Create graph4.py

lab/graph4.py

@@ -0,0 +1,162 @@
+__import__('pysqlite3')  # Workaround for sqlite3 error on live Streamlit.
+import sys
+sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')  # Workaround for sqlite3 error on live Streamlit.
+import graphviz
+import traceback
+import tempfile
+from langgraph.graph import StateGraph, END
+from langchain_openai import ChatOpenAI
+from pydantic import BaseModel, Field
+from typing import TypedDict, List, Literal, Dict, Any
+from langchain_core.output_parsers import StrOutputParser, JsonOutputParser
+from langchain.prompts import PromptTemplate
+from langchain.memory import ConversationBufferMemory
+from pdf_writer import generate_pdf
+from crew import CrewClass, Essay
+
+
+class GraphState(TypedDict):
+    topic: str
+    response: str
+    documents: List[str]
+    essay: Dict[str, Any]
+    pdf_name: str
+
+
+class RouteQuery(BaseModel):
+    """Route a user query to direct answer or research."""
+
+    way: Literal["edit_essay", "write_essay", "answer"] = Field(
+        ...,
+        description="Given a user question, choose to route it to write_essay, edit_essay, or answer",
+    )
+
+
+class EssayWriter:
+    def __init__(self):
+        self.model = ChatOpenAI(model="gpt-4o-mini-2024-07-18", temperature=0)
+        self.crew = CrewClass(llm=ChatOpenAI(model="gpt-4o-mini-2024-07-18", temperature=0.5))
+
+        self.memory = ConversationBufferMemory()
+        self.essay = {}
+        self.router_prompt = """
+                            You are a router, and your duty is to route the user to the correct expert.
+                            Always check conversation history and consider your move based on it.
+                            If the topic is something about memory or daily talk, route the user to the answer expert.
+                            If the topic starts with something like "Can you write" or the user requests an article or essay, route the user to the write_essay expert.
+                            If the topic is about editing an essay, route the user to the edit_essay expert.
+                            
+                            \nConversation History: {memory}
+                            \nTopic: {topic}
+                            """
+
+        self.simple_answer_prompt = """
+                            You are an expert, and you are providing a simple answer to the user's question.
+                            
+                            \nConversation History: {memory}
+                            \nTopic: {topic}
+                            """
+
+        builder = StateGraph(GraphState)
+
+        builder.add_node("answer", self.answer)
+        builder.add_node("write_essay", self.write_essay)
+        builder.add_node("edit_essay", self.edit_essay)
+
+        builder.set_conditional_entry_point(self.router_query, {
+            "write_essay": "write_essay",
+            "answer": "answer",
+            "edit_essay": "edit_essay",
+        })
+
+        builder.add_edge("write_essay", END)
+        builder.add_edge("edit_essay", END)
+        builder.add_edge("answer", END)
+
+        self.graph = builder.compile()
+        self.save_workflow_graph()
+
+
+    def router_query(self, state: GraphState):
+        print("**ROUTER**")
+        prompt = PromptTemplate.from_template(self.router_prompt)
+        memory = self.memory.load_memory_variables({})
+
+        router_query = self.model.with_structured_output(RouteQuery)
+        chain = prompt | router_query
+        result: RouteQuery = chain.invoke({"topic": state["topic"], "memory": memory})
+
+        print("Router Result: ", result.way)
+        return result.way
+
+    def answer(self, state: GraphState):
+        print("**ANSWER**")
+        prompt = PromptTemplate.from_template(self.simple_answer_prompt)
+        memory = self.memory.load_memory_variables({})
+        chain = prompt | self.model | StrOutputParser()
+        result = chain.invoke({"topic": state["topic"], "memory": memory})
+
+        self.memory.save_context(inputs={"input": state["topic"]}, outputs={"output": result})
+        return {"response": result}
+
+    def write_essay(self, state: GraphState):
+        print("**ESSAY COMPLETION**")
+        # Generate the essay using the crew
+        self.essay = self.crew.kickoff({"topic": state["topic"]})
+        # Save the conversation context
+        self.memory.save_context(inputs={"input": state["topic"]}, outputs={"output": str(self.essay)})
+        # Generate the PDF and return essay content for preview
+        pdf_name = generate_pdf(self.essay)
+        return {
+            "response": "Here is your essay! You can review it below before downloading.",
+            "essay": self.essay,
+            "pdf_name": pdf_name,
+        }
+
+    def edit_essay(self, state: GraphState):
+        print("**ESSAY EDIT**")
+        memory = self.memory.load_memory_variables({})
+
+        user_request = state["topic"]
+        parser = JsonOutputParser(pydantic_object=Essay)
+        prompt = PromptTemplate(
+            template=(
+                "Edit the JSON file as the user requested, and return the new JSON file."
+                "\n Request: {user_request} "
+                "\n Conversation History: {memory}"
+                "\n JSON File: {essay}"
+                " \n{format_instructions}"
+            ),
+            input_variables=["memory", "user_request", "essay"],
+            partial_variables={"format_instructions": parser.get_format_instructions()},
+        )
+
+        chain = prompt | self.model | parser
+
+        # Update the essay with the edits
+        self.essay = chain.invoke({"user_request": user_request, "memory": memory, "essay": self.essay})
+
+        # Save the conversation context
+        self.memory.save_context(inputs={"input": state["topic"]}, outputs={"output": str(self.essay)})
+
+        # Generate the PDF and return essay content for preview
+        pdf_name = generate_pdf(self.essay)
+        return {
+            "response": "Here is your edited essay! You can review it below before downloading.",
+            "essay": self.essay,
+            "pdf_name": pdf_name,
+        }
+        
+    def save_workflow_graph(self):
+        """Generate and save a dynamic LangGraph visualization to a fixed location."""
+        try:
+            graph_path = "/tmp/graph.png"  
+
+            # Generate the mermaid diagram and save it to a fixed file
+            with open(graph_path, "wb") as f:
+                f.write(self.graph.get_graph().draw_mermaid_png())
+
+            print(f"✅ Workflow visualization saved at: {graph_path}")
+
+        except Exception as e:
+            print(f"❌ Error generating graph: {e}")