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Harrison/callbacks (#1587)

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tool-patch
Harrison Chase 1 year ago committed by GitHub
parent 90846dcc28
commit 9f78717b3c
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4 changed files with 142 additions and 16 deletions
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110
docs/tracing/agent_with_tracing.ipynb
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@ -35,12 +35,28 @@
"\n",
"import langchain\n",
"from langchain.agents import Tool, initialize_agent, load_tools\n",
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.llms import OpenAI"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1b62cd48",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Agent run with tracing. Ensure that OPENAI_API_KEY is set appropriately to run this example.\n",
"\n",
"llm = OpenAI(temperature=0)\n",
"tools = load_tools([\"llm-math\"], llm=llm)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "bfa16b79-aa4b-4d41-a067-70d1f593f667",
"metadata": {
"tags": []
@ -70,16 +86,12 @@
"'1.0891804557407723'"
]
},
"execution_count": 2,
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Agent run with tracing. Ensure that OPENAI_API_KEY is set appropriately to run this example.\n",
"\n",
"llm = OpenAI(temperature=0)\n",
"tools = load_tools([\"llm-math\"], llm=llm)\n",
"agent = initialize_agent(\n",
" tools, llm, agent=\"zero-shot-react-description\", verbose=True\n",
")\n",
@ -87,10 +99,94 @@
"agent.run(\"What is 2 raised to .123243 power?\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4829eb1d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mQuestion: What is 2 raised to .123243 power?\n",
"Thought: I need a calculator to solve this problem.\n",
"Action:\n",
"```\n",
"{\n",
" \"action\": \"calculator\",\n",
" \"action_input\": \"2^0.123243\"\n",
"}\n",
"```\n",
"\u001b[0m\n",
"Observation: calculator is not a valid tool, try another one.\n",
"\u001b[32;1m\u001b[1;3mI made a mistake, I need to use the correct tool for this question.\n",
"Action:\n",
"```\n",
"{\n",
" \"action\": \"calculator\",\n",
" \"action_input\": \"2^0.123243\"\n",
"}\n",
"```\n",
"\n",
"\u001b[0m\n",
"Observation: calculator is not a valid tool, try another one.\n",
"\u001b[32;1m\u001b[1;3mI made a mistake, the tool name is actually \"calc\" instead of \"calculator\".\n",
"Action:\n",
"```\n",
"{\n",
" \"action\": \"calc\",\n",
" \"action_input\": \"2^0.123243\"\n",
"}\n",
"```\n",
"\n",
"\u001b[0m\n",
"Observation: calc is not a valid tool, try another one.\n",
"\u001b[32;1m\u001b[1;3mI made another mistake, the tool name is actually \"Calculator\" instead of \"calc\".\n",
"Action:\n",
"```\n",
"{\n",
" \"action\": \"Calculator\",\n",
" \"action_input\": \"2^0.123243\"\n",
"}\n",
"```\n",
"\n",
"\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.0891804557407723\n",
"\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3mThe final answer is 1.0891804557407723.\n",
"Final Answer: 1.0891804557407723\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'1.0891804557407723'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Agent run with tracing using a chat model\n",
"agent = initialize_agent(\n",
" tools, ChatOpenAI(temperature=0), agent=\"chat-zero-shot-react-description\", verbose=True\n",
")\n",
"\n",
"agent.run(\"What is 2 raised to .123243 power?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "25addd7f",
"id": "76abfd82",
"metadata": {},
"outputs": [],
"source": []
@ -112,7 +208,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
"version": "3.9.1"
}
},
"nbformat": 4,

35
langchain/chat_models/base.py
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@ -60,13 +60,44 @@ class BaseChatModel(BaseLanguageModel, BaseModel, ABC):
self, prompts: List[PromptValue], stop: Optional[List[str]] = None
) -> LLMResult:
prompt_messages = [p.to_messages() for p in prompts]
return self.generate(prompt_messages, stop=stop)
prompt_strings = [p.to_string() for p in prompts]
self.callback_manager.on_llm_start(
{"name": self.__class__.__name__}, prompt_strings, verbose=self.verbose
)
try:
output = self.generate(prompt_messages, stop=stop)
except (KeyboardInterrupt, Exception) as e:
self.callback_manager.on_llm_error(e, verbose=self.verbose)
raise e
self.callback_manager.on_llm_end(output, verbose=self.verbose)
return output
async def agenerate_prompt(
self, prompts: List[PromptValue], stop: Optional[List[str]] = None
) -> LLMResult:
prompt_messages = [p.to_messages() for p in prompts]
return await self.agenerate(prompt_messages, stop=stop)
prompt_strings = [p.to_string() for p in prompts]
if self.callback_manager.is_async:
await self.callback_manager.on_llm_start(
{"name": self.__class__.__name__}, prompt_strings, verbose=self.verbose
)
else:
self.callback_manager.on_llm_start(
{"name": self.__class__.__name__}, prompt_strings, verbose=self.verbose
)
try:
output = await self.agenerate(prompt_messages, stop=stop)
except (KeyboardInterrupt, Exception) as e:
if self.callback_manager.is_async:
await self.callback_manager.on_llm_error(e, verbose=self.verbose)
else:
self.callback_manager.on_llm_error(e, verbose=self.verbose)
raise e
if self.callback_manager.is_async:
await self.callback_manager.on_llm_end(output, verbose=self.verbose)
else:
self.callback_manager.on_llm_end(output, verbose=self.verbose)
return output
@abstractmethod
def _generate(

3
langchain/prompts/chat.py
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@ -7,6 +7,7 @@ from typing import Any, Callable, List, Sequence, Tuple, Type, Union
from pydantic import BaseModel, Field
from langchain.memory.buffer import get_buffer_string
from langchain.prompts.base import BasePromptTemplate, StringPromptTemplate
from langchain.prompts.prompt import PromptTemplate
from langchain.schema import (
@ -111,7 +112,7 @@ class ChatPromptValue(PromptValue):
def to_string(self) -> str:
"""Return prompt as string."""
return str(self.messages)
return get_buffer_string(self.messages)
def to_messages(self) -> List[BaseMessage]:
"""Return prompt as messages."""

10
tests/unit_tests/prompts/test_chat.py
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@ -70,12 +70,10 @@ def test_chat_prompt_template() -> None:
string = prompt.to_string()
expected = (
'[SystemMessage(content="Here\'s some context: context", '
'additional_kwargs={}), HumanMessage(content="Hello foo, '
"I'm bar. Thanks for the context\", additional_kwargs={}), "
"AIMessage(content=\"I'm an AI. I'm foo. I'm bar.\", additional_kwargs={}), "
"ChatMessage(content=\"I'm a generic message. I'm foo. I'm bar.\","
" additional_kwargs={}, role='test')]"
"System: Here's some context: context\n"
"Human: Hello foo, I'm bar. Thanks for the context\n"
"AI: I'm an AI. I'm foo. I'm bar.\n"
"test: I'm a generic message. I'm foo. I'm bar."
)
assert string == expected

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