You can assign an API key to the prediction API from the UI. Refer Chatflow Level for more details.
The Authorization header must be provided with the correct API key specified during a HTTP call.
"Authorization": "Bearer <your-api-key>"
Vector Upsert API
POST /api/v1/vector/upsert/{your-chatflowid}
Request Body
Key
Description
Type
Required
overrideConfig
Override existing flow configuration
object
No
stopNodeId
Node ID of the vector store. When you have multiple vector stores in a flow, you might not want to upsert all of them. Specifying stopNodeId will ensure only that specific vector store node is upserted.
array
No
Document Loaders with Upload
Some document loaders in Flowise allow user to upload files:
If the flow contains Document Loaders with Upload File functionality, the API looks slightly different. Instead of passing body as JSON, form-data is being used. This allows you to upload any files to the API.
It is user's responsibility to make sure the file type is compatible with the expected file type from document loader. For example, if a Text File Loader is being used, you should only upload file with .txt extension.
import requestsAPI_URL ="http://localhost:3000/api/v1/vector/upsert/<chatlfowid>"# use form data to upload filesform_data ={"files": ('state_of_the_union.txt',open('state_of_the_union.txt', 'rb'))}body_data ={"returnSourceDocuments":True}defquery(form_data): response = requests.post(API_URL, files=form_data, data=body_data)print(response)return response.json()output =query(form_data)print(output)
// use FormData to upload fileslet formData =newFormData();formData.append("files",input.files[0]);formData.append("returnSourceDocuments",true);asyncfunctionquery(formData) {constresponse=awaitfetch("http://localhost:3000/api/v1/vector/upsert/<chatlfowid>", { method:"POST", body: formData } );constresult=awaitresponse.json();return result;}query(formData).then((response) => {console.log(response);});
Document Loaders without Upload
For other Document Loaders nodes without Upload File functionality, the API body is in JSON format similar to Prediction API.
You can assign an API key to the prediction API from the UI. Refer Chatflow Level for more details.
The Authorization header must be provided with the correct API key specified during a HTTP call.
"Authorization": "Bearer <your-api-key>"
Message API
GET /api/v1/chatmessage/{your-chatflowid}
DELETE /api/v1/chatmessage/{your-chatflowid}
Query Parameters
Param
Type
Value
sessionId
string
sort
enum
ASC or DESC
startDate
string
endDate
string
Authentication
Message API is restricted to only Flowise admin user. Basic authentication must be provided in the headers if Flowise instance has been configured with FLOWISE_USERNAME and FLOWISE_PASSWORD. Refer App Level for more details.