverages a Retrieval-Augmented Generation (RAG) architecture to deliver accurate and contextually relevant answers to a wide range of user queries. This document details the project's architecture, implementation, and practical applications, serving as a comprehensive guide for developers, researchers, and anyone interested in the intersection of conversational AI and information retrieval.
---
### 1. Introduction
#### 1.1. Purpose and Objectives
The primary purpose of this project is to develop and showcase a sophisticated, yet user-friendly, conversational AI assistant named **Aether**. The core objective is to provide a tool that can accurately and efficiently answer questions about a specific corpus of documents—the "Ready Tensor" publications. This project demonstrates the practical application of Retrieval-Augmented Generation (RAG) models in creating intelligent systems that can comprehend and converse about specialized knowledge domains.
The specific objectives of this project are:
* To build a robust backend service using FastAPI and LangChain to orchestrate the RAG pipeline, which includes a re-ranking step to enhance search result relevance.
* To create an intuitive frontend interface with Next.js that allows users to interact with Aether in a conversational manner.
* To implement a data ingestion process that efficiently indexes the "Ready Tensor" publications into a Chroma vector database.
* To develop an intent detection mechanism that can differentiate between various user queries and provide tailored responses.
* To maintain a conversation history for each user session to provide a more natural and context-aware conversational experience.
#### 1.2. Intended Audience and Use Case
This publication is intended for:
* **Developers and AI Engineers** who are interested in building and deploying conversational AI applications.
* **Researchers and Data Scientists** who are exploring the capabilities of RAG models and their applications in information retrieval.
* **Students and Enthusiasts** who want to learn about the practical implementation of large language models and vector databases.
The primary use case for Aether is to serve as an intelligent search and summarization tool for the "Ready Tensor" publications. It allows users to ask questions in natural language and receive concise, accurate answers, eliminating the need to manually sift through lengthy documents.
---
### 2. Methodology
#### 2.1. System Architecture
Aether is comprised of two main components: a backend service and a frontend web application.
* **Backend (foundoune):** The backend is a FastAPI server that exposes a RESTful API for the frontend. It uses LangChain to orchestrate the RAG pipeline, which includes data ingestion, intent detection, and response generation. The "Ready Tensor" publications are ingested into a Chroma vector database, and Google's `embedding-001` model is used to create vector embeddings of the text. The `gemini-2.5-pro` model is used for generating natural language responses.
* **Frontend (aether-frontend):** The frontend is a Next.js application that provides a chat interface for users to interact with Aether. It communicates with the backend via API calls to send user queries and receive the assistant's responses.
#### 2.2. The RAG Pipeline
The core of Aether's functionality lies in its RAG pipeline, which can be broken down into the following sections:
**Core Functionality:**
The script creates a sophisticated question-answering system that interacts with a collection of publications. It uses a technique called Retrieval-Augmented Generation (RAG), which means it first finds relevant information from a knowledge base and then uses a powerful language model to generate a human-like answer based on that information.
**Key Features:**
* **Initialization and Data Loading:**
* When the `RAGAssistant` is created, it loads publication data from a JSON file.
* It uses two different Google Generative AI models: the powerful `gemini-2.5-pro` for generating detailed answers and the faster `gemini-1.5-flash` for quickly determining the user's intent.
* It also utilizes Google's embeddings model (`models/embedding-001`) to convert text into numerical representations for searching.
* **Data Ingestion and Vector Store:**
* For the first time it runs, it "ingests" the publication descriptions. This involves breaking the text into smaller chunks and storing them in a `Chroma` vector database.
* This database allows for very fast and efficient "semantic search," which finds text based on meaning rather than just keywords.
* On subsequent runs, it loads the already-created database to save time.
* **Intent Recognition:**
* When a user asks a question, the assistant first uses the `gemini-1.5-flash` model to determine the user's *intent* (e.g., are they asking for a list, a summary, or a specific detail?).
* It can identify intents like `list_all_publications`, `get_publication_details`, `summarize_publication`, and more.
* **Two-Path Query Processing:**
* **Fast Path:** If the user's intent is a straightforward request (like "list all publications" or "what are the details for 'Title X'"), the system directly calls a specific function to retrieve the answer from the loaded publication data without performing a vector search.
* **Slow Path (RAG):** For more general or complex questions, it falls back to the RAG process. It searches the `Chroma` vector database for the most relevant document chunks related to the user's question.
* **Re-ranking for Accuracy:**
* After retrieving an initial set of documents from the vector search, it uses a `CrossEncoder` model to re-rank them. This step further refines the search results to ensure the most relevant information is prioritized.
* **Response Generation:**
* Finally, it takes the top-ranked, most relevant information and feeds it, along with the original question, into the powerful `gemini-2.5-pro` model.
* This model then generates a comprehensive, well-formatted, and friendly response for the user.
* **Conversation History:**
* It maintains a memory of the conversation, allowing for follow-up questions and a more natural conversational flow.
---
### 3. Implementation
#### 3.1. Getting Started
To run Aether on your local machine, you will need to have the following prerequisites installed:
* Python 3.8 or higher
* Node.js 14.0 or higher
* pip and npm
```bash
**Backend Setup:**
# Clone the project repository from GitHub.
git clone <repository_url>
# Navigate to the foundoune directory.
cd foundoune
# Install the required Python packages:
pip install -r requirements.txt
# Set the DATA_PATH environment variable to the path of your project_1_publications.json file.
export DATA_PATH=/path/to/your/project_1_publications.json
# Add the GEMINI and GOOGLE api keys to the project’s environment.
export GEMINI_API_KEY="YOUR_GEMINI_API_KEY"
export GOOGLE_API_KEY="YOUR_GOOGLE_API_KEY"
# Start the FastAPI server:
uvicorn main:app --reload
**Frontend Setup:**
# Navigate to the aether-frontend directory.
cd aether-frontend
# Install the required npm packages:
npm install
# Start the Next.js development server:
npm run dev
```
Once both the backend and frontend are running, you can access the Aether chat interface by opening your web browser and navigating to `http://localhost:3000`.
#### 3.2. Live Demo
A live demo of Aether is available at: [https://aether.alkenacode.dev](https://aether.alkenacode.dev). This will allow you to interact with the assistant and experience its capabilities firsthand.
---
### 4. Results and Discussion
#### 4.1. Use Cases and Examples
Aether can be used to answer a wide variety of questions about the "Ready Tensor" publications. Here are a few examples:
* **Summarization:** "Can you summarize the main points of the 'Advancements in Neural Networks' publication?"
* **Specific Information Retrieval:** "Who are the authors of the 'Deep Learning for Computer Vision' paper?"
* **Comparative Analysis:** "What are the key differences between the 'Natural Language Processing with Transformers' and 'Recurrent Neural Networks for Sequential Data' publications?"
#### 4.2. Future Work
While Aether is a fully functional conversational AI assistant, there are several areas where it could be improved in the future:
* **Expanded Knowledge Base:** The current version of Aether is limited to the "Ready Tensor" publications. In the future, the knowledge base could be expanded to include a wider range of documents.
* **Improved Intent Detection:** The intent detection mechanism could be made more sophisticated to handle a wider range of user queries.
* **Support for Complex Queries:** The current version of Aether is best suited for answering relatively simple questions. In the future, the assistant could be enhanced to handle more complex queries that require reasoning and inference.
---
### 5. Conclusion
Aether is a powerful and versatile conversational AI assistant that demonstrates the potential of RAG models in creating intelligent systems that can understand and converse about specialized knowledge domains. By combining the power of large language models with the efficiency of vector databases, Aether provides a seamless and intuitive way for users to access and understand information.
We encourage you to try out Aether, explore its capabilities, and contribute to its development. The project is open-source and available on [https://github.com/Kiragu-Maina/aether-rag-assistant](https://github.com/Kiragu-Maina/aether-rag-assistant). We welcome your feedback and contributions.
|
Given the following content, create as much questions whose answer can be found within the content. Then, provide the answer to the questions. Ensure the answers are derived directly from the content. Format the questions and answers in the following JSON structure: {Question: '', Answer: ''}.
|
RAG-Based Learning & Code Assistant
# RAG-Based Learning & Code Assistant
## Abstract
The **RAG-Based Learning & Code Assistant** is a dual-purpose AI application designed to provide academic support to students and technical guidance to developers. Leveraging the power of **LangChain**, **Groq's LLaMA 3**, and **ChromaDB**, the system processes and retrieves information from user-uploaded documents. With an intuitive **Gradio** interface and fallback **TF-IDF** embeddings, the assistant ensures contextual and reliable responses.
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## Introduction
**Retrieval-Augmented Generation (RAG)** combines document retrieval with large language models to provide grounded and context-aware answers. This project implements a RAG-based assistant targeting two domains: education and software development. It uses **LangChain** for orchestration, **ChromaDB** for vector storage, and **Groq’s LLaMA 3** for response generation. The application is accessible via a web interface powered by **Gradio**.
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## Methodology
* Users upload documents (`.pdf`, `.txt`, `.md`, `.py`, etc.)
* Text is split into chunks using LangChain's `RecursiveCharacterTextSplitter`
* Embeddings are generated using HuggingFace models or fallback **TF-IDF**
* Chunks are stored in **ChromaDB**
* On receiving a query:
* Relevant chunks are retrieved
* The query + context is sent to **LLaMA 3 (via Groq API)**
* A response is generated and shown via Gradio
Separate vector stores and prompts are used for:
* **Learning Tutor** (for students)
* **Code Helper** (for developers)
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## Experiments
* Academic documents (e.g., textbooks, notes) were tested with the **Learning Tutor**
* Code documentation and Python files were used with the **Code Helper**
* Both transformer-based and fallback TF-IDF embeddings were tested
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## Results
* Responses were accurate and informative
* LLaMA 3 gave well-structured answers when combined with semantic retrieval
* TF-IDF worked acceptably when HuggingFace models were unavailable
* Gradio UI allowed real-time chat experience with source traceability
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## Conclusion
The **RAG-Based Learning & Code Assistant** demonstrates how combining retrieval, embeddings, and LLMs can create effective AI assistants. Its dual functionality for education and development makes it broadly useful. Future improvements could include:
* Cloud deployment (e.g., Hugging Face Spaces)
* Authentication and user sessions
* Support for more document types like `.docx` or `.xlsx`
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