langchain
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Published
20 hours ago •
langchain-ai
langchain-ai
Failure to run docpage examples
System Info
Version: 0.0.180 Python: 3.10.11 OS: MacOs Monterrey 12.5.1 (Apple Silicon)
Steps to reproduce:
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
# First, let's load the language model we're going to use to control the agent.
chat = ChatOpenAI(temperature=0)
# Next, let's load some tools to use. Note that the `llm-math` tool uses an LLM, so we need to pass that in.
llm = OpenAI(temperature=0)
tools = load_tools(["serpapi", "llm-math"], llm=llm)
# Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.
agent = initialize_agent(tools, chat, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
# Now let's test it out!
agent.run("What is EPAM price in NYSE? What is that number raised to the 0.23 power?")
Output:
{
"name": "OutputParserException",
"message": "Could not parse LLM output: Thought: I need to use a search engine to find the current price of EPAM on NYSE and a calculator to raise it to the 0.23 power.\n\nAction:\n```\n{\n \"action\": \"Search\",\n \"action_input\": \"EPAM NYSE price\"\n}\n```\n\n",
"stack": "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mJSONDecodeError\u001b[0m Traceback (most recent call last)\nFile \u001b[0;32m~/Projects/epam/python/langchain/env/lib/python3.10/site-packages/langchain/agents/chat/output_parser.py:21\u001b[0m, in \u001b[0;36mChatOutputParser.parse\u001b[0;34m(self, text)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 21\u001b[0m response \u001b[39m=\u001b[39m parse_json_markdown(text)\n\u001b[1;32m 22\u001b[0m \u001b[39mreturn\u001b[39;00m AgentAction(response[\u001b[39m\"\u001b[39m\u001b[39maction\u001b[39m\u001b[39m\"\u001b[39m], response[\u001b[39m\"\u001b[39m\u001b[39maction_input\u001b[39m\u001b[39m\"\u001b[39m], text)\n\nFile \u001b[0;32m~/Projects/epam/python/langchain/env/lib/python3.10/site-packages/langchain/output_parsers/json.py:17\u001b[0m, in \u001b[0;36mparse_json_markdown\u001b[0;34m(json_string)\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[39m# Parse the JSON string into a Python dictionary\u001b[39;00m\n\u001b[0;32m---> 17\u001b[0m parsed \u001b[39m=\u001b[39m json\u001b[39m.\u001b[39;49mloads(json_string)\n\u001b[1;32m 19\u001b[0m \u001b[39mreturn\u001b[39;00m parsed\n\nFile \u001b[0;32m/opt/homebrew/Cellar/[email protected]/3.10.11/Frameworks/Python.framework/Versions/3.10/lib/python3.10/json/__init__.py:346\u001b[0m, in \u001b[0;36mloads\u001b[0;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m 343\u001b[0m \u001b[39mif\u001b[39;00m (\u001b[39mcls\u001b[39m \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m object_hook \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m\n\u001b[1;32m 344\u001b[0m parse_int \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m parse_float \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m\n\u001b[1;32m 345\u001b[0m parse_constant \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m object_pairs_hook \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m kw):\n\u001b[0;32m--> 346\u001b[0m \u001b[39mreturn\u001b[39;00m _default_decoder\u001b[39m.\u001b[39;49mdecode(s)\n\u001b[1;32m 347\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mcls\u001b[39m \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\nFile \u001b[0;32m/opt/homebrew/Cellar/[email protected]/3.10.11/Frameworks/Python.framework/Versions/3.10/lib/python3.10/json/decoder.py:337\u001b[0m, in \u001b[0;36mJSONDecoder.decode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m 333\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Return the Python representation of ``s`` (a ``str`` instance\u001b[39;00m\n\u001b[1;32m 334\u001b[0m \u001b[39mcontaining a JSON document).\u001b[39;00m\n\u001b[1;32m 335\u001b[0m \n\u001b[1;32m 336\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 337\u001b[0m obj, end \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mraw_decode(s, idx\u001b[39m=\u001b[39;49m_w(s, \u001b[39m0\u001b[39;49m)\u001b[39m.\u001b[39;49mend())\n\u001b[1;32m 338\u001b[0m end \u001b[39m=\u001b[39m _w(s, end)\u001b[39m.\u001b[39mend()\n\nFile \u001b[0;32m/opt/homebrew/Cellar/[email protected]/3.10.11/Frameworks/Python.framework/Versions/3.10/lib/python3.10/json/decoder.py:355\u001b[0m, in \u001b[0;36mJSONDecoder.raw_decode\u001b[0;34m(self, s, idx)\u001b[0m\n\u001b[1;32m 354\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mStopIteration\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m--> 355\u001b[0m \u001b[39mraise\u001b[39;00m JSONDecodeError(\u001b[39m\"\u001b[39m\u001b[39mExpecting value\u001b[39m\u001b[39m\"\u001b[39m, s, err\u001b[39m.\u001b[39mvalue) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 356\u001b[0m \u001b[39mreturn\u001b[39;00m obj, end\n\n\u001b[0;31mJSONDecodeError\u001b[0m: Expecting value: line 1 column 1 (char 0)\n\nDuring handling of the above exception, another exception occurred:\n\n\u001b[0;31mOutputParserException\u001b[0m Traceback (most recent call last)\nCell \u001b[0;32mIn[13], line 19\u001b[0m\n\u001b[1;32m 16\u001b[0m agent \u001b[39m=\u001b[39m initialize_agent(tools, chat, agent\u001b[39m=\u001b[39mAgentType\u001b[39m.\u001b[39mCHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m 18\u001b[0m \u001b[39m# Now let's test it out!\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m agent\u001b[39m.\u001b[39;49mrun(\u001b[39m\"\u001b[39;49m\u001b[39mWhat is EPAM price in NYSE? What is that number raised to the 0.23 power?\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\nFile \u001b[0;32m~/Projects/epam/python/langchain/env/lib/python3.10/site-packages/langchain/chains/base.py:236\u001b[0m, in \u001b[0;36mChain.run\u001b[0;34m(self, callbacks, *args, **kwargs)\u001b[0m\n\u001b[1;32m 234\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(args) \u001b[39m!=\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m 235\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39m`run` supports only one positional argument.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 236\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m(args[\u001b[39m0\u001b[39;49m], callbacks\u001b[39m=\u001b[39;49mcallbacks)[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39moutput_keys[\u001b[39m0\u001b[39m]]\n\u001b[1;32m 238\u001b[0m \u001b[39mif\u001b[39;00m kwargs \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m args:\n\u001b[1;32m 239\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m(kwargs, callbacks\u001b[39m=\u001b[39mcallbacks)[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39moutput_keys[\u001b[39m0\u001b[39m]]\n\nFile \u001b[0;32m~/Projects/epam/python/langchain/env/lib/python3.10/site-packages/langchain/chains/base.py:140\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs, callbacks)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[39mexcept\u001b[39;00m (\u001b[39mKeyboardInterrupt\u001b[39;00m, \u001b[39mException\u001b[39;00m) \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 139\u001b[0m run_manager\u001b[39m.\u001b[39mon_chain_error(e)\n\u001b[0;32m--> 140\u001b[0m \u001b[39mraise\u001b[39;00m e\n\u001b[1;32m 141\u001b[0m run_manager\u001b[39m.\u001b[39mon_chain_end(outputs)\n\u001b[1;32m 142\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprep_outputs(inputs, outputs, return_only_outputs)\n\nFile \u001b[0;32m~/Projects/epam/python/langchain/env/lib/python3.10/site-packages/langchain/chains/base.py:134\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs, callbacks)\u001b[0m\n\u001b[1;32m 128\u001b[0m run_manager \u001b[39m=\u001b[39m callback_manager\u001b[39m.\u001b[39mon_chain_start(\n\u001b[1;32m 129\u001b[0m {\u001b[39m\"\u001b[39m\u001b[39mname\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m},\n\u001b[1;32m 130\u001b[0m inputs,\n\u001b[1;32m 131\u001b[0m )\n\u001b[1;32m 132\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 133\u001b[0m outputs \u001b[39m=\u001b[39m (\n\u001b[0;32m--> 134\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call(inputs, run_manager\u001b[39m=\u001b[39;49mrun_manager)\n\u001b[1;32m 135\u001b[0m \u001b[39mif\u001b[39;00m new_arg_supported\n\u001b[1;32m 136\u001b[0m \u001b[39melse\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_call(inputs)\n\u001b[1;32m 137\u001b[0m )\n\u001b[1;32m 138\u001b[0m \u001b[39mexcept\u001b[39;00m (\u001b[39mKeyboardInterrupt\u001b[39;00m, \u001b[39mException\u001b[39;00m) \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 139\u001b[0m run_manager\u001b[39m.\u001b[39mon_chain_error(e)\n\nFile \u001b[0;32m~/Projects/epam/python/langchain/env/lib/python3.10/site-packages/langchain/agents/agent.py:951\u001b[0m, in \u001b[0;36mAgentExecutor._call\u001b[0;34m(self, inputs, run_manager)\u001b[0m\n\u001b[1;32m 949\u001b[0m \u001b[39m# We now enter the agent loop (until it returns something).\u001b[39;00m\n\u001b[1;32m 950\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_should_continue(iterations, time_elapsed):\n\u001b[0;32m--> 951\u001b[0m next_step_output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_take_next_step(\n\u001b[1;32m 952\u001b[0m name_to_tool_map,\n\u001b[1;32m 953\u001b[0m color_mapping,\n\u001b[1;32m 954\u001b[0m inputs,\n\u001b[1;32m 955\u001b[0m intermediate_steps,\n\u001b[1;32m 956\u001b[0m run_manager\u001b[39m=\u001b[39;49mrun_manager,\n\u001b[1;32m 957\u001b[0m )\n\u001b[1;32m 958\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(next_step_output, AgentFinish):\n\u001b[1;32m 959\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_return(\n\u001b[1;32m 960\u001b[0m next_step_output, intermediate_steps, run_manager\u001b[39m=\u001b[39mrun_manager\n\u001b[1;32m 961\u001b[0m )\n\nFile \u001b[0;32m~/Projects/epam/python/langchain/env/lib/python3.10/site-packages/langchain/agents/agent.py:773\u001b[0m, in \u001b[0;36mAgentExecutor._take_next_step\u001b[0;34m(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)\u001b[0m\n\u001b[1;32m 771\u001b[0m raise_error \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[1;32m 772\u001b[0m \u001b[39mif\u001b[39;00m raise_error:\n\u001b[0;32m--> 773\u001b[0m \u001b[39mraise\u001b[39;00m e\n\u001b[1;32m 774\u001b[0m text \u001b[39m=\u001b[39m \u001b[39mstr\u001b[39m(e)\n\u001b[1;32m 775\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandle_parsing_errors, \u001b[39mbool\u001b[39m):\n\nFile \u001b[0;32m~/Projects/epam/python/langchain/env/lib/python3.10/site-packages/langchain/agents/agent.py:762\u001b[0m, in \u001b[0;36mAgentExecutor._take_next_step\u001b[0;34m(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)\u001b[0m\n\u001b[1;32m 756\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Take a single step in the thought-action-observation loop.\u001b[39;00m\n\u001b[1;32m 757\u001b[0m \n\u001b[1;32m 758\u001b[0m \u001b[39mOverride this to take control of how the agent makes and acts on choices.\u001b[39;00m\n\u001b[1;32m 759\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 760\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 761\u001b[0m \u001b[39m# Call the LLM to see what to do.\u001b[39;00m\n\u001b[0;32m--> 762\u001b[0m output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49magent\u001b[39m.\u001b[39;49mplan(\n\u001b[1;32m 763\u001b[0m intermediate_steps,\n\u001b[1;32m 764\u001b[0m callbacks\u001b[39m=\u001b[39;49mrun_manager\u001b[39m.\u001b[39;49mget_child() \u001b[39mif\u001b[39;49;00m run_manager \u001b[39melse\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m,\n\u001b[1;32m 765\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49minputs,\n\u001b[1;32m 766\u001b[0m )\n\u001b[1;32m 767\u001b[0m \u001b[39mexcept\u001b[39;00m OutputParserException \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 768\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandle_parsing_errors, \u001b[39mbool\u001b[39m):\n\nFile \u001b[0;32m~/Projects/epam/python/langchain/env/lib/python3.10/site-packages/langchain/agents/agent.py:444\u001b[0m, in \u001b[0;36mAgent.plan\u001b[0;34m(self, intermediate_steps, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m 442\u001b[0m full_inputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_full_inputs(intermediate_steps, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m 443\u001b[0m full_output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mllm_chain\u001b[39m.\u001b[39mpredict(callbacks\u001b[39m=\u001b[39mcallbacks, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mfull_inputs)\n\u001b[0;32m--> 444\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moutput_parser\u001b[39m.\u001b[39;49mparse(full_output)\n\nFile \u001b[0;32m~/Projects/epam/python/langchain/env/lib/python3.10/site-packages/langchain/agents/chat/output_parser.py:25\u001b[0m, in \u001b[0;36mChatOutputParser.parse\u001b[0;34m(self, text)\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[39mreturn\u001b[39;00m AgentAction(response[\u001b[39m\"\u001b[39m\u001b[39maction\u001b[39m\u001b[39m\"\u001b[39m], response[\u001b[39m\"\u001b[39m\u001b[39maction_input\u001b[39m\u001b[39m\"\u001b[39m], text)\n\u001b[1;32m 24\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m:\n\u001b[0;32m---> 25\u001b[0m \u001b[39mraise\u001b[39;00m OutputParserException(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mCould not parse LLM output: \u001b[39m\u001b[39m{\u001b[39;00mtext\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\n\u001b[0;31mOutputParserException\u001b[0m: Could not parse LLM output: Thought: I need to use a search engine to find the current price of EPAM on NYSE and a calculator to raise it to the 0.23 power.\n\nAction:\n```\n{\n \"action\": \"Search\",\n \"action_input\": \"EPAM NYSE price\"\n}\n```\n\n"
}
Who can help?
No response
Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
Reproduction
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
# First, let's load the language model we're going to use to control the agent.
chat = ChatOpenAI(temperature=0)
# Next, let's load some tools to use. Note that the `llm-math` tool uses an LLM, so we need to pass that in.
llm = OpenAI(temperature=0)
tools = load_tools(["serpapi", "llm-math"], llm=llm)
# Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.
agent = initialize_agent(tools, chat, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
# Now let's test it out!
agent.run("What is EPAM price in NYSE? What is that number raised to the 0.23 power?")
Expected behavior
Should work
