Merge branch 'main' into separate-completer-and-chat-settings

docs/source/developers/index.md

@@ -150,6 +150,84 @@ my-provider = "my_provider:MyEmbeddingsProvider"
 
 [Embeddings]: https://api.python.langchain.com/en/stable/embeddings/langchain_core.embeddings.Embeddings.html
 
+
+### Custom completion providers
+
+Any model provider derived from `BaseProvider` can be used as a completion provider.
+However, some providers may benefit from customizing handling of completion requests.
+
+There are two asynchronous methods which can be overridden in subclasses of `BaseProvider`:
+- `generate_inline_completions`: takes a request (`InlineCompletionRequest`) and returns `InlineCompletionReply`
+- `stream_inline_completions`: takes a request and yields an initiating reply (`InlineCompletionReply`) with `isIncomplete` set to `True` followed by subsequent chunks (`InlineCompletionStreamChunk`)
+
+When streaming all replies and chunks for given invocation of the `stream_inline_completions()` method should include a constant and unique string token identifying the stream. All chunks except for the last chunk for a given item should have the `done` value set to `False`.
+
+The following example demonstrates a custom implementation of the completion provider with both a method for sending multiple completions in one go, and streaming multiple completions concurrently.
+The implementation and explanation for the `merge_iterators` function used in this example can be found [here](https://stackoverflow.com/q/72445371/4877269).
+
+```python
+class MyCompletionProvider(BaseProvider, FakeListLLM):
+    id = "my_provider"
+    name = "My Provider"
+    model_id_key = "model"
+    models = ["model_a"]
+
+    def __init__(self, **kwargs):
+        kwargs["responses"] = ["This fake response will not be used for completion"]
+        super().__init__(**kwargs)
+
+    async def generate_inline_completions(self, request: InlineCompletionRequest):
+        return InlineCompletionReply(
+            list=InlineCompletionList(items=[
+                {"insertText": "An ant minding its own business"},
+                {"insertText": "A bug searching for a snack"}
+            ]),
+            reply_to=request.number,
+        )
+
+    async def stream_inline_completions(self, request: InlineCompletionRequest):
+        token_1 = f"t{request.number}s0"
+        token_2 = f"t{request.number}s1"
+
+        yield InlineCompletionReply(
+            list=InlineCompletionList(
+                items=[
+                    {"insertText": "An ", "isIncomplete": True, "token": token_1},
+                    {"insertText": "", "isIncomplete": True, "token": token_2}
+                ]
+            ),
+            reply_to=request.number,
+        )
+
+        # where merge_iterators
+        async for reply in merge_iterators([
+            self._stream("elephant dancing in the rain", request.number, token_1, start_with="An"),
+            self._stream("A flock of birds flying around a mountain", request.number, token_2)
+        ]):
+            yield reply
+
+    async def _stream(self, sentence, request_number, token, start_with = ""):
+        suggestion = start_with
+
+        for fragment in sentence.split():
+            await asyncio.sleep(0.75)
+            suggestion += " " + fragment
+            yield InlineCompletionStreamChunk(
+                type="stream",
+                response={"insertText": suggestion, "token": token},
+                reply_to=request_number,
+                done=False
+            )
+
+        # finally, send a message confirming that we are done
+        yield InlineCompletionStreamChunk(
+            type="stream",
+            response={"insertText": suggestion, "token": token},
+            reply_to=request_number,
+            done=True,
+        )
+```
+
 ## Prompt templates
 
 Each provider can define **prompt templates** for each supported format. A prompt

packages/jupyter-ai-magics/jupyter_ai_magics/completion_utils.py

@@ -0,0 +1,52 @@
+from typing import Dict
+
+from .models.completion import InlineCompletionRequest
+
+
+def token_from_request(request: InlineCompletionRequest, suggestion: int):
+    """Generate a deterministic token (for matching streamed messages)
+    using request number and suggestion number"""
+    return f"t{request.number}s{suggestion}"
+
+
+def template_inputs_from_request(request: InlineCompletionRequest) -> Dict:
+    suffix = request.suffix.strip()
+    filename = request.path.split("/")[-1] if request.path else "untitled"
+
+    return {
+        "prefix": request.prefix,
+        "suffix": suffix,
+        "language": request.language,
+        "filename": filename,
+        "stop": ["\n```"],
+    }
+
+
+def post_process_suggestion(suggestion: str, request: InlineCompletionRequest) -> str:
+    """Remove spurious fragments from the suggestion.
+
+    While most models (especially instruct and infill models do not require
+    any pre-processing, some models such as gpt-4 which only have chat APIs
+    may require removing spurious fragments. This function uses heuristics
+    and request data to remove such fragments.
+    """
+    # gpt-4 tends to add "```python" or similar
+    language = request.language or "python"
+    markdown_identifiers = {"ipython": ["ipython", "python", "py"]}
+    bad_openings = [
+        f"```{identifier}"
+        for identifier in markdown_identifiers.get(language, [language])
+    ] + ["```"]
+    for opening in bad_openings:
+        if suggestion.startswith(opening):
+            suggestion = suggestion[len(opening) :].lstrip()
+            # check for the prefix inclusion (only if there was a bad opening)
+            if suggestion.startswith(request.prefix):
+                suggestion = suggestion[len(request.prefix) :]
+            break
+
+    # check if the suggestion ends with a closing markdown identifier and remove it
+    if suggestion.rstrip().endswith("```"):
+        suggestion = suggestion.rstrip()[:-3].rstrip()
+
+    return suggestion

packages/jupyter-ai-magics/jupyter_ai_magics/models/completion.py

@@ -0,0 +1,81 @@
+from typing import List, Literal, Optional
+
+from langchain.pydantic_v1 import BaseModel
+
+
+class InlineCompletionRequest(BaseModel):
+    """Message send by client to request inline completions.
+
+    Prefix/suffix implementation is used to avoid the need for synchronising
+    the notebook state at every key press (subject to change in future)."""
+
+    # unique message ID generated by the client used to identify replies and
+    # to easily discard replies for older requests
+    number: int
+    # prefix should include full text of the current cell preceding the cursor
+    prefix: str
+    # suffix should include full text of the current cell preceding the cursor
+    suffix: str
+    # media type for the current language, e.g. `text/x-python`
+    mime: str
+    # whether to stream the response (if supported by the model)
+    stream: bool
+    # path to the notebook of file for which the completions are generated
+    path: Optional[str]
+    # language inferred from the document mime type (if possible)
+    language: Optional[str]
+    # identifier of the cell for which the completions are generated if in a notebook
+    # previous cells and following cells can be used to learn the wider context
+    cell_id: Optional[str]
+
+
+class InlineCompletionItem(BaseModel):
+    """The inline completion suggestion to be displayed on the frontend.
+
+    See JupyterLab `InlineCompletionItem` documentation for the details.
+    """
+
+    insertText: str
+    filterText: Optional[str]
+    isIncomplete: Optional[bool]
+    token: Optional[str]
+
+
+class CompletionError(BaseModel):
+    type: str
+    traceback: str
+
+
+class InlineCompletionList(BaseModel):
+    """Reflection of JupyterLab's `IInlineCompletionList`."""
+
+    items: List[InlineCompletionItem]
+
+
+class InlineCompletionReply(BaseModel):
+    """Message sent from model to client with the infill suggestions"""
+
+    list: InlineCompletionList
+    # number of request for which we are replying
+    reply_to: int
+    error: Optional[CompletionError]
+
+
+class InlineCompletionStreamChunk(BaseModel):
+    """Message sent from model to client with the infill suggestions"""
+
+    type: Literal["stream"] = "stream"
+    response: InlineCompletionItem
+    reply_to: int
+    done: bool
+    error: Optional[CompletionError]
+
+
+__all__ = [
+    "InlineCompletionRequest",
+    "InlineCompletionItem",
+    "CompletionError",
+    "InlineCompletionList",
+    "InlineCompletionReply",
+    "InlineCompletionStreamChunk",
+]

packages/jupyter-ai-magics/jupyter_ai_magics/partner_providers/openai.py

@@ -75,23 +75,17 @@ class AzureChatOpenAIProvider(BaseProvider, AzureChatOpenAI):
     id = "azure-chat-openai"
     name = "Azure OpenAI"
     models = ["*"]
-    model_id_key = "deployment_name"
+    model_id_key = "azure_deployment"
     model_id_label = "Deployment name"
     pypi_package_deps = ["langchain_openai"]
+    # Confusingly, langchain uses both OPENAI_API_KEY and AZURE_OPENAI_API_KEY for azure
+    # https://github.com/langchain-ai/langchain/blob/f2579096993ae460516a0aae1d3e09f3eb5c1772/libs/partners/openai/langchain_openai/llms/azure.py#L85
     auth_strategy = EnvAuthStrategy(name="AZURE_OPENAI_API_KEY")
     registry = True
 
     fields = [
-        TextField(
-            key="openai_api_base", label="Base API URL (required)", format="text"
-        ),
-        TextField(
-            key="openai_api_version", label="API version (required)", format="text"
-        ),
-        TextField(
-            key="openai_organization", label="Organization (optional)", format="text"
-        ),
-        TextField(key="openai_proxy", label="Proxy (optional)", format="text"),
+        TextField(key="azure_endpoint", label="Base API URL (required)", format="text"),
+        TextField(key="api_version", label="API version (required)", format="text"),
     ]
 
 

packages/jupyter-ai-magics/jupyter_ai_magics/providers.py

@@ -5,7 +5,17 @@
 import io
 import json
 from concurrent.futures import ThreadPoolExecutor
-from typing import Any, ClassVar, Coroutine, Dict, List, Literal, Optional, Union
+from typing import (
+    Any,
+    AsyncIterator,
+    ClassVar,
+    Coroutine,
+    Dict,
+    List,
+    Literal,
+    Optional,
+    Union,
+)
 
 from jsonpath_ng import parse
 from langchain.chat_models.base import BaseChatModel
@@ -20,6 +30,8 @@
 )
 from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain.schema import LLMResult
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.runnable import Runnable
 from langchain.utils import get_from_dict_or_env
 from langchain_community.chat_models import (
     BedrockChat,
@@ -46,6 +58,13 @@
 except:
     from pydantic.main import ModelMetaclass
 
+from . import completion_utils as completion
+from .models.completion import (
+    InlineCompletionList,
+    InlineCompletionReply,
+    InlineCompletionRequest,
+    InlineCompletionStreamChunk,
+)
 from .models.persona import Persona
 
 CHAT_SYSTEM_PROMPT = """
@@ -405,6 +424,71 @@ def is_chat_provider(self):
     def allows_concurrency(self):
         return True
 
+    async def generate_inline_completions(
+        self, request: InlineCompletionRequest
+    ) -> InlineCompletionReply:
+        chain = self._create_completion_chain()
+        model_arguments = completion.template_inputs_from_request(request)
+        suggestion = await chain.ainvoke(input=model_arguments)
+        suggestion = completion.post_process_suggestion(suggestion, request)
+        return InlineCompletionReply(
+            list=InlineCompletionList(items=[{"insertText": suggestion}]),
+            reply_to=request.number,
+        )
+
+    async def stream_inline_completions(
+        self, request: InlineCompletionRequest
+    ) -> AsyncIterator[InlineCompletionStreamChunk]:
+        chain = self._create_completion_chain()
+        token = completion.token_from_request(request, 0)
+        model_arguments = completion.template_inputs_from_request(request)
+        suggestion = ""
+
+        # send an incomplete `InlineCompletionReply`, indicating to the
+        # client that LLM output is about to streamed across this connection.
+        yield InlineCompletionReply(
+            list=InlineCompletionList(
+                items=[
+                    {
+                        # insert text starts empty as we do not pre-generate any part
+                        "insertText": "",
+                        "isIncomplete": True,
+                        "token": token,
+                    }
+                ]
+            ),
+            reply_to=request.number,
+        )
+
+        async for fragment in chain.astream(input=model_arguments):
+            suggestion += fragment
+            if suggestion.startswith("```"):
+                if "\n" not in suggestion:
+                    # we are not ready to apply post-processing
+                    continue
+                else:
+                    suggestion = completion.post_process_suggestion(suggestion, request)
+            elif suggestion.rstrip().endswith("```"):
+                suggestion = completion.post_process_suggestion(suggestion, request)
+            yield InlineCompletionStreamChunk(
+                type="stream",
+                response={"insertText": suggestion, "token": token},
+                reply_to=request.number,
+                done=False,
+            )
+
+        # finally, send a message confirming that we are done
+        yield InlineCompletionStreamChunk(
+            type="stream",
+            response={"insertText": suggestion, "token": token},
+            reply_to=request.number,
+            done=True,
+        )
+
+    def _create_completion_chain(self) -> Runnable:
+        prompt_template = self.get_completion_prompt_template()
+        return prompt_template | self | StrOutputParser()
+
 
 class AI21Provider(BaseProvider, AI21):
     id = "ai21"

packages/jupyter-ai/jupyter_ai/chat_handlers/help.py

@@ -62,4 +62,14 @@ def __init__(self, *args, chat_handlers: Dict[str, BaseChatHandler], **kwargs):
         self._chat_handlers = chat_handlers
 
     async def process_message(self, message: HumanChatMessage):
-        self.reply(_format_help_message(self._chat_handlers), message)
+        persona = self.config_manager.persona
+        lm_provider = self.config_manager.lm_provider
+        unsupported_slash_commands = (
+            lm_provider.unsupported_slash_commands if lm_provider else set()
+        )
+        self.reply(
+            _format_help_message(
+                self._chat_handlers, persona, unsupported_slash_commands
+            ),
+            message,
+        )