feat: implement gptme-util CLI for utilities

Makefile

@@ -52,7 +52,7 @@ lint:
 	! grep -r 'ToolUse("python"' ${SRCDIRS}
 	@# ruff
 	poetry run ruff check ${RUFF_ARGS}
-
+	poetry run pylint --disable=all --enable=duplicate-code gptme/
 
 format:
 	poetry run ruff check --fix-only ${RUFF_ARGS}

docs/cli.rst

@@ -21,3 +21,7 @@ This is the full CLI reference. For a more concise version, run ``gptme --help``
 .. click:: gptme.eval:main
    :prog: gptme-eval
    :nested: full
+
+.. click:: gptme.util.cli:main
+   :prog: gptme-util
+   :nested: full

docs/server.rst

@@ -13,7 +13,7 @@ It can be started by running the following command:
 
     gptme-server
 
-For more CLI usage, see :ref:`the CLI documentation <cli:gptme-server>`.
+For more CLI usage, see the :ref:`CLI reference <cli:gptme-server>`.
 
 There are a few different interfaces available:
 

gptme/llm_openai_models.py

@@ -1,15 +1,10 @@
-from typing import TypedDict
-from typing_extensions import NotRequired
+from typing import TYPE_CHECKING
 
+if TYPE_CHECKING:
+    from .models import _ModelDictMeta  # fmt: skip
 
-class _ModelDictMeta(TypedDict):
-    context: int
-    max_output: NotRequired[int]
-    price_input: NotRequired[float]
-    price_output: NotRequired[float]
 
-
-OPENAI_MODELS: dict[str, _ModelDictMeta] = {
+OPENAI_MODELS: dict[str, "_ModelDictMeta"] = {
     # GPT-4o
     "gpt-4o": {
         "context": 128_000,

gptme/logmanager.py

@@ -331,13 +331,19 @@ def _conversation_files() -> list[Path]:
 
 @dataclass(frozen=True)
 class ConversationMeta:
+    """Metadata about a conversation."""
+
     name: str
     path: str
     created: float
     modified: float
     messages: int
     branches: int
 
+    def format(self) -> str:
+        """Format a conversation for display."""
+        return f"{self.name}: {self.messages} messages, last modified {self.modified}"
+
 
 def get_conversations() -> Generator[ConversationMeta, None, None]:
     """Returns all conversations, excluding ones used for testing, evals, etc."""
@@ -368,6 +374,23 @@ def get_user_conversations() -> Generator[ConversationMeta, None, None]:
         yield conv
 
 
+def list_conversations(
+    limit: int = 20,
+    include_test: bool = False,
+) -> list[ConversationMeta]:
+    """
+    List conversations with a limit.
+
+    Args:
+        limit: Maximum number of conversations to return
+        include_test: Whether to include test conversations
+    """
+    conversation_iter = (
+        get_conversations() if include_test else get_user_conversations()
+    )
+    return list(islice(conversation_iter, limit))
+
+
 def _gen_read_jsonl(path: PathLike) -> Generator[Message, None, None]:
     with open(path) as file:
         for line in file.readlines():

gptme/message.py

@@ -183,7 +183,30 @@ def to_xml(self) -> str:
         attrs = f"role='{self.role}'"
         return f"<message {attrs}>\n{self.content}\n</message>"
 
-    def format(self, oneline: bool = False, highlight: bool = False) -> str:
+    def format(
+        self,
+        oneline: bool = False,
+        highlight: bool = False,
+        max_length: int | None = None,
+    ) -> str:
+        """Format the message for display.
+
+        Args:
+            oneline: Whether to format the message as a single line
+            highlight: Whether to highlight code blocks
+            max_length: Maximum length of the message. If None, no truncation is applied.
+                       If set, will truncate at first newline or max_length, whichever comes first.
+        """
+        if max_length is not None:
+            first_newline = self.content.find("\n")
+            max_length = (
+                min(max_length, first_newline) if first_newline != -1 else max_length
+            )
+            content = self.content[:max_length]
+            if len(content) < len(self.content):
+                content += "..."
+            temp_msg = self.replace(content=content)
+            return format_msgs([temp_msg], oneline=True, highlight=highlight)[0]
         return format_msgs([self], oneline=oneline, highlight=highlight)[0]
 
     def print(self, oneline: bool = False, highlight: bool = True) -> None:

gptme/prompts.py

@@ -17,7 +17,6 @@
 from .__version__ import __version__
 from .config import get_config, get_project_config
 from .message import Message
-from .tools import loaded_tools
 from .util import document_prompt_function
 
 PromptType = Literal["full", "short"]
@@ -199,6 +198,8 @@ def prompt_project() -> Generator[Message, None, None]:
 
 def prompt_tools(examples: bool = True) -> Generator[Message, None, None]:
     """Generate the tools overview prompt."""
+    from .tools import loaded_tools  # fmt: skip
+
     assert loaded_tools, "No tools loaded"
     prompt = "# Tools Overview"
     for tool in loaded_tools:

gptme/tools/chats.py

@@ -2,32 +2,15 @@
 List, search, and summarize past conversation logs.
 """
 
-import itertools
 import logging
 from pathlib import Path
-from textwrap import indent
-from typing import TYPE_CHECKING
 
 from ..message import Message
 from .base import ToolSpec, ToolUse
 
-if TYPE_CHECKING:
-    from ..logmanager import LogManager
-
-
 logger = logging.getLogger(__name__)
 
 
-def _format_message_snippet(msg: Message, max_length: int = 100) -> str:
-    """Format a message snippet for display."""
-    first_newline = msg.content.find("\n")
-    max_length = min(max_length, first_newline) if first_newline != -1 else max_length
-    content = msg.content[:max_length]
-    return f"{msg.role.capitalize()}: {content}" + (
-        "..." if len(content) <= len(msg.content) else ""
-    )
-
-
 def _get_matching_messages(log_manager, query: str, system=False) -> list[Message]:
     """Get messages matching the query."""
     return [
@@ -38,35 +21,6 @@ def _get_matching_messages(log_manager, query: str, system=False) -> list[Messag
     ]
 
 
-def _summarize_conversation(
-    log_manager: "LogManager", include_summary: bool
-) -> list[str]:
-    """Summarize a conversation."""
-    # noreorder
-    from ..llm import summarize as llm_summarize  # fmt: skip
-
-    summary_lines = []
-    if include_summary:
-        summary = llm_summarize(log_manager.log.messages)
-        summary_lines.append(indent(f"Summary: {summary.content}", "   "))
-    else:
-        non_system_messages = [msg for msg in log_manager.log if msg.role != "system"]
-        if non_system_messages:
-            first_msg = non_system_messages[0]
-            last_msg = non_system_messages[-1]
-
-            summary_lines.append(
-                f"   First message: {_format_message_snippet(first_msg)}"
-            )
-            if last_msg != first_msg:
-                summary_lines.append(
-                    f"   Last message:  {_format_message_snippet(last_msg)}"
-                )
-
-    summary_lines.append(f"   Total messages: {len(log_manager.log)}")
-    return summary_lines
-
-
 def list_chats(max_results: int = 5, include_summary: bool = False) -> None:
     """
     List recent chat conversations and optionally summarize them using an LLM.
@@ -77,24 +31,26 @@ def list_chats(max_results: int = 5, include_summary: bool = False) -> None:
             If True, uses an LLM to generate a comprehensive summary.
             If False, uses a simple strategy showing snippets of the first and last messages.
     """
-    # noreorder
-    from ..logmanager import LogManager, get_user_conversations  # fmt: skip
+    from ..llm import summarize  # fmt: skip
+    from ..logmanager import LogManager, list_conversations  # fmt: skip
 
-    conversations = list(itertools.islice(get_user_conversations(), max_results))
+    conversations = list_conversations(max_results)
     if not conversations:
         print("No conversations found.")
         return
 
     print(f"Recent conversations (showing up to {max_results}):")
     for i, conv in enumerate(conversations, 1):
-        print(f"\n{i}. {conv.name}")
+        print(f"\n{i}. {conv.format()}")
         print(f"   Created: {conv.created}")
 
         log_path = Path(conv.path)
         log_manager = LogManager.load(log_path)
 
-        summary_lines = _summarize_conversation(log_manager, include_summary)
-        print("\n".join(summary_lines))
+        # Use the LLM to generate a summary if requested
+        if include_summary:
+            summary = summarize(log_manager.log.messages)
+            print(f"   Summary: {summary.content}")
 
 
 def search_chats(query: str, max_results: int = 5, system=False) -> None:
@@ -106,11 +62,10 @@ def search_chats(query: str, max_results: int = 5, system=False) -> None:
         max_results (int): Maximum number of conversations to display.
         system (bool): Whether to include system messages in the search.
     """
-    # noreorder
-    from ..logmanager import LogManager, get_user_conversations  # fmt: skip
+    from ..logmanager import LogManager, list_conversations  # fmt: skip
 
     results: list[dict] = []
-    for conv in get_user_conversations():
+    for conv in list_conversations(max_results):
         log_path = Path(conv.path)
         log_manager = LogManager.load(log_path)
 
@@ -119,37 +74,31 @@ def search_chats(query: str, max_results: int = 5, system=False) -> None:
         if matching_messages:
             results.append(
                 {
-                    "conversation": conv.name,
+                    "conversation": conv,
                     "log_manager": log_manager,
                     "matching_messages": matching_messages,
                 }
             )
 
-        if len(results) >= max_results:
-            break
-
-    # Sort results by the number of matching messages, in descending order
-    results.sort(key=lambda x: len(x["matching_messages"]), reverse=True)
-
     if not results:
         print(f"No results found for query: '{query}'")
         return
 
+    # Sort results by the number of matching messages, in descending order
+    results.sort(key=lambda x: len(x["matching_messages"]), reverse=True)
+
     print(f"Search results for query: '{query}'")
     print(f"Found matches in {len(results)} conversation(s):")
 
     for i, result in enumerate(results, 1):
-        print(f"\n{i}. Conversation: {result['conversation']}")
+        conversation = result["conversation"]
+        print(f"\n{i}. {conversation.format()}")
         print(f"   Number of matching messages: {len(result['matching_messages'])}")
 
-        summary_lines = _summarize_conversation(
-            result["log_manager"], include_summary=False
-        )
-        print("\n".join(summary_lines))
-
+        # Show sample matches
         print("   Sample matches:")
         for j, msg in enumerate(result["matching_messages"][:3], 1):
-            print(f"     {j}. {_format_message_snippet(msg)}")
+            print(f"     {j}. {msg.format(max_length=100)}")
         if len(result["matching_messages"]) > 3:
             print(
                 f"     ... and {len(result['matching_messages']) - 3} more matching message(s)"
@@ -165,23 +114,18 @@ def read_chat(conversation: str, max_results: int = 5, incl_system=False) -> Non
         max_results (int): Maximum number of messages to display.
         incl_system (bool): Whether to include system messages.
     """
-    # noreorder
-    from ..logmanager import LogManager, get_conversations  # fmt: skip
-
-    conversations = list(get_conversations())
+    from ..logmanager import LogManager, list_conversations  # fmt: skip
 
-    for conv in conversations:
+    for conv in list_conversations():
         if conv.name == conversation:
             log_path = Path(conv.path)
             logmanager = LogManager.load(log_path)
             print(f"Reading conversation: {conversation}")
             i = 0
             for msg in logmanager.log:
                 if msg.role != "system" or incl_system:
-                    print(f"{i}. {_format_message_snippet(msg)}")
+                    print(f"{i}. {msg.format(max_length=100)}")
                     i += 1
-                else:
-                    print(f"{i}. (system message)")
                 if i >= max_results:
                     break
             break

gptme/util.py → gptme/util/__init__.py

@@ -1,3 +1,7 @@
+"""
+Utility package for gptme.
+"""
+
 import functools
 import io
 import logging
@@ -17,7 +21,7 @@
 from rich.console import Console
 from rich.syntax import Syntax
 
-from .clipboard import copy, set_copytext
+from ..clipboard import copy, set_copytext
 
 EMOJI_WARN = "⚠️"
 
@@ -319,8 +323,8 @@ def decorator(func):  # pragma: no cover
             return func
 
         # noreorder
-        from .message import len_tokens  # fmt: skip
-        from .tools import init_tools  # fmt: skip
+        from ..message import len_tokens  # fmt: skip
+        from ..tools import init_tools  # fmt: skip
 
         init_tools()