prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  RediSearch: search command doesn't support asyncio Pipeline
  The RediSearch search command returns an instance of the `Result` class except when the Redis client is a `Pipeline` because `Pipeline` returns itself instead of a result when you execute a command. There's code that checks for this in both the `SearchCommands` and `AsyncSearchCommands` classes:
  https://github.com/redis/redis-py/blob/4b0543d567aef36ac467ce495d831a24575d8d5b/redis/commands/search/commands.py#L414
  https://github.com/redis/redis-py/blob/4b0543d567aef36ac467ce495d831a24575d8d5b/redis/commands/search/commands.py#L883
  However, this check doesn't work if the `Pipeline` is from the `redis.asyncio.client` module. The following modification should fix the issue:
  ```python
  from redis.client import Pipeline
  from redis.asyncio.client import Pipeline as AsyncPipeline
  ...
          if isinstance(res, Pipeline) or isinstance(res, AsyncPipeline):
              return res
  ...
  ```
  I'm not sure if it makes sense to check for both `Pipeline` types or if the `SearchCommands` class should check for just `Pipeline` and the `AsyncSearchCommands` class should check for just `AsyncPipeline`. Let me know and I can make a PR. Or feel free to make the changes yourself if that's easier. Thanks!
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `redis/commands/search/__init__.py`
  Content:
  ```
  1 import redis
  2 
  3 from ...asyncio.client import Pipeline as AsyncioPipeline
  4 from .commands import AsyncSearchCommands, SearchCommands
  5 
  6 
  7 class Search(SearchCommands):
  8     """
  9     Create a client for talking to search.
  10     It abstracts the API of the module and lets you just use the engine.
  11     """
  12 
  13     class BatchIndexer:
  14         """
  15         A batch indexer allows you to automatically batch
  16         document indexing in pipelines, flushing it every N documents.
  17         """
  18 
  19         def __init__(self, client, chunk_size=1000):
  20 
  21             self.client = client
  22             self.execute_command = client.execute_command
  23             self._pipeline = client.pipeline(transaction=False, shard_hint=None)
  24             self.total = 0
  25             self.chunk_size = chunk_size
  26             self.current_chunk = 0
  27 
  28         def __del__(self):
  29             if self.current_chunk:
  30                 self.commit()
  31 
  32         def add_document(
  33             self,
  34             doc_id,
  35             nosave=False,
  36             score=1.0,
  37             payload=None,
  38             replace=False,
  39             partial=False,
  40             no_create=False,
  41             **fields,
  42         ):
  43             """
  44             Add a document to the batch query
  45             """
  46             self.client._add_document(
  47                 doc_id,
  48                 conn=self._pipeline,
  49                 nosave=nosave,
  50                 score=score,
  51                 payload=payload,
  52                 replace=replace,
  53                 partial=partial,
  54                 no_create=no_create,
  55                 **fields,
  56             )
  57             self.current_chunk += 1
  58             self.total += 1
  59             if self.current_chunk >= self.chunk_size:
  60                 self.commit()
  61 
  62         def add_document_hash(self, doc_id, score=1.0, replace=False):
  63             """
  64             Add a hash to the batch query
  65             """
  66             self.client._add_document_hash(
  67                 doc_id, conn=self._pipeline, score=score, replace=replace
  68             )
  69             self.current_chunk += 1
  70             self.total += 1
  71             if self.current_chunk >= self.chunk_size:
  72                 self.commit()
  73 
  74         def commit(self):
  75             """
  76             Manually commit and flush the batch indexing query
  77             """
  78             self._pipeline.execute()
  79             self.current_chunk = 0
  80 
  81     def __init__(self, client, index_name="idx"):
  82         """
  83         Create a new Client for the given index_name.
  84         The default name is `idx`
  85 
  86         If conn is not None, we employ an already existing redis connection
  87         """
  88         self.MODULE_CALLBACKS = {}
  89         self.client = client
  90         self.index_name = index_name
  91         self.execute_command = client.execute_command
  92         self._pipeline = client.pipeline
  93 
  94     def pipeline(self, transaction=True, shard_hint=None):
  95         """Creates a pipeline for the SEARCH module, that can be used for executing
  96         SEARCH commands, as well as classic core commands.
  97         """
  98         p = Pipeline(
  99             connection_pool=self.client.connection_pool,
  100             response_callbacks=self.MODULE_CALLBACKS,
  101             transaction=transaction,
  102             shard_hint=shard_hint,
  103         )
  104         p.index_name = self.index_name
  105         return p
  106 
  107 
  108 class AsyncSearch(Search, AsyncSearchCommands):
  109     class BatchIndexer(Search.BatchIndexer):
  110         """
  111         A batch indexer allows you to automatically batch
  112         document indexing in pipelines, flushing it every N documents.
  113         """
  114 
  115         async def add_document(
  116             self,
  117             doc_id,
  118             nosave=False,
  119             score=1.0,
  120             payload=None,
  121             replace=False,
  122             partial=False,
  123             no_create=False,
  124             **fields,
  125         ):
  126             """
  127             Add a document to the batch query
  128             """
  129             self.client._add_document(
  130                 doc_id,
  131                 conn=self._pipeline,
  132                 nosave=nosave,
  133                 score=score,
  134                 payload=payload,
  135                 replace=replace,
  136                 partial=partial,
  137                 no_create=no_create,
  138                 **fields,
  139             )
  140             self.current_chunk += 1
  141             self.total += 1
  142             if self.current_chunk >= self.chunk_size:
  143                 await self.commit()
  144 
  145         async def commit(self):
  146             """
  147             Manually commit and flush the batch indexing query
  148             """
  149             await self._pipeline.execute()
  150             self.current_chunk = 0
  151 
  152     def pipeline(self, transaction=True, shard_hint=None):
  153         """Creates a pipeline for the SEARCH module, that can be used for executing
  154         SEARCH commands, as well as classic core commands.
  155         """
  156         p = AsyncPipeline(
  157             connection_pool=self.client.connection_pool,
  158             response_callbacks=self.MODULE_CALLBACKS,
  159             transaction=transaction,
  160             shard_hint=shard_hint,
  161         )
  162         p.index_name = self.index_name
  163         return p
  164 
  165 
  166 class Pipeline(SearchCommands, redis.client.Pipeline):
  167     """Pipeline for the module."""
  168 
  169 
  170 class AsyncPipeline(AsyncSearchCommands, AsyncioPipeline):
  171     """AsyncPipeline for the module."""
  172
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/redis/commands/search/__init__.py b/redis/commands/search/__init__.py
  --- a/redis/commands/search/__init__.py
  +++ b/redis/commands/search/__init__.py
  @@ -167,5 +167,5 @@
       """Pipeline for the module."""
   
   
  -class AsyncPipeline(AsyncSearchCommands, AsyncioPipeline):
  +class AsyncPipeline(AsyncSearchCommands, AsyncioPipeline, Pipeline):
       """AsyncPipeline for the module."""
  
verification_info: {"golden_diff": "diff --git a/redis/commands/search/__init__.py b/redis/commands/search/__init__.py\n--- a/redis/commands/search/__init__.py\n+++ b/redis/commands/search/__init__.py\n@@ -167,5 +167,5 @@\n     \"\"\"Pipeline for the module.\"\"\"\n \n \n-class AsyncPipeline(AsyncSearchCommands, AsyncioPipeline):\n+class AsyncPipeline(AsyncSearchCommands, AsyncioPipeline, Pipeline):\n     \"\"\"AsyncPipeline for the module.\"\"\"\n", "issue": "RediSearch: search command doesn't support asyncio Pipeline\nThe RediSearch search command returns an instance of the `Result` class except when the Redis client is a `Pipeline` because `Pipeline` returns itself instead of a result when you execute a command. There's code that checks for this in both the `SearchCommands` and `AsyncSearchCommands` classes:\r\nhttps://github.com/redis/redis-py/blob/4b0543d567aef36ac467ce495d831a24575d8d5b/redis/commands/search/commands.py#L414\r\nhttps://github.com/redis/redis-py/blob/4b0543d567aef36ac467ce495d831a24575d8d5b/redis/commands/search/commands.py#L883\r\nHowever, this check doesn't work if the `Pipeline` is from the `redis.asyncio.client` module. The following modification should fix the issue:\r\n```python\r\nfrom redis.client import Pipeline\r\nfrom redis.asyncio.client import Pipeline as AsyncPipeline\r\n...\r\n        if isinstance(res, Pipeline) or isinstance(res, AsyncPipeline):\r\n            return res\r\n...\r\n```\r\nI'm not sure if it makes sense to check for both `Pipeline` types or if the `SearchCommands` class should check for just `Pipeline` and the `AsyncSearchCommands` class should check for just `AsyncPipeline`. Let me know and I can make a PR. Or feel free to make the changes yourself if that's easier. Thanks!\n", "before_files": [{"content": "import redis\n\nfrom ...asyncio.client import Pipeline as AsyncioPipeline\nfrom .commands import AsyncSearchCommands, SearchCommands\n\n\nclass Search(SearchCommands):\n    \"\"\"\n    Create a client for talking to search.\n    It abstracts the API of the module and lets you just use the engine.\n    \"\"\"\n\n    class BatchIndexer:\n        \"\"\"\n        A batch indexer allows you to automatically batch\n        document indexing in pipelines, flushing it every N documents.\n        \"\"\"\n\n        def __init__(self, client, chunk_size=1000):\n\n            self.client = client\n            self.execute_command = client.execute_command\n            self._pipeline = client.pipeline(transaction=False, shard_hint=None)\n            self.total = 0\n            self.chunk_size = chunk_size\n            self.current_chunk = 0\n\n        def __del__(self):\n            if self.current_chunk:\n                self.commit()\n\n        def add_document(\n            self,\n            doc_id,\n            nosave=False,\n            score=1.0,\n            payload=None,\n            replace=False,\n            partial=False,\n            no_create=False,\n            **fields,\n        ):\n            \"\"\"\n            Add a document to the batch query\n            \"\"\"\n            self.client._add_document(\n                doc_id,\n                conn=self._pipeline,\n                nosave=nosave,\n                score=score,\n                payload=payload,\n                replace=replace,\n                partial=partial,\n                no_create=no_create,\n                **fields,\n            )\n            self.current_chunk += 1\n            self.total += 1\n            if self.current_chunk >= self.chunk_size:\n                self.commit()\n\n        def add_document_hash(self, doc_id, score=1.0, replace=False):\n            \"\"\"\n            Add a hash to the batch query\n            \"\"\"\n            self.client._add_document_hash(\n                doc_id, conn=self._pipeline, score=score, replace=replace\n            )\n            self.current_chunk += 1\n            self.total += 1\n            if self.current_chunk >= self.chunk_size:\n                self.commit()\n\n        def commit(self):\n            \"\"\"\n            Manually commit and flush the batch indexing query\n            \"\"\"\n            self._pipeline.execute()\n            self.current_chunk = 0\n\n    def __init__(self, client, index_name=\"idx\"):\n        \"\"\"\n        Create a new Client for the given index_name.\n        The default name is `idx`\n\n        If conn is not None, we employ an already existing redis connection\n        \"\"\"\n        self.MODULE_CALLBACKS = {}\n        self.client = client\n        self.index_name = index_name\n        self.execute_command = client.execute_command\n        self._pipeline = client.pipeline\n\n    def pipeline(self, transaction=True, shard_hint=None):\n        \"\"\"Creates a pipeline for the SEARCH module, that can be used for executing\n        SEARCH commands, as well as classic core commands.\n        \"\"\"\n        p = Pipeline(\n            connection_pool=self.client.connection_pool,\n            response_callbacks=self.MODULE_CALLBACKS,\n            transaction=transaction,\n            shard_hint=shard_hint,\n        )\n        p.index_name = self.index_name\n        return p\n\n\nclass AsyncSearch(Search, AsyncSearchCommands):\n    class BatchIndexer(Search.BatchIndexer):\n        \"\"\"\n        A batch indexer allows you to automatically batch\n        document indexing in pipelines, flushing it every N documents.\n        \"\"\"\n\n        async def add_document(\n            self,\n            doc_id,\n            nosave=False,\n            score=1.0,\n            payload=None,\n            replace=False,\n            partial=False,\n            no_create=False,\n            **fields,\n        ):\n            \"\"\"\n            Add a document to the batch query\n            \"\"\"\n            self.client._add_document(\n                doc_id,\n                conn=self._pipeline,\n                nosave=nosave,\n                score=score,\n                payload=payload,\n                replace=replace,\n                partial=partial,\n                no_create=no_create,\n                **fields,\n            )\n            self.current_chunk += 1\n            self.total += 1\n            if self.current_chunk >= self.chunk_size:\n                await self.commit()\n\n        async def commit(self):\n            \"\"\"\n            Manually commit and flush the batch indexing query\n            \"\"\"\n            await self._pipeline.execute()\n            self.current_chunk = 0\n\n    def pipeline(self, transaction=True, shard_hint=None):\n        \"\"\"Creates a pipeline for the SEARCH module, that can be used for executing\n        SEARCH commands, as well as classic core commands.\n        \"\"\"\n        p = AsyncPipeline(\n            connection_pool=self.client.connection_pool,\n            response_callbacks=self.MODULE_CALLBACKS,\n            transaction=transaction,\n            shard_hint=shard_hint,\n        )\n        p.index_name = self.index_name\n        return p\n\n\nclass Pipeline(SearchCommands, redis.client.Pipeline):\n    \"\"\"Pipeline for the module.\"\"\"\n\n\nclass AsyncPipeline(AsyncSearchCommands, AsyncioPipeline):\n    \"\"\"AsyncPipeline for the module.\"\"\"\n", "path": "redis/commands/search/__init__.py"}], "after_files": [{"content": "import redis\n\nfrom ...asyncio.client import Pipeline as AsyncioPipeline\nfrom .commands import AsyncSearchCommands, SearchCommands\n\n\nclass Search(SearchCommands):\n    \"\"\"\n    Create a client for talking to search.\n    It abstracts the API of the module and lets you just use the engine.\n    \"\"\"\n\n    class BatchIndexer:\n        \"\"\"\n        A batch indexer allows you to automatically batch\n        document indexing in pipelines, flushing it every N documents.\n        \"\"\"\n\n        def __init__(self, client, chunk_size=1000):\n\n            self.client = client\n            self.execute_command = client.execute_command\n            self._pipeline = client.pipeline(transaction=False, shard_hint=None)\n            self.total = 0\n            self.chunk_size = chunk_size\n            self.current_chunk = 0\n\n        def __del__(self):\n            if self.current_chunk:\n                self.commit()\n\n        def add_document(\n            self,\n            doc_id,\n            nosave=False,\n            score=1.0,\n            payload=None,\n            replace=False,\n            partial=False,\n            no_create=False,\n            **fields,\n        ):\n            \"\"\"\n            Add a document to the batch query\n            \"\"\"\n            self.client._add_document(\n                doc_id,\n                conn=self._pipeline,\n                nosave=nosave,\n                score=score,\n                payload=payload,\n                replace=replace,\n                partial=partial,\n                no_create=no_create,\n                **fields,\n            )\n            self.current_chunk += 1\n            self.total += 1\n            if self.current_chunk >= self.chunk_size:\n                self.commit()\n\n        def add_document_hash(self, doc_id, score=1.0, replace=False):\n            \"\"\"\n            Add a hash to the batch query\n            \"\"\"\n            self.client._add_document_hash(\n                doc_id, conn=self._pipeline, score=score, replace=replace\n            )\n            self.current_chunk += 1\n            self.total += 1\n            if self.current_chunk >= self.chunk_size:\n                self.commit()\n\n        def commit(self):\n            \"\"\"\n            Manually commit and flush the batch indexing query\n            \"\"\"\n            self._pipeline.execute()\n            self.current_chunk = 0\n\n    def __init__(self, client, index_name=\"idx\"):\n        \"\"\"\n        Create a new Client for the given index_name.\n        The default name is `idx`\n\n        If conn is not None, we employ an already existing redis connection\n        \"\"\"\n        self.MODULE_CALLBACKS = {}\n        self.client = client\n        self.index_name = index_name\n        self.execute_command = client.execute_command\n        self._pipeline = client.pipeline\n\n    def pipeline(self, transaction=True, shard_hint=None):\n        \"\"\"Creates a pipeline for the SEARCH module, that can be used for executing\n        SEARCH commands, as well as classic core commands.\n        \"\"\"\n        p = Pipeline(\n            connection_pool=self.client.connection_pool,\n            response_callbacks=self.MODULE_CALLBACKS,\n            transaction=transaction,\n            shard_hint=shard_hint,\n        )\n        p.index_name = self.index_name\n        return p\n\n\nclass AsyncSearch(Search, AsyncSearchCommands):\n    class BatchIndexer(Search.BatchIndexer):\n        \"\"\"\n        A batch indexer allows you to automatically batch\n        document indexing in pipelines, flushing it every N documents.\n        \"\"\"\n\n        async def add_document(\n            self,\n            doc_id,\n            nosave=False,\n            score=1.0,\n            payload=None,\n            replace=False,\n            partial=False,\n            no_create=False,\n            **fields,\n        ):\n            \"\"\"\n            Add a document to the batch query\n            \"\"\"\n            self.client._add_document(\n                doc_id,\n                conn=self._pipeline,\n                nosave=nosave,\n                score=score,\n                payload=payload,\n                replace=replace,\n                partial=partial,\n                no_create=no_create,\n                **fields,\n            )\n            self.current_chunk += 1\n            self.total += 1\n            if self.current_chunk >= self.chunk_size:\n                await self.commit()\n\n        async def commit(self):\n            \"\"\"\n            Manually commit and flush the batch indexing query\n            \"\"\"\n            await self._pipeline.execute()\n            self.current_chunk = 0\n\n    def pipeline(self, transaction=True, shard_hint=None):\n        \"\"\"Creates a pipeline for the SEARCH module, that can be used for executing\n        SEARCH commands, as well as classic core commands.\n        \"\"\"\n        p = AsyncPipeline(\n            connection_pool=self.client.connection_pool,\n            response_callbacks=self.MODULE_CALLBACKS,\n            transaction=transaction,\n            shard_hint=shard_hint,\n        )\n        p.index_name = self.index_name\n        return p\n\n\nclass Pipeline(SearchCommands, redis.client.Pipeline):\n    \"\"\"Pipeline for the module.\"\"\"\n\n\nclass AsyncPipeline(AsyncSearchCommands, AsyncioPipeline, Pipeline):\n    \"\"\"AsyncPipeline for the module.\"\"\"\n", "path": "redis/commands/search/__init__.py"}]}