Merge branch 'master' into bagatur/locals_in_config

docs/extras/use_cases/graph/neptune_cypher_qa.ipynb

@@ -18,8 +18,8 @@
     "\n",
     "\n",
     "host = \"<neptune-host>\"\n",
-    "port = 80\n",
+    "port = 8182\n",
-    "use_https = False\n",
+    "use_https = True\n",
     "\n",
     "graph = NeptuneGraph(host=host, port=port, use_https=use_https)"
    ]

libs/langchain/langchain/agents/agent_toolkits/openapi/spec.py

@@ -55,6 +55,16 @@ def dereference_refs(spec_obj: dict, full_spec: dict) -> Union[dict, list]:
 
 @dataclass(frozen=True)
 class ReducedOpenAPISpec:
+    """A reduced OpenAPI spec.
+
+    This is a quick and dirty representation for OpenAPI specs.
+
+    Attributes:
+        servers: The servers in the spec.
+        description: The description of the spec.
+        endpoints: The endpoints in the spec.
+    """
+
     servers: List[dict]
     description: str
     endpoints: List[Tuple[str, str, dict]]

libs/langchain/langchain/agents/xml/base.py

@@ -10,6 +10,8 @@ from langchain.tools.base import BaseTool
 
 
 class XMLAgentOutputParser(AgentOutputParser):
+    """Output parser for XMLAgent."""
+
     def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
         if "</tool>" in text:
             tool, tool_input = text.split("</tool>")

libs/langchain/langchain/chains/natbot/crawler.py

@@ -49,6 +49,8 @@ class ElementInViewPort(TypedDict):
 
 
 class Crawler:
+    """A crawler for web pages."""
+
     def __init__(self) -> None:
         try:
             from playwright.sync_api import sync_playwright

libs/langchain/langchain/chains/query_constructor/parser.py

@@ -9,6 +9,7 @@ try:
 except ImportError:
 
     def v_args(*args: Any, **kwargs: Any) -> Any:  # type: ignore
+        """Dummy decorator for when lark is not installed."""
         return lambda _: None
 
     Transformer = object  # type: ignore

libs/langchain/langchain/chat_models/base.py

@@ -51,6 +51,8 @@ def _get_verbosity() -> bool:
 
 
 class BaseChatModel(BaseLanguageModel[BaseMessageChunk], ABC):
+    """Base class for chat models."""
+
     cache: Optional[bool] = None
     """Whether to cache the response."""
     verbose: bool = Field(default_factory=_get_verbosity)

libs/langchain/langchain/document_loaders/__init__.py

@@ -16,6 +16,16 @@
 """
 
 from langchain.document_loaders.acreom import AcreomLoader
+from langchain.document_loaders.airbyte import (
+    AirbyteCDKLoader,
+    AirbyteGongLoader,
+    AirbyteHubspotLoader,
+    AirbyteSalesforceLoader,
+    AirbyteShopifyLoader,
+    AirbyteStripeLoader,
+    AirbyteTypeformLoader,
+    AirbyteZendeskSupportLoader,
+)
 from langchain.document_loaders.airbyte_json import AirbyteJSONLoader
 from langchain.document_loaders.airtable import AirtableLoader
 from langchain.document_loaders.apify_dataset import ApifyDatasetLoader
@@ -188,7 +198,15 @@ TelegramChatLoader = TelegramChatFileLoader
 __all__ = [
     "AZLyricsLoader",
     "AcreomLoader",
+    "AirbyteCDKLoader",
+    "AirbyteGongLoader",
     "AirbyteJSONLoader",
+    "AirbyteHubspotLoader",
+    "AirbyteSalesforceLoader",
+    "AirbyteShopifyLoader",
+    "AirbyteStripeLoader",
+    "AirbyteTypeformLoader",
+    "AirbyteZendeskSupportLoader",
     "AirtableLoader",
     "AmazonTextractPDFLoader",
     "ApifyDatasetLoader",

libs/langchain/langchain/document_loaders/airbyte.py

@@ -19,6 +19,17 @@ class AirbyteCDKLoader(BaseLoader):
         record_handler: Optional[RecordHandler] = None,
         state: Optional[Any] = None,
     ) -> None:
+        """Initializes the loader.
+
+        Args:
+            config: The config to pass to the source connector.
+            source_class: The source connector class.
+            stream_name: The name of the stream to load.
+            record_handler: A function that takes in a record and an optional id and
+                returns a Document. If None, the record will be used as the document.
+                Defaults to None.
+            state: The state to pass to the source connector. Defaults to None.
+        """
         from airbyte_cdk.models.airbyte_protocol import AirbyteRecordMessage
         from airbyte_cdk.sources.embedded.base_integration import (
             BaseEmbeddedIntegration,
@@ -26,6 +37,8 @@ class AirbyteCDKLoader(BaseLoader):
         from airbyte_cdk.sources.embedded.runner import CDKRunner
 
         class CDKIntegration(BaseEmbeddedIntegration):
+            """A wrapper around the CDK integration."""
+
             def _handle_record(
                 self, record: AirbyteRecordMessage, id: Optional[str]
             ) -> Document:
@@ -50,6 +63,8 @@ class AirbyteCDKLoader(BaseLoader):
 
 
 class AirbyteHubspotLoader(AirbyteCDKLoader):
+    """Loads records from Hubspot using an Airbyte source connector."""
+
     def __init__(
         self,
         config: Mapping[str, Any],
@@ -57,6 +72,16 @@ class AirbyteHubspotLoader(AirbyteCDKLoader):
         record_handler: Optional[RecordHandler] = None,
         state: Optional[Any] = None,
     ) -> None:
+        """Initializes the loader.
+
+        Args:
+            config: The config to pass to the source connector.
+            stream_name: The name of the stream to load.
+            record_handler: A function that takes in a record and an optional id and
+                returns a Document. If None, the record will be used as the document.
+                Defaults to None.
+            state: The state to pass to the source connector. Defaults to None.
+        """
         source_class = guard_import(
             "source_hubspot", pip_name="airbyte-source-hubspot"
         ).SourceHubspot
@@ -70,6 +95,8 @@ class AirbyteHubspotLoader(AirbyteCDKLoader):
 
 
 class AirbyteStripeLoader(AirbyteCDKLoader):
+    """Loads records from Stripe using an Airbyte source connector."""
+
     def __init__(
         self,
         config: Mapping[str, Any],
@@ -77,6 +104,16 @@ class AirbyteStripeLoader(AirbyteCDKLoader):
         record_handler: Optional[RecordHandler] = None,
         state: Optional[Any] = None,
     ) -> None:
+        """Initializes the loader.
+
+        Args:
+            config: The config to pass to the source connector.
+            stream_name: The name of the stream to load.
+            record_handler: A function that takes in a record and an optional id and
+                returns a Document. If None, the record will be used as the document.
+                Defaults to None.
+            state: The state to pass to the source connector. Defaults to None.
+        """
         source_class = guard_import(
             "source_stripe", pip_name="airbyte-source-stripe"
         ).SourceStripe
@@ -90,6 +127,8 @@ class AirbyteStripeLoader(AirbyteCDKLoader):
 
 
 class AirbyteTypeformLoader(AirbyteCDKLoader):
+    """Loads records from Typeform using an Airbyte source connector."""
+
     def __init__(
         self,
         config: Mapping[str, Any],
@@ -97,6 +136,16 @@ class AirbyteTypeformLoader(AirbyteCDKLoader):
         record_handler: Optional[RecordHandler] = None,
         state: Optional[Any] = None,
     ) -> None:
+        """Initializes the loader.
+
+        Args:
+            config: The config to pass to the source connector.
+            stream_name: The name of the stream to load.
+            record_handler: A function that takes in a record and an optional id and
+                returns a Document. If None, the record will be used as the document.
+                Defaults to None.
+            state: The state to pass to the source connector. Defaults to None.
+        """
         source_class = guard_import(
             "source_typeform", pip_name="airbyte-source-typeform"
         ).SourceTypeform
@@ -110,6 +159,8 @@ class AirbyteTypeformLoader(AirbyteCDKLoader):
 
 
 class AirbyteZendeskSupportLoader(AirbyteCDKLoader):
+    """Loads records from Zendesk Support using an Airbyte source connector."""
+
     def __init__(
         self,
         config: Mapping[str, Any],
@@ -117,6 +168,16 @@ class AirbyteZendeskSupportLoader(AirbyteCDKLoader):
         record_handler: Optional[RecordHandler] = None,
         state: Optional[Any] = None,
     ) -> None:
+        """Initializes the loader.
+
+        Args:
+            config: The config to pass to the source connector.
+            stream_name: The name of the stream to load.
+            record_handler: A function that takes in a record and an optional id and
+                returns a Document. If None, the record will be used as the document.
+                Defaults to None.
+            state: The state to pass to the source connector. Defaults to None.
+        """
         source_class = guard_import(
             "source_zendesk_support", pip_name="airbyte-source-zendesk-support"
         ).SourceZendeskSupport
@@ -130,6 +191,8 @@ class AirbyteZendeskSupportLoader(AirbyteCDKLoader):
 
 
 class AirbyteShopifyLoader(AirbyteCDKLoader):
+    """Loads records from Shopify using an Airbyte source connector."""
+
     def __init__(
         self,
         config: Mapping[str, Any],
@@ -137,6 +200,16 @@ class AirbyteShopifyLoader(AirbyteCDKLoader):
         record_handler: Optional[RecordHandler] = None,
         state: Optional[Any] = None,
     ) -> None:
+        """Initializes the loader.
+
+        Args:
+            config: The config to pass to the source connector.
+            stream_name: The name of the stream to load.
+            record_handler: A function that takes in a record and an optional id and
+                returns a Document. If None, the record will be used as the document.
+                Defaults to None.
+            state: The state to pass to the source connector. Defaults to None.
+        """
         source_class = guard_import(
             "source_shopify", pip_name="airbyte-source-shopify"
         ).SourceShopify
@@ -150,6 +223,8 @@ class AirbyteShopifyLoader(AirbyteCDKLoader):
 
 
 class AirbyteSalesforceLoader(AirbyteCDKLoader):
+    """Loads records from Salesforce using an Airbyte source connector."""
+
     def __init__(
         self,
         config: Mapping[str, Any],
@@ -157,6 +232,16 @@ class AirbyteSalesforceLoader(AirbyteCDKLoader):
         record_handler: Optional[RecordHandler] = None,
         state: Optional[Any] = None,
     ) -> None:
+        """Initializes the loader.
+
+        Args:
+            config: The config to pass to the source connector.
+            stream_name: The name of the stream to load.
+            record_handler: A function that takes in a record and an optional id and
+                returns a Document. If None, the record will be used as the document.
+                Defaults to None.
+            state: The state to pass to the source connector. Defaults to None.
+        """
         source_class = guard_import(
             "source_salesforce", pip_name="airbyte-source-salesforce"
         ).SourceSalesforce
@@ -170,6 +255,8 @@ class AirbyteSalesforceLoader(AirbyteCDKLoader):
 
 
 class AirbyteGongLoader(AirbyteCDKLoader):
+    """Loads records from Gong using an Airbyte source connector."""
+
     def __init__(
         self,
         config: Mapping[str, Any],
@@ -177,6 +264,16 @@ class AirbyteGongLoader(AirbyteCDKLoader):
         record_handler: Optional[RecordHandler] = None,
         state: Optional[Any] = None,
     ) -> None:
+        """Initializes the loader.
+
+        Args:
+            config: The config to pass to the source connector.
+            stream_name: The name of the stream to load.
+            record_handler: A function that takes in a record and an optional id and
+                returns a Document. If None, the record will be used as the document.
+                Defaults to None.
+            state: The state to pass to the source connector. Defaults to None.
+        """
         source_class = guard_import(
             "source_gong", pip_name="airbyte-source-gong"
         ).SourceGong

libs/langchain/langchain/document_loaders/directory.py

@@ -1,6 +1,7 @@
 """Load documents from a directory."""
 import concurrent
 import logging
+import random
 from pathlib import Path
 from typing import Any, List, Optional, Type, Union
 
@@ -39,6 +40,10 @@ class DirectoryLoader(BaseLoader):
         show_progress: bool = False,
         use_multithreading: bool = False,
         max_concurrency: int = 4,
+        *,
+        sample_size: int = 0,
+        randomize_sample: bool = False,
+        sample_seed: Union[int, None] = None,
     ):
         """Initialize with a path to directory and how to glob over it.
 
@@ -55,6 +60,10 @@ class DirectoryLoader(BaseLoader):
             show_progress: Whether to show a progress bar. Defaults to False.
             use_multithreading: Whether to use multithreading. Defaults to False.
             max_concurrency: The maximum number of threads to use. Defaults to 4.
+            sample_size: The maximum number of files you would like to load from the
+                directory.
+            randomize_sample: Suffle the files to get a random sample.
+            sample_seed: set the seed of the random shuffle for reporoducibility.
         """
         if loader_kwargs is None:
             loader_kwargs = {}
@@ -68,6 +77,9 @@ class DirectoryLoader(BaseLoader):
         self.show_progress = show_progress
         self.use_multithreading = use_multithreading
         self.max_concurrency = max_concurrency
+        self.sample_size = sample_size
+        self.randomize_sample = randomize_sample
+        self.sample_seed = sample_seed
 
     def load_file(
         self, item: Path, path: Path, docs: List[Document], pbar: Optional[Any]
@@ -107,6 +119,14 @@ class DirectoryLoader(BaseLoader):
         docs: List[Document] = []
         items = list(p.rglob(self.glob) if self.recursive else p.glob(self.glob))
 
+        if self.sample_size > 0:
+            if self.randomize_sample:
+                randomizer = (
+                    random.Random(self.sample_seed) if self.sample_seed else random
+                )
+                randomizer.shuffle(items)  # type: ignore
+            items = items[: min(len(items), self.sample_size)]
+
         pbar = None
         if self.show_progress:
             try:

libs/langchain/langchain/document_loaders/googledrive.py

@@ -169,6 +169,8 @@ class GoogleDriveLoader(BaseLoader, BaseModel):
                 .execute()
             )
             values = result.get("values", [])
+            if not values:
+                continue  # empty sheet
 
             header = values[0]
             for i, row in enumerate(values[1:], start=1):

libs/langchain/langchain/document_loaders/parsers/audio.py

@@ -79,8 +79,10 @@ class OpenAIWhisperParser(BaseBlobParser):
 
 
 class OpenAIWhisperParserLocal(BaseBlobParser):
-    """Transcribe and parse audio files.
+    """Transcribe and parse audio files with OpenAI Whisper model.
-    Audio transcription with OpenAI Whisper model locally from transformers
+
+    Audio transcription with OpenAI Whisper model locally from transformers.
+
     Parameters:
     device - device to use
         NOTE: By default uses the gpu if available,
@@ -105,6 +107,15 @@ class OpenAIWhisperParserLocal(BaseBlobParser):
         lang_model: Optional[str] = None,
         forced_decoder_ids: Optional[Tuple[Dict]] = None,
     ):
+        """Initialize the parser.
+
+        Args:
+            device: device to use.
+            lang_model: whisper model to use, for example "openai/whisper-medium".
+              Defaults to None.
+            forced_decoder_ids: id states for decoder in a multilanguage model.
+              Defaults to None.
+        """
         try:
             from transformers import pipeline
         except ImportError:

libs/langchain/langchain/document_transformers/nuclia_text_transform.py

@@ -11,7 +11,7 @@ class NucliaTextTransformer(BaseDocumentTransformer):
     """
     The Nuclia Understanding API splits into paragraphs and sentences,
     identifies entities, provides a summary of the text and generates
-    embeddings for all the sentences.
+    embeddings for all sentences.
     """
 
     def __init__(self, nua: NucliaUnderstandingAPI):

libs/langchain/langchain/embeddings/awa.py

@@ -6,6 +6,14 @@ from langchain.embeddings.base import Embeddings
 
 
 class AwaEmbeddings(BaseModel, Embeddings):
+    """Embedding documents and queries with Awa DB.
+
+    Attributes:
+        client: The AwaEmbedding client.
+        model: The name of the model used for embedding.
+         Default is "all-mpnet-base-v2".
+    """
+
     client: Any  #: :meta private:
     model: str = "all-mpnet-base-v2"
 

libs/langchain/langchain/embeddings/embaas.py

@@ -13,7 +13,7 @@ EMBAAS_API_URL = "https://api.embaas.io/v1/embeddings/"
 
 
 class EmbaasEmbeddingsPayload(TypedDict):
-    """Payload for the embaas embeddings API."""
+    """Payload for the Embaas embeddings API."""
 
     model: str
     texts: List[str]

libs/langchain/langchain/evaluation/loading.py

@@ -24,7 +24,7 @@ from langchain.schema.language_model import BaseLanguageModel
 
 
 def load_dataset(uri: str) -> List[Dict]:
-    """Load a dataset from the `LangChainDatasets HuggingFace org <https://huggingface.co/LangChainDatasets>`_.
+    """Load a dataset from the `LangChainDatasets on HuggingFace <https://huggingface.co/LangChainDatasets>`_.
 
     Args:
         uri: The uri of the dataset to load.

libs/langchain/langchain/llms/anthropic.py

@@ -2,7 +2,7 @@ import re
 import warnings
 from typing import Any, AsyncIterator, Callable, Dict, Iterator, List, Mapping, Optional
 
-from pydantic import root_validator
+from pydantic import Field, root_validator
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
@@ -11,7 +11,12 @@ from langchain.callbacks.manager import (
 from langchain.llms.base import LLM
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.output import GenerationChunk
-from langchain.utils import check_package_version, get_from_dict_or_env
+from langchain.utils import (
+    check_package_version,
+    get_from_dict_or_env,
+    get_pydantic_field_names,
+)
+from langchain.utils.utils import build_extra_kwargs
 
 
 class _AnthropicCommon(BaseLanguageModel):
@@ -45,6 +50,16 @@ class _AnthropicCommon(BaseLanguageModel):
     HUMAN_PROMPT: Optional[str] = None
     AI_PROMPT: Optional[str] = None
     count_tokens: Optional[Callable[[str], int]] = None
+    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
+
+    @root_validator(pre=True)
+    def build_extra(cls, values: Dict) -> Dict:
+        extra = values.get("model_kwargs", {})
+        all_required_field_names = get_pydantic_field_names(cls)
+        values["model_kwargs"] = build_extra_kwargs(
+            extra, values, all_required_field_names
+        )
+        return values
 
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
@@ -77,6 +92,7 @@ class _AnthropicCommon(BaseLanguageModel):
             values["HUMAN_PROMPT"] = anthropic.HUMAN_PROMPT
             values["AI_PROMPT"] = anthropic.AI_PROMPT
             values["count_tokens"] = values["client"].count_tokens
+
         except ImportError:
             raise ImportError(
                 "Could not import anthropic python package. "
@@ -97,7 +113,7 @@ class _AnthropicCommon(BaseLanguageModel):
             d["top_k"] = self.top_k
         if self.top_p is not None:
             d["top_p"] = self.top_p
-        return d
+        return {**d, **self.model_kwargs}
 
     @property
     def _identifying_params(self) -> Mapping[str, Any]:

libs/langchain/langchain/llms/aviary.py

@@ -15,6 +15,13 @@ TIMEOUT = 60
 
 @dataclasses.dataclass
 class AviaryBackend:
+    """Aviary backend.
+
+    Attributes:
+        backend_url: The URL for the Aviary backend.
+        bearer: The bearer token for the Aviary backend.
+    """
+
     backend_url: str
     bearer: str
 
@@ -89,6 +96,14 @@ class Aviary(LLM):
 
     AVIARY_URL and AVIARY_TOKEN environment variables must be set.
 
+    Attributes:
+        model: The name of the model to use. Defaults to "amazon/LightGPT".
+        aviary_url: The URL for the Aviary backend. Defaults to None.
+        aviary_token: The bearer token for the Aviary backend. Defaults to None.
+        use_prompt_format: If True, the prompt template for the model will be ignored.
+            Defaults to True.
+        version: API version to use for Aviary. Defaults to None.
+
     Example:
         .. code-block:: python
 

libs/langchain/langchain/llms/fake.py

@@ -56,6 +56,8 @@ class FakeListLLM(LLM):
 
 
 class FakeStreamingListLLM(FakeListLLM):
+    """Fake streaming list LLM for testing purposes."""
+
     def stream(
         self,
         input: LanguageModelInput,

libs/langchain/langchain/llms/vllm.py

@@ -8,6 +8,8 @@ from langchain.schema.output import Generation, LLMResult
 
 
 class VLLM(BaseLLM):
+    """VLLM language model."""
+
     model: str = ""
     """The name or path of a HuggingFace Transformers model."""
 
@@ -54,6 +56,9 @@ class VLLM(BaseLLM):
     max_new_tokens: int = 512
     """Maximum number of tokens to generate per output sequence."""
 
+    logprobs: Optional[int] = None
+    """Number of log probabilities to return per output token."""
+
     client: Any  #: :meta private:
 
     @root_validator()
@@ -91,6 +96,7 @@ class VLLM(BaseLLM):
             "stop": self.stop,
             "ignore_eos": self.ignore_eos,
             "use_beam_search": self.use_beam_search,
+            "logprobs": self.logprobs,
         }
 
     def _generate(

libs/langchain/langchain/schema/messages.py

@@ -88,6 +88,8 @@ class BaseMessage(Serializable):
 
 
 class BaseMessageChunk(BaseMessage):
+    """A Message chunk, which can be concatenated with other Message chunks."""
+
     def _merge_kwargs_dict(
         self, left: Dict[str, Any], right: Dict[str, Any]
     ) -> Dict[str, Any]:
@@ -145,6 +147,8 @@ class HumanMessage(BaseMessage):
 
 
 class HumanMessageChunk(HumanMessage, BaseMessageChunk):
+    """A Human Message chunk."""
+
     pass
 
 
@@ -163,6 +167,8 @@ class AIMessage(BaseMessage):
 
 
 class AIMessageChunk(AIMessage, BaseMessageChunk):
+    """A Message chunk from an AI."""
+
     pass
 
 
@@ -178,6 +184,8 @@ class SystemMessage(BaseMessage):
 
 
 class SystemMessageChunk(SystemMessage, BaseMessageChunk):
+    """A System Message chunk."""
+
     pass
 
 
@@ -194,6 +202,8 @@ class FunctionMessage(BaseMessage):
 
 
 class FunctionMessageChunk(FunctionMessage, BaseMessageChunk):
+    """A Function Message chunk."""
+
     pass
 
 
@@ -210,6 +220,8 @@ class ChatMessage(BaseMessage):
 
 
 class ChatMessageChunk(ChatMessage, BaseMessageChunk):
+    """A Chat Message chunk."""
+
     pass
 
 

libs/langchain/langchain/schema/output.py

@@ -29,6 +29,8 @@ class Generation(Serializable):
 
 
 class GenerationChunk(Generation):
+    """A Generation chunk, which can be concatenated with other Generation chunks."""
+
     def __add__(self, other: GenerationChunk) -> GenerationChunk:
         if isinstance(other, GenerationChunk):
             generation_info = (
@@ -62,6 +64,13 @@ class ChatGeneration(Generation):
 
 
 class ChatGenerationChunk(ChatGeneration):
+    """A ChatGeneration chunk, which can be concatenated with other
+      ChatGeneration chunks.
+
+    Attributes:
+        message: The message chunk output by the chat model.
+    """
+
     message: BaseMessageChunk
 
     def __add__(self, other: ChatGenerationChunk) -> ChatGenerationChunk:

libs/langchain/langchain/schema/output_parser.py

@@ -56,6 +56,8 @@ class BaseLLMOutputParser(Serializable, Generic[T], ABC):
 class BaseGenerationOutputParser(
     BaseLLMOutputParser, Runnable[Union[str, BaseMessage], T]
 ):
+    """Base class to parse the output of an LLM call."""
+
     def invoke(
         self, input: Union[str, BaseMessage], config: Optional[RunnableConfig] = None
     ) -> T:

libs/langchain/langchain/schema/runnable.py

@@ -49,6 +49,8 @@ async def _gather_with_concurrency(n: Union[int, None], *coros: Coroutine) -> li
 
 
 class RunnableConfig(TypedDict, total=False):
+    """Configuration for a Runnable."""
+
     tags: List[str]
     """
     Tags for this call and any sub-calls (eg. a Chain calling an LLM).
@@ -104,6 +106,9 @@ Other = TypeVar("Other")
 
 
 class Runnable(Generic[Input, Output], ABC):
+    """A Runnable is a unit of work that can be invoked, batched, streamed, or
+    transformed."""
+
     def __or__(
         self,
         other: Union[
@@ -1300,6 +1305,13 @@ class RunnableBinding(Serializable, Runnable[Input, Output]):
 
 
 class RouterInput(TypedDict):
+    """A Router input.
+
+    Attributes:
+        key: The key to route on.
+        input: The input to pass to the selected runnable.
+    """
+
     key: str
     input: Any
 

libs/langchain/langchain/tools/multion/create_session.py

@@ -31,6 +31,14 @@ class CreateSessionSchema(BaseModel):
 
 
 class MultionCreateSession(BaseTool):
+    """Tool that creates a new Multion Browser Window with provided fields.
+
+    Attributes:
+        name: The name of the tool. Default: "create_multion_session"
+        description: The description of the tool.
+        args_schema: The schema for the tool's arguments.
+    """
+
     name: str = "create_multion_session"
     description: str = """Use this tool to create a new Multion Browser Window \
         with provided fields.Always the first step to run \

libs/langchain/langchain/tools/multion/update_session.py

@@ -34,6 +34,14 @@ class UpdateSessionSchema(BaseModel):
 
 
 class MultionUpdateSession(BaseTool):
+    """Tool that updates an existing Multion Browser Window with provided fields.
+
+    Attributes:
+        name: The name of the tool. Default: "update_multion_session"
+        description: The description of the tool.
+        args_schema: The schema for the tool's arguments. Default: UpdateSessionSchema
+    """
+
     name: str = "update_multion_session"
     description: str = """Use this tool to update \
         a existing corresponding \

libs/langchain/langchain/tools/nuclia/tool.py

@@ -28,6 +28,15 @@ logger = logging.getLogger(__name__)
 
 
 class NUASchema(BaseModel):
+    """Input for Nuclia Understanding API.
+
+    Attributes:
+        action: Action to perform. Either `push` or `pull`.
+        id: ID of the file to push or pull.
+        path: Path to the file to push (needed only for `push` action).
+        text: Text content to process (needed only for `push` action).
+    """
+
     action: str = Field(
         ...,
         description="Action to perform. Either `push` or `pull`.",

libs/langchain/langchain/utilities/portkey.py

@@ -4,6 +4,13 @@ from typing import Dict, Optional
 
 
 class Portkey:
+    """Portkey configuration.
+
+    Attributes:
+        base: The base URL for the Portkey API.
+          Default: "https://api.portkey.ai/v1/proxy"
+    """
+
     base = "https://api.portkey.ai/v1/proxy"
 
     @staticmethod

libs/langchain/langchain/utilities/spark_sql.py

@@ -7,6 +7,8 @@ if TYPE_CHECKING:
 
 
 class SparkSQL:
+    """SparkSQL is a utility class for interacting with Spark SQL."""
+
     def __init__(
         self,
         spark_session: Optional[SparkSession] = None,
@@ -16,10 +18,26 @@ class SparkSQL:
         include_tables: Optional[List[str]] = None,
         sample_rows_in_table_info: int = 3,
     ):
+        """Initialize a SparkSQL object.
+
+        Args:
+            spark_session: A SparkSession object.
+              If not provided, one will be created.
+            catalog: The catalog to use.
+              If not provided, the default catalog will be used.
+            schema: The schema to use.
+              If not provided, the default schema will be used.
+            ignore_tables: A list of tables to ignore.
+              If not provided, all tables will be used.
+            include_tables: A list of tables to include.
+              If not provided, all tables will be used.
+            sample_rows_in_table_info: The number of rows to include in the table info.
+              Defaults to 3.
+        """
         try:
             from pyspark.sql import SparkSession
         except ImportError:
-            raise ValueError(
+            raise ImportError(
                 "pyspark is not installed. Please install it with `pip install pyspark`"
             )
 

libs/langchain/langchain/utils/math.py

@@ -20,6 +20,8 @@ def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
 
     X_norm = np.linalg.norm(X, axis=1)
     Y_norm = np.linalg.norm(Y, axis=1)
+    # Ignore divide by zero errors run time warnings as those are handled below.
+    with np.errstate(divide="ignore", invalid="ignore"):
         similarity = np.dot(X, Y.T) / np.outer(X_norm, Y_norm)
     similarity[np.isnan(similarity) | np.isinf(similarity)] = 0.0
     return similarity

libs/langchain/langchain/utils/utils.py

@@ -141,7 +141,13 @@ def build_extra_kwargs(
     values: Dict[str, Any],
     all_required_field_names: Set[str],
 ) -> Dict[str, Any]:
-    """"""
+    """Build extra kwargs from values and extra_kwargs.
+
+    Args:
+        extra_kwargs: Extra kwargs passed in by user.
+        values: Values passed in by user.
+        all_required_field_names: All required field names for the pydantic class.
+    """
     for field_name in list(values):
         if field_name in extra_kwargs:
             raise ValueError(f"Found {field_name} supplied twice.")

libs/langchain/langchain/vectorstores/alibabacloud_opensearch.py

@@ -12,7 +12,8 @@ logger = logging.getLogger()
 
 
 class AlibabaCloudOpenSearchSettings:
-    """Opensearch Client Configuration
+    """Alibaba Cloud Opensearch Client Configuration.
+
     Attribute:
         endpoint (str) : The endpoint of opensearch instance, You can find it
           from the console of Alibaba Cloud OpenSearch.

libs/langchain/langchain/vectorstores/hologres.py

@@ -16,7 +16,17 @@ _LANGCHAIN_DEFAULT_TABLE_NAME = "langchain_pg_embedding"
 
 
 class HologresWrapper:
+    """Wrapper around Hologres service."""
+
     def __init__(self, connection_string: str, ndims: int, table_name: str) -> None:
+        """Initialize the wrapper.
+
+        Args:
+            connection_string: Hologres connection string.
+            ndims: Number of dimensions of the embedding output.
+            table_name: Name of the table to store embeddings and data.
+        """
+
         import psycopg2
 
         self.table_name = table_name

libs/langchain/langchain/vectorstores/pgembedding.py

@@ -87,6 +87,8 @@ class EmbeddingStore(BaseModel):
 
 
 class QueryResult:
+    """QueryResult is a result from a query."""
+
     EmbeddingStore: EmbeddingStore
     distance: float
 

libs/langchain/langchain/vectorstores/scann.py

@@ -18,6 +18,7 @@ from langchain.vectorstores.utils import DistanceStrategy
 
 
 def normalize(x: np.ndarray) -> np.ndarray:
+    """Normalize vectors to unit length."""
     x /= np.clip(np.linalg.norm(x, axis=-1, keepdims=True), 1e-12, None)
     return x
 

libs/langchain/poetry.lock

@@ -13608,7 +13608,7 @@ clarifai = ["clarifai"]
 cohere = ["cohere"]
 docarray = ["docarray"]
 embeddings = ["sentence-transformers"]
-extended-testing = ["amazon-textract-caller", "atlassian-python-api", "beautifulsoup4", "bibtexparser", "cassio", "chardet", "esprima", "feedparser", "geopandas", "gitpython", "gql", "html2text", "jinja2", "jq", "lxml", "mwparserfromhell", "mwxml", "newspaper3k", "openai", "openai", "pandas", "pdfminer-six", "pgvector", "psychicapi", "py-trello", "pymupdf", "pypdf", "pypdfium2", "pyspark", "rank-bm25", "rapidfuzz", "requests-toolbelt", "scikit-learn", "streamlit", "sympy", "telethon", "tqdm", "xata", "xinference", "xmltodict", "zep-python"]
+extended-testing = ["amazon-textract-caller", "anthropic", "atlassian-python-api", "beautifulsoup4", "bibtexparser", "cassio", "chardet", "esprima", "feedparser", "geopandas", "gitpython", "gql", "html2text", "jinja2", "jq", "lxml", "mwparserfromhell", "mwxml", "newspaper3k", "openai", "openai", "pandas", "pdfminer-six", "pgvector", "psychicapi", "py-trello", "pymupdf", "pypdf", "pypdfium2", "pyspark", "rank-bm25", "rapidfuzz", "requests-toolbelt", "scikit-learn", "streamlit", "sympy", "telethon", "tqdm", "xata", "xinference", "xmltodict", "zep-python"]
 javascript = ["esprima"]
 llms = ["anthropic", "clarifai", "cohere", "huggingface_hub", "manifest-ml", "nlpcloud", "openai", "openllm", "openlm", "torch", "transformers", "xinference"]
 openai = ["openai", "tiktoken"]
@@ -13619,4 +13619,4 @@ text-helpers = ["chardet"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "a8bc3bc0555543de183b659147b47d4b686843bb80a2be94ef5c319af3cb1ed0"
+content-hash = "a8fd5dbcab821e39c502724e13a2f85b718f3e06c7c3f98062de01a44cf1ff6e"

libs/langchain/pyproject.toml

@@ -373,6 +373,7 @@ extended_testing = [
  "feedparser",
  "xata",
  "xmltodict",
+ "anthropic",
 ]
 
 scheduled_testing = [

libs/langchain/tests/unit_tests/chat_models/test_anthropic.py

@@ -0,0 +1,27 @@
+"""Test Anthropic Chat API wrapper."""
+import os
+
+import pytest
+
+from langchain.chat_models import ChatAnthropic
+
+os.environ["ANTHROPIC_API_KEY"] = "foo"
+
+
+@pytest.mark.requires("anthropic")
+def test_anthropic_model_kwargs() -> None:
+    llm = ChatAnthropic(model_kwargs={"foo": "bar"})
+    assert llm.model_kwargs == {"foo": "bar"}
+
+
+@pytest.mark.requires("anthropic")
+def test_anthropic_invalid_model_kwargs() -> None:
+    with pytest.raises(ValueError):
+        ChatAnthropic(model_kwargs={"max_tokens_to_sample": 5})
+
+
+@pytest.mark.requires("anthropic")
+def test_anthropic_incorrect_field() -> None:
+    with pytest.warns(match="not default parameter"):
+        llm = ChatAnthropic(foo="bar")
+    assert llm.model_kwargs == {"foo": "bar"}