Towards AI

SEPTEMBER 29, 2024

This article shows you how to build a simple RAG chatbot in Python using Pinecone for the vector database and embedding model, OpenAI for the LLM, and LangChain for the RAG workflow. In such cases, the chatbot may produce responses that are fluent and confident but factually incorrect. Finally, we’ll upsert (i.e.,

Chatbots 111

Chatbots OpenAI LLM Large Language Models 111

AssemblyAI

AUGUST 31, 2023

For this, we create a small demo application that lets you load audio data and apply an LLM that can answer questions about your spoken data. The metadata contains the full JSON response of our API with more meta information: print(docs[0].metadata) page_content) # Runner's knee. Runner's knee is a condition.

Python 217

Python Metadata Large Language Models LLM 217

AssemblyAI

OCTOBER 16, 2023

For this, we create a small demo application with an LLM-powered query engine that lets you load audio data and ask questions about your data. The metadata contains the full JSON response of our API with more meta information: print(docs[0].metadata) Getting Started Create a new virtual environment: # Mac/Linux: python3 -m venv venv.

Python 200

Python Metadata Large Language Models OpenAI 200

AWS Machine Learning Blog

OCTOBER 24, 2024

Since the inception of AWS GenAIIC in May 2023, we have witnessed high customer demand for chatbots that can extract information and generate insights from massive and often heterogeneous knowledge bases. Implementation on AWS A RAG chatbot can be set up in a matter of minutes using Amazon Bedrock Knowledge Bases. doc,pdf, or.txt).

Chatbots 89

Chatbots Metadata Generative AI LLM 89

AWS Machine Learning Blog

MARCH 18, 2025

SQL is one of the key languages widely used across businesses, and it requires an understanding of databases and table metadata. Sonnet on Amazon Bedrock as our LLM to generate SQL queries for user inputs. This retrieved data is used as context, combined with the original prompt, to create an expanded prompt that is passed to the LLM.

LLM 80

LLM Metadata Large Language Models Python 80

Flipboard

DECEMBER 6, 2023

Enterprises turn to Retrieval Augmented Generation (RAG) as a mainstream approach to building Q&A chatbots. The end goal was to create a chatbot that would seamlessly integrate publicly available data, along with proprietary customer-specific Q4 data, while maintaining the highest level of security and data privacy.

Chatbots 163

Chatbots LLM Prompt Engineer Prompt Engineering 163

AssemblyAI

SEPTEMBER 26, 2023

Retrieval Augmented Generation (RAG) is a method to augment the relevance and transparency of Large Language Model (LLM) responses. In this approach, the LLM query retrieves relevant documents from a database and passes these into the LLM as additional context. as the LLM and Chroma as the retriever vector database.

Metadata 246

Metadata LLM Python OpenAI 246

AWS Machine Learning Blog

MAY 30, 2024

Instead, Vitech opted for Retrieval Augmented Generation (RAG), in which the LLM can use vector embeddings to perform a semantic search and provide a more relevant answer to users when interacting with the chatbot. The prompt guides the LLM on how to respond and interact based on the user question.

Chatbots 129

Chatbots Prompt Engineer Prompt Engineering Large Language Models 129

AWS Machine Learning Blog

MARCH 13, 2025

Moreover, employing an LLM for individual product categorization proved to be a costly endeavor. If it was a 4xx error, its written in the metadata of the Job. The PydanticOutputParser requires a schema to be able to parse the JSON generated by the LLM. The generated categories were often incomplete or mislabeled.

Categorization 102

Categorization Prompt Engineer Prompt Engineering LLM 102

AWS Machine Learning Blog

SEPTEMBER 15, 2023

Large language model (LLM) agents are programs that extend the capabilities of standalone LLMs with 1) access to external tools (APIs, functions, webhooks, plugins, and so on), and 2) the ability to plan and execute tasks in a self-directed fashion. We conclude the post with items to consider before deploying LLM agents to production.

LLM 128

LLM Prompt Engineer Prompt Engineering Large Language Models 128

NVIDIA

MAY 1, 2024

Chatbots powered by large-language AI models have transformed computing, and NVIDIA ChatRTX lets users interact with their local data, accelerated by NVIDIA RTX -powered Windows PCs and workstations. The latest version adds support for additional LLMs, including Gemma, the latest open, local LLM trained by Google.

AI Modeling 133

AI Modeling Neural Network Chatbots Metadata 133

Towards AI

DECEMBER 16, 2024

Image by the author One of the major challenges in using LLMs in business is that LLMs hallucinate. How can you entrust your clients to a chatbot that can go mad and tell them something inappropriate at any moment? Thats a problem, especially given that an LLM cant be fired or held accountable.

LLM 105

LLM Metadata Explainability Chatbots 105

IBM Journey to AI blog

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. Most of today’s largest foundation models, including the large language model (LLM) powering ChatGPT, have been trained on information culled from the internet. But how trustworthy is that training data?

Metadata 220

Metadata Explainability Automation Explainable AI 220

Towards AI

DECEMBER 16, 2024

Image by the author One of the major challenges in using LLMs in business is that LLMs hallucinate. How can you entrust your clients to a chatbot that can go mad and tell them something inappropriate at any moment? Thats a problem, especially given that an LLM cant be fired or held accountable.

LLM 98

LLM Metadata Explainability Chatbots 98

Towards AI

DECEMBER 16, 2024

Image by the author One of the major challenges in using LLMs in business is that LLMs hallucinate. How can you entrust your clients to a chatbot that can go mad and tell them something inappropriate at any moment? Thats a problem, especially given that an LLM cant be fired or held accountable.

LLM 98

LLM Metadata Explainability Chatbots 98

AWS Machine Learning Blog

MARCH 20, 2025

Amazon API Gateway (WebSocket API) facilitates real-time interactions, enabling users to query the knowledge base dynamically via a chatbot or other interfaces. Additionally, large language model (LLM)-based analysis is applied to derive further insights, such as video summaries and classifications.

Automation 105

Automation IDP Generative AI Prompt Engineer 105

Unite.AI

JUNE 20, 2024

Large Language Models (LLMs) are capable of understanding and generating human-like text, making them invaluable for a wide range of applications, such as chatbots, content generation, and language translation. However, deploying LLMs can be a challenging task due to their immense size and computational requirements.

Large Language Models 130

Large Language Models LLM Metadata BERT 130

AWS Machine Learning Blog

JANUARY 31, 2024

In this post, we show you how to securely create a movie chatbot by implementing RAG with your own data using Knowledge Bases for Amazon Bedrock. Solution overview The IMDb and Box Office Mojo Movies/TV/OTT licensable data package provides a wide range of entertainment metadata, including over 1.6

Chatbots 118

Chatbots Large Language Models Data Scientist Generative AI 118

Flipboard

DECEMBER 13, 2024

We discuss the solution components to build a multimodal knowledge base, drive agentic workflow, use metadata to address hallucinations, and also share the lessons learned through the solution development using multiple large language models (LLMs) and Amazon Bedrock Knowledge Bases.

Metadata 132

Metadata LLM Software Engineer Chatbots 132

Towards AI

FEBRUARY 28, 2024

get('source') for i in result['source_documents'])}") return result['result'], set(json.loads(i.metadata['metadata']).get('source') It can be used for various applications, from content indexing to interactive chatbots. Args: query (str): The query for which an answer is sought.

Chatbots 105

Chatbots Metadata Python LLM 105

Towards AI

DECEMBER 16, 2024

Image by the author One of the major challenges in using LLMs in business is that LLMs hallucinate. How can you entrust your clients to a chatbot that can go mad and tell them something inappropriate at any moment? Thats a problem, especially given that an LLM cant be fired or held accountable.

LLM 93

LLM Metadata Explainability Chatbots 93

AWS Machine Learning Blog

AUGUST 9, 2024

Question and answering (Q&A) using documents is a commonly used application in various use cases like customer support chatbots, legal research assistants, and healthcare advisors. The embedding representations of text chunks along with related metadata are indexed in OpenSearch Service.

Data Ingestion 129

Data Ingestion Metadata LLM Generative AI 129

AWS Machine Learning Blog

MARCH 10, 2025

Using advanced GenAI, CreditAI by Octus is a flagship conversational chatbot that supports natural language queries and real-time data access with source attribution, significantly reducing analysis time and streamlining research workflows. With this LLM, CreditAI was now able to respond better to broader, industry-wide queries than before.

DevOps 69

DevOps Metadata Auto-complete Automation 69

AWS Machine Learning Blog

DECEMBER 6, 2023

To create AI assistants that are capable of having discussions grounded in specialized enterprise knowledge, we need to connect these powerful but generic LLMs to internal knowledge bases of documents. The search precision can also be improved with metadata filtering.

Metadata 126

Metadata LLM NLP Conversational AI 126

AWS Machine Learning Blog

AUGUST 8, 2024

They used the metadata layer (schema information) over their data lake consisting of views (tables) and models (relationships) from their data reporting tool, Looker , as the source of truth. This post highlights how Twilio enabled natural language-driven data exploration of business intelligence (BI) data with RAG and Amazon Bedrock.

Metadata 123

Metadata LLM Prompt Engineer Prompt Engineering 123

Marktechpost

SEPTEMBER 29, 2024

Retrieval Augmented Generation (RAG) is an AI framework that optimizes the output of a Large Language Model (LLM) by referencing a credible knowledge base outside of its training sources. LLMs are crucial for driving intelligent chatbots and other NLP applications.

LLM 120

LLM Chatbots Metadata Large Language Models 120

AWS Machine Learning Blog

MARCH 5, 2025

To scale ground truth generation and curation, you can apply a risk-based approach in conjunction with a prompt-based strategy using LLMs. Its important to note that LLM-generated ground truth isnt a substitute for use case SME involvement. To convert the source document excerpt into ground truth, we provide a base LLM prompt template.

Generative AI 76

Generative AI LLM AI AI 76

Marktechpost

APRIL 1, 2024

Its integration into LLMs has resulted in widespread adoption, establishing RAG as a key technology in advancing chatbots and enhancing the suitability of LLMs for real-world applications. When it finds a match or multiple matches, it retrieves the related data, converts it to human-readable words, and passes it back to the LLM.

Metadata 125

Metadata LLM Large Language Models Categorization 125

AWS Machine Learning Blog

NOVEMBER 26, 2023

They can also introduce context and memory into LLMs by connecting and chaining LLM prompts to solve for varying use cases. Getting recommendations along with metadata makes it more convenient to provide additional context to LLMs. We are excited to launch LangChain integration.

Generative AI 124

Generative AI Metadata Software Engineer AI 124

AWS Machine Learning Blog

SEPTEMBER 13, 2024

This agent invokes a Lambda function that internally calls the Anthropic Claude Sonnet large language model (LLM) on Amazon Bedrock to perform preliminary analysis on the images. The LLM generates a summary of the damage, which is sent to an SQS queue, and is subsequently reviewed by the claim adjusters.

Metadata 132

Metadata Automation LLM NLP 132

AWS Machine Learning Blog

MARCH 12, 2024

In this post, we discuss how Leidos worked with AWS to develop an approach to privacy-preserving large language model (LLM) inference using AWS Nitro Enclaves. LLMs are designed to understand and generate human-like language, and are used in many industries, including government, healthcare, financial, and intellectual property.

Large Language Models 119

Large Language Models LLM Chatbots Natural Language Processing 119

Marktechpost

JULY 25, 2024

Prompt Engineering with LLaMA-2 Difficulty Level: Beginner This course covers the prompt engineering techniques that enhance the capabilities of large language models (LLMs) like LLaMA-2. Students will learn to write precise prompts, edit system messages, and incorporate prompt-response history to create AI assistant and chatbot behavior.

Large Language Models 113

Large Language Models Prompt Engineering Prompt Engineer Chatbots 113

AWS Machine Learning Blog

OCTOBER 6, 2023

The applications also extend into retail, where they can enhance customer experiences through dynamic chatbots and AI assistants, and into digital marketing, where they can organize customer feedback and recommend products based on descriptions and purchase behaviors. A media metadata store keeps the promotion movie list up to date.

Generative AI 137

Generative AI LLM Natural Language Processing Metadata 137

AWS Machine Learning Blog

MAY 14, 2024

You can build such chatbots following the same process. You can easily build such chatbots following the same process. UI and the Chatbot example application to test human-workflow scenario. A second real-time human workflow is initiated as decided by the LLM. This is an offline process that is part of the RLHF.

Generative AI 118

Generative AI LLM Chatbots Machine Learning 118

Towards AI

FEBRUARY 5, 2024

LLMs, Chatbots medium.com In Part 3a, we’ll discuss Document Loading and Splitting and build a simple RAG pipeline. loading webpage content by URL and pandas dataframe on the fly These loaders use standard document formats comprising content and associated metadata. LangChain 101 Course (updated) LangChain 101 course sessions.

Metadata 115

Metadata LLM ChatGPT Data Analysis 115

Towards AI

AUGUST 8, 2024

Take advantage of the current deal offered by Amazon (depending on location) to get our recent book, “Building LLMs for Production,” with 30% off right now! Featured Community post from the Discord Arwmoffat just released Manifest, a tool that lets you write a Python function and have an LLM execute it.

Neural Network 111

Neural Network Metadata Explainability LLM 111

deepsense.ai

JULY 30, 2023

This approach is as important for fine-tuning Large Language Models internally as for projects where you only use LLM-as-a-service for the inference part (think of, e.g., using OpenAI API for calling GPT models). During training, we log all the model metrics and metadata automatically. Why are these elements so important?

Large Language Models 98

Large Language Models LLM Machine Learning Automation 98