Connecting external data sources to LLMs to give them memory is a crucial part of many LLM applications. This can take the form of connecting to pre-existing large corpora of data, remembering user conversations, or even creating “new memories” by reflection. Underpinning all of all this is retrieval - the process of pulling relevant pieces of information into context.

Join LangChain’s Harrison Chase for a deep dive into retrieval where we dissect the challenges of finding relevant information from a large corpus of data. 

You’ll learn:
- What is memory and why is it important
- Types of memory
- Basics of semantic search
- Edge cases of semantic search
- Generative Agent examples

# About the session
Connecting external data sources to LLMs to give them memory is a crucial part of many LLM applications. This can take the form of connecting to pre-existing large corpora of data, remembering user conversations, or even creating “new memories” by reflection. Underpinning all of all this is retrieval - the process of pulling relevant pieces of information into context.

Join LangChain’s Harrison Chase for a deep dive into retrieval where we dissect the challenges of finding relevant information from a large corpus of data. 

## You'll learn
- What is memory and why is it important
- Types of memory
- Basics of semantic search
- Edge cases of semantic search
- Generative Agent examples

meetup.svg

Webinar

Join LangChain's Harrison Chase for a live session exploring different retrieval techniques for getting the most relevant context from your LLM application.

Memory for LLM Applications with LangChain's Harrison Chase | Zilliz Webinar

<h1>What to Except</h1>
<p>You’ll receive an email from Zoom with the details to login for the session. We look forward to seeing you at the upcoming webinar!</p>
<ul>
<li>Thursday, June 8, 2023</li>
<li>9:00 AM Pacific | 12:00PM Eastern</li>
<li>45-minute presentation followed by audience questions</li>
</ul>


You’re in!

Webinar

Memory for LLM applications: Different retrieval techniques for getting the most relevant context

What to Except