We have all searched countless PDFs, Word documents, and text files for that crucial information. It is annoying, sluggish, and unproductive. Here comes DeepSeek RAG Chatbot 3.0. 

No ordinary chatbot, this AI-powered retrieval assistant understands context, remembers earlier discussions, and establishes information linkages. This strong combo of BM25, FAISS, Neural Reranking, GraphRAG, and Chat Memory Integration retrieves documents quickly, accurately, and explainably. 

Imagine asking a chatbot about your uploaded research paper. DeepSeek RAG Chatbot 3.0 develops a Knowledge Graph, analyzes important idea linkages, and immediately retrieves the most relevant information instead of scanning hundreds of pages. It even recalls your questions, making follow-ups easier. 

Look at what makes this latest version game-changing. 

 

New Features in DeepSeek RAG Chatbot 3.0

GraphRAG Integration

New DeepSeek RAG Chatbot feature GraphRAG creates a Knowledge Graph from uploaded documents. It links significant ideas instead of processing text as individual fragments, making information retrieval more contextual and perceptive.

GraphRAG helps legal document analysts evaluate clause relationships. The chatbot provides an organized answer rather than fragments.

Chat Memory History Awareness

Traditional chatbots typically forget recent conversations. DeepSeek RAG Chatbot remembers past interactions to provide more clear and relevant replies.

Ask yourself, “What did the last portion of my document say about AI ethics?” when working on a project. Instead of repeating everything, the chatbot remembers earlier conversations and responds.

Improved Error Handling

Everyone hates buggy software. The latest DeepSeek RAG Chatbot version solves chat history cleaning difficulties, improving user experience. Improvements to query processing and response creation speed up and simplify interactions.

Installation & Setup

Traditional (Python/venv) Installation

Starting is easy. Create a virtual environment first by cloning the repository:

git clone https://github.com/SaiAkhil066/DeepSeek-RAG-Chatbot.git  
cd DeepSeek-RAG-Chatbot  
python -m venv venv  
source venv/bin/activate  # For macOS/Linux  
venv\Scripts\activate  # For Windows  
pip install --upgrade pip  
pip install -r requirements.txt  

Then download and set Ollama, the core inference engine of the chatbot:

ollama pull deepseek-r1:7b  
ollama pull nomic-embed-text  

Finally, start the chatbot by running:

ollama serve  
streamlit run app.py  

You may communicate with the chatbot using http://localhost:8501 once it starts.

Docker Installation

Docker provides a handy answer for people who would like containerized deployment. Should Ollama already be installed on your server, just create and launch the chatbot container:

docker-compose build  
docker-compose up  

Use this docker-compose.yml file to containerize Ollama and the chatbot:

version: "3.8"
services:
  ollama:
    image: ghcr.io/jmorganca/ollama:latest
    container_name: ollama
    ports:
      - "11434:11434"

  deepgraph-rag-service:
    container_name: deepgraph-rag-service
    build: .
    ports:
      - "8501:8501"
    environment:
      - OLLAMA_API_URL=http://ollama:11434
      - MODEL=deepseek-r1:7b
      - EMBEDDINGS_MODEL=nomic-embed-text:latest
      - CROSS_ENCODER_MODEL=cross-encoder/ms-marco-MiniLM-L-6-v2
    depends_on:
      - ollama

Run the chatbot with:

docker-compose build  
docker-compose up  

This integrates Ollama and the chatbot into Docker, removing compatibility concerns.

How the Chatbot Works

To guarantee accuracy and context awareness, DeepSeek RAG Chatbot retrieves in many steps.

Upload PDF, DOCX, or TXT files using the sidebar. The chatbot then retrieves appropriate text fragments using BM25 and FAISS. BM25 ranks results by term frequency, whereas FAISS uses vector-based similarity search to improve accuracy.

GraphRAG links essential concepts to provide context in a Knowledge Graph after retrieving relevant snippets. Next, a Cross-Encoder model refines search results based on query relevance via Neural Reranking.

HyDE expands retrieval with hypothetical replies to increase accuracy. After processing everything using DeepSeek-7B, the chatbot responds properly.

Chat Memory Awareness helps the chatbot remember past interactions and create a conversational flow rather than simply responding to queries.

Implementing GraphRAG

Building a Knowledge Graph from Text

from transformers import pipeline
import networkx as nx
import spacy

nlp = spacy.load("en_core_web_sm")

def extract_relations(text):
    doc = nlp(text)
    relations = []
    for token in doc:
        if token.dep_ in ("nsubj", "dobj"):
            relations.append((token.head.text, token.text))
    return relations

text = "DeepSeek RAG Chatbot retrieves documents using GraphRAG and BM25."
relations = extract_relations(text)

G = nx.DiGraph()
for head, tail in relations:
    G.add_edge(head, tail)

print(G.edges)

This code uses SpaCy and NetworkX to create a Knowledge Graph using text relationships.

Integrating with FAISS for Fast Retrieval

import faiss
import numpy as np
from sentence_transformers import SentenceTransformer

embedder = SentenceTransformer("all-MiniLM-L6-v2")

documents = ["GraphRAG enhances retrieval.", "BM25 uses keyword matching.", "FAISS speeds up search."]
doc_embeddings = np.array(embedder.encode(documents), dtype="float32")

index = faiss.IndexFlatL2(doc_embeddings.shape[1])
index.add(doc_embeddings)

query = "How does GraphRAG work?"
query_embedding = embedder.encode([query])
_, indices = index.search(query_embedding, 1)

print(f"Best match: {documents[indices[0][0]]}")

This application embeds text using a transformer model, saves it in an FAISS index, and finds the best query match. 

Conclusion 

DeepSeek RAG Chatbot 3.0 improves AI-powered document retrieval. It provides context-aware, accurate, and explainable replies using GraphRAG, FAISS, BM25, and Chat Memory. This chatbot handles quick information retrieval, smooth conversation history integration, and complex queries. 

Join the future wave of AI-driven knowledge retrieval now!