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- RAG Specialist fully implemented new style

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- Selection Specialist - VA version - fully implemented
- Correction of TRAICIE_ROLE_DEFINITION_SPECIALIST - adaptation to new style
- Removal of 'debug' statements
This commit is contained in:
Josako
2025-07-10 10:39:42 +02:00
parent 509ee95d81
commit 51fd16bcc6
40 changed files with 110 additions and 298 deletions
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5
common/eveai_model/tracked_mistral_embeddings.py
View File

@@ -44,7 +44,6 @@ class TrackedMistralAIEmbeddings(EveAIEmbeddings):
for i in range(0, len(texts), self.batch_size): for i in range(0, len(texts), self.batch_size):
batch = texts[i:i + self.batch_size] batch = texts[i:i + self.batch_size]
batch_num = i // self.batch_size + 1 batch_num = i // self.batch_size + 1
current_app.logger.debug(f"Processing embedding batch {batch_num}, size: {len(batch)}")
start_time = time.time() start_time = time.time()
try: try:
@@ -70,9 +69,6 @@ class TrackedMistralAIEmbeddings(EveAIEmbeddings):
} }
current_event.log_llm_metrics(metrics) current_event.log_llm_metrics(metrics)
current_app.logger.debug(f"Batch {batch_num} processed: {len(batch)} texts, "
f"{result.usage.total_tokens} tokens, {batch_time:.2f}s")
# If processing multiple batches, add a small delay to avoid rate limits # If processing multiple batches, add a small delay to avoid rate limits
if len(texts) > self.batch_size and i + self.batch_size < len(texts): if len(texts) > self.batch_size and i + self.batch_size < len(texts):
time.sleep(0.25) # 250ms pause between batches time.sleep(0.25) # 250ms pause between batches
@@ -82,7 +78,6 @@ class TrackedMistralAIEmbeddings(EveAIEmbeddings):
# If a batch fails, try to process each text individually # If a batch fails, try to process each text individually
for j, text in enumerate(batch): for j, text in enumerate(batch):
try: try:
current_app.logger.debug(f"Attempting individual embedding for item {i + j}")
single_start_time = time.time() single_start_time = time.time()
single_result = self.client.embeddings.create( single_result = self.client.embeddings.create(
model=self.model, model=self.model,

8
common/services/utils/human_answer_services.py
View File

@@ -18,8 +18,10 @@ class HumanAnswerServices:
@staticmethod @staticmethod
def check_additional_information(tenant_id: int, question: str, answer: str, language_iso: str) -> bool: def check_additional_information(tenant_id: int, question: str, answer: str, language_iso: str) -> bool:
return HumanAnswerServices._check_answer(tenant_id, question, answer, language_iso, result = HumanAnswerServices._check_answer(tenant_id, question, answer, language_iso,
"check_additional_information", "Check Additional Information") "check_additional_information", "Check Additional Information")
return result
@staticmethod @staticmethod
def get_answer_to_question(tenant_id: int, question: str, answer: str, language_iso: str) -> str: def get_answer_to_question(tenant_id: int, question: str, answer: str, language_iso: str) -> str:
@@ -66,7 +68,6 @@ class HumanAnswerServices:
chain = (setup | check_answer_prompt | structured_llm ) chain = (setup | check_answer_prompt | structured_llm )
raw_answer = chain.invoke(prompt_params) raw_answer = chain.invoke(prompt_params)
current_app.logger.debug(f"Raw answer: {raw_answer}")
return raw_answer.answer return raw_answer.answer
@@ -89,7 +90,6 @@ class HumanAnswerServices:
chain = (setup | check_answer_prompt | structured_llm) chain = (setup | check_answer_prompt | structured_llm)
raw_answer = chain.invoke(prompt_params) raw_answer = chain.invoke(prompt_params)
current_app.logger.debug(f"Raw answer: {raw_answer}")
return raw_answer.answer return raw_answer.answer

6
common/utils/cache/translation_cache.py vendored
View File

@@ -68,7 +68,6 @@ class TranslationCacheHandler(CacheHandler[TranslationCache]):
setattr(translation, column.name, value) setattr(translation, column.name, value)
current_app.logger.debug(f"Translation Cache Retrieved: {translation}")
metrics = { metrics = {
'total_tokens': translation.prompt_tokens + translation.completion_tokens, 'total_tokens': translation.prompt_tokens + translation.completion_tokens,
'prompt_tokens': translation.prompt_tokens, 'prompt_tokens': translation.prompt_tokens,
@@ -109,7 +108,6 @@ class TranslationCacheHandler(CacheHandler[TranslationCache]):
""" """
if not context: if not context:
context = 'No context provided.' context = 'No context provided.'
current_app.logger.debug(f"Getting translation for text: {text[:10]}..., target_lang: {target_lang}, source_lang: {source_lang}, context: {context[:10]}...")
def creator_func(hash_key: str) -> Optional[TranslationCache]: def creator_func(hash_key: str) -> Optional[TranslationCache]:
# Check if translation already exists in database # Check if translation already exists in database
@@ -125,8 +123,6 @@ class TranslationCacheHandler(CacheHandler[TranslationCache]):
'time_elapsed': 0, 'time_elapsed': 0,
'interaction_type': 'LLM' 'interaction_type': 'LLM'
} }
current_app.logger.debug(f"Found existing translation in DB: {existing_translation.cache_key}")
current_app.logger.debug(f"Metrics: {metrics}")
current_event.log_llm_metrics(metrics) current_event.log_llm_metrics(metrics)
db.session.commit() db.session.commit()
return existing_translation return existing_translation
@@ -165,7 +161,6 @@ class TranslationCacheHandler(CacheHandler[TranslationCache]):
# Generate the hash key using your existing method # Generate the hash key using your existing method
hash_key = self._generate_cache_key(text, target_lang, source_lang, context) hash_key = self._generate_cache_key(text, target_lang, source_lang, context)
current_app.logger.debug(f"Generated hash key: {hash_key}")
# Pass the hash_key to the get method # Pass the hash_key to the get method
return self.get(creator_func, hash_key=hash_key) return self.get(creator_func, hash_key=hash_key)
@@ -189,7 +184,6 @@ class TranslationCacheHandler(CacheHandler[TranslationCache]):
def translate_text(self, text_to_translate: str, target_lang: str, source_lang: str = None, context: str = None) \ def translate_text(self, text_to_translate: str, target_lang: str, source_lang: str = None, context: str = None) \
-> tuple[str, dict[str, int | float]]: -> tuple[str, dict[str, int | float]]:
target_language = current_app.config['SUPPORTED_LANGUAGE_ISO639_1_LOOKUP'][target_lang] target_language = current_app.config['SUPPORTED_LANGUAGE_ISO639_1_LOOKUP'][target_lang]
current_app.logger.debug(f"Target language: {target_language}")
prompt_params = { prompt_params = {
"text_to_translate": text_to_translate, "text_to_translate": text_to_translate,
"target_language": target_language, "target_language": target_language,

2
common/utils/chat_utils.py
View File

@@ -44,13 +44,11 @@ def get_default_chat_customisation(tenant_customisation=None):
if isinstance(tenant_customisation, str): if isinstance(tenant_customisation, str):
try: try:
tenant_customisation = json.loads(tenant_customisation) tenant_customisation = json.loads(tenant_customisation)
current_app.logger.debug(f"Converted JSON string to dict: {tenant_customisation}")
except json.JSONDecodeError as e: except json.JSONDecodeError as e:
current_app.logger.error(f"Error parsing JSON customisation: {e}") current_app.logger.error(f"Error parsing JSON customisation: {e}")
return default_customisation return default_customisation
# Update with tenant customization # Update with tenant customization
current_app.logger.debug(f"Tenant customisation - in default creation: {tenant_customisation}")
if tenant_customisation: if tenant_customisation:
for key, value in tenant_customisation.items(): for key, value in tenant_customisation.items():
if key in customisation: if key in customisation:

6
common/utils/mail_utils.py
View File

@@ -6,22 +6,17 @@ from flask import current_app
def send_email(to_email, to_name, subject, html): def send_email(to_email, to_name, subject, html):
current_app.logger.debug(f"Sending email to {to_email} with subject {subject}")
access_key = current_app.config['SW_EMAIL_ACCESS_KEY'] access_key = current_app.config['SW_EMAIL_ACCESS_KEY']
secret_key = current_app.config['SW_EMAIL_SECRET_KEY'] secret_key = current_app.config['SW_EMAIL_SECRET_KEY']
default_project_id = current_app.config['SW_PROJECT'] default_project_id = current_app.config['SW_PROJECT']
default_region = "fr-par" default_region = "fr-par"
current_app.logger.debug(f"Access Key: {access_key}\nSecret Key: {secret_key}\n"
f"Default Project ID: {default_project_id}\nDefault Region: {default_region}")
client = Client( client = Client(
access_key=access_key, access_key=access_key,
secret_key=secret_key, secret_key=secret_key,
default_project_id=default_project_id, default_project_id=default_project_id,
default_region=default_region default_region=default_region
) )
current_app.logger.debug(f"Scaleway Client Initialized")
tem = TemV1Alpha1API(client) tem = TemV1Alpha1API(client)
current_app.logger.debug(f"Tem Initialized")
from_ = CreateEmailRequestAddress(email=current_app.config['SW_EMAIL_SENDER'], from_ = CreateEmailRequestAddress(email=current_app.config['SW_EMAIL_SENDER'],
name=current_app.config['SW_EMAIL_NAME']) name=current_app.config['SW_EMAIL_NAME'])
to_ = CreateEmailRequestAddress(email=to_email, name=to_name) to_ = CreateEmailRequestAddress(email=to_email, name=to_name)
@@ -34,7 +29,6 @@ def send_email(to_email, to_name, subject, html):
html=html, html=html,
project_id=default_project_id, project_id=default_project_id,
) )
current_app.logger.debug(f"Email sent to {to_email}")
def html_to_text(html_content): def html_to_text(html_content):

1
common/utils/template_filters.py
View File

@@ -98,7 +98,6 @@ def get_pagination_html(pagination, endpoint, **kwargs):
if page: if page:
is_active = 'active' if page == pagination.page else '' is_active = 'active' if page == pagination.page else ''
url = url_for(endpoint, page=page, **kwargs) url = url_for(endpoint, page=page, **kwargs)
current_app.logger.debug(f"URL for page {page}: {url}")
html.append(f'<li class="page-item {is_active}"><a class="page-link" href="{url}">{page}</a></li>') html.append(f'<li class="page-item {is_active}"><a class="page-link" href="{url}">{page}</a></li>')
else: else:
html.append('<li class="page-item disabled"><span class="page-link">...</span></li>') html.append('<li class="page-item disabled"><span class="page-link">...</span></li>')

5
config/agents/globals/RAG_AGENT/1.1.0.yaml
View File

@@ -4,7 +4,8 @@ role: >
{tenant_name} Spokesperson. {custom_role} {tenant_name} Spokesperson. {custom_role}
goal: > goal: >
You get questions by a human correspondent, and give answers based on a given context, taking into account the history You get questions by a human correspondent, and give answers based on a given context, taking into account the history
of the current conversation. {custom_goal} of the current conversation.
{custom_goal}
backstory: > backstory: >
You are the primary contact for {tenant_name}. You are known by {name}, and can be addressed by this name, or you. You are You are the primary contact for {tenant_name}. You are known by {name}, and can be addressed by this name, or you. You are
a very good communicator, and adapt to the style used by the human asking for information (e.g. formal or informal). a very good communicator, and adapt to the style used by the human asking for information (e.g. formal or informal).
@@ -13,7 +14,7 @@ backstory: >
language the context provided to you is in. You are participating in a conversation, not writing e.g. an email. Do not language the context provided to you is in. You are participating in a conversation, not writing e.g. an email. Do not
include a salutation or closing greeting in your answer. include a salutation or closing greeting in your answer.
{custom_backstory} {custom_backstory}
full_model_name: "mistral.mistral-small-latest" full_model_name: "mistral.mistral-medium-latest"
temperature: 0.3 temperature: 0.3
metadata: metadata:
author: "Josako" author: "Josako"

14
config/prompts/globals/check_additional_information/1.0.0.yaml
View File

@@ -1,13 +1,17 @@
version: "1.0.0" version: "1.0.0"
content: > content: >
Check if additional information or questions are available in the following answer (answer in between triple Check if there are other elements available in the provided text (in between triple $) than answers to the
backquotes): following question (in between triple €):
```{answer}``` €€€
{question}
€€€
in addition to answers to the following question (in between triple backquotes): Provided text:
```{question}``` $$$
{answer}
$$$
Answer with True or False, without additional information. Answer with True or False, without additional information.
llm_model: "mistral.mistral-medium-latest" llm_model: "mistral.mistral-medium-latest"

2
config/prompts/globals/history/1.0.0.yaml
View File

@@ -4,7 +4,7 @@ content: |
question is understandable without that history. The conversation is a consequence of questions and context provided question is understandable without that history. The conversation is a consequence of questions and context provided
by the HUMAN, and the AI (you) answering back, in chronological order. The most recent (i.e. last) elements are the by the HUMAN, and the AI (you) answering back, in chronological order. The most recent (i.e. last) elements are the
most important when detailing the question. most important when detailing the question.
You answer by stating the detailed question in {language}. You return the only the detailed question in {language}. Without any additional information.
History: History:
```{history}``` ```{history}```
Question to be detailed: Question to be detailed:

2
config/specialists/traicie/TRAICIE_SELECTION_SPECIALIST/1.4.0.yaml
View File

@@ -93,7 +93,7 @@ arguments:
name: "Interaction Mode" name: "Interaction Mode"
type: "enum" type: "enum"
description: "The interaction mode the specialist will start working in." description: "The interaction mode the specialist will start working in."
allowed_values: ["orientation", "seduction"] allowed_values: ["orientation", "selection"]
default: "orientation" default: "orientation"
required: true required: true
results: results:

14
config/tasks/globals/RAG_TASK/1.1.0.yaml
View File

@@ -8,14 +8,14 @@ task_description: >
Use the following {language} in your communication, and cite the sources used at the end of the full conversation. Use the following {language} in your communication, and cite the sources used at the end of the full conversation.
If the question cannot be answered using the given context, answer "I have insufficient information to answer this If the question cannot be answered using the given context, answer "I have insufficient information to answer this
question." question."
Context (in between triple backquotes): Context (in between triple $):
```{context}``` $$${context}$$$
History (in between triple backquotes): History (in between triple €):
```{history}``` €€€{history}€€€
Question (in between triple backquotes): Question (in between triple £):
```{question}``` £££{question}£££
expected_output: > expected_output: >
Your answer.
metadata: metadata:
author: "Josako" author: "Josako"
date_added: "2025-01-08" date_added: "2025-01-08"

2
eveai_app/views/basic_views.py
View File

@@ -121,7 +121,6 @@ def view_content(content_type):
content_type (str): Type content (eg. 'changelog', 'terms', 'privacy') content_type (str): Type content (eg. 'changelog', 'terms', 'privacy')
""" """
try: try:
current_app.logger.debug(f"Showing content {content_type}")
major_minor = request.args.get('version') major_minor = request.args.get('version')
patch = request.args.get('patch') patch = request.args.get('patch')
@@ -163,5 +162,4 @@ def view_content(content_type):
@roles_accepted('Super User', 'Partner Admin', 'Tenant Admin') @roles_accepted('Super User', 'Partner Admin', 'Tenant Admin')
def release_notes(): def release_notes():
"""Doorverwijzen naar de nieuwe content view voor changelog""" """Doorverwijzen naar de nieuwe content view voor changelog"""
current_app.logger.debug(f"Redirecting to content viewer")
return redirect(prefixed_url_for('basic_bp.view_content', content_type='changelog')) return redirect(prefixed_url_for('basic_bp.view_content', content_type='changelog'))

2
eveai_app/views/document_forms.py
View File

@@ -137,14 +137,12 @@ class RetrieverForm(FlaskForm):
super().__init__(*args, **kwargs) super().__init__(*args, **kwargs)
tenant_id = session.get('tenant').get('id') tenant_id = session.get('tenant').get('id')
choices = TenantServices.get_available_types_for_tenant(tenant_id, "retrievers") choices = TenantServices.get_available_types_for_tenant(tenant_id, "retrievers")
current_app.logger.debug(f"Potential choices: {choices}")
# Dynamically populate the 'type' field using the constructor # Dynamically populate the 'type' field using the constructor
type_choices = [] type_choices = []
for key, value in choices.items(): for key, value in choices.items():
valid_catalog_types = value.get('valid_catalog_types', None) valid_catalog_types = value.get('valid_catalog_types', None)
if valid_catalog_types: if valid_catalog_types:
catalog_type = session.get('catalog').get('type') catalog_type = session.get('catalog').get('type')
current_app.logger.debug(f"Check {catalog_type} in {valid_catalog_types}")
if catalog_type in valid_catalog_types: if catalog_type in valid_catalog_types:
type_choices.append((key, value['name'])) type_choices.append((key, value['name']))
else: # Retriever type is valid for all catalog types else: # Retriever type is valid for all catalog types

4
eveai_app/views/document_views.py
View File

@@ -668,7 +668,6 @@ def handle_document_version_selection():
return redirect(prefixed_url_for('document_bp.document_versions_list')) return redirect(prefixed_url_for('document_bp.document_versions_list'))
action = request.form['action'] action = request.form['action']
current_app.logger.debug(f'Action: {action}')
match action: match action:
case 'edit_document_version': case 'edit_document_version':
@@ -747,7 +746,6 @@ def document_versions_list():
@document_bp.route('/view_document_version_markdown/<int:document_version_id>', methods=['GET']) @document_bp.route('/view_document_version_markdown/<int:document_version_id>', methods=['GET'])
@roles_accepted('Super User', 'Partner Admin', 'Tenant Admin') @roles_accepted('Super User', 'Partner Admin', 'Tenant Admin')
def view_document_version_markdown(document_version_id): def view_document_version_markdown(document_version_id):
current_app.logger.debug(f'Viewing document version markdown {document_version_id}')
# Retrieve document version # Retrieve document version
document_version = DocumentVersion.query.get_or_404(document_version_id) document_version = DocumentVersion.query.get_or_404(document_version_id)
@@ -759,7 +757,6 @@ def view_document_version_markdown(document_version_id):
markdown_filename = f"{document_version.id}.md" markdown_filename = f"{document_version.id}.md"
markdown_object_name = minio_client.generate_object_name(document_version.doc_id, document_version.language, markdown_object_name = minio_client.generate_object_name(document_version.doc_id, document_version.language,
document_version.id, markdown_filename) document_version.id, markdown_filename)
current_app.logger.debug(f'Markdown object name: {markdown_object_name}')
# Download actual markdown file # Download actual markdown file
file_data = minio_client.download_document_file( file_data = minio_client.download_document_file(
tenant_id, tenant_id,
@@ -769,7 +766,6 @@ def view_document_version_markdown(document_version_id):
# Decodeer de binaire data naar UTF-8 tekst # Decodeer de binaire data naar UTF-8 tekst
markdown_content = file_data.decode('utf-8') markdown_content = file_data.decode('utf-8')
current_app.logger.debug(f'Markdown content: {markdown_content}')
# Render de template met de markdown inhoud # Render de template met de markdown inhoud
return render_template( return render_template(

8
eveai_app/views/dynamic_form_base.py
View File

@@ -66,8 +66,6 @@ class OrderedListField(TextAreaField):
else: else:
existing_render_kw = {} existing_render_kw = {}
current_app.logger.debug(f"incomming render_kw for ordered list field: {existing_render_kw}")
# Stel nieuwe render_kw samen # Stel nieuwe render_kw samen
new_render_kw = { new_render_kw = {
'data-list-type': list_type, 'data-list-type': list_type,
@@ -91,8 +89,6 @@ class OrderedListField(TextAreaField):
if key != 'class': # Klassen hebben we al verwerkt if key != 'class': # Klassen hebben we al verwerkt
new_render_kw[key] = value new_render_kw[key] = value
current_app.logger.debug(f"final render_kw for ordered list field: {new_render_kw}")
# Update kwargs met de nieuwe gecombineerde render_kw # Update kwargs met de nieuwe gecombineerde render_kw
kwargs['render_kw'] = new_render_kw kwargs['render_kw'] = new_render_kw
@@ -327,7 +323,6 @@ class DynamicFormBase(FlaskForm):
for the collection_name and may also contain list_type definitions for the collection_name and may also contain list_type definitions
initial_data: Optional initial data for the fields initial_data: Optional initial data for the fields
""" """
current_app.logger.debug(f"Adding dynamic fields for collection {collection_name} with config: {config}")
if isinstance(initial_data, str): if isinstance(initial_data, str):
try: try:
@@ -535,7 +530,7 @@ class DynamicFormBase(FlaskForm):
list_types[list_type] = specialist_config[list_type] list_types[list_type] = specialist_config[list_type]
break break
except Exception as e: except Exception as e:
current_app.logger.debug(f"Error checking specialist {specialist_type}: {e}") current_app.logger.error(f"Error checking specialist {specialist_type}: {e}")
continue continue
except Exception as e: except Exception as e:
current_app.logger.error(f"Error retrieving specialist configurations: {e}") current_app.logger.error(f"Error retrieving specialist configurations: {e}")
@@ -575,7 +570,6 @@ class DynamicFormBase(FlaskForm):
# Parse JSON for special field types # Parse JSON for special field types
if field.type == 'BooleanField': if field.type == 'BooleanField':
data[original_field_name] = full_field_name in self.raw_formdata data[original_field_name] = full_field_name in self.raw_formdata
current_app.logger.debug(f"Value for {original_field_name} is {data[original_field_name]}")
elif isinstance(field, (TaggingFieldsField, TaggingFieldsFilterField, DynamicArgumentsField, OrderedListField)) and field.data: elif isinstance(field, (TaggingFieldsField, TaggingFieldsFilterField, DynamicArgumentsField, OrderedListField)) and field.data:
try: try:
data[original_field_name] = json.loads(field.data) data[original_field_name] = json.loads(field.data)

9
eveai_app/views/interaction_views.py
View File

@@ -76,7 +76,6 @@ def handle_chat_session_selection():
cs_id = ast.literal_eval(chat_session_identification).get('value') cs_id = ast.literal_eval(chat_session_identification).get('value')
action = request.form['action'] action = request.form['action']
current_app.logger.debug(f'Handle Chat Session Selection Action: {action}')
match action: match action:
case 'view_chat_session': case 'view_chat_session':
@@ -503,7 +502,6 @@ def execute_specialist(specialist_id):
if form.validate_on_submit(): if form.validate_on_submit():
# We're only interested in gathering the dynamic arguments # We're only interested in gathering the dynamic arguments
arguments = form.get_dynamic_data("arguments") arguments = form.get_dynamic_data("arguments")
current_app.logger.debug(f"Executing specialist {specialist.id} with arguments: {arguments}")
session_id = SpecialistServices.start_session() session_id = SpecialistServices.start_session()
execution_task = SpecialistServices.execute_specialist( execution_task = SpecialistServices.execute_specialist(
tenant_id=session.get('tenant').get('id'), tenant_id=session.get('tenant').get('id'),
@@ -512,7 +510,6 @@ def execute_specialist(specialist_id):
session_id=session_id, session_id=session_id,
user_timezone=session.get('tenant').get('timezone') user_timezone=session.get('tenant').get('timezone')
) )
current_app.logger.debug(f"Execution task for specialist {specialist.id} created: {execution_task}")
return redirect(prefixed_url_for('interaction_bp.session_interactions_by_session_id', session_id=session_id)) return redirect(prefixed_url_for('interaction_bp.session_interactions_by_session_id', session_id=session_id))
return render_template('interaction/execute_specialist.html', form=form) return render_template('interaction/execute_specialist.html', form=form)
@@ -620,7 +617,6 @@ def specialist_magic_link():
# Define the make valid for this magic link # Define the make valid for this magic link
specialist = Specialist.query.get(new_specialist_magic_link.specialist_id) specialist = Specialist.query.get(new_specialist_magic_link.specialist_id)
make_id = specialist.configuration.get('make', None) make_id = specialist.configuration.get('make', None)
current_app.logger.debug(f"make_id defined in specialist: {make_id}")
if make_id: if make_id:
new_specialist_magic_link.tenant_make_id = make_id new_specialist_magic_link.tenant_make_id = make_id
elif session.get('tenant').get('default_tenant_make_id'): elif session.get('tenant').get('default_tenant_make_id'):
@@ -707,10 +703,6 @@ def edit_specialist_magic_link(specialist_magic_link_id):
# Store the data URI in the form data # Store the data URI in the form data
form.qr_code_url.data = data_uri form.qr_code_url.data = data_uri
current_app.logger.debug(f"QR code generated successfully for {magic_link_code}")
current_app.logger.debug(f"QR code data URI starts with: {data_uri[:50]}...")
except Exception as e: except Exception as e:
current_app.logger.error(f"Failed to generate QR code: {str(e)}") current_app.logger.error(f"Failed to generate QR code: {str(e)}")
form.qr_code_url.data = "Error generating QR code" form.qr_code_url.data = "Error generating QR code"
@@ -794,7 +786,6 @@ def assets():
def handle_asset_selection(): def handle_asset_selection():
action = request.form.get('action') action = request.form.get('action')
asset_id = request.form.get('selected_row') asset_id = request.form.get('selected_row')
current_app.logger.debug(f"Action: {action}, Asset ID: {asset_id}")
if action == 'edit_asset': if action == 'edit_asset':
return redirect(prefixed_url_for('interaction_bp.edit_asset', asset_id=asset_id)) return redirect(prefixed_url_for('interaction_bp.edit_asset', asset_id=asset_id))

1
eveai_app/views/list_views/assets_list_view.py
View File

@@ -51,7 +51,6 @@ class AssetsListView(FilteredListView):
else: else:
return '' return ''
current_app.logger.debug(f"Assets retrieved: {pagination.items}")
rows = [ rows = [
[ [
{'value': item.id, 'class': '', 'type': 'text'}, {'value': item.id, 'class': '', 'type': 'text'},

2
eveai_app/views/list_views/document_list_view.py
View File

@@ -11,7 +11,6 @@ class DocumentListView(FilteredListView):
def get_query(self): def get_query(self):
catalog_id = session.get('catalog_id') catalog_id = session.get('catalog_id')
current_app.logger.debug(f"Catalog ID: {catalog_id}")
return Document.query.filter_by(catalog_id=catalog_id) return Document.query.filter_by(catalog_id=catalog_id)
def apply_filters(self, query): def apply_filters(self, query):
@@ -57,7 +56,6 @@ class DocumentListView(FilteredListView):
else: else:
return '' return ''
current_app.logger.debug(f"Items retrieved: {pagination.items}")
rows = [ rows = [
[ [
{'value': item.id, 'class': '', 'type': 'text'}, {'value': item.id, 'class': '', 'type': 'text'},

1
eveai_app/views/list_views/full_document_list_view.py
View File

@@ -19,7 +19,6 @@ class FullDocumentListView(FilteredListView):
def get_query(self): def get_query(self):
catalog_id = session.get('catalog_id') catalog_id = session.get('catalog_id')
current_app.logger.debug(f"Catalog ID: {catalog_id}")
# Fix: Selecteer alleen de id kolom in de subquery # Fix: Selecteer alleen de id kolom in de subquery
latest_version_subquery = ( latest_version_subquery = (

10
eveai_app/views/partner_views.py
View File

@@ -57,7 +57,6 @@ def edit_partner(partner_id):
form.tenant.data = tenant.name form.tenant.data = tenant.name
if form.validate_on_submit(): if form.validate_on_submit():
current_app.logger.debug(f"Form data for Partner: {form.data}")
# Populate the user with form data # Populate the user with form data
form.populate_obj(partner) form.populate_obj(partner)
update_logging_information(partner, dt.now(tz.utc)) update_logging_information(partner, dt.now(tz.utc))
@@ -88,8 +87,6 @@ def partners():
Tenant.name.label('name') Tenant.name.label('name')
).join(Tenant, Partner.tenant_id == Tenant.id).order_by(Partner.id)) ).join(Tenant, Partner.tenant_id == Tenant.id).order_by(Partner.id))
current_app.logger.debug(f'{format_query_results(query)}')
pagination = query.paginate(page=page, per_page=per_page) pagination = query.paginate(page=page, per_page=per_page)
the_partners = pagination.items the_partners = pagination.items
@@ -170,17 +167,10 @@ def edit_partner_service(partner_service_id):
form.add_dynamic_fields("configuration", partner_service_config, partner_service.configuration) form.add_dynamic_fields("configuration", partner_service_config, partner_service.configuration)
form.add_dynamic_fields("permissions", partner_service_config, partner_service.permissions) form.add_dynamic_fields("permissions", partner_service_config, partner_service.permissions)
if request.method == 'POST':
current_app.logger.debug(f"Form returned: {form.data}")
raw_form_data = request.form.to_dict()
current_app.logger.debug(f"Raw form data: {raw_form_data}")
if form.validate_on_submit(): if form.validate_on_submit():
form.populate_obj(partner_service) form.populate_obj(partner_service)
partner_service.configuration = form.get_dynamic_data('configuration') partner_service.configuration = form.get_dynamic_data('configuration')
partner_service.permissions = form.get_dynamic_data('permissions') partner_service.permissions = form.get_dynamic_data('permissions')
current_app.logger.debug(f"Partner Service configuration: {partner_service.configuration}")
current_app.logger.debug(f"Partner Service permissions: {partner_service.permissions}")
update_logging_information(partner_service, dt.now(tz.utc)) update_logging_information(partner_service, dt.now(tz.utc))

5
eveai_app/views/user_forms.py
View File

@@ -172,11 +172,6 @@ def validate_make_name(form, field):
# Check if tenant_make already exists in the database # Check if tenant_make already exists in the database
existing_make = TenantMake.query.filter_by(name=field.data).first() existing_make = TenantMake.query.filter_by(name=field.data).first()
if existing_make:
current_app.logger.debug(f'Existing make: {existing_make.id}')
current_app.logger.debug(f'Form has id: {hasattr(form, 'id')}')
if hasattr(form, 'id'):
current_app.logger.debug(f'Form has id: {form.id.data}')
if existing_make: if existing_make:
if not hasattr(form, 'id') or form.id.data != existing_make.id: if not hasattr(form, 'id') or form.id.data != existing_make.id:
raise ValidationError(f'A Make with name "{field.data}" already exists. Choose another name.') raise ValidationError(f'A Make with name "{field.data}" already exists. Choose another name.')

7
eveai_app/views/user_views.py
View File

@@ -147,12 +147,8 @@ def select_tenant():
# Start with a base query # Start with a base query
query = Tenant.query query = Tenant.query
current_app.logger.debug("We proberen het scherm op te bouwen")
current_app.logger.debug(f"Session: {session}")
# Apply different filters based on user role # Apply different filters based on user role
if current_user.has_roles('Partner Admin') and 'partner' in session: if current_user.has_roles('Partner Admin') and 'partner' in session:
current_app.logger.debug("We zitten in partner mode")
# Get the partner's management service # Get the partner's management service
management_service = next((service for service in session['partner']['services'] management_service = next((service for service in session['partner']['services']
if service.get('type') == 'MANAGEMENT_SERVICE'), None) if service.get('type') == 'MANAGEMENT_SERVICE'), None)
@@ -175,7 +171,6 @@ def select_tenant():
# Filter query to only show allowed tenants # Filter query to only show allowed tenants
query = query.filter(Tenant.id.in_(allowed_tenant_ids)) query = query.filter(Tenant.id.in_(allowed_tenant_ids))
current_app.logger.debug("We zitten na partner service selectie")
# Apply form filters (for both Super User and Partner Admin) # Apply form filters (for both Super User and Partner Admin)
if filter_form.validate_on_submit(): if filter_form.validate_on_submit():
if filter_form.types.data: if filter_form.types.data:
@@ -722,9 +717,7 @@ def edit_tenant_make(tenant_make_id):
form.populate_obj(tenant_make) form.populate_obj(tenant_make)
tenant_make.chat_customisation_options = form.get_dynamic_data("configuration") tenant_make.chat_customisation_options = form.get_dynamic_data("configuration")
# Verwerk allowed_languages als array # Verwerk allowed_languages als array
current_app.logger.debug(f"Allowed languages: {form.allowed_languages.data}")
tenant_make.allowed_languages = form.allowed_languages.data if form.allowed_languages.data else None tenant_make.allowed_languages = form.allowed_languages.data if form.allowed_languages.data else None
current_app.logger.debug(f"Updated allowed languages: {tenant_make.allowed_languages}")
# Update logging information # Update logging information
update_logging_information(tenant_make, dt.now(tz.utc)) update_logging_information(tenant_make, dt.now(tz.utc))

2
eveai_chat_workers/outputs/globals/q_a_output/q_a_output_v1_0.py
View File

@@ -4,4 +4,4 @@ from pydantic import BaseModel, Field
class QAOutput(BaseModel): class QAOutput(BaseModel):
answer: bool = Field(None, description="True or False") answer: bool = Field(None, description="Your answer, True or False")

2
eveai_chat_workers/outputs/globals/rag/rag_v1_0.py
View File

@@ -4,6 +4,6 @@ from pydantic import BaseModel, Field
class RAGOutput(BaseModel): class RAGOutput(BaseModel):
answer: str = Field(None, description="Answer to the questions asked") answer: str = Field(None, description="Answer to the questions asked, in Markdown format.")
insufficient_info: bool = Field(None, description="An indication if there's insufficient information to answer") insufficient_info: bool = Field(None, description="An indication if there's insufficient information to answer")

1
eveai_chat_workers/retrievers/base_retriever.py
View File

@@ -108,6 +108,5 @@ def get_retriever_class(retriever_type: str, type_version: str):
module_path = f"eveai_chat_workers.retrievers.{partner}.{retriever_type}.{major_minor}" module_path = f"eveai_chat_workers.retrievers.{partner}.{retriever_type}.{major_minor}"
else: else:
module_path = f"eveai_chat_workers.retrievers.globals.{retriever_type}.{major_minor}" module_path = f"eveai_chat_workers.retrievers.globals.{retriever_type}.{major_minor}"
current_app.logger.debug(f"Importing retriever class from {module_path}")
module = importlib.import_module(module_path) module = importlib.import_module(module_path)
return module.RetrieverExecutor return module.RetrieverExecutor

4
eveai_chat_workers/retrievers/globals/STANDARD_RAG/1_0.py
View File

@@ -116,8 +116,8 @@ class RetrieverExecutor(BaseRetriever):
)) ))
self.log_tuning('retrieve', { self.log_tuning('retrieve', {
"arguments": arguments.model_dump(), "arguments": arguments.model_dump(),
"similarity_threshold": self.similarity_threshold, "similarity_threshold": similarity_threshold,
"k": self.k, "k": k,
"query": compiled_query, "query": compiled_query,
"Raw Results": str(results), "Raw Results": str(results),
"Processed Results": [r.model_dump() for r in processed_results], "Processed Results": [r.model_dump() for r in processed_results],

2
eveai_chat_workers/specialists/base_specialist.py
View File

@@ -135,11 +135,9 @@ def get_specialist_class(specialist_type: str, type_version: str):
major_minor = '_'.join(type_version.split('.')[:2]) major_minor = '_'.join(type_version.split('.')[:2])
specialist_config = cache_manager.specialists_config_cache.get_config(specialist_type, type_version) specialist_config = cache_manager.specialists_config_cache.get_config(specialist_type, type_version)
partner = specialist_config.get("partner", None) partner = specialist_config.get("partner", None)
current_app.logger.debug(f"Specialist partner for {specialist_type} {type_version} is {partner}")
if partner: if partner:
module_path = f"eveai_chat_workers.specialists.{partner}.{specialist_type}.{major_minor}" module_path = f"eveai_chat_workers.specialists.{partner}.{specialist_type}.{major_minor}"
else: else:
module_path = f"eveai_chat_workers.specialists.globals.{specialist_type}.{major_minor}" module_path = f"eveai_chat_workers.specialists.globals.{specialist_type}.{major_minor}"
current_app.logger.debug(f"Importing specialist class from {module_path}")
module = importlib.import_module(module_path) module = importlib.import_module(module_path)
return module.SpecialistExecutor return module.SpecialistExecutor

11
eveai_chat_workers/specialists/crewai_base_classes.py
View File

@@ -40,7 +40,6 @@ class EveAICrewAIAgent(Agent):
Returns: Returns:
Output of the agent Output of the agent
""" """
current_app.logger.debug(f"Task Execution {task.name} by {self.name}")
# with current_event.create_span(f"Task Execution {task.name} by {self.name}"): # with current_event.create_span(f"Task Execution {task.name} by {self.name}"):
self.specialist.log_tuning(f"EveAI Agent {self.name}, Task {task.name} Start", {}) self.specialist.log_tuning(f"EveAI Agent {self.name}, Task {task.name} Start", {})
self.specialist.update_progress("EveAI Agent Task Start", self.specialist.update_progress("EveAI Agent Task Start",
@@ -134,11 +133,17 @@ class EveAICrewAIFlow(Flow):
return self.state return self.state
class Citation(BaseModel):
document_id: int
document_version_id: int
url: str
class EveAIFlowState(BaseModel): class EveAIFlowState(BaseModel):
"""Base class for all EveAI flow states""" """Base class for all EveAI flow states"""
answer: Optional[str] = None answer: Optional[str] = None
detailed_question: Optional[str] = None
question: Optional[str] = None question: Optional[str] = None
phase: Optional[str] = None phase: Optional[str] = None
form_request: Optional[Dict[str, Any]] = None form_request: Optional[Dict[str, Any]] = None
citations: Optional[Dict[str, Any]] = None citations: Optional[List[Citation]] = None

86
eveai_chat_workers/specialists/crewai_base_specialist.py
View File

@@ -78,14 +78,15 @@ class CrewAIBaseSpecialistExecutor(BaseSpecialistExecutor):
return "\n\n".join([ return "\n\n".join([
"\n\n".join([ "\n\n".join([
f"HUMAN:\n" f"HUMAN:\n"
f"{interaction.specialist_results['detailed_question']}" f"{interaction.specialist_arguments['question']}"
if interaction.specialist_results.get('detailed_question') else "", if interaction.specialist_arguments.get('question') else "",
f"{interaction.specialist_arguments.get('form_values')}" f"{interaction.specialist_arguments.get('form_values')}"
if interaction.specialist_arguments.get('form_values') else "", if interaction.specialist_arguments.get('form_values') else "",
f"AI:\n{interaction.specialist_results['answer']}" f"AI:\n{interaction.specialist_results['answer']}"
if interaction.specialist_results.get('answer') else "" if interaction.specialist_results.get('answer') else ""
]).strip() ]).strip()
for interaction in self._cached_session.interactions for interaction in self._cached_session.interactions
if interaction.specialist_arguments.get('question') != "Initialize"
]) ])
def _add_task_agent(self, task_name: str, agent_name: str): def _add_task_agent(self, task_name: str, agent_name: str):
@@ -120,10 +121,9 @@ class CrewAIBaseSpecialistExecutor(BaseSpecialistExecutor):
self._state_result_relations[state_name] = result_name self._state_result_relations[state_name] = result_name
def _config_default_state_result_relations(self): def _config_default_state_result_relations(self):
for default_attribute_name in ['answer', 'detailed_question', 'form_request', 'phase', 'citations']: for default_attribute_name in ['answer', 'form_request', 'phase', 'citations']:
self._add_state_result_relation(default_attribute_name) self._add_state_result_relation(default_attribute_name)
@abstractmethod @abstractmethod
def _config_state_result_relations(self): def _config_state_result_relations(self):
"""Configure the state-result relations by adding state-result combinations. Use _add_state_result_relation()""" """Configure the state-result relations by adding state-result combinations. Use _add_state_result_relation()"""
@@ -150,6 +150,7 @@ class CrewAIBaseSpecialistExecutor(BaseSpecialistExecutor):
agent_goal = agent_config.get('goal', '').replace('{custom_goal}', agent.goal or '') agent_goal = agent_config.get('goal', '').replace('{custom_goal}', agent.goal or '')
agent_goal = self._replace_system_variables(agent_goal) agent_goal = self._replace_system_variables(agent_goal)
agent_backstory = agent_config.get('backstory', '').replace('{custom_backstory}', agent.backstory or '') agent_backstory = agent_config.get('backstory', '').replace('{custom_backstory}', agent.backstory or '')
agent_backstory = self._replace_system_variables(agent_backstory)
agent_full_model_name = agent_config.get('full_model_name', 'mistral.mistral-large-latest') agent_full_model_name = agent_config.get('full_model_name', 'mistral.mistral-large-latest')
agent_temperature = agent_config.get('temperature', 0.3) agent_temperature = agent_config.get('temperature', 0.3)
llm = get_crewai_llm(agent_full_model_name, agent_temperature) llm = get_crewai_llm(agent_full_model_name, agent_temperature)
@@ -183,12 +184,9 @@ class CrewAIBaseSpecialistExecutor(BaseSpecialistExecutor):
"verbose": task.tuning "verbose": task.tuning
} }
task_name = task.type.lower() task_name = task.type.lower()
current_app.logger.debug(f"Task {task_name} is getting processed")
if task_name in self._task_pydantic_outputs: if task_name in self._task_pydantic_outputs:
task_kwargs["output_pydantic"] = self._task_pydantic_outputs[task_name] task_kwargs["output_pydantic"] = self._task_pydantic_outputs[task_name]
current_app.logger.debug(f"Task {task_name} has an output pydantic: {self._task_pydantic_outputs[task_name]}")
if task_name in self._task_agents: if task_name in self._task_agents:
current_app.logger.debug(f"Task {task_name} has an agent: {self._task_agents[task_name]}")
task_kwargs["agent"] = self._agents[self._task_agents[task_name]] task_kwargs["agent"] = self._agents[self._task_agents[task_name]]
# Instantiate the task with dynamic arguments # Instantiate the task with dynamic arguments
@@ -236,46 +234,6 @@ class CrewAIBaseSpecialistExecutor(BaseSpecialistExecutor):
The assets can be retrieved using their type name in lower case, e.g. rag_agent""" The assets can be retrieved using their type name in lower case, e.g. rag_agent"""
raise NotImplementedError raise NotImplementedError
def _detail_question(self, language: str, question: str) -> str:
"""Detail question based on conversation history"""
try:
with current_event.create_span("Specialist Detail Question"):
# Get LLM and template
template, llm = get_template("history", temperature=0.3)
language_template = create_language_template(template, language)
# Create prompt
history_prompt = ChatPromptTemplate.from_template(language_template)
# Create chain
chain = (
history_prompt |
llm |
StrOutputParser()
)
# Execute chain
detailed_question = chain.invoke({
"history": self.formatted_history,
"question": question
})
self.log_tuning("_detail_question", {
"cached_session_id": self._cached_session.session_id,
"cached_session.interactions": str(self._cached_session.interactions),
"original_question": question,
"history_used": self.formatted_history,
"detailed_question": detailed_question,
})
self.update_progress("Detail Question", {"name": self.type})
return detailed_question
except Exception as e:
current_app.logger.error(f"Error detailing question: {e}")
return question # Fallback to original question
def _retrieve_context(self, arguments: SpecialistArguments) -> tuple[str, list[dict[str, Any]]]: def _retrieve_context(self, arguments: SpecialistArguments) -> tuple[str, list[dict[str, Any]]]:
with current_event.create_span("Specialist Retrieval"): with current_event.create_span("Specialist Retrieval"):
self.log_tuning("Starting context retrieval", { self.log_tuning("Starting context retrieval", {
@@ -283,12 +241,8 @@ class CrewAIBaseSpecialistExecutor(BaseSpecialistExecutor):
"all arguments": arguments.model_dump(), "all arguments": arguments.model_dump(),
}) })
original_question = arguments.question
detailed_question = self._detail_question(arguments.language, original_question)
modified_arguments = arguments.model_copy(update={ modified_arguments = arguments.model_copy(update={
"query": detailed_question, "query": arguments.question
"original_query": original_question
}) })
@@ -361,11 +315,8 @@ class CrewAIBaseSpecialistExecutor(BaseSpecialistExecutor):
update_data = {} update_data = {}
state_dict = self.flow.state.model_dump() state_dict = self.flow.state.model_dump()
current_app.logger.debug(f"Updating specialist results with state: {state_dict}")
for state_name, result_name in self._state_result_relations.items(): for state_name, result_name in self._state_result_relations.items():
current_app.logger.debug(f"Try Updating {result_name} with {state_name}")
if state_name in state_dict and state_dict[state_name] is not None: if state_name in state_dict and state_dict[state_name] is not None:
current_app.logger.debug(f"Updating {result_name} with {state_name} = {state_dict[state_name]}")
update_data[result_name] = state_dict[state_name] update_data[result_name] = state_dict[state_name]
return specialist_results.model_copy(update=update_data) return specialist_results.model_copy(update=update_data)
@@ -383,35 +334,22 @@ class CrewAIBaseSpecialistExecutor(BaseSpecialistExecutor):
# Initialize the standard state values # Initialize the standard state values
self.flow.state.answer = None self.flow.state.answer = None
self.flow.state.detailed_question = None self.flow.state.question = None
self.flow.state.form_request = None self.flow.state.form_request = None
self.flow.state.phase = None self.flow.state.phase = None
self.flow.state.citations = [] self.flow.state.citations = []
@abstractmethod @abstractmethod
def execute(self, arguments: SpecialistArguments, formatted_context: str, citations: List[int]) -> SpecialistResult: def execute(self, arguments: SpecialistArguments,
formatted_context: Optional[str], citations: Optional[list[dict[str, Any]]]) -> SpecialistResult:
raise NotImplementedError raise NotImplementedError
def execute_specialist(self, arguments: SpecialistArguments) -> SpecialistResult: def execute_specialist(self, arguments: SpecialistArguments) -> SpecialistResult:
current_app.logger.debug(f"Retrievers for this specialist: {self.retrievers}")
if self.retrievers: if self.retrievers:
# Detail the incoming query formatted_context = None
if self._cached_session.interactions: citations = None
question = arguments.question result = self.execute(arguments, formatted_context, citations)
language = arguments.language
detailed_question = self._detail_question(language, question)
else:
detailed_question = arguments.question
modified_arguments = {
"question": detailed_question,
"original_question": arguments.question
}
detailed_arguments = arguments.model_copy(update=modified_arguments)
formatted_context, citations = self._retrieve_context(detailed_arguments)
result = self.execute(detailed_arguments, formatted_context, citations)
modified_result = { modified_result = {
"detailed_question": detailed_question,
"citations": citations, "citations": citations,
} }
intermediate_result = result.model_copy(update=modified_result) intermediate_result = result.model_copy(update=modified_result)

7
eveai_chat_workers/specialists/globals/RAG_SPECIALIST/1_0.py
View File

@@ -69,18 +69,12 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
def execute(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult: def execute(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
self.log_tuning("RAG Specialist execution started", {}) self.log_tuning("RAG Specialist execution started", {})
current_app.logger.debug(f"Arguments: {arguments.model_dump()}")
current_app.logger.debug(f"Formatted Context: {formatted_context}")
current_app.logger.debug(f"Formatted History: {self._formatted_history}")
current_app.logger.debug(f"Cached Chat Session: {self._cached_session}")
if not self._cached_session.interactions: if not self._cached_session.interactions:
specialist_phase = "initial" specialist_phase = "initial"
else: else:
specialist_phase = self._cached_session.interactions[-1].specialist_results.get('phase', 'initial') specialist_phase = self._cached_session.interactions[-1].specialist_results.get('phase', 'initial')
results = None results = None
current_app.logger.debug(f"Specialist Phase: {specialist_phase}")
match specialist_phase: match specialist_phase:
case "initial": case "initial":
@@ -191,7 +185,6 @@ class RAGFlow(EveAICrewAIFlow[RAGFlowState]):
raise e raise e
async def kickoff_async(self, inputs=None): async def kickoff_async(self, inputs=None):
current_app.logger.debug(f"Async kickoff {self.name}")
self.state.input = RAGSpecialistInput.model_validate(inputs) self.state.input = RAGSpecialistInput.model_validate(inputs)
result = await super().kickoff_async(inputs) result = await super().kickoff_async(inputs)
return self.state return self.state

18
eveai_chat_workers/specialists/globals/RAG_SPECIALIST/1_1.py
View File

@@ -1,6 +1,6 @@
import json import json
from os import wait from os import wait
from typing import Optional, List from typing import Optional, List, Dict, Any
from crewai.flow.flow import start, listen, and_ from crewai.flow.flow import start, listen, and_
from flask import current_app from flask import current_app
@@ -47,6 +47,7 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
def _config_state_result_relations(self): def _config_state_result_relations(self):
self._add_state_result_relation("rag_output") self._add_state_result_relation("rag_output")
self._add_state_result_relation("citations")
def _instantiate_specialist(self): def _instantiate_specialist(self):
verbose = self.tuning verbose = self.tuning
@@ -69,18 +70,12 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
def execute(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult: def execute(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
self.log_tuning("RAG Specialist execution started", {}) self.log_tuning("RAG Specialist execution started", {})
current_app.logger.debug(f"Arguments: {arguments.model_dump()}")
current_app.logger.debug(f"Formatted Context: {formatted_context}")
current_app.logger.debug(f"Formatted History: {self._formatted_history}")
current_app.logger.debug(f"Cached Chat Session: {self._cached_session}")
if not self._cached_session.interactions: if not self._cached_session.interactions:
specialist_phase = "initial" specialist_phase = "initial"
else: else:
specialist_phase = self._cached_session.interactions[-1].specialist_results.get('phase', 'initial') specialist_phase = self._cached_session.interactions[-1].specialist_results.get('phase', 'initial')
results = None results = None
current_app.logger.debug(f"Specialist Phase: {specialist_phase}")
match specialist_phase: match specialist_phase:
case "initial": case "initial":
@@ -112,6 +107,8 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
INSUFFICIENT_INFORMATION_MESSAGE, INSUFFICIENT_INFORMATION_MESSAGE,
arguments.language) arguments.language)
formatted_context, citations = self._retrieve_context(arguments)
if formatted_context: if formatted_context:
flow_inputs = { flow_inputs = {
"language": arguments.language, "language": arguments.language,
@@ -128,16 +125,18 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
flow_results.rag_output.answer = insufficient_info_message flow_results.rag_output.answer = insufficient_info_message
rag_output = flow_results.rag_output rag_output = flow_results.rag_output
else: else:
rag_output = RAGOutput(answer=insufficient_info_message, insufficient_info=True) rag_output = RAGOutput(answer=insufficient_info_message, insufficient_info=True)
self.flow.state.rag_output = rag_output self.flow.state.rag_output = rag_output
self.flow.state.citations = citations
self.flow.state.answer = rag_output.answer self.flow.state.answer = rag_output.answer
self.flow.state.phase = "rag" self.flow.state.phase = "rag"
results = RAGSpecialistResult.create_for_type(self.type, self.type_version) results = RAGSpecialistResult.create_for_type(self.type, self.type_version)
return results
class RAGSpecialistInput(BaseModel): class RAGSpecialistInput(BaseModel):
language: Optional[str] = Field(None, alias="language") language: Optional[str] = Field(None, alias="language")
@@ -156,6 +155,7 @@ class RAGFlowState(EveAIFlowState):
"""Flow state for RAG specialist that automatically updates from task outputs""" """Flow state for RAG specialist that automatically updates from task outputs"""
input: Optional[RAGSpecialistInput] = None input: Optional[RAGSpecialistInput] = None
rag_output: Optional[RAGOutput] = None rag_output: Optional[RAGOutput] = None
citations: Optional[List[Dict[str, Any]]] = None
class RAGFlow(EveAICrewAIFlow[RAGFlowState]): class RAGFlow(EveAICrewAIFlow[RAGFlowState]):
@@ -190,8 +190,6 @@ class RAGFlow(EveAICrewAIFlow[RAGFlowState]):
raise e raise e
async def kickoff_async(self, inputs=None): async def kickoff_async(self, inputs=None):
current_app.logger.debug(f"Async kickoff {self.name}")
current_app.logger.debug(f"Inputs: {inputs}")
self.state.input = RAGSpecialistInput.model_validate(inputs) self.state.input = RAGSpecialistInput.model_validate(inputs)
result = await super().kickoff_async(inputs) result = await super().kickoff_async(inputs)
return self.state return self.state

6
eveai_chat_workers/specialists/globals/SPIN_SPECIALIST/1_0.py
View File

@@ -216,9 +216,7 @@ class SPINFlow(EveAICrewAIFlow[SPINFlowState]):
async def execute_rag(self): async def execute_rag(self):
inputs = self.state.input.model_dump() inputs = self.state.input.model_dump()
try: try:
current_app.logger.debug("In execute_rag")
crew_output = await self.rag_crew.kickoff_async(inputs=inputs) crew_output = await self.rag_crew.kickoff_async(inputs=inputs)
current_app.logger.debug(f"Crew execution ended with output:\n{crew_output}")
self.specialist_executor.log_tuning("RAG Crew Output", crew_output.model_dump()) self.specialist_executor.log_tuning("RAG Crew Output", crew_output.model_dump())
output_pydantic = crew_output.pydantic output_pydantic = crew_output.pydantic
if not output_pydantic: if not output_pydantic:
@@ -277,11 +275,8 @@ class SPINFlow(EveAICrewAIFlow[SPINFlowState]):
if self.state.spin: if self.state.spin:
additional_questions = additional_questions + self.state.spin.questions additional_questions = additional_questions + self.state.spin.questions
inputs["additional_questions"] = additional_questions inputs["additional_questions"] = additional_questions
current_app.logger.debug(f"Prepared Answers: \n{inputs['prepared_answers']}")
current_app.logger.debug(f"Additional Questions: \n{additional_questions}")
try: try:
crew_output = await self.rag_consolidation_crew.kickoff_async(inputs=inputs) crew_output = await self.rag_consolidation_crew.kickoff_async(inputs=inputs)
current_app.logger.debug(f"Consolidation output after crew execution:\n{crew_output}")
self.specialist_executor.log_tuning("RAG Consolidation Crew Output", crew_output.model_dump()) self.specialist_executor.log_tuning("RAG Consolidation Crew Output", crew_output.model_dump())
output_pydantic = crew_output.pydantic output_pydantic = crew_output.pydantic
if not output_pydantic: if not output_pydantic:
@@ -295,7 +290,6 @@ class SPINFlow(EveAICrewAIFlow[SPINFlowState]):
raise e raise e
async def kickoff_async(self, inputs=None): async def kickoff_async(self, inputs=None):
current_app.logger.debug(f"Async kickoff {self.name}")
self.state.input = SPINSpecialistInput.model_validate(inputs) self.state.input = SPINSpecialistInput.model_validate(inputs)
result = await super().kickoff_async(inputs) result = await super().kickoff_async(inputs)
return self.state return self.state

5
eveai_chat_workers/specialists/specialist_typing.py
View File

@@ -1,4 +1,4 @@
from typing import Dict, Any, Optional from typing import Dict, Any, Optional, List
from pydantic import BaseModel, Field, model_validator from pydantic import BaseModel, Field, model_validator
from eveai_chat_workers.retrievers.retriever_typing import RetrieverArguments from eveai_chat_workers.retrievers.retriever_typing import RetrieverArguments
from common.extensions import cache_manager from common.extensions import cache_manager
@@ -103,10 +103,9 @@ class SpecialistResult(BaseModel):
# Structural optional fields available for all specialists # Structural optional fields available for all specialists
answer: Optional[str] = Field(None, description="Optional textual answer from the specialist") answer: Optional[str] = Field(None, description="Optional textual answer from the specialist")
detailed_question: Optional[str] = Field(None, description="Optional detailed question for the specialist")
form_request: Optional[Dict[str, Any]] = Field(None, description="Optional form definition to request user input") form_request: Optional[Dict[str, Any]] = Field(None, description="Optional form definition to request user input")
phase: Optional[str] = Field(None, description="Phase of the specialist's workflow") phase: Optional[str] = Field(None, description="Phase of the specialist's workflow")
citations: Optional[Dict[str, Any]] = Field(None, description="Citations for the specialist's answer") citations: Optional[List[Dict[str, Any]]] = Field(None, description="Citations for the specialist's answer")
@model_validator(mode='after') @model_validator(mode='after')
def validate_required_results(self) -> 'SpecialistResult': def validate_required_results(self) -> 'SpecialistResult':

23
eveai_chat_workers/specialists/traicie/TRAICIE_KO_INTERVIEW_DEFINITION_SPECIALIST/1_0.py
View File

@@ -71,11 +71,6 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
def execute(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult: def execute(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
self.log_tuning("Traicie KO Criteria Interview Definition Specialist execution started", {}) self.log_tuning("Traicie KO Criteria Interview Definition Specialist execution started", {})
current_app.logger.debug(f"Arguments: {arguments.model_dump()}")
current_app.logger.debug(f"Formatted Context: {formatted_context}")
current_app.logger.debug(f"Formatted History: {self._formatted_history}")
current_app.logger.debug(f"Cached Chat Session: {self._cached_session}")
if not self._cached_session.interactions: if not self._cached_session.interactions:
specialist_phase = "initial" specialist_phase = "initial"
else: else:
@@ -104,13 +99,9 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
raise EveAISpecialistExecutionError(self.tenant_id, self.specialist_id, self.session_id, raise EveAISpecialistExecutionError(self.tenant_id, self.specialist_id, self.session_id,
"Specialist is no Selection Specialist") "Specialist is no Selection Specialist")
current_app.logger.debug(f"Specialist Competencies:\n"
f"{selection_specialist.configuration.get("competencies", [])}")
ko_competencies = [] ko_competencies = []
for competency in selection_specialist.configuration.get("competencies", []): for competency in selection_specialist.configuration.get("competencies", []):
if competency["is_knockout"] is True and competency["assess"] is True: if competency["is_knockout"] is True and competency["assess"] is True:
current_app.logger.debug(f"Assessable Knockout competency: {competency}")
ko_competencies.append({"title": competency["title"], "description": competency["description"]}) ko_competencies.append({"title": competency["title"], "description": competency["description"]})
tone_of_voice = selection_specialist.configuration.get('tone_of_voice', 'Professional & Neutral') tone_of_voice = selection_specialist.configuration.get('tone_of_voice', 'Professional & Neutral')
@@ -118,7 +109,6 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
(item for item in TONE_OF_VOICE if item["name"] == tone_of_voice), (item for item in TONE_OF_VOICE if item["name"] == tone_of_voice),
None # fallback indien niet gevonden None # fallback indien niet gevonden
) )
current_app.logger.debug(f"Selected tone of voice: {selected_tone_of_voice}")
tone_of_voice_context = f"{selected_tone_of_voice["description"]}" tone_of_voice_context = f"{selected_tone_of_voice["description"]}"
language_level = selection_specialist.configuration.get('language_level', 'Standard') language_level = selection_specialist.configuration.get('language_level', 'Standard')
@@ -126,7 +116,6 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
(item for item in LANGUAGE_LEVEL if item["name"] == language_level), (item for item in LANGUAGE_LEVEL if item["name"] == language_level),
None None
) )
current_app.logger.debug(f"Selected language level: {selected_language_level}")
language_level_context = (f"{selected_language_level['description']}, " language_level_context = (f"{selected_language_level['description']}, "
f"corresponding to CEFR level {selected_language_level['cefr_level']}") f"corresponding to CEFR level {selected_language_level['cefr_level']}")
@@ -140,12 +129,9 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
} }
flow_results = self.flow.kickoff(inputs=flow_inputs) flow_results = self.flow.kickoff(inputs=flow_inputs)
current_app.logger.debug(f"Flow results: {flow_results}")
current_app.logger.debug(f"Flow state: {self.flow.state}")
new_type = "TRAICIE_KO_CRITERIA_QUESTIONS" new_type = "TRAICIE_KO_CRITERIA_QUESTIONS"
current_app.logger.debug(f"KO Criteria Questions:\n {self.flow.state.ko_questions}")
# Controleer of we een KOQuestions object hebben of een lijst van KOQuestion objecten # Controleer of we een KOQuestions object hebben of een lijst van KOQuestion objecten
if hasattr(self.flow.state.ko_questions, 'to_json'): if hasattr(self.flow.state.ko_questions, 'to_json'):
# Het is een KOQuestions object # Het is een KOQuestions object
@@ -161,8 +147,6 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
ko_questions_data = [q.model_dump() for q in self.flow.state.ko_questions] ko_questions_data = [q.model_dump() for q in self.flow.state.ko_questions]
json_str = json.dumps(ko_questions_data, ensure_ascii=False, indent=2) json_str = json.dumps(ko_questions_data, ensure_ascii=False, indent=2)
current_app.logger.debug(f"KO Criteria Questions json style:\n {json_str}")
try: try:
asset = db.session.query(EveAIAsset).filter( asset = db.session.query(EveAIAsset).filter(
EveAIAsset.type == new_type, EveAIAsset.type == new_type,
@@ -281,21 +265,17 @@ class KOFlow(EveAICrewAIFlow[KOFlowState]):
async def execute_ko_def_definition(self): async def execute_ko_def_definition(self):
inputs = self.state.input.model_dump() inputs = self.state.input.model_dump()
try: try:
current_app.logger.debug("Run execute_ko_interview_definition")
crew_output = await self.ko_def_crew.kickoff_async(inputs=inputs) crew_output = await self.ko_def_crew.kickoff_async(inputs=inputs)
# Unfortunately, crew_output will only contain the output of the latest task. # Unfortunately, crew_output will only contain the output of the latest task.
# As we will only take into account the flow state, we need to ensure both competencies and criteria # As we will only take into account the flow state, we need to ensure both competencies and criteria
# are copies to the flow state. # are copies to the flow state.
update = {} update = {}
for task in self.ko_def_crew.tasks: for task in self.ko_def_crew.tasks:
current_app.logger.debug(f"Task {task.name} output:\n{task.output}")
if task.name == "traicie_ko_criteria_interview_definition_task": if task.name == "traicie_ko_criteria_interview_definition_task":
# update["competencies"] = task.output.pydantic.competencies # update["competencies"] = task.output.pydantic.competencies
self.state.ko_questions = task.output.pydantic.ko_questions self.state.ko_questions = task.output.pydantic.ko_questions
# crew_output.pydantic = crew_output.pydantic.model_copy(update=update) # crew_output.pydantic = crew_output.pydantic.model_copy(update=update)
self.state.phase = "personal_contact_data" self.state.phase = "personal_contact_data"
current_app.logger.debug(f"State after execute_ko_def_definition: {self.state}")
current_app.logger.debug(f"State dump after execute_ko_def_definition: {self.state.model_dump()}")
return crew_output return crew_output
except Exception as e: except Exception as e:
current_app.logger.error(f"CREW execute_ko_def Kickoff Error: {str(e)}") current_app.logger.error(f"CREW execute_ko_def Kickoff Error: {str(e)}")
@@ -303,9 +283,6 @@ class KOFlow(EveAICrewAIFlow[KOFlowState]):
raise e raise e
async def kickoff_async(self, inputs=None): async def kickoff_async(self, inputs=None):
current_app.logger.debug(f"Async kickoff {self.name}")
current_app.logger.debug(f"Inputs: {inputs}")
self.state.input = KODefInput.model_validate(inputs) self.state.input = KODefInput.model_validate(inputs)
current_app.logger.debug(f"State: {self.state}")
result = await super().kickoff_async(inputs) result = await super().kickoff_async(inputs)
return self.state return self.state

3
eveai_chat_workers/specialists/traicie/TRAICIE_ROLE_DEFINITION_SPECIALIST/1_0.py
View File

@@ -143,8 +143,6 @@ class VacancyDefinitionFlow(EveAICrewAIFlow[VacancyDefFlowState]):
# update["criteria"] = task.output.pydantic.criteria # update["criteria"] = task.output.pydantic.criteria
self.state.criteria = task.output.pydantic.criteria self.state.criteria = task.output.pydantic.criteria
# crew_output.pydantic = crew_output.pydantic.model_copy(update=update) # crew_output.pydantic = crew_output.pydantic.model_copy(update=update)
current_app.logger.debug(f"State after execute_vac_def: {self.state}")
current_app.logger.debug(f"State dump after execute_vac_def: {self.state.model_dump()}")
return crew_output return crew_output
except Exception as e: except Exception as e:
current_app.logger.error(f"CREW execute_vac_def Kickoff Error: {str(e)}") current_app.logger.error(f"CREW execute_vac_def Kickoff Error: {str(e)}")
@@ -152,7 +150,6 @@ class VacancyDefinitionFlow(EveAICrewAIFlow[VacancyDefFlowState]):
raise e raise e
async def kickoff_async(self, inputs=None): async def kickoff_async(self, inputs=None):
current_app.logger.debug(f"Async kickoff {self.name}")
self.state.input = VacancyDefinitionSpecialistInput.model_validate(inputs) self.state.input = VacancyDefinitionSpecialistInput.model_validate(inputs)
result = await super().kickoff_async(inputs) result = await super().kickoff_async(inputs)
return self.state return self.state

7
eveai_chat_workers/specialists/traicie/TRAICIE_ROLE_DEFINITION_SPECIALIST/1_2.py
View File

@@ -168,20 +168,16 @@ class RoleDefinitionFlow(EveAICrewAIFlow[RoleDefFlowState]):
async def execute_role_definition (self): async def execute_role_definition (self):
inputs = self.state.input.model_dump() inputs = self.state.input.model_dump()
try: try:
current_app.logger.debug("In execute_role_definition")
crew_output = await self.role_definition_crew.kickoff_async(inputs=inputs) crew_output = await self.role_definition_crew.kickoff_async(inputs=inputs)
# Unfortunately, crew_output will only contain the output of the latest task. # Unfortunately, crew_output will only contain the output of the latest task.
# As we will only take into account the flow state, we need to ensure both competencies and criteria # As we will only take into account the flow state, we need to ensure both competencies and criteria
# are copies to the flow state. # are copies to the flow state.
update = {} update = {}
for task in self.role_definition_crew.tasks: for task in self.role_definition_crew.tasks:
current_app.logger.debug(f"Task {task.name} output:\n{task.output}")
if task.name == "traicie_get_competencies_task": if task.name == "traicie_get_competencies_task":
# update["competencies"] = task.output.pydantic.competencies # update["competencies"] = task.output.pydantic.competencies
self.state.competencies = task.output.pydantic.competencies self.state.competencies = task.output.pydantic.competencies
# crew_output.pydantic = crew_output.pydantic.model_copy(update=update) # crew_output.pydantic = crew_output.pydantic.model_copy(update=update)
current_app.logger.debug(f"State after execute_role_definition: {self.state}")
current_app.logger.debug(f"State dump after execute_role_definition: {self.state.model_dump()}")
return crew_output return crew_output
except Exception as e: except Exception as e:
current_app.logger.error(f"CREW execute_role_definition Kickoff Error: {str(e)}") current_app.logger.error(f"CREW execute_role_definition Kickoff Error: {str(e)}")
@@ -189,9 +185,6 @@ class RoleDefinitionFlow(EveAICrewAIFlow[RoleDefFlowState]):
raise e raise e
async def kickoff_async(self, inputs=None): async def kickoff_async(self, inputs=None):
current_app.logger.debug(f"Async kickoff {self.name}")
current_app.logger.debug(f"Inputs: {inputs}")
self.state.input = RoleDefinitionSpecialistInput.model_validate(inputs) self.state.input = RoleDefinitionSpecialistInput.model_validate(inputs)
current_app.logger.debug(f"State: {self.state}")
result = await super().kickoff_async(inputs) result = await super().kickoff_async(inputs)
return self.state return self.state

7
eveai_chat_workers/specialists/traicie/TRAICIE_ROLE_DEFINITION_SPECIALIST/1_3.py
View File

@@ -174,20 +174,16 @@ class RoleDefinitionFlow(EveAICrewAIFlow[RoleDefFlowState]):
async def execute_role_definition (self): async def execute_role_definition (self):
inputs = self.state.input.model_dump() inputs = self.state.input.model_dump()
try: try:
current_app.logger.debug("In execute_role_definition")
crew_output = await self.role_definition_crew.kickoff_async(inputs=inputs) crew_output = await self.role_definition_crew.kickoff_async(inputs=inputs)
# Unfortunately, crew_output will only contain the output of the latest task. # Unfortunately, crew_output will only contain the output of the latest task.
# As we will only take into account the flow state, we need to ensure both competencies and criteria # As we will only take into account the flow state, we need to ensure both competencies and criteria
# are copies to the flow state. # are copies to the flow state.
update = {} update = {}
for task in self.role_definition_crew.tasks: for task in self.role_definition_crew.tasks:
current_app.logger.debug(f"Task {task.name} output:\n{task.output}")
if task.name == "traicie_get_competencies_task": if task.name == "traicie_get_competencies_task":
# update["competencies"] = task.output.pydantic.competencies # update["competencies"] = task.output.pydantic.competencies
self.state.competencies = task.output.pydantic.competencies self.state.competencies = task.output.pydantic.competencies
# crew_output.pydantic = crew_output.pydantic.model_copy(update=update) # crew_output.pydantic = crew_output.pydantic.model_copy(update=update)
current_app.logger.debug(f"State after execute_role_definition: {self.state}")
current_app.logger.debug(f"State dump after execute_role_definition: {self.state.model_dump()}")
return crew_output return crew_output
except Exception as e: except Exception as e:
current_app.logger.error(f"CREW execute_role_definition Kickoff Error: {str(e)}") current_app.logger.error(f"CREW execute_role_definition Kickoff Error: {str(e)}")
@@ -195,9 +191,6 @@ class RoleDefinitionFlow(EveAICrewAIFlow[RoleDefFlowState]):
raise e raise e
async def kickoff_async(self, inputs=None): async def kickoff_async(self, inputs=None):
current_app.logger.debug(f"Async kickoff {self.name}")
current_app.logger.debug(f"Inputs: {inputs}")
self.state.input = RoleDefinitionSpecialistInput.model_validate(inputs) self.state.input = RoleDefinitionSpecialistInput.model_validate(inputs)
current_app.logger.debug(f"State: {self.state}")
result = await super().kickoff_async(inputs) result = await super().kickoff_async(inputs)
return self.state return self.state

94
eveai_chat_workers/specialists/traicie/TRAICIE_SELECTION_SPECIALIST/1_4.py
View File

@@ -61,6 +61,7 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
# Load the Tenant & set language # Load the Tenant & set language
self.tenant = Tenant.query.get_or_404(tenant_id) self.tenant = Tenant.query.get_or_404(tenant_id)
self.specialist_phase = "initial"
@property @property
def type(self) -> str: def type(self) -> str:
@@ -106,19 +107,13 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
def execute(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult: def execute(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
self.log_tuning("Traicie Selection Specialist execution started", {}) self.log_tuning("Traicie Selection Specialist execution started", {})
current_app.logger.debug(f"Arguments: {arguments.model_dump()}")
current_app.logger.debug(f"Formatted Context: {formatted_context}")
current_app.logger.debug(f"Formatted History: {self._formatted_history}")
current_app.logger.debug(f"Cached Chat Session: {self._cached_session}")
if not self._cached_session.interactions: if not self._cached_session.interactions:
specialist_phase = "initial" self.specialist_phase = "initial"
else: else:
specialist_phase = self._cached_session.interactions[-1].specialist_results.get('phase', 'initial') self.specialist_phase = self._cached_session.interactions[-1].specialist_results.get('phase', 'initial')
results = None results = None
current_app.logger.debug(f"Specialist phase: {specialist_phase}") match self.specialist_phase:
match specialist_phase:
case "initial": case "initial":
results = self.execute_initial_state(arguments, formatted_context, citations) results = self.execute_initial_state(arguments, formatted_context, citations)
case "start_selection_procedure": case "start_selection_procedure":
@@ -149,16 +144,21 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
interaction_mode = arguments.interaction_mode interaction_mode = arguments.interaction_mode
if not interaction_mode: if not interaction_mode:
interaction_mode = "selection" interaction_mode = "selection"
current_app.logger.debug(f"Interaction mode: {interaction_mode}")
welcome_message = self.specialist.configuration.get("welcome_message", "Welcome to our selection process.") welcome_message = self.specialist.configuration.get("welcome_message", "Welcome to our selection process.")
welcome_message = TranslationServices.translate(self.tenant_id, welcome_message, arguments.language) welcome_message = TranslationServices.translate(self.tenant_id, welcome_message, arguments.language)
if interaction_mode == "selection": if interaction_mode == "selection":
return self.execute_start_selection_procedure_state(arguments, formatted_context, citations, return self.execute_start_selection_procedure_state(arguments, formatted_context, citations,
welcome_message) welcome_message)
else: # We are in orientation mode, so we perform standard rag # We are in orientation mode, so we give a standard message, and move to rag state
return self.execute_rag_state(arguments, formatted_context, citations, welcome_message) start_selection_question = TranslationServices.translate(self.tenant_id, START_SELECTION_QUESTION,
arguments.language)
self.flow.state.answer = f"{welcome_message}\n\n{start_selection_question}"
self.flow.state.phase = "rag"
results = SelectionResult.create_for_type(self.type, self.type_version)
return results
def execute_start_selection_procedure_state(self, arguments: SpecialistArguments, formatted_context, citations, def execute_start_selection_procedure_state(self, arguments: SpecialistArguments, formatted_context, citations,
start_message=None) -> SpecialistResult: start_message=None) -> SpecialistResult:
@@ -172,7 +172,6 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
ko_questions = self._get_ko_questions() ko_questions = self._get_ko_questions()
fields = {} fields = {}
for ko_question in ko_questions.ko_questions: for ko_question in ko_questions.ko_questions:
current_app.logger.debug(f"KO Question: {ko_question}")
fields[ko_question.title] = { fields[ko_question.title] = {
"name": ko_question.title, "name": ko_question.title,
"description": ko_question.title, "description": ko_question.title,
@@ -213,11 +212,9 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
if not arguments.form_values: if not arguments.form_values:
raise EveAISpecialistExecutionError(self.tenant_id, self.specialist_id, self.session_id, raise EveAISpecialistExecutionError(self.tenant_id, self.specialist_id, self.session_id,
"No form values returned") "No form values returned")
current_app.logger.debug(f"Form values: {arguments.form_values}")
# Load the previous KO Questions # Load the previous KO Questions
previous_ko_questions = self._get_ko_questions().ko_questions previous_ko_questions = self._get_ko_questions().ko_questions
current_app.logger.debug(f"Previous KO Questions: {previous_ko_questions}")
# Evaluate KO Criteria # Evaluate KO Criteria
evaluation = "positive" evaluation = "positive"
@@ -355,39 +352,37 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
results = SelectionResult.create_for_type(self.type, self.type_version,) results = SelectionResult.create_for_type(self.type, self.type_version,)
return results return results
def execute_rag_state(self, arguments: SpecialistArguments, formatted_context, citations, welcome_message=None) \ def execute_rag_state(self, arguments: SpecialistArguments, formatted_context, citations) \
-> SpecialistResult: -> SpecialistResult:
self.log_tuning("Traicie Selection Specialist rag_state started", {}) self.log_tuning("Traicie Selection Specialist rag_state started", {})
start_selection_question = TranslationServices.translate(self.tenant_id, START_SELECTION_QUESTION, start_selection_question = TranslationServices.translate(self.tenant_id, START_SELECTION_QUESTION,
arguments.language) arguments.language)
if welcome_message:
answer = f"{welcome_message}\n\n{start_selection_question}"
else:
answer = ""
rag_results = None rag_output = None
if arguments.question:
if HumanAnswerServices.check_additional_information(self.tenant_id,
START_SELECTION_QUESTION,
arguments.question,
arguments.language):
rag_results = self.execute_rag(arguments, formatted_context, citations)
self.flow.state.rag_output = rag_results.rag_output
answer = f"{answer}\n{rag_results.answer}"
if HumanAnswerServices.check_affirmative_answer(self.tenant_id, if HumanAnswerServices.check_additional_information(self.tenant_id,
START_SELECTION_QUESTION, START_SELECTION_QUESTION,
arguments.question, arguments.question,
arguments.language): arguments.language):
return self.execute_start_selection_procedure_state(arguments, formatted_context, citations, answer) rag_output = self.execute_rag(arguments, formatted_context, citations)
self.flow.state.rag_output = rag_output
answer = rag_output.answer
else:
answer = ""
self.flow.state.answer = answer if HumanAnswerServices.check_affirmative_answer(self.tenant_id,
self.flow.state.phase = "rag" START_SELECTION_QUESTION,
self.flow.state.form_request = None arguments.question,
arguments.language):
return self.execute_start_selection_procedure_state(arguments, formatted_context, citations, answer)
else:
self.flow.state.answer = f"{answer}\n\n{start_selection_question}"
self.flow.state.phase = "rag"
self.flow.state.form_request = None
results = SelectionResult.create_for_type(self.type, self.type_version,) results = SelectionResult.create_for_type(self.type, self.type_version,)
return results return results
def execute_rag(self, arguments: SpecialistArguments, formatted_context, citations) -> RAGOutput: def execute_rag(self, arguments: SpecialistArguments, formatted_context, citations) -> RAGOutput:
self.log_tuning("RAG Specialist execution started", {}) self.log_tuning("RAG Specialist execution started", {})
@@ -395,6 +390,9 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
insufficient_info_message = TranslationServices.translate(self.tenant_id, insufficient_info_message = TranslationServices.translate(self.tenant_id,
INSUFFICIENT_INFORMATION_MESSAGE, INSUFFICIENT_INFORMATION_MESSAGE,
arguments.language) arguments.language)
formatted_context, citations = self._retrieve_context(arguments)
self.flow.state.citations = citations
if formatted_context: if formatted_context:
flow_inputs = { flow_inputs = {
"language": arguments.language, "language": arguments.language,
@@ -403,9 +401,11 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
"history": self.formatted_history, "history": self.formatted_history,
"name": self.specialist.configuration.get('name', ''), "name": self.specialist.configuration.get('name', ''),
} }
rag_output = self.flow.kickoff(inputs=flow_inputs) flow_results = self.flow.kickoff(inputs=flow_inputs)
if rag_output.rag_output.insufficient_info: if flow_results.rag_output.insufficient_info:
rag_output.rag_output.answer = insufficient_info_message flow_results.rag_output.answer = insufficient_info_message
rag_output = flow_results.rag_output
else: else:
rag_output = RAGOutput(answer=insufficient_info_message, insufficient_info=True) rag_output = RAGOutput(answer=insufficient_info_message, insufficient_info=True)
@@ -418,8 +418,11 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
START_SELECTION_QUESTION, START_SELECTION_QUESTION,
arguments.question, arguments.question,
arguments.language): arguments.language):
results = self.execute_rag(arguments, formatted_context, citations) rag_output = self.execute_rag(arguments, formatted_context, citations)
return results
self.flow.state.rag_output = rag_output
return rag_output
else: else:
return None return None
@@ -439,7 +442,6 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
ko_questions_data = minio_client.download_asset_file(self.tenant_id, ko_questions_asset.bucket_name, ko_questions_data = minio_client.download_asset_file(self.tenant_id, ko_questions_asset.bucket_name,
ko_questions_asset.object_name) ko_questions_asset.object_name)
ko_questions = KOQuestions.from_json(ko_questions_data) ko_questions = KOQuestions.from_json(ko_questions_data)
current_app.logger.debug(f"KO Questions: {ko_questions}")
return ko_questions return ko_questions
@@ -470,8 +472,8 @@ class SelectionInput(BaseModel):
history: Optional[str] = Field(None, alias="history") history: Optional[str] = Field(None, alias="history")
name: Optional[str] = Field(None, alias="name") name: Optional[str] = Field(None, alias="name")
# Selection elements # Selection elements
region: str = Field(..., alias="region") region: Optional[str] = Field(None, alias="region")
working_schedule: Optional[str] = Field(..., alias="working_schedule") working_schedule: Optional[str] = Field(None, alias="working_schedule")
start_date: Optional[date] = Field(None, alias="vacancy_text") start_date: Optional[date] = Field(None, alias="vacancy_text")
interaction_mode: Optional[str] = Field(None, alias="interaction_mode") interaction_mode: Optional[str] = Field(None, alias="interaction_mode")
tone_of_voice: Optional[str] = Field(None, alias="tone_of_voice") tone_of_voice: Optional[str] = Field(None, alias="tone_of_voice")
@@ -489,6 +491,7 @@ class SelectionFlowState(EveAIFlowState):
ko_criteria_answers: Optional[Dict[str, str]] = None ko_criteria_answers: Optional[Dict[str, str]] = None
personal_contact_data: Optional[PersonalContactData] = None personal_contact_data: Optional[PersonalContactData] = None
contact_time: Optional[str] = None contact_time: Optional[str] = None
citations: Optional[List[Dict[str, Any]]] = None
class SelectionResult(SpecialistResult): class SelectionResult(SpecialistResult):
@@ -530,7 +533,6 @@ class SelectionFlow(EveAICrewAIFlow[SelectionFlowState]):
raise e raise e
async def kickoff_async(self, inputs=None): async def kickoff_async(self, inputs=None):
current_app.logger.debug(f"Async kickoff {self.name}")
self.state.input = SelectionInput.model_validate(inputs) self.state.input = SelectionInput.model_validate(inputs)
result = await super().kickoff_async(inputs) result = await super().kickoff_async(inputs)
return self.state return self.state

10
eveai_workers/tasks.py
View File

@@ -586,7 +586,6 @@ def combine_chunks_for_markdown(potential_chunks, min_chars, max_chars, processo
# Force new chunk if pattern matches # Force new chunk if pattern matches
if chunking_patterns and matches_chunking_pattern(chunk, chunking_patterns): if chunking_patterns and matches_chunking_pattern(chunk, chunking_patterns):
if current_chunk and current_length >= min_chars: if current_chunk and current_length >= min_chars:
current_app.logger.debug(f"Chunk Length of chunk to embed: {len(current_chunk)} ")
actual_chunks.append(current_chunk) actual_chunks.append(current_chunk)
current_chunk = chunk current_chunk = chunk
current_length = chunk_length current_length = chunk_length
@@ -594,7 +593,6 @@ def combine_chunks_for_markdown(potential_chunks, min_chars, max_chars, processo
if current_length + chunk_length > max_chars: if current_length + chunk_length > max_chars:
if current_length >= min_chars: if current_length >= min_chars:
current_app.logger.debug(f"Chunk Length of chunk to embed: {len(current_chunk)} ")
actual_chunks.append(current_chunk) actual_chunks.append(current_chunk)
current_chunk = chunk current_chunk = chunk
current_length = chunk_length current_length = chunk_length
@@ -608,7 +606,6 @@ def combine_chunks_for_markdown(potential_chunks, min_chars, max_chars, processo
# Handle the last chunk # Handle the last chunk
if current_chunk and current_length >= 0: if current_chunk and current_length >= 0:
current_app.logger.debug(f"Chunk Length of chunk to embed: {len(current_chunk)} ")
actual_chunks.append(current_chunk) actual_chunks.append(current_chunk)
return actual_chunks return actual_chunks
@@ -630,7 +627,6 @@ def get_processor_for_document(catalog_id: int, file_type: str, sub_file_type: s
ValueError: If no matching processor is found ValueError: If no matching processor is found
""" """
try: try:
current_app.logger.debug(f"Getting processor for catalog {catalog_id}, file type {file_type}, file sub_type {sub_file_type} ")
# Start with base query for catalog # Start with base query for catalog
query = Processor.query.filter_by(catalog_id=catalog_id).filter_by(active=True) query = Processor.query.filter_by(catalog_id=catalog_id).filter_by(active=True)
@@ -647,7 +643,6 @@ def get_processor_for_document(catalog_id: int, file_type: str, sub_file_type: s
if not available_processors: if not available_processors:
raise EveAINoProcessorFound(catalog_id, file_type, sub_file_type) raise EveAINoProcessorFound(catalog_id, file_type, sub_file_type)
available_processor_types = [processor.type for processor in available_processors] available_processor_types = [processor.type for processor in available_processors]
current_app.logger.debug(f"Available processors for catalog {catalog_id}: {available_processor_types}")
# Find processor type that handles this file type # Find processor type that handles this file type
matching_processor_type = None matching_processor_type = None
@@ -657,17 +652,13 @@ def get_processor_for_document(catalog_id: int, file_type: str, sub_file_type: s
supported_types = config['file_types'] supported_types = config['file_types']
if isinstance(supported_types, str): if isinstance(supported_types, str):
supported_types = [t.strip() for t in supported_types.split(',')] supported_types = [t.strip() for t in supported_types.split(',')]
current_app.logger.debug(f"Supported types for processor type {proc_type}: {supported_types}")
if file_type in supported_types: if file_type in supported_types:
matching_processor_type = proc_type matching_processor_type = proc_type
break break
current_app.logger.debug(f"Processor type found for catalog {catalog_id}, file type {file_type}: {matching_processor_type}")
if not matching_processor_type: if not matching_processor_type:
raise EveAINoProcessorFound(catalog_id, file_type, sub_file_type) raise EveAINoProcessorFound(catalog_id, file_type, sub_file_type)
else:
current_app.logger.debug(f"Processor type found for file type: {file_type}: {matching_processor_type}")
processor = None processor = None
for proc in available_processors: for proc in available_processors:
@@ -678,7 +669,6 @@ def get_processor_for_document(catalog_id: int, file_type: str, sub_file_type: s
if not processor: if not processor:
raise EveAINoProcessorFound(catalog_id, file_type, sub_file_type) raise EveAINoProcessorFound(catalog_id, file_type, sub_file_type)
current_app.logger.debug(f"Processor found for catalog {catalog_id}, file type {file_type}: {processor}")
return processor return processor
except Exception as e: except Exception as e:

2
requirements.txt
View File

@@ -82,7 +82,7 @@ typing_extensions~=4.12.2
babel~=2.16.0 babel~=2.16.0
dogpile.cache~=1.3.3 dogpile.cache~=1.3.3
python-docx~=1.1.2 python-docx~=1.1.2
crewai~=0.121.0 crewai~=0.140.0
sseclient~=0.0.27 sseclient~=0.0.27
termcolor~=2.5.0 termcolor~=2.5.0
mistral-common~=1.5.5 mistral-common~=1.5.5