Try out this example using this Colab Notebook
Create Complete and Accurate Qdrant Points with the Power of Tensorlake
Let’s set the context for this example, you will build a LangGraph agent for a real estate company to help track who has signed property documents, when they signed, and who still needs to sign. You’ll learn how to:- Use Tensorlake’s Python SDK
- Extract structured data, markdown chunks, and figure and table summarization from research papers with unique reading order
- Create robust points (embeddings and payloads) to upsert into a Qdrant Collection
- Create a LangGraph agent that uses simple tools to answer questions like:
- Does computer science education improve problem solving skills?
- What are the key takeaways from papers by Bill Griswold?
- Why is learning to code hard for kids?
Prerequisites
- Python 3.10+
- An OpenAI API key
- A Tensorlake API key
- A Qdrant API key and Cluster URL
- Some sample research papers
Build and test your LangGraph Agent
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Setup