implement streaming for assistants

pyproject.toml

@@ -22,10 +22,10 @@ classifiers = [
 requires-python = ">=3.9"
 dependencies = [
     "aiofiles",
-    "anyio",
     "emoji",
     "fastapi",
     "httpx",
+    "httpx_sse",
     "importlib_metadata>=4.6; python_version<'3.10'",
     "packaging",
     "panel>=1.3.6,<1.4",
@@ -38,6 +38,8 @@ dependencies = [
     "questionary",
     "rich",
     "sqlalchemy>=2",
+    "sse-starlette",
+    "starlette",
     "tomlkit",
     "typer",
     "uvicorn",

ragna/_compat.py

@@ -1,7 +1,16 @@
+import builtins
 import sys
-from typing import Callable, Iterable, Iterator, Mapping, TypeVar
-
-__all__ = ["itertools_pairwise", "importlib_metadata_package_distributions"]
+from typing import (
+    AsyncIterator,
+    Awaitable,
+    Callable,
+    Iterable,
+    Iterator,
+    Mapping,
+    TypeVar,
+)
+
+__all__ = ["itertools_pairwise", "importlib_metadata_package_distributions", "anext"]
 
 T = TypeVar("T")
 
@@ -38,3 +47,17 @@ def _importlib_metadata_package_distributions() -> (
 
 
 importlib_metadata_package_distributions = _importlib_metadata_package_distributions()
+
+
+def _anext() -> Callable[[AsyncIterator[T]], Awaitable[T]]:
+    if sys.version_info[:2] >= (3, 10):
+        anext = builtins.anext
+    else:
+
+        async def anext(ait: AsyncIterator[T]) -> T:
+            return await ait.__anext__()
+
+    return anext
+
+
+anext = _anext()

ragna/assistants/_anthropic.py

@@ -1,4 +1,7 @@
-from typing import cast
+import json
+from typing import AsyncIterator, cast
+
+import httpx_sse
 
 from ragna.core import RagnaException, Source
 
@@ -30,9 +33,11 @@ def _instructize_prompt(self, prompt: str, sources: list[Source]) -> str:
 
     async def _call_api(
         self, prompt: str, sources: list[Source], *, max_new_tokens: int
-    ) -> str:
-        # # See https://docs.anthropic.com/claude/reference/complete_post
-        response = await self._client.post(
+    ) -> AsyncIterator[str]:
+        # See https://docs.anthropic.com/claude/reference/streaming
+        async with httpx_sse.aconnect_sse(
+            self._client,
+            "POST",
             "https://api.anthropic.com/v1/complete",
             headers={
                 "accept": "application/json",
@@ -45,13 +50,19 @@ async def _call_api(
                 "prompt": self._instructize_prompt(prompt, sources),
                 "max_tokens_to_sample": max_new_tokens,
                 "temperature": 0.0,
+                "stream": True,
             },
-        )
-        if response.is_error:
-            raise RagnaException(
-                status_code=response.status_code, response=response.json()
-            )
-        return cast(str, response.json()["completion"])
+        ) as event_source:
+            async for sse in event_source.aiter_sse():
+                data = json.loads(sse.data)
+                if data["type"] != "completion":
+                    continue
+                elif "error" in data:
+                    raise RagnaException(data["error"].pop("message"), **data["error"])
+                elif data["stop_reason"] is not None:
+                    break
+
+                yield cast(str, data["completion"])
 
 
 class ClaudeInstant(AnthropicApiAssistant):

ragna/assistants/_api.py

@@ -1,5 +1,8 @@
 import abc
 import os
+from typing import AsyncIterator
+
+import httpx
 
 import ragna
 from ragna.core import Assistant, EnvVarRequirement, Requirement, Source
@@ -13,8 +16,6 @@ def requirements(cls) -> list[Requirement]:
         return [EnvVarRequirement(cls._API_KEY_ENV_VAR)]
 
     def __init__(self) -> None:
-        import httpx
-
         self._client = httpx.AsyncClient(
             headers={"User-Agent": f"{ragna.__version__}/{self}"},
             timeout=60,
@@ -23,11 +24,14 @@ def __init__(self) -> None:
 
     async def answer(
         self, prompt: str, sources: list[Source], *, max_new_tokens: int = 256
-    ) -> str:
-        return await self._call_api(prompt, sources, max_new_tokens=max_new_tokens)
+    ) -> AsyncIterator[str]:
+        async for chunk in self._call_api(  # type: ignore[attr-defined, misc]
+            prompt, sources, max_new_tokens=max_new_tokens
+        ):
+            yield chunk
 
     @abc.abstractmethod
     async def _call_api(
         self, prompt: str, sources: list[Source], *, max_new_tokens: int
-    ) -> str:
+    ) -> AsyncIterator[str]:
         ...

ragna/assistants/_demo.py

@@ -1,6 +1,7 @@
 import re
 import sys
 import textwrap
+from typing import Iterator
 
 from ragna.core import Assistant, Source
 
@@ -26,11 +27,11 @@ def display_name(cls) -> str:
     def max_input_size(self) -> int:
         return sys.maxsize
 
-    def answer(self, prompt: str, sources: list[Source]) -> str:
+    def answer(self, prompt: str, sources: list[Source]) -> Iterator[str]:
         if re.search("markdown", prompt, re.IGNORECASE):
-            return self._markdown_answer()
+            yield self._markdown_answer()
         else:
-            return self._default_answer(prompt, sources)
+            yield self._default_answer(prompt, sources)
 
     def _markdown_answer(self) -> str:
         return textwrap.dedent(

ragna/assistants/_mosaicml.py

@@ -1,4 +1,4 @@
-from typing import cast
+from typing import AsyncIterator, cast
 
 from ragna.core import RagnaException, Source
 
@@ -29,7 +29,7 @@ def _instructize_prompt(self, prompt: str, sources: list[Source]) -> str:
 
     async def _call_api(
         self, prompt: str, sources: list[Source], *, max_new_tokens: int
-    ) -> str:
+    ) -> AsyncIterator[str]:
         instruction = self._instructize_prompt(prompt, sources)
         # https://docs.mosaicml.com/en/latest/inference.html#text-completion-requests
         response = await self._client.post(
@@ -47,7 +47,7 @@ async def _call_api(
             raise RagnaException(
                 status_code=response.status_code, response=response.json()
             )
-        return cast(str, response.json()["outputs"][0]).replace(instruction, "").strip()
+        yield cast(str, response.json()["outputs"][0]).replace(instruction, "").strip()
 
 
 class Mpt7bInstruct(MosaicmlApiAssistant):

ragna/assistants/_openai.py

@@ -1,6 +1,9 @@
-from typing import cast
+import json
+from typing import AsyncIterator, cast
 
-from ragna.core import RagnaException, Source
+import httpx_sse
+
+from ragna.core import Source
 
 from ._api import ApiAssistant
 
@@ -29,10 +32,12 @@ def _make_system_content(self, sources: list[Source]) -> str:
 
     async def _call_api(
         self, prompt: str, sources: list[Source], *, max_new_tokens: int
-    ) -> str:
+    ) -> AsyncIterator[str]:
         # See https://platform.openai.com/docs/api-reference/chat/create
-        # and https://platform.openai.com/docs/api-reference/chat/object
-        response = await self._client.post(
+        # and https://platform.openai.com/docs/api-reference/chat/streaming
+        async with httpx_sse.aconnect_sse(
+            self._client,
+            "POST",
             "https://api.openai.com/v1/chat/completions",
             headers={
                 "Content-Type": "application/json",
@@ -52,13 +57,16 @@ async def _call_api(
                 "model": self._MODEL,
                 "temperature": 0.0,
                 "max_tokens": max_new_tokens,
+                "stream": True,
             },
-        )
-        if response.is_error:
-            raise RagnaException(
-                status_code=response.status_code, response=response.json()
-            )
-        return cast(str, response.json()["choices"][0]["message"]["content"])
+        ) as event_source:
+            async for sse in event_source.aiter_sse():
+                data = json.loads(sse.data)
+                choice = data["choices"][0]
+                if choice["finish_reason"] is not None:
+                    break
+
+                yield cast(str, choice["delta"]["content"])
 
 
 class Gpt35Turbo16k(OpenaiApiAssistant):
@@ -73,9 +81,6 @@ class Gpt35Turbo16k(OpenaiApiAssistant):
     _CONTEXT_SIZE = 16_384
 
 
-Gpt35Turbo16k.__doc__ = "OOPS"
-
-
 class Gpt4(OpenaiApiAssistant):
     """[OpenAI GPT-4](https://platform.openai.com/docs/models/gpt-4)
 

ragna/core/_components.py

@@ -4,7 +4,7 @@
 import enum
 import functools
 import inspect
-from typing import Type
+from typing import AsyncIterable, AsyncIterator, Iterator, Optional, Type, Union
 
 import pydantic
 import pydantic.utils
@@ -138,11 +138,10 @@ class MessageRole(enum.Enum):
     ASSISTANT = "assistant"
 
 
-class Message(pydantic.BaseModel):
+class Message:
     """Data class for messages.
 
     Attributes:
-        content: The content of the message.
         role: The message producer.
         sources: The sources used to produce the message.
 
@@ -152,13 +151,61 @@ class Message(pydantic.BaseModel):
         - [ragna.core.Chat.answer][]
     """
 
-    content: str
-    role: MessageRole
-    sources: list[Source] = pydantic.Field(default_factory=list)
+    def __init__(
+        self,
+        content: Union[str, AsyncIterable[str]],
+        *,
+        role: MessageRole = MessageRole.SYSTEM,
+        sources: Optional[list[Source]] = None,
+    ) -> None:
+        if isinstance(content, str):
+            self._content: str = content
+        else:
+            self._content_stream: AsyncIterable[str] = content
+
+        self.role = role
+        self.sources = sources or []
+
+    async def __aiter__(self) -> AsyncIterator[str]:
+        if hasattr(self, "_content"):
+            yield self._content
+            return
+
+        chunks = []
+        async for chunk in self._content_stream:
+            chunks.append(chunk)
+            yield chunk
+
+        self._content = "".join(chunks)
+
+    async def read(self) -> str:
+        if not hasattr(self, "_content"):
+            # Since self.__aiter__ is already setting the self._content attribute, we
+            # only need to exhaust the content stream here.
+            async for _ in self:
+                pass
+        return self._content
+
+    @property
+    def content(self) -> str:
+        if not hasattr(self, "_content"):
+            raise RuntimeError(
+                "Message content cannot be accessed without having iterated over it, "
+                "e.g. `async for chunk in message`, or reading the content, e.g. "
+                "`await message.read()`, first."
+            )
+        return self._content
 
     def __str__(self) -> str:
         return self.content
 
+    def __repr__(self) -> str:
+        return (
+            f"{type(self).__name__}("
+            f"content={self.content}, role={self.role}, sources={self.sources}"
+            f")"
+        )
+
 
 class Assistant(Component, abc.ABC):
     """Abstract base class for assistants used in [ragna.core.Chat][]"""
@@ -171,7 +218,7 @@ def max_input_size(self) -> int:
         ...
 
     @abc.abstractmethod
-    def answer(self, prompt: str, sources: list[Source]) -> str:
+    def answer(self, prompt: str, sources: list[Source]) -> Iterator[str]:
         """Answer a prompt given some sources.
 
         Args: