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*Objective:* Develop a system to track and store LLM token usage data, enabling organizations to audit and analyze token usage.
#### Key Steps:
1. *Introduction:*
- Importance of tracking LLM token usage for auditing and cost management.
- Real-world use case of integrating OpenAI with DynamoDB to store token usage data.
2. *Setup:*
- Create a DynamoDB table named `token_logs` with a partition key `response_id`.
- Install necessary libraries (`boto3` for AWS interactions).
3. *Practical Implementation:*
- Initialize DynamoDB client and create a method to update the table.
- Set up the LangChain with OpenAI's GPT-4 model.
- Define a prompt template to get responses in JSON format.
- Extract response metadata (input tokens, output tokens, total tokens).
- Store question, answer, and metadata in DynamoDB.
- Demonstrate writing a sample entry to DynamoDB.
- Handle JSON responses and integrate error handling.
- Update DynamoDB with token usage data and timestamp.
4. *Conclusion:*
- Summarize the benefits of this implementation for auditing LLM usage.
- Encourage viewers to subscribe, like, and share the video for more tutorials.
*Personal Requests:*
- If you are watching this video for the first time, please try to subscribe to my channel, hit the like button, and share my videos with your family and friends.
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*Playlist Recommendations:*
- Check out my playlists on LangChain and Llama Index for more detailed tutorials and use cases.
*Thank You:*
- Thank you for watching and supporting the channel. Stay tuned for more tutorials, and happy learning!
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