Realtime Powerful RAG Pipeline using Neo4j and Langchain | iNeuron

Показать описание

Welcome to our in-depth session on building a real-time, powerful Retrieval-Augmented Generation (RAG) pipeline using Neo4j and Langchain!

In this session, we will guide you step-by-step through the process of creating an efficient and scalable RAG pipeline that leverages the graph database capabilities of Neo4j and the advanced features of Langchain.

📊 Tools and Technologies:

- Database: Neo4j
- Framework: Langchain
- Programming Language: Python
- Development Environment: Jupyter Notebook, any Python IDE

👥 Who Is This For?

- Data Scientists
- Data Engineers
- Developers interested in advanced data processing techniques
- Anyone looking to implement real-time data retrieval and generation systems

💡 Why Watch?

By the end of this session, you'll have a comprehensive understanding of how to build and optimize a real-time RAG pipeline using Neo4j and Langchain. This session provides practical insights and hands-on experience, making it an essential resource for anyone looking to enhance their data processing skills.

📣 Feedback and Suggestions:
We value your feedback! Please leave your comments and suggestions below to help us improve and create content that caters to your interests and needs.

Thank you for watching! If you found this session helpful, please like, share, and subscribe!

#DataScience #RAGPipeline #Neo4j #Langchain #RealTimeData #GraphDatabase #DataEngineering #TechTutorial

Phone: +91 8071176111

Рекомендации по теме

Realtime Powerful RAG Pipeline using Neo4j and Langchain | iNeuron

Realtime Powerful RAG Pipeline using Neo4j(Knowledge Graph Db) and Langchain #rag

Realtime Powerful RAG Pipeline using Neo4j and Langchain | iNeuron

Evaluating your RAG Pipeline in Real Time

Build a Large Language Model AI Chatbot using Retrieval Augmented Generation

Building Production-Ready RAG Applications: Jerry Liu

Build your own RAG (retrieval augmented generation) AI Chatbot using Python | Simple walkthrough

Build Real-Time AI Voice Assistant With RAG Pipeline And Memory | Mistral LLM | Ollama | LlamaIndex

6-Building Advanced RAG Q&A Project With Multiple Data Sources With Langchain

RAG Explained: How 'This' Company Implemented Retrieval-Augmented Generation | ANIMATED

AWS re:Invent 2023 - Real-time RAG: How to augment LLMs with Redis and Amazon Bedrock (DAT101)

Build Local End-to-End RAG Pipeline with Evaluation - BeyondLLM

How to build Multimodal Retrieval-Augmented Generation (RAG) with Gemini

Creating PRODUCTION level END TO END RAG pipeline| Fast, Accurate, Scalable, Simple

RAG Pipeline from Scratch Using OLlama Python & Llama2 | | Llama2 Setup in local PC #llama2 #ra...

Better Llama 2 with Retrieval Augmented Generation (RAG)

10 Powerful Use Cases for Retrieval-Augmented Generation (RAG)

'I want Llama3 to perform 10x with my private knowledge' - Local Agentic RAG w/ llama3

Building Real-Time RAG Pipeline With Mongodb and Pinecone Part-1 #rag #llm #mongodb #pinecone

Master SearXNG Setup in Docker for Powerful RAG Pipelines with LLMs | Easy Configuration & Usage

Session 7: RAG Evaluation with RAGAS and How to Improve Retrieval

Industrial-scale Web Scraping with AI & Proxy Networks

Building a RAG application using open-source models (Asking questions from a PDF using Llama2)

How To Build RAG ChatBot Including Graphical Interface - With Code

Track and Monitor RAG Pipelines using Weights & Biases (wandb)