Chatbots, Data Ingestion and Natural Language Processing

Build a contextual chatbot application using Knowledge Bases for Amazon Bedrock

AWS Machine Learning Blog

FEBRUARY 19, 2024

Modern chatbots can serve as digital agents, providing a new avenue for delivering 24/7 customer service and support across many industries. Their popularity stems from the ability to respond to customer inquiries in real time and handle multiple queries simultaneously in different languages.

Chatbots

Chatbots Data Ingestion Machine Learning Generative AI

8 Open-Source Tools for Retrieval-Augmented Generation (RAG) Implementation

Marktechpost

JANUARY 10, 2024

In simple terms, RAG is a natural language processing (NLP) approach that blends retrieval and generation models to enhance the quality of generated content. It addresses challenges faced by Large Language Models (LLMs), including limited knowledge access, lack of transparency, and hallucinations in answers.

Natural Language Processing

Natural Language Processing Large Language Models Data Ingestion NLP

Improving RAG Answer Quality Through Complex Reasoning

Towards AI

JULY 24, 2024

TLDR; In this article, we will explain multi-hop retrieval and how it can be leveraged to build RAG systems that require complex reasoning We will showcase the technique by building a Q&A chatbot in the healthcare domain using Indexify, OpenAI, and DSPy. Legal Industry: Creating a retrieval model for legal cases. pip install dspy-ai==2.0.8

Data Ingestion

Data Ingestion OpenAI Natural Language Processing Chatbots

Webinars

How to Achieve High-Accuracy Results When Using LLMs

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Improving RAG Answer Quality Through Complex Reasoning

Towards AI

JULY 24, 2024

TLDR; In this article, we will explain multi-hop retrieval and how it can be leveraged to build RAG systems that require complex reasoning We will showcase the technique by building a Q&A chatbot in the healthcare domain using Indexify, OpenAI, and DSPy. Legal Industry: Creating a retrieval model for legal cases. pip install dspy-ai==2.0.8

Data Ingestion

Data Ingestion OpenAI Natural Language Processing Chatbots

Chatbot on custom knowledge base using LLaMA index

Pragnakalp

JULY 13, 2023

LlamaIndex is an impressive data framework designed to support the development of applications utilizing LLMs (Large Language Models). It offers a wide range of essential tools that simplify tasks such as data ingestion, organization, retrieval, and integration with different application frameworks.

Chatbots

Chatbots Large Language Models LLM Data Ingestion

Personalize your generative AI applications with Amazon SageMaker Feature Store

AWS Machine Learning Blog

OCTOBER 6, 2023

Large language models (LLMs) are revolutionizing fields like search engines, natural language processing (NLP), healthcare, robotics, and code generation. For ingestion, data can be updated in an offline mode, whereas inference needs to happen in milliseconds.

Generative AI

Generative AI LLM Natural Language Processing Metadata

Build a powerful question answering bot with Amazon SageMaker, Amazon OpenSearch Service, Streamlit, and LangChain

AWS Machine Learning Blog

MAY 25, 2023

One of the most common applications of generative AI and large language models (LLMs) in an enterprise environment is answering questions based on the enterprise’s knowledge corpus. Amazon Lex provides the framework for building AI based chatbots. Amazon SageMaker Processing jobs for large scale data ingestion into OpenSearch.

LLM

LLM Data Ingestion Python ML

Build knowledge-powered conversational applications using LlamaIndex and Llama 2-Chat

AWS Machine Learning Blog

APRIL 8, 2024

One popular approach is using Retrieval Augmented Generation (RAG) to create Q&A systems that comprehend complex information and provide natural responses to queries. RAG allows models to tap into vast knowledge bases and deliver human-like dialogue for applications like chatbots and enterprise search assistants.

LLM

LLM Large Language Models Machine Learning Data Ingestion

A Guide on Semantic Search with Embedding Models

Pickl AI

DECEMBER 26, 2024

Semantic search uses Natural Language Processing (NLP) and Machine Learning to interpret the intent behind a users query, enabling more accurate and contextually relevant results. Embedding models are the backbone of semantic search, powering applications in Natural Language Processing (NLP), recommendation systems, and more.

BERT

BERT Natural Language Processing Machine Learning NLP

10 Integral Steps in LLM Application Development

Topbots

FEBRUARY 19, 2024

Networking Capabilities: Ensure your infrastructure has the networking capabilities to handle large volumes of data transfer. Data Pipeline Management: Set up efficient data pipelines for data ingestion, processing, and management. The effectiveness of an LLM system also hinges on its unique characteristics.

LLM

LLM Natural Language Processing Data Ingestion Automation

A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

SEPTEMBER 11, 2024

The benchmark used is the RoBERTa-Base, a popular model used in natural language processing (NLP) applications, that uses the transformer architecture. The automated process of data ingestion, processing, packaging, combination, and prediction is referred to by WorldQuant as their “alpha factory.”

ML

ML Deep Learning Algorithm Large Language Models

Reducing hallucinations in large language models with custom intervention using Amazon Bedrock Agents

Flipboard

NOVEMBER 26, 2024

In this post, we will set up our own custom agentic AI workflow using Amazon Bedrock Agents to intervene when LLM hallucinations are detected and route the user query to customer service agents through a human-in-the-loop process. The final agent response is shown in the chatbot UI(User Interface).

Large Language Models

Large Language Models LLM Natural Language Processing Responsible AI

Dive deep into vector data stores using Amazon Bedrock Knowledge Bases

AWS Machine Learning Blog

OCTOBER 11, 2024

The following diagram depicts the high-level steps of a RAG process to access an organization’s internal or external knowledge stores and pass the data to the LLM. The agent returns the LLM response to the chatbot UI or the automated process. The LLM response is passed back to the agent.

Metadata

Metadata Generative AI LLM Data Ingestion

Artificial Intelligence Zone

Build a contextual chatbot application using Knowledge Bases for Amazon Bedrock

8 Open-Source Tools for Retrieval-Augmented Generation (RAG) Implementation

Webinars

Trending Sources

Improving RAG Answer Quality Through Complex Reasoning

Webinars

Improving RAG Answer Quality Through Complex Reasoning

Chatbot on custom knowledge base using LLaMA index

Personalize your generative AI applications with Amazon SageMaker Feature Store

Build a powerful question answering bot with Amazon SageMaker, Amazon OpenSearch Service, Streamlit, and LangChain

Build knowledge-powered conversational applications using LlamaIndex and Llama 2-Chat

A Guide on Semantic Search with Embedding Models

10 Integral Steps in LLM Application Development

A review of purpose-built accelerators for financial services

Reducing hallucinations in large language models with custom intervention using Amazon Bedrock Agents

Dive deep into vector data stores using Amazon Bedrock Knowledge Bases

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