AI-driven Unstructured Data Extraction Using Apache Camel and LangChain4J
summary
Prototype converts conversation transcripts to POJOs using Apache Camel Quarkus + LangChain4j prototype and a local Ollama-hosted model, Granite 4 3B on Ollama (local CPU). It extracts entities, relations and composite fields (customer.name, customer.birthday), resolves coreferences including a name-change trap, and returns structured JSON via a JSON-schema-like output. Inference runs about 15 seconds on a small machine, yields a reasonable demo accuracy, and highlights the need for confidence scoring and closed vs open extraction choices.
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chapters (13)
- 0:00Introduction to AI data extraction
- 1:19What is unstructured data
- 3:07Demo setup and challenges
- 5:37Code walkthrough introduction
- 7:00LangChain4J configuration
- 8:23Defining extraction targets
- 9:48Prompt and contract setup
- 10:51Apache Camel integration
- 12:18Execution and results
- 14:27Key learnings and tips
- 16:33Next steps and accuracy
- 18:21Handling extraction errors
- 19:44Conclusion and Q&A
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