📖 docstrings retrievers consistency (#9422)

📜 
- updated the top-level descriptions to a consistent format;
- changed the format of several 100% internal functions from "name" to
"_name". So, these functions are not shown in the Top-level API
Reference page (with lists of classes/functions)

libs/langchain/langchain/retrievers/arxiv.py

@@ -6,8 +6,7 @@ from langchain.utilities.arxiv import ArxivAPIWrapper
 
 
 class ArxivRetriever(BaseRetriever, ArxivAPIWrapper):
-    """
-    Retriever for Arxiv.
+    """`Arxiv` retriever.
 
     It wraps load() to get_relevant_documents().
     It uses all ArxivAPIWrapper arguments without any change.

libs/langchain/langchain/retrievers/azure_cognitive_search.py

@@ -1,5 +1,3 @@
-"""Retriever for the Azure Cognitive Search service."""
-
 from __future__ import annotations
 
 import json
@@ -18,7 +16,7 @@ from langchain.utils import get_from_dict_or_env
 
 
 class AzureCognitiveSearchRetriever(BaseRetriever):
-    """Retriever for the Azure Cognitive Search service."""
+    """`Azure Cognitive Search` service retriever."""
 
     service_name: str = ""
     """Name of Azure Cognitive Search service"""

libs/langchain/langchain/retrievers/bm25.py

@@ -1,8 +1,3 @@
-"""
-BM25 Retriever without elastic search
-"""
-
-
 from __future__ import annotations
 
 from typing import Any, Callable, Dict, Iterable, List, Optional
@@ -16,7 +11,7 @@ def default_preprocessing_func(text: str) -> List[str]:
 
 
 class BM25Retriever(BaseRetriever):
-    """BM25 Retriever without elastic search."""
+    """`BM25` retriever without Elasticsearch."""
 
     vectorizer: Any
     """ BM25 vectorizer."""

libs/langchain/langchain/retrievers/chaindesk.py

@@ -11,7 +11,7 @@ from langchain.schema import BaseRetriever, Document
 
 
 class ChaindeskRetriever(BaseRetriever):
-    """Retriever for the Chaindesk API."""
+    """`Chaindesk API` retriever."""
 
     datastore_url: str
     top_k: Optional[int]

libs/langchain/langchain/retrievers/chatgpt_plugin_retriever.py

@@ -13,7 +13,7 @@ from langchain.schema import BaseRetriever, Document
 
 
 class ChatGPTPluginRetriever(BaseRetriever):
-    """Retrieves documents from a ChatGPT plugin."""
+    """`ChatGPT plugin` retriever."""
 
     url: str
     """URL of the ChatGPT plugin."""

libs/langchain/langchain/retrievers/databerry.py

@@ -11,7 +11,7 @@ from langchain.schema import BaseRetriever, Document
 
 
 class DataberryRetriever(BaseRetriever):
-    """Retriever for the Databerry API."""
+    """`Databerry API` retriever."""
 
     datastore_url: str
     top_k: Optional[int]

libs/langchain/langchain/retrievers/docarray.py

@@ -17,10 +17,9 @@ class SearchType(str, Enum):
 
 
 class DocArrayRetriever(BaseRetriever):
-    """
-    Retriever for DocArray Document Indices.
+    """`DocArray Document Indices` retriever.
 
-    Currently, supports 5 backends:
+    Currently, it supports 5 backends:
     InMemoryExactNNIndex, HnswDocumentIndex, QdrantDocumentIndex,
     ElasticDocIndex, and WeaviateDocumentIndex.
 

libs/langchain/langchain/retrievers/document_compressors/base.py

@@ -1,4 +1,3 @@
-"""Interface for retrieved document compressors."""
 from abc import ABC, abstractmethod
 from inspect import signature
 from typing import List, Optional, Sequence, Union
@@ -9,7 +8,7 @@ from langchain.schema import BaseDocumentTransformer, Document
 
 
 class BaseDocumentCompressor(BaseModel, ABC):
-    """Base abstraction interface for document compression."""
+    """Base class for document compressors."""
 
     @abstractmethod
     def compress_documents(
@@ -31,7 +30,7 @@ class BaseDocumentCompressor(BaseModel, ABC):
 
 
 class DocumentCompressorPipeline(BaseDocumentCompressor):
-    """Document compressor that uses a pipeline of transformers."""
+    """Document compressor that uses a pipeline of Transformers."""
 
     transformers: List[Union[BaseDocumentTransformer, BaseDocumentCompressor]]
     """List of document filters that are chained together and run in sequence."""

libs/langchain/langchain/retrievers/document_compressors/chain_extract.py

@@ -42,7 +42,7 @@ def _get_default_chain_prompt() -> PromptTemplate:
 
 
 class LLMChainExtractor(BaseDocumentCompressor):
-    """DocumentCompressor that uses an LLM chain to extract
+    """Document compressor that uses an LLM chain to extract
     the relevant parts of documents."""
 
     llm_chain: LLMChain

libs/langchain/langchain/retrievers/document_compressors/cohere_rerank.py

@@ -20,7 +20,7 @@ else:
 
 
 class CohereRerank(BaseDocumentCompressor):
-    """DocumentCompressor that uses Cohere's rerank API to compress documents."""
+    """Document compressor that uses `Cohere Rerank API`."""
 
     client: Client
     """Cohere client to use for compressing documents."""

libs/langchain/langchain/retrievers/elastic_search_bm25.py

@@ -11,7 +11,7 @@ from langchain.schema import BaseRetriever
 
 
 class ElasticSearchBM25Retriever(BaseRetriever):
-    """Retriever for the Elasticsearch using BM25 as a retrieval method.
+    """`Elasticsearch` retriever that uses `BM25`.
 
     To connect to an Elasticsearch instance that requires login credentials,
     including Elastic Cloud, use the Elasticsearch URL format

libs/langchain/langchain/retrievers/ensemble.py

@@ -13,8 +13,9 @@ from langchain.schema import BaseRetriever, Document
 
 
 class EnsembleRetriever(BaseRetriever):
-    """
-    This class ensemble the results of multiple retrievers by using rank fusion.
+    """Retriever that ensembles the multiple retrievers.
+
+    It uses a rank fusion.
 
     Args:
         retrievers: A list of retrievers to ensemble.

libs/langchain/langchain/retrievers/google_cloud_enterprise_search.py

@@ -17,11 +17,10 @@ if TYPE_CHECKING:
 
 
 class GoogleCloudEnterpriseSearchRetriever(BaseRetriever):
-    """Retriever for the Google Cloud Enterprise Search Service API.
-
-    For the detailed explanation of the Enterprise Search concepts
-    and configuration parameters refer to the product documentation.
+    """`Google Cloud Enterprise Search API` retriever.
 
+    For a detailed explanation of the Enterprise Search concepts
+    and configuration parameters, refer to the product documentation.
     https://cloud.google.com/generative-ai-app-builder/docs/enterprise-search-introduction
     """
 

libs/langchain/langchain/retrievers/kendra.py

@@ -9,7 +9,7 @@ from langchain.schema import BaseRetriever
 
 
 def clean_excerpt(excerpt: str) -> str:
-    """Cleans an excerpt from Kendra.
+    """Clean an excerpt from Kendra.
 
     Args:
         excerpt: The excerpt to clean.
@@ -25,7 +25,7 @@ def clean_excerpt(excerpt: str) -> str:
 
 
 def combined_text(item: "ResultItem") -> str:
-    """Combines a ResultItem title and excerpt into a single string.
+    """Combine a ResultItem title and excerpt into a single string.
 
     Args:
         item: the ResultItem of a Kendra search.
@@ -45,15 +45,15 @@ def combined_text(item: "ResultItem") -> str:
 
 
 DocumentAttributeValueType = Union[str, int, List[str], None]
-"""Possible types of a DocumentAttributeValue. Dates are also represented as str."""
+"""Possible types of a DocumentAttributeValue. 
+
+Dates are also represented as str.
+"""
 
 
 # Unexpected keyword argument "extra" for "__init_subclass__" of "object"
 class Highlight(BaseModel, extra=Extra.allow):  # type: ignore[call-arg]
-    """
-    Represents the information that can be
-    used to highlight key words in the excerpt.
-    """
+    """Information that highlights the key words in the excerpt."""
 
     BeginOffset: int
     """The zero-based location in the excerpt where the highlight starts."""
@@ -79,7 +79,7 @@ class TextWithHighLights(BaseModel, extra=Extra.allow):  # type: ignore[call-arg
 class AdditionalResultAttributeValue(  # type: ignore[call-arg]
     BaseModel, extra=Extra.allow
 ):
-    """The value of an additional result attribute."""
+    """Value of an additional result attribute."""
 
     TextWithHighlightsValue: TextWithHighLights
     """The text with highlights value."""
@@ -87,7 +87,7 @@ class AdditionalResultAttributeValue(  # type: ignore[call-arg]
 
 # Unexpected keyword argument "extra" for "__init_subclass__" of "object"
 class AdditionalResultAttribute(BaseModel, extra=Extra.allow):  # type: ignore[call-arg]
-    """An additional result attribute."""
+    """Additional result attribute."""
 
     Key: str
     """The key of the attribute."""
@@ -102,7 +102,7 @@ class AdditionalResultAttribute(BaseModel, extra=Extra.allow):  # type: ignore[c
 
 # Unexpected keyword argument "extra" for "__init_subclass__" of "object"
 class DocumentAttributeValue(BaseModel, extra=Extra.allow):  # type: ignore[call-arg]
-    """The value of a document attribute."""
+    """Value of a document attribute."""
 
     DateValue: Optional[str]
     """The date expressed as an ISO 8601 string."""
@@ -133,7 +133,7 @@ class DocumentAttributeValue(BaseModel, extra=Extra.allow):  # type: ignore[call
 
 # Unexpected keyword argument "extra" for "__init_subclass__" of "object"
 class DocumentAttribute(BaseModel, extra=Extra.allow):  # type: ignore[call-arg]
-    """A document attribute."""
+    """Document attribute."""
 
     Key: str
     """The key of the attribute."""
@@ -143,7 +143,7 @@ class DocumentAttribute(BaseModel, extra=Extra.allow):  # type: ignore[call-arg]
 
 # Unexpected keyword argument "extra" for "__init_subclass__" of "object"
 class ResultItem(BaseModel, ABC, extra=Extra.allow):  # type: ignore[call-arg]
-    """Abstract class that represents a result item."""
+    """Base class of a result item."""
 
     Id: Optional[str]
     """The ID of the relevant result item."""
@@ -199,7 +199,7 @@ class ResultItem(BaseModel, ABC, extra=Extra.allow):  # type: ignore[call-arg]
 
 
 class QueryResultItem(ResultItem):
-    """A Query API result item."""
+    """Query API result item."""
 
     DocumentTitle: TextWithHighLights
     """The document title."""
@@ -248,7 +248,7 @@ class QueryResultItem(ResultItem):
 
 
 class RetrieveResultItem(ResultItem):
-    """A Retrieve API result item."""
+    """Retrieve API result item."""
 
     DocumentTitle: Optional[str]
     """The document title."""
@@ -264,11 +264,12 @@ class RetrieveResultItem(ResultItem):
 
 # Unexpected keyword argument "extra" for "__init_subclass__" of "object"
 class QueryResult(BaseModel, extra=Extra.allow):  # type: ignore[call-arg]
-    """
-    Represents an Amazon Kendra Query API search result, which is composed of:
+    """`Amazon Kendra Query API` search result.
+
+    It is composed of:
         * Relevant suggested answers: either a text excerpt or table excerpt.
         * Matching FAQs or questions-answer from your FAQ file.
-        * Documents including an excerpt of each document with the its title.
+        * Documents including an excerpt of each document with its title.
     """
 
     ResultItems: List[QueryResultItem]
@@ -277,8 +278,9 @@ class QueryResult(BaseModel, extra=Extra.allow):  # type: ignore[call-arg]
 
 # Unexpected keyword argument "extra" for "__init_subclass__" of "object"
 class RetrieveResult(BaseModel, extra=Extra.allow):  # type: ignore[call-arg]
-    """
-    Represents an Amazon Kendra Retrieve API search result, which is composed of:
+    """`Amazon Kendra Retrieve API` search result.
+
+    It is composed of:
         * relevant passages or text excerpts given an input query.
     """
 
@@ -289,7 +291,7 @@ class RetrieveResult(BaseModel, extra=Extra.allow):  # type: ignore[call-arg]
 
 
 class AmazonKendraRetriever(BaseRetriever):
-    """Retriever for the Amazon Kendra Index.
+    """`Amazon Kendra Index` retriever.
 
     Args:
         index_id: Kendra index id

libs/langchain/langchain/retrievers/knn.py

@@ -30,7 +30,7 @@ def create_index(contexts: List[str], embeddings: Embeddings) -> np.ndarray:
 
 
 class KNNRetriever(BaseRetriever):
-    """KNN Retriever."""
+    """`KNN` retriever."""
 
     embeddings: Embeddings
     """Embeddings model to use."""

libs/langchain/langchain/retrievers/llama_index.py

@@ -6,7 +6,9 @@ from langchain.schema import BaseRetriever, Document
 
 
 class LlamaIndexRetriever(BaseRetriever):
-    """Retriever for the question-answering with sources over
+    """`LlamaIndex` retriever.
+
+    It is used for the question-answering with sources over
     an LlamaIndex data structure."""
 
     index: Any
@@ -40,7 +42,9 @@ class LlamaIndexRetriever(BaseRetriever):
 
 
 class LlamaIndexGraphRetriever(BaseRetriever):
-    """Retriever for question-answering with sources over an LlamaIndex
+    """`LlamaIndex` graph data structure retriever.
+
+    It is used for question-answering with sources over an LlamaIndex
     graph data structure."""
 
     graph: Any

libs/langchain/langchain/retrievers/metal.py

@@ -6,7 +6,7 @@ from langchain.schema import BaseRetriever, Document
 
 
 class MetalRetriever(BaseRetriever):
-    """Retriever that uses the Metal API."""
+    """`Metal API` retriever."""
 
     client: Any
     """The Metal client to use."""

libs/langchain/langchain/retrievers/milvus.py

@@ -12,7 +12,7 @@ from langchain.vectorstores.milvus import Milvus
 
 
 class MilvusRetriever(BaseRetriever):
-    """Retriever that uses the Milvus API."""
+    """`Milvus API` retriever."""
 
     embedding_function: Embeddings
     collection_name: str = "LangChainCollection"

libs/langchain/langchain/retrievers/multi_query.py

@@ -44,9 +44,10 @@ DEFAULT_QUERY_PROMPT = PromptTemplate(
 
 
 class MultiQueryRetriever(BaseRetriever):
+    """Given a query, use an LLM to write a set of queries.
 
-    """Given a user query, use an LLM to write a set of queries.
-    Retrieve docs for each query. Rake the unique union of all retrieved docs."""
+    Retrieve docs for each query. Rake the unique union of all retrieved docs.
+    """
 
     retriever: BaseRetriever
     llm_chain: LLMChain

libs/langchain/langchain/retrievers/parent_document_retriever.py

@@ -11,7 +11,7 @@ from langchain.vectorstores.base import VectorStore
 
 
 class ParentDocumentRetriever(BaseRetriever):
-    """Fetches small chunks, then fetches their parent documents.
+    """Retrieve small chunks then retrieve their parent documents.
 
     When splitting documents for retrieval, there are often conflicting desires:
 

libs/langchain/langchain/retrievers/pinecone_hybrid_search.py

@@ -29,13 +29,13 @@ def create_index(
     ids: Optional[List[str]] = None,
     metadatas: Optional[List[dict]] = None,
 ) -> None:
-    """
-    Create a Pinecone index from a list of contexts.
-    Modifies the index argument in-place.
+    """Create an index from a list of contexts.
+
+    It modifies the index argument in-place!
 
     Args:
         contexts: List of contexts to embed.
-        index: Pinecone index to use.
+        index: Index to use.
         embeddings: Embeddings model to use.
         sparse_encoder: Sparse encoder to use.
         ids: List of ids to use for the documents.
@@ -95,7 +95,7 @@ def create_index(
 
 
 class PineconeHybridSearchRetriever(BaseRetriever):
-    """Pinecone Hybrid Search Retriever."""
+    """`Pinecone Hybrid Search` retriever."""
 
     embeddings: Embeddings
     """Embeddings model to use."""

libs/langchain/langchain/retrievers/pubmed.py

@@ -6,7 +6,7 @@ from langchain.utilities.pubmed import PubMedAPIWrapper
 
 
 class PubMedRetriever(BaseRetriever, PubMedAPIWrapper):
-    """Retriever for PubMed API.
+    """`PubMed API` retriever.
 
     It wraps load() to get_relevant_documents().
     It uses all PubMedAPIWrapper arguments without any change.

libs/langchain/langchain/retrievers/re_phraser.py

@@ -23,9 +23,8 @@ DEFAULT_QUERY_PROMPT = PromptTemplate.from_template(DEFAULT_TEMPLATE)
 
 
 class RePhraseQueryRetriever(BaseRetriever):
-
-    """Given a user query, use an LLM to re-phrase it.
-    Then, retrieve docs for re-phrased query."""
+    """Given a query, use an LLM to re-phrase it.
+    Then, retrieve docs for the re-phrased query."""
 
     retriever: BaseRetriever
     llm_chain: LLMChain

libs/langchain/langchain/retrievers/remote_retriever.py

@@ -11,7 +11,7 @@ from langchain.schema import BaseRetriever, Document
 
 
 class RemoteLangChainRetriever(BaseRetriever):
-    """Retriever for remote LangChain API."""
+    """`LangChain API` retriever."""
 
     url: str
     """URL of the remote LangChain API."""

libs/langchain/langchain/retrievers/self_query/chroma.py

@@ -11,7 +11,7 @@ from langchain.chains.query_constructor.ir import (
 
 
 class ChromaTranslator(Visitor):
-    """Translate internal query language elements to valid filters."""
+    """Translate `Chroma` internal query language elements to valid filters."""
 
     allowed_operators = [Operator.AND, Operator.OR]
     """Subset of allowed logical operators."""

libs/langchain/langchain/retrievers/self_query/deeplake.py

@@ -35,7 +35,7 @@ def can_cast_to_float(string: str) -> bool:
 
 
 class DeepLakeTranslator(Visitor):
-    """Logic for converting internal query language elements to valid filters."""
+    """Translate `DeepLake` internal query language elements to valid filters."""
 
     allowed_operators = [Operator.AND, Operator.OR]
     """Subset of allowed logical operators."""

libs/langchain/langchain/retrievers/self_query/elasticsearch.py

@@ -11,7 +11,7 @@ from langchain.chains.query_constructor.ir import (
 
 
 class ElasticsearchTranslator(Visitor):
-    """Translate the internal query language elements to valid filters."""
+    """Translate `Elasticsearch` internal query language elements to valid filters."""
 
     allowed_comparators = [
         Comparator.EQ,

libs/langchain/langchain/retrievers/self_query/myscale.py

@@ -12,7 +12,7 @@ from langchain.chains.query_constructor.ir import (
 )
 
 
-def DEFAULT_COMPOSER(op_name: str) -> Callable:
+def _DEFAULT_COMPOSER(op_name: str) -> Callable:
     """
     Default composer for logical operators.
 
@@ -30,9 +30,10 @@ def DEFAULT_COMPOSER(op_name: str) -> Callable:
     return f
 
 
-def FUNCTION_COMPOSER(op_name: str) -> Callable:
+def _FUNCTION_COMPOSER(op_name: str) -> Callable:
     """
     Composer for functions.
+
     Args:
         op_name: Name of the function.
 
@@ -48,7 +49,7 @@ def FUNCTION_COMPOSER(op_name: str) -> Callable:
 
 
 class MyScaleTranslator(Visitor):
-    """Translate internal query language elements to valid filters."""
+    """Translate `MyScale` internal query language elements to valid filters."""
 
     allowed_operators = [Operator.AND, Operator.OR, Operator.NOT]
     """Subset of allowed logical operators."""
@@ -64,16 +65,16 @@ class MyScaleTranslator(Visitor):
     ]
 
     map_dict = {
-        Operator.AND: DEFAULT_COMPOSER("AND"),
-        Operator.OR: DEFAULT_COMPOSER("OR"),
-        Operator.NOT: DEFAULT_COMPOSER("NOT"),
-        Comparator.EQ: DEFAULT_COMPOSER("="),
-        Comparator.GT: DEFAULT_COMPOSER(">"),
-        Comparator.GTE: DEFAULT_COMPOSER(">="),
-        Comparator.LT: DEFAULT_COMPOSER("<"),
-        Comparator.LTE: DEFAULT_COMPOSER("<="),
-        Comparator.CONTAIN: FUNCTION_COMPOSER("has"),
-        Comparator.LIKE: DEFAULT_COMPOSER("ILIKE"),
+        Operator.AND: _DEFAULT_COMPOSER("AND"),
+        Operator.OR: _DEFAULT_COMPOSER("OR"),
+        Operator.NOT: _DEFAULT_COMPOSER("NOT"),
+        Comparator.EQ: _DEFAULT_COMPOSER("="),
+        Comparator.GT: _DEFAULT_COMPOSER(">"),
+        Comparator.GTE: _DEFAULT_COMPOSER(">="),
+        Comparator.LT: _DEFAULT_COMPOSER("<"),
+        Comparator.LTE: _DEFAULT_COMPOSER("<="),
+        Comparator.CONTAIN: _FUNCTION_COMPOSER("has"),
+        Comparator.LIKE: _DEFAULT_COMPOSER("ILIKE"),
     }
 
     def __init__(self, metadata_key: str = "metadata") -> None:

libs/langchain/langchain/retrievers/self_query/pinecone.py

@@ -11,7 +11,7 @@ from langchain.chains.query_constructor.ir import (
 
 
 class PineconeTranslator(Visitor):
-    """Translate the internal query language elements to valid filters."""
+    """Translate `Pinecone` internal query language elements to valid filters."""
 
     allowed_comparators = (
         Comparator.EQ,

libs/langchain/langchain/retrievers/self_query/qdrant.py

@@ -16,7 +16,7 @@ if TYPE_CHECKING:
 
 
 class QdrantTranslator(Visitor):
-    """Translate the internal query language elements to valid filters."""
+    """Translate `Qdrant` internal query language elements to valid filters."""
 
     allowed_comparators = (
         Comparator.EQ,

libs/langchain/langchain/retrievers/self_query/weaviate.py

@@ -11,7 +11,7 @@ from langchain.chains.query_constructor.ir import (
 
 
 class WeaviateTranslator(Visitor):
-    """Translate the internal query language elements to valid filters."""
+    """Translate `Weaviate` internal query language elements to valid filters."""
 
     allowed_operators = [Operator.AND, Operator.OR]
     """Subset of allowed logical operators."""

libs/langchain/langchain/retrievers/svm.py

@@ -26,7 +26,7 @@ def create_index(contexts: List[str], embeddings: Embeddings) -> np.ndarray:
 
 
 class SVMRetriever(BaseRetriever):
-    """SVM Retriever.
+    """`SVM` retriever.
 
     Largely based on
     https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb

libs/langchain/langchain/retrievers/tfidf.py

@@ -9,7 +9,7 @@ from langchain.schema import BaseRetriever, Document
 
 
 class TFIDFRetriever(BaseRetriever):
-    """TF-IDF Retriever.
+    """`TF-IDF` retriever.
 
     Largely based on
     https://github.com/asvskartheek/Text-Retrieval/blob/master/TF-IDF%20Search%20Engine%20(SKLEARN).ipynb

libs/langchain/langchain/retrievers/time_weighted_retriever.py

@@ -9,7 +9,7 @@ from langchain.vectorstores.base import VectorStore
 
 
 def _get_hours_passed(time: datetime.datetime, ref_time: datetime.datetime) -> float:
-    """Get the hours passed between two datetime objects."""
+    """Get the hours passed between two datetimes."""
     return (time - ref_time).total_seconds() / 3600
 
 

libs/langchain/langchain/retrievers/vespa_retriever.py

@@ -11,7 +11,7 @@ if TYPE_CHECKING:
 
 
 class VespaRetriever(BaseRetriever):
-    """Retriever that uses Vespa."""
+    """`Vespa` retriever."""
 
     app: Vespa
     """Vespa application to query."""

libs/langchain/langchain/retrievers/weaviate_hybrid_search.py

@@ -10,7 +10,11 @@ from langchain.schema import BaseRetriever
 
 
 class WeaviateHybridSearchRetriever(BaseRetriever):
-    """Retriever for the Weaviate's hybrid search."""
+    """`Weaviate hybrid search` retriever.
+
+    See the documentation:
+      https://weaviate.io/blog/hybrid-search-explained
+    """
 
     client: Any
     """keyword arguments to pass to the Weaviate client."""

libs/langchain/langchain/retrievers/web_research.py

@@ -24,7 +24,7 @@ logger = logging.getLogger(__name__)
 
 
 class SearchQueries(BaseModel):
-    """Search queries to run to research for the user's goal."""
+    """Search queries to research for the user's goal."""
 
     queries: List[str] = Field(
         ..., description="List of search queries to look up on Google"
@@ -66,7 +66,7 @@ class QuestionListOutputParser(PydanticOutputParser):
 
 
 class WebResearchRetriever(BaseRetriever):
-    """Retriever for web research based on the Google Search API."""
+    """`Google Search API` retriever."""
 
     # Inputs
     vectorstore: VectorStore = Field(

libs/langchain/langchain/retrievers/wikipedia.py

@@ -6,7 +6,7 @@ from langchain.utilities.wikipedia import WikipediaAPIWrapper
 
 
 class WikipediaRetriever(BaseRetriever, WikipediaAPIWrapper):
-    """Retriever for Wikipedia API.
+    """`Wikipedia API` retriever.
 
     It wraps load() to get_relevant_documents().
     It uses all WikipediaAPIWrapper arguments without any change.

libs/langchain/langchain/retrievers/zep.py

@@ -14,7 +14,7 @@ if TYPE_CHECKING:
 
 
 class ZepRetriever(BaseRetriever):
-    """Retriever for the Zep long-term memory store.
+    """`Zep` long-term memory store retriever.
 
     Search your user's long-term chat history with Zep.
 

libs/langchain/langchain/retrievers/zilliz.py

@@ -11,7 +11,7 @@ from langchain.vectorstores.zilliz import Zilliz
 
 
 class ZillizRetriever(BaseRetriever):
-    """Retriever for the Zilliz API."""
+    """`Zilliz API` retriever."""
 
     embedding_function: Embeddings
     """The underlying embedding function from which documents will be retrieved."""