fix(instructor/patch.py, tests/openai/test_multitask.py): Correct model checking and add new tests

instructor/patch.py

@@ -3,6 +3,7 @@
 import logging
 from collections.abc import Iterable
 from functools import wraps
+import pandas as pd
 from tenacity import Retrying, AsyncRetrying, stop_after_attempt, RetryError
 from json import JSONDecodeError
 from typing import (
@@ -213,8 +214,7 @@ def process_response(
 
     # ? This really hints at the fact that we need a better way of
     # ? attaching usage data and the raw response to the model we return.
-    if isinstance(response_model, IterableBase):
-        #! If the response model is a multitask, return the tasks
+    if isinstance(model, IterableBase):
         return [task for task in model.tasks]
 
     if isinstance(response_model, ParallelBase):
@@ -267,7 +267,7 @@ async def process_response_async(
 
     # ? This really hints at the fact that we need a better way of
     # ? attaching usage data and the raw response to the model we return.
-    if isinstance(response_model, IterableBase):
+    if isinstance(model, IterableBase):
         #! If the response model is a multitask, return the tasks
         return [task for task in model.tasks]
 

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "instructor"
-version = "0.5.0"
+version = "0.5.1"
 description = "structured outputs for llm"
 authors = ["Jason Liu <[email protected]>"]
 license = "MIT"

tests/openai/test_multitask.py

@@ -22,7 +22,6 @@ def test_multi_user(model, mode, client):
     def stream_extract(input: str) -> Iterable[User]:
         return client.chat.completions.create(
             model=model,
-            stream=True,
             response_model=Users,
             messages=[
                 {
@@ -54,6 +53,73 @@ def stream_extract(input: str) -> Iterable[User]:
 async def test_multi_user_tools_mode_async(model, mode, aclient):
     client = instructor.patch(aclient, mode=mode)
 
+    async def stream_extract(input: str) -> Iterable[User]:
+        return await client.chat.completions.create(
+            model=model,
+            response_model=Users,
+            messages=[
+                {
+                    "role": "user",
+                    "content": (
+                        f"Consider the data below:\n{input}"
+                        "Correctly segment it into entitites"
+                        "Make sure the JSON is correct"
+                    ),
+                },
+            ],
+            max_tokens=1000,
+        )
+
+    resp = []
+    async for user in await stream_extract(input="Jason is 20, Sarah is 30"):
+        resp.append(user)
+    print(resp)
+    assert len(resp) == 2
+    assert resp[0].name == "Jason"
+    assert resp[0].age == 20
+    assert resp[1].name == "Sarah"
+    assert resp[1].age == 30
+
+
+@pytest.mark.parametrize("model, mode", product(models, modes))
+def test_multi_user_stream(model, mode, client):
+    client = instructor.patch(client, mode=mode)
+
+    def stream_extract(input: str) -> Iterable[User]:
+        return client.chat.completions.create(
+            model=model,
+            stream=True,
+            response_model=Users,
+            messages=[
+                {
+                    "role": "system",
+                    "content": "You are a perfect entity extraction system",
+                },
+                {
+                    "role": "user",
+                    "content": (
+                        f"Consider the data below:\n{input}"
+                        "Correctly segment it into entitites"
+                        "Make sure the JSON is correct"
+                    ),
+                },
+            ],
+            max_tokens=1000,
+        )
+
+    resp = [user for user in stream_extract(input="Jason is 20, Sarah is 30")]
+    assert len(resp) == 2
+    assert resp[0].name == "Jason"
+    assert resp[0].age == 20
+    assert resp[1].name == "Sarah"
+    assert resp[1].age == 30
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model, mode", product(models, modes))
+async def test_multi_user_tools_mode_async_stream(model, mode, aclient):
+    client = instructor.patch(aclient, mode=mode)
+
     async def stream_extract(input: str) -> Iterable[User]:
         return await client.chat.completions.create(
             model=model,