Upload 52 files

src/agents/highlight_explain_agent/flow.py

@@ -1,27 +0,0 @@
-from langgraph.graph import StateGraph, START, END
-from .func import State, highlight_explain
-from langgraph.graph.state import CompiledStateGraph
-
-
-class HighlightExplainAgent:
-    def __init__(self):
-        self.builder = StateGraph(State)
-
-    @staticmethod
-    def routing(state: State):
-        pass
-
-    def node(self):
-        self.builder.add_node("highlight_explain", highlight_explain)
-
-    def edge(self):
-        self.builder.add_edge(START, "highlight_explain")
-        self.builder.add_edge("highlight_explain", END)
-
-    def __call__(self) -> CompiledStateGraph:
-        self.node()
-        self.edge()
-        return self.builder.compile()
-
-
-highlight_workflow = HighlightExplainAgent()()

src/agents/highlight_explain_agent/func.py

@@ -1,33 +1,57 @@
 from typing import TypedDict, AnyStr
 
-from .prompt import highlight_explain_chain
+from .prompt import highlight_explain_chain, highlight_explain_question_generate_chain
 
 
-class State(TypedDict):
-    domain: AnyStr
-    highlight_terms: AnyStr
-    before_highlight_paragraph: AnyStr
-    after_highlight_paragraph: AnyStr
-    question: AnyStr
-    explanation: AnyStr
-    language: AnyStr
+async def highlight_explain(
+    domain,
+    question,
+    highlight_terms,
+    before_highlight_paragraph,
+    after_highlight_paragraph,
+    language,
+):
+    adjacent_paragraphs = (
+        before_highlight_paragraph
+        + "**"
+        + highlight_terms
+        + "**"
+        + after_highlight_paragraph
+    )
+    response = await highlight_explain_chain.ainvoke(
+        {
+            "domain": domain,
+            "highlight_terms": highlight_terms,
+            "adjacent_paragraphs": adjacent_paragraphs,
+            "question": question,
+            "language": language,
+        }
+    )
+    return response.explanation
 
 
-async def highlight_explain(state: State):
+async def highlight_explain_question_generate(
+    domain,
+    question,
+    highlight_terms,
+    before_highlight_paragraph,
+    after_highlight_paragraph,
+    language,
+):
     adjacent_paragraphs = (
-        state["before_highlight_paragraph"]
+        before_highlight_paragraph
         + "**"
-        + state["highlight_terms"]
+        + highlight_terms
         + "**"
-        + state["after_highlight_paragraph"]
+        + after_highlight_paragraph
     )
-    response = await highlight_explain_chain.ainvoke(
+    response = await highlight_explain_question_generate_chain.ainvoke(
         {
-            "domain": state["domain"],
-            "highlight_terms": state["highlight_terms"],
+            "domain": domain,
+            "highlight_terms": highlight_terms,
             "adjacent_paragraphs": adjacent_paragraphs,
-            "question": state["question"],
-            "language": state["language"],
+            "question": question,
+            "language": language,
         }
     )
-    return {"explanation": response["explanation"]}
+    return response.questions

src/agents/highlight_explain_agent/prompt.py

@@ -1,13 +1,21 @@
 from pydantic import BaseModel, Field
 from langchain_core.prompts import ChatPromptTemplate
-from typing import Literal, Annotated, AnyStr, TypedDict
+from typing import List
 from src.config.llm import llm_2_0 as llm
 
 
-class HighlightExplain(TypedDict):
+class HighlightExplain(BaseModel):
     """Explain the highlight terms in a concise and easy to understand manner."""
 
-    explanation: Annotated[AnyStr, "The explanation of the highlight terms."]
+    explanation: str = Field(description="The explanation of the highlight terms.")
+
+
+class HighlightExplainQuestionGenerate(BaseModel):
+    """Gợi ý 3 câu hỏi liên quan"""
+
+    questions: List[str] = Field(
+        description="Các câu hỏi gợi ý liên quan"
+    )
 
 
 highlight_explain_prompt = ChatPromptTemplate(
@@ -33,7 +41,33 @@ The explanation must be primarily in {language}. But you can use {domain} domain
     ]
 )
 
+highlight_explain_question_generate_prompt = ChatPromptTemplate(
+    [
+        (
+            "system",
+            """Bạn là chuyên gia gợi ý câu hỏi ở lĩnh vực {domain}.
+Bạn được cung cấp với các từ khóa được nhấn và các đoạn văn xung quanh từ khóa.
+Bạn cần gợi ý các câu hỏi phù hợp với các từ khóa được nhấn và các đoạn văn xung quanh từ khóa.
+Gen ra 3 gợi ý
+
+Câu hỏi phải được viết chủ yếu bằng {language}. Nhưng bạn có thể sử dụng các từ khóa trong lĩnh vực {domain}.
+            """,
+        ),
+        (
+            "human",
+            """
+            Highlight terms: {highlight_terms}
+            Câu xung quanh highlight terms: {adjacent_paragraphs}
+            """,
+        ),
+    ]
+)
+
 
 highlight_explain_chain = highlight_explain_prompt | llm.with_structured_output(
     HighlightExplain
 )
+highlight_explain_question_generate_chain = (
+    highlight_explain_question_generate_prompt
+    | llm.with_structured_output(HighlightExplainQuestionGenerate)
+)

src/agents/primary_chatbot/prompt.py

@@ -2,7 +2,6 @@ from pydantic import BaseModel, Field
 from langchain_core.prompts import ChatPromptTemplate
 from typing import Literal
 
-# from src.config.llm import llm_2_0 as llm
 from src.config.llm import llm_2_0 as llm
 from typing import Optional
 

src/apis/routers/chat_router.py

@@ -9,7 +9,10 @@ from src.apis.interfaces.chat_interface import (
 )
 from src.apis.interfaces.entrance_eval_interface import TestResultsBody
 from src.agents.primary_chatbot.flow import primary_chat_agent, tutor_chat_agent
-from src.agents.highlight_explain_agent.flow import highlight_workflow
+from src.agents.highlight_explain_agent.func import (
+    highlight_explain,
+    highlight_explain_question_generate,
+)
 from src.agents.entrance_eval_agent.flow import entrance_eval_agent
 
 router = APIRouter(prefix="/ai", tags=["AI"])
@@ -48,19 +51,29 @@ async def tutor_chat(body: TutorChatBody):
 
 
 @router.post("/highlight_explain")
-async def highlight_explain(body: HighlightExplainBody):
-    response = await highlight_workflow.ainvoke(
-        {
-            "domain": body.domain,
-            "question": body.question,
-            "highlight_terms": body.highlight_terms,
-            "before_highlight_paragraph": body.before_highlight_paragraph,
-            "after_highlight_paragraph": body.after_highlight_paragraph,
-            "language": body.language,
-        }
+async def highlight_explain_router(body: HighlightExplainBody):
+    response = await highlight_explain(
+        body.domain,
+        body.question,
+        body.highlight_terms,
+        body.before_highlight_paragraph,
+        body.after_highlight_paragraph,
+        body.language,
     )
-    final_response = response["explanation"]
-    return JSONResponse(status_code=status.HTTP_200_OK, content=final_response)
+    return JSONResponse(status_code=status.HTTP_200_OK, content=response)
+
+
+@router.post("/highlight_explain_question_generate")
+async def highlight_explain_question_generate_router(body: HighlightExplainBody):
+    response = await highlight_explain_question_generate(
+        body.domain,
+        body.question,
+        body.highlight_terms,
+        body.before_highlight_paragraph,
+        body.after_highlight_paragraph,
+        body.language,
+    )
+    return JSONResponse(status_code=status.HTTP_200_OK, content=response)
 
 
 @router.post("/entrance_eval")