Build a Financial Document Assistant with Qdrant and Mistral AI

How it works

1. 1.Load financial documents automatically when uploaded or manually via the chat interface
2. 2.Split text content into logical chunks and convert them into embeddings using the embedding model
3. 3.Store all vectors in Qdrant for semantic search and fast document retrieval
4. 4.Receive user questions and retrieve the most relevant document sections from the vector store
5. 5.Send the retrieved context and query to Mistral LLM to generate precise, detailed answers

Use cases

• Extract key figures, ratios, and financial metrics from annual reports and SEC filings automatically
• Answer analyst and management questions about contract terms, deal structures, and financial provisions
• Summarize and explain complex regulatory documents, quarterly earnings reports, and investment prospectuses

Requirements

• Active Mistral AI account with API access to the embedding and Mistral chat models
• Qdrant instance (cloud or local) configured and accessible to store and retrieve document vectors

Service Value

Ideal as a smart automation service combining integrations and AI to produce ready-to-use results.