fix: add and enable OpenAI strict mode

src/raglite/_eval.py

@@ -5,7 +5,7 @@
 
 import numpy as np
 import pandas as pd
-from pydantic import BaseModel, Field, field_validator
+from pydantic import BaseModel, ConfigDict, Field, field_validator
 from sqlmodel import Session, func, select
 from tqdm.auto import tqdm, trange
 
@@ -25,10 +25,11 @@ def insert_evals(  # noqa: C901
     class QuestionResponse(BaseModel):
         """A specific question about the content of a set of document contexts."""
 
+        model_config = ConfigDict(
+            extra="forbid"  # Forbid extra attributes as required by OpenAI's strict mode.
+        )
         question: str = Field(
-            ...,
-            description="A specific question about the content of a set of document contexts.",
-            min_length=1,
+            ..., description="A specific question about the content of a set of document contexts."
         )
         system_prompt: ClassVar[str] = """
 You are given a set of contexts extracted from a document.
@@ -85,7 +86,7 @@ def validate_question(cls, value: str) -> str:
             # Extract a question from the seed chunk's related chunks.
             try:
                 question_response = extract_with_llm(
-                    QuestionResponse, related_chunks, config=config
+                    QuestionResponse, related_chunks, strict=True, config=config
                 )
             except ValueError:
                 continue
@@ -101,6 +102,9 @@ def validate_question(cls, value: str) -> str:
             class ContextEvalResponse(BaseModel):
                 """Indicate whether the provided context can be used to answer a given question."""
 
+                model_config = ConfigDict(
+                    extra="forbid"  # Forbid extra attributes as required by OpenAI's strict mode.
+                )
                 hit: bool = Field(
                     ...,
                     description="True if the provided context contains (a part of) the answer to the given question, false otherwise.",
@@ -118,7 +122,7 @@ class ContextEvalResponse(BaseModel):
             ):
                 try:
                     context_eval_response = extract_with_llm(
-                        ContextEvalResponse, str(candidate_chunk), config=config
+                        ContextEvalResponse, str(candidate_chunk), strict=True, config=config
                     )
                 except ValueError:  # noqa: PERF203
                     pass
@@ -132,10 +136,12 @@ class ContextEvalResponse(BaseModel):
             class AnswerResponse(BaseModel):
                 """Answer a question using the provided context."""
 
+                model_config = ConfigDict(
+                    extra="forbid"  # Forbid extra attributes as required by OpenAI's strict mode.
+                )
                 answer: str = Field(
                     ...,
                     description="A complete answer to the given question using the provided context.",
-                    min_length=1,
                 )
                 system_prompt: ClassVar[str] = f"""
 You are given a set of contexts extracted from a document.
@@ -152,6 +158,7 @@ class AnswerResponse(BaseModel):
                 answer_response = extract_with_llm(
                     AnswerResponse,
                     [str(relevant_chunk) for relevant_chunk in relevant_chunks],
+                    strict=True,
                     config=config,
                 )
             except ValueError:

src/raglite/_extract.py

@@ -2,7 +2,6 @@
 
 from typing import Any, TypeVar
 
-import litellm
 from litellm import completion, get_supported_openai_params  # type: ignore[attr-defined]
 from pydantic import BaseModel, ValidationError
 
@@ -14,6 +13,7 @@
 def extract_with_llm(
     return_type: type[T],
     user_prompt: str | list[str],
+    strict: bool = False,  # noqa: FBT001,FBT002
     config: RAGLiteConfig | None = None,
     **kwargs: Any,
 ) -> T:
@@ -33,29 +33,31 @@ class MyNameResponse(BaseModel):
     """
     # Load the default config if not provided.
     config = config or RAGLiteConfig()
-    # Update the system prompt with the JSON schema of the return type to help the LLM.
-    system_prompt = "\n".join(
-        (
-            return_type.system_prompt.strip(),  # type: ignore[attr-defined]
-            "Format your response according to this JSON schema:",
-            str(return_type.model_json_schema()),
-        )
+    # Check if the LLM supports the response format.
+    llm_provider = "llama-cpp-python" if config.embedder.startswith("llama-cpp") else None
+    llm_supports_response_format = "response_format" in (
+        get_supported_openai_params(model=config.llm, custom_llm_provider=llm_provider) or []
     )
-    # Constrain the reponse format to the JSON schema if it's supported by the LLM [1].
+    # Update the system prompt with the JSON schema of the return type to help the LLM.
+    system_prompt = getattr(return_type, "system_prompt", "").strip()
+    if not llm_supports_response_format or llm_provider == "llama-cpp-python":
+        system_prompt += f"\n\nFormat your response according to this JSON schema:\n{return_type.model_json_schema()!s}"
+    # Constrain the reponse format to the JSON schema if it's supported by the LLM [1]. Strict mode
+    # is disabled by default because it only supports a subset of JSON schema features [2].
     # [1] https://docs.litellm.ai/docs/completion/json_mode
+    # [2] https://platform.openai.com/docs/guides/structured-outputs#some-type-specific-keywords-are-not-yet-supported
     # TODO: Fall back to {"type": "json_object"} if JSON schema is not supported by the LLM.
-    llm_provider = "llama-cpp-python" if config.embedder.startswith("llama-cpp") else None
     response_format: dict[str, Any] | None = (
         {
             "type": "json_schema",
             "json_schema": {
                 "name": return_type.__name__,
                 "description": return_type.__doc__ or "",
                 "schema": return_type.model_json_schema(),
+                "strict": strict,
             },
         }
-        if "response_format"
-        in (get_supported_openai_params(model=config.llm, custom_llm_provider=llm_provider) or [])
+        if llm_supports_response_format
         else None
     )
     # Concatenate the user prompt if it is a list of strings.
@@ -64,9 +66,6 @@ class MyNameResponse(BaseModel):
             f'<context index="{i + 1}">\n{chunk.strip()}\n</context>'
             for i, chunk in enumerate(user_prompt)
         )
-    # Enable JSON schema validation.
-    enable_json_schema_validation = litellm.enable_json_schema_validation
-    litellm.enable_json_schema_validation = True
     # Extract structured data from the unstructured input.
     for _ in range(config.llm_max_tries):
         response = completion(
@@ -89,6 +88,4 @@ class MyNameResponse(BaseModel):
     else:
         error_message = f"Failed to extract {return_type} from input {user_prompt}."
         raise ValueError(error_message) from last_exception
-    # Restore the previous JSON schema validation setting.
-    litellm.enable_json_schema_validation = enable_json_schema_validation
     return instance

tests/test_extract.py

@@ -3,7 +3,7 @@
 from typing import ClassVar
 
 import pytest
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 from raglite import RAGLiteConfig
 from raglite._extract import extract_with_llm
@@ -13,29 +13,36 @@
     params=[
         pytest.param(RAGLiteConfig().llm, id="llama_cpp_python"),
         pytest.param("gpt-4o-mini", id="openai"),
-    ],
+    ]
 )
-def llm(
-    request: pytest.FixtureRequest,
-) -> str:
+def llm(request: pytest.FixtureRequest) -> str:
     """Get an LLM to test RAGLite with."""
     llm: str = request.param
     return llm
 
 
-def test_extract(llm: str) -> None:
+@pytest.mark.parametrize(
+    "strict", [pytest.param(False, id="strict=False"), pytest.param(True, id="strict=True")]
+)
+def test_extract(llm: str, strict: bool) -> None:  # noqa: FBT001
     """Test extracting structured data."""
     # Set the LLM.
     config = RAGLiteConfig(llm=llm)
 
-    # Extract structured data.
+    # Define the JSON schema of the response.
     class LoginResponse(BaseModel):
+        """The response to a login request."""
+
+        model_config = ConfigDict(extra="forbid" if strict else "allow")
         username: str = Field(..., description="The username.")
         password: str = Field(..., description="The password.")
         system_prompt: ClassVar[str] = "Extract the username and password from the input."
 
+    # Extract structured data.
     username, password = "cypher", "steak"
-    login_response = extract_with_llm(LoginResponse, f"{username} // {password}", config=config)
+    login_response = extract_with_llm(
+        LoginResponse, f"{username} // {password}", strict=strict, config=config
+    )
     # Validate the response.
     assert isinstance(login_response, LoginResponse)
     assert login_response.username == username