add google assistants

pyproject.toml

@@ -139,12 +139,14 @@ disallow_incomplete_defs = false
 
 [[tool.mypy.overrides]]
 module = [
+    "docx",
     "fitz",
+    "json_stream",
+    "json_stream.httpx",
     "lancedb",
     "param",
-    "pyarrow",
-    "docx",
     "pptx",
+    "pyarrow",
     "sentence_transformers",
 ]
 ignore_missing_imports = true

ragna/_utils.py

@@ -4,9 +4,23 @@
 import sys
 import threading
 from pathlib import Path
-from typing import Any, Callable, Optional, Union
+from typing import (
+    Any,
+    AsyncIterator,
+    Awaitable,
+    Callable,
+    Iterator,
+    Optional,
+    TypeVar,
+    Union,
+    cast,
+)
 from urllib.parse import SplitResult, urlsplit, urlunsplit
 
+from starlette.concurrency import iterate_in_threadpool, run_in_threadpool
+
+T = TypeVar("T")
+
 _LOCAL_ROOT = (
     Path(os.environ.get("RAGNA_LOCAL_ROOT", "~/.cache/ragna")).expanduser().resolve()
 )
@@ -125,3 +139,29 @@ def is_debugging() -> bool:
             if any(part.startswith(name) for part in parts):
                 return True
     return False
+
+
+def as_awaitable(
+    fn: Union[Callable[..., T], Callable[..., Awaitable[T]]],
+    *args: Any,
+    **kwargs: Any,
+) -> Awaitable[T]:
+    if inspect.iscoroutinefunction(fn):
+        fn = cast(Callable[..., Awaitable[T]], fn)
+        return fn(*args, **kwargs)
+    else:
+        fn = cast(Callable[..., T], fn)
+        return run_in_threadpool(fn, *args, **kwargs)
+
+
+def as_async_iterator(
+    fn: Union[Callable[..., Iterator[T]], Callable[..., AsyncIterator[T]]],
+    *args: Any,
+    **kwargs: Any,
+) -> AsyncIterator[T]:
+    if inspect.isasyncgenfunction(fn):
+        fn = cast(Callable[..., AsyncIterator[T]], fn)
+        return fn(*args, **kwargs)
+    else:
+        fn = cast(Callable[..., Iterator[T]], fn)
+        return iterate_in_threadpool(fn(*args, **kwargs))

ragna/assistants/__init__.py

@@ -1,6 +1,8 @@
 __all__ = [
     "Claude",
     "ClaudeInstant",
+    "GeminiPro",
+    "GeminiUltra",
     "Gpt35Turbo16k",
     "Gpt4",
     "Mpt7bInstruct",
@@ -10,6 +12,7 @@
 
 from ._anthropic import Claude, ClaudeInstant
 from ._demo import RagnaDemoAssistant
+from ._google import GeminiPro, GeminiUltra
 from ._mosaicml import Mpt7bInstruct, Mpt30bInstruct
 from ._openai import Gpt4, Gpt35Turbo16k
 

ragna/assistants/_anthropic.py

@@ -36,7 +36,7 @@ async def _call_api(
     ) -> AsyncIterator[str]:
         # See https://docs.anthropic.com/claude/reference/streaming
         async with httpx_sse.aconnect_sse(
-            self._client,
+            self._async_client,
             "POST",
             "https://api.anthropic.com/v1/complete",
             headers={

ragna/assistants/_api.py

@@ -1,10 +1,11 @@
 import abc
 import os
-from typing import AsyncIterator
+from typing import Any, AsyncIterator, Iterator
 
 import httpx
 
 import ragna
+from ragna._utils import as_async_iterator
 from ragna.core import Assistant, EnvVarRequirement, Requirement, Source
 
 
@@ -16,22 +17,28 @@ def requirements(cls) -> list[Requirement]:
         return [EnvVarRequirement(cls._API_KEY_ENV_VAR)]
 
     def __init__(self) -> None:
-        self._client = httpx.AsyncClient(
+        self._api_key = os.environ[self._API_KEY_ENV_VAR]
+
+        kwargs: dict[str, Any] = dict(
             headers={"User-Agent": f"{ragna.__version__}/{self}"},
             timeout=60,
         )
-        self._api_key = os.environ[self._API_KEY_ENV_VAR]
+        self._sync_client = httpx.Client(**kwargs)
+        self._async_client = httpx.AsyncClient(**kwargs)
 
     async def answer(
         self, prompt: str, sources: list[Source], *, max_new_tokens: int = 256
     ) -> AsyncIterator[str]:
-        async for chunk in self._call_api(  # type: ignore[attr-defined, misc]
-            prompt, sources, max_new_tokens=max_new_tokens
+        async for chunk in as_async_iterator(
+            self._call_api,
+            prompt,
+            sources,
+            max_new_tokens=max_new_tokens,
         ):
             yield chunk
 
     @abc.abstractmethod
-    async def _call_api(
+    def _call_api(
         self, prompt: str, sources: list[Source], *, max_new_tokens: int
-    ) -> AsyncIterator[str]:
+    ) -> Iterator[str]:
         ...

ragna/assistants/_google.py

@@ -0,0 +1,95 @@
+from typing import Iterator
+
+from ragna.core import PackageRequirement, Requirement, Source
+
+from ._api import ApiAssistant
+
+
+class GoogleApiAssistant(ApiAssistant):
+    _API_KEY_ENV_VAR = "GOOGLE_API_KEY"
+    _MODEL: str
+    _CONTEXT_SIZE: int
+
+    @classmethod
+    def requirements(cls) -> list[Requirement]:
+        return [
+            *super().requirements(),
+            PackageRequirement("json-stream"),
+        ]
+
+    @classmethod
+    def display_name(cls) -> str:
+        return f"Google/{cls._MODEL}"
+
+    @property
+    def max_input_size(self) -> int:
+        return self._CONTEXT_SIZE
+
+    def _instructize_prompt(self, prompt: str, sources: list[Source]) -> str:
+        # https://ai.google.dev/docs/prompt_best_practices#add-contextual-information
+        return "\n".join(
+            [
+                "Answer the prompt using only the pieces of context below.",
+                "If you don't know the answer, just say so. Don't try to make up additional context.",
+                f"Prompt: {prompt}",
+                *[f"\n{source.content}" for source in sources],
+            ]
+        )
+
+    def _call_api(
+        self, prompt: str, sources: list[Source], *, max_new_tokens: int
+    ) -> Iterator[str]:
+        import json_stream.httpx
+
+        with self._sync_client.stream(
+            "POST",
+            f"https://generativelanguage.googleapis.com/v1beta/models/{self._MODEL}:streamGenerateContent",
+            params={"key": self._api_key},
+            headers={"Content-Type": "application/json"},
+            json={
+                "contents": [
+                    {"parts": [{"text": self._instructize_prompt(prompt, sources)}]}
+                ],
+                # https://ai.google.dev/docs/safety_setting_gemini
+                "safetySettings": [
+                    {"category": f"HARM_CATEGORY_{category}", "threshold": "BLOCK_NONE"}
+                    for category in [
+                        "HARASSMENT",
+                        "HATE_SPEECH",
+                        "SEXUALLY_EXPLICIT",
+                        "DANGEROUS_CONTENT",
+                    ]
+                ],
+                # https://ai.google.dev/tutorials/rest_quickstart#configuration
+                "generationConfig": {
+                    "temperature": 0.0,
+                    "maxOutputTokens": max_new_tokens,
+                },
+            },
+        ) as response:
+            for chunk in json_stream.httpx.load(response, persistent=True):
+                yield chunk["candidates"][0]["content"]["parts"][0]["text"]
+
+
+class GeminiPro(GoogleApiAssistant):
+    """[Google Gemini Pro](https://ai.google.dev/models/gemini)
+
+    !!! info "Required environment variables"
+
+        - `GOOGLE_API_KEY`
+    """
+
+    _MODEL = "gemini-pro"
+    _CONTEXT_SIZE = 30_720
+
+
+class GeminiUltra(GoogleApiAssistant):
+    """[Google Gemini Ultra](https://ai.google.dev/models/gemini)
+
+    !!! info "Required environment variables"
+
+        - `GOOGLE_API_KEY`
+    """
+
+    _MODEL = "gemini-ultra"
+    _CONTEXT_SIZE = 30_720

ragna/assistants/_mosaicml.py

@@ -32,7 +32,7 @@ async def _call_api(
     ) -> AsyncIterator[str]:
         instruction = self._instructize_prompt(prompt, sources)
         # https://docs.mosaicml.com/en/latest/inference.html#text-completion-requests
-        response = await self._client.post(
+        response = await self._async_client.post(
             f"https://models.hosted-on.mosaicml.hosting/{self._MODEL}/v1/predict",
             headers={
                 "Authorization": f"{self._api_key}",

ragna/assistants/_openai.py

@@ -36,7 +36,7 @@ async def _call_api(
         # See https://platform.openai.com/docs/api-reference/chat/create
         # and https://platform.openai.com/docs/api-reference/chat/streaming
         async with httpx_sse.aconnect_sse(
-            self._client,
+            self._async_client,
             "POST",
             "https://api.openai.com/v1/chat/completions",
             headers={

ragna/core/_rag.py

@@ -1,7 +1,6 @@
 from __future__ import annotations
 
 import datetime
-import inspect
 import uuid
 from typing import (
     Any,
@@ -19,7 +18,8 @@
 )
 
 import pydantic
-from starlette.concurrency import iterate_in_threadpool, run_in_threadpool
+
+from ragna._utils import as_async_iterator, as_awaitable
 
 from ._components import Assistant, Component, Message, MessageRole, SourceStorage
 from ._document import Document, LocalDocument
@@ -256,34 +256,19 @@ def _unpack_chat_params(
             for fn, model in component_models.items()
         }
 
-    async def _run(
+    def _run(
         self, fn: Union[Callable[..., T], Callable[..., Awaitable[T]]], *args: Any
-    ) -> T:
+    ) -> Awaitable[T]:
         kwargs = self._unpacked_params[fn]
-        if inspect.iscoroutinefunction(fn):
-            fn = cast(Callable[..., Awaitable[T]], fn)
-            coro = fn(*args, **kwargs)
-        else:
-            fn = cast(Callable[..., T], fn)
-            coro = run_in_threadpool(fn, *args, **kwargs)
-
-        return await coro
+        return as_awaitable(fn, *args, **kwargs)
 
-    async def _run_gen(
+    def _run_gen(
         self,
         fn: Union[Callable[..., Iterator[T]], Callable[..., AsyncIterator[T]]],
         *args: Any,
     ) -> AsyncIterator[T]:
         kwargs = self._unpacked_params[fn]
-        if inspect.isasyncgenfunction(fn):
-            fn = cast(Callable[..., AsyncIterator[T]], fn)
-            async_gen = fn(*args, **kwargs)
-        else:
-            fn = cast(Callable[..., Iterator[T]], fn)
-            async_gen = iterate_in_threadpool(fn(*args, **kwargs))
-
-        async for item in async_gen:
-            yield item
+        return as_async_iterator(fn, *args, **kwargs)
 
     async def __aenter__(self) -> Chat:
         await self.prepare()

ragna/deploy/_ui/api_wrapper.py

@@ -18,7 +18,7 @@ class ApiWrapper(param.Parameterized):
     auth_token = param.String(default=None)
 
     def __init__(self, api_url, **params):
-        self.client = httpx.AsyncClient(base_url=api_url)
+        self.client = httpx.AsyncClient(base_url=api_url, timeout=60)
 
         super().__init__(**params)