Contextual provides a platform for creating enterprise-grade AI agents, grounded in your documents and data. See a demo of our agents here.

Our APIs provide powerful—yet simple—interfaces for ingesting data, creating agents, and interacting with our state-of-the-art Contextual RAG system.

Follow this guide to create your first agent!

Get your API Key

If you do not have access to the platform, you can request an API key via the Request Access button in the header of the documentation page or on the Contextual website.

Contextual uses API keys to authenticate requests. Only admins within a tenant can create API keys. To create a key:

Log into your tenant at app.contextual.ai
Click on API Keys in the sidebar
Click the Create API Key button in the upper right and follow the instructions
Save the generated key in a secure location

See https://github.com/ContextualAI/examples for an easy-to-follow Jupyter notebook.

Via curl commands

Step 1: Create a datastore

A datastore securely stores the files you would like to search and reference in your agents. Each agent must be associated with at least one datastore. You can create a datastore using the /datastores endpoint with the following command:


curl --request POST \
     --url https://api.contextual.ai/v1/datastores \
     --header 'accept: application/json' \
     --header 'authorization: Bearer $API_KEY' \
     --header 'content-type: application/json' \
     --data '{"name":"Test Datastore"}'

Note: Remember to replace $API_KEY with your key and feel free to rename the datastore

If the request is successful, the id of the newly created datastore will be returned to you. Be sure to save this id as you will need it in subsequent steps!

Step 2: Upload documents into your datastore

Now that you've created a datastore, you can start uploading documents into the platform. Uploaded documents are processed by Contextual in ways optimized for retrieval.

If you don't have your own documents handy, feel free to use our test documents, found here
For the best experience, please use renderable PDFs

You can upload a document using the following command:

curl --request POST \
     --url https://api.contextual.ai/v1/datastores/{datastore_id}/documents \
     --header 'accept: application/json' \
     --header 'authorization: Bearer $API_KEY' \
     --header 'content-type: multipart/form-data' \
     --form file=@'${file_path}'

Note: Remember to:

Replace {datastore_id} in the url path with the id from the previous step
Replace $API_KEY with your API key
Replace {file_path} with the path to the document on your machine

If the request is successful, the id of the uploaded document will be returned to you. Keep in mind that a given document may require a few minutes to fully process once uploaded.

To check the status of documents uploaded into the datastore, use this command:

curl --request GET \
     --url https://api.contextual.ai/v1/datastores/{datastore_id}/documents \
     --header 'accept: application/json' \
     --header 'authorization: Bearer $API_KEY'

Note: Remember to:

Replace {datastore_id} in the url path with the id from the previous step
Replace $API_KEY with your API key

You should see the document you uploaded in the list, along with its ingestion_job_status.

Step 3: Create an agent

Now that you have a datastore with some files, you can use the /agents endpoint to create your first agent.

curl --request POST \
     --url https://api.contextual.ai/v1/agents \
     --header 'accept: application/json' \
     --header 'authorization: Bearer $API_KEY' \
     --header 'content-type: application/json' \
     --data '
{
  "name": "Test",
  "description": "Test Agent",
  "datastore_ids": []
}
'

Note: Remember to:

Replace $API_KEY with your API key
Populate the datastore_ids list field with the datastore id from above

If the request is successful, the agent_id of the newly created agent will be returned to you. You'll need this to query your agent in the next step.

Step 4: Query your agent

Now that you've set up an agent and uploaded documents for use with it, you can use the /query endpoint to send messages:

curl --request POST \
     --url https://api.contextual.ai/v1/agents/{agent_id}/query \
     --header 'accept: application/json' \
     --header 'authorization: Bearer $API_KEY' \
     --header 'content-type: application/json' \
     --data '
{
  "stream": false,
  "messages": [
    {
      "role": "user",
      "content": "What is the revenue of Apple?"
    }
  ]
}
'

Note: Remember to:

Replace {agent_id} in the url path with the agent_id from the previous step
Replace $API_KEY with your API key
Replace the content field with a question that is relevant to the document(s) you uploaded

If the request is successful, you'll receive a response back that will contain:

The body of the response
The sources retrieved from the datastore that are relevant to the response
Attributions/citations of sources to specific text spans in the response

Note: You should wait to query your agent until at least one document in the datastore has finished processing. You can check the status of uploaded documents by following the instructions in the previous step.

🙌 Congrats! You've now created a basic agent in the Contextual platform, interacted with it, and evaluated its results.

Via Contextual API Docs

As an alternative to shell commands, you can also use the interactive areas of our documentation to interact with our APIs .

Step 1: Create a Datastore

To create a datastore via the API docs:

Expand the /datastores section in the sidebar
Select Create Datastore
Enter the name of your datastore in the Body Params field in the central panel
Input your API key in the Bearer field in the top of the right panel
Click the Try It! button
Save the returned datastore id for future reference

Step 2: Upload documents into your datastore

To upload a document:

Expand the /datastores section in the sidebar
Select Ingest Document
Enter the datastore id from the previous step in the datastore_id field
Click the upload button in the file field to select a file from your local machine for upload
Input your API key in the Bearer field in the top of the right panel
Click the Try It! button

Keep in mind that processing the document after upload can take a few minutes. To check the status of your uploaded document(s):

Expand the Datastores section in the sidebar
Select List Document
Enter the datastore id in the datastore_id field
Input your API key in the Bearer field in the top of the right panel
Click the Try It! button

Step 3: Create an agent

To create an agent linked to the datastore you created:

Expand the /agents section in the sidebar
Select Create Agent
Input a name and description in the respective fields in the central panel
Click Add String in the datastore_ids field and input the datastore_id from earlier
Input your API key in the Bearer field in the top of the right panel
Click the Try It! button

If successful, you will see a 200 response and the agent should also appear in your tenant. Save the returned agent id for the query step.

Step 4: Query your agent

To send a message to your agent:

Expand the /agents/{id}/query section in the sidebar
Select Query
Input the id from the prior step in the agent_id field
Click Add Object in the messages field and type a question that is relevant to the given document(s) you uploaded in the content field
Input your API key in the Bearer field in the top of the right panel
Click the Try It! button

If successful, you'll see a 200 response, the body of which will contain:

The body of the response
The sources retrieved from the datastore that are relevant to the response
Attributions/citations of sources to specific text spans in the response

WHAT'S NEXT

Now that you've covered the basics, feel free to explore our advanced features, including evaluation and tuning options, for deeper insights into your agent.

Evaluation & Tuning Guide