### Checked other resources

- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
- [X] I am sure that this is a bug in LangChain rather than my code.
- [X] The bug is not resolved by updating to the latest stable version of LangChain (or the specific integration package).

### Example Code

```python
from langchain_pinecone import PineconeVectorStore

# Dummy data for illustration purposes
dummy_docs = ...
dummy_embeddings = ...
dummy_index_name = ...

# Attempt to create a PineconeVectorStore retriever
vector_store_retriever = PineconeVectorStore.from_documents(dummy_docs, dummy_embeddings, index_name=dummy_index_name)

```

### Error Message and Stack Trace (if applicable)

```python
File "/home/ubuntu/app.py", line 31, in _get_vector_store_retriever
vector_store_retriever = PineconeVectorStore.from_documents(docs, self.embeddings, index_name=self.cfg.index_name)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/langchain_core/vectorstores.py", line 550, in from_documents
return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/langchain_pinecone/vectorstores.py", line 441, in from_texts
pinecone.add_texts(
File "/home/ubuntu/langchain_pinecone/vectorstores.py", line 158, in add_texts
async_res = [
^
File "/home/ubuntu/langchain_pinecone/vectorstores.py", line 159, in <listcomp>
self._index.upsert(
File "/home/ubuntu/pinecone/utils/error_handling.py", line 10, in inner_func
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/pinecone/data/index.py", line 168, in upsert
return self._upsert_batch(vectors, namespace, _check_type, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/pinecone/data/index.py", line 189, in _upsert_batch
return self._vector_api.upsert(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/pinecone/core/client/api_client.py", line 772, in __call__
return self.callable(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/pinecone/core/client/api/data_plane_api.py", line 1084, in __upsert
return self.call_with_http_info(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/pinecone/core/client/api_client.py", line 834, in call_with_http_info
return self.api_client.call_api(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/pinecone/core/client/api_client.py", line 417, in call_api
return self.pool.apply_async(self.__call_api, (resource_path,
^^^^^^^^^
File "/home/ubuntu/pinecone/core/client/api_client.py", line 103, in pool
self._pool = ThreadPool(self.pool_threads)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/multiprocessing/pool.py", line 930, in __init__
Pool.__init__(self, processes, initializer, initargs)
File "/usr/local/lib/python3.11/multiprocessing/pool.py", line 196, in __init__
self._change_notifier = self._ctx.SimpleQueue()
^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/multiprocessing/context.py", line 113, in SimpleQueue
return SimpleQueue(ctx=self.get_context())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/multiprocessing/queues.py", line 341, in __init__
self._rlock = ctx.Lock()
^^^^^^^^^^
File "/usr/local/lib/python3.11/multiprocessing/context.py", line 68, in Lock
return Lock(ctx=self.get_context())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/multiprocessing/synchronize.py", line 169, in __init__
SemLock.__init__(self, SEMAPHORE, 1, 1, ctx=ctx)
File "/usr/local/lib/python3.11/multiprocessing/synchronize.py", line 57, in __init__
sl = self._semlock = _multiprocessing.SemLock(
^^^^^^^^^^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory

```

### Description

- **Problem**: Encountering `FileNotFoundError: [Errno 2] No such file or directory` when trying to use `PineconeVectorStore.from_documents` in an AWS Lambda environment with the base image `python:3.11-slim`.
- **Expected Behavior**: The code should initialize a `PineconeVectorStore` retriever without errors.
- **Actual Behavior**: The initialization fails with a `FileNotFoundError` indicating an issue with multiprocessing in the slim Python 3.11 Docker image.

### System Info

- **Python Version**: 3.11

- **Platform**: Linux (AWS Lambda with python:3.11-slim base image)

- **Installed Packages**:

```
langchain==0.2.1
langchain-community==0.2.1
langchain-core==0.2.2
langchain-openai==0.1.8
langchain-pinecone==0.1.1
langchain-text-splitters==0.2.0
langdetect==1.0.9
langgraph==0.0.57
langsmith==0.1.63
pinecone-client==3.2.2
```
FileNotFoundError in PineconeVectorStore.from_documents on AWS Lambda 
This is a GitHub Issue
repo:langchain
owner:langchain-ai
Title : FileNotFoundError in PineconeVectorStore.from_documents on AWS Lambda 
Issue date:

--- start body ---
### Checked other resources

- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
- [X] I am sure that this is a bug in LangChain rather than my code.
- [X] The bug is not resolved by updating to the latest stable version of LangChain (or the specific integration package).

### Example Code

```python
from langchain_pinecone import PineconeVectorStore

# Dummy data for illustration purposes
dummy_docs = ...
dummy_embeddings = ...
dummy_index_name = ...

# Attempt to create a PineconeVectorStore retriever
vector_store_retriever = PineconeVectorStore.from_documents(dummy_docs, dummy_embeddings, index_name=dummy_index_name)

```

### Error Message and Stack Trace (if applicable)

```python
File "/home/ubuntu/app.py", line 31, in _get_vector_store_retriever
vector_store_retriever = PineconeVectorStore.from_documents(docs, self.embeddings, index_name=self.cfg.index_name)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/langchain_core/vectorstores.py", line 550, in from_documents
return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/langchain_pinecone/vectorstores.py", line 441, in from_texts
pinecone.add_texts(
File "/home/ubuntu/langchain_pinecone/vectorstores.py", line 158, in add_texts
async_res = [
^
File "/home/ubuntu/langchain_pinecone/vectorstores.py", line 159, in <listcomp>
self._index.upsert(
File "/home/ubuntu/pinecone/utils/error_handling.py", line 10, in inner_func
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/pinecone/data/index.py", line 168, in upsert
return self._upsert_batch(vectors, namespace, _check_type, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/pinecone/data/index.py", line 189, in _upsert_batch
return self._vector_api.upsert(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/pinecone/core/client/api_client.py", line 772, in __call__
return self.callable(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/pinecone/core/client/api/data_plane_api.py", line 1084, in __upsert
return self.call_with_http_info(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/pinecone/core/client/api_client.py", line 834, in call_with_http_info
return self.api_client.call_api(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/pinecone/core/client/api_client.py", line 417, in call_api
return self.pool.apply_async(self.__call_api, (resource_path,
^^^^^^^^^
File "/home/ubuntu/pinecone/core/client/api_client.py", line 103, in pool
self._pool = ThreadPool(self.pool_threads)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/multiprocessing/pool.py", line 930, in __init__
Pool.__init__(self, processes, initializer, initargs)
File "/usr/local/lib/python3.11/multiprocessing/pool.py", line 196, in __init__
self._change_notifier = self._ctx.SimpleQueue()
^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/multiprocessing/context.py", line 113, in SimpleQueue
return SimpleQueue(ctx=self.get_context())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/multiprocessing/queues.py", line 341, in __init__
self._rlock = ctx.Lock()
^^^^^^^^^^
File "/usr/local/lib/python3.11/multiprocessing/context.py", line 68, in Lock
return Lock(ctx=self.get_context())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/multiprocessing/synchronize.py", line 169, in __init__
SemLock.__init__(self, SEMAPHORE, 1, 1, ctx=ctx)
File "/usr/local/lib/python3.11/multiprocessing/synchronize.py", line 57, in __init__
sl = self._semlock = _multiprocessing.SemLock(
^^^^^^^^^^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory

```

### Description

- **Problem**: Encountering `FileNotFoundError: [Errno 2] No such file or directory` when trying to use `PineconeVectorStore.from_documents` in an AWS Lambda environment with the base image `python:3.11-slim`.
- **Expected Behavior**: The code should initialize a `PineconeVectorStore` retriever without errors.
- **Actual Behavior**: The initialization fails with a `FileNotFoundError` indicating an issue with multiprocessing in the slim Python 3.11 Docker image.

### System Info

- **Python Version**: 3.11

- **Platform**: Linux (AWS Lambda with python:3.11-slim base image)

- **Installed Packages**:

```
langchain==0.2.1
langchain-community==0.2.1
langchain-core==0.2.2
langchain-openai==0.1.8
langchain-pinecone==0.1.1
langchain-text-splitters==0.2.0
langdetect==1.0.9
langgraph==0.0.57
langsmith==0.1.63
pinecone-client==3.2.2
```
--- end body ---

The issue indicates a potential problem with the multiprocessing library in the Python 3.11-slim Docker image used in AWS Lambda. The FileNotFoundError suggests that the environment may not support certain operations that rely on file system access, which is common in serverless environments. This could lead to unexpected behavior when trying to initialize the PineconeVectorStore, especially if it relies on multiprocessing features that require file locks or semaphores.