mirror of
https://github.com/hwchase17/langchain
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985496f4be
Big docs refactor! Motivation is to make it easier for people to find resources they are looking for. To accomplish this, there are now three main sections: - Getting Started: steps for getting started, walking through most core functionality - Modules: these are different modules of functionality that langchain provides. Each part here has a "getting started", "how to", "key concepts" and "reference" section (except in a few select cases where it didnt easily fit). - Use Cases: this is to separate use cases (like summarization, question answering, evaluation, etc) from the modules, and provide a different entry point to the code base. There is also a full reference section, as well as extra resources (glossary, gallery, etc) Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
91 lines
2.4 KiB
Plaintext
91 lines
2.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "0c3f1df8",
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"metadata": {},
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"source": [
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"# Self Ask With Search\n",
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"\n",
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"This notebook showcases the Self Ask With Search chain."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "7e3b513e",
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"metadata": {},
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"outputs": [
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"name": "stdout",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m Yes.\n",
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"Follow up: Who is the reigning men's U.S. Open champion?\u001b[0m\n",
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"Intermediate answer: \u001b[36;1m\u001b[1;3mCarlos Alcaraz won the 2022 Men's single title while Poland's Iga Swiatek won the Women's single title defeating Tunisian's Ons Jabeur.\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mFollow up: Where is Carlos Alcaraz from?\u001b[0m\n",
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"Intermediate answer: \u001b[36;1m\u001b[1;3mEl Palmar, Spain\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mSo the final answer is: El Palmar, Spain\u001b[0m\n",
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"\u001b[1m> Finished AgentExecutor chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'El Palmar, Spain'"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from langchain import OpenAI, SerpAPIWrapper\n",
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"from langchain.agents import initialize_agent, Tool\n",
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"\n",
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"llm = OpenAI(temperature=0)\n",
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"search = SerpAPIWrapper()\n",
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"tools = [\n",
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" Tool(\n",
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" name=\"Intermediate Answer\",\n",
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" func=search.run\n",
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" )\n",
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"]\n",
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"\n",
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"self_ask_with_search = initialize_agent(tools, llm, agent=\"self-ask-with-search\", verbose=True)\n",
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"self_ask_with_search.run(\"What is the hometown of the reigning men's U.S. Open champion?\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.9.0 64-bit ('llm-env')",
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"language": "python",
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"name": "python3"
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"version": "3.9.0"
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