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121 lines
3.0 KiB
Plaintext
121 lines
3.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "5371a9bb",
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"metadata": {},
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"source": [
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"# Tracing Walkthrough"
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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": 1,
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"id": "17c04cc6-c93d-4b6c-a033-e897577f4ed1",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import os\n",
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"os.environ[\"LANGCHAIN_HANDLER\"] = \"langchain\"\n",
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"\n",
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"## Uncomment this if using hosted setup.\n",
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"\n",
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"# os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://langchain-api-gateway-57eoxz8z.uc.gateway.dev\" \n",
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"\n",
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"## Uncomment this if you want traces to be recorded to \"my_session\" instead of default.\n",
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"\n",
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"# os.environ[\"LANGCHAIN_SESSION\"] = \"my_session\" \n",
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"\n",
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"## Better to set this environment variable in the terminal\n",
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"## Uncomment this if using hosted version. Replace \"my_api_key\" with your actual API Key.\n",
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"\n",
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"# os.environ[\"LANGCHAIN_API_KEY\"] = \"my_api_key\" \n",
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"\n",
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"import langchain\n",
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"from langchain.agents import Tool, initialize_agent, load_tools\n",
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"from langchain.llms import OpenAI"
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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": "bfa16b79-aa4b-4d41-a067-70d1f593f667",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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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 I need to use a calculator to solve this.\n",
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"Action: Calculator\n",
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"Action Input: 2^.123243\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.0891804557407723\n",
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"\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
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"Final Answer: 1.0891804557407723\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished 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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"'1.0891804557407723'"
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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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"# Agent run with tracing. Ensure that OPENAI_API_KEY is set appropriately to run this example.\n",
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"\n",
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"llm = OpenAI(temperature=0)\n",
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"tools = load_tools([\"llm-math\"], llm=llm)\n",
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"agent = initialize_agent(\n",
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" tools, llm, agent=\"zero-shot-react-description\", verbose=True\n",
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")\n",
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"\n",
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"agent.run(\"What is 2 raised to .123243 power?\")"
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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": null,
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"id": "25addd7f",
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"metadata": {},
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"outputs": [],
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"source": []
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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 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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