Multi-agent Systems with Mistral AI, Milvus and Llama-agents

1 | © Copyright 8/16/23 Zilliz
Multi-agent Systems with
Mistral AI, Milvus and
Llama-agents
Stephen Batifol | Zilliz
Zilliz Webinar, Aug. 8th

Stephen Batifol
Developer Advocate, Zilliz/ Milvus
stephen.batifol@zilliz.com
linkedin.com/in/stephen-batifol/
@stephenbtl
Speaker

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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

Seamless integration with all popular AI toolkits

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

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

Basic RAG Architecture

5 lines starter

Naive RAG is limited

Naive RAG failure mode
Summarization

Implicit data

Multi-part questions

RAG is necessary but
not sufficient

Good dishes come from good ingredients
• Data collection
• Data cleaning
• Parsing & Chunking

Simplify and streamline
the conversion of
unstructured data into
state-of-the-art vector
embeddings, using
intuitive UI and Restful
APIs.
Pipelines
Easy. High-quality. Scalable.
Simplify the workflow
for developers, from
converting
unstructured data into
searchable vectors to
retrieving them from
vector databases
Deliver excellence in
every phase of vector
search pipeline
development and
deployment,
regardless of their
expertise
Ensure scalability for
managing large
datasets and
high-throughput
queries, maintaining
high performance with
min. customization or
infra changes
Zilliz Cloud Pipelines

Naive RAG Pipeline
⚠ Single-shot
⚠ No query understanding/planning
⚠ No tool use
⚠ No reflection, error correction
⚠ No memory (stateless)

First thing first
Measure it before you attempts to improve it!

01 Agentic RAG

Agentic RAG
✅ Multi-turn
✅ Query / task planning layer
✅ Tool interface for external environment
✅ Reflection
✅ Memory for personalization

Conversation Memory

ReAct Reasoning and Action) Prompting
Designed to:
• Integrate the reasoning capabilities of LLMs
• Ability to take actionable steps
Itʼs able to:
• Understand and process information
• Evaluate situations, take appropriate actions
• Communicate responses
• Track ongoing situations

ReAct Reasoning and Action) Prompting

Tool Use

02
RAG in action with Milvus
Lite

• Framework for building LLM Applications
• Focus on retrieving data and integrating with
LLMs
• Integrations with most AI popular tools
🦙 llama-index

🦙 llama-agents 🤖 by llama-index
• Build Stateful apps with LLMs
and Multi-Agents workflow
• Cycles and Branching
• Human-in-the-Loop
• Persistence

🦙 llama-agents 🤖 - Components

Mistral AI
• Mistral Embed
• Embedding Model focused on Retrieval, very useful
for RAG
• English only
• Mistral Nemo
• 12B model with 128k context length
• Strong Function Calling and Retrieval for its size
• Run Locally
• Mistral Large 2
• 123 Billions parameters with 128K context length
• Very strong Function Calling and Retrieval skills

pip install pymilvus
Milvus Lite

milvus.io
github.com/milvus-io/
@milvusio
@stephenbtl
/in/stephen-batifol
Thank you

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

Multi-agent Systems with Mistral AI, Milvus and Llama-agents

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