community: Update Replicate LLM and fix tests (#27655)

**Description:** 
- Fix bug in Replicate LLM class, where it was looking for parameter
names in a place where they no longer exist in pydantic 2, resulting in
the "Field required" validation error described in the issue.
- Fix Replicate LLM integration tests to:
  - Use active models on Replicate.
- Use the correct model parameter `max_new_tokens` as shown in the
[Replicate
docs](https://replicate.com/docs/guides/language-models/how-to-use#minimum-and-maximum-new-tokens).
  - Use callbacks instead of deprecated callback_manager.

**Issue:** #26937 

**Dependencies:** n/a

**Twitter handle:** n/a

---------

Signed-off-by: Fayvor Love <fayvor@gmail.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>

libs/community/langchain_community/llms/replicate.py

@@ -78,7 +78,7 @@ class Replicate(LLM):
     @classmethod
     def build_extra(cls, values: Dict[str, Any]) -> Any:
         """Build extra kwargs from additional params that were passed in."""
-        all_required_field_names = {field.alias for field in get_fields(cls).values()}
+        all_required_field_names = {field for field in get_fields(cls).keys()}
 
         input = values.pop("input", {})
         if input:

libs/community/tests/integration_tests/llms/test_replicate.py

@@ -1,16 +1,18 @@
 """Test Replicate API wrapper."""
 
-from langchain_core.callbacks import CallbackManager
-
 from langchain_community.llms.replicate import Replicate
 from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
 
-TEST_MODEL = "replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5"  # noqa: E501
+TEST_MODEL_HELLO = (
+    "replicate/hello-world:"
+    + "5c7d5dc6dd8bf75c1acaa8565735e7986bc5b66206b55cca93cb72c9bf15ccaa"
+)
+TEST_MODEL_LANG = "meta/meta-llama-3-8b-instruct"
 
 
 def test_replicate_call() -> None:
     """Test simple non-streaming call to Replicate."""
-    llm = Replicate(model=TEST_MODEL)
+    llm = Replicate(model=TEST_MODEL_HELLO)
     output = llm.invoke("What is LangChain")
     assert output
     assert isinstance(output, str)
@@ -19,9 +21,10 @@ def test_replicate_call() -> None:
 def test_replicate_streaming_call() -> None:
     """Test streaming call to Replicate."""
     callback_handler = FakeCallbackHandler()
-    callback_manager = CallbackManager([callback_handler])
 
-    llm = Replicate(streaming=True, callback_manager=callback_manager, model=TEST_MODEL)
+    llm = Replicate(
+        streaming=True, callbacks=[callback_handler], model=TEST_MODEL_HELLO
+    )
     output = llm.invoke("What is LangChain")
     assert output
     assert isinstance(output, str)
@@ -30,17 +33,17 @@ def test_replicate_streaming_call() -> None:
 def test_replicate_model_kwargs() -> None:
     """Test simple non-streaming call to Replicate."""
     llm = Replicate(  # type: ignore[call-arg]
-        model=TEST_MODEL, model_kwargs={"max_length": 100, "temperature": 0.01}
+        model=TEST_MODEL_LANG, model_kwargs={"max_new_tokens": 10, "temperature": 0.01}
     )
     long_output = llm.invoke("What is LangChain")
     llm = Replicate(  # type: ignore[call-arg]
-        model=TEST_MODEL, model_kwargs={"max_length": 10, "temperature": 0.01}
+        model=TEST_MODEL_LANG, model_kwargs={"max_new_tokens": 5, "temperature": 0.01}
     )
     short_output = llm.invoke("What is LangChain")
     assert len(short_output) < len(long_output)
-    assert llm.model_kwargs == {"max_length": 10, "temperature": 0.01}
+    assert llm.model_kwargs == {"max_new_tokens": 5, "temperature": 0.01}
 
 
 def test_replicate_input() -> None:
-    llm = Replicate(model=TEST_MODEL, input={"max_length": 10})
-    assert llm.model_kwargs == {"max_length": 10}
+    llm = Replicate(model=TEST_MODEL_LANG, input={"max_new_tokens": 10})
+    assert llm.model_kwargs == {"max_new_tokens": 10}