community[patch]: fix top_p type hint (#15452)

fix: https://github.com/langchain-ai/langchain/issues/15341

@efriis

libs/community/langchain_community/embeddings/ollama.py

@@ -97,7 +97,7 @@ class OllamaEmbeddings(BaseModel, Embeddings):
     will give more diverse answers, while a lower value (e.g. 10)
     will be more conservative. (Default: 40)"""
 
-    top_p: Optional[int] = None
+    top_p: Optional[float] = None
     """Works together with top-k. A higher value (e.g., 0.95) will lead
     to more diverse text, while a lower value (e.g., 0.5) will
     generate more focused and conservative text. (Default: 0.9)"""

libs/community/langchain_community/llms/nlpcloud.py

@@ -38,7 +38,7 @@ class NLPCloud(LLM):
     """Whether or not to remove the end sequence token."""
     bad_words: List[str] = []
     """List of tokens not allowed to be generated."""
-    top_p: int = 1
+    top_p: float = 1.0
     """Total probability mass of tokens to consider at each step."""
     top_k: int = 50
     """The number of highest probability tokens to keep for top-k filtering."""

libs/community/langchain_community/llms/ollama.py

@@ -90,7 +90,7 @@ class _OllamaCommon(BaseLanguageModel):
     will give more diverse answers, while a lower value (e.g. 10)
     will be more conservative. (Default: 40)"""
 
-    top_p: Optional[int] = None
+    top_p: Optional[float] = None
     """Works together with top-k. A higher value (e.g., 0.95) will lead
     to more diverse text, while a lower value (e.g., 0.5) will
     generate more focused and conservative text. (Default: 0.9)"""

libs/partners/google-genai/langchain_google_genai/chat_models.py

@@ -443,7 +443,7 @@ Supported examples:
     top_k: Optional[int] = None
     """Decode using top-k sampling: consider the set of top_k most probable tokens.
        Must be positive."""
-    top_p: Optional[int] = None
+    top_p: Optional[float] = None
     """The maximum cumulative probability of tokens to consider when sampling.
 
         The model uses combined Top-k and nucleus sampling.