Merge branch 'main' into patch-1

.github/workflows/ruff.yml

@@ -31,13 +31,7 @@ jobs:
           python3 -m pip install -r requirements.txt
           python3 -m pip install -r requirements-doc.txt
       - name: Run Continuous Integration Action
-        run: |
-          set -e -o pipefail
-          export CUSTOM_PACKAGES="${{ env.CUSTOM_PACKAGES }}" &&
-          export CUSTOM_FLAGS="${{ env.CUSTOM_FLAGS }}" &&
-          curl -sSL https://raw.githubusercontent.com/gao-hongnan/omniverse/2fd5de1b8103e955cd5f022ab016b72fa901fa8f/scripts/devops/continuous-integration/lint_ruff.sh -o lint_ruff.sh
-          chmod +x lint_ruff.sh
-          bash lint_ruff.sh | tee ${{ env.WORKING_DIRECTORY }}/${{ env.RUFF_OUTPUT_FILENAME }}
+        uses: astral-sh/ruff-action@v1
       - name: Upload Artifacts
         uses: actions/upload-artifact@v3
         with:

docs/hooks/hide_lines.py

@@ -5,9 +5,9 @@
 
 @mkdocs.plugins.event_priority(0)
 # pylint: disable=unused-argument
-def on_startup(command: str, dirty: bool) -> None:
+def on_startup(command: str, dirty: bool) -> None:  # noqa: ARG001
     """Monkey patch Highlight extension to hide lines in code blocks."""
-    original = highlight.Highlight.highlight
+    original = highlight.Highlight.highlight  # type: ignore
 
     def patched(self: Any, src: str, *args: Any, **kwargs: Any) -> Any:
         lines = src.splitlines(keepends=True)

docs/tutorials/7-synthetic-data-generation.ipynb

@@ -565,7 +565,7 @@
     "\n",
     "for metric,size in product(METRICS,SIZES):\n",
     "    metric_name, score_fn = metric\n",
-    "    score_fns[f\"{metric_name}@{size}\"] = lambda predictions,labels : score_fn(predictions[:size],labels)"
+    "    score_fns[f\"{metric_name}@{size}\"] = lambda predictions,labels, fn=score_fn, k=size: fn(predictions[:k],labels) # type: ignore"
    ]
   },
   {

instructor/client.py

@@ -507,12 +507,7 @@ def from_openai(
             instructor.Mode.MD_JSON,
         }
 
-    if provider in {Provider.DATABRICKS}:
-        assert mode in {
-            instructor.Mode.MD_JSON
-        }, "Databricks provider only supports `MD_JSON` mode."
-
-    if provider in {Provider.OPENAI}:
+    if provider in {Provider.OPENAI, Provider.DATABRICKS}:
         assert mode in {
             instructor.Mode.TOOLS,
             instructor.Mode.JSON,

instructor/process_response.py

@@ -20,7 +20,11 @@
 from instructor.dsl.partial import PartialBase
 from instructor.dsl.simple_type import AdapterBase, ModelAdapter, is_simple_type
 from instructor.function_calls import OpenAISchema, openai_schema
-from instructor.utils import merge_consecutive_messages
+from instructor.utils import (
+    merge_consecutive_messages,
+    extract_system_messages,
+    combine_system_messages,
+)
 from instructor.multimodal import convert_messages
 
 logger = logging.getLogger("instructor")
@@ -332,20 +336,15 @@ def handle_anthropic_tools(
         "name": response_model.__name__,
     }
 
-    system_messages = [
-        m["content"] for m in new_kwargs["messages"] if m["role"] == "system"
-    ]
+    system_messages = extract_system_messages(new_kwargs.get("messages", []))
 
-    if "system" in new_kwargs and system_messages:
-        raise ValueError(
-            "Only a single system message is supported - either set it as a message in the messages array or use the system parameter"
+    if system_messages:
+        new_kwargs["system"] = combine_system_messages(
+            new_kwargs.get("system"), system_messages
         )
 
-    if "system" not in new_kwargs:
-        new_kwargs["system"] = "\n\n".join(system_messages)
-
     new_kwargs["messages"] = [
-        m for m in new_kwargs["messages"] if m["role"] != "system"
+        m for m in new_kwargs.get("messages", []) if m["role"] != "system"
     ]
 
     return response_model, new_kwargs
@@ -354,25 +353,18 @@ def handle_anthropic_tools(
 def handle_anthropic_json(
     response_model: type[T], new_kwargs: dict[str, Any]
 ) -> tuple[type[T], dict[str, Any]]:
-    openai_system_messages = [
-        message["content"]
-        for message in new_kwargs.get("messages", [])
-        if message["role"] == "system"
-    ]
+    system_messages = extract_system_messages(new_kwargs.get("messages", []))
 
-    if "system" in new_kwargs and openai_system_messages:
-        raise ValueError(
-            "Only a single System message is supported - either set it using the system parameter or in the list of messages"
+    if system_messages:
+        new_kwargs["system"] = combine_system_messages(
+            new_kwargs.get("system"), system_messages
         )
 
-    if not "system" in new_kwargs:
-        new_kwargs["system"] = "\n\n".join(openai_system_messages)
-
     new_kwargs["messages"] = [
-        m for m in new_kwargs["messages"] if m["role"] != "system"
+        m for m in new_kwargs.get("messages", []) if m["role"] != "system"
     ]
 
-    message = dedent(
+    json_schema_message = dedent(
         f"""
         As a genius expert, your task is to understand the content and provide
         the parsed objects in json that match the following json_schema:\n
@@ -383,7 +375,9 @@ def handle_anthropic_json(
         """
     )
 
-    new_kwargs["system"] = f"{new_kwargs.get('system', '')}\n\n{message}".strip()
+    new_kwargs["system"] = combine_system_messages(
+        new_kwargs.get("system"), [{"type": "text", "text": json_schema_message}]
+    )
 
     return response_model, new_kwargs
 
@@ -664,22 +658,19 @@ def handle_response_model(
             # This is cause cohere uses 'message' and 'chat_history' instead of 'messages'
             return handle_cohere_modes(new_kwargs)
         # Handle images without a response model
-        if autodetect_images and "messages" in new_kwargs:
+        if "messages" in new_kwargs:
             messages = convert_messages(
                 new_kwargs["messages"],
                 mode,
                 autodetect_images=autodetect_images,
             )
             if mode in {Mode.ANTHROPIC_JSON, Mode.ANTHROPIC_TOOLS}:
                 # Handle OpenAI style or Anthropic style messages
-                new_kwargs["messages"] = [
-                    m for m in messages if m["role"] != "system"
-                ]
+                new_kwargs["messages"] = [m for m in messages if m["role"] != "system"]
                 if "system" not in new_kwargs:
-                    system_messages = (m for m in messages if m["role"] == "system")
-                    system_message = next(system_messages, None)
+                    system_message = extract_system_messages(messages)
                     if system_message:
-                        new_kwargs["system"] = system_message["content"]
+                        new_kwargs["system"] = system_message
             else:
                 new_kwargs["messages"] = messages
         return None, new_kwargs

instructor/utils.py

@@ -10,7 +10,10 @@
     Callable,
     Generic,
     Protocol,
+    Union,
+    TypedDict,
     TypeVar,
+    cast
 )
 from pydantic import BaseModel
 import os
@@ -131,7 +134,6 @@ def update_total_usage(
     response: T_Model | None,
     total_usage: OpenAIUsage | AnthropicUsage,
 ) -> T_Model | ChatCompletion | None:
-
     if response is None:
         return None
 
@@ -369,3 +371,52 @@ def update_gemini_kwargs(kwargs: dict[str, Any]) -> dict[str, Any]:
 
 def disable_pydantic_error_url():
     os.environ["PYDANTIC_ERRORS_INCLUDE_URL"] = "0"
+
+
+class SystemMessage(TypedDict, total=False):
+    type: str
+    text: str
+    cache_control: dict[str, str]
+
+
+def combine_system_messages(
+    existing_system: Union[str, list[SystemMessage], None],  # noqa: UP007
+    new_system: Union[str, list[SystemMessage]],  # noqa: UP007
+) -> Union[str, list[SystemMessage]]:  # noqa: UP007
+    if existing_system is None:
+        return new_system
+
+    if isinstance(existing_system, str) and isinstance(new_system, str):
+        return f"{existing_system}\n\n{new_system}"
+
+    if isinstance(existing_system, list) and isinstance(new_system, list):
+        return existing_system + new_system
+
+    if isinstance(existing_system, str) and isinstance(new_system, list):
+        return [SystemMessage(type="text", text=existing_system)] + new_system
+
+    if isinstance(existing_system, list) and isinstance(new_system, str):
+        return existing_system + [SystemMessage(type="text", text=new_system)]
+
+    raise ValueError("Unsupported system message type combination")
+
+
+def extract_system_messages(messages: list[dict[str, Any]]) -> list[SystemMessage]:
+    def convert_message(content: Union[str, dict[str, Any]]) -> SystemMessage:  # noqa: UP007
+        if isinstance(content, str):
+            return SystemMessage(type="text", text=content)
+        elif isinstance(content, dict):
+            return SystemMessage(**content)
+        else:
+            raise ValueError(f"Unsupported content type: {type(content)}")
+
+    result: list[SystemMessage] = []
+    for m in messages:
+        if m["role"] == "system":
+            # System message must always be a string or list of dictionaries
+            content = cast(Union[str, list[dict[str, Any]]], m["content"])  # noqa: UP007
+            if isinstance(content, list):
+                result.extend(convert_message(item) for item in content)
+            else:
+                result.append(convert_message(content))
+    return result

make_desc.py

@@ -1,5 +1,5 @@
 import os
-from typing import Optional, List, Set, Literal
+from typing import Optional, Literal
 import asyncio
 from openai import AsyncOpenAI
 import typer
@@ -15,7 +15,7 @@
 
 
 async def generate_ai_frontmatter(
-    client: AsyncOpenAI, title: str, content: str, categories: List[str]
+    client: AsyncOpenAI, title: str, content: str, categories: list[str]
 ):
     """
     Generate a description and categories for the given content using AI.
@@ -35,8 +35,8 @@ class DescriptionAndCategories(BaseModel):
         reasoning: str = Field(
             ..., description="The reasoning for the correct categories"
         )
-        tags: List[str]
-        categories: List[
+        tags: list[str]
+        categories: list[
             Literal[
                 "OpenAI",
                 "Anthropic",
@@ -72,7 +72,7 @@ class DescriptionAndCategories(BaseModel):
     return response
 
 
-def get_all_categories(root_dir: str) -> Set[str]:
+def get_all_categories(root_dir: str) -> set[str]:
     """
     Read all markdown files and extract unique categories.
 
@@ -113,7 +113,7 @@ def preview_categories(root_dir: str) -> None:
 
 
 async def process_file(
-    client: AsyncOpenAI, file_path: str, categories: List[str], enable_comments: bool
+    client: AsyncOpenAI, file_path: str, categories: list[str], enable_comments: bool
 ) -> None:
     """
     Process a single file, adding or updating the description and categories in the front matter.
@@ -143,7 +143,7 @@ async def process_file(
 
 async def process_files(
     root_dir: str,
-    api_key: Optional[str] = None,
+    api_key: Optional[str] = None,  # noqa: ARG001
     use_categories: bool = False,
     enable_comments: bool = False,
 ) -> None:

tests/llm/test_anthropic/evals/test_simple.py

@@ -20,18 +20,19 @@ class User(BaseModel):
 
         @field_validator("name")
         def name_is_uppercase(cls, v: str):
-            assert v.isupper(), "Name must be uppercase, please fix"
+            assert v.isupper(), f"{v} is not an uppercased string. Note that all characters in {v} must be uppercase (EG. TIM SARAH ADAM)."
             return v
 
     resp = client.messages.create(
         model="claude-3-haiku-20240307",
-        max_tokens=1024,
+        max_tokens=4096,
         max_retries=2,
+        system="Make sure to follow the instructions carefully and return a response object that matches the json schema requested. Age is an integer.",
         messages=[
             {
                 "role": "user",
                 "content": "Extract John is 18 years old.",
-            }
+            },
         ],
         response_model=User,
     )  # type: ignore
@@ -53,7 +54,7 @@ class User(BaseModel):
 
     resp = client.messages.create(
         model="claude-3-haiku-20240307",
-        max_tokens=1024,
+        max_tokens=4096,
         max_retries=0,
         messages=[
             {
@@ -83,6 +84,7 @@ class User(BaseModel):
         model="claude-3-haiku-20240307",
         max_tokens=1024,
         max_retries=0,
+        system="Make sure to follow the instructions carefully and return a response object that matches the json schema requested. Family members here is just asking for a list of names",
         messages=[
             {
                 "role": "user",
@@ -132,7 +134,7 @@ class User(BaseModel):
 
     resp = client.messages.create(
         model="claude-3-haiku-20240307",
-        max_tokens=1024,
+        max_tokens=4096,
         max_retries=2,
         messages=[
             {
@@ -185,7 +187,10 @@ class User(BaseModel):
         max_tokens=1024,
         max_retries=0,
         messages=[
-            {"role": "system", "content": "EVERYTHING MUST BE IN ALL CAPS"},
+            {
+                "role": "system",
+                "content": "Please make sure to follow the instructions carefully and return a valid response object. All strings must be fully capitalised in all caps. (Eg. THIS IS AN UPPERCASE STRING) and age is an integer.",
+            },
             {
                 "role": "user",
                 "content": "Create a user for a model with a name and age.",