Update truncation logic to handle multi-part message content

src/khoj/processor/conversation/anthropic/utils.py

@@ -203,7 +203,10 @@ def format_messages_for_anthropic(messages: list[ChatMessage], system_prompt: st
     system_prompt = system_prompt or ""
     for message in messages.copy():
         if message.role == "system":
-            system_prompt += message.content
+            if isinstance(message.content, list):
+                system_prompt += "\n".join([part["text"] for part in message.content if part["type"] == "text"])
+            else:
+                system_prompt += message.content
             messages.remove(message)
     system_prompt = None if is_none_or_empty(system_prompt) else system_prompt
 

src/khoj/processor/conversation/google/utils.py

@@ -301,7 +301,10 @@ def format_messages_for_gemini(
     messages = deepcopy(original_messages)
     for message in messages.copy():
         if message.role == "system":
-            system_prompt += message.content
+            if isinstance(message.content, list):
+                system_prompt += "\n".join([part["text"] for part in message.content if part["type"] == "text"])
+            else:
+                system_prompt += message.content
             messages.remove(message)
     system_prompt = None if is_none_or_empty(system_prompt) else system_prompt
 

src/khoj/processor/conversation/utils.py

@@ -4,14 +4,12 @@ import logging
 import math
 import mimetypes
 import os
-import queue
 import re
 import uuid
 from dataclasses import dataclass
 from datetime import datetime
 from enum import Enum
 from io import BytesIO
-from time import perf_counter
 from typing import Any, Callable, Dict, List, Optional
 
 import PIL.Image
@@ -20,8 +18,9 @@ import requests
 import tiktoken
 import yaml
 from langchain.schema import ChatMessage
+from llama_cpp import LlamaTokenizer
 from llama_cpp.llama import Llama
-from transformers import AutoTokenizer
+from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast
 
 from khoj.database.adapters import ConversationAdapters
 from khoj.database.models import ChatModel, ClientApplication, KhojUser
@@ -382,7 +381,7 @@ def gather_raw_query_files(
 
 def generate_chatml_messages_with_context(
     user_message,
-    system_message=None,
+    system_message: str = None,
     conversation_log={},
     model_name="gpt-4o-mini",
     loaded_model: Optional[Llama] = None,
@@ -480,7 +479,7 @@ def generate_chatml_messages_with_context(
         if len(chatml_messages) >= 3 * lookback_turns:
             break
 
-    messages = []
+    messages: list[ChatMessage] = []
 
     if not is_none_or_empty(generated_asset_results):
         messages.append(
@@ -517,6 +516,11 @@ def generate_chatml_messages_with_context(
     if not is_none_or_empty(system_message):
         messages.append(ChatMessage(content=system_message, role="system"))
 
+    # Normalize message content to list of chatml dictionaries
+    for message in messages:
+        if isinstance(message.content, str):
+            message.content = [{"type": "text", "text": message.content}]
+
     # Truncate oldest messages from conversation history until under max supported prompt size by model
     messages = truncate_messages(messages, max_prompt_size, model_name, loaded_model, tokenizer_name)
 
@@ -524,14 +528,11 @@ def generate_chatml_messages_with_context(
     return messages[::-1]
 
 
-def truncate_messages(
+def get_encoder(
-    messages: list[ChatMessage],
-    max_prompt_size: int,
     model_name: str,
     loaded_model: Optional[Llama] = None,
     tokenizer_name=None,
-) -> list[ChatMessage]:
+) -> tiktoken.Encoding | PreTrainedTokenizer | PreTrainedTokenizerFast | LlamaTokenizer:
-    """Truncate messages to fit within max prompt size supported by model"""
     default_tokenizer = "gpt-4o"
 
     try:
@@ -554,6 +555,48 @@ def truncate_messages(
             logger.debug(
                 f"Fallback to default chat model tokenizer: {default_tokenizer}.\nConfigure tokenizer for model: {model_name} in Khoj settings to improve context stuffing."
             )
+    return encoder
+
+
+def count_tokens(
+    message_content: str | list[str | dict],
+    encoder: PreTrainedTokenizer | PreTrainedTokenizerFast | LlamaTokenizer | tiktoken.Encoding,
+) -> int:
+    """
+    Count the total number of tokens in a list of messages.
+
+    Assumes each images takes 500 tokens for approximation.
+    """
+    if isinstance(message_content, list):
+        image_count = 0
+        message_content_parts: list[str] = []
+        # Collate message content into single string to ease token counting
+        for part in message_content:
+            if isinstance(part, dict) and part.get("type") == "text":
+                message_content_parts.append(part["text"])
+            elif isinstance(part, dict) and part.get("type") == "image_url":
+                image_count += 1
+            elif isinstance(part, str):
+                message_content_parts.append(part)
+            else:
+                logger.warning(f"Unknown message type: {part}. Skipping.")
+        message_content = "\n".join(message_content_parts).rstrip()
+        return len(encoder.encode(message_content)) + image_count * 500
+    elif isinstance(message_content, str):
+        return len(encoder.encode(message_content))
+    else:
+        return len(encoder.encode(json.dumps(message_content)))
+
+
+def truncate_messages(
+    messages: list[ChatMessage],
+    max_prompt_size: int,
+    model_name: str,
+    loaded_model: Optional[Llama] = None,
+    tokenizer_name=None,
+) -> list[ChatMessage]:
+    """Truncate messages to fit within max prompt size supported by model"""
+    encoder = get_encoder(model_name, loaded_model, tokenizer_name)
 
     # Extract system message from messages
     system_message = None
@@ -562,35 +605,55 @@ def truncate_messages(
             system_message = messages.pop(idx)
             break
 
-    # TODO: Handle truncation of multi-part message.content, i.e when message.content is a list[dict] rather than a string
-    system_message_tokens = (
-        len(encoder.encode(system_message.content)) if system_message and type(system_message.content) == str else 0
-    )
-
-    tokens = sum([len(encoder.encode(message.content)) for message in messages if type(message.content) == str])
-
     # 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.)
-    while (tokens + system_message_tokens + 4 * len(messages)) > max_prompt_size and len(messages) > 1:
+    system_message_tokens = count_tokens(system_message.content, encoder) if system_message else 0
-        messages.pop()
+    tokens = sum([count_tokens(message.content, encoder) for message in messages])
-        tokens = sum([len(encoder.encode(message.content)) for message in messages if type(message.content) == str])
+    total_tokens = tokens + system_message_tokens + 4 * len(messages)
+
+    while total_tokens > max_prompt_size and (len(messages) > 1 or len(messages[0].content) > 1):
+        if len(messages[-1].content) > 1:
+            # The oldest content part is earlier in content list. So pop from the front.
+            messages[-1].content.pop(0)
+        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)
 
     # Truncate current message if still over max supported prompt size by model
-    if (tokens + system_message_tokens) > max_prompt_size:
+    total_tokens = tokens + system_message_tokens + 4 * len(messages)
-        current_message = "\n".join(messages[0].content.split("\n")[:-1]) if type(messages[0].content) == str else ""
+    if total_tokens > max_prompt_size:
-        original_question = "\n".join(messages[0].content.split("\n")[-1:]) if type(messages[0].content) == str else ""
+        # At this point, a single message with a single content part of type dict should remain
-        original_question = f"\n{original_question}"
+        assert (
-        original_question_tokens = len(encoder.encode(original_question))
+            len(messages) == 1 and len(messages[0].content) == 1 and isinstance(messages[0].content[0], dict)
+        ), "Expected a single message with a single content part remaining at this point in truncation"
+
+        # Collate message content into single string to ease truncation
+        part = messages[0].content[0]
+        message_content: str = part["text"] if part["type"] == "text" else json.dumps(part)
+        message_role = messages[0].role
+
+        remaining_context = "\n".join(message_content.split("\n")[:-1])
+        original_question = "\n" + "\n".join(message_content.split("\n")[-1:])
+
+        original_question_tokens = count_tokens(original_question, encoder)
         remaining_tokens = max_prompt_size - system_message_tokens
         if remaining_tokens > original_question_tokens:
             remaining_tokens -= original_question_tokens
-            truncated_message = encoder.decode(encoder.encode(current_message)[:remaining_tokens]).strip()
+            truncated_context = encoder.decode(encoder.encode(remaining_context)[:remaining_tokens]).strip()
-            messages = [ChatMessage(content=truncated_message + original_question, role=messages[0].role)]
+            truncated_content = truncated_context + original_question
         else:
-            truncated_message = encoder.decode(encoder.encode(original_question)[:remaining_tokens]).strip()
+            truncated_content = encoder.decode(encoder.encode(original_question)[:remaining_tokens]).strip()
-            messages = [ChatMessage(content=truncated_message, role=messages[0].role)]
+        messages = [ChatMessage(content=[{"type": "text", "text": truncated_content}], role=message_role)]
+
+        truncated_snippet = (
+            f"{truncated_content[:1000]}\n...\n{truncated_content[-1000:]}"
+            if len(truncated_content) > 2000
+            else truncated_content
+        )
         logger.debug(
-            f"Truncate current message to fit within max prompt size of {max_prompt_size} supported by {model_name} model:\n {truncated_message[:1000]}..."
+            f"Truncate current message to fit within max prompt size of {max_prompt_size} supported by {model_name} model:\n {truncated_snippet}"
         )
 
     if system_message:

src/khoj/utils/state.py

@@ -6,6 +6,7 @@ from typing import Any, Dict, List
 
 from apscheduler.schedulers.background import BackgroundScheduler
 from openai import OpenAI
+from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
 from whisper import Whisper
 
 from khoj.database.models import ProcessLock
@@ -40,7 +41,7 @@ khoj_version: str = None
 device = get_device()
 chat_on_gpu: bool = True
 anonymous_mode: bool = False
-pretrained_tokenizers: Dict[str, Any] = dict()
+pretrained_tokenizers: Dict[str, PreTrainedTokenizer | PreTrainedTokenizerFast] = dict()
 billing_enabled: bool = (
     os.getenv("STRIPE_API_KEY") is not None
     and os.getenv("STRIPE_SIGNING_SECRET") is not None

tests/test_conversation_utils.py

@@ -1,3 +1,5 @@
+from copy import deepcopy
+
 import tiktoken
 from langchain.schema import ChatMessage
 
@@ -5,7 +7,7 @@ from khoj.processor.conversation import utils
 
 
 class TestTruncateMessage:
-    max_prompt_size = 10
+    max_prompt_size = 40
     model_name = "gpt-4o-mini"
     encoder = tiktoken.encoding_for_model(model_name)
 
@@ -15,45 +17,108 @@ class TestTruncateMessage:
 
         # Act
         truncated_chat_history = utils.truncate_messages(chat_history, self.max_prompt_size, self.model_name)
-        tokens = sum([len(self.encoder.encode(message.content)) for message in truncated_chat_history])
+        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 len(chat_history) < 50
-        assert len(chat_history) > 1
+        assert len(chat_history) > 5
         assert tokens <= self.max_prompt_size
 
+    def test_truncate_message_only_oldest_big(self):
+        # Arrange
+        chat_history = generate_chat_history(5)
+        big_chat_message = ChatMessage(role="user", content=generate_content(100, suffix="Question?"))
+        chat_history.append(big_chat_message)
+
+        # 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])
+
+        # Assert
+        # The original object has been modified. Verify certain properties
+        assert len(chat_history) == 5
+        assert tokens <= self.max_prompt_size
+
+    def test_truncate_message_with_image(self):
+        # Arrange
+        image_content_item = {"type": "image_url", "image_url": {"url": "placeholder"}}
+        content_list = [{"type": "text", "text": f"{index}"} for index in range(100)]
+        content_list += [image_content_item, {"type": "text", "text": "Question?"}]
+        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])
+
+        # 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])
+
+        # 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"
+
+    def test_truncate_message_with_content_list(self):
+        # Arrange
+        chat_history = generate_chat_history(5)
+        content_list = [{"type": "text", "text": f"{index}"} for index in range(100)]
+        content_list += [{"type": "text", "text": "Question?"}]
+        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])
+
+        # 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])
+
+        # Assert
+        # The original object has been modified. Verify certain properties
+        assert (
+            len(chat_history) == 1
+        ), "Only most recent message should be present as it itself is larger than context size"
+        assert len(truncated_chat_history[0].content) < len(
+            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"
+
     def test_truncate_message_first_large(self):
         # Arrange
         chat_history = generate_chat_history(5)
-        big_chat_message = ChatMessage(role="user", content=f"{generate_content(6)}\nQuestion?")
+        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([len(self.encoder.encode(message.content)) for message in chat_history])
+        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([len(self.encoder.encode(message.content)) for message in truncated_chat_history])
+        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 len(chat_history) == 1
+        assert (
-        assert truncated_chat_history[0] != copy_big_chat_message
+            len(chat_history) == 1
-        assert tokens <= self.max_prompt_size
+        ), "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"
 
-    def test_truncate_message_last_large(self):
+    def test_truncate_message_large_system_message_first(self):
         # Arrange
         chat_history = generate_chat_history(5)
         chat_history[0].role = "system"  # Mark the first message as system message
-        big_chat_message = ChatMessage(role="user", content=f"{generate_content(11)}\nQuestion?")
+        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)
-        initial_tokens = sum([len(self.encoder.encode(message.content)) 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)
-        final_tokens = sum([len(self.encoder.encode(message.content)) 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.
@@ -62,46 +127,52 @@ class TestTruncateMessage:
         )  # Because the system_prompt is popped off from the chat_messages list
         assert len(truncated_chat_history) < 10
         assert len(truncated_chat_history) > 1
-        assert truncated_chat_history[0] != copy_big_chat_message
+        assert truncated_chat_history[0] != copy_big_chat_message, "Original message should be modified"
-        assert initial_tokens > self.max_prompt_size
+        assert truncated_chat_history[0].content[0]["text"].endswith("\nQuestion?"), "Query should be preserved"
-        assert final_tokens <= self.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, "Final tokens should be within max prompt size"
 
     def test_truncate_single_large_non_system_message(self):
         # Arrange
-        big_chat_message = ChatMessage(role="user", content=f"{generate_content(11)}\nQuestion?")
+        big_chat_message = ChatMessage(role="user", content=generate_content(100, suffix="Question?"))
         copy_big_chat_message = big_chat_message.copy()
         chat_messages = [big_chat_message]
-        initial_tokens = sum([len(self.encoder.encode(message.content)) for message in chat_messages])
+        initial_tokens = sum([utils.count_tokens(message.content, self.encoder) for message in chat_messages])
 
         # Act
         truncated_chat_history = utils.truncate_messages(chat_messages, self.max_prompt_size, self.model_name)
-        final_tokens = sum([len(self.encoder.encode(message.content)) 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 initial_tokens > self.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
+        assert final_tokens <= self.max_prompt_size, "Final tokens should be within max prompt size"
-        assert len(chat_messages) == 1
+        assert (
-        assert truncated_chat_history[0] != copy_big_chat_message
+            len(chat_messages) == 1
+        ), "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"
 
     def test_truncate_single_large_question(self):
         # Arrange
-        big_chat_message_content = " ".join(["hi"] * (self.max_prompt_size + 1))
+        big_chat_message_content = [{"type": "text", "text": " ".join(["hi"] * (self.max_prompt_size + 1))}]
         big_chat_message = ChatMessage(role="user", content=big_chat_message_content)
         copy_big_chat_message = big_chat_message.copy()
         chat_messages = [big_chat_message]
-        initial_tokens = sum([len(self.encoder.encode(message.content)) for message in chat_messages])
+        initial_tokens = sum([utils.count_tokens(message.content, self.encoder) for message in chat_messages])
 
         # Act
         truncated_chat_history = utils.truncate_messages(chat_messages, self.max_prompt_size, self.model_name)
-        final_tokens = sum([len(self.encoder.encode(message.content)) 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 initial_tokens > self.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
+        assert final_tokens <= self.max_prompt_size, "Final tokens should be within max prompt size"
-        assert len(chat_messages) == 1
+        assert (
-        assert truncated_chat_history[0] != copy_big_chat_message
+            len(chat_messages) == 1
+        ), "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"
 
 
 def test_load_complex_raw_json_string():
@@ -116,12 +187,12 @@ def test_load_complex_raw_json_string():
     assert parsed_json == expeced_json
 
 
-def generate_content(count):
+def generate_content(count, suffix=""):
-    return " ".join([f"{index}" for index, _ in enumerate(range(count))])
+    return [{"type": "text", "text": " ".join([f"{index}" for index, _ in enumerate(range(count))]) + "\n" + suffix}]
 
 
 def generate_chat_history(count):
     return [
-        ChatMessage(role="user" if index % 2 == 0 else "assistant", content=f"{index}")
+        ChatMessage(role="user" if index % 2 == 0 else "assistant", content=[{"type": "text", "text": f"{index}"}])
         for index, _ in enumerate(range(count))
     ]