Delete table_maker.py

table_maker.py

@@ -1,106 +0,0 @@
-from pydantic_graph import BaseNode, End, GraphRunContext, Graph
-from pydantic_ai import Agent
-from pydantic_ai.common_tools.tavily import tavily_search_tool
-from dataclasses import dataclass
-from pydantic import Field, BaseModel
-from typing import  List, Dict, Optional, Any
-from pydantic_ai.models.gemini import GeminiModel
-from pydantic_ai.providers.google_gla import GoogleGLAProvider
-from dotenv import load_dotenv
-import os
-from tavily import TavilyClient
-from IPython.display import Image, display
-import requests
-
-load_dotenv()
-google_api_key=os.getenv('google_api_key')
-tavily_key=os.getenv('tavily_key')
-tavily_client = TavilyClient(api_key=tavily_key)
-llm=GeminiModel('gemini-2.0-flash', provider=GoogleGLAProvider(api_key=google_api_key))
-
-@dataclass
-class State:
-    query:str
-    research:List[str]
-    table:dict
-    preliminary_research:str
-    research_plan:List[str]
-
-#define the table row and table schema
-class Table_row(BaseModel):
-    data: List[str] = Field(description='the data of the row')
-class Table(BaseModel): 
-    rows: List[Table_row] = Field(description='the rows of the table')
-    columns: List[str] = Field(description='the columns of the table')
-    
-
-    
-class table_maker_node(BaseNode[State]):
-    async def run(self, ctx: GraphRunContext[State])->End:
-        table_agent=Agent(llm, result_type=Table, system_prompt="generate a detailed table in a dictionary format based on the research and the query")
-        table=await table_agent.run(f'query:{ctx.state.query}, research:{ctx.state.research}')
-        ctx.state.table={'data':[row.data for row in table.data.rows], 'columns':table.data.columns}
-        return End(ctx.state.table)
-    
-
-class data_research_node(BaseNode[State]):
-    async def run(self, ctx: GraphRunContext[State])->table_maker_node:
-        for i in ctx.state.research_plan:   
-            response = tavily_client.search(i.search_query)
-
-            for i in response.get('results'):
-                if i.get('score')>0.50:
-                    ctx.state.research.append(i.get('content'))
-        return table_maker_node()
-    
-
-
-class search_query(BaseModel):
-    search_query: str = Field(description='the detailed web search query for the research')
-    
-class Research_plan(BaseModel):
-    search_queries: List[search_query] = Field(description='the detailed web search queries for the research')
-
-research_plan_agent=Agent(llm, result_type=Research_plan, system_prompt='generate a detailed research plan breaking down the research into smaller parts based on the query and the preliminary search')
-
-class Research_plan_node(BaseNode[State]):
-    async def run(self, ctx: GraphRunContext[State])->data_research_node:
-        
-        prompt=(f'query:{ctx.state.query}, preliminary_search:{ctx.state.preliminary_research}')
-        result=await research_plan_agent.run(prompt)
-        ctx.state.research_plan=result.data.search_queries
-        return data_research_node()
-
-class preliminary_search_node(BaseNode[State]):
-    async def run(self, ctx: GraphRunContext[State]) -> Research_plan_node:
-        search_agent=Agent(llm, tools=[tavily_search_tool(tavily_key)], system_prompt="do a websearch based on the query")
-        prompt = (' Do a preliminary search to get a global idea of the subject that the user wants to do reseach on as well as the necessary informations to do a search on.\n'
-                  f'The subject is based on the query: {ctx.state.query}, return the results of the search.')
-        result=await search_agent.run(prompt)
-        ctx.state.preliminary_research=result.data
-        return Research_plan_node()
-
-    
-class table_maker_engine:
-    def __init__(self):
-        self.graph=Graph(nodes=[preliminary_search_node, Research_plan_node, data_research_node, table_maker_node])
-        self.state=State(query='', research=[], table={}, preliminary_research='', research_plan=[])
-
-    async def chat(self,query:str):
-        """Chat with the table maker engine,
-        Args:
-            query (str): The query to search for
-        Returns:
-            str: The response from the table maker engine
-        """
-        self.state.query=query
-        response=await self.graph.run(preliminary_search_node(),state=self.state)
-        return response.output
-    
-    def display_graph(self):
-        """Display the graph of the table maker engine
-        Returns:
-            Image: The image of the graph
-        """
-        image=self.graph.mermaid_image()
-        return display(Image(image))