{ "cells": [ { "cell_type": "markdown", "id": "23234b50-e6c6-4c87-9f97-259c15f36894", "metadata": { "tags": [] }, "source": [ "# Only streaming final agent output" ] }, { "cell_type": "markdown", "id": "29dd6333-307c-43df-b848-65001c01733b", "metadata": {}, "source": [ "If you only want the final output of an agent to be streamed, you can use the callback ``FinalStreamingStdOutCallbackHandler``.\n", "For this, the underlying LLM has to support streaming as well." ] }, { "cell_type": "code", "execution_count": 1, "id": "e4592215-6604-47e2-89ff-5db3af6d1e40", "metadata": { "tags": [] }, "outputs": [], "source": [ "from langchain.agents import load_tools\n", "from langchain.agents import initialize_agent\n", "from langchain.agents import AgentType\n", "from langchain.callbacks.streaming_stdout_final_only import FinalStreamingStdOutCallbackHandler\n", "from langchain.llms import OpenAI" ] }, { "cell_type": "markdown", "id": "19a813f7", "metadata": {}, "source": [ "Let's create the underlying LLM with ``streaming = True`` and pass a new instance of ``FinalStreamingStdOutCallbackHandler``." ] }, { "cell_type": "code", "execution_count": 2, "id": "7fe81ef4", "metadata": {}, "outputs": [], "source": [ "llm = OpenAI(streaming=True, callbacks=[FinalStreamingStdOutCallbackHandler()], temperature=0)" ] }, { "cell_type": "code", "execution_count": 4, "id": "ff45b85d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Konrad Adenauer became Chancellor of Germany in 1949, 74 years ago in 2023." ] }, { "data": { "text/plain": [ "'Konrad Adenauer became Chancellor of Germany in 1949, 74 years ago in 2023.'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tools = load_tools([\"wikipedia\", \"llm-math\"], llm=llm)\n", "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=False)\n", "agent.run(\"It's 2023 now. How many years ago did Konrad Adenauer become Chancellor of Germany.\")" ] }, { "cell_type": "markdown", "id": "53a743b8", "metadata": {}, "source": [ "### Handling custom answer prefixes" ] }, { "cell_type": "markdown", "id": "23602c62", "metadata": {}, "source": [ "By default, we assume that the token sequence ``\"\\nFinal\", \" Answer\", \":\"`` indicates that the agent has reached an answers. We can, however, also pass a custom sequence to use as answer prefix." ] }, { "cell_type": "code", "execution_count": 5, "id": "5662a638", "metadata": {}, "outputs": [], "source": [ "llm = OpenAI(\n", " streaming=True,\n", " callbacks=[FinalStreamingStdOutCallbackHandler(answer_prefix_tokens=[\"\\nThe\", \" answer\", \":\"])],\n", " temperature=0\n", ")" ] }, { "cell_type": "markdown", "id": "b1a96cc0", "metadata": {}, "source": [ "Be aware you likely need to include whitespaces and new line characters in your token. " ] }, { "cell_type": "code", "execution_count": null, "id": "9278b522", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.3" } }, "nbformat": 4, "nbformat_minor": 5 }