Extract action items
curl --request POST \
--url https://api.assemblyai.com/lemur/v3/generate/action-items \
--header 'Authorization: <api-key>' \
--header 'Content-Type: application/json' \
--data '
{
"transcript_ids": [
"64nygnr62k-405c-4ae8-8a6b-d90b40ff3cce"
],
"context": "This is an interview about wildfires.",
"final_model": "anthropic/claude-3-5-sonnet",
"temperature": 0,
"max_output_size": 3000,
"answer_format": "Bullet Points"
}
'import requests
url = "https://api.assemblyai.com/lemur/v3/generate/action-items"
payload = {
"transcript_ids": ["64nygnr62k-405c-4ae8-8a6b-d90b40ff3cce"],
"context": "This is an interview about wildfires.",
"final_model": "anthropic/claude-3-5-sonnet",
"temperature": 0,
"max_output_size": 3000,
"answer_format": "Bullet Points"
}
headers = {
"Authorization": "<api-key>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: '<api-key>', 'Content-Type': 'application/json'},
body: JSON.stringify({
transcript_ids: ['64nygnr62k-405c-4ae8-8a6b-d90b40ff3cce'],
context: 'This is an interview about wildfires.',
final_model: 'anthropic/claude-3-5-sonnet',
temperature: 0,
max_output_size: 3000,
answer_format: 'Bullet Points'
})
};
fetch('https://api.assemblyai.com/lemur/v3/generate/action-items', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => "https://api.assemblyai.com/lemur/v3/generate/action-items",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'transcript_ids' => [
'64nygnr62k-405c-4ae8-8a6b-d90b40ff3cce'
],
'context' => 'This is an interview about wildfires.',
'final_model' => 'anthropic/claude-3-5-sonnet',
'temperature' => 0,
'max_output_size' => 3000,
'answer_format' => 'Bullet Points'
]),
CURLOPT_HTTPHEADER => [
"Authorization: <api-key>",
"Content-Type: application/json"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://api.assemblyai.com/lemur/v3/generate/action-items"
payload := strings.NewReader("{\n \"transcript_ids\": [\n \"64nygnr62k-405c-4ae8-8a6b-d90b40ff3cce\"\n ],\n \"context\": \"This is an interview about wildfires.\",\n \"final_model\": \"anthropic/claude-3-5-sonnet\",\n \"temperature\": 0,\n \"max_output_size\": 3000,\n \"answer_format\": \"Bullet Points\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Authorization", "<api-key>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://api.assemblyai.com/lemur/v3/generate/action-items")
.header("Authorization", "<api-key>")
.header("Content-Type", "application/json")
.body("{\n \"transcript_ids\": [\n \"64nygnr62k-405c-4ae8-8a6b-d90b40ff3cce\"\n ],\n \"context\": \"This is an interview about wildfires.\",\n \"final_model\": \"anthropic/claude-3-5-sonnet\",\n \"temperature\": 0,\n \"max_output_size\": 3000,\n \"answer_format\": \"Bullet Points\"\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.assemblyai.com/lemur/v3/generate/action-items")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"transcript_ids\": [\n \"64nygnr62k-405c-4ae8-8a6b-d90b40ff3cce\"\n ],\n \"context\": \"This is an interview about wildfires.\",\n \"final_model\": \"anthropic/claude-3-5-sonnet\",\n \"temperature\": 0,\n \"max_output_size\": 3000,\n \"answer_format\": \"Bullet Points\"\n}"
response = http.request(request)
puts response.read_body{
"request_id": "5e1b27c2-691f-4414-8bc5-f14678442f9e",
"usage": {
"input_tokens": 27,
"output_tokens": 3
},
"response": "Here are some potential action items based on the transcript:\n\n- Monitor air quality levels in affected areas and issue warnings as needed.\n\n- Advise vulnerable populations like children, the elderly, and those with respiratory conditions to limit time outdoors.\n\n- Have schools cancel outdoor activities when air quality is poor.\n\n- Educate the public on health impacts of smoke inhalation and precautions to take.\n\n- Track progression of smoke plumes using weather and air quality monitoring systems.\n\n- Coordinate cross-regionally to manage smoke exposure as air masses shift.\n\n- Plan for likely increase in such events due to climate change. Expand monitoring and forecasting capabilities.\n\n- Conduct research to better understand health impacts of wildfire smoke and mitigation strategies.\n\n- Develop strategies to prevent and manage wildfires to limit air quality impacts.\n"
}{
"error": "This is a sample error message"
}{
"error": "Authentication error, API token missing/invalid"
}{
"error": "Not found"
}{
"error": "Too Many Requests"
}{
"error": "Internal Server Error"
}LeMUR
Extract action items
Use LeMUR to generate a list of action items from a transcript
POST
/
lemur
/
v3
/
generate
/
action-items
Extract action items
curl --request POST \
--url https://api.assemblyai.com/lemur/v3/generate/action-items \
--header 'Authorization: <api-key>' \
--header 'Content-Type: application/json' \
--data '
{
"transcript_ids": [
"64nygnr62k-405c-4ae8-8a6b-d90b40ff3cce"
],
"context": "This is an interview about wildfires.",
"final_model": "anthropic/claude-3-5-sonnet",
"temperature": 0,
"max_output_size": 3000,
"answer_format": "Bullet Points"
}
'import requests
url = "https://api.assemblyai.com/lemur/v3/generate/action-items"
payload = {
"transcript_ids": ["64nygnr62k-405c-4ae8-8a6b-d90b40ff3cce"],
"context": "This is an interview about wildfires.",
"final_model": "anthropic/claude-3-5-sonnet",
"temperature": 0,
"max_output_size": 3000,
"answer_format": "Bullet Points"
}
headers = {
"Authorization": "<api-key>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: '<api-key>', 'Content-Type': 'application/json'},
body: JSON.stringify({
transcript_ids: ['64nygnr62k-405c-4ae8-8a6b-d90b40ff3cce'],
context: 'This is an interview about wildfires.',
final_model: 'anthropic/claude-3-5-sonnet',
temperature: 0,
max_output_size: 3000,
answer_format: 'Bullet Points'
})
};
fetch('https://api.assemblyai.com/lemur/v3/generate/action-items', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => "https://api.assemblyai.com/lemur/v3/generate/action-items",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'transcript_ids' => [
'64nygnr62k-405c-4ae8-8a6b-d90b40ff3cce'
],
'context' => 'This is an interview about wildfires.',
'final_model' => 'anthropic/claude-3-5-sonnet',
'temperature' => 0,
'max_output_size' => 3000,
'answer_format' => 'Bullet Points'
]),
CURLOPT_HTTPHEADER => [
"Authorization: <api-key>",
"Content-Type: application/json"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://api.assemblyai.com/lemur/v3/generate/action-items"
payload := strings.NewReader("{\n \"transcript_ids\": [\n \"64nygnr62k-405c-4ae8-8a6b-d90b40ff3cce\"\n ],\n \"context\": \"This is an interview about wildfires.\",\n \"final_model\": \"anthropic/claude-3-5-sonnet\",\n \"temperature\": 0,\n \"max_output_size\": 3000,\n \"answer_format\": \"Bullet Points\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Authorization", "<api-key>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://api.assemblyai.com/lemur/v3/generate/action-items")
.header("Authorization", "<api-key>")
.header("Content-Type", "application/json")
.body("{\n \"transcript_ids\": [\n \"64nygnr62k-405c-4ae8-8a6b-d90b40ff3cce\"\n ],\n \"context\": \"This is an interview about wildfires.\",\n \"final_model\": \"anthropic/claude-3-5-sonnet\",\n \"temperature\": 0,\n \"max_output_size\": 3000,\n \"answer_format\": \"Bullet Points\"\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.assemblyai.com/lemur/v3/generate/action-items")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"transcript_ids\": [\n \"64nygnr62k-405c-4ae8-8a6b-d90b40ff3cce\"\n ],\n \"context\": \"This is an interview about wildfires.\",\n \"final_model\": \"anthropic/claude-3-5-sonnet\",\n \"temperature\": 0,\n \"max_output_size\": 3000,\n \"answer_format\": \"Bullet Points\"\n}"
response = http.request(request)
puts response.read_body{
"request_id": "5e1b27c2-691f-4414-8bc5-f14678442f9e",
"usage": {
"input_tokens": 27,
"output_tokens": 3
},
"response": "Here are some potential action items based on the transcript:\n\n- Monitor air quality levels in affected areas and issue warnings as needed.\n\n- Advise vulnerable populations like children, the elderly, and those with respiratory conditions to limit time outdoors.\n\n- Have schools cancel outdoor activities when air quality is poor.\n\n- Educate the public on health impacts of smoke inhalation and precautions to take.\n\n- Track progression of smoke plumes using weather and air quality monitoring systems.\n\n- Coordinate cross-regionally to manage smoke exposure as air masses shift.\n\n- Plan for likely increase in such events due to climate change. Expand monitoring and forecasting capabilities.\n\n- Conduct research to better understand health impacts of wildfire smoke and mitigation strategies.\n\n- Develop strategies to prevent and manage wildfires to limit air quality impacts.\n"
}{
"error": "This is a sample error message"
}{
"error": "Authentication error, API token missing/invalid"
}{
"error": "Not found"
}{
"error": "Too Many Requests"
}{
"error": "Internal Server Error"
}Authorizations
Body
application/json
Params to generate action items from transcripts
A list of completed transcripts with text. Up to a maximum of 100 files or 100 hours, whichever is lower. Use either transcript_ids or input_text as input into LeMUR.
Custom formatted transcript data. Maximum size is the context limit of the selected model, which defaults to 100000. Use either transcript_ids or input_text as input into LeMUR.
Context to provide the model. This can be a string or a free-form JSON value.
The model that is used for the final prompt after compression is performed.
Available options:
anthropic/claude-3-5-sonnet, anthropic/claude-3-opus, anthropic/claude-3-haiku, anthropic/claude-3-sonnet, anthropic/claude-2-1, anthropic/claude-2, default, anthropic/claude-instant-1-2, basic, assemblyai/mistral-7b Max output size in tokens, up to 4000
The temperature to use for the model. Higher values result in answers that are more creative, lower values are more conservative. Can be any value between 0.0 and 1.0 inclusive.
Required range:
0 <= x <= 1How you want the action items to be returned. This can be any text. Defaults to "Bullet Points".
⌘I