docs: update integration api refs (#25195)

- [x] toolkits
- [x] retrievers (in this repo)

libs/community/langchain_community/retrievers/bedrock.py

@@ -19,11 +19,18 @@ class RetrievalConfig(BaseModel, extra="allow"):  # type: ignore[call-arg]
 
 
 class AmazonKnowledgeBasesRetriever(BaseRetriever):
-    """`Amazon Bedrock Knowledge Bases` retrieval.
+    """Amazon Bedrock Knowledge Bases retriever.
 
     See https://aws.amazon.com/bedrock/knowledge-bases for more info.
 
-    Args:
+    Setup:
+        Install ``langchain-aws``:
+
+        .. code-block:: bash
+
+            pip install -U langchain-aws
+
+    Key init args:
         knowledge_base_id: Knowledge Base ID.
         region_name: The aws region e.g., `us-west-2`.
             Fallback to AWS_DEFAULT_REGION env variable or region specified in
@@ -35,7 +42,7 @@ class AmazonKnowledgeBasesRetriever(BaseRetriever):
         client: boto3 client for bedrock agent runtime.
         retrieval_config: Configuration for retrieval.
 
-    Example:
+    Instantiate:
         .. code-block:: python
 
             from langchain_community.retrievers import AmazonKnowledgeBasesRetriever
@@ -48,7 +55,48 @@ class AmazonKnowledgeBasesRetriever(BaseRetriever):
                     }
                 },
             )
-    """
+
+    Usage:
+        .. code-block:: python
+
+            query = "..."
+
+            retriever.invoke(query)
+
+    Use within a chain:
+        .. code-block:: python
+
+            from langchain_aws import ChatBedrockConverse
+            from langchain_core.output_parsers import StrOutputParser
+            from langchain_core.prompts import ChatPromptTemplate
+            from langchain_core.runnables import RunnablePassthrough
+            from langchain_openai import ChatOpenAI
+
+            prompt = ChatPromptTemplate.from_template(
+                \"\"\"Answer the question based only on the context provided.
+
+            Context: {context}
+
+            Question: {question}\"\"\"
+            )
+
+            llm = ChatBedrockConverse(
+                model_id="anthropic.claude-3-5-sonnet-20240620-v1:0"
+            )
+
+            def format_docs(docs):
+                return "\\n\\n".join(doc.page_content for doc in docs)
+
+            chain = (
+                {"context": retriever | format_docs, "question": RunnablePassthrough()}
+                | prompt
+                | llm
+                | StrOutputParser()
+            )
+
+            chain.invoke("...")
+
+    """  # noqa: E501
 
     knowledge_base_id: str
     region_name: Optional[str] = None