core[patch], openai[patch]: Chat openai stream logprobs (#16218)

libs/core/langchain_core/outputs/chat_generation.py

@@ -5,6 +5,7 @@ from typing import Any, Dict, List, Literal
 from langchain_core.messages import BaseMessage, BaseMessageChunk
 from langchain_core.outputs.generation import Generation
 from langchain_core.pydantic_v1 import root_validator
+from langchain_core.utils._merge import merge_dicts
 
 
 class ChatGeneration(Generation):
@@ -53,14 +54,13 @@ class ChatGenerationChunk(ChatGeneration):
 
     def __add__(self, other: ChatGenerationChunk) -> ChatGenerationChunk:
         if isinstance(other, ChatGenerationChunk):
-            generation_info = (
-                {**(self.generation_info or {}), **(other.generation_info or {})}
-                if self.generation_info is not None or other.generation_info is not None
-                else None
+            generation_info = merge_dicts(
+                self.generation_info or {},
+                other.generation_info or {},
             )
             return ChatGenerationChunk(
                 message=self.message + other.message,
-                generation_info=generation_info,
+                generation_info=generation_info or None,
             )
         else:
             raise TypeError(

libs/core/langchain_core/outputs/generation.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 from typing import Any, Dict, List, Literal, Optional
 
 from langchain_core.load import Serializable
+from langchain_core.utils._merge import merge_dicts
 
 
 class Generation(Serializable):
@@ -40,14 +41,13 @@ class GenerationChunk(Generation):
 
     def __add__(self, other: GenerationChunk) -> GenerationChunk:
         if isinstance(other, GenerationChunk):
-            generation_info = (
-                {**(self.generation_info or {}), **(other.generation_info or {})}
-                if self.generation_info is not None or other.generation_info is not None
-                else None
+            generation_info = merge_dicts(
+                self.generation_info or {},
+                other.generation_info or {},
             )
             return GenerationChunk(
                 text=self.text + other.text,
-                generation_info=generation_info,
+                generation_info=generation_info or None,
             )
         else:
             raise TypeError(

libs/core/langchain_core/utils/_merge.py

@@ -0,0 +1,44 @@
+from __future__ import annotations
+
+from typing import Any, Dict
+
+
+def merge_dicts(left: Dict[str, Any], right: Dict[str, Any]) -> Dict[str, Any]:
+    """Merge two dicts, handling specific scenarios where a key exists in both
+    dictionaries but has a value of None in 'left'. In such cases, the method uses the
+    value from 'right' for that key in the merged dictionary.
+
+    Example:
+        If left = {"function_call": {"arguments": None}} and
+        right = {"function_call": {"arguments": "{\n"}}
+        then, after merging, for the key "function_call",
+        the value from 'right' is used,
+        resulting in merged = {"function_call": {"arguments": "{\n"}}.
+    """
+    merged = left.copy()
+    for k, v in right.items():
+        if k not in merged:
+            merged[k] = v
+        elif merged[k] is None and v:
+            merged[k] = v
+        elif v is None:
+            continue
+        elif merged[k] == v:
+            continue
+        elif type(merged[k]) != type(v):
+            raise TypeError(
+                f'additional_kwargs["{k}"] already exists in this message,'
+                " but with a different type."
+            )
+        elif isinstance(merged[k], str):
+            merged[k] += v
+        elif isinstance(merged[k], dict):
+            merged[k] = merge_dicts(merged[k], v)
+        elif isinstance(merged[k], list):
+            merged[k] = merged[k] + v
+        else:
+            raise TypeError(
+                f"Additional kwargs key {k} already exists in left dict and value has "
+                f"unsupported type {type(merged[k])}."
+            )
+    return merged

libs/partners/openai/langchain_openai/chat_models/base.py

@@ -404,15 +404,19 @@ class ChatOpenAI(BaseChatModel):
             chunk = _convert_delta_to_message_chunk(
                 choice["delta"], default_chunk_class
             )
-            finish_reason = choice.get("finish_reason")
-            generation_info = (
-                dict(finish_reason=finish_reason) if finish_reason is not None else None
-            )
+            generation_info = {}
+            if finish_reason := choice.get("finish_reason"):
+                generation_info["finish_reason"] = finish_reason
+            logprobs = choice.get("logprobs")
+            if logprobs:
+                generation_info["logprobs"] = logprobs
             default_chunk_class = chunk.__class__
-            chunk = ChatGenerationChunk(message=chunk, generation_info=generation_info)
+            chunk = ChatGenerationChunk(
+                message=chunk, generation_info=generation_info or None
+            )
             yield chunk
             if run_manager:
-                run_manager.on_llm_new_token(chunk.text, chunk=chunk)
+                run_manager.on_llm_new_token(chunk.text, chunk=chunk, logprobs=logprobs)
 
     def _generate(
         self,
@@ -492,15 +496,21 @@ class ChatOpenAI(BaseChatModel):
             chunk = _convert_delta_to_message_chunk(
                 choice["delta"], default_chunk_class
             )
-            finish_reason = choice.get("finish_reason")
-            generation_info = (
-                dict(finish_reason=finish_reason) if finish_reason is not None else None
-            )
+            generation_info = {}
+            if finish_reason := choice.get("finish_reason"):
+                generation_info["finish_reason"] = finish_reason
+            logprobs = choice.get("logprobs")
+            if logprobs:
+                generation_info["logprobs"] = logprobs
             default_chunk_class = chunk.__class__
-            chunk = ChatGenerationChunk(message=chunk, generation_info=generation_info)
+            chunk = ChatGenerationChunk(
+                message=chunk, generation_info=generation_info or None
+            )
             yield chunk
             if run_manager:
-                await run_manager.on_llm_new_token(token=chunk.text, chunk=chunk)
+                await run_manager.on_llm_new_token(
+                    token=chunk.text, chunk=chunk, logprobs=logprobs
+                )
 
     async def _agenerate(
         self,

libs/partners/openai/tests/integration_tests/chat_models/test_base.py

@@ -391,3 +391,37 @@ def test_invoke() -> None:
 
     result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
     assert isinstance(result.content, str)
+
+
+def test_logprobs() -> None:
+    llm = ChatOpenAI()
+    result = llm.generate([[HumanMessage(content="I'm PickleRick")]], logprobs=True)
+    assert result.generations[0][0].generation_info
+    assert "content" in result.generations[0][0].generation_info["logprobs"]
+
+
+async def test_async_logprobs() -> None:
+    llm = ChatOpenAI()
+    result = await llm.agenerate(
+        [[HumanMessage(content="I'm PickleRick")]], logprobs=True
+    )
+    assert result.generations[0][0].generation_info
+    assert "content" in result.generations[0][0].generation_info["logprobs"]
+
+
+def test_logprobs_streaming() -> None:
+    llm = ChatOpenAI()
+    result = llm.generate(
+        [[HumanMessage(content="I'm PickleRick")]], logprobs=True, stream=True
+    )
+    assert result.generations[0][0].generation_info
+    assert "content" in result.generations[0][0].generation_info["logprobs"]
+
+
+async def test_async_logprobs_streaming() -> None:
+    llm = ChatOpenAI()
+    result = await llm.agenerate(
+        [[HumanMessage(content="I'm PickleRick")]], logprobs=True, stream=True
+    )
+    assert result.generations[0][0].generation_info
+    assert "content" in result.generations[0][0].generation_info["logprobs"]