Course overview
Practical session to build a RAG agent for analyzing company financial results: integrate sources, vectorize with Pinecone, orchestrate in n8n and synthesize findings with GPT/Gemini into Google Docs. Covers environment prep, stepwise assembly and security checks.
Objective: create a RAG agent to ingest financial documents, index them and produce consolidated analyses.
Prepare Pinecone, model access (GPT/Gemini), n8n and Google Docs for textual output.
Progressively build pipelines: extraction, embeddings, contextual retrieval and model querying.
Security checkpoint: verify controls on access to sensitive data and API calls.
1lessons
1lessons
4activities
4exercises
0videos
Skills and topics covered
Ingestion and chunking of financial PDF reportsEmbedding creation and vector searchDocumented response to complex financial questionsGenerate an analysis report in Google DocsApplied AI for business functionsData & BI researchn8nGPTGeminiPineconeGoogle Docsavancé
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