protected override string ExtractIntent(Item item)
{
try
{
Phrase phrase = new Phrase(item.Name);
if (phrase.Task != null)
{
string verb = phrase.Task.Verb.ToLower();
string noun = phrase.Task.Article.ToLower();
Intent intent = Storage.NewSuggestionsContext.Intents.FirstOrDefault(i => i.Verb == verb && i.Noun == noun);
if (intent != null)
return intent.WorkflowType;
}
}
catch (Exception ex)
{
TraceLog.TraceException("Could not initialize NLP engine", ex);
}
return base.ExtractIntent(item);
}
private Status GenerateSuggestions(WorkflowInstance workflowInstance, ServerEntity entity, Dictionary<string, string> suggestionList)
{
Item item = entity as Item;
if (item == null)
{
TraceLog.TraceError("Entity is not an Item");
return Status.Error;
}
User user = UserContext.GetUser(item.UserID, true);
if (user == null)
{
TraceLog.TraceError("Could not find the user associated with Item " + item.Name);
return Status.Error;
}
ADGraphAPI adApi = new ADGraphAPI();
string adRefreshToken = null;
// check for FB and/or AD credentials
UserCredential cred = user.GetCredential(UserCredential.FacebookConsent);
if (cred != null && cred.AccessToken != null) { adApi.FacebookAccessToken = cred.AccessToken; }
cred = user.GetCredential(UserCredential.CloudADConsent);
if (cred != null && cred.RenewalToken != null) { adRefreshToken = cred.RenewalToken; }
if (adApi.FacebookAccessToken == null && adRefreshToken == null)
{ // user not having either token is not an error condition, but there is no way to generate suggestions
// just move forward from this state
return Status.Complete;
}
// if a refresh token exists for AD, get an access token from Azure ACS for the Azure AD service
if (adRefreshToken != null)
{
try
{
AccessTokenRequestWithRefreshToken request = new AccessTokenRequestWithRefreshToken(new Uri(AzureOAuthConfiguration.GetTokenUri()))
{
RefreshToken = adRefreshToken,
ClientId = AzureOAuthConfiguration.GetClientIdentity(),
ClientSecret = AzureOAuthConfiguration.ClientSecret,
Scope = AzureOAuthConfiguration.RelyingPartyRealm,
};
OAuthMessage message = OAuthClient.GetAccessToken(request);
AccessTokenResponse authzResponse = message as AccessTokenResponse;
adApi.ADAccessToken = authzResponse.AccessToken;
// workaround for ACS trashing the refresh token
if (!String.IsNullOrEmpty(authzResponse.RefreshToken))
{
TraceLog.TraceInfo("Storing new CloudAD refresh token");
user.AddCredential(UserCredential.CloudADConsent, authzResponse.AccessToken, null, authzResponse.RefreshToken);
UserContext.SaveChanges();
}
}
catch (Exception ex)
{
TraceLog.TraceException("Could not contact ACS to get an access token", ex);
// Facebook credentials are not available
if (adApi.FacebookAccessToken == null)
return Status.Pending; // could be a temporary outage, do not move off this state
}
}
// extract a subject hint if one hasn't been discovered yet
string subjectHint = GetInstanceData(workflowInstance, ActivityVariables.SubjectHint);
if (String.IsNullOrEmpty(subjectHint))
{
try
{
Phrase phrase = new Phrase(item.Name);
if (phrase.Task != null)
{
subjectHint = phrase.Task.Subject;
if (!String.IsNullOrWhiteSpace(subjectHint))
StoreInstanceData(workflowInstance, ActivityVariables.SubjectHint, subjectHint);
}
}
catch (Exception ex)
{
TraceLog.TraceException("Could not initialize NLP engine", ex);
}
}
// get contacts from Cloud AD and Facebook via the AD Graph Person service
// TODO: also get local contacts from the Contacts folder
try
{
var results = adApi.Query(subjectHint ?? "");
foreach (var subject in results)
{
// serialize an existing contact corresponding to the subject,
// or generate a new serialized contact if one wasn't found
Item contact = MakeContact(workflowInstance, item, subject);
suggestionList[contact.Name] = JsonSerializer.Serialize(contact);
}
}
catch (Exception ex)
{
TraceLog.TraceException("Could not contact Person Service", ex);
return Status.Error;
}
// inexact match
return Status.Pending;
}
private Status GenerateSuggestions(WorkflowInstance workflowInstance, ServerEntity entity, Dictionary<string, string> suggestionList)
{
Item item = entity as Item;
if (item == null)
{
TraceLog.TraceError("Entity is not an Item");
return Status.Error;
}
// if an intent was already matched, return it now
var intentFV = item.GetFieldValue(ExtendedFieldNames.Intent);
if (intentFV != null && !String.IsNullOrWhiteSpace(intentFV.Value))
{
var wt = SuggestionsContext.WorkflowTypes.FirstOrDefault(t => t.Type == intentFV.Value);
if (wt != null)
{
suggestionList[wt.Type] = wt.Type;
return Status.Complete;
}
}
// run NLP engine over the task name to extract intent (verb / noun) as well as a subject hint
string name = item.Name;
string verb = null;
string noun = null;
string subject = null;
try
{
Phrase phrase = new Phrase(name);
if (phrase.Task != null)
{
verb = phrase.Task.Verb.ToLower();
noun = phrase.Task.Article.ToLower();
subject = phrase.Task.Subject;
if (!String.IsNullOrWhiteSpace(subject))
StoreInstanceData(workflowInstance, ActivityVariables.SubjectHint, subject);
}
}
catch (Exception ex)
{
TraceLog.TraceException("Could not initialize NLP engine", ex);
}
// if NLP failed (e.g. data file not found), do "poor man's NLP" - assume a structure like <verb> <noun> [{for,with} <subject>]
if (verb == null)
{
string sentence = name.ToLower();
// remove extra whitespace and filler words
StringBuilder sb = new StringBuilder();
foreach (var word in sentence.Split(new char[] { ' ', '\n', '\t' }, StringSplitOptions.RemoveEmptyEntries))
{
bool add = true;
foreach (var filler in "a;an;the".Split(';'))
if (word == filler)
{
add = false;
break;
}
if (add)
sb.AppendFormat("{0} ", word);
}
sentence = sb.ToString().Trim();
// poor man's NLP - assume first word is verb, second word is noun
string[] parts = sentence.Split(' ');
if (parts.Length >= 2)
{
verb = parts[0];
noun = parts[1];
}
if (parts.Length >= 4)
{
if (parts[2] == "for" || parts[2] == "with")
{
// capitalize and store the word following "for" in the SubjectHint workflow parameter
subject = parts[3];
subject = subject.Substring(0, 1).ToUpper() + subject.Substring(1);
StoreInstanceData(workflowInstance, ActivityVariables.SubjectHint, subject);
}
}
}
// try to find an intent that exactly matches the verb/noun extracted by the NLP step
Intent intent = SuggestionsContext.Intents.FirstOrDefault(i => i.Verb == verb && i.Noun == noun);
if (intent != null)
{
var wt = SuggestionsContext.WorkflowTypes.FirstOrDefault(t => t.Type == intent.WorkflowType);
if (wt != null)
{
suggestionList[intent.WorkflowType] = wt.Type;
return Status.Complete;
}
}
// get a list of all approximate matches and surface as suggestions to the user
var intentList = SuggestionsContext.Intents.Where(i => i.Verb == verb || i.Noun == noun);
foreach (var i in intentList)
suggestionList[i.WorkflowType] = i.WorkflowType;
// if there are no suggestions, we can terminate this workflow
if (suggestionList.Count == 0)
{
TraceLog.TraceError("No possible intents were found, terminating workflow");
return Status.Error;
}
return Status.Pending;
}