/**
 * ReAct agent service -- a FlowProcessor that implements a streaming ReAct
 * (Reasoning + Acting) agent with tool execution.
 *
 * The agent:
 * 1. Receives an AgentRequest (a user question)
 * 2. Builds a ReAct prompt with available tools
 * 3. Iteratively calls the LLM, parses Thought/Action/Action Input/Final Answer
 * 4. Executes tools and feeds observations back to the LLM
 * 5. Sends streaming AgentResponse chunks (thought, observation, answer, error)
 *
 * Tools can be registered statically (hardcoded fallback) or dynamically via
 * config-push. When a "tool" section is present in config, tools are built
 * from that config; otherwise the 3 default tools are used for backward compat.
 *
 * Python reference: trustgraph-flow/trustgraph/agent/react/service.py
 */

import {
  makeFlowProcessor,
  makeConsumerSpec,
  makeProducerSpec,
  makeRequestResponseSpec,
  makeFlowProcessorProgram,
  errorMessage,
  type ProcessorConfig,
  type FlowContext,
  type FlowProcessorRuntime,
  type AgentRequest,
  type AgentResponse,
  type TextCompletionRequest,
  type TextCompletionResponse,
  type GraphRagRequest,
  type GraphRagResponse,
  type DocumentRagRequest,
  type DocumentRagResponse,
  type TriplesQueryRequest,
  type TriplesQueryResponse,
  type ToolRequest,
  type ToolResponse,
  type EffectConfigHandler,
  type EffectRequestOptions,
  type EffectRequestResponse,
  type FlowRequestOptions,
  type FlowRequestor,
  type FlowResourceNotFoundError,
  type MessagingDeliveryError,
  type Spec,
} from "@trustgraph/base";
import { Context, Effect, Layer, Ref } from "effect";
import * as O from "effect/Option";
import * as S from "effect/Schema";

import {
  createKnowledgeQueryTool,
  createDocumentQueryTool,
  createTriplesQueryTool,
  createMcpTool,
  type ExplainData,
} from "./tools.js";
import { buildReActPrompt } from "./prompt.js";
import { filterToolsByGroupAndState } from "../tool-filter.js";
import type { AgentTool, ToolArg } from "./types.js";

const MAX_ITERATIONS = 10;

class AgentToolExecutionError extends S.TaggedErrorClass<AgentToolExecutionError>()(
  "AgentToolExecutionError",
  {
    message: S.String,
  },
) {}

const UnknownRecord = S.Record(S.String, S.Unknown);
const ToolArgumentConfig = S.StructWithRest(
  S.Struct({
    name: S.optionalKey(S.String),
    type: S.optionalKey(S.String),
    description: S.optionalKey(S.String),
  }),
  [UnknownRecord],
);
const ToolConfigEntry = S.StructWithRest(
  S.Struct({
    type: S.optionalKey(S.String),
    name: S.optionalKey(S.String),
    description: S.optionalKey(S.String),
    arguments: ToolArgumentConfig.pipe(S.Array, S.optionalKey),
  }),
  [UnknownRecord],
);
type ToolConfigEntry = typeof ToolConfigEntry.Type;

const decodeRawToolConfig = S.decodeUnknownOption(S.Record(S.String, S.String));
const decodeToolConfigEntry = S.decodeUnknownOption(ToolConfigEntry.pipe(S.fromJsonString));

export interface AgentRuntimeService {
  readonly configureTools: (
    config: Record<string, unknown>,
    version: number,
  ) => Effect.Effect<void>;
  readonly getConfiguredTools: Effect.Effect<ReadonlyArray<AgentTool> | null>;
}

export class AgentRuntime extends Context.Service<AgentRuntime, AgentRuntimeService>()(
  "@trustgraph/flow/agent/react/service/AgentRuntime",
) {}

const toEffectRequestOptions = <TRes>(
  options: FlowRequestOptions<TRes> | undefined,
): EffectRequestOptions<TRes> | undefined => {
  if (options === undefined) return undefined;
  return {
    ...(options.timeoutMs === undefined ? {} : { timeoutMs: options.timeoutMs }),
    ...(options.recipient === undefined
      ? {}
      : {
          recipient: (response: TRes) =>
            Effect.promise(() => options.recipient?.(response) ?? Promise.resolve(true)),
        }),
  };
};

const toPromiseRequestor = <TReq, TRes>(
  requestor: EffectRequestResponse<TReq, TRes>,
): FlowRequestor<TReq, TRes> => ({
  request: (request, options) =>
    Effect.runPromise(requestor.request(request, toEffectRequestOptions(options))),
  stop: () => Effect.runPromise(requestor.stop),
});

const buildConfiguredTool = (
  toolId: string,
  data: ToolConfigEntry,
): Effect.Effect<AgentTool | null> =>
  Effect.gen(function* () {
    const implType = data.type ?? "";
    const name = data.name ?? "";
    const description = data.description ?? "";
    const config = { ...data } as Record<string, unknown>;

    if (name.length === 0) {
      yield* Effect.logWarning(`[AgentService] Skipping tool with no name: ${toolId}`);
      return null;
    }

    switch (implType) {
      case "knowledge-query":
        return {
          name,
          description:
            description.length > 0
              ? description
              : "Query the knowledge graph for information about entities and their relationships.",
          args: [{ name: "question", type: "string", description: "The question to ask" }],
          config,
          execute: () => Promise.resolve(""),
        };

      case "document-query":
        return {
          name,
          description:
            description.length > 0
              ? description
              : "Search documents for relevant information.",
          args: [{ name: "question", type: "string", description: "The question to search for" }],
          config,
          execute: () => Promise.resolve(""),
        };

      case "triples-query":
        return {
          name,
          description:
            description.length > 0
              ? description
              : "Query for specific triples in the knowledge graph.",
          args: [
            { name: "subject", type: "string", description: "Subject entity (optional)" },
            { name: "predicate", type: "string", description: "Predicate/relationship (optional)" },
            { name: "object", type: "string", description: "Object entity (optional)" },
          ],
          config,
          execute: () => Promise.resolve(""),
        };

      case "mcp-tool": {
        const args: ToolArg[] = (data.arguments ?? []).map((arg) => ({
          name: arg.name ?? "",
          type: arg.type ?? "string",
          description: arg.description ?? "",
        }));

        return {
          name,
          description,
          args,
          config,
          execute: () => Promise.resolve(""),
        };
      }

      default:
        yield* Effect.logWarning(`[AgentService] Unknown tool type "${implType}" for ${name}`);
        return null;
    }
  });

const loadConfiguredTools = Effect.fn("AgentRuntime.loadConfiguredTools")(function* (
  config: Record<string, unknown>,
  version: number,
) {
  yield* Effect.log(`[AgentService] Loading tool configuration version ${version}`);

  if (!("tool" in config) || typeof config.tool !== "object" || config.tool === null) {
    yield* Effect.log("[AgentService] No tool config found, using default tools");
    return null;
  }

  const rawConfig = decodeRawToolConfig(config.tool);
  if (O.isNone(rawConfig)) {
    yield* Effect.logError("[AgentService] Tool config must be an object of JSON strings");
    return null;
  }

  const tools: AgentTool[] = [];
  for (const [toolId, toolValue] of Object.entries(rawConfig.value)) {
    const decoded = decodeToolConfigEntry(toolValue);
    if (O.isNone(decoded)) {
      yield* Effect.logError(`[AgentService] Failed to parse tool config ${toolId}`);
      continue;
    }

    const tool = yield* buildConfiguredTool(toolId, decoded.value);
    if (tool === null) continue;

    tools.push(tool);
    yield* Effect.log(`[AgentService] Registered tool: ${tool.name} (${tool.config?.type ?? "unknown"})`);
  }

  yield* Effect.log(`[AgentService] ${tools.length} tools loaded from config`);
  return tools.length > 0 ? tools : null;
});

export const makeAgentRuntime = Effect.gen(function* () {
  const configuredToolsRef = yield* Ref.make<ReadonlyArray<AgentTool> | null>(null);

  return AgentRuntime.of({
    configureTools: Effect.fn("AgentRuntime.configureTools")(function* (config, version) {
      const tools = yield* loadConfiguredTools(config, version);
      yield* Ref.set(configuredToolsRef, tools);
    }),
    getConfiguredTools: Ref.get(configuredToolsRef),
  });
});

export const AgentRuntimeLive = Layer.effect(AgentRuntime, makeAgentRuntime);

const onToolsConfig = Effect.fn("AgentService.onToolsConfig")(function* (
  config: Record<string, unknown>,
  version: number,
) {
  const runtime = yield* AgentRuntime;
  yield* runtime.configureTools(config, version);
});

const wireTools = Effect.fn("AgentService.wireTools")(function* (
  tools: ReadonlyArray<AgentTool>,
  flowCtx: FlowContext<AgentRuntime>,
  collection: string | undefined,
  onExplain: (data: ExplainData) => void,
) {
  const graphRag = yield* flowCtx.flow.requestorEffect<GraphRagRequest, GraphRagResponse>("graph-rag");
  const docRag = yield* flowCtx.flow.requestorEffect<DocumentRagRequest, DocumentRagResponse>("doc-rag");
  const triples = yield* flowCtx.flow.requestorEffect<TriplesQueryRequest, TriplesQueryResponse>("triples");
  const mcpTool = yield* flowCtx.flow.requestorEffect<ToolRequest, ToolResponse>("mcp-tool");

  return tools.map((tool) => {
    const implType = tool.config?.type as string | undefined;

    switch (implType) {
      case "knowledge-query": {
        const live = createKnowledgeQueryTool(
          toPromiseRequestor(graphRag),
          collection,
          onExplain,
        );
        return { ...tool, execute: live.execute };
      }
      case "document-query": {
        const live = createDocumentQueryTool(
          toPromiseRequestor(docRag),
          collection,
        );
        return { ...tool, execute: live.execute };
      }
      case "triples-query": {
        const live = createTriplesQueryTool(
          toPromiseRequestor(triples),
          collection,
        );
        return { ...tool, execute: live.execute };
      }
      case "mcp-tool": {
        const live = createMcpTool(
          toPromiseRequestor(mcpTool),
          tool.name,
          tool.description,
          tool.args,
        );
        return { ...tool, execute: live.execute };
      }
      default:
        return tool;
    }
  });
});

const defaultTools = Effect.fn("AgentService.defaultTools")(function* (
  flowCtx: FlowContext<AgentRuntime>,
  collection: string | undefined,
  onExplain: (data: ExplainData) => void,
) {
  const graphRag = yield* flowCtx.flow.requestorEffect<GraphRagRequest, GraphRagResponse>("graph-rag");
  const docRag = yield* flowCtx.flow.requestorEffect<DocumentRagRequest, DocumentRagResponse>("doc-rag");
  const triples = yield* flowCtx.flow.requestorEffect<TriplesQueryRequest, TriplesQueryResponse>("triples");

  return [
    createKnowledgeQueryTool(
      toPromiseRequestor(graphRag),
      collection,
      onExplain,
    ),
    createDocumentQueryTool(
      toPromiseRequestor(docRag),
      collection,
    ),
    createTriplesQueryTool(
      toPromiseRequestor(triples),
      collection,
    ),
  ];
});

const executeTool = (
  tool: AgentTool,
  input: string,
): Effect.Effect<string> =>
  Effect.tryPromise({
    try: () => tool.execute(input),
    catch: (cause) => AgentToolExecutionError.make({ message: errorMessage(cause) }),
  }).pipe(
    Effect.catch((error: AgentToolExecutionError) =>
      Effect.succeed(`Error executing tool: ${error.message}`),
    ),
  );

type AgentHandlerError =
  | FlowResourceNotFoundError
  | MessagingDeliveryError;

const onAgentRequest = Effect.fn("AgentService.onRequest")(function* (
  msg: AgentRequest,
  properties: Record<string, string>,
  flowCtx: FlowContext<AgentRuntime>,
): Effect.fn.Return<void, AgentHandlerError, AgentRuntime> {
  const requestId = properties.id;
  if (requestId === undefined || requestId.length === 0) return;

  const responseProducer = yield* flowCtx.flow.producerEffect<AgentResponse>("agent-response");

  yield* Effect.gen(function* () {
    const runtime = yield* AgentRuntime;
    const explainEvents: ExplainData[] = [];
    const onExplain = (data: ExplainData) => {
      explainEvents.push(data);
    };

    const configuredTools = yield* runtime.getConfiguredTools;
    let tools = configuredTools !== null
      ? yield* wireTools(configuredTools, flowCtx, msg.collection, onExplain)
      : yield* defaultTools(flowCtx, msg.collection, onExplain);

    tools = filterToolsByGroupAndState(tools, msg.group, msg.state);

    const { system, prompt: initialPrompt } = buildReActPrompt(
      tools,
      msg.question,
    );

    const llmClient = yield* flowCtx.flow.requestorEffect<
      TextCompletionRequest,
      TextCompletionResponse
    >("llm");

    let conversation = initialPrompt;

    for (let iteration = 0; iteration < MAX_ITERATIONS; iteration++) {
      yield* Effect.log(
        `[AgentService] Iteration ${iteration + 1}/${MAX_ITERATIONS} for request ${requestId}`,
      );

      const llmResponse = yield* llmClient.request({
        system,
        prompt: conversation,
      });

      if (llmResponse.error !== undefined) {
        yield* responseProducer.send(requestId, {
          chunk_type: "error",
          content: `LLM error: ${llmResponse.error.message}`,
          end_of_dialog: true,
        });
        return;
      }

      const text = llmResponse.response;
      const parsed = parseReActResponse(text);

      if (parsed.thought.length > 0) {
        yield* responseProducer.send(requestId, {
          chunk_type: "thought",
          content: parsed.thought,
          end_of_message: true,
        });
      }

      if (parsed.finalAnswer.length > 0) {
        for (const explain of explainEvents) {
          yield* responseProducer.send(requestId, {
            chunk_type: "explain",
            content: "",
            explain_id: explain.explainId,
            explain_triples: explain.triples,
          } as AgentResponse);
        }

        yield* responseProducer.send(requestId, {
          chunk_type: "answer",
          content: parsed.finalAnswer,
          end_of_message: true,
          end_of_dialog: true,
        });
        return;
      }

      if (parsed.action.length > 0 && parsed.actionInput.length > 0) {
        const tool = tools.find((candidate) => candidate.name === parsed.action);
        const observation = tool === undefined
          ? `Unknown tool: ${parsed.action}. Available tools: ${tools.map((candidate) => candidate.name).join(", ")}`
          : yield* executeTool(tool, parsed.actionInput);

        yield* responseProducer.send(requestId, {
          chunk_type: "observation",
          content: observation,
          end_of_message: true,
        });

        conversation += `\n${text}\nObservation: ${observation}\n`;
      } else if (parsed.finalAnswer.length === 0) {
        conversation += `\n${text}\nObservation: You must either use a tool (Action + Action Input) or provide a Final Answer.\n`;
      }
    }

    yield* responseProducer.send(requestId, {
      chunk_type: "error",
      content:
        "Maximum reasoning iterations reached without a final answer. " +
        "The agent was unable to complete the task within the allowed steps.",
      end_of_message: true,
      end_of_dialog: true,
    });
  }).pipe(
    Effect.catch((error: unknown) =>
      Effect.logError(`[AgentService] Error processing request ${requestId}`, {
        error: errorMessage(error),
      }).pipe(
        Effect.flatMap(() =>
          responseProducer.send(requestId, {
            chunk_type: "error",
            content: `Agent error: ${errorMessage(error)}`,
            end_of_message: true,
            end_of_dialog: true,
          }),
        ),
      ),
    ),
  );
});

export const makeAgentSpecs = (): ReadonlyArray<Spec<AgentRuntime>> => [
  makeConsumerSpec<AgentRequest, AgentHandlerError, AgentRuntime>(
    "agent-request",
    onAgentRequest,
  ),
  makeProducerSpec<AgentResponse>("agent-response"),
  makeRequestResponseSpec<TextCompletionRequest, TextCompletionResponse>(
    "llm",
    "text-completion-request",
    "text-completion-response",
  ),
  makeRequestResponseSpec<GraphRagRequest, GraphRagResponse>(
    "graph-rag",
    "graph-rag-request",
    "graph-rag-response",
  ),
  makeRequestResponseSpec<DocumentRagRequest, DocumentRagResponse>(
    "doc-rag",
    "document-rag-request",
    "document-rag-response",
  ),
  makeRequestResponseSpec<TriplesQueryRequest, TriplesQueryResponse>(
    "triples",
    "triples-request",
    "triples-response",
  ),
  makeRequestResponseSpec<ToolRequest, ToolResponse>(
    "mcp-tool",
    "mcp-tool-request",
    "mcp-tool-response",
  ),
];

export const makeAgentConfigHandlers = (): ReadonlyArray<
  EffectConfigHandler<never, AgentRuntime>
> => [onToolsConfig];

export type AgentService = FlowProcessorRuntime<AgentRuntime>;

export function makeAgentService(config: ProcessorConfig): AgentService {
  const runtime = Effect.runSync(makeAgentRuntime);
  const service = makeFlowProcessor(config, {
    specifications: makeAgentSpecs(),
    provide: (effect) => effect.pipe(Effect.provideService(AgentRuntime, runtime)),
  });
  service.registerConfigHandler((pushedConfig, version) =>
    Effect.runPromise(onToolsConfig(pushedConfig, version).pipe(
      Effect.provideService(AgentRuntime, runtime),
    )),
  );
  Effect.runSync(Effect.log("[AgentService] Service initialized"));
  return service;
}

export const AgentService = makeAgentService;

/**
 * Simple line-based parser for ReAct LLM output.
 *
 * Extracts Thought, Action, Action Input, and Final Answer sections.
 * For the MVP this avoids the complexity of the streaming parser --
 * we parse the complete response at once.
 */
function parseReActResponse(text: string): {
  thought: string;
  action: string;
  actionInput: string;
  finalAnswer: string;
} {
  let thought = "";
  let action = "";
  let actionInput = "";
  let finalAnswer = "";

  const lines = text.split("\n");
  let currentSection: "thought" | "action" | "action_input" | null = null;

  for (let i = 0; i < lines.length; i++) {
    const line = lines[i];
    const trimmed = line.trimStart();

    if (trimmed.startsWith("Final Answer:")) {
      // Everything from "Final Answer:" to end of text is the answer
      const firstLine = trimmed.slice("Final Answer:".length).trim();
      const remainingLines = lines.slice(i + 1).join("\n").trim();
      finalAnswer =
        firstLine + (remainingLines.length > 0 ? "\n" + remainingLines : "");
      break;
    } else if (trimmed.startsWith("Thought:")) {
      currentSection = "thought";
      const content = trimmed.slice("Thought:".length).trim();
      if (content.length > 0) {
        thought += (thought.length > 0 ? "\n" : "") + content;
      }
    } else if (trimmed.startsWith("Action Input:")) {
      currentSection = "action_input";
      const content = trimmed.slice("Action Input:".length).trim();
      if (content.length > 0) {
        actionInput += content;
      }
    } else if (trimmed.startsWith("Action:")) {
      currentSection = "action";
      const content = trimmed.slice("Action:".length).trim();
      if (content.length > 0) {
        action = content;
      }
    } else if (trimmed.startsWith("Observation:")) {
      // Stop processing -- observations are injected by us, not the LLM
      currentSection = null;
    } else if (trimmed.length > 0 && currentSection !== null) {
      // Continuation line for current section
      switch (currentSection) {
        case "thought":
          thought += "\n" + trimmed;
          break;
        case "action":
          // Action should be a single line (tool name), but handle multi-line
          action += " " + trimmed;
          break;
        case "action_input":
          actionInput += "\n" + trimmed;
          break;
      }
    }
  }

  return {
    thought: thought.trim(),
    action: action.trim(),
    actionInput: actionInput.trim(),
    finalAnswer: finalAnswer.trim(),
  };
}

export const program = makeFlowProcessorProgram<ProcessorConfig, never, AgentRuntime>({
  id: "agent",
  specs: () => makeAgentSpecs(),
  configHandlers: () => makeAgentConfigHandlers(),
  layer: () => AgentRuntimeLive,
});

export function run(): Promise<void> {
  return Effect.runPromise(program);
}