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update FLOW_ID to LANGFLOW_CHAT_FLOW_ID

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Gabriel Luiz Freitas Almeida 2025-09-04 10:23:34 -03:00
parent e1d58c7421
commit d77ebb5f37
1 changed files with 193 additions and 77 deletions
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270
src/services/chat_service.py
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@ -1,43 +1,75 @@
from config.settings import clients, LANGFLOW_URL, FLOW_ID
from agent import async_chat, async_langflow, async_chat_stream, async_langflow_stream
from auth_context import set_auth_context
import json
from agent import async_chat, async_chat_stream, async_langflow
from auth_context import set_auth_context
from config.settings import LANGFLOW_CHAT_FLOW_ID, LANGFLOW_URL, clients
class ChatService:
async def chat(self, prompt: str, user_id: str = None, jwt_token: str = None, previous_response_id: str = None, stream: bool = False):
async def chat(
self,
prompt: str,
user_id: str = None,
jwt_token: str = None,
previous_response_id: str = None,
stream: bool = False,
):
"""Handle chat requests using the patched OpenAI client"""
if not prompt:
raise ValueError("Prompt is required")
# Set authentication context for this request so tools can access it
if user_id and jwt_token:
set_auth_context(user_id, jwt_token)
if stream:
return async_chat_stream(clients.patched_async_client, prompt, user_id, previous_response_id=previous_response_id)
return async_chat_stream(
clients.patched_async_client,
prompt,
user_id,
previous_response_id=previous_response_id,
)
else:
response_text, response_id = await async_chat(clients.patched_async_client, prompt, user_id, previous_response_id=previous_response_id)
response_text, response_id = await async_chat(
clients.patched_async_client,
prompt,
user_id,
previous_response_id=previous_response_id,
)
response_data = {"response": response_text}
if response_id:
response_data["response_id"] = response_id
return response_data
async def langflow_chat(self, prompt: str, user_id: str = None, jwt_token: str = None, previous_response_id: str = None, stream: bool = False):
async def langflow_chat(
self,
prompt: str,
user_id: str = None,
jwt_token: str = None,
previous_response_id: str = None,
stream: bool = False,
):
"""Handle Langflow chat requests"""
if not prompt:
raise ValueError("Prompt is required")
if not LANGFLOW_URL or not FLOW_ID:
raise ValueError("LANGFLOW_URL and FLOW_ID environment variables are required")
if not LANGFLOW_URL or not LANGFLOW_CHAT_FLOW_ID:
raise ValueError(
"LANGFLOW_URL and LANGFLOW_CHAT_FLOW_ID environment variables are required"
)
# Prepare extra headers for JWT authentication
extra_headers = {}
if jwt_token:
extra_headers['X-LANGFLOW-GLOBAL-VAR-JWT'] = jwt_token
extra_headers["X-LANGFLOW-GLOBAL-VAR-JWT"] = jwt_token
# Get context variables for filters, limit, and threshold
from auth_context import get_search_filters, get_search_limit, get_score_threshold
from auth_context import (
get_score_threshold,
get_search_filters,
get_search_limit,
)
filters = get_search_filters()
limit = get_search_limit()
score_threshold = get_score_threshold()
@ -49,86 +81,130 @@ class ChatService:
# Map frontend filter names to backend field names
field_mapping = {
"data_sources": "filename",
"document_types": "mimetype",
"owners": "owner"
"document_types": "mimetype",
"owners": "owner",
}
for filter_key, values in filters.items():
if values is not None and isinstance(values, list) and len(values) > 0:
# Map frontend key to backend field name
field_name = field_mapping.get(filter_key, filter_key)
if len(values) == 1:
# Single value filter
filter_clauses.append({"term": {field_name: values[0]}})
else:
# Multiple values filter
filter_clauses.append({"terms": {field_name: values}})
if filter_clauses:
filter_expression["filter"] = filter_clauses
# Add limit and score threshold to the filter expression (only if different from defaults)
if limit and limit != 10: # 10 is the default limit
filter_expression["limit"] = limit
if score_threshold and score_threshold != 0: # 0 is the default threshold
filter_expression["score_threshold"] = score_threshold
# Pass the complete filter expression as a single header to Langflow (only if we have something to send)
if filter_expression:
print(f"Sending OpenRAG query filter to Langflow: {json.dumps(filter_expression, indent=2)}")
extra_headers['X-LANGFLOW-GLOBAL-VAR-OPENRAG-QUERY-FILTER'] = json.dumps(filter_expression)
print(
f"Sending OpenRAG query filter to Langflow: {json.dumps(filter_expression, indent=2)}"
)
extra_headers["X-LANGFLOW-GLOBAL-VAR-OPENRAG-QUERY-FILTER"] = json.dumps(
filter_expression
)
# Ensure the Langflow client exists; try lazy init if needed
langflow_client = await clients.ensure_langflow_client()
if not langflow_client:
raise ValueError("Langflow client not initialized. Ensure LANGFLOW is reachable or set LANGFLOW_KEY.")
raise ValueError(
"Langflow client not initialized. Ensure LANGFLOW is reachable or set LANGFLOW_KEY."
)
if stream:
from agent import async_langflow_chat_stream
return async_langflow_chat_stream(langflow_client, FLOW_ID, prompt, user_id, extra_headers=extra_headers, previous_response_id=previous_response_id)
return async_langflow_chat_stream(
langflow_client,
LANGFLOW_CHAT_FLOW_ID,
prompt,
user_id,
extra_headers=extra_headers,
previous_response_id=previous_response_id,
)
else:
from agent import async_langflow_chat
response_text, response_id = await async_langflow_chat(langflow_client, FLOW_ID, prompt, user_id, extra_headers=extra_headers, previous_response_id=previous_response_id)
response_text, response_id = await async_langflow_chat(
langflow_client,
LANGFLOW_CHAT_FLOW_ID,
prompt,
user_id,
extra_headers=extra_headers,
previous_response_id=previous_response_id,
)
response_data = {"response": response_text}
if response_id:
response_data["response_id"] = response_id
return response_data
async def upload_context_chat(self, document_content: str, filename: str,
user_id: str = None, jwt_token: str = None, previous_response_id: str = None, endpoint: str = "langflow"):
async def upload_context_chat(
self,
document_content: str,
filename: str,
user_id: str = None,
jwt_token: str = None,
previous_response_id: str = None,
endpoint: str = "langflow",
):
"""Send document content as user message to get proper response_id"""
document_prompt = f"I'm uploading a document called '{filename}'. Here is its content:\n\n{document_content}\n\nPlease confirm you've received this document and are ready to answer questions about it."
if endpoint == "langflow":
# Prepare extra headers for JWT authentication
extra_headers = {}
if jwt_token:
extra_headers['X-LANGFLOW-GLOBAL-VAR-JWT'] = jwt_token
extra_headers["X-LANGFLOW-GLOBAL-VAR-JWT"] = jwt_token
# Ensure the Langflow client exists; try lazy init if needed
langflow_client = await clients.ensure_langflow_client()
if not langflow_client:
raise ValueError("Langflow client not initialized. Ensure LANGFLOW is reachable or set LANGFLOW_KEY.")
response_text, response_id = await async_langflow(langflow_client, FLOW_ID, document_prompt, extra_headers=extra_headers, previous_response_id=previous_response_id)
raise ValueError(
"Langflow client not initialized. Ensure LANGFLOW is reachable or set LANGFLOW_KEY."
)
response_text, response_id = await async_langflow(
langflow_client,
LANGFLOW_CHAT_FLOW_ID,
document_prompt,
extra_headers=extra_headers,
previous_response_id=previous_response_id,
)
else: # chat
# Set auth context for chat tools and provide user_id
if user_id and jwt_token:
set_auth_context(user_id, jwt_token)
response_text, response_id = await async_chat(clients.patched_async_client, document_prompt, user_id, previous_response_id=previous_response_id)
response_text, response_id = await async_chat(
clients.patched_async_client,
document_prompt,
user_id,
previous_response_id=previous_response_id,
)
return response_text, response_id
async def get_chat_history(self, user_id: str):
"""Get chat conversation history for a user"""
from agent import get_user_conversations
if not user_id:
return {"error": "User ID is required", "conversations": []}
conversations_dict = get_user_conversations(user_id)
print(f"[DEBUG] get_chat_history for user {user_id}: found {len(conversations_dict)} conversations")
print(
f"[DEBUG] get_chat_history for user {user_id}: found {len(conversations_dict)} conversations"
)
# Convert conversations dict to list format with metadata
conversations = []
for response_id, conversation_state in conversations_dict.items():
@ -139,47 +215,67 @@ class ChatService:
message_data = {
"role": msg["role"],
"content": msg["content"],
"timestamp": msg.get("timestamp").isoformat() if msg.get("timestamp") else None
"timestamp": msg.get("timestamp").isoformat()
if msg.get("timestamp")
else None,
}
if msg.get("response_id"):
message_data["response_id"] = msg["response_id"]
messages.append(message_data)
if messages: # Only include conversations with actual messages
# Generate title from first user message
first_user_msg = next((msg for msg in messages if msg["role"] == "user"), None)
title = first_user_msg["content"][:50] + "..." if first_user_msg and len(first_user_msg["content"]) > 50 else first_user_msg["content"] if first_user_msg else "New chat"
conversations.append({
"response_id": response_id,
"title": title,
"endpoint": "chat",
"messages": messages,
"created_at": conversation_state.get("created_at").isoformat() if conversation_state.get("created_at") else None,
"last_activity": conversation_state.get("last_activity").isoformat() if conversation_state.get("last_activity") else None,
"previous_response_id": conversation_state.get("previous_response_id"),
"total_messages": len(messages)
})
first_user_msg = next(
(msg for msg in messages if msg["role"] == "user"), None
)
title = (
first_user_msg["content"][:50] + "..."
if first_user_msg and len(first_user_msg["content"]) > 50
else first_user_msg["content"]
if first_user_msg
else "New chat"
)
conversations.append(
{
"response_id": response_id,
"title": title,
"endpoint": "chat",
"messages": messages,
"created_at": conversation_state.get("created_at").isoformat()
if conversation_state.get("created_at")
else None,
"last_activity": conversation_state.get(
"last_activity"
).isoformat()
if conversation_state.get("last_activity")
else None,
"previous_response_id": conversation_state.get(
"previous_response_id"
),
"total_messages": len(messages),
}
)
# Sort by last activity (most recent first)
conversations.sort(key=lambda c: c["last_activity"], reverse=True)
return {
"user_id": user_id,
"endpoint": "chat",
"conversations": conversations,
"total_conversations": len(conversations)
"total_conversations": len(conversations),
}
async def get_langflow_history(self, user_id: str):
"""Get langflow conversation history for a user"""
from agent import get_user_conversations
if not user_id:
return {"error": "User ID is required", "conversations": []}
conversations_dict = get_user_conversations(user_id)
# Convert conversations dict to list format with metadata
conversations = []
for response_id, conversation_state in conversations_dict.items():
@ -190,34 +286,54 @@ class ChatService:
message_data = {
"role": msg["role"],
"content": msg["content"],
"timestamp": msg.get("timestamp").isoformat() if msg.get("timestamp") else None
"timestamp": msg.get("timestamp").isoformat()
if msg.get("timestamp")
else None,
}
if msg.get("response_id"):
message_data["response_id"] = msg["response_id"]
messages.append(message_data)
if messages: # Only include conversations with actual messages
# Generate title from first user message
first_user_msg = next((msg for msg in messages if msg["role"] == "user"), None)
title = first_user_msg["content"][:50] + "..." if first_user_msg and len(first_user_msg["content"]) > 50 else first_user_msg["content"] if first_user_msg else "New chat"
conversations.append({
"response_id": response_id,
"title": title,
"endpoint": "langflow",
"messages": messages,
"created_at": conversation_state.get("created_at").isoformat() if conversation_state.get("created_at") else None,
"last_activity": conversation_state.get("last_activity").isoformat() if conversation_state.get("last_activity") else None,
"previous_response_id": conversation_state.get("previous_response_id"),
"total_messages": len(messages)
})
first_user_msg = next(
(msg for msg in messages if msg["role"] == "user"), None
)
title = (
first_user_msg["content"][:50] + "..."
if first_user_msg and len(first_user_msg["content"]) > 50
else first_user_msg["content"]
if first_user_msg
else "New chat"
)
conversations.append(
{
"response_id": response_id,
"title": title,
"endpoint": "langflow",
"messages": messages,
"created_at": conversation_state.get("created_at").isoformat()
if conversation_state.get("created_at")
else None,
"last_activity": conversation_state.get(
"last_activity"
).isoformat()
if conversation_state.get("last_activity")
else None,
"previous_response_id": conversation_state.get(
"previous_response_id"
),
"total_messages": len(messages),
}
)
# Sort by last activity (most recent first)
conversations.sort(key=lambda c: c["last_activity"], reverse=True)
return {
"user_id": user_id,
"endpoint": "langflow",
"conversations": conversations,
"total_conversations": len(conversations)
"total_conversations": len(conversations),
}