Files
DocsGPT/application/api/answer/services/conversation_service.py
T
81b6ee5daa Pg 4 (#2390)
* feat: postgres tests

* feat: mongo cutoff

* feat: mongo cutoff

* feat: adjust docs and compose files

* fix: mini code mongo removals

* fix: tests and k8s mongo stuff

* feat: test fixes

* fix: ruff

* fix: vale

* Potential fix for pull request finding 'CodeQL / Clear-text logging of sensitive information'

Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>

* fix: mini suggestions

* vale lint fix 2

* fix: codeql columns thing

* fix: test mongo

* fix: tests coverage

* feat: better tests 4

* feat: more tests

* feat: decent coverage

* fix: ruff fixes

* fix: remove mongo mock

* feat: enhance workflow engine and API routes; add document retrieval and source handling

* feat: e2e tests

* fix: mcp, mongo and more

* fix: mini codeql warning

* fix: agent chunk view

* fix: mini issues

* fix: more pg fixes

* feat: postgres prep on start

* feat: qa tests

* fix: mini improvements

* fix: tests

---------

Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
Co-authored-by: Siddhant Rai <siddhant.rai.5686@gmail.com>
2026-04-18 13:13:57 +01:00

281 lines
11 KiB
Python

"""Conversation persistence service backed by Postgres.
Handles create / append / update / compression for conversations during
the answer-streaming path. Connections are opened per-operation rather
than held for the duration of a stream.
"""
import logging
from datetime import datetime, timezone
from typing import Any, Dict, List, Optional
from sqlalchemy import text as sql_text
from application.core.settings import settings
from application.storage.db.base_repository import looks_like_uuid
from application.storage.db.repositories.agents import AgentsRepository
from application.storage.db.repositories.conversations import ConversationsRepository
from application.storage.db.session import db_readonly, db_session
logger = logging.getLogger(__name__)
class ConversationService:
def get_conversation(
self, conversation_id: str, user_id: str
) -> Optional[Dict[str, Any]]:
"""Retrieve a conversation with owner-or-shared access control.
Returns a dict in the legacy Mongo shape — ``queries`` is a list
of message dicts (prompt/response/...) — for compatibility with
the streaming pipeline that consumes this shape.
"""
if not conversation_id or not user_id:
return None
try:
with db_readonly() as conn:
repo = ConversationsRepository(conn)
conv = repo.get_any(conversation_id, user_id)
if conv is None:
logger.warning(
f"Conversation not found or unauthorized - ID: {conversation_id}, User: {user_id}"
)
return None
messages = repo.get_messages(str(conv["id"]))
conv["queries"] = messages
conv["_id"] = str(conv["id"])
return conv
except Exception as e:
logger.error(f"Error fetching conversation: {str(e)}", exc_info=True)
return None
def save_conversation(
self,
conversation_id: Optional[str],
question: str,
response: str,
thought: str,
sources: List[Dict[str, Any]],
tool_calls: List[Dict[str, Any]],
llm: Any,
model_id: str,
decoded_token: Dict[str, Any],
index: Optional[int] = None,
api_key: Optional[str] = None,
agent_id: Optional[str] = None,
is_shared_usage: bool = False,
shared_token: Optional[str] = None,
attachment_ids: Optional[List[str]] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> str:
"""Save or update a conversation in Postgres.
Returns the string conversation id (PG UUID as string, or the
caller-provided id if it was already a UUID).
"""
if decoded_token is None:
raise ValueError("Invalid or missing authentication token")
user_id = decoded_token.get("sub")
if not user_id:
raise ValueError("User ID not found in token")
current_time = datetime.now(timezone.utc)
# Trim huge inline source text to a reasonable max before persist.
for source in sources:
if "text" in source and isinstance(source["text"], str):
source["text"] = source["text"][:1000]
message_payload = {
"prompt": question,
"response": response,
"thought": thought,
"sources": sources,
"tool_calls": tool_calls,
"attachments": attachment_ids,
"model_id": model_id,
"timestamp": current_time,
}
if metadata:
message_payload["metadata"] = metadata
if conversation_id is not None and index is not None:
with db_session() as conn:
repo = ConversationsRepository(conn)
conv = repo.get_any(conversation_id, user_id)
if conv is None:
raise ValueError("Conversation not found or unauthorized")
conv_pg_id = str(conv["id"])
repo.update_message_at(conv_pg_id, index, message_payload)
repo.truncate_after(conv_pg_id, index)
return conversation_id
elif conversation_id:
with db_session() as conn:
repo = ConversationsRepository(conn)
conv = repo.get_any(conversation_id, user_id)
if conv is None:
raise ValueError("Conversation not found or unauthorized")
conv_pg_id = str(conv["id"])
# append_message expects 'metadata' key either way; normalise.
append_payload = dict(message_payload)
append_payload.setdefault("metadata", metadata or {})
repo.append_message(conv_pg_id, append_payload)
return conversation_id
else:
messages_summary = [
{
"role": "system",
"content": "You are a helpful assistant that creates concise conversation titles. "
"Summarize conversations in 3 words or less using the same language as the user.",
},
{
"role": "user",
"content": "Summarise following conversation in no more than 3 words, "
"respond ONLY with the summary, use the same language as the "
"user query \n\nUser: " + question + "\n\n" + "AI: " + response,
},
]
completion = llm.gen(
model=model_id, messages=messages_summary, max_tokens=500
)
if not completion or not completion.strip():
completion = question[:50] if question else "New Conversation"
resolved_api_key: Optional[str] = None
resolved_agent_id: Optional[str] = None
if api_key:
with db_readonly() as conn:
agent = AgentsRepository(conn).find_by_key(api_key)
if agent:
resolved_api_key = agent.get("key")
if agent_id:
resolved_agent_id = agent_id
with db_session() as conn:
repo = ConversationsRepository(conn)
conv = repo.create(
user_id,
completion,
agent_id=resolved_agent_id,
api_key=resolved_api_key,
is_shared_usage=bool(resolved_agent_id and is_shared_usage),
shared_token=(
shared_token
if (resolved_agent_id and is_shared_usage)
else None
),
)
conv_pg_id = str(conv["id"])
append_payload = dict(message_payload)
append_payload.setdefault("metadata", metadata or {})
repo.append_message(conv_pg_id, append_payload)
return conv_pg_id
def update_compression_metadata(
self, conversation_id: str, compression_metadata: Dict[str, Any]
) -> None:
"""Persist compression flags and append a compression point.
Mirrors the Mongo-era ``$set`` + ``$push $slice`` on
``compression_metadata`` but goes through the PG repo API.
"""
try:
with db_session() as conn:
repo = ConversationsRepository(conn)
# conversation_id here comes from the streaming pipeline
# which has already resolved it; accept either UUID or
# legacy id for safety.
conv = repo.get_by_legacy_id(conversation_id)
conv_pg_id = (
str(conv["id"]) if conv is not None else conversation_id
)
repo.set_compression_flags(
conv_pg_id,
is_compressed=True,
last_compression_at=compression_metadata.get("timestamp"),
)
repo.append_compression_point(
conv_pg_id,
compression_metadata,
max_points=settings.COMPRESSION_MAX_HISTORY_POINTS,
)
logger.info(
f"Updated compression metadata for conversation {conversation_id}"
)
except Exception as e:
logger.error(
f"Error updating compression metadata: {str(e)}", exc_info=True
)
raise
def append_compression_message(
self, conversation_id: str, compression_metadata: Dict[str, Any]
) -> None:
"""Append a synthetic compression summary message to the conversation."""
try:
summary = compression_metadata.get("compressed_summary", "")
if not summary:
return
timestamp = compression_metadata.get(
"timestamp", datetime.now(timezone.utc)
)
with db_session() as conn:
repo = ConversationsRepository(conn)
conv = repo.get_by_legacy_id(conversation_id)
conv_pg_id = (
str(conv["id"]) if conv is not None else conversation_id
)
repo.append_message(conv_pg_id, {
"prompt": "[Context Compression Summary]",
"response": summary,
"thought": "",
"sources": [],
"tool_calls": [],
"attachments": [],
"model_id": compression_metadata.get("model_used"),
"timestamp": timestamp,
})
logger.info(
f"Appended compression summary to conversation {conversation_id}"
)
except Exception as e:
logger.error(
f"Error appending compression summary: {str(e)}", exc_info=True
)
def get_compression_metadata(
self, conversation_id: str
) -> Optional[Dict[str, Any]]:
"""Fetch the stored compression metadata JSONB blob for a conversation."""
try:
with db_readonly() as conn:
repo = ConversationsRepository(conn)
conv = repo.get_by_legacy_id(conversation_id)
if conv is None:
# Fallback to UUID lookup without user scoping — the
# caller already holds an authenticated conversation
# id from the streaming path. Gate on id shape so a
# non-UUID (legacy ObjectId that wasn't backfilled)
# doesn't reach CAST — the cast raises and spams the
# logs with a stack trace on every call.
if not looks_like_uuid(conversation_id):
return None
result = conn.execute(
sql_text(
"SELECT compression_metadata FROM conversations "
"WHERE id = CAST(:id AS uuid)"
),
{"id": conversation_id},
)
row = result.fetchone()
return row[0] if row is not None else None
return conv.get("compression_metadata") if conv else None
except Exception as e:
logger.error(
f"Error getting compression metadata: {str(e)}", exc_info=True
)
return None