Pricing

Orchestrate your large-scale RAG pipelines with Milvus and Swiftask

Swiftask interfaces with Milvus to transform your massive knowledge bases into dynamic data sources for your AI agents.

Result:

Ensure maximum precision for your agents, even on data volumes exceeding millions of vectors.

The complexity of high-volume RAG architectures

Managing RAG (Retrieval-Augmented Generation) pipelines on terabytes of data presents major challenges: search latency, vector index maintenance, and retrieval accuracy.

Main negative impacts:

Increased latency: Unoptimized vector search slows down agent response times, degrading the end-user experience.
Complex index management: Maintaining data freshness and Milvus index performance requires robust orchestration that standard systems struggle to provide.
Risk of hallucinations: Without precise retrieval within massive datasets, the AI agent risks providing answers disconnected from real context.

Swiftask acts as the intelligent orchestration layer on top of Milvus. We automate the data pipeline, from ingestion to contextual retrieval, to ensure total reliability.

BEFORE / AFTER

What changes with Swiftask

Classic RAG architecture

A fragmented infrastructure where vector processing is decoupled from the agent. Queries are slow, index updates are manual, and precision decreases as the database grows.

Swiftask + Milvus ecosystem

A unified feedback loop. Swiftask indexes, queries, and refines results via Milvus in real-time. Performance remains constant, regardless of your corpus size.

Deploying your RAG pipeline in 4 steps

STEP 1 : Connect to your Milvus cluster

Configure access to your Milvus instance in Swiftask via API. The connection is secure and optimized for large volumes.

STEP 2 : Configure the ingestion pipeline

Define data flows that feed your Milvus collections. Swiftask handles chunking and embedding automatically.

STEP 3 : Set retrieval strategies

Adjust search parameters (top-k, cosine similarity) to maximize the relevance of retrieved snippets.

STEP 4 : Continuous optimization

Monitor the performance of your RAG queries and refine your agent prompts based on Swiftask logs.

Advanced features for your pipelines

Swiftask analyzes the semantics of each user query to query Milvus with precision, filtering out irrelevant results.

Target connector: The agent performs the right actions in milvus based on event context.
Automated actions: Hybrid search (vector + keywords). Automatic index update management. Multi-collection support. Integration with the market's most performant LLMs.
Native governance: All interactions between Swiftask and Milvus are monitored to ensure total transparency regarding resource usage.

Each action is contextualized and executed automatically at the right time.

Each Swiftask agent uses a dedicated identity (e.g. agent-milvus@swiftask.ai ). You keep full visibility on every action and every sent message.

Key takeaway: The agent automates repetitive decisions and leaves high-value actions to your teams.

Why choose this duo for your AI projects

1. Horizontal scalability

Milvus handles billions of vectors. Swiftask orchestrates their use without performance loss.

2. Contextual precision

The combination of Swiftask AI and Milvus power drastically reduces context errors.

3. Accelerated deployment

Avoid months of custom development with our ready-to-use connectors.

4. Data governance

Maintain full control over your sensitive data with pipelines that meet enterprise standards.

5. Multi-LLM support

Change your AI engine without modifying your underlying RAG infrastructure.

Security and compliance

Swiftask applies enterprise-grade security standards for your milvus automations.

End-to-end encryption: All data transiting between Swiftask and Milvus is encrypted.
Granular access control: Define strict permissions on Milvus collections accessible by your agents.
Exhaustive audit logs: Complete traceability of every query made to the vector database.
GDPR/ISO compliance: Infrastructure designed to meet the strictest security requirements.

To learn more about compliance, visit the Swiftask governance page for detailed security architecture information.

RESULTS

Technical performance measured

Metric	Before	After
RAG response time	Several seconds	< 500ms
Data volume	Memory-limited	Billions of vectors
Retrieval accuracy	Inconsistent	> 95% relevance
Maintenance	Manual and time-consuming	Automated by Swiftask

Take action with milvus

Ensure maximum precision for your agents, even on data volumes exceeding millions of vectors.