refactor: ♻️ refactoring LLM to avoid repetition

.github/ISSUE_TEMPLATE/bug-report-🐛.md

@@ -1,43 +1,55 @@
 ---
 name: "Bug report \U0001F41B"
 about: Create a report to help us fix your bug
-title: ''
+title: ""
 labels: bug
-assignees: ''
-
+assignees: ""
 ---
 
 **Describe the bug**
 A clear and concise description of what the bug is.
 
 **To Reproduce**
 Steps to reproduce the behavior:
+
 1. Go to '...'
 2. Click on '....'
 3. Scroll down to '....'
 4. See error
 
-**Expected behavior**
-A clear and concise description of what you expected to happen.
-
-**Screenshots**
-If applicable, add screenshots to help explain your problem.
-
 **Environment**
+
 - Operating System: [e.g. MacOS]
 - Python Version: [e.g. 3.10.6]
 - Continue Version: [e.g. v0.0.207]
 
-**Console logs**
+**Logs**
+
 ```
 REPLACE THIS SECTION WITH CONSOLE LOGS OR A SCREENSHOT...
+```
+
+To get the Continue server logs:
+
+1. cmd+shift+p (MacOS) / ctrl+shift+p (Windows)
+2. Search for and then select "Continue: View Continue Server Logs"
+3. Scroll to the bottom of `continue.log` and copy the last 100 lines or so
+
+To get the VS Code console logs:
 
-To get the console logs in VS Code:
 1. cmd+shift+p (MacOS) / ctrl+shift+p (Windows)
 2. Search for and then select "Developer: Toggle Developer Tools"
 3. Select Console
 4. Read the console logs
-```
+
+If the problem is related to LLM prompting:
+
+1. Hover the problematic response in the Continue UI
+2. Click the "magnifying glass" icon
+3. Copy the contents of the `continue_logs.txt` file that opens
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
 
 **Additional context**
 Add any other context about the problem here.

.github/ISSUE_TEMPLATE/feature-request-💪.md

@@ -1,10 +1,9 @@
 ---
 name: "Feature request \U0001F4AA"
 about: Suggest an idea for this project
-title: ''
+title: ""
 labels: enhancement
 assignees: TyDunn
-
 ---
 
 **Is your feature request related to a problem? Please describe.**
@@ -13,8 +12,5 @@ A clear and concise description of what the problem is. Ex. I'm always frustrate
 **Describe the solution you'd like**
 A clear and concise description of what you want to happen.
 
-**Describe alternatives you've considered**
-A clear and concise description of any alternative solutions or features you've considered.
-
 **Additional context**
 Add any other context or screenshots about the feature request here.

continuedev/src/continuedev/core/abstract_sdk.py

@@ -71,10 +71,6 @@ async def add_directory(self, path: str):
     async def delete_directory(self, path: str):
         pass
 
-    @abstractmethod
-    async def get_user_secret(self, env_var: str) -> str:
-        pass
-
     config: ContinueConfig
 
     @abstractmethod

continuedev/src/continuedev/core/autopilot.py

@@ -507,7 +507,7 @@ async def accept_user_input(self, user_input: str):
         if self.session_info is None:
 
             async def create_title():
-                title = await self.continue_sdk.models.medium.complete(
+                title = await self.continue_sdk.models.medium._complete(
                     f'Give a short title to describe the current chat session. Do not put quotes around the title. The first message was: "{user_input}". Do not use more than 10 words. The title is: ',
                     max_tokens=20,
                 )

continuedev/src/continuedev/core/sdk.py

@@ -94,14 +94,7 @@ def write_log(self, message: str):
         self.history.timeline[self.history.current_index].logs.append(message)
 
     async def start_model(self, llm: LLM):
-        kwargs = {}
-        if llm.requires_api_key:
-            kwargs["api_key"] = await self.get_user_secret(llm.requires_api_key)
-        if llm.requires_unique_id:
-            kwargs["unique_id"] = self.ide.unique_id
-        if llm.requires_write_log:
-            kwargs["write_log"] = self.write_log
-        await llm.start(**kwargs)
+        await llm.start(unique_id=self.ide.unique_id, write_log=self.write_log)
 
     async def _ensure_absolute_path(self, path: str) -> str:
         if os.path.isabs(path):
@@ -211,10 +204,6 @@ async def delete_directory(self, path: str):
         path = await self._ensure_absolute_path(path)
         return await self.run_step(FileSystemEditStep(edit=DeleteDirectory(path=path)))
 
-    async def get_user_secret(self, env_var: str) -> str:
-        # TODO support error prompt dynamically set on env_var
-        return await self.ide.getUserSecret(env_var)
-
     _last_valid_config: ContinueConfig = None
 
     def _load_config_dot_py(self) -> ContinueConfig:

continuedev/src/continuedev/libs/llm/__init__.py

@@ -1,66 +1,77 @@
-from abc import ABC, abstractproperty
-from typing import Any, Coroutine, Dict, Generator, List, Optional, Union
+from abc import ABC
+from typing import Any, Callable, Coroutine, Dict, Generator, List, Optional, Union
 
 from ...core.main import ChatMessage
 from ...models.main import ContinueBaseModel
+from ..util.count_tokens import DEFAULT_ARGS, count_tokens
 
 
 class LLM(ContinueBaseModel, ABC):
-    requires_api_key: Optional[str] = None
-    requires_unique_id: bool = False
-    requires_write_log: bool = False
     title: Optional[str] = None
     system_message: Optional[str] = None
 
+    context_length: int = 2048
+    "The maximum context length of the LLM in tokens, as counted by count_tokens."
+
+    unique_id: Optional[str] = None
+    "The unique ID of the user."
+
+    model: str
+    "The model name"
+
     prompt_templates: dict = {}
 
+    write_log: Optional[Callable[[str], None]] = None
+    "A function that takes a string and writes it to the log."
+
+    api_key: Optional[str] = None
+    "The API key for the LLM provider."
+
     class Config:
         arbitrary_types_allowed = True
         extra = "allow"
 
     def dict(self, **kwargs):
         original_dict = super().dict(**kwargs)
         original_dict.pop("write_log", None)
-        original_dict["name"] = self.name
         original_dict["class_name"] = self.__class__.__name__
         return original_dict
 
-    @abstractproperty
-    def name(self):
-        """Return the name of the LLM."""
-        raise NotImplementedError
+    def collect_args(self, **kwargs) -> Any:
+        """Collect the arguments for the LLM."""
+        args = {**DEFAULT_ARGS.copy(), "model": self.model, "max_tokens": 1024}
+        args.update(kwargs)
+        return args
 
-    async def start(self, *, api_key: Optional[str] = None, **kwargs):
+    async def start(
+        self, write_log: Callable[[str], None] = None, unique_id: Optional[str] = None
+    ):
         """Start the connection to the LLM."""
-        raise NotImplementedError
+        self.write_log = write_log
+        self.unique_id = unique_id
 
     async def stop(self):
         """Stop the connection to the LLM."""
-        raise NotImplementedError
+        pass
 
-    async def complete(
+    async def _complete(
         self, prompt: str, with_history: List[ChatMessage] = None, **kwargs
     ) -> Coroutine[Any, Any, str]:
         """Return the completion of the text with the given temperature."""
         raise NotImplementedError
 
-    def stream_complete(
+    def _stream_complete(
         self, prompt, with_history: List[ChatMessage] = None, **kwargs
     ) -> Generator[Union[Any, List, Dict], None, None]:
         """Stream the completion through generator."""
         raise NotImplementedError
 
-    async def stream_chat(
+    async def _stream_chat(
         self, messages: List[ChatMessage] = None, **kwargs
     ) -> Generator[Union[Any, List, Dict], None, None]:
         """Stream the chat through generator."""
         raise NotImplementedError
 
     def count_tokens(self, text: str):
         """Return the number of tokens in the given text."""
-        raise NotImplementedError
-
-    @abstractproperty
-    def context_length(self) -> int:
-        """Return the context length of the LLM in tokens, as counted by count_tokens."""
-        raise NotImplementedError
+        return count_tokens(self.model, text)

continuedev/src/continuedev/libs/llm/anthropic.py

@@ -1,47 +1,36 @@
-from typing import Any, Callable, Coroutine, Dict, Generator, List, Optional, Union
+from typing import Any, Coroutine, Dict, Generator, List, Union
 
 from anthropic import AI_PROMPT, HUMAN_PROMPT, AsyncAnthropic
 
 from ...core.main import ChatMessage
 from ..llm import LLM
-from ..util.count_tokens import DEFAULT_ARGS, compile_chat_messages, count_tokens
+from ..util.count_tokens import compile_chat_messages
 
 
 class AnthropicLLM(LLM):
     api_key: str
+    "Anthropic API key"
+
     model: str = "claude-2"
 
-    requires_write_log = True
     _async_client: AsyncAnthropic = None
 
     class Config:
         arbitrary_types_allowed = True
 
-    write_log: Optional[Callable[[str], None]] = None
-
     async def start(
         self,
-        *,
-        api_key: Optional[str] = None,
-        write_log: Callable[[str], None],
         **kwargs,
     ):
-        self.write_log = write_log
+        await super().start(**kwargs)
         self._async_client = AsyncAnthropic(api_key=self.api_key)
 
-    async def stop(self):
-        pass
-
-    @property
-    def name(self):
-        return self.model
+        if self.model == "claude-2":
+            self.context_length = 100_000
 
-    @property
-    def default_args(self):
-        return {**DEFAULT_ARGS, "model": self.model}
+    def collect_args(self, **kwargs) -> Any:
+        args = super().collect_args(**kwargs)
 
-    def _transform_args(self, args: Dict[str, Any]) -> Dict[str, Any]:
-        args = args.copy()
         if "max_tokens" in args:
             args["max_tokens_to_sample"] = args["max_tokens"]
             del args["max_tokens"]
@@ -51,15 +40,6 @@ def _transform_args(self, args: Dict[str, Any]) -> Dict[str, Any]:
             del args["presence_penalty"]
         return args
 
-    def count_tokens(self, text: str):
-        return count_tokens(self.model, text)
-
-    @property
-    def context_length(self):
-        if self.model == "claude-2":
-            return 100000
-        raise Exception(f"Unknown Anthropic model {self.model}")
-
     def __messages_to_prompt(self, messages: List[Dict[str, str]]) -> str:
         prompt = ""
 
@@ -76,13 +56,11 @@ def __messages_to_prompt(self, messages: List[Dict[str, str]]) -> str:
         prompt += AI_PROMPT
         return prompt
 
-    async def stream_complete(
+    async def _stream_complete(
         self, prompt, with_history: List[ChatMessage] = None, **kwargs
     ) -> Generator[Union[Any, List, Dict], None, None]:
-        args = self.default_args.copy()
-        args.update(kwargs)
+        args = self.collect_args(**kwargs)
         args["stream"] = True
-        args = self._transform_args(args)
         prompt = f"{HUMAN_PROMPT} {prompt} {AI_PROMPT}"
 
         self.write_log(f"Prompt: \n\n{prompt}")
@@ -95,13 +73,11 @@ async def stream_complete(
 
         self.write_log(f"Completion: \n\n{completion}")
 
-    async def stream_chat(
+    async def _stream_chat(
         self, messages: List[ChatMessage] = None, **kwargs
     ) -> Generator[Union[Any, List, Dict], None, None]:
-        args = self.default_args.copy()
-        args.update(kwargs)
+        args = self.collect_args(**kwargs)
         args["stream"] = True
-        args = self._transform_args(args)
 
         messages = compile_chat_messages(
             args["model"],
@@ -123,11 +99,10 @@ async def stream_chat(
 
         self.write_log(f"Completion: \n\n{completion}")
 
-    async def complete(
+    async def _complete(
         self, prompt: str, with_history: List[ChatMessage] = None, **kwargs
     ) -> Coroutine[Any, Any, str]:
-        args = {**self.default_args, **kwargs}
-        args = self._transform_args(args)
+        args = self.collect_args(**kwargs)
 
         messages = compile_chat_messages(
             args["model"],