Merge branch 'master' into python-3.14

Author: sydney-runkle

libs/cli/langchain_cli/integration_template/integration_template/chat_models.py

@@ -36,20 +36,20 @@ class Chat__ModuleName__(BaseChatModel):
 
     # TODO: Populate with relevant params.
     Key init args — completion params:
-        model: str
+        model:
             Name of __ModuleName__ model to use.
-        temperature: float
+        temperature:
             Sampling temperature.
-        max_tokens: int | None
+        max_tokens:
             Max number of tokens to generate.
 
     # TODO: Populate with relevant params.
     Key init args — client params:
-        timeout: float | None
+        timeout:
             Timeout for requests.
-        max_retries: int
+        max_retries:
             Max number of retries.
-        api_key: str | None
+        api_key:
             __ModuleName__ API key. If not passed in will be read from env var
             __MODULE_NAME___API_KEY.
 

libs/cli/langchain_cli/integration_template/integration_template/vectorstores.py

@@ -37,16 +37,16 @@ class __ModuleName__VectorStore(VectorStore):
 
     # TODO: Populate with relevant params.
     Key init args — indexing params:
-        collection_name: str
+        collection_name:
             Name of the collection.
-        embedding_function: Embeddings
+        embedding_function:
             Embedding function to use.
 
     # TODO: Populate with relevant params.
     Key init args — client params:
-        client: Client | None
+        client:
             Client to use.
-        connection_args: dict | None
+        connection_args:
             Connection arguments.
 
     # TODO: Replace with relevant init params.

libs/core/langchain_core/indexing/base.py

@@ -522,10 +522,10 @@ def upsert(self, items: Sequence[Document], /, **kwargs: Any) -> UpsertResponse:
 
         When an ID is specified and the content already exists in the vectorstore,
         the upsert method should update the content with the new data. If the content
-        does not exist, the upsert method should add the item to the vectorstore.
+        does not exist, the upsert method should add the item to the `VectorStore`.
 
         Args:
-            items: Sequence of documents to add to the vectorstore.
+            items: Sequence of documents to add to the `VectorStore`.
             **kwargs: Additional keyword arguments.
 
         Returns:
@@ -545,10 +545,10 @@ async def aupsert(
 
         When an ID is specified and the item already exists in the vectorstore,
         the upsert method should update the item with the new data. If the item
-        does not exist, the upsert method should add the item to the vectorstore.
+        does not exist, the upsert method should add the item to the `VectorStore`.
 
         Args:
-            items: Sequence of documents to add to the vectorstore.
+            items: Sequence of documents to add to the `VectorStore`.
             **kwargs: Additional keyword arguments.
 
         Returns:

libs/core/langchain_core/language_models/base.py

@@ -200,14 +200,14 @@ def generate_prompt(
                 pure text generation models and `BaseMessage` objects for chat models).
             stop: Stop words to use when generating. Model output is cut off at the
                 first occurrence of any of these substrings.
-            callbacks: Callbacks to pass through. Used for executing additional
+            callbacks: `Callbacks` to pass through. Used for executing additional
                 functionality, such as logging or streaming, throughout generation.
             **kwargs: Arbitrary additional keyword arguments. These are usually passed
                 to the model provider API call.
 
         Returns:
             An `LLMResult`, which contains a list of candidate `Generation` objects for
-            each input prompt and additional model provider-specific output.
+                each input prompt and additional model provider-specific output.
 
         """
 
@@ -237,14 +237,14 @@ async def agenerate_prompt(
                 pure text generation models and `BaseMessage` objects for chat models).
             stop: Stop words to use when generating. Model output is cut off at the
                 first occurrence of any of these substrings.
-            callbacks: Callbacks to pass through. Used for executing additional
+            callbacks: `Callbacks` to pass through. Used for executing additional
                 functionality, such as logging or streaming, throughout generation.
             **kwargs: Arbitrary additional keyword arguments. These are usually passed
                 to the model provider API call.
 
         Returns:
             An `LLMResult`, which contains a list of candidate `Generation` objects for
-            each input prompt and additional model provider-specific output.
+                each input prompt and additional model provider-specific output.
 
         """
 
@@ -269,8 +269,7 @@ def get_token_ids(self, text: str) -> list[int]:
 
         Returns:
             A list of ids corresponding to the tokens in the text, in order they occur
-            in the text.
-
+                in the text.
         """
         if self.custom_get_token_ids is not None:
             return self.custom_get_token_ids(text)

libs/core/langchain_core/language_models/chat_models.py

@@ -838,13 +838,13 @@ def generate(
         1. Take advantage of batched calls,
         2. Need more output from the model than just the top generated value,
         3. Are building chains that are agnostic to the underlying language model
-           type (e.g., pure text completion models vs chat models).
+            type (e.g., pure text completion models vs chat models).
 
         Args:
             messages: List of list of messages.
             stop: Stop words to use when generating. Model output is cut off at the
                 first occurrence of any of these substrings.
-            callbacks: Callbacks to pass through. Used for executing additional
+            callbacks: `Callbacks` to pass through. Used for executing additional
                 functionality, such as logging or streaming, throughout generation.
             tags: The tags to apply.
             metadata: The metadata to apply.
@@ -854,8 +854,8 @@ def generate(
                 to the model provider API call.
 
         Returns:
-            An LLMResult, which contains a list of candidate Generations for each input
-            prompt and additional model provider-specific output.
+            An `LLMResult`, which contains a list of candidate `Generations` for each
+                input prompt and additional model provider-specific output.
 
         """
         ls_structured_output_format = kwargs.pop(
@@ -956,13 +956,13 @@ async def agenerate(
         1. Take advantage of batched calls,
         2. Need more output from the model than just the top generated value,
         3. Are building chains that are agnostic to the underlying language model
-           type (e.g., pure text completion models vs chat models).
+            type (e.g., pure text completion models vs chat models).
 
         Args:
             messages: List of list of messages.
             stop: Stop words to use when generating. Model output is cut off at the
                 first occurrence of any of these substrings.
-            callbacks: Callbacks to pass through. Used for executing additional
+            callbacks: `Callbacks` to pass through. Used for executing additional
                 functionality, such as logging or streaming, throughout generation.
             tags: The tags to apply.
             metadata: The metadata to apply.
@@ -972,8 +972,8 @@ async def agenerate(
                 to the model provider API call.
 
         Returns:
-            An LLMResult, which contains a list of candidate Generations for each input
-            prompt and additional model provider-specific output.
+            An `LLMResult`, which contains a list of candidate `Generations` for each
+                input prompt and additional model provider-specific output.
 
         """
         ls_structured_output_format = kwargs.pop(
@@ -1510,115 +1510,121 @@ def with_structured_output(
                 If `schema` is a Pydantic class then the model output will be a
                 Pydantic instance of that class, and the model-generated fields will be
                 validated by the Pydantic class. Otherwise the model output will be a
-                dict and will not be validated. See `langchain_core.utils.function_calling.convert_to_openai_tool`
-                for more on how to properly specify types and descriptions of
-                schema fields when specifying a Pydantic or `TypedDict` class.
+                dict and will not be validated.
+
+                See `langchain_core.utils.function_calling.convert_to_openai_tool` for
+                more on how to properly specify types and descriptions of schema fields
+                when specifying a Pydantic or `TypedDict` class.
 
             include_raw:
                 If `False` then only the parsed structured output is returned. If
                 an error occurs during model output parsing it will be raised. If `True`
                 then both the raw model response (a `BaseMessage`) and the parsed model
                 response will be returned. If an error occurs during output parsing it
-                will be caught and returned as well. The final output is always a dict
-                with keys `'raw'`, `'parsed'`, and `'parsing_error'`.
+                will be caught and returned as well.
+
+                The final output is always a `dict` with keys `'raw'`, `'parsed'`, and
+                `'parsing_error'`.
 
         Raises:
             ValueError: If there are any unsupported `kwargs`.
             NotImplementedError: If the model does not implement
                 `with_structured_output()`.
 
         Returns:
-            A Runnable that takes same inputs as a `langchain_core.language_models.chat.BaseChatModel`.
+            A `Runnable` that takes same inputs as a
+                `langchain_core.language_models.chat.BaseChatModel`. If `include_raw` is
+                `False` and `schema` is a Pydantic class, `Runnable` outputs an instance
+                of `schema` (i.e., a Pydantic object). Otherwise, if `include_raw` is
+                `False` then `Runnable` outputs a `dict`.
 
-                If `include_raw` is False and `schema` is a Pydantic class, Runnable outputs
-                an instance of `schema` (i.e., a Pydantic object).
+                If `include_raw` is `True`, then `Runnable` outputs a `dict` with keys:
 
-                Otherwise, if `include_raw` is False then Runnable outputs a dict.
+                - `'raw'`: `BaseMessage`
+                - `'parsed'`: `None` if there was a parsing error, otherwise the type
+                    depends on the `schema` as described above.
+                - `'parsing_error'`: `BaseException | None`
 
-                If `include_raw` is True, then Runnable outputs a dict with keys:
+        Example: Pydantic schema (`include_raw=False`):
 
-                - `'raw'`: BaseMessage
-                - `'parsed'`: None if there was a parsing error, otherwise the type
-                    depends on the `schema` as described above.
-                - `'parsing_error'`: BaseException | None
+        ```python
+        from pydantic import BaseModel
 
-        Example: Pydantic schema (include_raw=False):
-            ```python
-            from pydantic import BaseModel
 
+        class AnswerWithJustification(BaseModel):
+            '''An answer to the user question along with justification for the answer.'''
 
-            class AnswerWithJustification(BaseModel):
-                '''An answer to the user question along with justification for the answer.'''
+            answer: str
+            justification: str
 
-                answer: str
-                justification: str
 
+        model = ChatModel(model="model-name", temperature=0)
+        structured_model = model.with_structured_output(AnswerWithJustification)
 
-            model = ChatModel(model="model-name", temperature=0)
-            structured_model = model.with_structured_output(AnswerWithJustification)
+        structured_model.invoke(
+            "What weighs more a pound of bricks or a pound of feathers"
+        )
 
-            structured_model.invoke(
-                "What weighs more a pound of bricks or a pound of feathers"
-            )
+        # -> AnswerWithJustification(
+        #     answer='They weigh the same',
+        #     justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
+        # )
+        ```
 
-            # -> AnswerWithJustification(
-            #     answer='They weigh the same',
-            #     justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
-            # )
-            ```
+        Example: Pydantic schema (`include_raw=True`):
 
-        Example: Pydantic schema (include_raw=True):
-            ```python
-            from pydantic import BaseModel
+        ```python
+        from pydantic import BaseModel
 
 
-            class AnswerWithJustification(BaseModel):
-                '''An answer to the user question along with justification for the answer.'''
+        class AnswerWithJustification(BaseModel):
+            '''An answer to the user question along with justification for the answer.'''
 
-                answer: str
-                justification: str
+            answer: str
+            justification: str
 
 
-            model = ChatModel(model="model-name", temperature=0)
-            structured_model = model.with_structured_output(
-                AnswerWithJustification, include_raw=True
-            )
+        model = ChatModel(model="model-name", temperature=0)
+        structured_model = model.with_structured_output(
+            AnswerWithJustification, include_raw=True
+        )
 
-            structured_model.invoke(
-                "What weighs more a pound of bricks or a pound of feathers"
-            )
-            # -> {
-            #     'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
-            #     'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
-            #     'parsing_error': None
-            # }
-            ```
+        structured_model.invoke(
+            "What weighs more a pound of bricks or a pound of feathers"
+        )
+        # -> {
+        #     'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
+        #     'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
+        #     'parsing_error': None
+        # }
+        ```
 
-        Example: Dict schema (include_raw=False):
-            ```python
-            from pydantic import BaseModel
-            from langchain_core.utils.function_calling import convert_to_openai_tool
+        Example: `dict` schema (`include_raw=False`):
 
+        ```python
+        from pydantic import BaseModel
+        from langchain_core.utils.function_calling import convert_to_openai_tool
 
-            class AnswerWithJustification(BaseModel):
-                '''An answer to the user question along with justification for the answer.'''
 
-                answer: str
-                justification: str
+        class AnswerWithJustification(BaseModel):
+            '''An answer to the user question along with justification for the answer.'''
 
+            answer: str
+            justification: str
 
-            dict_schema = convert_to_openai_tool(AnswerWithJustification)
-            model = ChatModel(model="model-name", temperature=0)
-            structured_model = model.with_structured_output(dict_schema)
 
-            structured_model.invoke(
-                "What weighs more a pound of bricks or a pound of feathers"
-            )
-            # -> {
-            #     'answer': 'They weigh the same',
-            #     'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
-            # }
-            ```
+        dict_schema = convert_to_openai_tool(AnswerWithJustification)
+        model = ChatModel(model="model-name", temperature=0)
+        structured_model = model.with_structured_output(dict_schema)
+
+        structured_model.invoke(
+            "What weighs more a pound of bricks or a pound of feathers"
+        )
+        # -> {
+        #     'answer': 'They weigh the same',
+        #     'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
+        # }
+        ```
 
         !!! warning "Behavior changed in 0.2.26"
             Added support for TypedDict class.