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docs: Upgrade examples with RunnableWithMessageHistory to langgraph memory (#26855)
This PR updates the documentation examples that used RunnableWithMessageHistory to show how to achieve the same implementation with langgraph memory. Some of the underlying PRs (not all of them): - docs[patch]: update chatbot tutorial and migration guide (#26780) - docs[patch]: update chatbot memory how-to (#26790) - docs[patch]: update chatbot tools how-to (#26816) - docs: update chat history in rag how-to (#26821) - docs: update trim messages notebook (#26793) - docs: clean up imports in how to guide for rag qa with chat history (#26825) - docs[patch]: update conversational rag tutorial (#26814) --------- Co-authored-by: ccurme <chester.curme@gmail.com> Co-authored-by: Vadym Barda <vadym@langchain.dev> Co-authored-by: mercyspirit <ziying.qiu@gmail.com> Co-authored-by: aqiu7 <aqiu7@gatech.edu> Co-authored-by: John <43506685+Coniferish@users.noreply.github.com> Co-authored-by: Erick Friis <erick@langchain.dev> Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com> Co-authored-by: Subhrajyoty Roy <subhrajyotyroy@gmail.com> Co-authored-by: Rajendra Kadam <raj.725@outlook.com> Co-authored-by: Christophe Bornet <cbornet@hotmail.com> Co-authored-by: Devin Gaffney <itsme@devingaffney.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
This commit is contained in:
Eugene Yurtsev
GitHub
@@ -581,12 +581,38 @@ def trim_messages(
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) -> list[BaseMessage]:
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"""Trim messages to be below a token count.
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trim_messages can be used to reduce the size of a chat history to a specified token
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count or specified message count.
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In either case, if passing the trimmed chat history back into a chat model
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directly, the resulting chat history should usually satisfy the following
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properties:
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1. The resulting chat history should be valid. Most chat models expect that chat
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history starts with either (1) a `HumanMessage` or (2) a `SystemMessage` followed
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by a `HumanMessage`. To achieve this, set `start_on="human"`.
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In addition, generally a `ToolMessage` can only appear after an `AIMessage`
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that involved a tool call.
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Please see the following link for more information about messages:
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https://python.langchain.com/docs/concepts/#messages
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2. It includes recent messages and drops old messages in the chat history.
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To achieve this set the `strategy="last"`.
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3. Usually, the new chat history should include the `SystemMessage` if it
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was present in the original chat history since the `SystemMessage` includes
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special instructions to the chat model. The `SystemMessage` is almost always
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the first message in the history if present. To achieve this set the
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`include_system=True`.
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**Note** The examples below show how to configure `trim_messages` to achieve
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a behavior consistent with the above properties.
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Args:
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messages: Sequence of Message-like objects to trim.
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max_tokens: Max token count of trimmed messages.
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token_counter: Function or llm for counting tokens in a BaseMessage or a list of
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BaseMessage. If a BaseLanguageModel is passed in then
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BaseLanguageModel.get_num_tokens_from_messages() will be used.
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Set to `len` to count the number of **messages** in the chat history.
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strategy: Strategy for trimming.
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- "first": Keep the first <= n_count tokens of the messages.
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- "last": Keep the last <= n_count tokens of the messages.
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@@ -633,11 +659,97 @@ def trim_messages(
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``strategy`` is specified.
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Example:
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Trim chat history based on token count, keeping the SystemMessage if
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present, and ensuring that the chat history starts with a HumanMessage (
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or a SystemMessage followed by a HumanMessage).
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.. code-block:: python
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from typing import List
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from langchain_core.messages import trim_messages, AIMessage, BaseMessage, HumanMessage, SystemMessage
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from langchain_core.messages import (
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AIMessage,
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HumanMessage,
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BaseMessage,
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SystemMessage,
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trim_messages,
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)
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messages = [
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SystemMessage("you're a good assistant, you always respond with a joke."),
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HumanMessage("i wonder why it's called langchain"),
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AIMessage(
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'Well, I guess they thought "WordRope" and "SentenceString" just didn\'t have the same ring to it!'
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),
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HumanMessage("and who is harrison chasing anyways"),
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AIMessage(
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"Hmmm let me think.\n\nWhy, he's probably chasing after the last cup of coffee in the office!"
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),
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HumanMessage("what do you call a speechless parrot"),
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]
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trim_messages(
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messages,
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max_tokens=45,
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strategy="last",
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token_counter=ChatOpenAI(model="gpt-4o"),
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# Most chat models expect that chat history starts with either:
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# (1) a HumanMessage or
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# (2) a SystemMessage followed by a HumanMessage
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start_on="human",
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# Usually, we want to keep the SystemMessage
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# if it's present in the original history.
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# The SystemMessage has special instructions for the model.
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include_system=True,
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allow_partial=False,
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)
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.. code-block:: python
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[
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SystemMessage(content="you're a good assistant, you always respond with a joke."),
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HumanMessage(content='what do you call a speechless parrot'),
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]
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Trim chat history based on the message count, keeping the SystemMessage if
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present, and ensuring that the chat history starts with a HumanMessage (
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or a SystemMessage followed by a HumanMessage).
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trim_messages(
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messages,
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# When `len` is passed in as the token counter function,
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# max_tokens will count the number of messages in the chat history.
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max_tokens=4,
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strategy="last",
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# Passing in `len` as a token counter function will
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# count the number of messages in the chat history.
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token_counter=len,
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# Most chat models expect that chat history starts with either:
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# (1) a HumanMessage or
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# (2) a SystemMessage followed by a HumanMessage
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start_on="human",
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# Usually, we want to keep the SystemMessage
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# if it's present in the original history.
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# The SystemMessage has special instructions for the model.
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include_system=True,
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allow_partial=False,
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)
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.. code-block:: python
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[
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SystemMessage(content="you're a good assistant, you always respond with a joke."),
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HumanMessage(content='and who is harrison chasing anyways'),
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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!"),
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HumanMessage(content='what do you call a speechless parrot'),
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]
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Trim chat history using a custom token counter function that counts the
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number of tokens in each message.
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.. code-block:: python
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messages = [
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SystemMessage("This is a 4 token text. The full message is 10 tokens."),
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@@ -670,18 +782,6 @@ def trim_messages(
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count += default_msg_prefix_len + len(msg.content) * default_content_len + default_msg_suffix_len
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return count
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First 30 tokens, not allowing partial messages:
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.. code-block:: python
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trim_messages(messages, max_tokens=30, token_counter=dummy_token_counter, strategy="first")
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.. code-block:: python
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[
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SystemMessage("This is a 4 token text. The full message is 10 tokens."),
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HumanMessage("This is a 4 token text. The full message is 10 tokens.", id="first"),
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]
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First 30 tokens, allowing partial messages:
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.. code-block:: python
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@@ -700,108 +800,6 @@ def trim_messages(
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HumanMessage("This is a 4 token text. The full message is 10 tokens.", id="first"),
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AIMessage( [{"type": "text", "text": "This is the FIRST 4 token block."}], id="second"),
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]
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First 30 tokens, allowing partial messages, have to end on HumanMessage:
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.. code-block:: python
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trim_messages(
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messages,
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max_tokens=30,
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token_counter=dummy_token_counter,
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strategy="first"
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allow_partial=True,
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end_on="human",
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)
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.. code-block:: python
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[
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SystemMessage("This is a 4 token text. The full message is 10 tokens."),
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HumanMessage("This is a 4 token text. The full message is 10 tokens.", id="first"),
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]
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Last 30 tokens, including system message, not allowing partial messages:
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.. code-block:: python
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trim_messages(messages, max_tokens=30, include_system=True, token_counter=dummy_token_counter, strategy="last")
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.. code-block:: python
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[
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SystemMessage("This is a 4 token text. The full message is 10 tokens."),
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HumanMessage("This is a 4 token text. The full message is 10 tokens.", id="third"),
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AIMessage("This is a 4 token text. The full message is 10 tokens.", id="fourth"),
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]
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Last 40 tokens, including system message, allowing partial messages:
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.. code-block:: python
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trim_messages(
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messages,
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max_tokens=40,
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token_counter=dummy_token_counter,
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strategy="last",
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allow_partial=True,
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include_system=True
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)
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.. code-block:: python
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[
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SystemMessage("This is a 4 token text. The full message is 10 tokens."),
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AIMessage(
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[{"type": "text", "text": "This is the FIRST 4 token block."},],
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id="second",
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),
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HumanMessage("This is a 4 token text. The full message is 10 tokens.", id="third"),
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AIMessage("This is a 4 token text. The full message is 10 tokens.", id="fourth"),
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]
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Last 30 tokens, including system message, allowing partial messages, end on HumanMessage:
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.. code-block:: python
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trim_messages(
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messages,
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max_tokens=30,
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token_counter=dummy_token_counter,
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strategy="last",
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end_on="human",
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include_system=True,
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allow_partial=True,
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)
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.. code-block:: python
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[
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SystemMessage("This is a 4 token text. The full message is 10 tokens."),
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AIMessage(
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[{"type": "text", "text": "This is the FIRST 4 token block."},],
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id="second",
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),
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HumanMessage("This is a 4 token text. The full message is 10 tokens.", id="third"),
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]
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Last 40 tokens, including system message, allowing partial messages, start on HumanMessage:
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.. code-block:: python
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trim_messages(
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messages,
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max_tokens=40,
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token_counter=dummy_token_counter,
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strategy="last",
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include_system=True,
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allow_partial=True,
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start_on="human"
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)
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.. code-block:: python
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[
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SystemMessage("This is a 4 token text. The full message is 10 tokens."),
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HumanMessage("This is a 4 token text. The full message is 10 tokens.", id="third"),
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AIMessage("This is a 4 token text. The full message is 10 tokens.", id="fourth"),
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]
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""" # noqa: E501
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if start_on and strategy == "first":
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