docs: update integration api refs (#25195)

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

libs/cli/langchain_cli/integration_template/integration_template/retrievers.py

@@ -44,7 +44,7 @@ class __ModuleName__Retriever(BaseRetriever):
 
             retriever.invoke(query)
 
-        .. code-block:: python
+        .. code-block:: none
 
             # TODO: Example output.
 
@@ -67,7 +67,7 @@ class __ModuleName__Retriever(BaseRetriever):
             llm = ChatOpenAI(model="gpt-3.5-turbo-0125")
 
             def format_docs(docs):
-                return "\n\n".join(doc.page_content for doc in docs)
+                return "\\n\\n".join(doc.page_content for doc in docs)
 
             chain = (
                 {"context": retriever | format_docs, "question": RunnablePassthrough()}
@@ -78,7 +78,7 @@ class __ModuleName__Retriever(BaseRetriever):
 
             chain.invoke("...")
 
-        .. code-block:: python
+        .. code-block:: none
 
              # TODO: Example output.
 

libs/cli/langchain_cli/integration_template/integration_template/toolkits.py

@@ -41,7 +41,7 @@ class __ModuleName__Toolkit(BaseToolKit):
 
             toolkit.get_tools()
 
-        .. code-block:: python
+        .. code-block:: none
 
             # TODO: Example output.
 
@@ -61,7 +61,7 @@ class __ModuleName__Toolkit(BaseToolKit):
             for event in events:
                 event["messages"][-1].pretty_print()
 
-        .. code-block:: python
+        .. code-block:: none
 
              # TODO: Example output.
 

libs/community/langchain_community/agent_toolkits/github/toolkit.py

@@ -164,9 +164,105 @@ class GitHubToolkit(BaseToolkit):
 
         See [Security](https://python.langchain.com/docs/security) for more information.
 
+    Setup:
+        See detailed installation instructions here:
+        https://python.langchain.com/v0.2/docs/integrations/tools/github/#installation
+
+        You will need to install ``pygithub`` and set the following environment
+        variables:
+
+        .. code-block:: bash
+
+            pip install -U pygithub
+            export GITHUB_APP_ID="your-app-id"
+            export GITHUB_APP_PRIVATE_KEY="path-to-private-key"
+            export GITHUB_REPOSITORY="your-github-repository"
+
+    Instantiate:
+        .. code-block:: python
+
+            from langchain_community.agent_toolkits.github.toolkit import GitHubToolkit
+            from langchain_community.utilities.github import GitHubAPIWrapper
+
+            github = GitHubAPIWrapper()
+            toolkit = GitHubToolkit.from_github_api_wrapper(github)
+
+    Tools:
+        .. code-block:: python
+
+            tools = toolkit.get_tools()
+            for tool in tools:
+                print(tool.name)
+
+        .. code-block:: none
+
+            Get Issues
+            Get Issue
+            Comment on Issue
+            List open pull requests (PRs)
+            Get Pull Request
+            Overview of files included in PR
+            Create Pull Request
+            List Pull Requests' Files
+            Create File
+            Read File
+            Update File
+            Delete File
+            Overview of existing files in Main branch
+            Overview of files in current working branch
+            List branches in this repository
+            Set active branch
+            Create a new branch
+            Get files from a directory
+            Search issues and pull requests
+            Search code
+            Create review request
+
+    Use within an agent:
+        .. code-block:: python
+
+            from langchain_openai import ChatOpenAI
+            from langgraph.prebuilt import create_react_agent
+
+            # Select example tool
+            tools = [tool for tool in toolkit.get_tools() if tool.name == "Get Issue"]
+            assert len(tools) == 1
+            tools[0].name = "get_issue"
+
+            llm = ChatOpenAI(model="gpt-4o-mini")
+            agent_executor = create_react_agent(llm, tools)
+
+            example_query = "What is the title of issue 24888?"
+
+            events = agent_executor.stream(
+                {"messages": [("user", example_query)]},
+                stream_mode="values",
+            )
+            for event in events:
+                event["messages"][-1].pretty_print()
+
+        .. code-block:: none
+
+             ================================[1m Human Message [0m=================================
+
+            What is the title of issue 24888?
+            ==================================[1m Ai Message [0m==================================
+            Tool Calls:
+            get_issue (call_iSYJVaM7uchfNHOMJoVPQsOi)
+            Call ID: call_iSYJVaM7uchfNHOMJoVPQsOi
+            Args:
+                issue_number: 24888
+            =================================[1m Tool Message [0m=================================
+            Name: get_issue
+
+            {"number": 24888, "title": "Standardize KV-Store Docs", "body": "..."
+            ==================================[1m Ai Message [0m==================================
+
+            The title of issue 24888 is "Standardize KV-Store Docs".
+
     Parameters:
         tools: List[BaseTool]. The tools in the toolkit. Default is an empty list.
-    """
+    """  # noqa: E501
 
     tools: List[BaseTool] = []
 

libs/community/langchain_community/agent_toolkits/gmail/toolkit.py

@@ -39,9 +39,81 @@ class GmailToolkit(BaseToolkit):
 
         See https://python.langchain.com/docs/security for more information.
 
+    Setup:
+        You will need a Google credentials.json file to use this toolkit.
+        See instructions here: https://python.langchain.com/v0.2/docs/integrations/tools/gmail/#setup
+
+    Key init args:
+        api_resource: Optional. The Google API resource. Default is None.
+
+    Instantiate:
+        .. code-block:: python
+
+            from langchain_google_community import GmailToolkit
+
+            toolkit = GmailToolkit()
+
+    Tools:
+        .. code-block:: python
+
+            toolkit.get_tools()
+
+        .. code-block:: none
+
+            [GmailCreateDraft(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),
+            GmailSendMessage(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),
+            GmailSearch(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),
+            GmailGetMessage(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),
+            GmailGetThread(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>)]
+
+    Use within an agent:
+        .. code-block:: python
+
+            from langchain_openai import ChatOpenAI
+            from langgraph.prebuilt import create_react_agent
+
+            llm = ChatOpenAI(model="gpt-4o-mini")
+
+            agent_executor = create_react_agent(llm, tools)
+
+            example_query = "Draft an email to fake@fake.com thanking them for coffee."
+
+            events = agent_executor.stream(
+                {"messages": [("user", example_query)]},
+                stream_mode="values",
+            )
+            for event in events:
+                event["messages"][-1].pretty_print()
+
+        .. code-block:: none
+
+             ================================[1m Human Message [0m=================================
+
+            Draft an email to fake@fake.com thanking them for coffee.
+            ==================================[1m Ai Message [0m==================================
+            Tool Calls:
+            create_gmail_draft (call_slGkYKZKA6h3Mf1CraUBzs6M)
+            Call ID: call_slGkYKZKA6h3Mf1CraUBzs6M
+            Args:
+                message: Dear Fake,
+
+            I wanted to take a moment to thank you for the coffee yesterday. It was a pleasure catching up with you. Let's do it again soon!
+
+            Best regards,
+            [Your Name]
+                to: ['fake@fake.com']
+                subject: Thank You for the Coffee
+            =================================[1m Tool Message [0m=================================
+            Name: create_gmail_draft
+
+            Draft created. Draft Id: r-7233782721440261513
+            ==================================[1m Ai Message [0m==================================
+
+            I have drafted an email to fake@fake.com thanking them for the coffee. You can review and send it from your email draft with the subject "Thank You for the Coffee".
+
     Parameters:
         api_resource: Optional. The Google API resource. Default is None.
-    """
+    """  # noqa: E501
 
     api_resource: Resource = Field(default_factory=build_resource_service)
 

libs/community/langchain_community/agent_toolkits/openapi/toolkit.py

@@ -38,7 +38,125 @@ class RequestsToolkit(BaseToolkit):
         what network access it has.
 
         See https://python.langchain.com/docs/security for more information.
-    """
+
+    Setup:
+        Install ``langchain-community``.
+
+        .. code-block:: bash
+
+            pip install -U langchain-community
+
+    Key init args:
+        requests_wrapper: langchain_community.utilities.requests.GenericRequestsWrapper
+            wrapper for executing requests.
+        allow_dangerous_requests: bool
+            Defaults to False. Must "opt-in" to using dangerous requests by setting to True.
+
+    Instantiate:
+        .. code-block:: python
+
+            from langchain_community.agent_toolkits.openapi.toolkit import RequestsToolkit
+            from langchain_community.utilities.requests import TextRequestsWrapper
+
+            toolkit = RequestsToolkit(
+                requests_wrapper=TextRequestsWrapper(headers={}),
+                allow_dangerous_requests=ALLOW_DANGEROUS_REQUEST,
+            )
+
+    Tools:
+        .. code-block:: python
+
+            tools = toolkit.get_tools()
+            tools
+
+        .. code-block:: none
+
+            [RequestsGetTool(requests_wrapper=TextRequestsWrapper(headers={}, aiosession=None, auth=None, response_content_type='text', verify=True), allow_dangerous_requests=True),
+            RequestsPostTool(requests_wrapper=TextRequestsWrapper(headers={}, aiosession=None, auth=None, response_content_type='text', verify=True), allow_dangerous_requests=True),
+            RequestsPatchTool(requests_wrapper=TextRequestsWrapper(headers={}, aiosession=None, auth=None, response_content_type='text', verify=True), allow_dangerous_requests=True),
+            RequestsPutTool(requests_wrapper=TextRequestsWrapper(headers={}, aiosession=None, auth=None, response_content_type='text', verify=True), allow_dangerous_requests=True),
+            RequestsDeleteTool(requests_wrapper=TextRequestsWrapper(headers={}, aiosession=None, auth=None, response_content_type='text', verify=True), allow_dangerous_requests=True)]
+
+    Use within an agent:
+        .. code-block:: python
+
+            from langchain_openai import ChatOpenAI
+            from langgraph.prebuilt import create_react_agent
+
+
+            api_spec = \"\"\"
+            openapi: 3.0.0
+            info:
+              title: JSONPlaceholder API
+              version: 1.0.0
+            servers:
+              - url: https://jsonplaceholder.typicode.com
+            paths:
+              /posts:
+                get:
+                  summary: Get posts
+                  parameters: &id001
+                    - name: _limit
+                      in: query
+                      required: false
+                      schema:
+                        type: integer
+                      example: 2
+                      description: Limit the number of results
+            \"\"\"
+
+            system_message = \"\"\"
+            You have access to an API to help answer user queries.
+            Here is documentation on the API:
+            {api_spec}
+            \"\"\".format(api_spec=api_spec)
+
+            llm = ChatOpenAI(model="gpt-4o-mini")
+            agent_executor = create_react_agent(llm, tools, state_modifier=system_message)
+
+            example_query = "Fetch the top two posts. What are their titles?"
+
+            events = agent_executor.stream(
+                {"messages": [("user", example_query)]},
+                stream_mode="values",
+            )
+            for event in events:
+                event["messages"][-1].pretty_print()
+
+        .. code-block:: none
+
+             ================================[1m Human Message [0m=================================
+
+            Fetch the top two posts. What are their titles?
+            ==================================[1m Ai Message [0m==================================
+            Tool Calls:
+            requests_get (call_RV2SOyzCnV5h2sm4WPgG8fND)
+            Call ID: call_RV2SOyzCnV5h2sm4WPgG8fND
+            Args:
+                url: https://jsonplaceholder.typicode.com/posts?_limit=2
+            =================================[1m Tool Message [0m=================================
+            Name: requests_get
+
+            [
+            {
+                "userId": 1,
+                "id": 1,
+                "title": "sunt aut facere repellat provident occaecati excepturi optio reprehenderit",
+                "body": "quia et suscipit..."
+            },
+            {
+                "userId": 1,
+                "id": 2,
+                "title": "qui est esse",
+                "body": "est rerum tempore vitae..."
+            }
+            ]
+            ==================================[1m Ai Message [0m==================================
+
+            The titles of the top two posts are:
+            1. "sunt aut facere repellat provident occaecati excepturi optio reprehenderit"
+            2. "qui est esse"
+    """  # noqa: E501
 
     requests_wrapper: TextRequestsWrapper
     """The requests wrapper."""

libs/community/langchain_community/agent_toolkits/slack/toolkit.py

@@ -21,7 +21,73 @@ class SlackToolkit(BaseToolkit):
 
     Parameters:
         client: The Slack client.
-    """
+
+    Setup:
+        Install ``slack_sdk`` and set environment variable ``SLACK_USER_TOKEN``.
+
+        .. code-block:: bash
+
+            pip install -U slack_sdk
+            export SLACK_USER_TOKEN="your-user-token"
+
+    Key init args:
+        client: slack_sdk.WebClient
+            The Slack client.
+
+    Instantiate:
+        .. code-block:: python
+
+            from langchain_community.agent_toolkits import SlackToolkit
+
+            toolkit = SlackToolkit()
+
+    Tools:
+        .. code-block:: python
+
+            tools = toolkit.get_tools()
+            tools
+
+        .. code-block:: none
+
+            [SlackGetChannel(client=<slack_sdk.web.client.WebClient object at 0x113caa8c0>),
+            SlackGetMessage(client=<slack_sdk.web.client.WebClient object at 0x113caa4d0>),
+            SlackScheduleMessage(client=<slack_sdk.web.client.WebClient object at 0x113caa440>),
+            SlackSendMessage(client=<slack_sdk.web.client.WebClient object at 0x113caa410>)]
+
+    Use within an agent:
+        .. code-block:: python
+
+            from langchain_openai import ChatOpenAI
+            from langgraph.prebuilt import create_react_agent
+
+            llm = ChatOpenAI(model="gpt-4o-mini")
+            agent_executor = create_react_agent(llm, tools)
+
+            example_query = "When was the #general channel created?"
+
+            events = agent_executor.stream(
+                {"messages": [("user", example_query)]},
+                stream_mode="values",
+            )
+            for event in events:
+                message = event["messages"][-1]
+                if message.type != "tool":  # mask sensitive information
+                    event["messages"][-1].pretty_print()
+
+        .. code-block:: none
+
+             ================================[1m Human Message [0m=================================
+
+            When was the #general channel created?
+            ==================================[1m Ai Message [0m==================================
+            Tool Calls:
+            get_channelid_name_dict (call_NXDkALjoOx97uF1v0CoZTqtJ)
+            Call ID: call_NXDkALjoOx97uF1v0CoZTqtJ
+            Args:
+            ==================================[1m Ai Message [0m==================================
+
+            The #general channel was created on timestamp 1671043305.
+    """  # noqa: E501
 
     client: WebClient = Field(default_factory=login)
 

libs/community/langchain_community/retrievers/arxiv.py

@@ -10,9 +10,76 @@ from langchain_community.utilities.arxiv import ArxivAPIWrapper
 class ArxivRetriever(BaseRetriever, ArxivAPIWrapper):
     """`Arxiv` retriever.
 
-    It wraps load() to get_relevant_documents().
+    Setup:
-    It uses all ArxivAPIWrapper arguments without any change.
+        Install ``arxiv``:
-    """
+
+        .. code-block:: bash
+
+            pip install -U arxiv
+
+    Key init args:
+        load_max_docs: int
+            maximum number of documents to load
+        get_ful_documents: bool
+            whether to return full document text or snippets
+
+    Instantiate:
+        .. code-block:: python
+
+            from langchain_community.retrievers import ArxivRetriever
+
+            retriever = ArxivRetriever(
+                load_max_docs=2,
+                get_ful_documents=True,
+            )
+
+    Usage:
+        .. code-block:: python
+
+            docs = retriever.invoke("What is the ImageBind model?")
+            docs[0].metadata
+
+        .. code-block:: none
+
+            {'Entry ID': 'http://arxiv.org/abs/2305.05665v2',
+            'Published': datetime.date(2023, 5, 31),
+            'Title': 'ImageBind: One Embedding Space To Bind Them All',
+            'Authors': 'Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, Ishan Misra'}
+
+    Use within a chain:
+        .. code-block:: python
+
+            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 = ChatOpenAI(model="gpt-3.5-turbo-0125")
+
+            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("What is the ImageBind model?")
+
+        .. code-block:: none
+
+             'The ImageBind model is an approach to learn a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data...'
+    """  # noqa: E501
 
     get_full_documents: bool = False
 

libs/community/langchain_community/retrievers/azure_ai_search.py

@@ -19,7 +19,71 @@ DEFAULT_URL_SUFFIX = "search.windows.net"
 
 
 class AzureAISearchRetriever(BaseRetriever):
-    """`Azure AI Search` service retriever."""
+    """`Azure AI Search` service retriever.
+
+    Setup:
+        See here for more detail: https://python.langchain.com/v0.2/docs/integrations/retrievers/azure_ai_search/
+
+        We will need to install the below dependencies and set the required
+        environment variables:
+
+        .. code-block:: bash
+
+            pip install -U langchain-community azure-identity azure-search-documents
+            export AZURE_AI_SEARCH_SERVICE_NAME="<YOUR_SEARCH_SERVICE_NAME>"
+            export AZURE_AI_SEARCH_INDEX_NAME="<YOUR_SEARCH_INDEX_NAME>"
+            export AZURE_AI_SEARCH_API_KEY="<YOUR_API_KEY>"
+
+    Key init args:
+        content_key: str
+        top_k: int
+        index_name: str
+
+    Instantiate:
+        .. code-block:: python
+
+            from langchain_community.retrievers import AzureAISearchRetriever
+
+            retriever = AzureAISearchRetriever(
+                content_key="content", top_k=1, index_name="langchain-vector-demo"
+            )
+
+    Usage:
+        .. code-block:: python
+
+            retriever.invoke("here is my unstructured query string")
+
+    Use within a chain:
+        .. code-block:: python
+
+            from langchain_core.output_parsers import StrOutputParser
+            from langchain_core.prompts import ChatPromptTemplate
+            from langchain_core.runnables import RunnablePassthrough
+            from langchain_openai import AzureChatOpenAI
+
+            prompt = ChatPromptTemplate.from_template(
+                \"\"\"Answer the question based only on the context provided.
+
+            Context: {context}
+
+            Question: {question}\"\"\"
+            )
+
+            llm = AzureChatOpenAI(azure_deployment="gpt-35-turbo")
+
+            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
 
     service_name: str = ""
     """Name of Azure AI Search service"""

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

libs/community/langchain_community/retrievers/milvus.py

@@ -15,7 +15,73 @@ from langchain_community.vectorstores.milvus import Milvus
 
 
 class MilvusRetriever(BaseRetriever):
-    """`Milvus API` retriever."""
+    """Milvus API retriever.
+
+    See detailed instructions here: https://python.langchain.com/v0.2/docs/integrations/retrievers/milvus_hybrid_search/
+
+    Setup:
+        Install ``langchain-milvus`` and other dependencies:
+
+        .. code-block:: bash
+
+            pip install -U pymilvus[model] langchain-milvus
+
+    Key init args:
+        collection: Milvus Collection
+
+    Instantiate:
+        .. code-block:: python
+
+            retriever = MilvusCollectionHybridSearchRetriever(collection=collection)
+
+    Usage:
+        .. code-block:: python
+
+            query = "What are the story about ventures?"
+
+            retriever.invoke(query)
+
+        .. code-block:: none
+
+            [Document(page_content="In 'The Lost Expedition' by Caspian Grey...", metadata={'doc_id': '449281835035545843'}),
+            Document(page_content="In 'The Phantom Pilgrim' by Rowan Welles...", metadata={'doc_id': '449281835035545845'}),
+            Document(page_content="In 'The Dreamwalker's Journey' by Lyra Snow..", metadata={'doc_id': '449281835035545846'})]
+
+    Use within a chain:
+        .. code-block:: python
+
+            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 = ChatOpenAI(model="gpt-3.5-turbo-0125")
+
+            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("What novels has Lila written and what are their contents?")
+
+        .. code-block:: none
+
+             "Lila Rose has written 'The Memory Thief,' which follows a charismatic thief..."
+
+    """  # noqa: E501
 
     embedding_function: Embeddings
     collection_name: str = "LangChainCollection"

libs/community/langchain_community/retrievers/wikipedia.py

@@ -10,9 +10,66 @@ from langchain_community.utilities.wikipedia import WikipediaAPIWrapper
 class WikipediaRetriever(BaseRetriever, WikipediaAPIWrapper):
     """`Wikipedia API` retriever.
 
-    It wraps load() to get_relevant_documents().
+    Setup:
-    It uses all WikipediaAPIWrapper arguments without any change.
+        Install the ``wikipedia`` dependency:
-    """
+
+        .. code-block:: bash
+
+            pip install -U wikipedia
+
+    Instantiate:
+        .. code-block:: python
+
+            from langchain_community.retrievers import WikipediaRetriever
+
+            retriever = WikipediaRetriever()
+
+    Usage:
+        .. code-block:: python
+
+            docs = retriever.invoke("TOKYO GHOUL")
+            print(docs[0].page_content[:100])
+
+        .. code-block:: none
+
+            Tokyo Ghoul (Japanese: 東京喰種（トーキョーグール）, Hepburn: Tōkyō Gūru) is a Japanese dark fantasy
+
+    Use within a chain:
+        .. code-block:: python
+
+            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 = ChatOpenAI(model="gpt-3.5-turbo-0125")
+
+            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(
+                "Who is the main character in `Tokyo Ghoul` and does he transform into a ghoul?"
+            )
+
+        .. code-block:: none
+
+             'The main character in Tokyo Ghoul is Ken Kaneki, who transforms into a ghoul after receiving an organ transplant from a ghoul named Rize.'
+    """  # noqa: E501
 
     def _get_relevant_documents(
         self, query: str, *, run_manager: CallbackManagerForRetrieverRun