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docs: update trim messages notebook (#26793)
Update trim messages notebook to include common use cases and explain what the desired behavior is
This commit is contained in:
Eugene Yurtsev
GitHub
parent
15d49d3df2
commit
54d5b74b00
@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "markdown",
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"id": "b5ee5b75-6876-4d62-9ade-5a7a808ae5a2",
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"id": "eaad9a82-0592-4315-9931-0621054bdd0e",
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"metadata": {},
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"source": [
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"# How to trim messages\n",
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@ -22,33 +22,77 @@
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"\n",
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"All models have finite context windows, meaning there's a limit to how many tokens they can take as input. If you have very long messages or a chain/agent that accumulates a long message is history, you'll need to manage the length of the messages you're passing in to the model.\n",
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"\n",
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"The `trim_messages` util provides some basic strategies for trimming a list of messages to be of a certain token length.\n",
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"[trim_messages](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.utils.trim_messages.html) can be used to reduce the size of a chat history to a specified token count or specified message count.\n",
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"\n",
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"## Getting the last `max_tokens` tokens\n",
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"\n",
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"To get the last `max_tokens` in the list of Messages we can set `strategy=\"last\"`. Notice that for our `token_counter` we can pass in a function (more on that below) or a language model (since language models have a message token counting method). It makes sense to pass in a model when you're trimming your messages to fit into the context window of that specific model:"
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"If passing the trimmed chat history back into a chat model directly, the trimmed chat history should satisfy the following properties:\n",
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"\n",
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"1. The resulting chat history should be **valid**. Most chat models expect that chat\n",
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" history starts with either (1) a `HumanMessage` or (2) a [SystemMessage](/docs/concepts/#systemmessage) followed\n",
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" by a `HumanMessage`. In addition, generally a `ToolMessage` can only appear after an `AIMessage`\n",
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" that involved a tool call. This can be achieved by setting `start_on=\"human\"`.\n",
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"2. It includes recent messages and drops old messages in the chat history.\n",
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" This can be achieved by setting `strategy=\"last\"`.\n",
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"4. Usually, the new chat history should include the `SystemMessage` if it\n",
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" was present in the original chat history since the `SystemMessage` includes\n",
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" special instructions to the chat model. The `SystemMessage` is almost always\n",
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" the first message in the history if present. This can be achieved by setting\n",
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" `include_system=True`."
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]
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},
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{
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"cell_type": "markdown",
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"id": "e4bffc37-78c0-46c3-ad0c-b44de0ed3e90",
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"metadata": {},
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"source": [
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"## Trimming based on token count\n",
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"\n",
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"Here, we'll trim the chat history based on token count. The trimmed chat history will produce a **valid** chat history that includes the `SystemMessage`.\n",
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"\n",
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"To keep the most recent messages, we set `strategy=\"last\"`. We'll also set `include_system=True` to include the `SystemMessage`, and `start_on=\"human\"` to make sure the resulting chat history is valid. \n",
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"\n",
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"This is a good default configuration when using `trim_messages` based on token count. Remember to adjust `token_counter` and `max_tokens` for your use case.\n",
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"\n",
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"Notice that for our `token_counter` we can pass in a function (more on that below) or a language model (since language models have a message token counting method). It makes sense to pass in a model when you're trimming your messages to fit into the context window of that specific model:"
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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": "c974633b-3bd0-4844-8a8f-85e3e25f13fe",
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"id": "c91edeb2-9978-4665-9fdb-fc96cdb51caa",
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"metadata": {},
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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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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"source": [
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"pip install -qU langchain-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": "40ea972c-d424-4bc4-9f2e-82f01c3d7598",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[AIMessage(content=\"Hmmm let me think.\\n\\nWhy, he's probably chasing after the last cup of coffee in the office!\"),\n",
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" HumanMessage(content='what do you call a speechless parrot')]"
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"[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\", additional_kwargs={}, response_metadata={}),\n",
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" HumanMessage(content='what do you call a speechless parrot', additional_kwargs={}, response_metadata={})]"
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]
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},
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"execution_count": 1,
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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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"# pip install -U langchain-openai\n",
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"from langchain_core.messages import (\n",
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" AIMessage,\n",
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" HumanMessage,\n",
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@ -70,36 +114,66 @@
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" HumanMessage(\"what do you call a speechless parrot\"),\n",
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"]\n",
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"\n",
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"\n",
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"trim_messages(\n",
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" messages,\n",
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" max_tokens=45,\n",
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" # Keep the last <= n_count tokens of the messages.\n",
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" strategy=\"last\",\n",
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" # highlight-start\n",
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" # Remember to adjust based on your model\n",
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" # or else pass a custom token_encoder\n",
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" token_counter=ChatOpenAI(model=\"gpt-4o\"),\n",
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" # highlight-end\n",
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" # Most chat models expect that chat history starts with either:\n",
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" # (1) a HumanMessage or\n",
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" # (2) a SystemMessage followed by a HumanMessage\n",
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" # highlight-start\n",
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" # Remember to adjust based on the desired conversation\n",
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" # length\n",
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" max_tokens=45,\n",
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" # highlight-end\n",
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" # Most chat models expect that chat history starts with either:\n",
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" # (1) a HumanMessage or\n",
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" # (2) a SystemMessage followed by a HumanMessage\n",
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" # start_on=\"human\" makes sure we produce a valid chat history\n",
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" start_on=\"human\",\n",
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" # Usually, we want to keep the SystemMessage\n",
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" # if it's present in the original history.\n",
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" # The SystemMessage has special instructions for the model.\n",
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" include_system=True,\n",
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" allow_partial=False,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d3f46654-c4b2-4136-b995-91c3febe5bf9",
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"id": "28fcfc94-0d4a-415c-9506-8ae7634253a2",
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"metadata": {},
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"source": [
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"If we want to always keep the initial system message we can specify `include_system=True`:"
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"## Trimming based on message count\n",
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"\n",
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"Alternatively, we can trim the chat history based on **message count**, by setting `token_counter=len`. In this case, each message will count as a single token, and `max_tokens` will control\n",
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"the maximum number of messages.\n",
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"\n",
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"This is a good default configuration when using `trim_messages` based on message count. Remember to adjust `max_tokens` for your use case."
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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": "589b0223-3a73-44ec-8315-2dba3ee6117d",
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"execution_count": 3,
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"id": "c8fdedae-0e6b-4901-a222-81fc95e265c2",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\"),\n",
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" HumanMessage(content='what do you call a speechless parrot')]"
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"[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\", additional_kwargs={}, response_metadata={}),\n",
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" HumanMessage(content='and who is harrison chasing anyways', additional_kwargs={}, response_metadata={}),\n",
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" AIMessage(content=\"Hmmm let me think.\\n\\nWhy, he's probably chasing after the last cup of coffee in the office!\", additional_kwargs={}, response_metadata={}),\n",
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" HumanMessage(content='what do you call a speechless parrot', additional_kwargs={}, response_metadata={})]"
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]
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},
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"execution_count": 2,
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -107,36 +181,56 @@
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"source": [
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"trim_messages(\n",
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" messages,\n",
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" max_tokens=45,\n",
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" # Keep the last <= n_count tokens of the messages.\n",
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" strategy=\"last\",\n",
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" token_counter=ChatOpenAI(model=\"gpt-4o\"),\n",
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" # highlight-next-line\n",
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" token_counter=len,\n",
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" # When token_counter=len, each message\n",
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" # will be counted as a single token.\n",
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" # highlight-start\n",
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" # Remember to adjust for your use case\n",
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" max_tokens=5,\n",
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" # highlight-end\n",
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" # Most chat models expect that chat history starts with either:\n",
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" # (1) a HumanMessage or\n",
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" # (2) a SystemMessage followed by a HumanMessage\n",
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" # start_on=\"human\" makes sure we produce a valid chat history\n",
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" start_on=\"human\",\n",
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" # Usually, we want to keep the SystemMessage\n",
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" # if it's present in the original history.\n",
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" # The SystemMessage has special instructions for the model.\n",
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" include_system=True,\n",
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")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "8a8b542c-04d1-4515-8d82-b999ea4fac4f",
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"id": "9367857f-7f9a-4d17-9f9c-6ffc5aae909c",
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"metadata": {},
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"source": [
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"## Advanced Usage\n",
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"\n",
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"You can use `trim_message` as a building-block to create more complex processing logic.\n",
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"\n",
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"If we want to allow splitting up the contents of a message we can specify `allow_partial=True`:"
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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": 3,
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"id": "8c46a209-dddd-4d01-81f6-f6ae55d3225c",
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"execution_count": 4,
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"id": "8bcca1fe-674c-4713-bacc-8e8e6d6f56c3",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\"),\n",
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" AIMessage(content=\"\\nWhy, he's probably chasing after the last cup of coffee in the office!\"),\n",
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" HumanMessage(content='what do you call a speechless parrot')]"
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"[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\", additional_kwargs={}, response_metadata={}),\n",
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" AIMessage(content=\"\\nWhy, he's probably chasing after the last cup of coffee in the office!\", additional_kwargs={}, response_metadata={}),\n",
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" HumanMessage(content='what do you call a speechless parrot', additional_kwargs={}, response_metadata={})]"
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]
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},
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"execution_count": 3,
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -154,26 +248,26 @@
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},
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{
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"cell_type": "markdown",
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"id": "306adf9c-41cd-495c-b4dc-e4f43dd7f8f8",
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"id": "245bee9b-e515-4e89-8f2a-84bda9a25de8",
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"metadata": {},
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"source": [
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"If we need to make sure that our first message (excluding the system message) is always of a specific type, we can specify `start_on`:"
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"By default, the `SystemMessage` will not be included, so you can drop it by either setting `include_system=False` or by dropping the `include_system` argument."
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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": 4,
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"id": "878a730b-fe44-4e9d-ab65-7b8f7b069de8",
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"execution_count": 5,
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"id": "94351736-28a1-44a3-aac7-82356c81d171",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\"),\n",
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" HumanMessage(content='what do you call a speechless parrot')]"
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"[AIMessage(content=\"Hmmm let me think.\\n\\nWhy, he's probably chasing after the last cup of coffee in the office!\", additional_kwargs={}, response_metadata={}),\n",
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" HumanMessage(content='what do you call a speechless parrot', additional_kwargs={}, response_metadata={})]"
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]
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},
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"execution_count": 4,
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -181,11 +275,9 @@
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"source": [
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"trim_messages(\n",
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" messages,\n",
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" max_tokens=60,\n",
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" max_tokens=45,\n",
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" strategy=\"last\",\n",
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" token_counter=ChatOpenAI(model=\"gpt-4o\"),\n",
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" include_system=True,\n",
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" start_on=\"human\",\n",
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")"
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]
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},
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@ -194,25 +286,23 @@
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"id": "7f5d391d-235b-4091-b2de-c22866b478f3",
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"metadata": {},
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"source": [
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"## Getting the first `max_tokens` tokens\n",
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"\n",
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"We can perform the flipped operation of getting the *first* `max_tokens` by specifying `strategy=\"first\"`:"
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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": 5,
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"execution_count": 6,
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"id": "5f56ae54-1a39-4019-9351-3b494c003d5b",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\"),\n",
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" HumanMessage(content=\"i wonder why it's called langchain\")]"
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"[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\", additional_kwargs={}, response_metadata={}),\n",
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" HumanMessage(content=\"i wonder why it's called langchain\", additional_kwargs={}, response_metadata={})]"
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]
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},
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"execution_count": 5,
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -238,18 +328,36 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 60,
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"id": "d930c089-e8e6-4980-9d39-11d41e794772",
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"metadata": {},
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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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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"source": [
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"pip install -qU tiktoken"
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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": 7,
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"id": "1c1c3b1e-2ece-49e7-a3b6-e69877c1633b",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[AIMessage(content=\"Hmmm let me think.\\n\\nWhy, he's probably chasing after the last cup of coffee in the office!\"),\n",
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" HumanMessage(content='what do you call a speechless parrot')]"
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"[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\", additional_kwargs={}, response_metadata={}),\n",
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" HumanMessage(content='what do you call a speechless parrot', additional_kwargs={}, response_metadata={})]"
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]
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},
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"execution_count": 6,
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -257,7 +365,6 @@
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"source": [
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"from typing import List\n",
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"\n",
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"# pip install tiktoken\n",
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"import tiktoken\n",
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"from langchain_core.messages import BaseMessage, ToolMessage\n",
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"\n",
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@ -298,9 +405,25 @@
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"\n",
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"trim_messages(\n",
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" messages,\n",
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" max_tokens=45,\n",
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" strategy=\"last\",\n",
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" # highlight-next-line\n",
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" token_counter=tiktoken_counter,\n",
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" # Keep the last <= n_count tokens of the messages.\n",
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" strategy=\"last\",\n",
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" # When token_counter=len, each message\n",
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" # will be counted as a single token.\n",
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" # highlight-start\n",
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" # Remember to adjust for your use case\n",
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" max_tokens=45,\n",
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" # highlight-end\n",
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" # Most chat models expect that chat history starts with either:\n",
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" # (1) a HumanMessage or\n",
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" # (2) a SystemMessage followed by a HumanMessage\n",
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" # start_on=\"human\" makes sure we produce a valid chat history\n",
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" start_on=\"human\",\n",
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" # Usually, we want to keep the SystemMessage\n",
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" # if it's present in the original history.\n",
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" # The SystemMessage has special instructions for the model.\n",
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" include_system=True,\n",
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")"
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]
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},
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@ -311,22 +434,22 @@
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"source": [
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"## Chaining\n",
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"\n",
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"`trim_messages` can be used in an imperatively (like above) or declaratively, making it easy to compose with other components in a chain"
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"`trim_messages` can be used imperatively (like above) or declaratively, making it easy to compose with other components in a chain"
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]
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},
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{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"execution_count": 62,
|
||||
"id": "96aa29b2-01e0-437c-a1ab-02fb0141cb57",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"AIMessage(content='A: A \"Polly-gone\"!', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 32, 'total_tokens': 41}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_66b29dffce', 'finish_reason': 'stop', 'logprobs': None}, id='run-83e96ddf-bcaa-4f63-824c-98b0f8a0d474-0', usage_metadata={'input_tokens': 32, 'output_tokens': 9, 'total_tokens': 41})"
|
||||
"AIMessage(content='A \"polygon!\"', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 4, 'prompt_tokens': 32, 'total_tokens': 36, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3537616b13', 'finish_reason': 'stop', 'logprobs': None}, id='run-995342be-0443-4e33-9b54-153f5c8771d3-0', usage_metadata={'input_tokens': 32, 'output_tokens': 4, 'total_tokens': 36})"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"execution_count": 62,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -340,7 +463,15 @@
|
||||
" max_tokens=45,\n",
|
||||
" strategy=\"last\",\n",
|
||||
" token_counter=llm,\n",
|
||||
" # Usually, we want to keep the SystemMessage\n",
|
||||
" # if it's present in the original history.\n",
|
||||
" # The SystemMessage has special instructions for the model.\n",
|
||||
" include_system=True,\n",
|
||||
" # Most chat models expect that chat history starts with either:\n",
|
||||
" # (1) a HumanMessage or\n",
|
||||
" # (2) a SystemMessage followed by a HumanMessage\n",
|
||||
" # start_on=\"human\" makes sure we produce a valid chat history\n",
|
||||
" start_on=\"human\",\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"chain = trimmer | llm\n",
|
||||
@ -359,18 +490,18 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 63,
|
||||
"id": "1ff02d0a-353d-4fac-a77c-7c2c5262abd9",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\"),\n",
|
||||
" HumanMessage(content='what do you call a speechless parrot')]"
|
||||
"[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\", additional_kwargs={}, response_metadata={}),\n",
|
||||
" HumanMessage(content='what do you call a speechless parrot', additional_kwargs={}, response_metadata={})]"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"execution_count": 63,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -391,17 +522,17 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 6,
|
||||
"id": "a9517858-fc2f-4dc3-898d-bf98a0e905a0",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"AIMessage(content='A \"polly-no-wanna-cracker\"!', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 32, 'total_tokens': 42}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_5bf7397cd3', 'finish_reason': 'stop', 'logprobs': None}, id='run-054dd309-3497-4e7b-b22a-c1859f11d32e-0', usage_metadata={'input_tokens': 32, 'output_tokens': 10, 'total_tokens': 42})"
|
||||
"AIMessage(content='A polygon! (Because it\\'s a \"poly-gone\" quiet!)', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 32, 'total_tokens': 46, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_e375328146', 'finish_reason': 'stop', 'logprobs': None}, id='run-8569a119-ca02-4232-bee1-20caea61cd6d-0', usage_metadata={'input_tokens': 32, 'output_tokens': 14, 'total_tokens': 46})"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -425,7 +556,15 @@
|
||||
" max_tokens=45,\n",
|
||||
" strategy=\"last\",\n",
|
||||
" token_counter=llm,\n",
|
||||
" # Usually, we want to keep the SystemMessage\n",
|
||||
" # if it's present in the original history.\n",
|
||||
" # The SystemMessage has special instructions for the model.\n",
|
||||
" include_system=True,\n",
|
||||
" # Most chat models expect that chat history starts with either:\n",
|
||||
" # (1) a HumanMessage or\n",
|
||||
" # (2) a SystemMessage followed by a HumanMessage\n",
|
||||
" # start_on=\"human\" makes sure we produce a valid chat history\n",
|
||||
" start_on=\"human\",\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"chain = trimmer | llm\n",
|
||||
@ -471,7 +610,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.4"
|
||||
"version": "3.11.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user