Readme updates

README.md

@@ -1,6 +1,21 @@
-# Paper Query
+# StrainReliefChat
+
+StrainReliefChat is a naive modular RAG/CAG application for learning more about the StrainRelief drug discovery tool! StrainRelief is a tool developed by Prescient Design to accelerate small molecule drug discovery by using mahcine learning to predict ligand strain energies with quantum mechanical accuracy.
+
+StrainReliefChat is built using a hybrid retrieval and cached augmented generation (RAG/CAG) approach:
+
+1. The full StrainRelief [paper](https://arxiv.org/abs/2503.13352) is loaded and cached in the context window for all queries.
+2. Reference papers cited in StrainRelief are embedded and available for retrieval.
+3. The StrainRelief code [repository](https://github.com/prescient-design/StrainRelief) is embedded and available for retrieval.
+
+The chatbot is deployed with streamlit and is available [**here**](https://strain-relief.streamlit.app/).
+
+**Note**: *StrainReliefChat runs by default using OpenAI's GPT-4.1-nano model, however for optimal performance using GPT-4.1 you will need to enter your own OpenAI API Key.*
+
+# PaperQuery
+
+StrainReliefChat™️ is built on the more general PaperQuery framework...
 
-[intorduction]
 
 # Setup
 
@@ -13,22 +28,23 @@ pre-commit install
 ```
 
 ## API Keys
-API keys are stored in ~/.config/my_api_keys. Youo will need to create and populate this yourself to run the app. Example format:
+API keys are stored in ~/.config/my_api_keys. You will need to create and populate this yourself to run the app locally. Example format:
 ```
 TEMPLATE_API_KEY=some_key
 ```
 
 # Usage
 
+Both StrainReliefChat and PaperQuery are available through the command line or a streamlit app.
+
 ## Streamlit App
 
-There are two streamlit applications: `strain_relief_app.py` contains the StrainRelief paper in the context and references a vectorstore of the code and all paper references.
-```
-streamlit run src/paper_query/ui/strain_relief_app.py
-```
-`custom_app.py` allows customisation of the chatbot, including which model is used and what/how much context is used. Any of the commandline chatbots below can be chosen.
+- **StrainReliefChat**: locally at `src/paper_query/ui/strain_relief_app.py`, deployed [here](https://strain-relief.streamlit.app/).
+
+- **PaperQuery**: locally at `src/paper_query/ui/custom_app.py`, deployment coming soon.
+
 ```
-streamlit run src/paper_query/ui/custom_app.py
+streamlit run path/to/app.py
 ```
 
 ## Commandline Chatbots
@@ -40,88 +56,15 @@ streamlit run src/paper_query/ui/custom_app.py
 `paper-query [model] [provider] [paper]` is a CLI for a chatbot querying a single paper. The entire paper is held in context.
 
 ### code-query
-`code-query [model] [provider] [paper]` is a CLI chatbot for querying a single paper. The entire paper is held in context.
+`code-query [model] [provider] [paper] [code]` is a RAG CLI chatbot for querying a paper and it's code repository. The entire paper is held in context.
 
 ### paper-query-plus
 `paper-query-plus [model] [provider] [paper] [references]` is a RAG CLI chatbot for querying a paper and all of its references. The paper is held in context, references are stored in embeddings.
 
+### hybrid-query
+`hybrid-query [model] [provider] [paper] [references] [code]` is a RAG CLI chatbot for querying both code repositories and literature. The paper is held in context, references are stored in embeddings. StrainReliefChat is an example of this.
+
 ## Unit Tests
 - `pytest tests/` - runs all tests (unit, app and integration)
 - `pytest tests/ -m "app"` - runs all streamlit app tests
 - `pytest tests/ -m "integration"` - runs all integration tests
-
-## Preprocessing
-1. Load
-    - use grobid (in langchain) to extract from scientific paper
-    - GROBID is not reading in the title, authors or abstract.
-    - use tree sitter to extract from code. Can I extract straight from a github repo instead of havig it locally?
-2. Split
-    - look at different methods: how do i evaluate which is better?
-    - split pdf by sections of the paper, fewer sections with more info in each (document-structured based splitting)
-3. Embed
-    - does pdf and code need to be embedded using the same method?
-    - do I want to use a "sentence similarity" or a "document question answering" embedding? (or something else entirely?)
-4. Store
-    - can stroe locally for now but would be good to use DB longer term. Alex and Claude both suggested ChromaDB - have a look.
-
-![image](assets/load_to_store.png)
-
-# Retrieval Augmented Generation
-1. Question
-2. Retrieval
-3. Prompt
-    - what do i want me standard prompt to be? how do I evaluate these?
-4. LLM
-    - what LLM do i want to use? something open source
-5. Answer
-
-![image](assets/rag_qna.png)
-
-# Workflow
-1. paper_query_v0: a simple chatbot built in a modular and expandable fashion.
-
-- LangChain chatbot tutorials
-- LangChain RAG tutorials
-- simple cli chatbot functionality
-
-2. paper_query_v2: load the StrainRelief paper and use in context
-
-- Expand BaseChatbot class for new functionality.
-- PyPDFLoader for paper loading (option to load images)
-- model selection and stroage of api keys
-- add llama 3.1 8B instruct model
-
-3. streamlit interface
-
-- chat function wiht streaming
-- sidebar for selection of chatbot class and all required parameters
-
-4. paper_query_v2: RAG system for references
-
-5. paper_query_v3: RAG system to query the code repository.
-
-6. paper_query_v4: queries both the the code and the papers(s) in a single model.
-
-7.  QA: Qualtiy assurance questions. Can be used to make iterative improvements.
-
-8. Deployment: add a local deployment using Docker and AWS.
-
-9. paper_query_lite: fork paper_query and create MVP for the hybrid model. No uploading your own files. Then share this.
-
-### Additional:
-- QA: Write a selection of questions to QA any iterative improvements of the final model.
-- Chat History: Can I be smart adding StrainRelief to the context and history? StrainRelief ~10,500 tokens out of 128,000. After several questions you will lose the initial history because StrainReleif is included in every question.
-- Vectorstore: check if pdfs are saved previosuly and if so then don't load them.
-- Retrieval: LLM extraction and compression for RAG. Ablation test this.
-    ```
-    compressor = LLMChainExtractor.from_llm(llm)
-    self.retriever = ContextualCompressionRetrieve(base_compressor=compressor, base_retriever=base_retriever)
-    ```
-- Loaders: look at how PaperQA2 parse their papers (GROBID?). Can I emulate that? does it have a noticable difference?
-- Loaders: find a smart way of loading the title and author of each paper for referencing.
-- Embeddings: lokk at how PaperQA2 do thier embeddings. Can I emulate that? does it have a noticable difference?
-- Streamlit: add tab explaining the process used for each chatbot.
-- Streamlit: make upload file more intiative with labels/comments.
-- Streamlit: infer model provider from model and make model selection a dropdown (with free text option?).
-- Streamlit: deploy to streamlit cloud. Need a way to prevent people using my openai tokens before doing this.
-- General: fix circular imports do that assets_dir is defined globally.