import { Effect } from "effect" import { LLMError, LLMEvent, type ProviderMetadata, type ToolCall } from "../../schema" import { eventError, parseToolInput, type ToolAccumulator } from "../shared" type StreamKey = string | number /** * One pending streamed tool call. Providers emit the tool identity and JSON * argument text across separate chunks; `input` is the raw JSON string collected * so far, not the parsed object. */ export interface PendingTool extends ToolAccumulator { readonly providerExecuted?: boolean readonly providerMetadata?: ProviderMetadata } /** * Sparse parser state keyed by the provider's stream-local tool identifier. * * This key is not the final tool-call id (`call_...`). It is the id/index the * provider uses while streaming a partial call: OpenAI Chat / Anthropic / * Bedrock use numeric content indexes, while OpenAI Responses uses string * `item_id`s. The generic keeps each protocol internally consistent. */ export type State = Partial> /** * Result of adding argument text to one pending tool call. It returns both the * next `tools` state and the updated `tool` because parsers often need the * current id/name immediately. `events` contains lifecycle and delta events * produced by the append; metadata-only deltas update identity without output. */ export interface AppendOutcome { readonly tools: State readonly tool: PendingTool readonly events: ReadonlyArray } /** Create empty accumulator state for one provider stream. */ export const empty = (): State => ({}) const withTool = (tools: State, key: K, tool: PendingTool): State => { return { ...tools, [key]: tool } } const withoutTool = (tools: State, key: K): State => { const next = { ...tools } delete next[key] return next } const inputStart = (tool: PendingTool) => LLMEvent.toolInputStart({ id: tool.id, name: tool.name, providerMetadata: tool.providerMetadata, }) const inputDelta = (tool: PendingTool, text: string) => LLMEvent.toolInputDelta({ id: tool.id, name: tool.name, text, }) const toolCall = (route: string, tool: PendingTool, inputOverride?: string) => parseToolInput(route, tool.name, inputOverride ?? tool.input).pipe( Effect.map( (input): ToolCall => LLMEvent.toolCall({ id: tool.id, name: tool.name, input, providerExecuted: tool.providerExecuted ? true : undefined, providerMetadata: tool.providerMetadata, }), ), ) /** Store the updated tool and produce the optional public delta event. */ const appendTool = ( tools: State, key: K, tool: PendingTool, text: string, ): AppendOutcome => { const events: LLMEvent[] = [] if (!tools[key]) events.push(inputStart(tool)) if (text.length > 0) events.push(inputDelta(tool, text)) return { tools: withTool(tools, key, tool), tool, events, } } export const isError = (result: AppendOutcome | LLMError): result is LLMError => result instanceof LLMError /** * Register a tool call whose start event arrived before any argument deltas. * Used by Anthropic `content_block_start`, Bedrock `contentBlockStart`, and * OpenAI Responses `response.output_item.added`. */ export const start = ( tools: State, key: K, tool: Omit & { readonly input?: string }, ) => withTool(tools, key, { ...tool, input: tool.input ?? "" }) /** * Append a streamed argument delta, starting the tool if this provider encodes * identity on the first delta instead of a separate start event. OpenAI Chat has * this shape: `tool_calls[].index` is the stream key, and `id` / `name` may only * appear on the first delta for that index. */ export const appendOrStart = ( route: string, tools: State, key: K, delta: { readonly id?: string; readonly name?: string; readonly text: string }, missingToolMessage: string, ): AppendOutcome | LLMError => { const current = tools[key] const id = delta.id ?? current?.id const name = delta.name ?? current?.name if (!id || !name) return eventError(route, missingToolMessage) const tool = { id, name, input: `${current?.input ?? ""}${delta.text}`, providerExecuted: current?.providerExecuted, providerMetadata: current?.providerMetadata, } if (current && delta.text.length === 0 && current.id === id && current.name === name) return { tools, tool: current, events: [] } return appendTool(tools, key, tool, delta.text) } /** * Append argument text to a tool that must already have been started. This keeps * protocols honest when their stream grammar promises a start event before any * argument delta. */ export const appendExisting = ( route: string, tools: State, key: K, text: string, missingToolMessage: string, ): AppendOutcome | LLMError => { const current = tools[key] if (!current) return eventError(route, missingToolMessage) if (text.length === 0) return { tools, tool: current, events: [] } return appendTool(tools, key, { ...current, input: `${current.input}${text}` }, text) } /** * Finalize one pending tool call: parse the accumulated raw JSON, remove it * from state, and return the optional public `tool-call` event. Missing keys are * a no-op because some providers emit stop events for non-tool content blocks. */ export const finish = (route: string, tools: State, key: K) => Effect.gen(function* () { const tool = tools[key] if (!tool) return { tools } return { tools: withoutTool(tools, key), events: [ LLMEvent.toolInputEnd({ id: tool.id, name: tool.name, providerMetadata: tool.providerMetadata }), yield* toolCall(route, tool), ], } }) /** * Finalize one pending tool call with an authoritative final input string. * OpenAI Responses can send accumulated deltas and then repeat the completed * arguments on `response.output_item.done`; the final value wins. */ export const finishWithInput = (route: string, tools: State, key: K, input: string) => Effect.gen(function* () { const tool = tools[key] if (!tool) return { tools } return { tools: withoutTool(tools, key), events: [ LLMEvent.toolInputEnd({ id: tool.id, name: tool.name, providerMetadata: tool.providerMetadata }), yield* toolCall(route, tool, input), ], } }) /** * Finalize every pending tool call at once. OpenAI Chat has this shape: it does * not emit per-tool stop events, so all accumulated calls finish when the choice * receives a terminal `finish_reason`. */ export const finishAll = (route: string, tools: State) => Effect.gen(function* () { const pending = Object.values(tools).filter( (tool): tool is PendingTool => tool !== undefined, ) return { tools: empty(), events: yield* Effect.forEach(pending, (tool) => toolCall(route, tool).pipe( Effect.map((call) => [ LLMEvent.toolInputEnd({ id: tool.id, name: tool.name, providerMetadata: tool.providerMetadata }), call, ]), ), ).pipe(Effect.map((events) => events.flat())), } }) export * as ToolStream from "./tool-stream"