feat(ollama): logprobs support in Ollama (#34218)

Closes #34207 

---

Expose log probabilities from the Ollama Python SDK through
`ChatOllama`. The ollama client already returns a `logprobs` field on
chat responses for supported models, but `ChatOllama` had no way to
request or surface it.

## Changes
- Add `logprobs` and `top_logprobs` fields to `ChatOllama`, forwarded to
the client via `_build_chat_params`. Setting `top_logprobs` without
`logprobs=True` auto-enables it with a warning; setting it with
`logprobs=False` raises a `ValueError`
- Surface per-token logprobs on intermediate streaming chunks (both sync
`_create_chat_stream` and async `_create_async_chat_stream`) via
`response_metadata["logprobs"]`, accumulated into the final response on
`invoke()`
- Bump minimum `ollama` SDK from `>=0.6.0` to `>=0.6.1` — the version
that added logprobs support

---------

Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>

libs/partners/ollama/langchain_ollama/chat_models.py

@@ -44,6 +44,7 @@ from __future__ import annotations
 import ast
 import json
 import logging
+import warnings
 from collections.abc import AsyncIterator, Callable, Iterator, Mapping, Sequence
 from operator import itemgetter
 from typing import Any, Literal, cast
@@ -83,7 +84,7 @@ from langchain_core.utils.function_calling import (
 )
 from langchain_core.utils.pydantic import TypeBaseModel, is_basemodel_subclass
 from ollama import AsyncClient, Client, Message
-from pydantic import BaseModel, PrivateAttr, model_validator
+from pydantic import BaseModel, PrivateAttr, field_validator, model_validator
 from pydantic.json_schema import JsonSchemaValue
 from pydantic.v1 import BaseModel as BaseModelV1
 from typing_extensions import Self, is_typeddict
@@ -626,6 +627,31 @@ class ChatOllama(BaseChatModel):
     same prompt.
     """
 
+    logprobs: bool | None = None
+    """Whether to return logprobs.
+
+    !!! note
+
+        When streaming, per-token logprobs are available on each intermediate
+        chunk (via `response_metadata["logprobs"]`) and are accumulated into the
+        final aggregated response when using `invoke()`.
+    """
+
+    top_logprobs: int | None = None
+    """Number of most likely tokens to return at each token position, each with
+    an associated log probability. Must be a positive integer.
+
+    If set without `logprobs=True`, `logprobs` will be enabled automatically.
+    """
+
+    @field_validator("top_logprobs")
+    @classmethod
+    def _validate_top_logprobs(cls, v: int | None) -> int | None:
+        if v is not None and v < 1:
+            msg = "`top_logprobs` must be a positive integer."
+            raise ValueError(msg)
+        return v
+
     stop: list[str] | None = None
     """Sets the stop tokens to use."""
 
@@ -772,6 +798,8 @@ class ChatOllama(BaseChatModel):
             "model": kwargs.pop("model", self.model),
             "think": kwargs.pop("reasoning", self.reasoning),
             "format": kwargs.pop("format", self.format),
+            "logprobs": kwargs.pop("logprobs", self.logprobs),
+            "top_logprobs": kwargs.pop("top_logprobs", self.top_logprobs),
             "options": options_dict,
             "keep_alive": kwargs.pop("keep_alive", self.keep_alive),
             **kwargs,
@@ -790,6 +818,23 @@ class ChatOllama(BaseChatModel):
     @model_validator(mode="after")
     def _set_clients(self) -> Self:
         """Set clients to use for ollama."""
+        if self.top_logprobs is not None and self.logprobs is not True:
+            if self.logprobs is False:
+                msg = (
+                    "`top_logprobs` is set but `logprobs` is explicitly `False`. "
+                    "Either set `logprobs=True` to use `top_logprobs`, or remove "
+                    "`top_logprobs`."
+                )
+                raise ValueError(msg)
+            # logprobs is None (unset) — auto-enable as convenience
+            self.logprobs = True
+            warnings.warn(
+                "`top_logprobs` is set but `logprobs` was not explicitly enabled. "
+                "Setting `logprobs=True` automatically.",
+                UserWarning,
+                stacklevel=2,
+            )
+
         client_kwargs = self.client_kwargs or {}
 
         cleaned_url, auth_headers = parse_url_with_auth(self.base_url)
@@ -1096,7 +1141,12 @@ class ChatOllama(BaseChatModel):
                     generation_info["model_provider"] = "ollama"
                     _ = generation_info.pop("message", None)
                 else:
-                    generation_info = None
+                    chunk_logprobs = stream_resp.get("logprobs")
+                    generation_info = (
+                        {"logprobs": chunk_logprobs}
+                        if chunk_logprobs is not None
+                        else None
+                    )
 
                 additional_kwargs = {}
                 if (
@@ -1173,7 +1223,12 @@ class ChatOllama(BaseChatModel):
                     generation_info["model_provider"] = "ollama"
                     _ = generation_info.pop("message", None)
                 else:
-                    generation_info = None
+                    chunk_logprobs = stream_resp.get("logprobs")
+                    generation_info = (
+                        {"logprobs": chunk_logprobs}
+                        if chunk_logprobs is not None
+                        else None
+                    )
 
                 additional_kwargs = {}
                 if (