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  1. README.md +29 -3
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README.md CHANGED
@@ -60,15 +60,19 @@ receive clear, understandable answers.
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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. **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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- 5. **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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- 6. **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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@@ -95,6 +99,28 @@ To get started with MedRAG:
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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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+ -
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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.
doc/images/MediRAg.drawio.png CHANGED