CONNECT APP
Build with LLMWhisperer
Artificial Intelligence (AI)
- API key
MCP
Give your agent LLMWhisperer tools
Every LLMWhisperer action is exposed as an MCP tool on Pipedream's remote server. Point a client at it with your end user's ID and Connect resolves that user's LLMWhisperer account for each tool call — you store no tokens.
// accessToken: mint a short-lived token with the Connect SDK — see the MCP guide
const transport = new StreamableHTTPClientTransport(
new URL("https://remote.mcp.pipedream.net/v3"),
{
requestInit: {
headers: {
Authorization: `Bearer ${accessToken}`,
"x-pd-project-id": "{project_id}",
"x-pd-environment": "production",
"x-pd-external-user-id": "{external_user_id}", // any stable ID for this user in your system
"x-pd-app-slug": "llmwhisperer",
},
},
},
)
const mcp = new Client({ name: "my-agent", version: "1.0.0" })
await mcp.connect(transport)
const { tools } = await mcp.listTools()
// e.g. run Extract Text:
const result = await mcp.callTool({
name: "llmwhisperer-extract-text",
arguments: {
processingMode: "Processing Mode",
outputMode: "Output Mode",
},
})# access_token: mint a short-lived token with the Connect SDK — see the MCP guide
headers = {
"Authorization": f"Bearer {access_token}",
"x-pd-project-id": "{project_id}",
"x-pd-environment": "production",
"x-pd-external-user-id": "{external_user_id}", # any stable ID for this user in your system
"x-pd-app-slug": "llmwhisperer",
}
async with streamablehttp_client("https://remote.mcp.pipedream.net/v3", headers=headers) as (read, write, _):
async with ClientSession(read, write) as session:
await session.initialize()
tools = await session.list_tools()
# e.g. run Extract Text:
result = await session.call_tool("llmwhisperer-extract-text", {
"processingMode": "Processing Mode",
"outputMode": "Output Mode",
})API PROXY
Call the LLMWhisperer API directly
For an endpoint with no pre-built tool, the Connect proxy forwards your request to the LLMWhisperer API with the connected user's credentials attached. You store no tokens and write no refresh logic.
const resp = await pd.proxy.get({
externalUserId: "{external_user_id}", // any stable ID for this user in your system
accountId: "apn_xxxxxxx",
url: "https://llmwhisperer-api.unstract.com/v1/get-usage-info",
})
// Any allowed LLMWhisperer endpoint works here. Pipedream attaches the
// connected account's credentials to the outgoing request.# The path segment is the target URL, URL-safe base64 encoded:
# https://llmwhisperer-api.unstract.com/v1/get-usage-info
curl "https://api.pipedream.com/v1/connect/{project_id}/proxy/aHR0cHM6Ly9sbG13aGlzcGVyZXItYXBpLnVuc3RyYWN0LmNvbS92MS9nZXQtdXNhZ2UtaW5mbw?external_user_id={external_user_id}&account_id=apn_xxxxxxx" \
-H "Authorization: Bearer {access_token}" \
-H "x-pd-environment: production"SDK
Run LLMWhisperer actions from your backend
Connect a user's LLMWhisperer account once, then run Extract Text on their behalf from your own code — TypeScript, Python, or plain HTTP.
import { PipedreamClient } from "@pipedream/sdk"
const pd = new PipedreamClient({
projectId: process.env.PIPEDREAM_PROJECT_ID!,
clientId: process.env.PIPEDREAM_CLIENT_ID!,
clientSecret: process.env.PIPEDREAM_CLIENT_SECRET!,
projectEnvironment: "production",
})
const result = await pd.actions.run({
id: "llmwhisperer-extract-text",
externalUserId: "{external_user_id}", // any stable ID for this user in your system
configuredProps: {
llmwhisperer: { authProvisionId: "apn_xxxxxxx" },
processingMode: "Processing Mode",
outputMode: "Output Mode",
},
})from pipedream import Pipedream
pd = Pipedream(
client_id="{oauth_client_id}",
client_secret="{oauth_client_secret}",
project_id="{project_id}",
project_environment="production",
)
result = pd.actions.run(
id="llmwhisperer-extract-text",
external_user_id="{external_user_id}", # any stable ID for this user in your system
configured_props={
"llmwhisperer": {"authProvisionId": "apn_xxxxxxx"},
"processingMode": "Processing Mode",
"outputMode": "Output Mode",
},
)curl -X POST https://api.pipedream.com/v1/connect/{project_id}/actions/run \
-H "Content-Type: application/json" \
-H "X-PD-Environment: production" \
-H "Authorization: Bearer {access_token}" \
-d '{
"external_user_id": "{external_user_id}",
"id": "llmwhisperer-extract-text",
"configured_props": {
"llmwhisperer": { "authProvisionId": "apn_xxxxxxx" },
"processingMode": "Processing Mode",
"outputMode": "Output Mode"
}
}'TOOLS
LLMWhisperer actions
On-demand operations your product or agent can configure and run on behalf of a connected user.
-
Extract Text
actionConvert your PDF/scanned documents to text format which can be used by LLMs. See the documentationRead-onlyv0.1.4 -
Get Status
actionGet the status of the whisper process. This can be used to check the status of the conversion process when the conversion is done in async mode. See the documentationRead-onlyv0.0.2 -
Highlight Locations
actionGenerate highlight locations for a search term in the document. See the documentationRead-onlyv0.0.2 -
Retrieve Extracted Text
actionRetrieve the extracted text executed through the whisper API. This can be used to retrieve the text of the conversion process when the conversion is done in async mode. See the documentationRead-onlyv0.0.2
EVENTS
LLMWhisperer triggers
Event sources your backend can deploy for users and receive through a webhook.
No LLMWhisperer triggers are available yet.
MULTI-APP
Use LLMWhisperer with other popular apps
Most products don't stop at one integration. Pair LLMWhisperer with the other apps your users rely on, and ship use cases that span both.
- App slug
- llmwhisperer
- Authentication
- API key
- Categories
- Artificial Intelligence (AI)
- Actions
- 4
- Triggers
- 0
- API proxy
- Available