use with_structured_output

backend/extraction/utils.py

@@ -20,13 +20,12 @@ def _rm_titles(kv: dict) -> dict:
 # PUBLIC API
 
 
-def convert_json_schema_to_openai_schema(
+def update_json_schema(
     schema: dict,
     *,
-    rm_titles: bool = True,
     multi: bool = True,
 ) -> dict:
-    """Convert JSON schema to a corresponding OpenAI function call."""
+    """Add missing fields to JSON schema and add support for multiple records."""
     if multi:
         # Wrap the schema in an object called "Root" with a property called: "data"
         # which will be a json array of the original schema.
@@ -43,10 +42,6 @@ def convert_json_schema_to_openai_schema(
     else:
         raise NotImplementedError("Only multi is supported for now.")
 
-    schema_.pop("definitions", None)
-
-    return {
-        "name": "extractor",
-        "description": "Extract information matching the given schema.",
-        "parameters": _rm_titles(schema_) if rm_titles else schema_,
-    }
+    schema_["title"] = "extractor"
+    schema_["description"] = "Extract information matching the given schema."
+    return schema_

backend/server/extraction_runnable.py

@@ -5,7 +5,6 @@
 
 from fastapi import HTTPException
 from jsonschema import Draft202012Validator, exceptions
-from langchain.chains.openai_functions import create_openai_fn_runnable
 from langchain.text_splitter import TokenTextSplitter
 from langchain_core.messages import AIMessage, HumanMessage
 from langchain_core.prompts import ChatPromptTemplate
@@ -15,9 +14,7 @@
 from typing_extensions import TypedDict
 
 from db.models import Example, Extractor
-from extraction.utils import (
-    convert_json_schema_to_openai_schema,
-)
+from extraction.utils import update_json_schema
 from server.settings import CHUNK_SIZE, MODEL_NAME, get_model
 from server.validators import validate_json_schema
 
@@ -161,22 +158,22 @@ def get_examples_from_extractor(extractor: Extractor) -> List[Dict[str, Any]]:
 async def extraction_runnable(extraction_request: ExtractRequest) -> ExtractResponse:
     """An end point to extract content from a given text object."""
     # TODO: Add validation for model context window size
-    schema = extraction_request.json_schema
+    schema = update_json_schema(extraction_request.json_schema)
     try:
         Draft202012Validator.check_schema(schema)
     except exceptions.ValidationError as e:
         raise HTTPException(status_code=422, detail=f"Invalid schema: {e.message}")
 
-    openai_function = convert_json_schema_to_openai_schema(schema)
-    function_name = openai_function["name"]
     prompt = _make_prompt_template(
         extraction_request.instructions,
         extraction_request.examples,
-        function_name,
+        schema["title"],
     )
-    runnable = create_openai_fn_runnable(
-        functions=[openai_function], llm=model, prompt=prompt
+    # N.B. method must be consistent with examples in _make_prompt_template
+    runnable = prompt | model.with_structured_output(
+        schema=schema, method="function_calling"
     )
+
     return await runnable.ainvoke({"text": extraction_request.text})
 
 

backend/tests/integration_tests/test_extraction.py

@@ -78,7 +78,7 @@ class Person(BaseModel):
             json={
                 "input": {
                     "text": text,
-                    "schema": Person(),
+                    "schema": Person.schema(),
                     "instructions": "Redact all names using the characters `######`",
                     "examples": examples,
                 }

backend/tests/unit_tests/test_utils.py

@@ -1,13 +1,11 @@
 from langchain.pydantic_v1 import BaseModel, Field
 
-from extraction.utils import (
-    convert_json_schema_to_openai_schema,
-)
+from extraction.utils import update_json_schema
 from server.extraction_runnable import ExtractionExample, _make_prompt_template
 
 
-def test_convert_json_schema_to_openai_schema() -> None:
-    """Test converting a JSON schema to an OpenAI schema."""
+def test_update_json_schema() -> None:
+    """Test updating JSON schema."""
 
     class Person(BaseModel):
         name: str = Field(..., description="The name of the person.")
@@ -33,33 +31,34 @@ class Person(BaseModel):
         "type": "object",
     }
 
-    openai_schema = convert_json_schema_to_openai_schema(schema)
-    assert openai_schema == {
-        "description": "Extract information matching the given schema.",
-        "name": "extractor",
-        "parameters": {
-            "properties": {
-                "data": {
-                    "items": {
-                        "properties": {
-                            "age": {
-                                "description": "The age of the person.",
-                                "type": "integer",
-                            },
-                            "name": {
-                                "description": "The name of the person.",
-                                "type": "string",
-                            },
+    updated_schema = update_json_schema(schema)
+    assert updated_schema == {
+        "type": "object",
+        "properties": {
+            "data": {
+                "type": "array",
+                "items": {
+                    "title": "Person",
+                    "type": "object",
+                    "properties": {
+                        "name": {
+                            "title": "Name",
+                            "description": "The name of the person.",
+                            "type": "string",
+                        },
+                        "age": {
+                            "title": "Age",
+                            "description": "The age of the person.",
+                            "type": "integer",
                         },
-                        "required": ["name", "age"],
-                        "type": "object",
                     },
-                    "type": "array",
-                }
-            },
-            "required": ["data"],
-            "type": "object",
+                    "required": ["name", "age"],
+                },
+            }
         },
+        "required": ["data"],
+        "title": "extractor",
+        "description": "Extract information matching the given schema.",
     }