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import asyncio
import json
from collections import defaultdict
from typing import AsyncGenerator, Dict, List, Optional, Tuple
import cachetools.func
import regex as re
import requests
import structlog
from codegate.api import v1_models
from codegate.api.v1_models import (
AlertConversation,
ChatMessage,
Conversation,
PartialQuestionAnswer,
PartialQuestions,
QuestionAnswer,
QuestionType,
TokenUsageAggregate,
TokenUsageByModel,
)
from codegate.db.connection import alert_queue
from codegate.db.models import Alert, GetPromptWithOutputsRow, TokenUsage
logger = structlog.get_logger("codegate")
SYSTEM_PROMPTS = [
"Given the following... please reply with a short summary that is 4-12 words in length, "
"you should summarize what the user is asking for OR what the user is trying to accomplish. "
"You should only respond with the summary, no additional text or explanation, "
"you don't need ending punctuation.",
]
@cachetools.func.ttl_cache(maxsize=128, ttl=20 * 60)
def fetch_latest_version() -> str:
url = "https://api.github.com/repos/stacklok/codegate/releases/latest"
headers = {"Accept": "application/vnd.github+json", "X-GitHub-Api-Version": "2022-11-28"}
response = requests.get(url, headers=headers, timeout=5)
response.raise_for_status()
data = response.json()
return data.get("tag_name", "unknown")
async def generate_sse_events() -> AsyncGenerator[str, None]:
"""
SSE generator from queue
"""
while True:
message = await alert_queue.get()
yield f"data: {message}\n\n"
async def _is_system_prompt(message: str) -> bool:
"""
Check if the message is a system prompt.
"""
for prompt in SYSTEM_PROMPTS:
if prompt in message or message in prompt:
return True
return False
async def parse_request(request_str: str) -> Tuple[Optional[List[str]], str]: # noqa: C901
"""
Parse the request string from the pipeline and return the message and the model.
"""
try:
request = json.loads(request_str)
except Exception as e:
logger.warning(f"Error parsing request: {request_str}. {e}")
return None, ""
model = request.get("model", "")
messages = []
for message in request.get("messages", []):
role = message.get("role")
if not role == "user":
continue
content = message.get("content")
message_str = ""
if isinstance(content, str):
message_str = content
elif isinstance(content, list):
for content_part in content:
if isinstance(content_part, dict) and content_part.get("type") == "text":
message_str = content_part.get("text")
if message_str and not await _is_system_prompt(message_str):
messages.append(message_str)
# We couldn't get anything from the messages, try the prompt
if not messages:
message_prompt = request.get("prompt", "")
if message_prompt and not await _is_system_prompt(message_prompt):
messages.append(message_prompt)
# If still we don't have anything, return None string
if not messages:
return None, model
# Respond with the messages and the model
return messages, model
async def parse_output(output_str: str) -> Optional[str]: # noqa: C901
"""
Parse the output string from the pipeline and return the message.
"""
try:
if output_str is None:
return None
output = json.loads(output_str)
except Exception as e:
logger.warning(f"Error parsing output: {output_str}. {e}")
return None
def _parse_single_output(single_output: dict) -> str:
single_output_message = ""
for choice in single_output.get("choices", []):
if not isinstance(choice, dict):
continue
content_dict = choice.get("delta", {}) or choice.get("message", {})
single_output_message += content_dict.get("content", "")
return single_output_message
full_output_message = ""
if isinstance(output, list):
for output_chunk in output:
output_message = ""
if isinstance(output_chunk, dict):
output_message = _parse_single_output(output_chunk)
elif isinstance(output_chunk, str):
try:
output_decoded = json.loads(output_chunk)
output_message = _parse_single_output(output_decoded)
except Exception:
logger.error(f"Error reading chunk: {output_chunk}")
else:
logger.warning(
f"Could not handle output: {output_chunk}", out_type=type(output_chunk)
)
full_output_message += output_message
elif isinstance(output, dict):
full_output_message = _parse_single_output(output)
return full_output_message
async def _get_partial_question_answer(
row: GetPromptWithOutputsRow,
) -> Optional[PartialQuestionAnswer]:
"""
Parse a row from the get_prompt_with_outputs query and return a PartialConversation
The row contains the raw request and output strings from the pipeline.
"""
async with asyncio.TaskGroup() as tg:
request_task = tg.create_task(parse_request(row.request))
output_task = tg.create_task(parse_output(row.output))
request_user_msgs, model = request_task.result()
output_msg_str = output_task.result()
# If we couldn't parse the request, return None
if not request_user_msgs:
return None
request_message = PartialQuestions(
messages=request_user_msgs,
timestamp=row.timestamp,
message_id=row.id,
provider=row.provider,
type=row.type,
)
if output_msg_str:
output_message = ChatMessage(
message=output_msg_str,
timestamp=row.output_timestamp,
message_id=row.output_id,
)
else:
output_message = None
token_usage = TokenUsage.from_db(
input_cost=row.input_cost,
input_tokens=row.input_tokens,
output_tokens=row.output_tokens,
output_cost=row.output_cost,
)
# Use the model to update the token cost
provider = row.provider
# TODO: This should come from the database. For now, we are manually changing copilot to openai
# Change copilot provider to openai
if provider == "copilot":
provider = "openai"
model_token_usage = TokenUsageByModel(
model=model, token_usage=token_usage, provider_type=provider
)
alerts: List[v1_models.Alert] = [
v1_models.Alert.from_db_model(db_alert) for db_alert in row.alerts
]
return PartialQuestionAnswer(
partial_questions=request_message,
answer=output_message,
model_token_usage=model_token_usage,
alerts=alerts,
)
def parse_question_answer(input_text: str) -> str:
# Remove the <environment_details>...</environment_details> pattern if present
env_details_pattern = r"\n<environment_details>.*?</environment_details>"
input_text = re.sub(env_details_pattern, "", input_text, flags=re.DOTALL).strip()
# Check for the <task>...</task> pattern first
task_pattern = r"^<task>(.*?)</task>"
task_match = re.search(task_pattern, input_text, re.DOTALL)
if task_match:
return task_match.group(1).strip()
# If no <task>...</task>, check for "Context: xxx \n\nQuery: xxx"
context_query_pattern = r"^Context:.*?\n\n\s*Query:\s*(.*)$"
context_query_match = re.search(context_query_pattern, input_text, re.DOTALL)
if context_query_match:
return context_query_match.group(1).strip()
# If no pattern matches, return the original input text
return input_text
def _clean_secrets_from_message(message: str) -> str:
pattern = re.compile(r"REDACTED<(\$?[^>]+)>")
return pattern.sub("REDACTED_SECRET", message)
def _group_partial_messages( # noqa: C901
pq_list: List[PartialQuestions],
) -> List[List[PartialQuestions]]:
"""
A PartialQuestion is an object that contains several user messages provided from a
chat conversation. Example:
- PartialQuestion(messages=["Hello"], timestamp=2022-01-01T00:00:00Z)
- PartialQuestion(messages=["Hello", "How are you?"], timestamp=2022-01-01T00:00:01Z)
In the above example both PartialQuestions are part of the same conversation and should be
matched together.
Group PartialQuestions objects such that:
- If one PartialQuestion (pq) is a subset of another pq's messages, group them together.
- If multiple subsets exist for the same superset, choose only the one
closest in timestamp to the superset.
- Leave any unpaired pq by itself.
- Finally, sort the resulting groups by the earliest timestamp in each group.
"""
# 0) Clean secrets from messages
for pq in pq_list:
pq.messages = [_clean_secrets_from_message(msg) for msg in pq.messages]
# 1) Sort by length of messages descending (largest/most-complete first),
# then by timestamp ascending for stable processing.
pq_list_sorted = sorted(pq_list, key=lambda x: (-len(x.messages), x.timestamp))
used = set()
groups = []
# 2) Iterate in order of "largest messages first"
for sup in pq_list_sorted:
if sup.message_id in used:
continue # Already grouped
# Find all potential subsets of 'sup' that are not yet used
# (If sup's messages == sub's messages, that also counts, because sub ⊆ sup)
possible_subsets: List[PartialQuestions] = []
for sub in pq_list_sorted:
if sub.message_id == sup.message_id or sub.message_id in used:
continue
if (
set(sub.messages).issubset(set(sup.messages))
and sub.provider == sup.provider
and set(sub.messages) != set(sup.messages)
):
possible_subsets.append(sub)
# 3) If there are no subsets, check for time-based grouping
if not possible_subsets:
new_group = [sup]
used.add(sup.message_id)
for other in pq_list_sorted:
if other.message_id in used or other.message_id == sup.message_id:
continue
if abs((other.timestamp - sup.timestamp).total_seconds()) <= 5 and set(
other.messages
) & set(
sup.messages
): # At least one message in common
new_group.append(other)
used.add(other.message_id)
groups.append(new_group)
else:
# 4) Group subsets by messages to discard duplicates e.g.: 2 subsets with single 'hello'
subs_group_by_messages = defaultdict(list)
for q in possible_subsets:
subs_group_by_messages[tuple(q.messages)].append(q)
new_group = [sup]
used.add(sup.message_id)
for subs_same_message in subs_group_by_messages.values():
# If more than one pick the one subset closest in time to sup
closest_subset = min(
subs_same_message, key=lambda s: abs(s.timestamp - sup.timestamp)
)
new_group.append(closest_subset)
used.add(closest_subset.message_id)
groups.append(new_group)
# 5) Sort the groups by the earliest timestamp within each group
groups.sort(key=lambda g: min(pq.timestamp for pq in g))
return groups
def _get_question_answer_from_partial(
partial_question_answer: PartialQuestionAnswer,
) -> QuestionAnswer:
"""
Get a QuestionAnswer object from a PartialQuestionAnswer object. PartialQuestionAnswer
contains a list of messages as question. QuestionAnswer contains a single message as question.
"""
# Get the last user message as the question
message_str = partial_question_answer.partial_questions.messages[-1]
if (
partial_question_answer.partial_questions.provider == "copilot"
and partial_question_answer.partial_questions.type == "chat"
):
message_str = "\n".join(partial_question_answer.partial_questions.messages)
# sanitize answer from reserved words
if partial_question_answer.answer and partial_question_answer.answer.message:
partial_question_answer.answer.message = re.sub(
r"(question_about_specific_files|question_about_specific_code|unknown)\s*",
"",
partial_question_answer.answer.message,
).strip()
question = ChatMessage(
message=message_str,
timestamp=partial_question_answer.partial_questions.timestamp,
message_id=partial_question_answer.partial_questions.message_id,
)
return QuestionAnswer(question=question, answer=partial_question_answer.answer)
async def match_conversations(
partial_question_answers: List[Optional[PartialQuestionAnswer]],
) -> Tuple[List[Conversation], Dict[str, Conversation]]:
"""
Match partial conversations to form a complete conversation.
"""
grouped_partial_questions = _group_partial_messages(
[partial_qs_a.partial_questions for partial_qs_a in partial_question_answers]
)
# Create the conversation objects
conversations = []
map_q_id_to_conversation = {}
for group in grouped_partial_questions:
questions_answers: List[QuestionAnswer] = []
token_usage_agg = TokenUsageAggregate(tokens_by_model={}, token_usage=TokenUsage())
alerts: List[v1_models.Alert] = []
first_partial_qa = None
for partial_question in sorted(group, key=lambda x: x.timestamp):
# Partial questions don't contain the answer, so we need to find the corresponding
# valid partial question answer
selected_partial_qa = None
for partial_qa in partial_question_answers:
if partial_question.message_id == partial_qa.partial_questions.message_id:
selected_partial_qa = partial_qa
break
# check if we have a question and answer, otherwise do not add it
# if the question is a FIM question, we should add it even if there is no answer
# not add Chat questions without answers
if selected_partial_qa and (
selected_partial_qa.answer is not None
or selected_partial_qa.partial_questions.type == QuestionType.fim
):
# if we don't have a first question, set it. We will use it
# to set the conversation timestamp and provider
first_partial_qa = first_partial_qa or selected_partial_qa
qa = _get_question_answer_from_partial(selected_partial_qa)
qa.question.message = parse_question_answer(qa.question.message)
questions_answers.append(qa)
deduped_alerts = await remove_duplicate_alerts(selected_partial_qa.alerts)
alerts.extend(deduped_alerts)
token_usage_agg.add_model_token_usage(selected_partial_qa.model_token_usage)
# if we have a conversation with at least one question and answer
if len(questions_answers) > 0 and first_partial_qa is not None:
if token_usage_agg.token_usage.input_tokens == 0:
token_usage_agg = None
conversation = Conversation(
question_answers=questions_answers,
provider=first_partial_qa.partial_questions.provider,
type=first_partial_qa.partial_questions.type,
chat_id=first_partial_qa.partial_questions.message_id,
conversation_timestamp=first_partial_qa.partial_questions.timestamp,
token_usage_agg=token_usage_agg,
alerts=alerts,
)
for qa in questions_answers:
map_q_id_to_conversation[qa.question.message_id] = conversation
conversations.append(conversation)
return conversations, map_q_id_to_conversation
async def _process_prompt_output_to_partial_qa(
prompts_outputs: List[GetPromptWithOutputsRow],
) -> List[PartialQuestionAnswer]:
"""
Process the prompts and outputs to PartialQuestionAnswer objects.
"""
# Parse the prompts and outputs in parallel
async with asyncio.TaskGroup() as tg:
tasks = [tg.create_task(_get_partial_question_answer(row)) for row in prompts_outputs]
return [task.result() for task in tasks if task.result() is not None]
async def parse_messages_in_conversations(
prompts_outputs: List[GetPromptWithOutputsRow],
) -> Tuple[List[Conversation], Dict[str, Conversation]]:
"""
Get all the messages from the database and return them as a list of conversations.
"""
partial_question_answers = await _process_prompt_output_to_partial_qa(prompts_outputs)
conversations, map_q_id_to_conversation = await match_conversations(partial_question_answers)
return conversations, map_q_id_to_conversation
async def parse_row_alert_conversation(
row: Alert, map_q_id_to_conversation: Dict[str, Conversation]
) -> Optional[AlertConversation]:
"""
Parse a row from the get_alerts_with_prompt_and_output query and return a Conversation
The row contains the raw request and output strings from the pipeline.
"""
conversation = map_q_id_to_conversation.get(row.prompt_id)
if conversation is None:
return None
code_snippet = json.loads(row.code_snippet) if row.code_snippet else None
trigger_string = None
if row.trigger_string:
try:
trigger_string = json.loads(row.trigger_string)
except Exception:
trigger_string = row.trigger_string
return AlertConversation(
conversation=conversation,
alert_id=row.id,
code_snippet=code_snippet,
trigger_string=trigger_string,
trigger_type=row.trigger_type,
trigger_category=row.trigger_category,
timestamp=row.timestamp,
)
async def parse_get_alert_conversation(
alerts: List[Alert],
prompts_outputs: List[GetPromptWithOutputsRow],
) -> List[AlertConversation]:
"""
Parse a list of rows from the get_alerts_with_prompt_and_output query and return a list of
AlertConversation
The rows contain the raw request and output strings from the pipeline.
"""
_, map_q_id_to_conversation = await parse_messages_in_conversations(prompts_outputs)
dedup_alerts = await remove_duplicate_alerts(alerts)
async with asyncio.TaskGroup() as tg:
tasks = [
tg.create_task(parse_row_alert_conversation(row, map_q_id_to_conversation))
for row in dedup_alerts
]
return [task.result() for task in tasks if task.result() is not None]
async def parse_workspace_token_usage(
prompts_outputs: List[GetPromptWithOutputsRow],
) -> TokenUsageAggregate:
"""
Parse the token usage from the workspace.
"""
partial_question_answers = await _process_prompt_output_to_partial_qa(prompts_outputs)
token_usage_agg = TokenUsageAggregate(tokens_by_model={}, token_usage=TokenUsage())
for p_qa in partial_question_answers:
token_usage_agg.add_model_token_usage(p_qa.model_token_usage)
return token_usage_agg
async def remove_duplicate_alerts(alerts: List[v1_models.Alert]) -> List[v1_models.Alert]:
unique_alerts = []
seen = defaultdict(list)
for alert in sorted(
alerts, key=lambda x: x.timestamp, reverse=True
): # Sort alerts by timestamp descending
# Handle trigger_string based on its type
trigger_string_content = ""
if isinstance(alert.trigger_string, dict):
# If it's a dict, use relevant fields for deduplication
trigger_string_content = json.dumps(
{
"name": alert.trigger_string.get("name"),
"type": alert.trigger_string.get("type"),
"status": alert.trigger_string.get("status"),
}
)
elif isinstance(alert.trigger_string, str):
# If it's a string, use the part before "Context" if it exists
trigger_string_content = alert.trigger_string.split("Context")[0]
else:
# For any other case, convert to string
trigger_string_content = str(alert.trigger_string)
key = (
alert.code_snippet,
alert.trigger_type,
alert.trigger_category,
trigger_string_content,
)
# If key exists and new alert is more recent, replace it
if key in seen:
existing_alert = seen[key]
if abs((alert.timestamp - existing_alert.timestamp).total_seconds()) < 5:
seen[key] = alert # Replace with newer alert
continue
seen[key] = alert
unique_alerts.append(alert)
return unique_alerts