mirror of https://github.com/hwchase17/langchain
Merge branch 'master' into azure_search_fix
Mohammad Mohtashim
2 weeks ago
committed by
GitHub
commit
b8c03a0bb8
@ -0,0 +1,171 @@
|
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{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
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"metadata": {},
|
||||
"source": [
|
||||
"# How to use callbacks in async environments\n",
|
||||
"\n",
|
||||
":::info Prerequisites\n",
|
||||
"\n",
|
||||
"This guide assumes familiarity with the following concepts:\n",
|
||||
"\n",
|
||||
"- [Callbacks](/docs/concepts/#callbacks)\n",
|
||||
"- [Custom callback handlers](/docs/how_to/custom_callbacks)\n",
|
||||
"\n",
|
||||
":::\n",
|
||||
"\n",
|
||||
"If you are planning to use the async APIs, it is recommended to use and extend [`AsyncCallbackHandler`](https://api.python.langchain.com/en/latest/callbacks/langchain_core.callbacks.base.AsyncCallbackHandler.html) to avoid blocking the runloop.\n",
|
||||
"\n",
|
||||
"**Note**: if you use a sync `CallbackHandler` while using an async method to run your LLM / Chain / Tool / Agent, it will still work. However, under the hood, it will be called with [`run_in_executor`](https://docs.python.org/3/library/asyncio-eventloop.html#asyncio.loop.run_in_executor) which can cause issues if your `CallbackHandler` is not thread-safe."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# | output: false\n",
|
||||
"# | echo: false\n",
|
||||
"\n",
|
||||
"%pip install -qU langchain langchain_anthropic\n",
|
||||
"\n",
|
||||
"import getpass\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
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||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"zzzz....\n",
|
||||
"Hi! I just woke up. Your llm is starting\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: Here\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: 's\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: a\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: little\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: joke\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: for\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: you\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: :\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: \n",
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"\n",
|
||||
"Why\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: can\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: 't\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: a\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: bicycle\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: stan\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: d up\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: by\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: itself\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: ?\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: Because\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: it\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: 's\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: two\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: -\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: tire\n",
|
||||
"zzzz....\n",
|
||||
"Hi! I just woke up. Your llm is ending\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
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||||
"text/plain": [
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||||
"LLMResult(generations=[[ChatGeneration(text=\"Here's a little joke for you:\\n\\nWhy can't a bicycle stand up by itself? Because it's two-tire\", message=AIMessage(content=\"Here's a little joke for you:\\n\\nWhy can't a bicycle stand up by itself? Because it's two-tire\", id='run-8afc89e8-02c0-4522-8480-d96977240bd4-0'))]], llm_output={}, run=[RunInfo(run_id=UUID('8afc89e8-02c0-4522-8480-d96977240bd4'))])"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import asyncio\n",
|
||||
"from typing import Any, Dict, List\n",
|
||||
"\n",
|
||||
"from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler\n",
|
||||
"from langchain_anthropic import ChatAnthropic\n",
|
||||
"from langchain_core.messages import HumanMessage\n",
|
||||
"from langchain_core.outputs import LLMResult\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class MyCustomSyncHandler(BaseCallbackHandler):\n",
|
||||
" def on_llm_new_token(self, token: str, **kwargs) -> None:\n",
|
||||
" print(f\"Sync handler being called in a `thread_pool_executor`: token: {token}\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class MyCustomAsyncHandler(AsyncCallbackHandler):\n",
|
||||
" \"\"\"Async callback handler that can be used to handle callbacks from langchain.\"\"\"\n",
|
||||
"\n",
|
||||
" async def on_llm_start(\n",
|
||||
" self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any\n",
|
||||
" ) -> None:\n",
|
||||
" \"\"\"Run when chain starts running.\"\"\"\n",
|
||||
" print(\"zzzz....\")\n",
|
||||
" await asyncio.sleep(0.3)\n",
|
||||
" class_name = serialized[\"name\"]\n",
|
||||
" print(\"Hi! I just woke up. Your llm is starting\")\n",
|
||||
"\n",
|
||||
" async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:\n",
|
||||
" \"\"\"Run when chain ends running.\"\"\"\n",
|
||||
" print(\"zzzz....\")\n",
|
||||
" await asyncio.sleep(0.3)\n",
|
||||
" print(\"Hi! I just woke up. Your llm is ending\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# To enable streaming, we pass in `streaming=True` to the ChatModel constructor\n",
|
||||
"# Additionally, we pass in a list with our custom handler\n",
|
||||
"chat = ChatAnthropic(\n",
|
||||
" model=\"claude-3-sonnet-20240229\",\n",
|
||||
" max_tokens=25,\n",
|
||||
" streaming=True,\n",
|
||||
" callbacks=[MyCustomSyncHandler(), MyCustomAsyncHandler()],\n",
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||||
")\n",
|
||||
"\n",
|
||||
"await chat.agenerate([[HumanMessage(content=\"Tell me a joke\")]])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Next steps\n",
|
||||
"\n",
|
||||
"You've now learned how to create your own custom callback handlers.\n",
|
||||
"\n",
|
||||
"Next, check out the other how-to guides in this section, such as [how to attach callbacks to a runnable](/docs/how_to/callbacks_attach)."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"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": "3.10.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
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||||
}
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@ -0,0 +1,144 @@
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||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# How to attach callbacks to a module\n",
|
||||
"\n",
|
||||
":::info Prerequisites\n",
|
||||
"\n",
|
||||
"This guide assumes familiarity with the following concepts:\n",
|
||||
"\n",
|
||||
"- [Callbacks](/docs/concepts/#callbacks)\n",
|
||||
"- [Custom callback handlers](/docs/how_to/custom_callbacks)\n",
|
||||
"- [Chaining runnables](/docs/how_to/sequence)\n",
|
||||
"- [Attach runtime arguments to a Runnable](/docs/how_to/binding)\n",
|
||||
"\n",
|
||||
":::\n",
|
||||
"\n",
|
||||
"If you are composing a chain of runnables and want to reuse callbacks across multiple executions, you can attach callbacks with the [`.with_config()`](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.with_config) method. This saves you the need to pass callbacks in each time you invoke the chain.\n",
|
||||
"\n",
|
||||
"Here's an example:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# | output: false\n",
|
||||
"# | echo: false\n",
|
||||
"\n",
|
||||
"%pip install -qU langchain langchain_anthropic\n",
|
||||
"\n",
|
||||
"import getpass\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Chain RunnableSequence started\n",
|
||||
"Chain ChatPromptTemplate started\n",
|
||||
"Chain ended, outputs: messages=[HumanMessage(content='What is 1 + 2?')]\n",
|
||||
"Chat model started\n",
|
||||
"Chat model ended, response: generations=[[ChatGeneration(text='1 + 2 = 3', message=AIMessage(content='1 + 2 = 3', response_metadata={'id': 'msg_01LjC57hgrmzVhEma4yXdLKF', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}}, id='run-393950f9-79b9-4fd6-ac6e-50d93d75b906-0'))]] llm_output={'id': 'msg_01LjC57hgrmzVhEma4yXdLKF', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}} run=None\n",
|
||||
"Chain ended, outputs: content='1 + 2 = 3' response_metadata={'id': 'msg_01LjC57hgrmzVhEma4yXdLKF', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}} id='run-393950f9-79b9-4fd6-ac6e-50d93d75b906-0'\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"AIMessage(content='1 + 2 = 3', response_metadata={'id': 'msg_01LjC57hgrmzVhEma4yXdLKF', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}}, id='run-393950f9-79b9-4fd6-ac6e-50d93d75b906-0')"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from typing import Any, Dict, List\n",
|
||||
"\n",
|
||||
"from langchain_anthropic import ChatAnthropic\n",
|
||||
"from langchain_core.callbacks import BaseCallbackHandler\n",
|
||||
"from langchain_core.messages import BaseMessage\n",
|
||||
"from langchain_core.outputs import LLMResult\n",
|
||||
"from langchain_core.prompts import ChatPromptTemplate\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class LoggingHandler(BaseCallbackHandler):\n",
|
||||
" def on_chat_model_start(\n",
|
||||
" self, serialized: Dict[str, Any], messages: List[List[BaseMessage]], **kwargs\n",
|
||||
" ) -> None:\n",
|
||||
" print(\"Chat model started\")\n",
|
||||
"\n",
|
||||
" def on_llm_end(self, response: LLMResult, **kwargs) -> None:\n",
|
||||
" print(f\"Chat model ended, response: {response}\")\n",
|
||||
"\n",
|
||||
" def on_chain_start(\n",
|
||||
" self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs\n",
|
||||
" ) -> None:\n",
|
||||
" print(f\"Chain {serialized.get('name')} started\")\n",
|
||||
"\n",
|
||||
" def on_chain_end(self, outputs: Dict[str, Any], **kwargs) -> None:\n",
|
||||
" print(f\"Chain ended, outputs: {outputs}\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"callbacks = [LoggingHandler()]\n",
|
||||
"llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\")\n",
|
||||
"prompt = ChatPromptTemplate.from_template(\"What is 1 + {number}?\")\n",
|
||||
"\n",
|
||||
"chain = prompt | llm\n",
|
||||
"\n",
|
||||
"chain_with_callbacks = chain.with_config(callbacks=callbacks)\n",
|
||||
"\n",
|
||||
"chain_with_callbacks.invoke({\"number\": \"2\"})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The bound callbacks will run for all nested module runs.\n",
|
||||
"\n",
|
||||
"## Next steps\n",
|
||||
"\n",
|
||||
"You've now learned how to attach callbacks to a chain.\n",
|
||||
"\n",
|
||||
"Next, check out the other how-to guides in this section, such as how to [pass callbacks in at runtime](/docs/how_to/callbacks_runtime)."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"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": "3.10.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@ -0,0 +1,136 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# How to pass callbacks into a module constructor\n",
|
||||
"\n",
|
||||
":::info Prerequisites\n",
|
||||
"\n",
|
||||
"This guide assumes familiarity with the following concepts:\n",
|
||||
"\n",
|
||||
"- [Callbacks](/docs/concepts/#callbacks)\n",
|
||||
"- [Custom callback handlers](/docs/how_to/custom_callbacks)\n",
|
||||
"\n",
|
||||
":::\n",
|
||||
"\n",
|
||||
"Most LangChain modules allow you to pass `callbacks` directly into the constructor. In this case, the callbacks will only be called for that instance (and any nested runs).\n",
|
||||
"\n",
|
||||
"Here's an example:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# | output: false\n",
|
||||
"# | echo: false\n",
|
||||
"\n",
|
||||
"%pip install -qU langchain langchain_anthropic\n",
|
||||
"\n",
|
||||
"import getpass\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Chat model started\n",
|
||||
"Chat model ended, response: generations=[[ChatGeneration(text='1 + 2 = 3', message=AIMessage(content='1 + 2 = 3', response_metadata={'id': 'msg_01CdKsRmeS9WRb8BWnHDEHm7', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}}, id='run-2d7fdf2a-7405-4e17-97c0-67e6b2a65305-0'))]] llm_output={'id': 'msg_01CdKsRmeS9WRb8BWnHDEHm7', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}} run=None\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"AIMessage(content='1 + 2 = 3', response_metadata={'id': 'msg_01CdKsRmeS9WRb8BWnHDEHm7', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}}, id='run-2d7fdf2a-7405-4e17-97c0-67e6b2a65305-0')"
|
||||
]
|
||||
},
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from typing import Any, Dict, List\n",
|
||||
"\n",
|
||||
"from langchain_anthropic import ChatAnthropic\n",
|
||||
"from langchain_core.callbacks import BaseCallbackHandler\n",
|
||||
"from langchain_core.messages import BaseMessage\n",
|
||||
"from langchain_core.outputs import LLMResult\n",
|
||||
"from langchain_core.prompts import ChatPromptTemplate\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class LoggingHandler(BaseCallbackHandler):\n",
|
||||
" def on_chat_model_start(\n",
|
||||
" self, serialized: Dict[str, Any], messages: List[List[BaseMessage]], **kwargs\n",
|
||||
" ) -> None:\n",
|
||||
" print(\"Chat model started\")\n",
|
||||
"\n",
|
||||
" def on_llm_end(self, response: LLMResult, **kwargs) -> None:\n",
|
||||
" print(f\"Chat model ended, response: {response}\")\n",
|
||||
"\n",
|
||||
" def on_chain_start(\n",
|
||||
" self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs\n",
|
||||
" ) -> None:\n",
|
||||
" print(f\"Chain {serialized.get('name')} started\")\n",
|
||||
"\n",
|
||||
" def on_chain_end(self, outputs: Dict[str, Any], **kwargs) -> None:\n",
|
||||
" print(f\"Chain ended, outputs: {outputs}\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"callbacks = [LoggingHandler()]\n",
|
||||
"llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\", callbacks=callbacks)\n",
|
||||
"prompt = ChatPromptTemplate.from_template(\"What is 1 + {number}?\")\n",
|
||||
"\n",
|
||||
"chain = prompt | llm\n",
|
||||
"\n",
|
||||
"chain.invoke({\"number\": \"2\"})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can see that we only see events from the chat model run - no chain events from the prompt or broader chain.\n",
|
||||
"\n",
|
||||
"## Next steps\n",
|
||||
"\n",
|
||||
"You've now learned how to pass callbacks into a constructor.\n",
|
||||
"\n",
|
||||
"Next, check out the other how-to guides in this section, such as how to [pass callbacks at runtime](/docs/how_to/callbacks_runtime)."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"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": "3.10.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@ -0,0 +1,140 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# How to pass callbacks in at runtime\n",
|
||||
"\n",
|
||||
":::info Prerequisites\n",
|
||||
"\n",
|
||||
"This guide assumes familiarity with the following concepts:\n",
|
||||
"\n",
|
||||
"- [Callbacks](/docs/concepts/#callbacks)\n",
|
||||
"- [Custom callback handlers](/docs/how_to/custom_callbacks)\n",
|
||||
"\n",
|
||||
":::\n",
|
||||
"\n",
|
||||
"In many cases, it is advantageous to pass in handlers instead when running the object. When we pass through [`CallbackHandlers`](https://api.python.langchain.com/en/latest/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html#langchain-core-callbacks-base-basecallbackhandler) using the `callbacks` keyword arg when executing an run, those callbacks will be issued by all nested objects involved in the execution. For example, when a handler is passed through to an Agent, it will be used for all callbacks related to the agent and all the objects involved in the agent's execution, in this case, the Tools and LLM.\n",
|
||||
"\n",
|
||||
"This prevents us from having to manually attach the handlers to each individual nested object. Here's an example:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# | output: false\n",
|
||||
"# | echo: false\n",
|
||||
"\n",
|
||||
"%pip install -qU langchain langchain_anthropic\n",
|
||||
"\n",
|
||||
"import getpass\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Chain RunnableSequence started\n",
|
||||
"Chain ChatPromptTemplate started\n",
|
||||
"Chain ended, outputs: messages=[HumanMessage(content='What is 1 + 2?')]\n",
|
||||
"Chat model started\n",
|
||||
"Chat model ended, response: generations=[[ChatGeneration(text='1 + 2 = 3', message=AIMessage(content='1 + 2 = 3', response_metadata={'id': 'msg_01D8Tt5FdtBk5gLTfBPm2tac', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}}, id='run-bb0dddd8-85f3-4e6b-8553-eaa79f859ef8-0'))]] llm_output={'id': 'msg_01D8Tt5FdtBk5gLTfBPm2tac', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}} run=None\n",
|
||||
"Chain ended, outputs: content='1 + 2 = 3' response_metadata={'id': 'msg_01D8Tt5FdtBk5gLTfBPm2tac', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}} id='run-bb0dddd8-85f3-4e6b-8553-eaa79f859ef8-0'\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"AIMessage(content='1 + 2 = 3', response_metadata={'id': 'msg_01D8Tt5FdtBk5gLTfBPm2tac', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}}, id='run-bb0dddd8-85f3-4e6b-8553-eaa79f859ef8-0')"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from typing import Any, Dict, List\n",
|
||||
"\n",
|
||||
"from langchain_anthropic import ChatAnthropic\n",
|
||||
"from langchain_core.callbacks import BaseCallbackHandler\n",
|
||||
"from langchain_core.messages import BaseMessage\n",
|
||||
"from langchain_core.outputs import LLMResult\n",
|
||||
"from langchain_core.prompts import ChatPromptTemplate\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class LoggingHandler(BaseCallbackHandler):\n",
|
||||
" def on_chat_model_start(\n",
|
||||
" self, serialized: Dict[str, Any], messages: List[List[BaseMessage]], **kwargs\n",
|
||||
" ) -> None:\n",
|
||||
" print(\"Chat model started\")\n",
|
||||
"\n",
|
||||
" def on_llm_end(self, response: LLMResult, **kwargs) -> None:\n",
|
||||
" print(f\"Chat model ended, response: {response}\")\n",
|
||||
"\n",
|
||||
" def on_chain_start(\n",
|
||||
" self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs\n",
|
||||
" ) -> None:\n",
|
||||
" print(f\"Chain {serialized.get('name')} started\")\n",
|
||||
"\n",
|
||||
" def on_chain_end(self, outputs: Dict[str, Any], **kwargs) -> None:\n",
|
||||
" print(f\"Chain ended, outputs: {outputs}\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"callbacks = [LoggingHandler()]\n",
|
||||
"llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\")\n",
|
||||
"prompt = ChatPromptTemplate.from_template(\"What is 1 + {number}?\")\n",
|
||||
"\n",
|
||||
"chain = prompt | llm\n",
|
||||
"\n",
|
||||
"chain.invoke({\"number\": \"2\"}, config={\"callbacks\": callbacks})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"If there are already existing callbacks associated with a module, these will run in addition to any passed in at runtime.\n",
|
||||
"\n",
|
||||
"## Next steps\n",
|
||||
"\n",
|
||||
"You've now learned how to pass callbacks at runtime.\n",
|
||||
"\n",
|
||||
"Next, check out the other how-to guides in this section, such as how to [pass callbacks into a module constructor](/docs/how_to/custom_callbacks)."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"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": "3.10.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@ -0,0 +1,141 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# How to create custom callback handlers\n",
|
||||
"\n",
|
||||
":::info Prerequisites\n",
|
||||
"\n",
|
||||
"This guide assumes familiarity with the following concepts:\n",
|
||||
"\n",
|
||||
"- [Callbacks](/docs/concepts/#callbacks)\n",
|
||||
"\n",
|
||||
":::\n",
|
||||
"\n",
|
||||
"LangChain has some built-in callback handlers, but you will often want to create your own handlers with custom logic.\n",
|
||||
"\n",
|
||||
"To create a custom callback handler, we need to determine the [event(s)](https://api.python.langchain.com/en/latest/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html#langchain-core-callbacks-base-basecallbackhandler) we want our callback handler to handle as well as what we want our callback handler to do when the event is triggered. Then all we need to do is attach the callback handler to the object, for example via [the constructor](/docs/how_to/callbacks_constructor) or [at runtime](/docs/how_to/callbacks_runtime).\n",
|
||||
"\n",
|
||||
"In the example below, we'll implement streaming with a custom handler.\n",
|
||||
"\n",
|
||||
"In our custom callback handler `MyCustomHandler`, we implement the `on_llm_new_token` handler to print the token we have just received. We then attach our custom handler to the model object as a constructor callback."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# | output: false\n",
|
||||
"# | echo: false\n",
|
||||
"\n",
|
||||
"%pip install -qU langchain langchain_anthropic\n",
|
||||
"\n",
|
||||
"import getpass\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"My custom handler, token: Here\n",
|
||||
"My custom handler, token: 's\n",
|
||||
"My custom handler, token: a\n",
|
||||
"My custom handler, token: bear\n",
|
||||
"My custom handler, token: joke\n",
|
||||
"My custom handler, token: for\n",
|
||||
"My custom handler, token: you\n",
|
||||
"My custom handler, token: :\n",
|
||||
"My custom handler, token: \n",
|
||||
"\n",
|
||||
"Why\n",
|
||||
"My custom handler, token: di\n",
|
||||
"My custom handler, token: d the\n",
|
||||
"My custom handler, token: bear\n",
|
||||
"My custom handler, token: dissol\n",
|
||||
"My custom handler, token: ve\n",
|
||||
"My custom handler, token: in\n",
|
||||
"My custom handler, token: water\n",
|
||||
"My custom handler, token: ?\n",
|
||||
"My custom handler, token: \n",
|
||||
"Because\n",
|
||||
"My custom handler, token: it\n",
|
||||
"My custom handler, token: was\n",
|
||||
"My custom handler, token: a\n",
|
||||
"My custom handler, token: polar\n",
|
||||
"My custom handler, token: bear\n",
|
||||
"My custom handler, token: !\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from langchain_anthropic import ChatAnthropic\n",
|
||||
"from langchain_core.callbacks import BaseCallbackHandler\n",
|
||||
"from langchain_core.prompts import ChatPromptTemplate\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class MyCustomHandler(BaseCallbackHandler):\n",
|
||||
" def on_llm_new_token(self, token: str, **kwargs) -> None:\n",
|
||||
" print(f\"My custom handler, token: {token}\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"prompt = ChatPromptTemplate.from_messages([\"Tell me a joke about {animal}\"])\n",
|
||||
"\n",
|
||||
"# To enable streaming, we pass in `streaming=True` to the ChatModel constructor\n",
|
||||
"# Additionally, we pass in our custom handler as a list to the callbacks parameter\n",
|
||||
"model = ChatAnthropic(\n",
|
||||
" model=\"claude-3-sonnet-20240229\", streaming=True, callbacks=[MyCustomHandler()]\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"chain = prompt | model\n",
|
||||
"\n",
|
||||
"response = chain.invoke({\"animal\": \"bears\"})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can see [this reference page](https://api.python.langchain.com/en/latest/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html#langchain-core-callbacks-base-basecallbackhandler) for a list of events you can handle. Note that the `handle_chain_*` events run for most LCEL runnables.\n",
|
||||
"\n",
|
||||
"## Next steps\n",
|
||||
"\n",
|
||||
"You've now learned how to create your own custom callback handlers.\n",
|
||||
"\n",
|
||||
"Next, check out the other how-to guides in this section, such as [how to attach callbacks to a runnable](/docs/how_to/callbacks_attach)."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"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": "3.10.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Loading…
Reference in New Issue