Add more extraction code

langchain_benchmarks/extraction/evaluators.py

@@ -0,0 +1,27 @@
+from langchain.smith import RunEvalConfig
+from pydantic import BaseModel
+
+
+def get_eval_config(eval_llm: BaseModel) -> RunEvalConfig:
+    """Get the evaluation configuration for the email task."""
+    return RunEvalConfig(
+        evaluators=[
+            RunEvalConfig.LabeledScoreString(
+                criteria={
+                    "accuracy": """
+    Score 1: The answer is incorrect and unrelated to the question or reference document.
+    Score 3: The answer is partially correct but has more than one omission or major errors.
+    Score 5: The answer is mostly correct but has more than one omission or major error.
+    Score 7: The answer is mostly correct but has at most one omission or major error.
+    Score 9: The answer is mostly correct with no omissions and only minor errors, and aligns with the reference document.
+    Score 10: The answer is correct, complete, and aligns with the reference document. Extra information is acceptable if it is sensible.
+
+    If the reference answer contains multiple alternatives, the predicted answer must only match one of the alternatives to be considered correct.
+    If the predicted answer contains additional helpful and accurate information that is not present in the reference answer, it should still be considered correct and not be penalized.
+    """  # noqa
+                },
+                llm=eval_llm,
+                normalize_by=10.0,
+            ),
+        ],
+    )

langchain_benchmarks/extraction/implementations.py

@@ -0,0 +1,67 @@
+"""Default implementations of LLMs that can be used for extraction."""
+from typing import Type, Optional, List, Any, Dict
+
+from langchain.chains.openai_functions import convert_to_openai_function
+from langchain.chat_models import ChatOpenAI
+from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser
+from langchain.schema.runnable import Runnable
+from langsmith.client import Client
+from pydantic import BaseModel
+
+from langchain_benchmarks.extraction.evaluators import get_eval_config
+from langchain_benchmarks.schema import ExtractionTask
+
+# PUBLIC API
+
+def create_openai_function_based_extractor(
+    llm: Runnable,
+    schema: Type[BaseModel],
+) -> Runnable[dict, dict]:
+    """Create an extraction chain that uses an LLM to extract a schema.
+
+    The underlying functionality is exclusively for LLMs that support
+    extraction using openai functions format.
+
+    Args:
+        llm: The LLM to use for extraction.
+        schema: The schema to extract.
+
+    Returns:
+        An llm that will extract the schema
+    """
+    openai_functions = [convert_to_openai_function(schema)]
+    llm_kwargs = {
+        "functions": openai_functions,
+        "function_call": {"name": openai_functions[0]["name"]},
+    }
+    output_parser = JsonOutputFunctionsParser()
+    extraction_chain = (
+        llm.bind(**llm_kwargs) | output_parser | (lambda x: {"output": x})
+    )
+    return extraction_chain
+
+
+def run_on_dataset(
+    task: ExtractionTask,
+    llm: Runnable,
+    *,
+    tags: Optional[List[str]] = None,
+    **kwargs: Any,
+) -> Dict[str, Any]:
+    """Run an LLM on a dataset.
+
+    Args:
+        task: The task to run on.
+        llm: The LLM to run.
+        tags: The tags to use for the run.
+        kwargs: Additional arguments to pass to the client.
+    """
+    client = Client()
+    eval_llm = ChatOpenAI(model="gpt-4", temperature=0.0, model_kwargs={"seed": 42})
+    return client.run_on_dataset(
+        dataset_name=task.name,
+        llm_or_chain_factory=create_openai_function_based_extractor(llm, task.schema),
+        evaluation=get_eval_config(eval_llm),
+        tags=tags,
+        **kwargs,
+    )

langchain_benchmarks/extraction/email_task.py → langchain_benchmarks/extraction/tasks/email_task.py

@@ -1,7 +1,7 @@
 from enum import Enum
 from typing import Optional, List
 
-from langchain.smith import RunEvalConfig
+from langchain.prompts import ChatPromptTemplate
 from pydantic import BaseModel, Field
 
 from langchain_benchmarks.schema import ExtractionTask
@@ -33,35 +33,22 @@ class Email(BaseModel):
     tone: ToneEnum = Field(..., description="The tone of the email.")
 
 
-def get_eval_config(eval_llm: BaseModel) -> RunEvalConfig:
-    """Get the evaluation configuration for the email task."""
-    return RunEvalConfig(
-        evaluators=[
-            RunEvalConfig.LabeledScoreString(
-                criteria={
-                    "accuracy": """
-    Score 1: The answer is incorrect and unrelated to the question or reference document.
-    Score 3: The answer is partially correct but has more than one omission or major errors.
-    Score 5: The answer is mostly correct but has more than one omission or major error.
-    Score 7: The answer is mostly correct but has at most one omission or major error.
-    Score 9: The answer is mostly correct with no omissions and only minor errors, and aligns with the reference document.
-    Score 10: The answer is correct, complete, and aligns with the reference document. Extra information is acceptable if it is sensible.
-
-    If the reference answer contains multiple alternatives, the predicted answer must only match one of the alternatives to be considered correct.
-    If the predicted answer contains additional helpful and accurate information that is not present in the reference answer, it should still be considered correct and not be penalized.
-    """  # noqa
-                },
-                llm=eval_llm,
-                normalize_by=10.0,
-            ),
-        ],
-    )
-
+# This is a default prompt that works for chat models.
+DEFAULT_CHAT_MODEL_PROMPT = ChatPromptTemplate.from_messages(
+    [
+        ("system", "You are an expert researcher."),
+        (
+            "human",
+            "What can you tell me about the following email? Make sure to "
+            "answer in the correct format: {schema}",
+        ),
+    ]
+)
 
 EMAIL_EXTRACTION_TASK = ExtractionTask(
     name="Email Extraction",
     dataset_id="https://smith.langchain.com/public/36bdfe7d-3cd1-4b36-b957-d12d95810a2b/d",
-    model=Email,
+    schema=Email,
     description="""\
 A dataset of 42 real emails deduped from a spam folder, with semantic HTML tags removed, \
 as well as a script for initial extraction and formatting of other emails from \
@@ -71,4 +58,5 @@ def get_eval_config(eval_llm: BaseModel) -> RunEvalConfig:
 
 See https://github.com/jacoblee93/oss-model-extraction-evals.
     """,
+    instructions=DEFAULT_CHAT_MODEL_PROMPT,
 )

langchain_benchmarks/registration.py

@@ -1,6 +1,6 @@
 """Registry of environments for ease of access."""
 
-from langchain_benchmarks.extraction import email_task
+from langchain_benchmarks.extraction.tasks import email_task
 from langchain_benchmarks.schema import Registry
 from langchain_benchmarks.tool_usage.tasks import (
     type_writer,

langchain_benchmarks/schema.py

@@ -2,6 +2,7 @@
 import dataclasses
 from typing import List, Callable, Any, Optional, Type, Union
 
+from langchain.prompts import ChatPromptTemplate
 from langchain.tools import BaseTool
 from pydantic import BaseModel
 from tabulate import tabulate
@@ -68,8 +69,16 @@ class ToolUsageTask(BaseTask):
 class ExtractionTask(BaseTask):
     """A definition for an extraction task."""
 
-    model: Type[BaseModel]
-    """Get the model for the task."""
+    schema: Type[BaseModel]
+    """Get schema that specifies what should be extracted."""
+
+    # We might want to make this optional / or support more types
+    # and add validation, but let's wait until we have more examples
+    instructions: ChatPromptTemplate
+    """Get the prompt for the task.
+
+    This is the default prompt to use for the task.
+    """
 
 
 @dataclasses.dataclass(frozen=False)

tests/unit_tests/extraction/test_email_extraction.py

@@ -1,3 +1,2 @@
 def test_email_extraction() -> None:
     """Try to import the email task."""
-    from langchain_benchmarks.extraction import email_task  # noqa: F401

tests/unit_tests/extraction/test_import_stuff.py

@@ -0,0 +1,3 @@
+def test_import_stuff() -> None:
+    """Test that all imports work."""
+    from langchain_benchmarks.extraction import evaluators, implementations  # noqa: F401