Allow multiple source CSV files for normalisation. Implement real model node. Add normalizarion prompt. Implement normalization setup. Start on RAG retreival functions
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+24
-21
@@ -1,24 +1,27 @@
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// import { SystemMessage } from "@langchain/core/messages";
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// import { GraphNode } from "@langchain/langgraph";
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// import { MessagesState } from "../state";
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// import { arithmeticTools } from "../tools/arithmetic";
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// import { ChatOpenAI } from "@langchain/openai"
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import { HumanMessage, SystemMessage } from "@langchain/core/messages";
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import { GraphNode } from "@langchain/langgraph";
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import { MessagesState } from "../state";
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import { ChatOpenAI } from "@langchain/openai"
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import { hydratePrompt } from "../prompts/hydratePrompt";
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// const model = new ChatOpenAI({
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// model: "gpt-5-mini"
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// });
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export function createModelNode(tools: any, promptPath: string): GraphNode<typeof MessagesState> {
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return async (state) => {
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const sysPrompt = hydratePrompt(promptPath, state.disinformationTitle)
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// const modelWithTools = model.bindTools(arithmeticTools);
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const model = new ChatOpenAI({
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model: "gpt-5-mini"
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});
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const modelWithTools = model.bindTools(tools);
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// export const llmCall: GraphNode<typeof MessagesState> = async (state) => {
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// const response = await modelWithTools.invoke([
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// new SystemMessage(
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// "You are a helpful assistant tasked with performing arithmetic on a set of inputs. Any calculation, no matter how trivial, should be done with tools. Output the final answer with %%% on each side"
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// ),
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// ...state.messages,
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// ]);
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// return {
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// messages: [response],
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// llmCalls: 1,
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// };
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// };
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const response = await modelWithTools.invoke([
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new SystemMessage(
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sysPrompt
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),
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...state.messages,
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]);
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return {
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messages: [response]
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};
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};
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}
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