Building an Agentic RAG locally with Ollama and Milvus

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With the rise of Open-Source LLMs like Llama, Mistral, Gemma, and more, it has become apparent that LLMs might also be useful even when run locally. In this talk, we will see how to deploy an Agentic Retrieval Augmented Generation (RAG) setup using Ollama, with Milvus as the vector database on your laptop. That way, you can also avoid being Rate Limited by OpenAI like I have been in the past.

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1 | © Copyright 8/16/23 Zilliz
Stephen Batifol | Zilliz
Unstructured Data Meetup, June 25th
Using LLM Agents with Llama
3, LangGraph and Milvus

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2 | © Copyright 8/16/23 Zilliz
Stephen Batifol
Developer Advocate, EMEA, Zilliz
stephen.batifol@zilliz.com
https://www.linkedin.com/in/stephen-batifol/
https://twitter.com/stephenbtl
Speaker

3 | © Copyright 8/16/23 Zilliz
3 | © Copyright 8/16/23 Zilliz
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Milvus is an open-source vector database for GenAI projects. pip install on your
laptop, plug into popular AI dev tools, and push to production with a single line of
code.
Easy Setup
Pip-install to start
coding in a notebook
within seconds.
Reusable Code
Write once, and
deploy with one line
of code into the
production
environment
Integration
Plug into OpenAI,
Langchain,
LlmaIndex, and
many more
Feature-rich
Dense & sparse
embeddings,
filtering, reranking
and beyond

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4 | © Copyright 8/16/23 Zilliz
Seamless integration with all popular AI toolkits

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5
RAG
(Retrieval Augmented Generation)

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Basic Idea
Use RAG to force the LLM to work with your data
by injecting it via a vector database like Milvus

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7 | © Copyright 8/16/23 Zilliz
Basic RAG Architecture

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01 Tech Stack

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• Framework for building LLM Applications
• Focus on retrieving data and integrating with
LLMs
• Integrations with most AI popular tools
🦜🔗 LangChain

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🦜🕸 LangGraph by LangChain
• Build Stateful apps with LLMs and Multi-Agents workflow
• Cycles and Branching
• Human-in-the-Loop
• Persistence

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Ollama
• Run LLMs anywhere
• Run Embedding Models

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02 Agentic RAG

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• Routing: Adaptive RAG
• Route Questions to different retrieval approaches
• Fallback: Corrective RAG
• Fallback to web search if docs are not relevant to query
• Self-Correction: Self-RAG
• Try to fix answers with hallucinations or don’t address question
General Ideas

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15 | © Copyright 8/16/23 Zilliz
General Ideas for Agents
• Reflection
• Self-Correction Mechanism
• Planning:
• The agent doesn’t just react to the query
• Lays out a step-by-step process to retrieve or generate the best answer
• Tool use
• Search for Knowledge Base in Milvus
• Search the web for more information

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16 | © Copyright 8/16/23 Zilliz
General Ideas

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17
Demo!

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milvus.io
github.com/milvus-io/
@milvusio
@stephenbtl
/in/stephen-batifol
Questions?

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02 Advanced RAG techniques

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● Divide & Conquer
○ Query Enhancement: better express or process the query intent.
○ Indexing Enhancement: data cleanup, better parser and chunking
○ Retriever Enhancement: more retrievers and hybrid search strategy
○ Generator Enhancement: prompt engineering and more powerful LLM
Types of RAG Enhancement Techniques

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21 | © Copyright 8/16/23 Zilliz
Meta Storage
Root Query Data Index
Coordinator Service
Proxy
Proxy
etcd
Log Broker
SDK
Load Balancer
DDL/DCL
DML
NOTIFICATION
CONTROL SIGNAL
Object Storage
Minio / S3 / AzureBlob
Log Snapshot Delta File Index File
Worker Node QUERY DATA DATA
Message Storage
VECTOR
DATABASE
Access Layer
Query Node Data Node Index Node
Milvus Architecture

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原版一模一样【微信：741003700 】【(msvu毕业证书）圣文森山大学毕业证成绩单】【微信：741003700 】学位证，留信学历认证（真实可查，永久存档）原件一模一样纸张工艺/offer、在读证明、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。本公司拥有海外各大学样板无数，能完美还原。 1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700 【主营项目】一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！二.真实使馆公证(即留学回国人员证明,不成功不收费) 三.真实教育部学历学位认证（教育部存档！教育部��服网站永久可查）四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度) 如果您处于以下几种情况： ◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】 ◇面对父母的压力，希望尽快拿到； ◇不清楚认证流程以及材料该如何准备； ◇回国时间很长，忘记办理； ◇回国马上就要找工作，办给用人单位看； ◇企事业单位必须要求办理的 ◇需要报考公务员、购买免税车、落转户口 ◇申请留学生创业基金留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才办理(msvu毕业证书）圣文森山大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。办理(msvu毕业证书）圣文森山大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：校徽：象征着学校的荣誉和传承。校名:学校英文全称授予学位：本部分将注明获得的具体学位名称。毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。办理(msvu毕业证书）圣文森山大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。综上所述，办理(msvu毕业证书）圣文森山大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

How to Avoid Learning the Linux-Kernel Memory Model

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The Linux-kernel memory model (LKMM) is a powerful tool for developing highly concurrent Linux-kernel code, but it also has a steep learning curve. Wouldn't it be great to get most of LKMM's benefits without the learning curve? This talk will describe how to do exactly that by using the standard Linux-kernel APIs (locking, reference counting, RCU) along with a simple rules of thumb, thus gaining most of LKMM's power with less learning. And the full LKMM is always there when you need it!

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The lecture titled "Automating AppSec" delves into the critical challenges associated with manual application security (AppSec) processes and outlines strategic approaches for incorporating automation to enhance efficiency, accuracy, and scalability. The lecture is structured to highlight the inherent difficulties in traditional AppSec practices, emphasizing the labor-intensive triage of issues, the complexity of identifying responsible owners for security flaws, and the challenges of implementing security checks within CI/CD pipelines. Furthermore, it provides actionable insights on automating these processes to not only mitigate these pains but also to enable a more proactive and scalable security posture within development cycles. The Pains of Manual AppSec: This section will explore the time-consuming and error-prone nature of manually triaging security issues, including the difficulty of prioritizing vulnerabilities based on their actual risk to the organization. It will also discuss the challenges in determining ownership for remediation tasks, a process often complicated by cross-functional teams and microservices architectures. Additionally, the inefficiencies of manual checks within CI/CD gates will be examined, highlighting how they can delay deployments and introduce security risks. Automating CI/CD Gates: Here, the focus shifts to the automation of security within the CI/CD pipelines. The lecture will cover methods to seamlessly integrate security tools that automatically scan for vulnerabilities as part of the build process, thereby ensuring that security is a core component of the development lifecycle. Strategies for configuring automated gates that can block or flag builds based on the severity of detected issues will be discussed, ensuring that only secure code progresses through the pipeline. Triaging Issues with Automation: This segment addresses how automation can be leveraged to intelligently triage and prioritize security issues. It will cover technologies and methodologies for automatically assessing the context and potential impact of vulnerabilities, facilitating quicker and more accurate decision-making. The use of automated alerting and reporting mechanisms to ensure the right stakeholders are informed in a timely manner will also be discussed. Identifying Ownership Automatically: Automating the process of identifying who owns the responsibility for fixing specific security issues is critical for efficient remediation. This part of the lecture will explore tools and practices for mapping vulnerabilities to code owners, leveraging version control and project management tools. Three Tips to Scale the Shift Left Program: Finally, the lecture will offer three practical tips for organizations looking to scale their Shift Left security programs. These will include recommendations on fostering a security culture within development teams, employing DevSecOps principles to integrate security throughout the development

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一比一原版(msvu毕业证书）圣文森山大学毕业证如何办理

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Building an Agentic RAG locally with Ollama and Milvus

1. 1 | © Copyright 8/16/23 Zilliz 1 | © Copyright 8/16/23 Zilliz Stephen Batifol | Zilliz Unstructured Data Meetup, June 25th Using LLM Agents with Llama 3, LangGraph and Milvus

2. 2 | © Copyright 8/16/23 Zilliz 2 | © Copyright 8/16/23 Zilliz Stephen Batifol Developer Advocate, EMEA, Zilliz stephen.batifol@zilliz.com https://www.linkedin.com/in/stephen-batifol/ https://twitter.com/stephenbtl Speaker

3. 3 | © Copyright 8/16/23 Zilliz 3 | © Copyright 8/16/23 Zilliz 27K+ GitHub Stars 25M+ Downloads 250+ Contributors 2,600 + Forks Milvus is an open-source vector database for GenAI projects. pip install on your laptop, plug into popular AI dev tools, and push to production with a single line of code. Easy Setup Pip-install to start coding in a notebook within seconds. Reusable Code Write once, and deploy with one line of code into the production environment Integration Plug into OpenAI, Langchain, LlmaIndex, and many more Feature-rich Dense & sparse embeddings, filtering, reranking and beyond

5. 5 | © Copyright 8/16/23 Zilliz 5 | © Copyright 8/16/23 Zilliz | © Copyright 8/16/23 Zilliz 5 RAG (Retrieval Augmented Generation)

6. 6 | © Copyright 8/16/23 Zilliz 6 | © Copyright 8/16/23 Zilliz Basic Idea Use RAG to force the LLM to work with your data by injecting it via a vector database like Milvus

9. 9 | © Copyright 8/16/23 Zilliz 9 | © Copyright 8/16/23 Zilliz • Framework for building LLM Applications • Focus on retrieving data and integrating with LLMs • Integrations with most AI popular tools 🦜🔗 LangChain

10. 10 | © Copyright 8/16/23 Zilliz 10 | © Copyright 8/16/23 Zilliz 🦜🕸 LangGraph by LangChain • Build Stateful apps with LLMs and Multi-Agents workflow • Cycles and Branching • Human-in-the-Loop • Persistence

14. 14 | © Copyright 8/16/23 Zilliz 14 | © Copyright 8/16/23 Zilliz • Routing: Adaptive RAG • Route Questions to different retrieval approaches • Fallback: Corrective RAG • Fallback to web search if docs are not relevant to query • Self-Correction: Self-RAG • Try to fix answers with hallucinations or don’t address question General Ideas

15. 15 | © Copyright 8/16/23 Zilliz 15 | © Copyright 8/16/23 Zilliz General Ideas for Agents • Reflection • Self-Correction Mechanism • Planning: • The agent doesn’t just react to the query • Lays out a step-by-step process to retrieve or generate the best answer • Tool use • Search for Knowledge Base in Milvus • Search the web for more information

18. 18 | © Copyright 8/16/23 Zilliz 18 | © Copyright 8/16/23 Zilliz milvus.io github.com/milvus-io/ @milvusio @stephenbtl /in/stephen-batifol Questions?

20. 20 | © Copyright 8/16/23 Zilliz 20 | © Copyright 8/16/23 Zilliz ● Divide & Conquer ○ Query Enhancement: better express or process the query intent. ○ Indexing Enhancement: data cleanup, better parser and chunking ○ Retriever Enhancement: more retrievers and hybrid search strategy ○ Generator Enhancement: prompt engineering and more powerful LLM Types of RAG Enhancement Techniques

21. 21 | © Copyright 8/16/23 Zilliz 21 | © Copyright 8/16/23 Zilliz Meta Storage Root Query Data Index Coordinator Service Proxy Proxy etcd Log Broker SDK Load Balancer DDL/DCL DML NOTIFICATION CONTROL SIGNAL Object Storage Minio / S3 / AzureBlob Log Snapshot Delta File Index File Worker Node QUERY DATA DATA Message Storage VECTOR DATABASE Access Layer Query Node Data Node Index Node Milvus Architecture

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