Merge branch 'master' into pprados/06-pdfplumber

.github/scripts/check_diff.py

@@ -39,7 +39,6 @@ IGNORED_PARTNERS = [
 
 PY_312_MAX_PACKAGES = [
     "libs/partners/huggingface",  # https://github.com/pytorch/pytorch/issues/130249
-    "libs/partners/pinecone",
     "libs/partners/voyageai",
 ]
 

docs/docs/integrations/vectorstores/google_bigquery_vector_search.ipynb

@@ -331,7 +331,7 @@
     "-   Dictionary-based Filters\n",
     "    -   You can pass a dictionary (dict) where the keys represent metadata fields and the values specify the filter condition. This method applies an equality filter between the key and the corresponding value. When multiple key-value pairs are provided, they are combined using a logical AND operation.\n",
     "-   SQL-based Filters\n",
-    "    -   Alternatively, you can provide a string representing an SQL WHERE clause to define more complex filtering conditions. This allows for greater flexibility, supporting SQL expressions such as comparison operators and logical operators."
+    "    -   Alternatively, you can provide a string representing an SQL WHERE clause to define more complex filtering conditions. This allows for greater flexibility, supporting SQL expressions such as comparison operators and logical operators. Learn more about [BigQuery operators](https://cloud.google.com/bigquery/docs/reference/standard-sql/operators)."
    ]
   },
   {
@@ -356,7 +356,7 @@
    "source": [
     "# SQL-based Filters\n",
     "# This should return \"Banana\", \"Apples and oranges\" and \"Cars and airplanes\" documents.\n",
-    "docs = store.similarity_search_by_vector(query_vector, filter={\"len = 6 AND len > 17\"})\n",
+    "docs = store.similarity_search_by_vector(query_vector, filter=\"len = 6 AND len > 17\")\n",
     "print(docs)"
    ]
   },

libs/community/langchain_community/chat_models/perplexity.py

@@ -3,19 +3,23 @@
 from __future__ import annotations
 
 import logging
+from operator import itemgetter
 from typing import (
     Any,
     Dict,
     Iterator,
     List,
+    Literal,
     Mapping,
     Optional,
     Tuple,
     Type,
+    TypeVar,
     Union,
 )
 
 from langchain_core.callbacks import CallbackManagerForLLMRun
+from langchain_core.language_models import LanguageModelInput
 from langchain_core.language_models.chat_models import (
     BaseChatModel,
     generate_from_stream,
@@ -34,17 +38,27 @@ from langchain_core.messages import (
     SystemMessageChunk,
     ToolMessageChunk,
 )
+from langchain_core.output_parsers import JsonOutputParser, PydanticOutputParser
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.utils import (
-    from_env,
-    get_pydantic_field_names,
+from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
+from langchain_core.utils import from_env, get_pydantic_field_names
+from langchain_core.utils.pydantic import (
+    is_basemodel_subclass,
 )
-from pydantic import ConfigDict, Field, model_validator
+from pydantic import BaseModel, ConfigDict, Field, TypeAdapter, model_validator
 from typing_extensions import Self
 
+_BM = TypeVar("_BM", bound=BaseModel)
+_DictOrPydanticClass = Union[Dict[str, Any], Type[_BM], Type]
+_DictOrPydantic = Union[Dict, _BM]
+
 logger = logging.getLogger(__name__)
 
 
+def _is_pydantic_class(obj: Any) -> bool:
+    return isinstance(obj, type) and is_basemodel_subclass(obj)
+
+
 class ChatPerplexity(BaseChatModel):
     """`Perplexity AI` Chat models API.
 
@@ -282,3 +296,99 @@ class ChatPerplexity(BaseChatModel):
     def _llm_type(self) -> str:
         """Return type of chat model."""
         return "perplexitychat"
+
+    def with_structured_output(
+        self,
+        schema: Optional[_DictOrPydanticClass] = None,
+        *,
+        method: Literal["json_schema"] = "json_schema",
+        include_raw: bool = False,
+        strict: Optional[bool] = None,
+        **kwargs: Any,
+    ) -> Runnable[LanguageModelInput, _DictOrPydantic]:
+        """Model wrapper that returns outputs formatted to match the given schema for Preplexity.
+        Currently, Preplexity only supports "json_schema" method for structured output
+        as per their official documentation: https://docs.perplexity.ai/guides/structured-outputs
+
+        Args:
+            schema:
+                The output schema. Can be passed in as:
+
+                - a JSON Schema,
+                - a TypedDict class,
+                - or a Pydantic class
+
+            method: The method for steering model generation, currently only support:
+
+                - "json_schema": Use the JSON Schema to parse the model output
+
+
+            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".
+
+            kwargs: Additional keyword args aren't supported.
+
+        Returns:
+            A Runnable that takes same inputs as a :class:`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 True, then Runnable outputs a dict with keys:
+
+            - "raw": BaseMessage
+            - "parsed": None if there was a parsing error, otherwise the type depends on the ``schema`` as described above.
+            - "parsing_error": Optional[BaseException]
+
+        """  # noqa: E501
+        if method == "json_schema":
+            if schema is None:
+                raise ValueError(
+                    "schema must be specified when method is not 'json_schema'. "
+                    "Received None."
+                )
+            is_pydantic_schema = _is_pydantic_class(schema)
+            if is_pydantic_schema and hasattr(
+                schema, "model_json_schema"
+            ):  # accounting for pydantic v1 and v2
+                response_format = schema.model_json_schema()  # type: ignore[union-attr]
+            elif is_pydantic_schema:
+                response_format = schema.schema()  # type: ignore[union-attr]
+            elif isinstance(schema, dict):
+                response_format = schema
+            elif type(schema).__name__ == "_TypedDictMeta":
+                adapter = TypeAdapter(schema)  # if use passes typeddict
+                response_format = adapter.json_schema()
+
+            llm = self.bind(
+                response_format={
+                    "type": "json_schema",
+                    "json_schema": {"schema": response_format},
+                }
+            )
+            output_parser = (
+                PydanticOutputParser(pydantic_object=schema)  # type: ignore[arg-type]
+                if is_pydantic_schema
+                else JsonOutputParser()
+            )
+        else:
+            raise ValueError(
+                f"Unrecognized method argument. Expected 'json_schema' Received:\
+                    '{method}'"
+            )
+
+        if include_raw:
+            parser_assign = RunnablePassthrough.assign(
+                parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
+            )
+            parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
+            parser_with_fallback = parser_assign.with_fallbacks(
+                [parser_none], exception_key="parsing_error"
+            )
+            return RunnableMap(raw=llm) | parser_with_fallback
+        else:
+            return llm | output_parser

libs/community/langchain_community/vectorstores/oraclevs.py

@@ -22,6 +22,8 @@ from typing import (
     cast,
 )
 
+from numpy.typing import NDArray
+
 if TYPE_CHECKING:
     from oracledb import Connection
 
@@ -47,6 +49,31 @@ logging.basicConfig(
 T = TypeVar("T", bound=Callable[..., Any])
 
 
+def _get_connection(client: Any) -> Connection | None:
+    # Dynamically import oracledb and the required classes
+    try:
+        import oracledb
+    except ImportError as e:
+        raise ImportError(
+            "Unable to import oracledb, please install with `pip install -U oracledb`."
+        ) from e
+
+    # check if ConnectionPool exists
+    connection_pool_class = getattr(oracledb, "ConnectionPool", None)
+
+    if isinstance(client, oracledb.Connection):
+        return client
+    elif connection_pool_class and isinstance(client, connection_pool_class):
+        return client.acquire()
+    else:
+        valid_types = "oracledb.Connection"
+        if connection_pool_class:
+            valid_types += " or oracledb.ConnectionPool"
+        raise TypeError(
+            f"Expected client of type {valid_types}, got {type(client).__name__}"
+        )
+
+
 def _handle_exceptions(func: T) -> T:
     @functools.wraps(func)
     def wrapper(*args: Any, **kwargs: Any) -> Any:
@@ -70,7 +97,7 @@ def _handle_exceptions(func: T) -> T:
     return cast(T, wrapper)
 
 
-def _table_exists(client: Connection, table_name: str) -> bool:
+def _table_exists(connection: Connection, table_name: str) -> bool:
     try:
         import oracledb
     except ImportError as e:
@@ -79,7 +106,7 @@ def _table_exists(client: Connection, table_name: str) -> bool:
         ) from e
 
     try:
-        with client.cursor() as cursor:
+        with connection.cursor() as cursor:
             cursor.execute(f"SELECT COUNT(*) FROM {table_name}")
             return True
     except oracledb.DatabaseError as ex:
@@ -106,7 +133,7 @@ def _compare_version(version: str, target_version: str) -> bool:
 
 
 @_handle_exceptions
-def _index_exists(client: Connection, index_name: str) -> bool:
+def _index_exists(connection: Connection, index_name: str) -> bool:
     # Check if the index exists
     query = """
         SELECT index_name 
@@ -114,7 +141,7 @@ def _index_exists(client: Connection, index_name: str) -> bool:
         WHERE upper(index_name) = upper(:idx_name)
         """
 
-    with client.cursor() as cursor:
+    with connection.cursor() as cursor:
         # Execute the query
         cursor.execute(query, idx_name=index_name.upper())
         result = cursor.fetchone()
@@ -146,16 +173,16 @@ def _get_index_name(base_name: str) -> str:
 
 
 @_handle_exceptions
-def _create_table(client: Connection, table_name: str, embedding_dim: int) -> None:
+def _create_table(connection: Connection, table_name: str, embedding_dim: int) -> None:
     cols_dict = {
         "id": "RAW(16) DEFAULT SYS_GUID() PRIMARY KEY",
         "text": "CLOB",
-        "metadata": "CLOB",
+        "metadata": "JSON",
         "embedding": f"vector({embedding_dim}, FLOAT32)",
     }
 
-    if not _table_exists(client, table_name):
-        with client.cursor() as cursor:
+    if not _table_exists(connection, table_name):
+        with connection.cursor() as cursor:
             ddl_body = ", ".join(
                 f"{col_name} {col_type}" for col_name, col_type in cols_dict.items()
             )
@@ -168,43 +195,45 @@ def _create_table(client: Connection, table_name: str, embedding_dim: int) -> No
 
 @_handle_exceptions
 def create_index(
-    client: Connection,
+    client: Any,
     vector_store: OracleVS,
     params: Optional[dict[str, Any]] = None,
 ) -> None:
-    """Create an index on the vector store.
-
-    Args:
-        client: The OracleDB connection object.
-        vector_store: The vector store object.
-        params: Optional parameters for the index creation.
-
-    Raises:
-        ValueError: If an invalid parameter is provided.
-    """
+    connection = _get_connection(client)
+    if connection is None:
+        raise ValueError("Failed to acquire a connection.")
     if params:
         if params["idx_type"] == "HNSW":
             _create_hnsw_index(
-                client, vector_store.table_name, vector_store.distance_strategy, params
+                connection,
+                vector_store.table_name,
+                vector_store.distance_strategy,
+                params,
             )
         elif params["idx_type"] == "IVF":
             _create_ivf_index(
-                client, vector_store.table_name, vector_store.distance_strategy, params
+                connection,
+                vector_store.table_name,
+                vector_store.distance_strategy,
+                params,
             )
         else:
             _create_hnsw_index(
-                client, vector_store.table_name, vector_store.distance_strategy, params
+                connection,
+                vector_store.table_name,
+                vector_store.distance_strategy,
+                params,
             )
     else:
         _create_hnsw_index(
-            client, vector_store.table_name, vector_store.distance_strategy, params
+            connection, vector_store.table_name, vector_store.distance_strategy, params
         )
     return
 
 
 @_handle_exceptions
 def _create_hnsw_index(
-    client: Connection,
+    connection: Connection,
     table_name: str,
     distance_strategy: DistanceStrategy,
     params: Optional[dict[str, Any]] = None,
@@ -278,8 +307,8 @@ def _create_hnsw_index(
     ddl = ddl_assembly.format(**config)
 
     # Check if the index exists
-    if not _index_exists(client, config["idx_name"]):
-        with client.cursor() as cursor:
+    if not _index_exists(connection, config["idx_name"]):
+        with connection.cursor() as cursor:
             cursor.execute(ddl)
             logger.info("Index created successfully...")
     else:
@@ -288,7 +317,7 @@ def _create_hnsw_index(
 
 @_handle_exceptions
 def _create_ivf_index(
-    client: Connection,
+    connection: Connection,
     table_name: str,
     distance_strategy: DistanceStrategy,
     params: Optional[dict[str, Any]] = None,
@@ -350,8 +379,8 @@ def _create_ivf_index(
     ddl = ddl_assembly.format(**config)
 
     # Check if the index exists
-    if not _index_exists(client, config["idx_name"]):
-        with client.cursor() as cursor:
+    if not _index_exists(connection, config["idx_name"]):
+        with connection.cursor() as cursor:
             cursor.execute(ddl)
         logger.info("Index created successfully...")
     else:
@@ -359,7 +388,7 @@ def _create_ivf_index(
 
 
 @_handle_exceptions
-def drop_table_purge(client: Connection, table_name: str) -> None:
+def drop_table_purge(client: Any, table_name: str) -> None:
     """Drop a table and purge it from the database.
 
     Args:
@@ -369,9 +398,11 @@ def drop_table_purge(client: Connection, table_name: str) -> None:
     Raises:
         RuntimeError: If an error occurs while dropping the table.
     """
-    if _table_exists(client, table_name):
-        cursor = client.cursor()
-        with cursor:
+    connection = _get_connection(client)
+    if connection is None:
+        raise ValueError("Failed to acquire a connection.")
+    if _table_exists(connection, table_name):
+        with connection.cursor() as cursor:
             ddl = f"DROP TABLE {table_name} PURGE"
             cursor.execute(ddl)
         logger.info("Table dropped successfully...")
@@ -381,7 +412,7 @@ def drop_table_purge(client: Connection, table_name: str) -> None:
 
 
 @_handle_exceptions
-def drop_index_if_exists(client: Connection, index_name: str) -> None:
+def drop_index_if_exists(client: Any, index_name: str) -> None:
     """Drop an index if it exists.
 
     Args:
@@ -391,9 +422,12 @@ def drop_index_if_exists(client: Connection, index_name: str) -> None:
     Raises:
         RuntimeError: If an error occurs while dropping the index.
     """
-    if _index_exists(client, index_name):
+    connection = _get_connection(client)
+    if connection is None:
+        raise ValueError("Failed to acquire a connection.")
+    if _index_exists(connection, index_name):
         drop_query = f"DROP INDEX {index_name}"
-        with client.cursor() as cursor:
+        with connection.cursor() as cursor:
             cursor.execute(drop_query)
             logger.info(f"Index {index_name} has been dropped.")
     else:
@@ -426,7 +460,7 @@ class OracleVS(VectorStore):
 
     def __init__(
         self,
-        client: Connection,
+        client: Any,
         embedding_function: Union[
             Callable[[str], List[float]],
             Embeddings,
@@ -445,8 +479,11 @@ class OracleVS(VectorStore):
             ) from e
 
         self.insert_mode = "array"
+        connection = _get_connection(client)
+        if connection is None:
+            raise ValueError("Failed to acquire a connection.")
 
-        if client.thin is True:
+        if hasattr(connection, "thin") and connection.thin:
             if oracledb.__version__ == "2.1.0":
                 raise Exception(
                     "Oracle DB python thin client driver version 2.1.0 not supported"
@@ -494,8 +531,7 @@ class OracleVS(VectorStore):
             self.table_name = table_name
             self.distance_strategy = distance_strategy
             self.params = params
-
-            _create_table(client, table_name, embedding_dim)
+            _create_table(connection, table_name, embedding_dim)
         except oracledb.DatabaseError as db_err:
             logger.exception(f"Database error occurred while create table: {db_err}")
             raise RuntimeError(
@@ -613,13 +649,16 @@ class OracleVS(VectorStore):
                 )
             ]
 
-        with self.client.cursor() as cursor:
+        connection = _get_connection(self.client)
+        if connection is None:
+            raise ValueError("Failed to acquire a connection.")
+        with connection.cursor() as cursor:
             cursor.executemany(
                 f"INSERT INTO {self.table_name} (id, embedding, metadata, "
                 f"text) VALUES (:1, :2, :3, :4)",
                 docs,
             )
-            self.client.commit()
+            connection.commit()
         return processed_ids
 
     def similarity_search(
@@ -630,6 +669,7 @@ class OracleVS(VectorStore):
         **kwargs: Any,
     ) -> List[Document]:
         """Return docs most similar to query."""
+        embedding: List[float] = []
         if isinstance(self.embedding_function, Embeddings):
             embedding = self.embedding_function.embed_query(query)
         documents = self.similarity_search_by_vector(
@@ -657,6 +697,7 @@ class OracleVS(VectorStore):
         **kwargs: Any,
     ) -> List[Tuple[Document, float]]:
         """Return docs most similar to query."""
+        embedding: List[float] = []
         if isinstance(self.embedding_function, Embeddings):
             embedding = self.embedding_function.embed_query(query)
         docs_and_scores = self.similarity_search_by_vector_with_relevance_scores(
@@ -707,25 +748,27 @@ class OracleVS(VectorStore):
             embedding_arr = array.array("f", embedding)
 
         query = f"""
-        SELECT id,
-          text,
-          metadata,
-          vector_distance(embedding, :embedding,
-          {_get_distance_function(self.distance_strategy)}) as distance
-        FROM {self.table_name}
-        ORDER BY distance
-        FETCH APPROX FIRST {k} ROWS ONLY
+            SELECT id,
+              text,
+              metadata,
+              vector_distance(embedding, :embedding,
+              {_get_distance_function(self.distance_strategy)}) as distance
+            FROM {self.table_name}
+            ORDER BY distance
+            FETCH APPROX FIRST {k} ROWS ONLY
         """
+
         # Execute the query
-        with self.client.cursor() as cursor:
+        connection = _get_connection(self.client)
+        if connection is None:
+            raise ValueError("Failed to acquire a connection.")
+        with connection.cursor() as cursor:
             cursor.execute(query, embedding=embedding_arr)
             results = cursor.fetchall()
 
             # Filter results if filter is provided
             for result in results:
-                metadata = json.loads(
-                    self._get_clob_value(result[2]) if result[2] is not None else "{}"
-                )
+                metadata = dict(result[2]) if isinstance(result[2], dict) else {}
 
                 # Apply filtering based on the 'filter' dictionary
                 if filter:
@@ -761,7 +804,7 @@ class OracleVS(VectorStore):
         k: int,
         filter: Optional[Dict[str, Any]] = None,
         **kwargs: Any,
-    ) -> List[Tuple[Document, float, np.ndarray]]:
+    ) -> List[Tuple[Document, float, NDArray[np.float32]]]:
         embedding_arr: Any
         if self.insert_mode == "clob":
             embedding_arr = json.dumps(embedding)
@@ -771,27 +814,29 @@ class OracleVS(VectorStore):
         documents = []
 
         query = f"""
-        SELECT id,
-          text,
-          metadata,
-          vector_distance(embedding, :embedding, {
+            SELECT id,
+              text,
+              metadata,
+              vector_distance(embedding, :embedding, {
             _get_distance_function(self.distance_strategy)
         }) as distance,
-          embedding
-        FROM {self.table_name}
-        ORDER BY distance
-        FETCH APPROX FIRST {k} ROWS ONLY
+              embedding
+            FROM {self.table_name}
+            ORDER BY distance
+            FETCH APPROX FIRST {k} ROWS ONLY
         """
 
         # Execute the query
-        with self.client.cursor() as cursor:
+        connection = _get_connection(self.client)
+        if connection is None:
+            raise ValueError("Failed to acquire a connection.")
+        with connection.cursor() as cursor:
             cursor.execute(query, embedding=embedding_arr)
             results = cursor.fetchall()
 
             for result in results:
                 page_content_str = self._get_clob_value(result[1])
-                metadata_str = self._get_clob_value(result[2])
-                metadata = json.loads(metadata_str)
+                metadata = result[2] if isinstance(result[2], dict) else {}
 
                 # Apply filter if provided and matches; otherwise, add all
                 # documents
@@ -984,9 +1029,12 @@ class OracleVS(VectorStore):
             f"id{i}": hashed_id for i, hashed_id in enumerate(hashed_ids, start=1)
         }
 
-        with self.client.cursor() as cursor:
+        connection = _get_connection(self.client)
+        if connection is None:
+            raise ValueError("Failed to acquire a connection.")
+        with connection.cursor() as cursor:
             cursor.execute(ddl, bind_vars)
-            self.client.commit()
+            connection.commit()
 
     @classmethod
     @_handle_exceptions
@@ -997,10 +1045,10 @@ class OracleVS(VectorStore):
         metadatas: Optional[List[dict]] = None,
         **kwargs: Any,
     ) -> OracleVS:
-        """Return VectorStore initialized from texts and embeddings."""
-        client = kwargs.get("client")
+        client: Any = kwargs.get("client", None)
         if client is None:
             raise ValueError("client parameter is required...")
+
         params = kwargs.get("params", {})
 
         table_name = str(kwargs.get("table_name", "langchain"))

libs/langchain/langchain/chat_models/base.py

@@ -118,6 +118,7 @@ def init_chat_model(
             - 'ollama'              -> langchain-ollama
             - 'google_anthropic_vertex'    -> langchain-google-vertexai
             - 'deepseek'            -> langchain-deepseek
+            - 'ibm'            -> langchain-ibm
             - 'nvidia'              -> langchain-nvidia-ai-endpoints
 
             Will attempt to infer model_provider from model if not specified. The
@@ -428,6 +429,11 @@ def _init_chat_model_helper(
         from langchain_nvidia_ai_endpoints import ChatNVIDIA
 
         return ChatNVIDIA(model=model, **kwargs)
+    elif model_provider == "ibm":
+        _check_pkg("langchain_ibm")
+        from langchain_ibm import ChatWatsonx
+
+        return ChatWatsonx(model_id=model, **kwargs)
     else:
         supported = ", ".join(_SUPPORTED_PROVIDERS)
         raise ValueError(
@@ -453,11 +459,12 @@ _SUPPORTED_PROVIDERS = {
     "bedrock_converse",
     "google_anthropic_vertex",
     "deepseek",
+    "ibm",
 }
 
 
 def _attempt_infer_model_provider(model_name: str) -> Optional[str]:
-    if any(model_name.startswith(pre) for pre in ("gpt-3", "gpt-4", "o1")):
+    if any(model_name.startswith(pre) for pre in ("gpt-3", "gpt-4", "o1", "o3")):
         return "openai"
     elif model_name.startswith("claude"):
         return "anthropic"

libs/partners/deepseek/langchain_deepseek/chat_models.py

@@ -1,9 +1,10 @@
 """DeepSeek chat models."""
 
-from typing import Dict, Optional, Union
+from typing import Dict, Optional, Type, Union
 
 import openai
-from langchain_core.outputs import ChatResult
+from langchain_core.messages import AIMessageChunk
+from langchain_core.outputs import ChatGenerationChunk, ChatResult
 from langchain_core.utils import from_env, secret_from_env
 from langchain_openai.chat_models.base import BaseChatOpenAI
 from pydantic import ConfigDict, Field, SecretStr, model_validator
@@ -218,3 +219,23 @@ class ChatDeepSeek(BaseChatOpenAI):
             )
 
         return rtn
+
+    def _convert_chunk_to_generation_chunk(
+        self,
+        chunk: dict,
+        default_chunk_class: Type,
+        base_generation_info: Optional[Dict],
+    ) -> Optional[ChatGenerationChunk]:
+        generation_chunk = super()._convert_chunk_to_generation_chunk(
+            chunk,
+            default_chunk_class,
+            base_generation_info,
+        )
+        if (choices := chunk.get("choices")) and generation_chunk:
+            top = choices[0]
+            if reasoning_content := top.get("delta", {}).get("reasoning_content"):
+                if isinstance(generation_chunk.message, AIMessageChunk):
+                    generation_chunk.message.additional_kwargs["reasoning_content"] = (
+                        reasoning_content
+                    )
+        return generation_chunk

libs/partners/deepseek/pyproject.toml

@@ -7,11 +7,11 @@ authors = []
 license = {text = "MIT"}
 requires-python = "<4.0,>=3.9"
 dependencies = [
-    "langchain-core<1.0.0,>=0.3.33",
-    "langchain-openai<1.0.0,>=0.3.3",
+    "langchain-core<1.0.0,>=0.3.34",
+    "langchain-openai<1.0.0,>=0.3.5",
 ]
 name = "langchain-deepseek"
-version = "0.1.1"
+version = "0.1.2"
 description = "An integration package connecting DeepSeek and LangChain"
 readme = "README.md"
 

libs/partners/deepseek/tests/integration_tests/test_chat_models.py

@@ -1,9 +1,10 @@
 """Test ChatDeepSeek chat model."""
 
-from typing import Type
+from typing import Optional, Type
 
 import pytest
 from langchain_core.language_models import BaseChatModel
+from langchain_core.messages import AIMessageChunk, BaseMessageChunk
 from langchain_core.tools import BaseTool
 from langchain_tests.integration_tests import ChatModelIntegrationTests
 
@@ -38,3 +39,13 @@ def test_reasoning_content() -> None:
     assert response.content
     assert response.additional_kwargs["reasoning_content"]
     raise ValueError()
+
+
+@pytest.mark.xfail(reason="Takes > 30s to run.")
+def test_reasoning_content_streaming() -> None:
+    chat_model = ChatDeepSeek(model="deepseek-reasoner")
+    full: Optional[BaseMessageChunk] = None
+    for chunk in chat_model.stream("What is the square root of 256256?"):
+        full = chunk if full is None else full + chunk
+    assert isinstance(full, AIMessageChunk)
+    assert full.additional_kwargs["reasoning_content"]

libs/partners/deepseek/uv.lock

@@ -367,7 +367,7 @@ wheels = [
 
 [[package]]
 name = "langchain-core"
-version = "0.3.33"
+version = "0.3.34"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jsonpatch" },
@@ -378,14 +378,14 @@ dependencies = [
     { name = "tenacity" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/57/b3/426268e07273c395affc6dd02cdf89803888121cfc59ce60922f363aeff8/langchain_core-0.3.33.tar.gz", hash = "sha256:b5dd93a4e7f8198d2fc6048723b0bfecf7aaf128b0d268cbac19c34c1579b953", size = 331492 }
+sdist = { url = "https://files.pythonhosted.org/packages/b9/c8/a4394a5bdfc820f539bd6983b1408964723ed43ce8cfafbcc7cada69c015/langchain_core-0.3.34.tar.gz", hash = "sha256:26504cf1e8e6c310adad907b890d4e3c147581cfa7434114f6dc1134fe4bc6d3", size = 524756 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/98/78/463bc92174555cc04b3e234faa169bb8b58f36fff77892d7b8ae2b4f58e4/langchain_core-0.3.33-py3-none-any.whl", hash = "sha256:269706408a2223f863ff1f9616f31903a5712403199d828b50aadbc4c28b553a", size = 412656 },
+    { url = "https://files.pythonhosted.org/packages/9f/65/27a586c8871a0632d747059eb97855b49ac6dea12b263a79f6c1b4f18b99/langchain_core-0.3.34-py3-none-any.whl", hash = "sha256:a057ebeddd2158d3be14bde341b25640ddf958b6989bd6e47160396f5a8202ae", size = 412955 },
 ]
 
 [[package]]
 name = "langchain-deepseek"
-version = "0.1.1"
+version = "0.1.2"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },
@@ -414,8 +414,8 @@ typing = [
 
 [package.metadata]
 requires-dist = [
-    { name = "langchain-core", specifier = ">=0.3.33,<1.0.0" },
-    { name = "langchain-openai", specifier = ">=0.3.3,<1.0.0" },
+    { name = "langchain-core", specifier = ">=0.3.34,<1.0.0" },
+    { name = "langchain-openai", specifier = ">=0.3.5,<1.0.0" },
 ]
 
 [package.metadata.requires-dev]
@@ -436,7 +436,7 @@ typing = [{ name = "mypy", specifier = ">=1.10,<2.0" }]
 
 [[package]]
 name = "langchain-openai"
-version = "0.3.3"
+version = "0.3.5"
 source = { directory = "../openai" }
 dependencies = [
     { name = "langchain-core" },
@@ -446,11 +446,42 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
-    { name = "langchain-core", specifier = ">=0.3.33,<0.4.0" },
+    { name = "langchain-core", specifier = ">=0.3.34,<1.0.0" },
     { name = "openai", specifier = ">=1.58.1,<2.0.0" },
     { name = "tiktoken", specifier = ">=0.7,<1" },
 ]
 
+[package.metadata.requires-dev]
+codespell = [{ name = "codespell", specifier = ">=2.2.0,<3.0.0" }]
+dev = [{ name = "langchain-core", directory = "../../core" }]
+lint = [{ name = "ruff", specifier = ">=0.5,<1.0" }]
+test = [
+    { name = "freezegun", specifier = ">=1.2.2,<2.0.0" },
+    { name = "langchain-core", directory = "../../core" },
+    { name = "langchain-tests", directory = "../../standard-tests" },
+    { name = "numpy", marker = "python_full_version < '3.12'", specifier = ">=1,<2" },
+    { name = "numpy", marker = "python_full_version >= '3.12'", specifier = ">=1.26.0,<2.0.0" },
+    { name = "pytest", specifier = ">=7.3.0,<8.0.0" },
+    { name = "pytest-asyncio", specifier = ">=0.21.1,<1.0.0" },
+    { name = "pytest-cov", specifier = ">=4.1.0,<5.0.0" },
+    { name = "pytest-mock", specifier = ">=3.10.0,<4.0.0" },
+    { name = "pytest-socket", specifier = ">=0.6.0,<1.0.0" },
+    { name = "pytest-watcher", specifier = ">=0.3.4,<1.0.0" },
+    { name = "pytest-xdist", specifier = ">=3.6.1,<4.0.0" },
+    { name = "syrupy", specifier = ">=4.0.2,<5.0.0" },
+]
+test-integration = [
+    { name = "httpx", specifier = ">=0.27.0,<1.0.0" },
+    { name = "numpy", marker = "python_full_version < '3.12'", specifier = ">=1,<2" },
+    { name = "numpy", marker = "python_full_version >= '3.12'", specifier = ">=1.26.0,<2.0.0" },
+    { name = "pillow", specifier = ">=10.3.0,<11.0.0" },
+]
+typing = [
+    { name = "langchain-core", directory = "../../core" },
+    { name = "mypy", specifier = ">=1.10,<2.0" },
+    { name = "types-tqdm", specifier = ">=4.66.0.5,<5.0.0.0" },
+]
+
 [[package]]
 name = "langchain-tests"
 version = "0.3.10"

libs/partners/mongodb/README.md

@@ -1,3 +1,3 @@
 This package has moved!
 
-https://github.com/langchain-ai/langchain-mongodb/tree/main/libs/mongodb
+https://github.com/langchain-ai/langchain-mongodb

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

@@ -316,57 +316,6 @@ def _convert_delta_to_message_chunk(
         return default_class(content=content, id=id_)  # type: ignore
 
 
-def _convert_chunk_to_generation_chunk(
-    chunk: dict, default_chunk_class: Type, base_generation_info: Optional[Dict]
-) -> Optional[ChatGenerationChunk]:
-    if chunk.get("type") == "content.delta":  # from beta.chat.completions.stream
-        return None
-    token_usage = chunk.get("usage")
-    choices = (
-        chunk.get("choices", [])
-        # from beta.chat.completions.stream
-        or chunk.get("chunk", {}).get("choices", [])
-    )
-
-    usage_metadata: Optional[UsageMetadata] = (
-        _create_usage_metadata(token_usage) if token_usage else None
-    )
-    if len(choices) == 0:
-        # logprobs is implicitly None
-        generation_chunk = ChatGenerationChunk(
-            message=default_chunk_class(content="", usage_metadata=usage_metadata)
-        )
-        return generation_chunk
-
-    choice = choices[0]
-    if choice["delta"] is None:
-        return None
-
-    message_chunk = _convert_delta_to_message_chunk(
-        choice["delta"], default_chunk_class
-    )
-    generation_info = {**base_generation_info} if base_generation_info else {}
-
-    if finish_reason := choice.get("finish_reason"):
-        generation_info["finish_reason"] = finish_reason
-        if model_name := chunk.get("model"):
-            generation_info["model_name"] = model_name
-        if system_fingerprint := chunk.get("system_fingerprint"):
-            generation_info["system_fingerprint"] = system_fingerprint
-
-    logprobs = choice.get("logprobs")
-    if logprobs:
-        generation_info["logprobs"] = logprobs
-
-    if usage_metadata and isinstance(message_chunk, AIMessageChunk):
-        message_chunk.usage_metadata = usage_metadata
-
-    generation_chunk = ChatGenerationChunk(
-        message=message_chunk, generation_info=generation_info or None
-    )
-    return generation_chunk
-
-
 def _update_token_usage(
     overall_token_usage: Union[int, dict], new_usage: Union[int, dict]
 ) -> Union[int, dict]:
@@ -692,6 +641,59 @@ class BaseChatOpenAI(BaseChatModel):
             combined["system_fingerprint"] = system_fingerprint
         return combined
 
+    def _convert_chunk_to_generation_chunk(
+        self,
+        chunk: dict,
+        default_chunk_class: Type,
+        base_generation_info: Optional[Dict],
+    ) -> Optional[ChatGenerationChunk]:
+        if chunk.get("type") == "content.delta":  # from beta.chat.completions.stream
+            return None
+        token_usage = chunk.get("usage")
+        choices = (
+            chunk.get("choices", [])
+            # from beta.chat.completions.stream
+            or chunk.get("chunk", {}).get("choices", [])
+        )
+
+        usage_metadata: Optional[UsageMetadata] = (
+            _create_usage_metadata(token_usage) if token_usage else None
+        )
+        if len(choices) == 0:
+            # logprobs is implicitly None
+            generation_chunk = ChatGenerationChunk(
+                message=default_chunk_class(content="", usage_metadata=usage_metadata)
+            )
+            return generation_chunk
+
+        choice = choices[0]
+        if choice["delta"] is None:
+            return None
+
+        message_chunk = _convert_delta_to_message_chunk(
+            choice["delta"], default_chunk_class
+        )
+        generation_info = {**base_generation_info} if base_generation_info else {}
+
+        if finish_reason := choice.get("finish_reason"):
+            generation_info["finish_reason"] = finish_reason
+            if model_name := chunk.get("model"):
+                generation_info["model_name"] = model_name
+            if system_fingerprint := chunk.get("system_fingerprint"):
+                generation_info["system_fingerprint"] = system_fingerprint
+
+        logprobs = choice.get("logprobs")
+        if logprobs:
+            generation_info["logprobs"] = logprobs
+
+        if usage_metadata and isinstance(message_chunk, AIMessageChunk):
+            message_chunk.usage_metadata = usage_metadata
+
+        generation_chunk = ChatGenerationChunk(
+            message=message_chunk, generation_info=generation_info or None
+        )
+        return generation_chunk
+
     def _stream(
         self,
         messages: List[BaseMessage],
@@ -727,7 +729,7 @@ class BaseChatOpenAI(BaseChatModel):
                 for chunk in response:
                     if not isinstance(chunk, dict):
                         chunk = chunk.model_dump()
-                    generation_chunk = _convert_chunk_to_generation_chunk(
+                    generation_chunk = self._convert_chunk_to_generation_chunk(
                         chunk,
                         default_chunk_class,
                         base_generation_info if is_first_chunk else {},
@@ -895,7 +897,7 @@ class BaseChatOpenAI(BaseChatModel):
                 async for chunk in response:
                     if not isinstance(chunk, dict):
                         chunk = chunk.model_dump()
-                    generation_chunk = _convert_chunk_to_generation_chunk(
+                    generation_chunk = self._convert_chunk_to_generation_chunk(
                         chunk,
                         default_chunk_class,
                         base_generation_info if is_first_chunk else {},

libs/partners/openai/pyproject.toml

@@ -12,7 +12,7 @@ dependencies = [
     "tiktoken<1,>=0.7",
 ]
 name = "langchain-openai"
-version = "0.3.4"
+version = "0.3.5"
 description = "An integration package connecting OpenAI and LangChain"
 readme = "README.md"
 

libs/partners/openai/uv.lock

@@ -520,7 +520,7 @@ typing = [
 
 [[package]]
 name = "langchain-openai"
-version = "0.3.4"
+version = "0.3.5"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },

libs/partners/pinecone/pyproject.toml

@@ -5,16 +5,16 @@ build-backend = "pdm.backend"
 [project]
 authors = []
 license = {text = "MIT"}
-requires-python = "<3.13,>=3.9"
+requires-python = "<3.14,>=3.9"
 dependencies = [
-    "langchain-core<1.0.0,>=0.3.29",
+    "langchain-core<1.0.0,>=0.3.34",
     "pinecone<6.0.0,>=5.4.0",
     "aiohttp<3.11,>=3.10",
-    "numpy<2.0.0,>=1.26.0",
+    "numpy<2.0.0,>=1.26.4",
     "langchain-tests<1.0.0,>=0.3.7",
 ]
 name = "langchain-pinecone"
-version = "0.2.2"
+version = "0.2.3"
 description = "An integration package connecting Pinecone and LangChain"
 readme = "README.md"
 

libs/partners/pinecone/uv.lock

@@ -1,5 +1,5 @@
 version = 1
-requires-python = ">=3.9, <3.13"
+requires-python = ">=3.9, <3.14"
 resolution-markers = [
     "python_full_version >= '3.12.4'",
     "python_full_version >= '3.12' and python_full_version < '3.12.4'",
@@ -75,6 +75,21 @@ wheels = [
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+    { url = "https://files.pythonhosted.org/packages/1f/63/654c185dfe3cf5d4a0d35b6ee49ee6ca91922c694eaa90732e1ba4b40ef1/aiohttp-3.10.11-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:703938e22434d7d14ec22f9f310559331f455018389222eed132808cd8f44127", size = 577381 },
+    { url = "https://files.pythonhosted.org/packages/4e/c4/ee9c350acb202ba2eb0c44b0f84376b05477e870444192a9f70e06844c28/aiohttp-3.10.11-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:9bc50b63648840854e00084c2b43035a62e033cb9b06d8c22b409d56eb098413", size = 393289 },
+    { url = "https://files.pythonhosted.org/packages/3d/7c/30d161a7e3b208cef1b922eacf2bbb8578b7e5a62266a6a2245a1dd044dc/aiohttp-3.10.11-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:5f0463bf8b0754bc744e1feb61590706823795041e63edf30118a6f0bf577461", size = 388859 },
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@@ -121,7 +136,7 @@ dependencies = [
     { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
     { name = "idna" },
     { name = "sniffio" },
-    { name = "typing-extensions" },
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