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Improve truncation with tool use and Anthropic caching

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- Cache last anthropic message. Given research mode now uses function
  calling paradigm and not the old research mode structure.
- Cache tool definitions passed to anthropic models
- Stop dropping first message if by assistant as seems like Anthropic
  API doesn't complain about it any more.

- Drop tool result when tool call is truncated as invalid state
- Do not truncate tool use message content, just drop the whole tool
  use message.

  AI model APIs need tool use assistant message content in specific
  form (e.g with thinking etc.). So dropping content items breaks
  expected tool use message content format.

Handle tool use scenarios where iteration query isn't set for retry
This commit is contained in:
Debanjum
2025-06-24 02:13:04 -07:00
parent 786b06bb3f
commit aa081913bf
3 changed files with 51 additions and 33 deletions
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25
src/khoj/processor/conversation/anthropic/utils.py
View File

@@ -82,6 +82,9 @@ def anthropic_completion_with_backoff(
anthropic.types.ToolParam(name=tool.name, description=tool.description, input_schema=tool.schema)
for tool in tools
]
# Cache tool definitions
last_tool = model_kwargs["tools"][-1]
last_tool["cache_control"] = {"type": "ephemeral"}
elif response_schema:
tool = create_tool_definition(response_schema)
model_kwargs["tools"] = [
@@ -291,11 +294,10 @@ def format_messages_for_anthropic(raw_messages: list[ChatMessage], system_prompt
else:
system = None
# Anthropic requires the first message to be a 'user' message
if len(messages) == 1:
# Anthropic requires the first message to be a user message unless its a tool call
message_type = messages[0].additional_kwargs.get("message_type", None)
if len(messages) == 1 and message_type != "tool_call":
messages[0].role = "user"
elif len(messages) > 1 and messages[0].role == "assistant":
messages = messages[1:]
for message in messages:
# Handle tool call and tool result message types from additional_kwargs
@@ -361,18 +363,15 @@ def format_messages_for_anthropic(raw_messages: list[ChatMessage], system_prompt
if isinstance(block, dict) and "cache_control" in block:
del block["cache_control"]
# Add cache control to the last content block of second to last message.
# In research mode, this message content is list of iterations, updated after each research iteration.
# Caching it should improve research efficiency.
cache_message = messages[-2]
# Add cache control to the last content block of last message.
# Caching should improve research efficiency.
cache_message = messages[-1]
if isinstance(cache_message.content, list) and cache_message.content:
# Add cache control to the last content block only if it's a text block with non-empty content
last_block = cache_message.content[-1]
if (
isinstance(last_block, dict)
and last_block.get("type") == "text"
and last_block.get("text")
and last_block.get("text").strip()
if isinstance(last_block, dict) and (
(last_block.get("type") == "text" and last_block.get("text", "").strip())
or (last_block.get("type") == "tool_result" and last_block.get("content", []))
):
last_block["cache_control"] = {"type": "ephemeral"}

38
src/khoj/processor/conversation/utils.py
View File

@@ -184,7 +184,7 @@ def construct_iteration_history(
iteration_history.append(ChatMessageModel(by="you", message=query_message_content))
for iteration in previous_iterations:
if not iteration.query:
if not iteration.query or isinstance(iteration.query, str):
iteration_history.append(
ChatMessageModel(
by="you",
@@ -336,7 +336,7 @@ def construct_tool_chat_history(
),
}
for iteration in previous_iterations:
if not iteration.query:
if not iteration.query or isinstance(iteration.query, str):
chat_history.append(
ChatMessageModel(
by="you",
@@ -806,6 +806,15 @@ def count_tokens(
return len(encoder.encode(json.dumps(message_content)))
def count_total_tokens(messages: list[ChatMessage], encoder, system_message: Optional[ChatMessage]) -> Tuple[int, int]:
"""Count total tokens in messages including system message"""
system_message_tokens = count_tokens(system_message.content, encoder) if system_message else 0
message_tokens = sum([count_tokens(message.content, encoder) for message in messages])
# Reserves 4 tokens to demarcate each message (e.g <|im_start|>user, <|im_end|>, <|endoftext|> etc.)
total_tokens = message_tokens + system_message_tokens + 4 * len(messages)
return total_tokens, system_message_tokens
def truncate_messages(
messages: list[ChatMessage],
max_prompt_size: int,
@@ -824,23 +833,30 @@ def truncate_messages(
break
# Drop older messages until under max supported prompt size by model
# Reserves 4 tokens to demarcate each message (e.g <|im_start|>user, <|im_end|>, <|endoftext|> etc.)
system_message_tokens = count_tokens(system_message.content, encoder) if system_message else 0
tokens = sum([count_tokens(message.content, encoder) for message in messages])
total_tokens = tokens + system_message_tokens + 4 * len(messages)
total_tokens, system_message_tokens = count_total_tokens(messages, encoder, system_message)
while total_tokens > max_prompt_size and (len(messages) > 1 or len(messages[0].content) > 1):
if len(messages[-1].content) > 1:
# If the last message has more than one content part, pop the oldest content part.
# For tool calls, the whole message should dropped, assistant's tool call content being truncated annoys AI APIs.
if len(messages[-1].content) > 1 and messages[-1].additional_kwargs.get("message_type") != "tool_call":
# The oldest content part is earlier in content list. So pop from the front.
messages[-1].content.pop(0)
# Otherwise, pop the last message if it has only one content part or is a tool call.
else:
# The oldest message is the last one. So pop from the back.
messages.pop()
tokens = sum([count_tokens(message.content, encoder) for message in messages])
total_tokens = tokens + system_message_tokens + 4 * len(messages)
dropped_message = messages.pop()
# Drop tool result pair of tool call, if tool call message has been removed
if (
dropped_message.additional_kwargs.get("message_type") == "tool_call"
and messages
and messages[-1].additional_kwargs.get("message_type") == "tool_result"
):
messages.pop()
total_tokens, _ = count_total_tokens(messages, encoder, system_message)
# Truncate current message if still over max supported prompt size by model
total_tokens = tokens + system_message_tokens + 4 * len(messages)
total_tokens, _ = count_total_tokens(messages, encoder, system_message)
if total_tokens > max_prompt_size:
# At this point, a single message with a single content part of type dict should remain
assert (

21
tests/test_conversation_utils.py
View File

@@ -48,17 +48,18 @@ class TestTruncateMessage:
big_chat_message = ChatMessage(role="user", content=content_list)
copy_big_chat_message = deepcopy(big_chat_message)
chat_history = [big_chat_message]
tokens = sum([utils.count_tokens(message.content, self.encoder) for message in chat_history])
initial_tokens = sum([utils.count_tokens(message.content, self.encoder) for message in chat_history])
# Act
truncated_chat_history = utils.truncate_messages(chat_history, self.max_prompt_size, self.model_name)
tokens = sum([utils.count_tokens(message.content, self.encoder) for message in truncated_chat_history])
final_tokens = sum([utils.count_tokens(message.content, self.encoder) for message in truncated_chat_history])
# Assert
# The original object has been modified. Verify certain properties
assert truncated_chat_history[0] != copy_big_chat_message, "Original message should be modified"
assert truncated_chat_history[0].content[-1]["text"] == "Question?", "Query should be preserved"
assert tokens <= self.max_prompt_size, "Truncated message should be within max prompt size"
assert initial_tokens > self.max_prompt_size, "Initial tokens should be greater than max prompt size"
assert final_tokens <= self.max_prompt_size, "Truncated message should be within max prompt size"
def test_truncate_message_with_content_list(self):
# Arrange
@@ -68,11 +69,11 @@ class TestTruncateMessage:
big_chat_message = ChatMessage(role="user", content=content_list)
copy_big_chat_message = deepcopy(big_chat_message)
chat_history.insert(0, big_chat_message)
tokens = sum([utils.count_tokens(message.content, self.encoder) for message in chat_history])
initial_tokens = sum([utils.count_tokens(message.content, self.encoder) for message in chat_history])
# Act
truncated_chat_history = utils.truncate_messages(chat_history, self.max_prompt_size, self.model_name)
tokens = sum([utils.count_tokens(message.content, self.encoder) for message in truncated_chat_history])
final_tokens = sum([utils.count_tokens(message.content, self.encoder) for message in truncated_chat_history])
# Assert
# The original object has been modified. Verify certain properties
@@ -83,7 +84,8 @@ class TestTruncateMessage:
copy_big_chat_message.content
), "message content list should be modified"
assert truncated_chat_history[0].content[-1]["text"] == "Question?", "Query should be preserved"
assert tokens <= self.max_prompt_size, "Truncated message should be within max prompt size"
assert initial_tokens > self.max_prompt_size, "Initial tokens should be greater than max prompt size"
assert final_tokens <= self.max_prompt_size, "Truncated message should be within max prompt size"
def test_truncate_message_first_large(self):
# Arrange
@@ -91,11 +93,11 @@ class TestTruncateMessage:
big_chat_message = ChatMessage(role="user", content=generate_content(100, suffix="Question?"))
copy_big_chat_message = big_chat_message.copy()
chat_history.insert(0, big_chat_message)
tokens = sum([utils.count_tokens(message.content, self.encoder) for message in chat_history])
initial_tokens = sum([utils.count_tokens(message.content, self.encoder) for message in chat_history])
# Act
truncated_chat_history = utils.truncate_messages(chat_history, self.max_prompt_size, self.model_name)
tokens = sum([utils.count_tokens(message.content, self.encoder) for message in truncated_chat_history])
final_tokens = sum([utils.count_tokens(message.content, self.encoder) for message in truncated_chat_history])
# Assert
# The original object has been modified. Verify certain properties
@@ -104,7 +106,8 @@ class TestTruncateMessage:
), "Only most recent message should be present as it itself is larger than context size"
assert truncated_chat_history[0] != copy_big_chat_message, "Original message should be modified"
assert truncated_chat_history[0].content[0]["text"].endswith("\nQuestion?"), "Query should be preserved"
assert tokens <= self.max_prompt_size, "Truncated message should be within max prompt size"
assert initial_tokens > self.max_prompt_size, "Initial tokens should be greater than max prompt size"
assert final_tokens <= self.max_prompt_size, "Truncated message should be within max prompt size"
def test_truncate_message_large_system_message_first(self):
# Arrange