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#1	import { BaseLanguageModel } from "@langchain/core/language_models/base";
#2	import {
#3	AIMessage,
#4	HumanMessage,
#5	SystemMessage,
#6	BaseMessage,
#7	} from "@langchain/core/messages";
#8	import { z } from "zod";
#9	import { LLM, LLMResponse } from "./base";
#10	import { LLMConfig, Message } from "../types/index";
#11	// Import the schemas directly into LangchainLLM
#12	import { FactRetrievalSchema, MemoryUpdateSchema } from "../prompts";
#13	// Import graph tool argument schemas
#14	import {
#15	GraphExtractEntitiesArgsSchema,
#16	GraphRelationsArgsSchema,
#17	GraphSimpleRelationshipArgsSchema, // Used for delete tool
#18	} from "../graphs/tools";
#19
#20	const convertToLangchainMessages = (messages: Message[]): BaseMessage[] => {
#21	return messages.map((msg) => {
#22	const content =
#23	typeof msg.content === "string"
#24	? msg.content
#25	: JSON.stringify(msg.content);
#26	switch (msg.role?.toLowerCase()) {
#27	case "system":
#28	return new SystemMessage(content);
#29	case "user":
#30	case "human":
#31	return new HumanMessage(content);
#32	case "assistant":
#33	case "ai":
#34	return new AIMessage(content);
#35	default:
#36	console.warn(
#37	`Unsupported message role '${msg.role}' for Langchain. Treating as 'human'.`,
#38	);
#39	return new HumanMessage(content);
#40	}
#41	});
#42	};
#43
#44	export class LangchainLLM implements LLM {
#45	private llmInstance: BaseLanguageModel;
#46	private modelName: string;
#47
#48	constructor(config: LLMConfig) {
#49	if (!config.model \|\| typeof config.model !== "object") {
#50	throw new Error(
#51	"Langchain provider requires an initialized Langchain instance passed via the 'model' field in the LLM config.",
#52	);
#53	}
#54	if (typeof (config.model as any).invoke !== "function") {
#55	throw new Error(
#56	"Provided Langchain 'instance' in the 'model' field does not appear to be a valid Langchain language model (missing invoke method).",
#57	);
#58	}
#59	this.llmInstance = config.model as BaseLanguageModel;
#60	this.modelName =
#61	(this.llmInstance as any).modelId \|\|
#62	(this.llmInstance as any).model \|\|
#63	"langchain-model";
#64	}
#65
#66	async generateResponse(
#67	messages: Message[],
#68	response_format?: { type: string },
#69	tools?: any[],
#70	): Promise<string \| LLMResponse> {
#71	const langchainMessages = convertToLangchainMessages(messages);
#72	let runnable: any = this.llmInstance;
#73	const invokeOptions: Record<string, any> = {};
#74	let isStructuredOutput = false;
#75	let selectedSchema: z.ZodSchema<any> \| null = null;
#76	let isToolCallResponse = false;
#77
#78	// --- Internal Schema Selection Logic (runs regardless of response_format) ---
#79	const systemPromptContent =
#80	(messages.find((m) => m.role === "system")?.content as string) \|\| "";
#81	const userPromptContent =
#82	(messages.find((m) => m.role === "user")?.content as string) \|\| "";
#83	const toolNames = tools?.map((t) => t.function.name) \|\| [];
#84
#85	// Prioritize tool call argument schemas
#86	if (toolNames.includes("extract_entities")) {
#87	selectedSchema = GraphExtractEntitiesArgsSchema;
#88	isToolCallResponse = true;
#89	} else if (toolNames.includes("establish_relationships")) {
#90	selectedSchema = GraphRelationsArgsSchema;
#91	isToolCallResponse = true;
#92	} else if (toolNames.includes("delete_graph_memory")) {
#93	selectedSchema = GraphSimpleRelationshipArgsSchema;
#94	isToolCallResponse = true;
#95	}
#96	// Check for memory prompts if no tool schema matched
#97	else if (
#98	systemPromptContent.includes("Personal Information Organizer") &&
#99	systemPromptContent.includes("extract relevant pieces of information")
#100	) {
#101	selectedSchema = FactRetrievalSchema;
#102	} else if (
#103	userPromptContent.includes("smart memory manager") &&
#104	userPromptContent.includes("Compare newly retrieved facts")
#105	) {
#106	selectedSchema = MemoryUpdateSchema;
#107	}
#108
#109	// --- Apply Structured Output if Schema Selected ---
#110	if (
#111	selectedSchema &&
#112	typeof (this.llmInstance as any).withStructuredOutput === "function"
#113	) {
#114	// Apply if a schema was selected (for memory or single tool calls)
#115	if (
#116	!isToolCallResponse \|\|
#117	(isToolCallResponse && tools && tools.length === 1)
#118	) {
#119	try {
#120	runnable = (this.llmInstance as any).withStructuredOutput(
#121	selectedSchema,
#122	{ name: tools?.[0]?.function.name },
#123	);
#124	isStructuredOutput = true;
#125	} catch (e) {
#126	isStructuredOutput = false; // Ensure flag is false on error
#127	// No fallback to response_format here unless explicitly passed
#128	if (response_format?.type === "json_object") {
#129	invokeOptions.response_format = { type: "json_object" };
#130	}
#131	}
#132	} else if (isToolCallResponse) {
#133	// If multiple tools, don't apply structured output, handle via tool binding below
#134	}
#135	} else if (selectedSchema && response_format?.type === "json_object") {
#136	// Schema selected, but no .withStructuredOutput. Try basic response_format only if explicitly requested.
#137	if (
#138	(this.llmInstance as any)._identifyingParams?.response_format \|\|
#139	(this.llmInstance as any).response_format
#140	) {
#141	invokeOptions.response_format = { type: "json_object" };
#142	}
#143	} else if (!selectedSchema && response_format?.type === "json_object") {
#144	// Explicit JSON request, but no schema inferred. Try basic response_format.
#145	if (
#146	(this.llmInstance as any)._identifyingParams?.response_format \|\|
#147	(this.llmInstance as any).response_format
#148	) {
#149	invokeOptions.response_format = { type: "json_object" };
#150	}
#151	}
#152
#153	// --- Handle tool binding ---
#154	if (tools && tools.length > 0) {
#155	if (typeof (runnable as any).bindTools === "function") {
#156	try {
#157	runnable = (runnable as any).bindTools(tools);
#158	} catch (e) {}
#159	} else {
#160	}
#161	}
#162
#163	// --- Invoke and Process Response ---
#164	try {
#165	const response = await runnable.invoke(langchainMessages, invokeOptions);
#166
#167	if (isStructuredOutput && !isToolCallResponse) {
#168	// Memory prompt with structured output
#169	return JSON.stringify(response);
#170	} else if (isStructuredOutput && isToolCallResponse) {
#171	// Tool call with structured arguments
#172	if (response?.tool_calls && Array.isArray(response.tool_calls)) {
#173	const mappedToolCalls = response.tool_calls.map((call: any) => ({
#174	name: call.name \|\| tools?.[0]?.function.name \|\| "unknown_tool",
#175	arguments:
#176	typeof call.args === "string"
#177	? call.args
#178	: JSON.stringify(call.args),
#179	}));
#180	return {
#181	content: response.content \|\| "",
#182	role: "assistant",
#183	toolCalls: mappedToolCalls,
#184	};
#185	} else {
#186	// Direct object response for tool args
#187	return {
#188	content: "",
#189	role: "assistant",
#190	toolCalls: [
#191	{
#192	name: tools?.[0]?.function.name \|\| "unknown_tool",
#193	arguments: JSON.stringify(response),
#194	},
#195	],
#196	};
#197	}
#198	} else if (
#199	response &&
#200	response.tool_calls &&
#201	Array.isArray(response.tool_calls)
#202	) {
#203	// Standard tool call response (no structured output used/failed)
#204	const mappedToolCalls = response.tool_calls.map((call: any) => ({
#205	name: call.name \|\| "unknown_tool",
#206	arguments:
#207	typeof call.args === "string"
#208	? call.args
#209	: JSON.stringify(call.args),
#210	}));
#211	return {
#212	content: response.content \|\| "",
#213	role: "assistant",
#214	toolCalls: mappedToolCalls,
#215	};
#216	} else if (response && typeof response.content === "string") {
#217	// Standard text response
#218	return response.content;
#219	} else {
#220	// Fallback for unexpected formats
#221	return JSON.stringify(response);
#222	}
#223	} catch (error) {
#224	throw error;
#225	}
#226	}
#227
#228	async generateChat(messages: Message[]): Promise<LLMResponse> {
#229	const langchainMessages = convertToLangchainMessages(messages);
#230	try {
#231	const response = await this.llmInstance.invoke(langchainMessages);
#232	if (response && typeof response.content === "string") {
#233	return {
#234	content: response.content,
#235	role: (response as BaseMessage).lc_id ? "assistant" : "assistant",
#236	};
#237	} else {
#238	console.warn(
#239	`Unexpected response format from Langchain instance (${this.modelName}) for generateChat:`,
#240	response,
#241	);
#242	return {
#243	content: JSON.stringify(response),
#244	role: "assistant",
#245	};
#246	}
#247	} catch (error) {
#248	console.error(
#249	`Error invoking Langchain instance (${this.modelName}) for generateChat:`,
#250	error,
#251	);
#252	throw error;
#253	}
#254	}
#255	}
#256

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