+ );
+};
+
+export default App;
diff --git a/app/frontend/src/assets/react.svg b/app/frontend/src/assets/react.svg
new file mode 100644
index 0000000..6c87de9
--- /dev/null
+++ b/app/frontend/src/assets/react.svg
@@ -0,0 +1 @@
+
\ No newline at end of file
diff --git a/app/frontend/src/index.css b/app/frontend/src/index.css
new file mode 100644
index 0000000..bd6213e
--- /dev/null
+++ b/app/frontend/src/index.css
@@ -0,0 +1,3 @@
+@tailwind base;
+@tailwind components;
+@tailwind utilities;
\ No newline at end of file
diff --git a/app/frontend/src/main.tsx b/app/frontend/src/main.tsx
new file mode 100644
index 0000000..bef5202
--- /dev/null
+++ b/app/frontend/src/main.tsx
@@ -0,0 +1,10 @@
+import { StrictMode } from 'react'
+import { createRoot } from 'react-dom/client'
+import './index.css'
+import App from './App.tsx'
+
+createRoot(document.getElementById('root')!).render(
+
+
+ ,
+)
diff --git a/app/frontend/src/vite-env.d.ts b/app/frontend/src/vite-env.d.ts
new file mode 100644
index 0000000..9dff957
--- /dev/null
+++ b/app/frontend/src/vite-env.d.ts
@@ -0,0 +1,9 @@
+///
+import type { TestingLibraryMatchers } from "@testing-library/jest-dom/matchers";
+
+declare global {
+ namespace jest {
+ interface Matchers
+ extends TestingLibraryMatchers {}
+ }
+}
\ No newline at end of file
diff --git a/app/frontend/tailwind.config.js b/app/frontend/tailwind.config.js
new file mode 100644
index 0000000..0546a7b
--- /dev/null
+++ b/app/frontend/tailwind.config.js
@@ -0,0 +1,11 @@
+/** @type {import('tailwindcss').Config} */
+export default {
+ content: [
+ "./src/**/*.{js,jsx,ts,tsx}",
+ ],
+ theme: {
+ extend: {},
+ },
+ plugins: [require("daisyui")],
+}
+
diff --git a/app/frontend/tests/setup.ts b/app/frontend/tests/setup.ts
new file mode 100644
index 0000000..54ebe26
--- /dev/null
+++ b/app/frontend/tests/setup.ts
@@ -0,0 +1,9 @@
+import { expect, afterEach } from "vitest";
+import { cleanup } from "@testing-library/react";
+import * as matchers from "@testing-library/jest-dom/matchers";
+
+expect.extend(matchers);
+
+afterEach(() => {
+ cleanup();
+});
\ No newline at end of file
diff --git a/app/frontend/tsconfig.app.json b/app/frontend/tsconfig.app.json
new file mode 100644
index 0000000..358ca9b
--- /dev/null
+++ b/app/frontend/tsconfig.app.json
@@ -0,0 +1,26 @@
+{
+ "compilerOptions": {
+ "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.app.tsbuildinfo",
+ "target": "ES2020",
+ "useDefineForClassFields": true,
+ "lib": ["ES2020", "DOM", "DOM.Iterable"],
+ "module": "ESNext",
+ "skipLibCheck": true,
+
+ /* Bundler mode */
+ "moduleResolution": "bundler",
+ "allowImportingTsExtensions": true,
+ "isolatedModules": true,
+ "moduleDetection": "force",
+ "noEmit": true,
+ "jsx": "react-jsx",
+
+ /* Linting */
+ "strict": true,
+ "noUnusedLocals": true,
+ "noUnusedParameters": true,
+ "noFallthroughCasesInSwitch": true,
+ "noUncheckedSideEffectImports": true
+ },
+ "include": ["src"]
+}
diff --git a/app/frontend/tsconfig.json b/app/frontend/tsconfig.json
new file mode 100644
index 0000000..1ffef60
--- /dev/null
+++ b/app/frontend/tsconfig.json
@@ -0,0 +1,7 @@
+{
+ "files": [],
+ "references": [
+ { "path": "./tsconfig.app.json" },
+ { "path": "./tsconfig.node.json" }
+ ]
+}
diff --git a/app/frontend/tsconfig.node.json b/app/frontend/tsconfig.node.json
new file mode 100644
index 0000000..db0becc
--- /dev/null
+++ b/app/frontend/tsconfig.node.json
@@ -0,0 +1,24 @@
+{
+ "compilerOptions": {
+ "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.node.tsbuildinfo",
+ "target": "ES2022",
+ "lib": ["ES2023"],
+ "module": "ESNext",
+ "skipLibCheck": true,
+
+ /* Bundler mode */
+ "moduleResolution": "bundler",
+ "allowImportingTsExtensions": true,
+ "isolatedModules": true,
+ "moduleDetection": "force",
+ "noEmit": true,
+
+ /* Linting */
+ "strict": true,
+ "noUnusedLocals": true,
+ "noUnusedParameters": true,
+ "noFallthroughCasesInSwitch": true,
+ "noUncheckedSideEffectImports": true
+ },
+ "include": ["vite.config.ts"]
+}
diff --git a/app/frontend/vite.config.ts b/app/frontend/vite.config.ts
new file mode 100644
index 0000000..915b45e
--- /dev/null
+++ b/app/frontend/vite.config.ts
@@ -0,0 +1,17 @@
+import { defineConfig } from 'vite'
+import react from '@vitejs/plugin-react'
+
+// https://vite.dev/config/
+export default defineConfig({
+ plugins: [react()],
+ server: {
+ host: true,
+ strictPort: true,
+ port: 5173
+ },
+ test: {
+ globals: true,
+ environment: "jsdom",
+ setupFiles: "./tests/setup.ts",
+ },
+});
diff --git a/app/llmops/config.yaml b/app/llmops/config.yaml
index eaaf910..af642d2 100644
--- a/app/llmops/config.yaml
+++ b/app/llmops/config.yaml
@@ -15,7 +15,7 @@ rag:
testing:
query: "如何治疗乳腺癌?"
evaluation:
- evaluation_dataset_csv_path: "../../../../data/qa_dataset_20250401b.csv"
+ evaluation_dataset_csv_path: "../../../../data/qa_dataset_20250409_onlyBreast.csv"
evaluation_dataset_column_question: question
evaluation_dataset_column_answer: answer
ls_chat_model_provider:
diff --git a/app/llmops/src/rag_adaptive_evaluation/prompts_library.py b/app/llmops/src/rag_adaptive_evaluation/prompts_library.py
index 33d23a7..143e1a6 100644
--- a/app/llmops/src/rag_adaptive_evaluation/prompts_library.py
+++ b/app/llmops/src/rag_adaptive_evaluation/prompts_library.py
@@ -6,8 +6,9 @@ Use the vectorstore for questions on these topics. Otherwise, use web-search.
system_retriever_grader = """You are a grader assessing relevance of a retrieved document to a user question. \n
If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. \n
- It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \n
- Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question."""
+ You must make sure to read carefully that the document contains a sentence or chunk of sentences that is exactly related but not closely related to the question subject (e.g. must be the exact disease or subject in question). \n
+ The goal is to filter out erroneous retrievals. \n
+ Must return a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question."""
system_hallucination_grader = """You are a grader assessing whether an LLM generation is grounded in / supported by a set of retrieved facts. \n
Give a binary score 'yes' or 'no'. 'Yes' means that the answer is grounded in / supported by the set of facts."""
@@ -18,6 +19,25 @@ system_answer_grader = """You are a grader assessing whether an answer addresses
system_question_rewriter = """You a question re-writer that converts an input question to a better version that is optimized \n
for vectorstore retrieval. Look at the input and try to reason about the underlying semantic intent / meaning."""
+# prompt for question answering based on retrieved documents
+qa_prompt_template = """You are an expert at answering questions based on the following retrieved context.\n
+Before answering the question, you must have your own thought process what are the general scopes to cover when answering this question, step-by-step. Do not include this thought process in the answer.\n
+Then, given your thought process, you must read the provided context carefully and extract the relevant information.\n
+
+If the question is about medical question, you must answer the question in a medical way and assume that the audience is a junior doctor or a medical student: \n
+1. For cancer diseases, you must include comprehensive treatment advices that encompasses multidisciplinary treatment options that included but not limited to surgery, chemotherapy, radiology, internal medicine (drugs), nutritional ratio (protein), etc. You must layout out the treatment options like what are the first-line, second-line treatment etc.\n
+2. For cancer diseases, don't consider context that is not primary tumor/cancer related, unless the question specifically mention it is secondary tumor/cancer related.\n
+3. If the question didn't state the stage of the cancer disease, you must reply with treatment options for each stage of the cancer disease, if they are availalbe in the provided context. If they are not available in the provided context, give a general one.\n
+
+You must not use any information that is not present in the provided context to answer the question. Make sure to remove those information not present in the provided context.\n
+If you don't know the answer, just say that you don't know.\n
+Provide the answer in a concise and organized manner. \n
+
+Question: {question} \n
+Context: {context} \n
+Answer:
+"""
+
# Evaluation
CORRECTNESS_PROMPT = """You are an impartial judge. Evaluate Student Answer against Ground Truth for conceptual similarity and correctness.
diff --git a/app/llmops/src/rag_adaptive_evaluation/run.py b/app/llmops/src/rag_adaptive_evaluation/run.py
index 033fd0d..3a1291b 100644
--- a/app/llmops/src/rag_adaptive_evaluation/run.py
+++ b/app/llmops/src/rag_adaptive_evaluation/run.py
@@ -18,11 +18,14 @@ from typing_extensions import TypedDict
from langchain_core.prompts import ChatPromptTemplate
from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain.prompts import PromptTemplate, HumanMessagePromptTemplate
+
from langchain.schema import Document
from pprint import pprint
from langgraph.graph import END, StateGraph, START
from langsmith import Client
+
from data_models import (
RouteQuery,
GradeDocuments,
@@ -34,7 +37,8 @@ from prompts_library import (
system_retriever_grader,
system_hallucination_grader,
system_answer_grader,
- system_question_rewriter
+ system_question_rewriter,
+ qa_prompt_template
)
from evaluators import (
@@ -141,18 +145,22 @@ def go(args):
##########################################
### Generate
- from langchain import hub
from langchain_core.output_parsers import StrOutputParser
- # Prompt
- prompt = hub.pull("rlm/rag-prompt")
+ # Create a PromptTemplate with the given prompt
+ new_prompt_template = PromptTemplate(
+ input_variables=["context", "question"],
+ template=qa_prompt_template,
+ )
- # Post-processing
- def format_docs(docs):
- return "\n\n".join(doc.page_content for doc in docs)
+ # Create a new HumanMessagePromptTemplate with the new PromptTemplate
+ new_human_message_prompt_template = HumanMessagePromptTemplate(
+ prompt=new_prompt_template
+ )
+ prompt_qa = ChatPromptTemplate.from_messages([new_human_message_prompt_template])
# Chain
- rag_chain = prompt | llm | StrOutputParser()
+ rag_chain = prompt_qa | llm | StrOutputParser()
##########################################