> ## Documentation Index
> Fetch the complete documentation index at: https://docs.unstructured.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Pinecone
Batch process all your records to store structured outputs in Pinecone.
The requirements are as follows.
* A Pinecone account. [Get an account](https://app.pinecone.io/).
* A Pinecone API key in your Pinecone account. To create an API key, do the following:
1. [Sign in to your Pinecone account and open the API keys page](https://app.pinecone.io/organizations/-/projects/-/keys).
2. Click **+ API key**.
3. For **API key name**, enter some descriptive name for the API key.
4. Click **Create key**.
5. Copy the generated API key to some secure location. You will not be able to access this API key again after you close the dialog.
* A Pinecone serverless index in your Pinecone account.
Creating a serverless index on your own is optional.
An index is not required to exist in advance.
When you set up the connector, at runtime, the index behavior is as follows:
For the [Unstructured UI](/ui/overview) and [Unstructured API](/api-reference/overview):
* Your workflow must contain an embedder node, and the embedder node must specify the embedding model that Unstructured will use to generate the embeedings.
* If an existing index name is specified,
and the number of dimensions that Unstructured generates does not match the number of dimensions that are specified in the existing index's embedding settings, the run will fail.
You must change the number of dimensions in your workflow's embedder node or your existing index's embedding settings to match, and try the run again.
* If an index name is not specified, Unstructured creates a new index in your Pinecone account. The
new index's name will be `u--`.
For [Unstructured Ingest](/open-source/ingestion/overview):
* If an existing index name is specified, and Unstructured generates embeddings,
but the number of dimensions that are generated does not match the existing index's embedding settings, the run will fail.
You must change your Unstructured embedding settings or your existing index's embedding settings to match, and try the run again.
* If an index name is not specified, Unstructured creates a new index in your Pinecone account. The new index's name will be `unstructuredautocreated`.
If you create a new index or use an existing one, Unstructured recommends that all records in the target index have a field
named `record_id` with a string data type.
Unstructured can use this field to do intelligent document overwrites. Without this field, duplicate documents
might be written to the index or, in some cases, the operation could fail altogether.
To create a serverless index on your own, do the following:
1. [Sign in to your Pinecone account and open the Create a new index page](https://app.pinecone.io/organizations/-/projects/-/create-index/serverless).
2. For **Enter index name**, enter some descriptive name for the index.
3. For **Configuration**, select the check box labelled **Custom settings**, or click the tile labelled **Manual configuration**.
Do not click any of the other tiles, such as **text-embedding-3-large**. Clicking any of these other tiles will cause Pinecone to generate embeddings instead of
having Unstructured generate them. If Pinecone generates embeddings instead of Unstructured, this could cause any related Unstructured workflows to fail.
4. For **Vector type**, select **Dense**.
5. For **Dimension**, enter the number of dimensions for the embeddings that Unstructured will generate.
The number of dimensions that you enter here must match the number of dimensions for the embedding model that you use in any related Unstructured workflows or ingestion pipelines. If these numbers do not
match in both places, this could cause any related Unstructured workflows or ingestion pipelines to fail.
6. For **Metric**, select **cosine**.
7. Leave **Capacity mode** set to **Serverless**.
8. You can leave **Cloud provider** and **Region** set to their default values, or you can select a cloud provider and region that is closest to you, if available.
9. Click **Create index**.
* Within a Pinecone serverless index, custom [namespaces](https://docs.pinecone.io/guides/index-data/indexing-overview#namespaces) are supported but are not required.
The Pinecone connector dependencies.
```bash theme={null}
pip install "unstructured-ingest[pinecone]"
```
You might also need to install additional dependencies, depending on your needs. [Learn more](/open-source/ingestion/ingest-dependencies).
The following environment variables:
* `PINECONE_API_KEY` - The Pinecone API, represented by `--api-key` (CLI) or `api_key` (Python, in the `PineconeAccessConfig` object).
* `PINECONE_INDEX_NAME` - The Pinecone serverless index name, represented by `--index-name` (CLI) or `index_name` (Python). If no value is provided, see the beginning of this article for the behavior at run time.
* `PINECONE_NAMESPACE_NAME` - The name of any custom namespace in the Pinecone index to upsert data into, represented by `--namespace` (CLI) or `namespace` (Python). If no value is provided, the default namespace of `default` will be used.
Now call the Unstructured CLI or Python SDK. The source connector can be any of the ones supported.
This example uses the local source connector.
This example sends files to Unstructured for processing by default. To process files locally instead, see the instructions at the end of this page.
```bash CLI theme={null}
#!/usr/bin/env bash
# Chunking and embedding are optional.
unstructured-ingest \
local \
--input-path $LOCAL_FILE_INPUT_DIR \
--output-dir $LOCAL_FILE_OUTPUT_DIR \
--strategy hi_res \
--chunk-elements \
--embedding-provider huggingface \
--num-processes 2 \
--verbose \
--api-key $UNSTRUCTURED_API_KEY \
--partition-endpoint $UNSTRUCTURED_API_URL \
--additional-partition-args="{\"split_pdf_page\":\"true\", \"split_pdf_allow_failed\":\"true\", \"split_pdf_concurrency_level\": 15}" \
pinecone \
--api-key "$PINECONE_API_KEY" \
--index-name "$PINECONE_INDEX_NAME" \
--namespace "$PINECONE_NAMESPACE_NAME" \
--batch-size 80
```
```python Python Ingest theme={null}
import os
from unstructured_ingest.pipeline.pipeline import Pipeline
from unstructured_ingest.interfaces import ProcessorConfig
from unstructured_ingest.processes.connectors.pinecone import (
PineconeConnectionConfig,
PineconeAccessConfig,
PineconeUploaderConfig,
PineconeUploadStagerConfig
)
from unstructured_ingest.processes.connectors.local import (
LocalIndexerConfig,
LocalDownloaderConfig,
LocalConnectionConfig
)
from unstructured_ingest.processes.partitioner import PartitionerConfig
from unstructured_ingest.processes.chunker import ChunkerConfig
from unstructured_ingest.processes.embedder import EmbedderConfig
# Chunking and embedding are optional.
if __name__ == "__main__":
Pipeline.from_configs(
context=ProcessorConfig(),
indexer_config=LocalIndexerConfig(input_path=os.getenv("LOCAL_FILE_INPUT_DIR")),
downloader_config=LocalDownloaderConfig(),
source_connection_config=LocalConnectionConfig(),
partitioner_config=PartitionerConfig(
partition_by_api=True,
api_key=os.getenv("UNSTRUCTURED_API_KEY"),
partition_endpoint=os.getenv("UNSTRUCTURED_API_URL"),
strategy="hi_res",
additional_partition_args={
"split_pdf_page": True,
"split_pdf_allow_failed": True,
"split_pdf_concurrency_level": 15
}
),
chunker_config=ChunkerConfig(chunking_strategy="by_title"),
embedder_config=EmbedderConfig(embedding_provider="huggingface"),
destination_connection_config=PineconeConnectionConfig(
access_config=PineconeAccessConfig(
api_key=os.getenv("PINECONE_API_KEY")
),
index_name=os.getenv("PINECONE_INDEX_NAME")
),
stager_config=PineconeUploadStagerConfig(),
uploader_config=PineconeUploaderConfig(
batch_size=100,
namespace=os.getenv("PINECONE_NAMESPACE_NAME")
)
).run()
```
For the Unstructured Ingest CLI and the Unstructured Ingest Python library, you can use the `--partition-by-api` option (CLI) or `partition_by_api` (Python) parameter to specify where files are processed:
* To do local file processing, omit `--partition-by-api` (CLI) or `partition_by_api` (Python), or explicitly specify `partition_by_api=False` (Python).
Local file processing does not use an Unstructured API key or API URL, so you can also omit the following, if they appear:
* `--api-key $UNSTRUCTURED_API_KEY` (CLI) or `api_key=os.getenv("UNSTRUCTURED_API_KEY")` (Python)
* `--partition-endpoint $UNSTRUCTURED_API_URL` (CLI) or `partition_endpoint=os.getenv("UNSTRUCTURED_API_URL")` (Python)
* The environment variables `UNSTRUCTURED_API_KEY` and `UNSTRUCTURED_API_URL`
* To send files to the legacy [Unstructured Partition Endpoint](/api-reference/legacy-api/partition/overview) for processing, specify `--partition-by-api` (CLI) or `partition_by_api=True` (Python).
Unstructured also requires an Unstructured API key and API URL, by adding the following:
* `--api-key $UNSTRUCTURED_API_KEY` (CLI) or `api_key=os.getenv("UNSTRUCTURED_API_KEY")` (Python)
* `--partition-endpoint $UNSTRUCTURED_API_URL` (CLI) or `partition_endpoint=os.getenv("UNSTRUCTURED_API_URL")` (Python)
* The environment variables `UNSTRUCTURED_API_KEY` and `UNSTRUCTURED_API_URL`, representing your API key and API URL, respectively.
You must specify the API URL only if you are not using the default API URL for Unstructured Ingest, which applies to **Let's Go**, **Pay-As-You-Go**, and **Business SaaS** accounts.
The default API URL for Unstructured Ingest is `https://api.unstructuredapp.io/general/v0/general`, which is the API URL for the legacy[Unstructured Partition Endpoint](/api-reference/legacy-api/partition/overview). However, you should always use the URL that was provided to you when your Unstructured account was created. If you do not have this URL, email Unstructured Support at [support@unstructured.io](mailto:support@unstructured.io).
If you do not have an API key, [get one now](/api-reference/legacy-api/partition/overview).
If you are using a **Business** account, the process
for generating Unstructured API keys, and the Unstructured API URL that you use, are different.
For instructions, see your Unstructured account administrator, or email Unstructured Support at [support@unstructured.io](mailto:support@unstructured.io).