community[patch]: assign missed default (#26326)

Assigning missed defaults in various classes. Most clients were being
assigned during the `model_validator(mode="before")` step, so this
change should amount to a no-op in those cases.

---

This PR was autogenerated using gritql

```shell

grit apply 'class_definition(name=$C, $body, superclasses=$S) where {    
    $C <: ! "Config", // Does not work in this scope, but works after class_definition
    $body <: block($statements),
    $statements <: some bubble assignment(left=$x, right=$y, type=$t) as $A where {
        or {
            $y <: `Field($z)`,
            $x <: "model_config"
        }
    },
    // And has either Any or Optional fields without a default
    $statements <: some bubble assignment(left=$x, right=$y, type=$t) as $A where {
        $t <: or {
            r"Optional.*",
            r"Any",
            r"Union[None, .*]",
            r"Union[.*, None, .*]",
            r"Union[.*, None]",
        },
        $y <: ., // Match empty node        
        $t => `$t = None`,
    },    
}
' --language python .

```

libs/community/langchain_community/chains/openapi/chain.py

@@ -30,7 +30,7 @@ class OpenAPIEndpointChain(Chain, BaseModel):
     """Chain interacts with an OpenAPI endpoint using natural language."""
 
     api_request_chain: LLMChain
-    api_response_chain: Optional[LLMChain]
+    api_response_chain: Optional[LLMChain] = None
     api_operation: APIOperation
     requests: Requests = Field(exclude=True, default_factory=Requests)
     param_mapping: _ParamMapping = Field(alias="param_mapping")

libs/community/langchain_community/chat_models/baidu_qianfan_endpoint.py

@@ -346,7 +346,7 @@ class QianfanChatEndpoint(BaseChatModel):
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
     """extra params for model invoke using with `do`."""
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
 
     # It could be empty due to the use of Console API
     # And they're not list here

libs/community/langchain_community/chat_models/jinachat.py

@@ -171,7 +171,7 @@ class JinaChat(BaseChatModel):
         """Return whether this model can be serialized by Langchain."""
         return False
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
     temperature: float = 0.7
     """What sampling temperature to use."""
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)

libs/community/langchain_community/chat_models/litellm.py

@@ -215,7 +215,7 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
 class ChatLiteLLM(BaseChatModel):
     """Chat model that uses the LiteLLM API."""
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
     model: str = "gpt-3.5-turbo"
     model_name: Optional[str] = None
     """Model name to use."""

libs/community/langchain_community/chat_models/llamacpp.py

@@ -67,7 +67,7 @@ class ChatLlamaCpp(BaseChatModel):
 
     """
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
 
     model_path: str
     """The path to the Llama model file."""

libs/community/langchain_community/chat_models/minimax.py

@@ -369,7 +369,7 @@ class MiniMaxChat(BaseChatModel):
             **self.model_kwargs,
         }
 
-    _client: Any
+    _client: Any = None
     model: str = "abab6.5-chat"
     """Model name to use."""
     max_tokens: int = 256

libs/community/langchain_community/chat_models/premai.py

@@ -305,7 +305,7 @@ class ChatPremAI(BaseChatModel, BaseModel):
     streaming: Optional[bool] = False
     """Whether to stream the responses or not."""
 
-    client: Any
+    client: Any = None
 
     model_config = ConfigDict(
         populate_by_name=True,

libs/community/langchain_community/chat_models/tongyi.py

@@ -434,7 +434,7 @@ class ChatTongyi(BaseChatModel):
     def lc_secrets(self) -> Dict[str, str]:
         return {"dashscope_api_key": "DASHSCOPE_API_KEY"}
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
     model_name: str = Field(default="qwen-turbo", alias="model")
     """Model name to use.
     callable multimodal model:

libs/community/langchain_community/chat_models/yuan2.py

@@ -76,7 +76,7 @@ class ChatYuan2(BaseChatModel):
             chat = ChatYuan2()
     """
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
     async_client: Any = Field(default=None, exclude=True)  #: :meta private:
 
     model_name: str = Field(default="yuan2", alias="model")

libs/community/langchain_community/cross_encoders/huggingface.py

@@ -23,7 +23,7 @@ class HuggingFaceCrossEncoder(BaseModel, BaseCrossEncoder):
             )
     """
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
     model_name: str = DEFAULT_MODEL_NAME
     """Model name to use."""
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)

libs/community/langchain_community/cross_encoders/sagemaker_endpoint.py

@@ -61,7 +61,7 @@ class SagemakerEndpointCrossEncoder(BaseModel, BaseCrossEncoder):
                credentials_profile_name=credentials_profile_name
            )
    """
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
 
     endpoint_name: str = ""
     """The name of the endpoint from the deployed Sagemaker model.

libs/community/langchain_community/document_compressors/llmlingua_filter.py

@@ -49,7 +49,7 @@ class LLMLinguaCompressor(BaseDocumentCompressor):
         "dynamic_context_compression_ratio": 0.4,
     }
     """Extra compression arguments"""
-    lingua: Any
+    lingua: Any = None
     """The instance of the llm linqua"""
 
     @model_validator(mode="before")

libs/community/langchain_community/document_compressors/openvino_rerank.py

@@ -21,9 +21,9 @@ class OpenVINOReranker(BaseDocumentCompressor):
     OpenVINO rerank models.
     """
 
-    ov_model: Any
+    ov_model: Any = None
     """OpenVINO model object."""
-    tokenizer: Any
+    tokenizer: Any = None
     """Tokenizer for embedding model."""
     model_name_or_path: str
     """HuggingFace model id."""

libs/community/langchain_community/embeddings/baichuan.py

@@ -59,7 +59,7 @@ class BaichuanTextEmbeddings(BaseModel, Embeddings):
             vectors = embeddings.embed_query(text)
     """  # noqa: E501
 
-    session: Any  #: :meta private:
+    session: Any = None  #: :meta private:
     model_name: str = Field(default="Baichuan-Text-Embedding", alias="model")
     """The model used to embed the documents."""
     baichuan_api_key: SecretStr = Field(

libs/community/langchain_community/embeddings/baidu_qianfan_endpoint.py

@@ -71,7 +71,7 @@ class QianfanEmbeddingsEndpoint(BaseModel, Embeddings):
     endpoint: str = ""
     """Endpoint of the Qianfan Embedding, required if custom model used."""
 
-    client: Any
+    client: Any = None
     """Qianfan client"""
 
     init_kwargs: Dict[str, Any] = Field(default_factory=dict)

libs/community/langchain_community/embeddings/bedrock.py

@@ -47,7 +47,7 @@ class BedrockEmbeddings(BaseModel, Embeddings):
             )
     """
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
     """Bedrock client."""
     region_name: Optional[str] = None
     """The aws region e.g., `us-west-2`. Fallsback to AWS_DEFAULT_REGION env variable

libs/community/langchain_community/embeddings/cohere.py

@@ -30,9 +30,9 @@ class CohereEmbeddings(BaseModel, Embeddings):
             )
     """
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
     """Cohere client."""
-    async_client: Any  #: :meta private:
+    async_client: Any = None  #: :meta private:
     """Cohere async client."""
     model: str = "embed-english-v2.0"
     """Model name to use."""

libs/community/langchain_community/embeddings/dashscope.py

@@ -101,7 +101,7 @@ class DashScopeEmbeddings(BaseModel, Embeddings):
 
     """
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
     """The DashScope client."""
     model: str = "text-embedding-v1"
     dashscope_api_key: Optional[str] = None

libs/community/langchain_community/embeddings/fastembed.py

@@ -38,12 +38,12 @@ class FastEmbedEmbeddings(BaseModel, Embeddings):
     Unknown behavior for values > 512.
     """
 
-    cache_dir: Optional[str]
+    cache_dir: Optional[str] = None
     """The path to the cache directory.
     Defaults to `local_cache` in the parent directory
     """
 
-    threads: Optional[int]
+    threads: Optional[int] = None
     """The number of threads single onnxruntime session can use.
     Defaults to None
     """
@@ -65,7 +65,7 @@ class FastEmbedEmbeddings(BaseModel, Embeddings):
     Defaults to `None`.
     """
 
-    _model: Any  # : :meta private:
+    _model: Any = None  # : :meta private:
 
     model_config = ConfigDict(extra="allow", protected_namespaces=())
 

libs/community/langchain_community/embeddings/ipex_llm.py

@@ -49,7 +49,7 @@ class IpexLLMBgeEmbeddings(BaseModel, Embeddings):
             )
     """
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
     model_name: str = DEFAULT_BGE_MODEL
     """Model name to use."""
     cache_folder: Optional[str] = None

libs/community/langchain_community/embeddings/laser.py

@@ -27,7 +27,7 @@ class LaserEmbeddings(BaseModel, Embeddings):
         embeddings = encoder.encode_sentences(["Hello", "World"])
     """
 
-    lang: Optional[str]
+    lang: Optional[str] = None
     """The language or language code you'd like to use
     If empty, this implementation will default
     to using a multilingual earlier LASER encoder model (called laser2)
@@ -35,7 +35,7 @@ class LaserEmbeddings(BaseModel, Embeddings):
     https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200
     """
 
-    _encoder_pipeline: Any  # : :meta private:
+    _encoder_pipeline: Any = None  # : :meta private:
 
     model_config = ConfigDict(
         extra="forbid",

libs/community/langchain_community/embeddings/llamacpp.py

@@ -19,7 +19,7 @@ class LlamaCppEmbeddings(BaseModel, Embeddings):
             llama = LlamaCppEmbeddings(model_path="/path/to/model.bin")
     """
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
     model_path: str
 
     n_ctx: int = Field(512, alias="n_ctx")

libs/community/langchain_community/embeddings/localai.py

@@ -141,7 +141,7 @@ class LocalAIEmbeddings(BaseModel, Embeddings):
 
     """
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
     model: str = "text-embedding-ada-002"
     deployment: str = model
     openai_api_version: Optional[str] = None

libs/community/langchain_community/embeddings/modelscope_hub.py

@@ -17,7 +17,7 @@ class ModelScopeEmbeddings(BaseModel, Embeddings):
             embed = ModelScopeEmbeddings(model_id=model_id, model_revision="v1.0.0")
     """
 
-    embed: Any
+    embed: Any = None
     model_id: str = "damo/nlp_corom_sentence-embedding_english-base"
     """Model name to use."""
     model_revision: Optional[str] = None

libs/community/langchain_community/embeddings/openvino.py

@@ -31,9 +31,9 @@ class OpenVINOEmbeddings(BaseModel, Embeddings):
             )
     """
 
-    ov_model: Any
+    ov_model: Any = None
     """OpenVINO model object."""
-    tokenizer: Any
+    tokenizer: Any = None
     """Tokenizer for embedding model."""
     model_name_or_path: str
     """HuggingFace model id."""

libs/community/langchain_community/embeddings/oracleai.py

@@ -28,7 +28,7 @@ class OracleEmbeddings(BaseModel, Embeddings):
     """Get Embeddings"""
 
     """Oracle Connection"""
-    conn: Any
+    conn: Any = None
     """Embedding Parameters"""
     params: Dict[str, Any]
     """Proxy"""

libs/community/langchain_community/embeddings/tensorflow_hub.py

@@ -19,7 +19,7 @@ class TensorflowHubEmbeddings(BaseModel, Embeddings):
             tf = TensorflowHubEmbeddings(model_url=url)
     """
 
-    embed: Any  #: :meta private:
+    embed: Any = None  #: :meta private:
     model_url: str = DEFAULT_MODEL_URL
     """Model name to use."""
 

libs/community/langchain_community/llms/aleph_alpha.py

@@ -25,7 +25,7 @@ class AlephAlpha(LLM):
             aleph_alpha = AlephAlpha(aleph_alpha_api_key="my-api-key")
     """
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
     model: Optional[str] = "luminous-base"
     """Model name to use."""
 

libs/community/langchain_community/llms/aphrodite.py

@@ -156,7 +156,7 @@ class Aphrodite(BaseLLM):
     """Holds any model parameters valid for `aphrodite.LLM` call not explicitly
     specified."""
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
 
     @pre_init
     def validate_environment(cls, values: Dict) -> Dict:

libs/community/langchain_community/llms/baidu_qianfan_endpoint.py

@@ -123,7 +123,7 @@ class QianfanLLMEndpoint(LLM):
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
     """extra params for model invoke using with `do`."""
 
-    client: Any
+    client: Any = None
 
     qianfan_ak: Optional[SecretStr] = Field(default=None, alias="api_key")
     qianfan_sk: Optional[SecretStr] = Field(default=None, alias="secret_key")

libs/community/langchain_community/llms/cohere.py

@@ -76,8 +76,8 @@ def acompletion_with_retry(llm: Cohere, **kwargs: Any) -> Any:
 class BaseCohere(Serializable):
     """Base class for Cohere models."""
 
-    client: Any  #: :meta private:
-    async_client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
+    async_client: Any = None  #: :meta private:
     model: Optional[str] = Field(default=None)
     """Model name to use."""
 

libs/community/langchain_community/llms/ctranslate2.py

@@ -41,9 +41,9 @@ class CTranslate2(BaseLLM):
     sampling_temperature: float = 1
     """Sampling temperature to generate more random samples."""
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
 
-    tokenizer: Any  #: :meta private:
+    tokenizer: Any = None  #: :meta private:
 
     ctranslate2_kwargs: Dict[str, Any] = Field(default_factory=dict)
     """

libs/community/langchain_community/llms/exllamav2.py

@@ -30,7 +30,7 @@ class ExLlamaV2(LLM):
     - Add support for custom stop sequences
     """
 
-    client: Any
+    client: Any = None
     model_path: str
     exllama_cache: Any = None
     config: Any = None

libs/community/langchain_community/llms/gooseai.py

@@ -30,7 +30,7 @@ class GooseAI(LLM):
 
     """
 
-    client: Any
+    client: Any = None
 
     model_name: str = "gpt-neo-20b"
     """Model name to use"""

libs/community/langchain_community/llms/huggingface_pipeline.py

@@ -60,7 +60,7 @@ class HuggingFacePipeline(BaseLLM):
             hf = HuggingFacePipeline(pipeline=pipe)
     """
 
-    pipeline: Any  #: :meta private:
+    pipeline: Any = None  #: :meta private:
     model_id: str = DEFAULT_MODEL_ID
     """Model name to use."""
     model_kwargs: Optional[dict] = None

libs/community/langchain_community/llms/huggingface_text_gen_inference.py

@@ -100,8 +100,8 @@ class HuggingFaceTextGenInference(LLM):
     """Holds any text-generation-inference server parameters not explicitly specified"""
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
     """Holds any model parameters valid for `call` not explicitly specified"""
-    client: Any
-    async_client: Any
+    client: Any = None
+    async_client: Any = None
 
     model_config = ConfigDict(
         extra="forbid",

libs/community/langchain_community/llms/ipex_llm.py

@@ -25,9 +25,9 @@ class IpexLLM(LLM):
     """Model name or model path to use."""
     model_kwargs: Optional[dict] = None
     """Keyword arguments passed to the model."""
-    model: Any  #: :meta private:
+    model: Any = None  #: :meta private:
     """IpexLLM model."""
-    tokenizer: Any  #: :meta private:
+    tokenizer: Any = None  #: :meta private:
     """Huggingface tokenizer model."""
     streaming: bool = True
     """Whether to stream the results, token by token."""

libs/community/langchain_community/llms/llamacpp.py

@@ -28,7 +28,7 @@ class LlamaCpp(LLM):
             llm = LlamaCpp(model_path="/path/to/llama/model")
     """
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
     model_path: str
     """The path to the Llama model file."""
 

libs/community/langchain_community/llms/manifest.py

@@ -9,7 +9,7 @@ from pydantic import ConfigDict
 class ManifestWrapper(LLM):
     """HazyResearch's Manifest library."""
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
     llm_kwargs: Optional[Dict] = None
 
     model_config = ConfigDict(

libs/community/langchain_community/llms/mlx_pipeline.py

@@ -38,9 +38,9 @@ class MLXPipeline(LLM):
 
     model_id: str = DEFAULT_MODEL_ID
     """Model name to use."""
-    model: Any  #: :meta private:
+    model: Any = None  #: :meta private:
     """Model."""
-    tokenizer: Any  #: :meta private:
+    tokenizer: Any = None  #: :meta private:
     """Tokenizer."""
     tokenizer_config: Optional[dict] = None
     """

libs/community/langchain_community/llms/nlpcloud.py

@@ -19,7 +19,7 @@ class NLPCloud(LLM):
             nlpcloud = NLPCloud(model="finetuned-gpt-neox-20b")
     """
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
     model_name: str = "finetuned-gpt-neox-20b"
     """Model name to use."""
     gpu: bool = True

libs/community/langchain_community/llms/petals.py

@@ -29,10 +29,10 @@ class Petals(LLM):
 
     """
 
-    client: Any
+    client: Any = None
     """The client to use for the API calls."""
 
-    tokenizer: Any
+    tokenizer: Any = None
     """The tokenizer to use for the API calls."""
 
     model_name: str = "bigscience/bloom-petals"

libs/community/langchain_community/llms/predictionguard.py

@@ -30,7 +30,7 @@ class PredictionGuard(LLM):
                                     })
     """
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
     model: Optional[str] = "MPT-7B-Instruct"
     """Model name to use."""
 

libs/community/langchain_community/llms/self_hosted.py

@@ -126,11 +126,11 @@ class SelfHostedPipeline(LLM):
             )
     """
 
-    pipeline_ref: Any  #: :meta private:
-    client: Any  #: :meta private:
+    pipeline_ref: Any = None  #: :meta private:
+    client: Any = None  #: :meta private:
     inference_fn: Callable = _generate_text  #: :meta private:
     """Inference function to send to the remote hardware."""
-    hardware: Any
+    hardware: Any = None
     """Remote hardware to send the inference function to."""
     model_load_fn: Callable
     """Function to load the model remotely on the server."""

libs/community/langchain_community/llms/self_hosted_hugging_face.py

@@ -160,7 +160,7 @@ class SelfHostedHuggingFaceLLM(SelfHostedPipeline):
     """Device to use for inference. -1 for CPU, 0 for GPU, 1 for second GPU, etc."""
     model_kwargs: Optional[dict] = None
     """Keyword arguments to pass to the model."""
-    hardware: Any
+    hardware: Any = None
     """Remote hardware to send the inference function to."""
     model_reqs: List[str] = ["./", "transformers", "torch"]
     """Requirements to install on hardware to inference the model."""

libs/community/langchain_community/llms/tongyi.py

@@ -238,7 +238,7 @@ class Tongyi(BaseLLM):
     def lc_secrets(self) -> Dict[str, str]:
         return {"dashscope_api_key": "DASHSCOPE_API_KEY"}
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
     model_name: str = Field(default="qwen-plus", alias="model")
 
     """Model name to use."""

libs/community/langchain_community/llms/vllm.py

@@ -72,7 +72,7 @@ class VLLM(BaseLLM):
     vllm_kwargs: Dict[str, Any] = Field(default_factory=dict)
     """Holds any model parameters valid for `vllm.LLM` call not explicitly specified."""
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
 
     @pre_init
     def validate_environment(cls, values: Dict) -> Dict:

libs/community/langchain_community/llms/volcengine_maas.py

@@ -12,7 +12,7 @@ from pydantic import BaseModel, Field, SecretStr
 class VolcEngineMaasBase(BaseModel):
     """Base class for VolcEngineMaas models."""
 
-    client: Any
+    client: Any = None
 
     volc_engine_maas_ak: Optional[SecretStr] = None
     """access key for volc engine"""

libs/community/langchain_community/llms/watsonxllm.py

@@ -93,7 +93,7 @@ class WatsonxLLM(BaseLLM):
     streaming: bool = False
     """ Whether to stream the results or not. """
 
-    watsonx_model: Any
+    watsonx_model: Any = None
 
     model_config = ConfigDict(
         extra="forbid",

libs/community/langchain_community/llms/weight_only_quantization.py

@@ -62,7 +62,7 @@ class WeightOnlyQuantPipeline(LLM):
             hf = WeightOnlyQuantPipeline(pipeline=pipe)
     """
 
-    pipeline: Any  #: :meta private:
+    pipeline: Any = None  #: :meta private:
     model_id: str = DEFAULT_MODEL_ID
     """Model name or local path to use."""
 

libs/community/langchain_community/retrievers/bm25.py

@@ -15,7 +15,7 @@ def default_preprocessing_func(text: str) -> List[str]:
 class BM25Retriever(BaseRetriever):
     """`BM25` retriever without Elasticsearch."""
 
-    vectorizer: Any
+    vectorizer: Any = None
     """ BM25 vectorizer."""
     docs: List[Document] = Field(repr=False)
     """ List of documents."""

libs/community/langchain_community/retrievers/docarray.py

@@ -38,7 +38,7 @@ class DocArrayRetriever(BaseRetriever):
         top_k: Number of documents to return
     """
 
-    index: Any
+    index: Any = None
     embeddings: Embeddings
     search_field: str
     content_field: str

libs/community/langchain_community/retrievers/google_vertex_ai_search.py

@@ -239,7 +239,7 @@ class GoogleVertexAISearchRetriever(BaseRetriever, _BaseGoogleVertexAISearchRetr
     """
 
     # type is SearchServiceClient but can't be set due to optional imports
-    _client: Any
+    _client: Any = None
     _serving_config: str
 
     model_config = ConfigDict(
@@ -405,7 +405,7 @@ class GoogleVertexAIMultiTurnSearchRetriever(
     """Vertex AI Search Conversation ID."""
 
     # type is ConversationalSearchServiceClient but can't be set due to optional imports
-    _client: Any
+    _client: Any = None
     _serving_config: str
 
     model_config = ConfigDict(

libs/community/langchain_community/retrievers/knn.py

@@ -35,7 +35,7 @@ class KNNRetriever(BaseRetriever):
 
     embeddings: Embeddings
     """Embeddings model to use."""
-    index: Any
+    index: Any = None
     """Index of embeddings."""
     texts: List[str]
     """List of texts to index."""

libs/community/langchain_community/retrievers/llama_index.py

@@ -12,7 +12,7 @@ class LlamaIndexRetriever(BaseRetriever):
     It is used for the question-answering with sources over
     an LlamaIndex data structure."""
 
-    index: Any
+    index: Any = None
     """LlamaIndex index to query."""
     query_kwargs: Dict = Field(default_factory=dict)
     """Keyword arguments to pass to the query method."""
@@ -48,7 +48,7 @@ class LlamaIndexGraphRetriever(BaseRetriever):
     It is used for question-answering with sources over an LlamaIndex
     graph data structure."""
 
-    graph: Any
+    graph: Any = None
     """LlamaIndex graph to query."""
     query_configs: List[Dict] = Field(default_factory=list)
     """List of query configs to pass to the query method."""

libs/community/langchain_community/retrievers/nanopq.py

@@ -31,7 +31,7 @@ class NanoPQRetriever(BaseRetriever):
 
     embeddings: Embeddings
     """Embeddings model to use."""
-    index: Any
+    index: Any = None
     """Index of embeddings."""
     texts: List[str]
     """List of texts to index."""

libs/community/langchain_community/retrievers/pinecone_hybrid_search.py

@@ -104,9 +104,9 @@ class PineconeHybridSearchRetriever(BaseRetriever):
     embeddings: Embeddings
     """Embeddings model to use."""
     """description"""
-    sparse_encoder: Any
+    sparse_encoder: Any = None
     """Sparse encoder to use."""
-    index: Any
+    index: Any = None
     """Pinecone index to use."""
     top_k: int = 4
     """Number of documents to return."""

libs/community/langchain_community/retrievers/qdrant_sparse_vector_retriever.py

@@ -36,7 +36,7 @@ from langchain_community.vectorstores.qdrant import Qdrant, QdrantException
 class QdrantSparseVectorRetriever(BaseRetriever):
     """Qdrant sparse vector retriever."""
 
-    client: Any
+    client: Any = None
     """'qdrant_client' instance to use."""
     collection_name: str
     """Qdrant collection name."""

libs/community/langchain_community/retrievers/svm.py

@@ -35,7 +35,7 @@ class SVMRetriever(BaseRetriever):
 
     embeddings: Embeddings
     """Embeddings model to use."""
-    index: Any
+    index: Any = None
     """Index of embeddings."""
     texts: List[str]
     """List of texts to index."""

libs/community/langchain_community/retrievers/tfidf.py

@@ -17,11 +17,11 @@ class TFIDFRetriever(BaseRetriever):
     https://github.com/asvskartheek/Text-Retrieval/blob/master/TF-IDF%20Search%20Engine%20(SKLEARN).ipynb
     """
 
-    vectorizer: Any
+    vectorizer: Any = None
     """TF-IDF vectorizer."""
     docs: List[Document]
     """Documents."""
-    tfidf_array: Any
+    tfidf_array: Any = None
     """TF-IDF array."""
     k: int = 4
     """Number of documents to return."""

libs/community/langchain_community/retrievers/weaviate_hybrid_search.py

@@ -16,7 +16,7 @@ class WeaviateHybridSearchRetriever(BaseRetriever):
       https://weaviate.io/blog/hybrid-search-explained
     """
 
-    client: Any
+    client: Any = None
     """keyword arguments to pass to the Weaviate client."""
     index_name: str
     """The name of the index to use."""

libs/community/langchain_community/tools/edenai/audio_text_to_speech.py

@@ -43,11 +43,11 @@ class EdenAiTextToSpeechTool(EdenaiTool):
 
     # optional params see api documentation for more info
     return_type: Literal["url", "wav"] = "url"
-    rate: Optional[int]
-    pitch: Optional[int]
-    volume: Optional[int]
-    audio_format: Optional[str]
-    sampling_rate: Optional[int]
+    rate: Optional[int] = None
+    pitch: Optional[int] = None
+    volume: Optional[int] = None
+    audio_format: Optional[str] = None
+    sampling_rate: Optional[int] = None
     voice_models: Dict[str, str] = Field(default_factory=dict)
 
     voice: Literal["MALE", "FEMALE"]

libs/community/langchain_community/tools/powerbi/tool.py

@@ -31,7 +31,7 @@ class QueryPowerBITool(BaseTool):
 
     Example Input: "How many rows are in table1?"
     """  # noqa: E501
-    llm_chain: Any
+    llm_chain: Any = None
     powerbi: PowerBIDataset = Field(exclude=True)
     examples: Optional[str] = DEFAULT_FEWSHOT_EXAMPLES
     session_cache: Dict[str, Any] = Field(default_factory=dict, exclude=True)

libs/community/langchain_community/utilities/asknews.py

@@ -12,8 +12,8 @@ from pydantic import BaseModel, ConfigDict, model_validator
 class AskNewsAPIWrapper(BaseModel):
     """Wrapper for AskNews API."""
 
-    asknews_sync: Any  #: :meta private:
-    asknews_async: Any  #: :meta private:
+    asknews_sync: Any = None  #: :meta private:
+    asknews_async: Any = None  #: :meta private:
     asknews_client_id: Optional[str] = None
     """Client ID for the AskNews API."""
     asknews_client_secret: Optional[str] = None

libs/community/langchain_community/utilities/awslambda.py

@@ -21,7 +21,7 @@ class LambdaWrapper(BaseModel):
 
     """
 
-    lambda_client: Any  #: :meta private:
+    lambda_client: Any = None  #: :meta private:
     """The configured boto3 client"""
     function_name: Optional[str] = None
     """The name of your lambda function"""

libs/community/langchain_community/utilities/dalle_image_generator.py

@@ -27,7 +27,7 @@ class DallEAPIWrapper(BaseModel):
     2. save your OPENAI_API_KEY in an environment variable
     """
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
     async_client: Any = Field(default=None, exclude=True)  #: :meta private:
     model_name: str = Field(default="dall-e-2", alias="model")
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)

libs/community/langchain_community/utilities/dataherald.py

@@ -18,7 +18,7 @@ class DataheraldAPIWrapper(BaseModel):
 
     """
 
-    dataherald_client: Any  #: :meta private:
+    dataherald_client: Any = None  #: :meta private:
     db_connection_id: str
     dataherald_api_key: Optional[str] = None
 

libs/community/langchain_community/utilities/github.py

@@ -29,8 +29,8 @@ def _import_tiktoken() -> Any:
 class GitHubAPIWrapper(BaseModel):
     """Wrapper for GitHub API."""
 
-    github: Any  #: :meta private:
-    github_repo_instance: Any  #: :meta private:
+    github: Any = None  #: :meta private:
+    github_repo_instance: Any = None  #: :meta private:
     github_repository: Optional[str] = None
     github_app_id: Optional[str] = None
     github_app_private_key: Optional[str] = None

libs/community/langchain_community/utilities/gitlab.py

@@ -15,8 +15,8 @@ if TYPE_CHECKING:
 class GitLabAPIWrapper(BaseModel):
     """Wrapper for GitLab API."""
 
-    gitlab: Any  #: :meta private:
-    gitlab_repo_instance: Any  #: :meta private:
+    gitlab: Any = None  #: :meta private:
+    gitlab_repo_instance: Any = None  #: :meta private:
     gitlab_repository: Optional[str] = None
     """The name of the GitLab repository, in the form {username}/{repo-name}."""
     gitlab_personal_access_token: Optional[str] = None

libs/community/langchain_community/utilities/google_finance.py

@@ -22,7 +22,7 @@ class GoogleFinanceAPIWrapper(BaseModel):
         google_Finance.run('langchain')
     """
 
-    serp_search_engine: Any
+    serp_search_engine: Any = None
     serp_api_key: Optional[SecretStr] = None
 
     model_config = ConfigDict(

libs/community/langchain_community/utilities/google_jobs.py

@@ -22,7 +22,7 @@ class GoogleJobsAPIWrapper(BaseModel):
         google_Jobs.run('langchain')
     """
 
-    serp_search_engine: Any
+    serp_search_engine: Any = None
     serp_api_key: Optional[SecretStr] = None
 
     model_config = ConfigDict(

libs/community/langchain_community/utilities/google_lens.py

@@ -27,7 +27,7 @@ class GoogleLensAPIWrapper(BaseModel):
         google_lens.run('langchain')
     """
 
-    serp_search_engine: Any
+    serp_search_engine: Any = None
     serp_api_key: Optional[SecretStr] = None
 
     model_config = ConfigDict(

libs/community/langchain_community/utilities/google_places_api.py

@@ -34,7 +34,7 @@ class GooglePlacesAPIWrapper(BaseModel):
     """
 
     gplaces_api_key: Optional[str] = None
-    google_map_client: Any  #: :meta private:
+    google_map_client: Any = None  #: :meta private:
     top_k_results: Optional[int] = None
 
     model_config = ConfigDict(

libs/community/langchain_community/utilities/google_search.py

@@ -51,7 +51,7 @@ class GoogleSearchAPIWrapper(BaseModel):
 
     """
 
-    search_engine: Any  #: :meta private:
+    search_engine: Any = None  #: :meta private:
     google_api_key: Optional[str] = None
     google_cse_id: Optional[str] = None
     k: int = 10

libs/community/langchain_community/utilities/google_trends.py

@@ -26,7 +26,7 @@ class GoogleTrendsAPIWrapper(BaseModel):
         google_trends.run('langchain')
     """
 
-    serp_search_engine: Any
+    serp_search_engine: Any = None
     serp_api_key: Optional[SecretStr] = None
 
     model_config = ConfigDict(

libs/community/langchain_community/utilities/graphql.py

@@ -14,7 +14,7 @@ class GraphQLAPIWrapper(BaseModel):
     custom_headers: Optional[Dict[str, str]] = None
     fetch_schema_from_transport: Optional[bool] = None
     graphql_endpoint: str
-    gql_client: Any  #: :meta private:
+    gql_client: Any = None  #: :meta private:
     gql_function: Callable[[str], Any]  #: :meta private:
 
     model_config = ConfigDict(

libs/community/langchain_community/utilities/jira.py

@@ -10,8 +10,8 @@ from pydantic import BaseModel, ConfigDict, model_validator
 class JiraAPIWrapper(BaseModel):
     """Wrapper for Jira API."""
 
-    jira: Any  #: :meta private:
-    confluence: Any
+    jira: Any = None  #: :meta private:
+    confluence: Any = None
     jira_username: Optional[str] = None
     jira_api_token: Optional[str] = None
     jira_instance_url: Optional[str] = None

libs/community/langchain_community/utilities/openweathermap.py

@@ -16,7 +16,7 @@ class OpenWeatherMapAPIWrapper(BaseModel):
     3. pip install pyowm
     """
 
-    owm: Any
+    owm: Any = None
     openweathermap_api_key: Optional[str] = None
 
     model_config = ConfigDict(

libs/community/langchain_community/utilities/serpapi.py

@@ -40,7 +40,7 @@ class SerpAPIWrapper(BaseModel):
             serpapi = SerpAPIWrapper()
     """
 
-    search_engine: Any  #: :meta private:
+    search_engine: Any = None  #: :meta private:
     params: dict = Field(
         default={
             "engine": "google",

libs/community/langchain_community/utilities/stackexchange.py

@@ -7,7 +7,7 @@ from pydantic import BaseModel, Field, model_validator
 class StackExchangeAPIWrapper(BaseModel):
     """Wrapper for Stack Exchange API."""
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
     max_results: int = 3
     """Max number of results to include in output."""
     query_type: Literal["all", "title", "body"] = "all"

libs/community/langchain_community/utilities/steam.py

@@ -13,7 +13,7 @@ from langchain_community.tools.steam.prompt import (
 class SteamWebAPIWrapper(BaseModel):
     """Wrapper for Steam API."""
 
-    steam: Any  # for python-steam-api
+    steam: Any = None  # for python-steam-api
 
     # operations: a list of dictionaries, each representing a specific operation that
     # can be performed with the API

libs/community/langchain_community/utilities/twilio.py

@@ -26,7 +26,7 @@ class TwilioAPIWrapper(BaseModel):
             twilio.run('test', '+12484345508')
     """
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
     account_sid: Optional[str] = None
     """Twilio account string identifier."""
     auth_token: Optional[str] = None

libs/community/langchain_community/utilities/wolfram_alpha.py

@@ -18,7 +18,7 @@ class WolframAlphaAPIWrapper(BaseModel):
 
     """
 
-    wolfram_client: Any  #: :meta private:
+    wolfram_client: Any = None  #: :meta private:
     wolfram_alpha_appid: Optional[str] = None
 
     model_config = ConfigDict(