Fix Anthropic FIM with muxing.

In the context of muxing, the code determining which mapper to use
when receiving requests to be routed towards Anthropic was relying in
`is_fim_request` only, and was not taking into account if the actual
endpoint receiving the request was the legacy one
(i.e. `/completions`) or the current one
(i.e. `/chat/completions`). This caused the use of the wrong mapper,
which led to an empty text content for the FIM request.

A better way to determine which mapper to use is looking at the
effective type, since that's the real source of truth for the
translation.

Author: blkt

src/codegate/muxing/router.py

@@ -138,7 +138,15 @@ async def route_to_dest_provider(
             # TODO this should be improved
             match model_route.endpoint.provider_type:
                 case ProviderType.anthropic:
-                    if is_fim_request:
+                    # Note: despite `is_fim_request` being true, our
+                    # integration tests query the `/chat/completions`
+                    # endpoint, which causes the
+                    # `anthropic_from_legacy_openai` to incorrectly
+                    # populate the struct.
+                    #
+                    # Checking for the actual type is a much more
+                    # reliable way of determining the right mapper.
+                    if isinstance(parsed, openai.LegacyCompletionRequest):
                         completion_function = anthropic.acompletion
                         from_openai = anthropic_from_legacy_openai
                         to_openai = anthropic_to_legacy_openai

src/codegate/providers/anthropic/provider.py

@@ -11,7 +11,15 @@
 from codegate.providers.anthropic.completion_handler import AnthropicCompletion
 from codegate.providers.base import BaseProvider, ModelFetchError
 from codegate.providers.fim_analyzer import FIMAnalyzer
-from codegate.types.anthropic import ChatCompletionRequest, stream_generator
+from codegate.types.anthropic import (
+    ChatCompletionRequest,
+    single_message,
+    single_response,
+    stream_generator,
+)
+from codegate.types.generators import (
+    completion_handler_replacement,
+)
 
 logger = structlog.get_logger("codegate")
 
@@ -118,18 +126,29 @@ async def create_message(
             body = await request.body()
 
             if os.getenv("CODEGATE_DEBUG_ANTHROPIC") is not None:
-                print(f"{create_message.__name__}: {body}")
+                print(f"{body.decode('utf-8')}")
 
             req = ChatCompletionRequest.model_validate_json(body)
             is_fim_request = FIMAnalyzer.is_fim_request(request.url.path, req)
 
-            return await self.process_request(
-                req,
-                x_api_key,
-                self.base_url,
-                is_fim_request,
-                request.state.detected_client,
-            )
+            if req.stream:
+                return await self.process_request(
+                    req,
+                    x_api_key,
+                    self.base_url,
+                    is_fim_request,
+                    request.state.detected_client,
+                )
+            else:
+                return await self.process_request(
+                    req,
+                    x_api_key,
+                    self.base_url,
+                    is_fim_request,
+                    request.state.detected_client,
+                    completion_handler=completion_handler_replacement(single_message),
+                    stream_generator=single_response,
+                )
 
 
 async def dumper(stream):

src/codegate/providers/ollama/completion_handler.py

@@ -73,7 +73,7 @@ async def _ollama_dispatcher(  # noqa: C901
         stream = openai_stream_generator(prepend(first, stream))
 
     if isinstance(first, OpenAIChatCompletion):
-        stream = openai_single_response_generator(first, stream)
+        stream = openai_single_response_generator(first)
 
     async for item in stream:
         yield item

src/codegate/types/anthropic/__init__.py

@@ -1,6 +1,8 @@
 from ._generators import (
     acompletion,
     message_wrapper,
+    single_message,
+    single_response,
     stream_generator,
 )
 from ._request_models import (
@@ -49,6 +51,8 @@
 __all__ = [
     "acompletion",
     "message_wrapper",
+    "single_message",
+    "single_response",
     "stream_generator",
     "AssistantMessage",
     "CacheControl",

src/codegate/types/anthropic/_generators.py

@@ -12,6 +12,7 @@
     ContentBlockDelta,
     ContentBlockStart,
     ContentBlockStop,
+    Message,
     MessageDelta,
     MessageError,
     MessagePing,
@@ -27,7 +28,7 @@ async def stream_generator(stream: AsyncIterator[Any]) -> AsyncIterator[str]:
     try:
         async for chunk in stream:
             try:
-                body = chunk.json(exclude_defaults=True, exclude_unset=True)
+                body = chunk.json(exclude_unset=True)
             except Exception as e:
                 logger.error("failed serializing payload", exc_info=e)
                 err = MessageError(
@@ -37,7 +38,7 @@ async def stream_generator(stream: AsyncIterator[Any]) -> AsyncIterator[str]:
                         message=str(e),
                     ),
                 )
-                body = err.json(exclude_defaults=True, exclude_unset=True)
+                body = err.json(exclude_unset=True)
                 yield f"event: error\ndata: {body}\n\n"
 
             data = f"event: {chunk.type}\ndata: {body}\n\n"
@@ -55,10 +56,60 @@ async def stream_generator(stream: AsyncIterator[Any]) -> AsyncIterator[str]:
                 message=str(e),
             ),
         )
-        body = err.json(exclude_defaults=True, exclude_unset=True)
+        body = err.json(exclude_unset=True)
         yield f"event: error\ndata: {body}\n\n"
 
 
+async def single_response(stream: AsyncIterator[Any]) -> AsyncIterator[str]:
+    """Wraps a single response object in an AsyncIterator. This is
+    meant to be used for non-streaming responses.
+
+    """
+    resp = await anext(stream)
+    yield resp.model_dump_json(exclude_unset=True)
+
+
+async def single_message(request, api_key, base_url, stream=None, is_fim_request=None):
+    headers = {
+        "anthropic-version": "2023-06-01",
+        "x-api-key": api_key,
+        "accept": "application/json",
+        "content-type": "application/json",
+    }
+    payload = request.model_dump_json(exclude_unset=True)
+
+    if os.getenv("CODEGATE_DEBUG_ANTHROPIC") is not None:
+        print(payload)
+
+    client = httpx.AsyncClient()
+    async with client.stream(
+        "POST",
+        f"{base_url}/v1/messages",
+        headers=headers,
+        content=payload,
+        timeout=60,  # TODO this should not be hardcoded
+    ) as resp:
+        match resp.status_code:
+            case 200:
+                text = await resp.aread()
+                if os.getenv("CODEGATE_DEBUG_ANTHROPIC") is not None:
+                    print(text.decode("utf-8"))
+                yield Message.model_validate_json(text)
+            case 400 | 401 | 403 | 404 | 413 | 429:
+                text = await resp.aread()
+                if os.getenv("CODEGATE_DEBUG_ANTHROPIC") is not None:
+                    print(text.decode("utf-8"))
+                yield MessageError.model_validate_json(text)
+            case 500 | 529:
+                text = await resp.aread()
+                if os.getenv("CODEGATE_DEBUG_ANTHROPIC") is not None:
+                    print(text.decode("utf-8"))
+                yield MessageError.model_validate_json(text)
+            case _:
+                logger.error(f"unexpected status code {resp.status_code}", provider="anthropic")
+                raise ValueError(f"unexpected status code {resp.status_code}", provider="anthropic")
+
+
 async def acompletion(request, api_key, base_url):
     headers = {
         "anthropic-version": "2023-06-01",
@@ -86,9 +137,13 @@ async def acompletion(request, api_key, base_url):
                     yield event
             case 400 | 401 | 403 | 404 | 413 | 429:
                 text = await resp.aread()
+                if os.getenv("CODEGATE_DEBUG_ANTHROPIC") is not None:
+                    print(text.decode("utf-8"))
                 yield MessageError.model_validate_json(text)
             case 500 | 529:
                 text = await resp.aread()
+                if os.getenv("CODEGATE_DEBUG_ANTHROPIC") is not None:
+                    print(text.decode("utf-8"))
                 yield MessageError.model_validate_json(text)
             case _:
                 logger.error(f"unexpected status code {resp.status_code}", provider="anthropic")

src/codegate/types/anthropic/_request_models.py

@@ -155,6 +155,7 @@ class ToolDef(pydantic.BaseModel):
     Literal["auto"],
     Literal["any"],
     Literal["tool"],
+    Literal["none"],
 ]
 
 

src/codegate/types/generators.py

@@ -1,37 +1,27 @@
-import os
 from typing import (
-    Any,
-    AsyncIterator,
+    Callable,
 )
 
-import pydantic
 import structlog
 
 logger = structlog.get_logger("codegate")
 
 
-# Since different providers typically use one of these formats for streaming
-# responses, we have a single stream generator for each format that is then plugged
-# into the adapter.
+def completion_handler_replacement(
+    completion_handler: Callable,
+):
+    async def _inner(
+        request,
+        base_url,
+        api_key,
+        stream=None,
+        is_fim_request=None,
+    ):
+        # Execute e.g. acompletion from Anthropic types
+        return completion_handler(
+            request,
+            api_key,
+            base_url,
+        )
 
-
-async def sse_stream_generator(stream: AsyncIterator[Any]) -> AsyncIterator[str]:
-    """OpenAI-style SSE format"""
-    try:
-        async for chunk in stream:
-            if isinstance(chunk, pydantic.BaseModel):
-                # alternatively we might want to just dump the whole object
-                # this might even allow us to tighten the typing of the stream
-                chunk = chunk.model_dump_json(exclude_none=True, exclude_unset=True)
-            try:
-                if os.getenv("CODEGATE_DEBUG_OPENAI") is not None:
-                    print(chunk)
-                yield f"data: {chunk}\n\n"
-            except Exception as e:
-                logger.error("failed generating output payloads", exc_info=e)
-                yield f"data: {str(e)}\n\n"
-    except Exception as e:
-        logger.error("failed generating output payloads", exc_info=e)
-        yield f"data: {str(e)}\n\n"
-    finally:
-        yield "data: [DONE]\n\n"
+    return _inner

src/codegate/types/ollama/_generators.py

@@ -23,7 +23,7 @@ async def stream_generator(
     try:
         async for chunk in stream:
             try:
-                body = chunk.model_dump_json(exclude_none=True, exclude_unset=True)
+                body = chunk.model_dump_json(exclude_unset=True)
                 data = f"{body}\n"
 
                 if os.getenv("CODEGATE_DEBUG_OLLAMA") is not None:

src/codegate/types/openai/_generators.py

@@ -50,26 +50,13 @@ async def stream_generator(stream: AsyncIterator[StreamingChatCompletion]) -> As
 
 async def single_response_generator(
     first: ChatCompletion,
-    stream: AsyncIterator[ChatCompletion],
 ) -> AsyncIterator[ChatCompletion]:
     """Wraps a single response object in an AsyncIterator. This is
     meant to be used for non-streaming responses.
 
     """
     yield first.model_dump_json(exclude_none=True, exclude_unset=True)
 
-    # Note: this async for loop is necessary to force Python to return
-    # an AsyncIterator. This is necessary because of the wiring at the
-    # Provider level expecting an AsyncIterator rather than a single
-    # response payload.
-    #
-    # Refactoring this means adding a code path specific for when we
-    # expect single response payloads rather than an SSE stream.
-    async for item in stream:
-        if item:
-            logger.error("no further items were expected", item=item)
-        yield item.model_dump_json(exclude_none=True, exclude_unset=True)
-
 
 async def completions_streaming(request, api_key, base_url):
     if base_url is None: