advanced~6h
Build a Multi-Source Context Engineering Workflow [Hands-On]
A full hands-on build of a multi-agent research assistant gathering context from documents, memory, web search, and arXiv — using Tensorlake, Zep, Firecrawl, Milvus, and CrewAI, deployed as a Streamlit app with citations.
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▶📚 Prerequisites(1)
🎓 Learning objectives
- •Design a context-engineering pipeline that aggregates context from 4 distinct sources (documents, memory, web, academic papers)
- •Use Tensorlake to convert complex documents into RAG-ready chunks
- •Use Zep's temporal knowledge graphs as a memory layer for chat history and user data
- •Build a context-filtering agent and a synthesizer agent using CrewAI, and deploy the result with citations
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📂 Subtopics
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Workflow Overview: A Multi-Agent Research Assistant Over 4 Context Sources
Building the Retrieval Step: Tensorlake, Milvus Indexing, Firecrawl and arXiv
Compression and Selection: Filtering Aggregated Context Before Generation
The Complete Working Example: Kicking Off the Workflow and the Streamlit App