Build Robust RAG System with Qdrant Vector: Advanced Techniques
- Rajesh
- 0
Introduction Recent advances in AI & ML have significantly transformed information retrieval and data processing. Another important feature is the RAG model, which combines standardized retrieval techniques with powerful generative models to produce more accurate and contextually relevant responses. When paired with a robust vector database like Qdrant, RAG can be further optimized to handle…
Read MoreRevolutionizing Query Rewrite and Extension: RAG Advanced Approach with HyDE
- Rajesh
- 0
Introduction In the present-day times of database management and information retrieval, enhancement of query processing is very important. Techniques containing query rewrite and extension play an important role in optimizing search operations. In this article, we delve into the innovative approach of RAG (Recursive Aggregation Graph) Advanced, coupled with HyDE, exploring its principles, applications, advantages…
Read MoreLeveraging RAG Rerank Technique for Prompt Compression and Retrieving Correct Responses
- Rajesh
- 0
Introduction: The utilization of Large Language Models has increased across various domains of natural language processing. As these models develop, their increased size and complexity present important challenges concerning efficiency, prompt interaction, and response accuracy. Addressing these challenges, the RAG rerank technique emerges as a crucial solution, combining the strengths of retrieval and generation models.…
Read MoreIntroduction to Retrieval Augmented Generation (RAG)
- Rajesh
- 0
In today’s field of artificial intelligence, where language models are highly valued, one of the most critical requirements is to ensure that the answers generated can be reliably accurate. Retrieval Augmented Generation (RAG) is an innovative artificial intelligence system that aims to improve the quality of responses produced by LLM using additional data sources. But…
Read MoreDocument Loaders in Langchain
- Naveen
- 0
In this article, we will be looking at multiple ways which langchain uses to load document to bring information from various sources and prepare it for processing. These loaders act like data connectors, fetching information and converting it into a format Langchain understands. There are a lot of document loaders in LangChain and you can…
Read MoreA Practitioners Guide to Retrieval Augmented Generation (RAG)
- Naveen
- 0
“The power of artificial intelligence is the power to transform humans.” – Fei-Fei Li Welcome to our comprehensive guide on Retrieval Augmented Generation (RAG), a revolutionary technique that combines powerful search capabilities with generative AI to enhance AI systems like langchain, a leading large language model. In this guide, we will explore the concept of…
Read MoreBuilding First Prompt Templates with OpenAI Language Models
- Naveen
- 0
Introduction In this blog post, we will explore how to build your first prompt and prompt templates using OpenAI Language Models. Language models are powerful tools that can generate text based on the input provided to them. By creating prompts and prompt templates, we can guide the language models to generate specific types of text…
Read MoreExploring LangChain: The Framework for Building AI Applications
- Naveen
- 0
In this article we will be exploring the framework that we can use to integrate AI intoour applications and build AI-powered applications. AI is being widely used, and it is the future, regardless of the type of application. I’llalso explain the kinds of applications you can build with AI. Integrating AI hasbecome a must to…
Read MoreHow to Remove Duplicates from a List in Python
- Naveen
- 0
In this article, we will learn how to remove duplicates from a list in Python. We have a list of names that contains duplicate entries, and our goal is to remove these additional names efficiently. While one approach could be to iterate through the list multiple times and check the frequency of each name, this…
Read MoreHow to use *args and **kwargs in Python
- Naveen
- 0
I have come to see that most new python programmers have a hard time figuring out the *args and **kwargs magic variables. So what are they ? First of all, let me tell you that it is not necessary to write *args or **kwargs. Only the * (asterisk) is necessary. You could have also written…
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