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Browse files- README.md +29 -3
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README.md
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- Uses DSPy to dynamically generate prompts that guide the retrieval process.
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- Helps in crafting responses that are both contextually relevant and easy to understand.
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4. **
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- Utilizes a RAG model to combine real-time retrieval with language generation.
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- Semantic caching improves the response time by reusing answers to similar questions.
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- Employs a vector database for fast and effective retrieval of information.
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- Enhances the bot's ability to search and retrieve relevant content from large datasets.
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- Includes tools to monitor and log the system’s performance.
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- Helps in maintaining system integrity and ensuring reliable operation.
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poetry run python app.py
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```
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## License
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This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
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- Uses DSPy to dynamically generate prompts that guide the retrieval process.
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- Helps in crafting responses that are both contextually relevant and easy to understand.
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4. **LlamaIndex streaming workflows**:
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- Uses LlamaIndex to construct the streaming workflow.
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- Helps in crafting responses that are both contextually relevant and easy to understand.
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5. **Retrieval-Augmented Generation (RAG) with Semantic Caching**:
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- Utilizes a RAG model to combine real-time retrieval with language generation.
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- Semantic caching improves the response time by reusing answers to similar questions.
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6. **Vector Database**:
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- Employs a vector database for fast and effective retrieval of information.
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- Enhances the bot's ability to search and retrieve relevant content from large datasets.
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7. **Observability**:
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- Includes tools to monitor and log the system’s performance.
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- Helps in maintaining system integrity and ensuring reliable operation.
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poetry run python app.py
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```
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## To-Do List
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### High Priority
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- [ ] Implement comprehensive observability tools to monitor and log system performance effectively.
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- [ ] Explore and implement semantic chunking to enhance retrieval performance and accuracy.
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### Medium Priority
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- [ ] Experiment with different embeddings and other models to enhance retrieval performance and accuracy.
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- [ ] Experiment with different embeddings and other models to improve the accuracy and relevance of bot responses.
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- [ ] Index all five DailyMed datasets to ensure complete data coverage and retrieval capabilities.
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- [x] https://dailymed-data.nlm.nih.gov/public-release-files/dm_spl_release_human_rx_part1.zip
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- [ ] https://dailymed-data.nlm.nih.gov/public-release-files/dm_spl_release_human_rx_part2.zip
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- [ ] https://dailymed-data.nlm.nih.gov/public-release-files/dm_spl_release_human_rx_part3.zip
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- [ ] https://dailymed-data.nlm.nih.gov/public-release-files/dm_spl_release_human_rx_part4.zip
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- [ ] https://dailymed-data.nlm.nih.gov/public-release-files/dm_spl_release_human_rx_part5.zip
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### Low Priority
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- [ ] Add an LLM agent to further enhance the system’s capabilities and improve interaction dynamics.
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## License
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This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
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doc/images/MediRAg.drawio.png
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