Elasticsearch adds a new vector search optimized profile to Elastic Cloud on Microsoft Azure.

Vector Search optimized hardware profile: what you need to know

Vector Search, HNSW, and Memory

Getting Started

Migrating existing Elastic Cloud deployments

Conclusion

Elastic Cloud adds Elasticsearch Vector Database optimized profile to Microsoft Azure

Integrate Elasticsearch's search relevance with ChatGPT's question-answering capability to enhance your domain-specific knowledge base.

What is ChatGPT?

Limitations of ChatGPT & how to minimize them

Elasticsearch — you know, for search!

How to use ChatGPT with Elasticsearch

Technical setup

Elastic Cloud setup

Eland

Elasticsearch index and web crawler

Connecting with OpenAI API

Python backend setup

Clone or download the python program

Sample chat responses

Elasticsearch’s robust retrieval + the power of ChatGPT

The limitations of ChatGPT include its knowledge cutoff date, its lack of knowledge about domain-specific content, and occasional hallucinations. Meaning, ChatGPT often exacerbates when it lacks access to relevant information, which can result in incorrect answers or misleading information.

What are the limitations of ChatGPT?

To minimize the limitations of ChatGPT, you can provide ChatGPT access to specific documents relevant to your domain and questions, and enable ChatGPT’s language understanding capabilities to generate tailored responses. This can be accomplished by connecting ChatGPT to a search engine like Elasticsearch.

How to minimize the limitations of ChatGPT?

Python interface accepts user questions.

Search request is sent to Elasticsearch.

Documentation body and original url are returned to python.

API call is made to OpenAI ChatCompletion.

Generated response is returned to python.

Python adds on original documentation source url to generated response and prints it to the screen for the user.

ChatGPT and Elasticsearch integration: OpenAI meets private data

ChatGPT and Elasticsearch: OpenAI meets private data

Explore how integrating Elasticsearch as a caching layer optimizes Generative AI performance by reducing token costs and response times, demonstrated through real-world testing and practical examples.

RAG overview

Challenges of generative AI at scale

Elastic as caching layer

Evaluating semantic similarity: tolerance vs. resistance

Semantic tolerance

Semantic resistance

Illustrating semantic similarity with an HR example

Impact of the similarity parameter on semantic tolerance and resistance

Use cases

Testing it out

Wrap-up

 RAG is a highly efficient mechanism in natural language processing. It operates by taking a given prompt or question and retrieving relevant information from a large dataset.

What is Retriever-Augmented Generation (RAG)?

Semantic Tolerance, mirroring Recall, is a concept where the similarity function is evaluated with a broader lens, allowing for a wider range of semantic similarities between questions. This leniency can result in more matches, potentially reducing the computational load on the LLM. However, it may also lead to less precise matches.

What is Semantic Tolerance in the context of Elasticsearch?

Optimizing GenAI: integrate Elasticsearch as a GenAI caching layer

Elasticsearch as a GenAI caching layer

Elasticsearch adds a new vector search optimized profile for GCP.

GCP Vector Search optimized instances: what you need to know

Elastic Cloud adds Elasticsearch Vector Database optimized instance to Google Cloud

Learn how Elastic is at the forefront of the generative AI & Large Language Models (LLMs) revolution, helping users take LLMs to new...

The iPhone societal shift: A game-changer in technology

Large language models (LLMs) following a similar pattern: The next big wave

The upcoming societal shift

Seizing the future with large language models

Elastic is at the forefront of the LLM revolution, helping users elevate LLMs by providing real-time information and integrating them into search, observability, and security systems for data analysis.

How is Elastic involved in the LLM revolution?

The generative AI societal shift: Elastic's Gen AI & LLMs journey

Learn about Elasticsearch retrievers, including Standard, kNN, text_expansion, and RRF. Discover how to use retrievers with examples.

Initial types of Retrievers

How are Retrievers different, and why are they useful?

Retrievers usage examples

Standard - Search All the Text!

kNN - Search all the Dense Vectors!

text_expansion - Search all the Sparse Vectors!

rrf - Combine All the Things!

Current restrictions with Retrievers

Retrievers are a new abstraction layer added to the Search API in Elasticsearch. They offer the convenience of configuring multi-stage retrieval pipelines within a single `_search` API call.

What are retrievers in Elasticsearch?

Retrievers come with certain restrictions users should be aware of. For example, only the query element is allowed when using compound retrievers. Additionally, sub-retrievers may not use elements restricted from having a compound retriever as part of the retriever tree.

What are the current restrictions of Elasticsearch retrievers?

Elasticsearch retrievers - How to use search retrievers in Elasticsearch

Introducing Retrievers - Search All the Things!

Explore the synergy between LangChain and Elasticsearch and how they are pushing the boundaries of what's possible with large language models (LLMs).

Understanding LangChain

Elasticsearch and LangChain

Elastic’s first contribution to LangChain

LangChain is a library designed to leverage the power of LLMs and combine them with other sources of computation or knowledge to create powerful applications. It is a toolbox of sorts, providing a standard interface for LLMs and facilitating their integration with other tools. LangChain makes developing applications that can answer questions over specific documents, power chatbots, and even create decision-making agents easier. All of this is done by blending LLMs with other computations (for example, the ability to perform complex maths) and knowledge bases (providing real-time inventory, for example), thus enabling the development of applications that were previously out of reach.

What is Langchain?

Elastic's ability to handle hybrid scoring adds a layer of flexibility and precision to the applications developed with LangChain. In addition to providing efficient storage and search capabilities, Elastic can also supply LLMs with vital context and can be used to store the chat history between users and LLMs. Also, with the recent contribution from Elastic, LangChain has extended its capabilities, offering developers an easier and more efficient way to generate embeddings using Elasticsearch models.

How does LangChain integrates with Elastic?

Elasticsearch & LangChain: unlocking the potential of LLMs

Elasticsearch and LangChain: unlocking the potential of large language models (LLMs)

Discover how Retrieval Augmented Generation (RAG) & Role-Based Access Control (RBAC) integrate to protect data and boost AI capabilities.

Data protection in LLMs

Types of data companies need to consider protecting

Internal vs. public data

Customer-specific data

RAG without RBAC

Elastic RBAC features

RAG with RBAC

Wrapup

RAG & RBAC integration: Protect data and boost AI capabilities

Learn how to implement vector search and kNN using the Elasticsearch APIs via HTTP or Python.

Author

Jeff Vestal

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