@gensx/openai/dist/cjs/openai.cjs.map

A wrapper around the OpenAI SDK for GenSX.

{"version":3,"file":"openai.cjs","sources":["@gensx/openai/../../../../src/openai.ts"],"sourcesContent":["/* eslint-disable @typescript-eslint/no-explicit-any */\n\n/* eslint-disable @typescript-eslint/no-unsafe-member-access */\n/* eslint-disable @typescript-eslint/no-unsafe-call */\n\nimport type { ComponentOpts, WrapOptions } from \"@gensx/core\";\n\nimport { Component, wrap } from \"@gensx/core\";\nimport { OpenAI as OriginalOpenAI } from \"openai\";\nimport { RunnableToolFunctionWithParse } from \"openai/lib/RunnableFunction.mjs\";\n\n// Aggregates streaming ChatCompletionChunk objects into a single ChatCompletion object\nfunction aggregateChatCompletionChunks(chunks: unknown[]): unknown {\n  if (chunks.length === 0) {\n    return null;\n  }\n\n  // If we only have one chunk and it's a complete ChatCompletion (not streaming), return it as-is\n  if (chunks.length === 1) {\n    const singleChunk = chunks[0] as any;\n    // Check if it's a complete ChatCompletion response (has choices array with content, not deltas)\n    if (singleChunk?.choices?.[0]?.message?.content !== undefined) {\n      return singleChunk;\n    }\n  }\n\n  // Multiple chunks or streaming chunks - aggregate them\n  const firstChunk = chunks[0] as any;\n  if (!firstChunk || typeof firstChunk !== \"object\") {\n    return chunks;\n  }\n\n  // Initialize the aggregated response structure\n  const aggregated: any = {\n    id: firstChunk.id ?? \"chatcmpl-\" + String(Date.now()),\n    object: \"chat.completion\",\n    created: firstChunk.created ?? Math.floor(Date.now() / 1000),\n    model: firstChunk.model ?? \"gpt-3.5-turbo\",\n    choices: [],\n  };\n\n  // Process each chunk\n  for (const chunk of chunks) {\n    const typedChunk = chunk as any;\n\n    if (!typedChunk?.choices) continue;\n\n    for (const choice of typedChunk.choices) {\n      const index = parseInt(String(choice.index), 10);\n\n      aggregated.choices[index] ??= {\n        index,\n        message: {\n          role: \"assistant\",\n          content: \"\",\n        },\n        finish_reason: null,\n      };\n\n      // Accumulate content from delta\n      if (choice.delta?.content) {\n        const existingContent = String(\n          aggregated.choices[index].message.content,\n        );\n        const newContent = String(choice.delta.content);\n        aggregated.choices[index].message.content =\n          existingContent + newContent;\n      }\n\n      // Set role if present\n      if (choice.delta?.role) {\n        aggregated.choices[index].message.role = choice.delta.role;\n      }\n\n      // Set finish reason from the last chunk that has it\n      if (choice.finish_reason) {\n        aggregated.choices[index].finish_reason = choice.finish_reason;\n      }\n\n      // Handle tool calls if present\n      if (choice.delta?.tool_calls) {\n        aggregated.choices[index].message.tool_calls ??= [];\n\n        for (const toolCallDelta of choice.delta.tool_calls) {\n          const toolCallIndex = toolCallDelta.index;\n\n          if (!aggregated.choices[index].message.tool_calls[toolCallIndex]) {\n            aggregated.choices[index].message.tool_calls[toolCallIndex] = {\n              id: toolCallDelta.id ?? \"\",\n              type: toolCallDelta.type ?? \"function\",\n              function: {\n                name: toolCallDelta.function?.name ?? \"\",\n                arguments: toolCallDelta.function?.arguments ?? \"\",\n              },\n            };\n          } else {\n            // Accumulate function arguments\n            if (toolCallDelta.function?.arguments) {\n              const existingArgs = String(\n                aggregated.choices[index].message.tool_calls[toolCallIndex]\n                  .function.arguments,\n              );\n              const newArgs = String(toolCallDelta.function.arguments);\n              aggregated.choices[index].message.tool_calls[\n                toolCallIndex\n              ].function.arguments = existingArgs + newArgs;\n            }\n          }\n        }\n      }\n    }\n\n    // Update usage from the last chunk that has it\n    if (typedChunk.usage) {\n      aggregated.usage = typedChunk.usage;\n    }\n  }\n\n  // Convert choices object to array\n  aggregated.choices = Object.values(\n    aggregated.choices as Record<string, unknown>,\n  );\n\n  return aggregated;\n}\n\n/**\n * A pre-wrapped version of the OpenAI SDK that makes all methods available as GenSX components.\n *\n * @example\n * ```ts\n * import { openai } from \"@gensx/openai\";\n *\n * // Use chat completions\n * const completion = await openai.chat.completions.create({\n *   model: \"gpt-4.1-mini\",\n *   messages: [{ role: \"user\", content: \"Hello!\" }]\n * });\n *\n * // Use embeddings\n * const embedding = await openai.embeddings.create({\n *   model: \"text-embedding-3-small\",\n *   input: \"Hello world!\"\n * });\n * ```\n */\n\nexport class OpenAI extends OriginalOpenAI {\n  constructor(config?: ConstructorParameters<typeof OriginalOpenAI>[0]) {\n    super(config);\n    return wrapOpenAI(this);\n  }\n}\n\nexport const wrapOpenAI = (\n  openAiInstance: OriginalOpenAI,\n  opts: WrapOptions = {},\n) => {\n  let wrapped: OriginalOpenAI;\n  // Create a wrapped instance\n  wrapped = wrap(openAiInstance, {\n    ...opts,\n    // Add metadata to component options\n    getComponentOpts: (\n      path: string[],\n      args: unknown,\n    ): Partial<ComponentOpts> => {\n      // Always set aggregation for chat completions since we can't detect stream parameter at wrap time\n      if (path.includes(\"completions\")) {\n        return {\n          aggregator: aggregateChatCompletionChunks,\n          __streamingResultKey: \"stream\",\n        };\n      }\n\n      if (args === undefined || typeof args !== \"object\") {\n        return {};\n      }\n\n      // Extract relevant metadata from args\n      const {\n        model,\n        temperature,\n        max_tokens,\n        top_p,\n        frequency_penalty,\n        presence_penalty,\n        stream,\n      } = args as Record<string, unknown>;\n\n      return {\n        metadata: {\n          llm: {\n            provider: inferProvider(openAiInstance.baseURL),\n            model,\n            temperature,\n            max_tokens,\n            top_p,\n            frequency_penalty,\n            presence_penalty,\n            stream,\n          },\n        },\n      };\n    },\n    replacementImplementations: {\n      \"OpenAI.beta.chat.completions.runTools\": (_target, value) => {\n        if (typeof value === \"function\") {\n          const componentOpts = opts.getComponentOpts?.(\n            [\"OpenAI\", \"beta\", \"chat\", \"completions\", \"runTools\"],\n            value,\n          );\n\n          const fn = Component(\n            \"OpenAI.beta.chat.completions.runTools\",\n            async (\n              ...params: Parameters<\n                typeof openAiInstance.beta.chat.completions.runTools\n              >\n            ) => {\n              const [first, ...rest] = params;\n              const { tools } = first;\n\n              // Wrap each tool with GenSX functionality\n              const wrappedTools = tools.map((tool) => {\n                if ((tool as any).$brand === \"auto-parseable-tool\") {\n                  const newTool = { ...tool };\n\n                  Object.defineProperty(newTool, \"$brand\", {\n                    value: (tool as any).$brand,\n                  });\n\n                  const boundCallback = (tool as any).$callback.bind(newTool);\n\n                  Object.defineProperty(newTool, \"$callback\", {\n                    value: Component(\n                      `Tool.${tool.function.name}`,\n                      boundCallback as (input?: object) => unknown,\n                    ),\n                  });\n                  Object.defineProperty(newTool, \"$parseRaw\", {\n                    value: (tool as any).$parseRaw,\n                  });\n\n                  return newTool;\n                } else {\n                  const runnableTool =\n                    tool as RunnableToolFunctionWithParse<object>;\n                  return {\n                    ...runnableTool,\n                    function: {\n                      ...runnableTool.function,\n                      function: Component(\n                        `Tool.${runnableTool.function.name}`,\n                        runnableTool.function.function as (\n                          input?: object,\n                        ) => unknown,\n                      ),\n                    },\n                  };\n                }\n              });\n\n              const result = (await value.apply(\n                (wrapped as any).beta.chat.completions,\n                [\n                  {\n                    ...first,\n                    tools: wrappedTools as (typeof params)[0][\"tools\"],\n                  },\n                  ...rest,\n                ],\n              )) as Record<string, unknown>;\n\n              return result;\n            },\n            {\n              ...componentOpts,\n            },\n          );\n\n          return fn;\n        }\n\n        console.warn(\n          \"beta.chat.completions.runTools is not a function. Type: \",\n          typeof value,\n        );\n        return value;\n      },\n    },\n  });\n\n  return wrapped;\n};\n\nfunction inferProvider(baseURL: string) {\n  if (baseURL.includes(\"openai\")) {\n    return \"openai\";\n  }\n  if (baseURL.includes(\"anthropic\")) {\n    return \"anthropic\";\n  }\n  if (baseURL.includes(\"groq\")) {\n    return \"groq\";\n  }\n  if (baseURL.includes(\"gemini\")) {\n    return \"google\";\n  }\n  if (baseURL.includes(\"claude\")) {\n    return \"anthropic\";\n  }\n  if (baseURL.includes(\"perplexity\")) {\n    return \"perplexity\";\n  }\n  if (baseURL.includes(\"grok\")) {\n    return \"grok\";\n  }\n\n  return 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