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feat: answer routes re-structure for better maintainability and reuse

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This commit is contained in:
Siddhant Rai
2025-07-23 20:07:42 +05:30
parent 4185e64c65
commit 76973a4b4c
20 changed files with 970 additions and 993 deletions
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7
application/api/__init__.py
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from flask_restx import Api
api = Api(
version="1.0",
title="DocsGPT API",
description="API for DocsGPT",
)

19
application/api/answer/__init__.py
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from flask import Blueprint
from application.api import api
from application.api.answer.routes.answer import AnswerResource
from application.api.answer.routes.base import answer_ns
from application.api.answer.routes.stream import StreamResource
answer = Blueprint("answer", __name__)
api.add_namespace(answer_ns)
def init_answer_routes():
api.add_resource(StreamResource, "/stream")
api.add_resource(AnswerResource, "/api/answer")
init_answer_routes()

914
application/api/answer/routes.py
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import asyncio
import datetime
import json
import logging
import os
import traceback
from bson.dbref import DBRef
from bson.objectid import ObjectId
from flask import Blueprint, make_response, request, Response
from flask_restx import fields, Namespace, Resource
from application.agents.agent_creator import AgentCreator
from application.core.mongo_db import MongoDB
from application.core.settings import settings
from application.error import bad_request
from application.extensions import api
from application.llm.llm_creator import LLMCreator
from application.retriever.retriever_creator import RetrieverCreator
from application.utils import check_required_fields, limit_chat_history
logger = logging.getLogger(__name__)
mongo = MongoDB.get_client()
db = mongo[settings.MONGO_DB_NAME]
conversations_collection = db["conversations"]
sources_collection = db["sources"]
prompts_collection = db["prompts"]
agents_collection = db["agents"]
user_logs_collection = db["user_logs"]
attachments_collection = db["attachments"]
answer = Blueprint("answer", __name__)
answer_ns = Namespace("answer", description="Answer related operations", path="/")
api.add_namespace(answer_ns)
gpt_model = ""
# to have some kind of default behaviour
if settings.LLM_PROVIDER == "openai":
gpt_model = "gpt-4o-mini"
elif settings.LLM_PROVIDER == "anthropic":
gpt_model = "claude-2"
elif settings.LLM_PROVIDER == "groq":
gpt_model = "llama3-8b-8192"
elif settings.LLM_PROVIDER == "novita":
gpt_model = "deepseek/deepseek-r1"
if settings.LLM_NAME: # in case there is particular model name configured
gpt_model = settings.LLM_NAME
# load the prompts
current_dir = os.path.dirname(
os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
)
with open(os.path.join(current_dir, "prompts", "chat_combine_default.txt"), "r") as f:
chat_combine_template = f.read()
with open(os.path.join(current_dir, "prompts", "chat_reduce_prompt.txt"), "r") as f:
chat_reduce_template = f.read()
with open(os.path.join(current_dir, "prompts", "chat_combine_creative.txt"), "r") as f:
chat_combine_creative = f.read()
with open(os.path.join(current_dir, "prompts", "chat_combine_strict.txt"), "r") as f:
chat_combine_strict = f.read()
api_key_set = settings.API_KEY is not None
embeddings_key_set = settings.EMBEDDINGS_KEY is not None
async def async_generate(chain, question, chat_history):
result = await chain.arun({"question": question, "chat_history": chat_history})
return result
def run_async_chain(chain, question, chat_history):
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
result = {}
try:
answer = loop.run_until_complete(async_generate(chain, question, chat_history))
finally:
loop.close()
result["answer"] = answer
return result
def get_agent_key(agent_id, user_id):
if not agent_id:
return None, False, None
try:
agent = agents_collection.find_one({"_id": ObjectId(agent_id)})
if agent is None:
raise Exception("Agent not found", 404)
is_owner = agent.get("user") == user_id
if is_owner:
agents_collection.update_one(
{"_id": ObjectId(agent_id)},
{"$set": {"lastUsedAt": datetime.datetime.now(datetime.timezone.utc)}},
)
return str(agent["key"]), False, None
is_shared_with_user = agent.get(
"shared_publicly", False
) or user_id in agent.get("shared_with", [])
if is_shared_with_user:
return str(agent["key"]), True, agent.get("shared_token")
raise Exception("Unauthorized access to the agent", 403)
except Exception as e:
logger.error(f"Error in get_agent_key: {str(e)}", exc_info=True)
raise
def get_data_from_api_key(api_key):
data = agents_collection.find_one({"key": api_key})
if not data:
raise Exception("Invalid API Key, please generate a new key", 401)
source = data.get("source")
if isinstance(source, DBRef):
source_doc = db.dereference(source)
data["source"] = str(source_doc["_id"])
data["retriever"] = source_doc.get("retriever", data.get("retriever"))
else:
data["source"] = {}
return data
def get_retriever(source_id: str):
doc = sources_collection.find_one({"_id": ObjectId(source_id)})
if doc is None:
raise Exception("Source document does not exist", 404)
retriever_name = None if "retriever" not in doc else doc["retriever"]
return retriever_name
def is_azure_configured():
return (
settings.OPENAI_API_BASE
and settings.OPENAI_API_VERSION
and settings.AZURE_DEPLOYMENT_NAME
)
def save_conversation(
conversation_id,
question,
response,
thought,
source_log_docs,
tool_calls,
llm,
decoded_token,
index=None,
api_key=None,
agent_id=None,
is_shared_usage=False,
shared_token=None,
attachment_ids=None,
):
current_time = datetime.datetime.now(datetime.timezone.utc)
if conversation_id is not None and index is not None:
conversations_collection.update_one(
{"_id": ObjectId(conversation_id), f"queries.{index}": {"$exists": True}},
{
"$set": {
f"queries.{index}.prompt": question,
f"queries.{index}.response": response,
f"queries.{index}.thought": thought,
f"queries.{index}.sources": source_log_docs,
f"queries.{index}.tool_calls": tool_calls,
f"queries.{index}.timestamp": current_time,
f"queries.{index}.attachments": attachment_ids,
}
},
)
##remove following queries from the array
conversations_collection.update_one(
{"_id": ObjectId(conversation_id), f"queries.{index}": {"$exists": True}},
{"$push": {"queries": {"$each": [], "$slice": index + 1}}},
)
elif conversation_id is not None and conversation_id != "None":
conversations_collection.update_one(
{"_id": ObjectId(conversation_id)},
{
"$push": {
"queries": {
"prompt": question,
"response": response,
"thought": thought,
"sources": source_log_docs,
"tool_calls": tool_calls,
"timestamp": current_time,
"attachments": attachment_ids,
}
}
},
)
else:
# create new conversation
# generate summary
messages_summary = [
{
"role": "assistant",
"content": "Summarise following conversation in no more than 3 "
"words, respond ONLY with the summary, use the same "
"language as the user query",
},
{
"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=gpt_model, messages=messages_summary, max_tokens=30)
conversation_data = {
"user": decoded_token.get("sub"),
"date": datetime.datetime.utcnow(),
"name": completion,
"queries": [
{
"prompt": question,
"response": response,
"thought": thought,
"sources": source_log_docs,
"tool_calls": tool_calls,
"timestamp": current_time,
"attachments": attachment_ids,
}
],
}
if api_key:
if agent_id:
conversation_data["agent_id"] = agent_id
if is_shared_usage:
conversation_data["is_shared_usage"] = is_shared_usage
conversation_data["shared_token"] = shared_token
api_key_doc = agents_collection.find_one({"key": api_key})
if api_key_doc:
conversation_data["api_key"] = api_key_doc["key"]
conversation_id = conversations_collection.insert_one(
conversation_data
).inserted_id
return conversation_id
def get_prompt(prompt_id):
if prompt_id == "default":
prompt = chat_combine_template
elif prompt_id == "creative":
prompt = chat_combine_creative
elif prompt_id == "strict":
prompt = chat_combine_strict
else:
prompt = prompts_collection.find_one({"_id": ObjectId(prompt_id)})["content"]
return prompt
def complete_stream(
question,
agent,
retriever,
conversation_id,
user_api_key,
decoded_token,
isNoneDoc=False,
index=None,
should_save_conversation=True,
attachment_ids=None,
agent_id=None,
is_shared_usage=False,
shared_token=None,
):
try:
response_full, thought, source_log_docs, tool_calls = "", "", [], []
answer = agent.gen(query=question, retriever=retriever)
for line in answer:
if "answer" in line:
response_full += str(line["answer"])
data = json.dumps({"type": "answer", "answer": line["answer"]})
yield f"data: {data}\n\n"
elif "sources" in line:
truncated_sources = []
source_log_docs = line["sources"]
for source in line["sources"]:
truncated_source = source.copy()
if "text" in truncated_source:
truncated_source["text"] = (
truncated_source["text"][:100].strip() + "..."
)
truncated_sources.append(truncated_source)
if len(truncated_sources) > 0:
data = json.dumps({"type": "source", "source": truncated_sources})
yield f"data: {data}\n\n"
elif "tool_calls" in line:
tool_calls = line["tool_calls"]
elif "thought" in line:
thought += line["thought"]
data = json.dumps({"type": "thought", "thought": line["thought"]})
yield f"data: {data}\n\n"
elif "type" in line:
data = json.dumps(line)
yield f"data: {data}\n\n"
if isNoneDoc:
for doc in source_log_docs:
doc["source"] = "None"
llm = LLMCreator.create_llm(
settings.LLM_PROVIDER,
api_key=settings.API_KEY,
user_api_key=user_api_key,
decoded_token=decoded_token,
)
if should_save_conversation:
conversation_id = save_conversation(
conversation_id,
question,
response_full,
thought,
source_log_docs,
tool_calls,
llm,
decoded_token,
index,
api_key=user_api_key,
attachment_ids=attachment_ids,
agent_id=agent_id,
is_shared_usage=is_shared_usage,
shared_token=shared_token,
)
else:
conversation_id = None
# send data.type = "end" to indicate that the stream has ended as json
data = json.dumps({"type": "id", "id": str(conversation_id)})
yield f"data: {data}\n\n"
retriever_params = retriever.get_params()
user_logs_collection.insert_one(
{
"action": "stream_answer",
"level": "info",
"user": decoded_token.get("sub"),
"api_key": user_api_key,
"question": question,
"response": response_full,
"sources": source_log_docs,
"retriever_params": retriever_params,
"attachments": attachment_ids,
"timestamp": datetime.datetime.now(datetime.timezone.utc),
}
)
data = json.dumps({"type": "end"})
yield f"data: {data}\n\n"
except Exception as e:
logger.error(f"Error in stream: {str(e)}", exc_info=True)
data = json.dumps(
{
"type": "error",
"error": "Please try again later. We apologize for any inconvenience.",
}
)
yield f"data: {data}\n\n"
return
@answer_ns.route("/stream")
class Stream(Resource):
stream_model = api.model(
"StreamModel",
{
"question": fields.String(
required=True, description="Question to be asked"
),
"history": fields.List(
fields.String, required=False, description="Chat history"
),
"conversation_id": fields.String(
required=False, description="Conversation ID"
),
"prompt_id": fields.String(
required=False, default="default", description="Prompt ID"
),
"chunks": fields.Integer(
required=False, default=2, description="Number of chunks"
),
"token_limit": fields.Integer(required=False, description="Token limit"),
"retriever": fields.String(required=False, description="Retriever type"),
"api_key": fields.String(required=False, description="API key"),
"active_docs": fields.String(
required=False, description="Active documents"
),
"isNoneDoc": fields.Boolean(
required=False, description="Flag indicating if no document is used"
),
"index": fields.Integer(
required=False, description="Index of the query to update"
),
"save_conversation": fields.Boolean(
required=False,
default=True,
description="Whether to save the conversation",
),
"attachments": fields.List(
fields.String, required=False, description="List of attachment IDs"
),
},
)
@api.expect(stream_model)
@api.doc(description="Stream a response based on the question and retriever")
def post(self):
data = request.get_json()
required_fields = ["question"]
if "index" in data:
required_fields = ["question", "conversation_id"]
missing_fields = check_required_fields(data, required_fields)
if missing_fields:
return missing_fields
save_conv = data.get("save_conversation", True)
try:
question = data["question"]
history = limit_chat_history(
json.loads(data.get("history", "[]")), gpt_model=gpt_model
)
conversation_id = data.get("conversation_id")
prompt_id = data.get("prompt_id", "default")
attachment_ids = data.get("attachments", [])
index = data.get("index", None)
chunks = int(data.get("chunks", 2))
token_limit = data.get("token_limit", settings.DEFAULT_MAX_HISTORY)
retriever_name = data.get("retriever", "classic")
agent_id = data.get("agent_id", None)
agent_type = settings.AGENT_NAME
decoded_token = getattr(request, "decoded_token", None)
user_sub = decoded_token.get("sub") if decoded_token else None
agent_key, is_shared_usage, shared_token = get_agent_key(agent_id, user_sub)
if agent_key:
data.update({"api_key": agent_key})
else:
agent_id = None
if "api_key" in data:
data_key = get_data_from_api_key(data["api_key"])
chunks = int(data_key.get("chunks", 2))
prompt_id = data_key.get("prompt_id", "default")
source = {"active_docs": data_key.get("source")}
retriever_name = data_key.get("retriever", retriever_name)
user_api_key = data["api_key"]
agent_type = data_key.get("agent_type", agent_type)
if is_shared_usage:
decoded_token = request.decoded_token
else:
decoded_token = {"sub": data_key.get("user")}
is_shared_usage = False
elif "active_docs" in data:
source = {"active_docs": data["active_docs"]}
retriever_name = get_retriever(data["active_docs"]) or retriever_name
user_api_key = None
decoded_token = request.decoded_token
else:
source = {}
user_api_key = None
decoded_token = request.decoded_token
if not decoded_token:
return make_response({"error": "Unauthorized"}, 401)
attachments = get_attachments_content(
attachment_ids, decoded_token.get("sub")
)
logger.info(
f"/stream - request_data: {data}, source: {source}, attachments: {len(attachments)}",
extra={"data": json.dumps({"request_data": data, "source": source})},
)
prompt = get_prompt(prompt_id)
if "isNoneDoc" in data and data["isNoneDoc"] is True:
chunks = 0
agent = AgentCreator.create_agent(
agent_type,
endpoint="stream",
llm_name=settings.LLM_PROVIDER,
gpt_model=gpt_model,
api_key=settings.API_KEY,
user_api_key=user_api_key,
prompt=prompt,
chat_history=history,
decoded_token=decoded_token,
attachments=attachments,
)
retriever = RetrieverCreator.create_retriever(
retriever_name,
source=source,
chat_history=history,
prompt=prompt,
chunks=chunks,
token_limit=token_limit,
gpt_model=gpt_model,
user_api_key=user_api_key,
decoded_token=decoded_token,
)
return Response(
complete_stream(
question=question,
agent=agent,
retriever=retriever,
conversation_id=conversation_id,
user_api_key=user_api_key,
decoded_token=decoded_token,
isNoneDoc=data.get("isNoneDoc"),
index=index,
should_save_conversation=save_conv,
attachment_ids=attachment_ids,
agent_id=agent_id,
is_shared_usage=is_shared_usage,
shared_token=shared_token,
),
mimetype="text/event-stream",
)
except ValueError:
message = "Malformed request body"
logger.error(f"/stream - error: {message}")
return Response(
error_stream_generate(message),
status=400,
mimetype="text/event-stream",
)
except Exception as e:
logger.error(
f"/stream - error: {str(e)} - traceback: {traceback.format_exc()}",
extra={"error": str(e), "traceback": traceback.format_exc()},
)
status_code = 400
return Response(
error_stream_generate("Unknown error occurred"),
status=status_code,
mimetype="text/event-stream",
)
def error_stream_generate(err_response):
data = json.dumps({"type": "error", "error": err_response})
yield f"data: {data}\n\n"
@answer_ns.route("/api/answer")
class Answer(Resource):
answer_model = api.model(
"AnswerModel",
{
"question": fields.String(
required=True, description="The question to answer"
),
"history": fields.List(
fields.String, required=False, description="Conversation history"
),
"conversation_id": fields.String(
required=False, description="Conversation ID"
),
"prompt_id": fields.String(
required=False, default="default", description="Prompt ID"
),
"chunks": fields.Integer(
required=False, default=2, description="Number of chunks"
),
"token_limit": fields.Integer(required=False, description="Token limit"),
"retriever": fields.String(required=False, description="Retriever type"),
"api_key": fields.String(required=False, description="API key"),
"active_docs": fields.String(
required=False, description="Active documents"
),
"isNoneDoc": fields.Boolean(
required=False, description="Flag indicating if no document is used"
),
},
)
@api.expect(answer_model)
@api.doc(description="Provide an answer based on the question and retriever")
def post(self):
data = request.get_json()
required_fields = ["question"]
missing_fields = check_required_fields(data, required_fields)
if missing_fields:
return missing_fields
try:
question = data["question"]
history = limit_chat_history(
json.loads(data.get("history", "[]")), gpt_model=gpt_model
)
conversation_id = data.get("conversation_id")
prompt_id = data.get("prompt_id", "default")
chunks = int(data.get("chunks", 2))
token_limit = data.get("token_limit", settings.DEFAULT_MAX_HISTORY)
retriever_name = data.get("retriever", "classic")
agent_type = settings.AGENT_NAME
if "api_key" in data:
data_key = get_data_from_api_key(data["api_key"])
chunks = int(data_key.get("chunks", 2))
prompt_id = data_key.get("prompt_id", "default")
source = {"active_docs": data_key.get("source")}
retriever_name = data_key.get("retriever", retriever_name)
user_api_key = data["api_key"]
agent_type = data_key.get("agent_type", agent_type)
decoded_token = {"sub": data_key.get("user")}
elif "active_docs" in data:
source = {"active_docs": data["active_docs"]}
retriever_name = get_retriever(data["active_docs"]) or retriever_name
user_api_key = None
decoded_token = request.decoded_token
else:
source = {}
user_api_key = None
decoded_token = request.decoded_token
if not decoded_token:
return make_response({"error": "Unauthorized"}, 401)
prompt = get_prompt(prompt_id)
logger.info(
f"/api/answer - request_data: {data}, source: {source}",
extra={"data": json.dumps({"request_data": data, "source": source})},
)
agent = AgentCreator.create_agent(
agent_type,
endpoint="api/answer",
llm_name=settings.LLM_PROVIDER,
gpt_model=gpt_model,
api_key=settings.API_KEY,
user_api_key=user_api_key,
prompt=prompt,
chat_history=history,
decoded_token=decoded_token,
)
retriever = RetrieverCreator.create_retriever(
retriever_name,
source=source,
chat_history=history,
prompt=prompt,
chunks=chunks,
token_limit=token_limit,
gpt_model=gpt_model,
user_api_key=user_api_key,
decoded_token=decoded_token,
)
response_full = ""
source_log_docs = []
tool_calls = []
stream_ended = False
thought = ""
for line in complete_stream(
question=question,
agent=agent,
retriever=retriever,
conversation_id=conversation_id,
user_api_key=user_api_key,
decoded_token=decoded_token,
isNoneDoc=data.get("isNoneDoc"),
index=None,
should_save_conversation=False,
):
try:
event_data = line.replace("data: ", "").strip()
event = json.loads(event_data)
if event["type"] == "answer":
response_full += event["answer"]
elif event["type"] == "source":
source_log_docs = event["source"]
elif event["type"] == "tool_calls":
tool_calls = event["tool_calls"]
elif event["type"] == "thought":
thought = event["thought"]
elif event["type"] == "error":
logger.error(f"Error from stream: {event['error']}")
return bad_request(500, event["error"])
elif event["type"] == "end":
stream_ended = True
except (json.JSONDecodeError, KeyError) as e:
logger.warning(f"Error parsing stream event: {e}, line: {line}")
continue
if not stream_ended:
logger.error("Stream ended unexpectedly without an 'end' event.")
return bad_request(500, "Stream ended unexpectedly.")
if data.get("isNoneDoc"):
for doc in source_log_docs:
doc["source"] = "None"
llm = LLMCreator.create_llm(
settings.LLM_PROVIDER,
api_key=settings.API_KEY,
user_api_key=user_api_key,
decoded_token=decoded_token,
)
result = {"answer": response_full, "sources": source_log_docs}
result["conversation_id"] = str(
save_conversation(
conversation_id,
question,
response_full,
thought,
source_log_docs,
tool_calls,
llm,
decoded_token,
api_key=user_api_key,
)
)
retriever_params = retriever.get_params()
user_logs_collection.insert_one(
{
"action": "api_answer",
"level": "info",
"user": decoded_token.get("sub"),
"api_key": user_api_key,
"question": question,
"response": response_full,
"sources": source_log_docs,
"retriever_params": retriever_params,
"timestamp": datetime.datetime.now(datetime.timezone.utc),
}
)
except Exception as e:
logger.error(
f"/api/answer - error: {str(e)} - traceback: {traceback.format_exc()}",
extra={"error": str(e), "traceback": traceback.format_exc()},
)
return bad_request(500, str(e))
return make_response(result, 200)
@answer_ns.route("/api/search")
class Search(Resource):
search_model = api.model(
"SearchModel",
{
"question": fields.String(
required=True, description="The question to search"
),
"chunks": fields.Integer(
required=False, default=2, description="Number of chunks"
),
"api_key": fields.String(
required=False, description="API key for authentication"
),
"active_docs": fields.String(
required=False, description="Active documents for retrieval"
),
"retriever": fields.String(required=False, description="Retriever type"),
"token_limit": fields.Integer(
required=False, description="Limit for tokens"
),
"isNoneDoc": fields.Boolean(
required=False, description="Flag indicating if no document is used"
),
},
)
@api.expect(search_model)
@api.doc(
description="Search for relevant documents based on the question and retriever"
)
def post(self):
data = request.get_json()
required_fields = ["question"]
missing_fields = check_required_fields(data, required_fields)
if missing_fields:
return missing_fields
try:
question = data["question"]
chunks = int(data.get("chunks", 2))
token_limit = data.get("token_limit", settings.DEFAULT_MAX_HISTORY)
retriever_name = data.get("retriever", "classic")
if "api_key" in data:
data_key = get_data_from_api_key(data["api_key"])
chunks = int(data_key.get("chunks", 2))
source = {"active_docs": data_key.get("source")}
user_api_key = data["api_key"]
decoded_token = {"sub": data_key.get("user")}
elif "active_docs" in data:
source = {"active_docs": data["active_docs"]}
user_api_key = None
decoded_token = request.decoded_token
else:
source = {}
user_api_key = None
decoded_token = request.decoded_token
if not decoded_token:
return make_response({"error": "Unauthorized"}, 401)
logger.info(
f"/api/answer - request_data: {data}, source: {source}",
extra={"data": json.dumps({"request_data": data, "source": source})},
)
retriever = RetrieverCreator.create_retriever(
retriever_name,
source=source,
chat_history=[],
prompt="default",
chunks=chunks,
token_limit=token_limit,
gpt_model=gpt_model,
user_api_key=user_api_key,
decoded_token=decoded_token,
)
docs = retriever.search(question)
retriever_params = retriever.get_params()
user_logs_collection.insert_one(
{
"action": "api_search",
"level": "info",
"user": decoded_token.get("sub"),
"api_key": user_api_key,
"question": question,
"sources": docs,
"retriever_params": retriever_params,
"timestamp": datetime.datetime.now(datetime.timezone.utc),
}
)
if data.get("isNoneDoc"):
for doc in docs:
doc["source"] = "None"
except Exception as e:
logger.error(
f"/api/search - error: {str(e)} - traceback: {traceback.format_exc()}",
extra={"error": str(e), "traceback": traceback.format_exc()},
)
return bad_request(500, str(e))
return make_response(docs, 200)
def get_attachments_content(attachment_ids, user):
"""
Retrieve content from attachment documents based on their IDs.
Args:
attachment_ids (list): List of attachment document IDs
user (str): User identifier to verify ownership
Returns:
list: List of dictionaries containing attachment content and metadata
"""
if not attachment_ids:
return []
attachments = []
for attachment_id in attachment_ids:
try:
attachment_doc = attachments_collection.find_one(
{"_id": ObjectId(attachment_id), "user": user}
)
if attachment_doc:
attachments.append(attachment_doc)
except Exception as e:
logger.error(
f"Error retrieving attachment {attachment_id}: {e}", exc_info=True
)
return attachments

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application/api/answer/routes/__init__.py Normal file
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application/api/answer/routes/answer.py Normal file
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import logging
import traceback
from flask import make_response, request
from flask_restx import fields, Resource
from application.api import api
from application.api.answer.routes.base import answer_ns, BaseAnswerResource
from application.api.answer.services.stream_processor import StreamProcessor
logger = logging.getLogger(__name__)
@answer_ns.route("/api/answer")
class AnswerResource(Resource, BaseAnswerResource):
def __init__(self, *args, **kwargs):
Resource.__init__(self, *args, **kwargs)
BaseAnswerResource.__init__(self)
answer_model = answer_ns.model(
"AnswerModel",
{
"question": fields.String(
required=True, description="Question to be asked"
),
"history": fields.List(
fields.String,
required=False,
description="Conversation history (only for new conversations)",
),
"conversation_id": fields.String(
required=False,
description="Existing conversation ID (loads history)",
),
"prompt_id": fields.String(
required=False, default="default", description="Prompt ID"
),
"chunks": fields.Integer(
required=False, default=2, description="Number of chunks"
),
"token_limit": fields.Integer(required=False, description="Token limit"),
"retriever": fields.String(required=False, description="Retriever type"),
"api_key": fields.String(required=False, description="API key"),
"active_docs": fields.String(
required=False, description="Active documents"
),
"isNoneDoc": fields.Boolean(
required=False, description="Flag indicating if no document is used"
),
"save_conversation": fields.Boolean(
required=False,
default=True,
description="Whether to save the conversation",
),
},
)
@api.expect(answer_model)
@api.doc(description="Provide a response based on the question and retriever")
def post(self):
data = request.get_json()
if error := self.validate_request(data):
return error
processor = StreamProcessor(data, None)
try:
processor.initialize()
if not processor.decoded_token:
return make_response({"error": "Unauthorized"}, 401)
agent = processor.create_agent()
retriever = processor.create_retriever()
stream = self.complete_stream(
question=data["question"],
agent=agent,
retriever=retriever,
conversation_id=processor.conversation_id,
user_api_key=processor.agent_config.get("user_api_key"),
decoded_token=processor.decoded_token,
isNoneDoc=data.get("isNoneDoc"),
index=None,
should_save_conversation=data.get("save_conversation", True),
)
conversation_id, response, sources, tool_calls, thought, error = (
self.process_response_stream(stream)
)
if error:
return make_response({"error": error}, 400)
result = {
"conversation_id": conversation_id,
"answer": response,
"sources": sources,
"tool_calls": tool_calls,
"thought": thought,
}
except Exception as e:
logger.error(
f"/api/answer - error: {str(e)} - traceback: {traceback.format_exc()}",
extra={"error": str(e), "traceback": traceback.format_exc()},
)
return make_response({"error": str(e)}, 500)
return make_response(result, 200)

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import datetime
import json
import logging
from typing import Any, Dict, Generator, List, Optional
from flask import Response
from flask_restx import Namespace
from application.api.answer.services.conversation_service import ConversationService
from application.core.mongo_db import MongoDB
from application.core.settings import settings
from application.llm.llm_creator import LLMCreator
from application.utils import check_required_fields, get_gpt_model
logger = logging.getLogger(__name__)
answer_ns = Namespace("answer", description="Answer related operations", path="/")
class BaseAnswerResource:
"""Shared base class for answer endpoints"""
def __init__(self):
mongo = MongoDB.get_client()
db = mongo[settings.MONGO_DB_NAME]
self.user_logs_collection = db["user_logs"]
self.gpt_model = get_gpt_model()
self.conversation_service = ConversationService()
def validate_request(
self, data: Dict[str, Any], require_conversation_id: bool = False
) -> Optional[Response]:
"""Common request validation"""
required_fields = ["question"]
if require_conversation_id:
required_fields.append("conversation_id")
if missing_fields := check_required_fields(data, required_fields):
return missing_fields
return None
def complete_stream(
self,
question: str,
agent: Any,
retriever: Any,
conversation_id: Optional[str],
user_api_key: Optional[str],
decoded_token: Dict[str, Any],
isNoneDoc: bool = False,
index: Optional[int] = None,
should_save_conversation: bool = True,
attachment_ids: Optional[List[str]] = None,
agent_id: Optional[str] = None,
is_shared_usage: bool = False,
shared_token: Optional[str] = None,
) -> Generator[str, None, None]:
"""
Generator function that streams the complete conversation response.
Args:
question: The user's question
agent: The agent instance
retriever: The retriever instance
conversation_id: Existing conversation ID
user_api_key: User's API key if any
decoded_token: Decoded JWT token
isNoneDoc: Flag for document-less responses
index: Index of message to update
should_save_conversation: Whether to persist the conversation
attachment_ids: List of attachment IDs
agent_id: ID of agent used
is_shared_usage: Flag for shared agent usage
shared_token: Token for shared agent
Yields:
Server-sent event strings
"""
try:
response_full, thought, source_log_docs, tool_calls = "", "", [], []
for line in agent.gen(query=question, retriever=retriever):
if "answer" in line:
response_full += str(line["answer"])
data = json.dumps({"type": "answer", "answer": line["answer"]})
yield f"data: {data}\n\n"
elif "sources" in line:
truncated_sources = []
source_log_docs = line["sources"]
for source in line["sources"]:
truncated_source = source.copy()
if "text" in truncated_source:
truncated_source["text"] = (
truncated_source["text"][:100].strip() + "..."
)
truncated_sources.append(truncated_source)
if truncated_sources:
data = json.dumps(
{"type": "source", "source": truncated_sources}
)
yield f"data: {data}\n\n"
elif "tool_calls" in line:
tool_calls = line["tool_calls"]
elif "thought" in line:
thought += line["thought"]
data = json.dumps({"type": "thought", "thought": line["thought"]})
yield f"data: {data}\n\n"
elif "type" in line:
data = json.dumps(line)
yield f"data: {data}\n\n"
if isNoneDoc:
for doc in source_log_docs:
doc["source"] = "None"
llm = LLMCreator.create_llm(
settings.LLM_PROVIDER,
api_key=settings.API_KEY,
user_api_key=user_api_key,
decoded_token=decoded_token,
)
if should_save_conversation:
conversation_id = self.conversation_service.save_conversation(
conversation_id,
question,
response_full,
thought,
source_log_docs,
tool_calls,
llm,
self.gpt_model,
decoded_token,
index=index,
api_key=user_api_key,
agent_id=agent_id,
is_shared_usage=is_shared_usage,
shared_token=shared_token,
attachment_ids=attachment_ids,
)
else:
conversation_id = None
# Send conversation ID
data = json.dumps({"type": "id", "id": str(conversation_id)})
yield f"data: {data}\n\n"
# Log the interaction
retriever_params = retriever.get_params()
self.user_logs_collection.insert_one(
{
"action": "stream_answer",
"level": "info",
"user": decoded_token.get("sub"),
"api_key": user_api_key,
"question": question,
"response": response_full,
"sources": source_log_docs,
"retriever_params": retriever_params,
"attachments": attachment_ids,
"timestamp": datetime.datetime.now(datetime.timezone.utc),
}
)
# End of stream
data = json.dumps({"type": "end"})
yield f"data: {data}\n\n"
except Exception as e:
logger.error(f"Error in stream: {str(e)}", exc_info=True)
data = json.dumps(
{
"type": "error",
"error": "Please try again later. We apologize for any inconvenience.",
}
)
yield f"data: {data}\n\n"
return
def process_response_stream(self, stream):
"""Process the stream response for non-streaming endpoint"""
conversation_id = ""
response_full = ""
source_log_docs = []
tool_calls = []
thought = ""
stream_ended = False
for line in stream:
try:
event_data = line.replace("data: ", "").strip()
event = json.loads(event_data)
if event["type"] == "id":
conversation_id = event["id"]
elif event["type"] == "answer":
response_full += event["answer"]
elif event["type"] == "source":
source_log_docs = event["source"]
elif event["type"] == "tool_calls":
tool_calls = event["tool_calls"]
elif event["type"] == "thought":
thought = event["thought"]
elif event["type"] == "error":
logger.error(f"Error from stream: {event['error']}")
return None, None, None, None, event["error"]
elif event["type"] == "end":
stream_ended = True
except (json.JSONDecodeError, KeyError) as e:
logger.warning(f"Error parsing stream event: {e}, line: {line}")
continue
if not stream_ended:
logger.error("Stream ended unexpectedly without an 'end' event.")
return None, None, None, None, "Stream ended unexpectedly"
return (
conversation_id,
response_full,
source_log_docs,
tool_calls,
thought,
None,
)
def error_stream_generate(self, err_response):
data = json.dumps({"type": "error", "error": err_response})
yield f"data: {data}\n\n"

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import logging
import traceback
from flask import make_response, request, Response
from flask_restx import fields, Resource
from application.api import api
from application.api.answer.routes.base import answer_ns, BaseAnswerResource
from application.api.answer.services.stream_processor import StreamProcessor
logger = logging.getLogger(__name__)
@answer_ns.route("/stream")
class StreamResource(Resource, BaseAnswerResource):
def __init__(self, *args, **kwargs):
Resource.__init__(self, *args, **kwargs)
BaseAnswerResource.__init__(self)
stream_model = answer_ns.model(
"StreamModel",
{
"question": fields.String(
required=True, description="Question to be asked"
),
"history": fields.List(
fields.String,
required=False,
description="Conversation history (only for new conversations)",
),
"conversation_id": fields.String(
required=False,
description="Existing conversation ID (loads history)",
),
"prompt_id": fields.String(
required=False, default="default", description="Prompt ID"
),
"chunks": fields.Integer(
required=False, default=2, description="Number of chunks"
),
"token_limit": fields.Integer(required=False, description="Token limit"),
"retriever": fields.String(required=False, description="Retriever type"),
"api_key": fields.String(required=False, description="API key"),
"active_docs": fields.String(
required=False, description="Active documents"
),
"isNoneDoc": fields.Boolean(
required=False, description="Flag indicating if no document is used"
),
"index": fields.Integer(
required=False, description="Index of the query to update"
),
"save_conversation": fields.Boolean(
required=False,
default=True,
description="Whether to save the conversation",
),
"attachments": fields.List(
fields.String, required=False, description="List of attachment IDs"
),
},
)
@api.expect(stream_model)
@api.doc(description="Stream a response based on the question and retriever")
def post(self):
data = request.get_json()
if error := self.validate_request(data, "index" in data):
return error
decoded_token = getattr(request, "decoded_token", None)
if not decoded_token:
return make_response({"error": "Unauthorized"}, 401)
processor = StreamProcessor(data, decoded_token)
try:
processor.initialize()
agent = processor.create_agent()
retriever = processor.create_retriever()
return Response(
self.complete_stream(
question=data["question"],
agent=agent,
retriever=retriever,
conversation_id=processor.conversation_id,
user_api_key=processor.agent_config.get("user_api_key"),
decoded_token=processor.decoded_token,
isNoneDoc=data.get("isNoneDoc"),
index=data.get("index"),
should_save_conversation=data.get("save_conversation", True),
attachment_ids=data.get("attachments", []),
agent_id=data.get("agent_id"),
is_shared_usage=processor.is_shared_usage,
shared_token=processor.shared_token,
),
mimetype="text/event-stream",
)
except ValueError:
message = "Malformed request body"
logger.error(f"/stream - error: {message}")
return Response(
self.error_stream_generate(message),
status=400,
mimetype="text/event-stream",
)
except Exception as e:
logger.error(
f"/stream - error: {str(e)} - traceback: {traceback.format_exc()}",
extra={"error": str(e), "traceback": traceback.format_exc()},
)
return Response(
self.error_stream_generate("Unknown error occurred"),
status=400,
mimetype="text/event-stream",
)

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application/api/answer/services/__init__.py Normal file
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application/api/answer/services/conversation_service.py Normal file
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import logging
from datetime import datetime, timezone
from typing import Any, Dict, List, Optional
from application.core.mongo_db import MongoDB
from application.core.settings import settings
from bson import ObjectId
logger = logging.getLogger(__name__)
class ConversationService:
def __init__(self):
mongo = MongoDB.get_client()
db = mongo[settings.MONGO_DB_NAME]
self.conversations_collection = db["conversations"]
self.agents_collection = db["agents"]
def get_conversation(
self, conversation_id: str, user_id: str
) -> Optional[Dict[str, Any]]:
"""Retrieve a conversation with proper access control"""
if not conversation_id or not user_id:
return None
try:
conversation = self.conversations_collection.find_one(
{
"_id": ObjectId(conversation_id),
"$or": [{"user": user_id}, {"shared_with": user_id}],
}
)
if not conversation:
logger.warning(
f"Conversation not found or unauthorized - ID: {conversation_id}, User: {user_id}"
)
return None
conversation["_id"] = str(conversation["_id"])
return conversation
except Exception as e:
logger.error(f"Error fetching conversation: {str(e)}", exc_info=True)
return None
def save_conversation(
self,
conversation_id: Optional[str],
question: str,
response: str,
thought: str,
sources: List[Dict[str, Any]],
tool_calls: List[Dict[str, Any]],
llm: Any,
gpt_model: str,
decoded_token: Dict[str, Any],
index: Optional[int] = None,
api_key: Optional[str] = None,
agent_id: Optional[str] = None,
is_shared_usage: bool = False,
shared_token: Optional[str] = None,
attachment_ids: Optional[List[str]] = None,
) -> str:
"""Save or update a conversation in the database"""
user_id = decoded_token.get("sub")
if not user_id:
raise ValueError("User ID not found in token")
current_time = datetime.now(timezone.utc)
if conversation_id is not None and index is not None:
# Update existing conversation with new query
result = self.conversations_collection.update_one(
{
"_id": ObjectId(conversation_id),
"user": user_id,
f"queries.{index}": {"$exists": True},
},
{
"$set": {
f"queries.{index}.prompt": question,
f"queries.{index}.response": response,
f"queries.{index}.thought": thought,
f"queries.{index}.sources": sources,
f"queries.{index}.tool_calls": tool_calls,
f"queries.{index}.timestamp": current_time,
f"queries.{index}.attachments": attachment_ids,
}
},
)
if result.matched_count == 0:
raise ValueError("Conversation not found or unauthorized")
self.conversations_collection.update_one(
{
"_id": ObjectId(conversation_id),
"user": user_id,
f"queries.{index}": {"$exists": True},
},
{"$push": {"queries": {"$each": [], "$slice": index + 1}}},
)
return conversation_id
elif conversation_id:
# Append new message to existing conversation
result = self.conversations_collection.update_one(
{"_id": ObjectId(conversation_id), "user": user_id},
{
"$push": {
"queries": {
"prompt": question,
"response": response,
"thought": thought,
"sources": sources,
"tool_calls": tool_calls,
"timestamp": current_time,
"attachments": attachment_ids,
}
}
},
)
if result.matched_count == 0:
raise ValueError("Conversation not found or unauthorized")
return conversation_id
else:
# Create new conversation
messages_summary = [
{
"role": "assistant",
"content": "Summarise following conversation in no more than 3 "
"words, respond ONLY with the summary, use the same "
"language as the user query",
},
{
"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=gpt_model, messages=messages_summary, max_tokens=30
)
conversation_data = {
"user": user_id,
"date": current_time,
"name": completion,
"queries": [
{
"prompt": question,
"response": response,
"thought": thought,
"sources": sources,
"tool_calls": tool_calls,
"timestamp": current_time,
"attachments": attachment_ids,
}
],
}
if api_key:
if agent_id:
conversation_data["agent_id"] = agent_id
if is_shared_usage:
conversation_data["is_shared_usage"] = is_shared_usage
conversation_data["shared_token"] = shared_token
agent = self.agents_collection.find_one({"key": api_key})
if agent:
conversation_data["api_key"] = agent["key"]
result = self.conversations_collection.insert_one(conversation_data)
return str(result.inserted_id)

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import datetime
import json
import logging
import os
from pathlib import Path
from typing import Any, Dict, Optional
from bson.dbref import DBRef
from bson.objectid import ObjectId
from application.agents.agent_creator import AgentCreator
from application.api.answer.services.conversation_service import ConversationService
from application.core.mongo_db import MongoDB
from application.core.settings import settings
from application.retriever.retriever_creator import RetrieverCreator
from application.utils import get_gpt_model, limit_chat_history
logger = logging.getLogger(__name__)
def get_prompt(prompt_id: str, prompts_collection=None) -> str:
"""
Get a prompt by preset name or MongoDB ID
"""
current_dir = Path(__file__).resolve().parents[3]
prompts_dir = current_dir / "prompts"
preset_mapping = {
"default": "chat_combine_default.txt",
"creative": "chat_combine_creative.txt",
"strict": "chat_combine_strict.txt",
"reduce": "chat_reduce_prompt.txt",
}
if prompt_id in preset_mapping:
file_path = os.path.join(prompts_dir, preset_mapping[prompt_id])
try:
with open(file_path, "r") as f:
return f.read()
except FileNotFoundError:
raise FileNotFoundError(f"Prompt file not found: {file_path}")
try:
if not prompts_collection:
mongo = MongoDB.get_client()
db = mongo[settings.MONGO_DB_NAME]
prompts_collection = db["prompts"]
prompt_doc = prompts_collection.find_one({"_id": ObjectId(prompt_id)})
if not prompt_doc:
raise ValueError(f"Prompt with ID {prompt_id} not found")
return prompt_doc["content"]
except Exception as e:
raise ValueError(f"Invalid prompt ID: {prompt_id}") from e
class StreamProcessor:
def __init__(
self, request_data: Dict[str, Any], decoded_token: Optional[Dict[str, Any]]
):
mongo = MongoDB.get_client()
self.db = mongo[settings.MONGO_DB_NAME]
self.agents_collection = self.db["agents"]
self.attachments_collection = self.db["attachments"]
self.prompts_collection = self.db["prompts"]
self.data = request_data
self.decoded_token = decoded_token
self.initial_user_id = (
self.decoded_token.get("sub") if self.decoded_token is not None else None
)
self.conversation_id = self.data.get("conversation_id")
self.source = (
{"active_docs": self.data["active_docs"]}
if "active_docs" in self.data
else {}
)
self.attachments = []
self.history = []
self.agent_config = {}
self.retriever_config = {}
self.is_shared_usage = False
self.shared_token = None
self.gpt_model = get_gpt_model()
self.conversation_service = ConversationService()
def initialize(self):
"""Initialize all required components for processing"""
self._configure_agent()
self._configure_retriever()
self._load_conversation_history()
self._process_attachments()
def _load_conversation_history(self):
"""Load conversation history either from DB or request"""
if self.conversation_id and self.initial_user_id:
conversation = self.conversation_service.get_conversation(
self.conversation_id, self.initial_user_id
)
if not conversation:
raise ValueError("Conversation not found or unauthorized")
self.history = [
{"prompt": query["prompt"], "response": query["response"]}
for query in conversation.get("queries", [])
]
else:
self.history = limit_chat_history(
json.loads(self.data.get("history", "[]")), gpt_model=self.gpt_model
)
def _process_attachments(self):
"""Process any attachments in the request"""
attachment_ids = self.data.get("attachments", [])
self.attachments = self._get_attachments_content(
attachment_ids, self.initial_user_id
)
def _get_attachments_content(self, attachment_ids, user_id):
"""
Retrieve content from attachment documents based on their IDs.
"""
if not attachment_ids:
return []
attachments = []
for attachment_id in attachment_ids:
try:
attachment_doc = self.attachments_collection.find_one(
{"_id": ObjectId(attachment_id), "user": user_id}
)
if attachment_doc:
attachments.append(attachment_doc)
except Exception as e:
logger.error(
f"Error retrieving attachment {attachment_id}: {e}", exc_info=True
)
return attachments
def _get_agent_key(self, agent_id: Optional[str], user_id: Optional[str]) -> tuple:
"""Get API key for agent with access control"""
if not agent_id:
return None, False, None
try:
agent = self.agents_collection.find_one({"_id": ObjectId(agent_id)})
if agent is None:
raise Exception("Agent not found")
is_owner = agent.get("user") == user_id
is_shared_with_user = agent.get(
"shared_publicly", False
) or user_id in agent.get("shared_with", [])
if not (is_owner or is_shared_with_user):
raise Exception("Unauthorized access to the agent")
if is_owner:
self.agents_collection.update_one(
{"_id": ObjectId(agent_id)},
{
"$set": {
"lastUsedAt": datetime.datetime.now(datetime.timezone.utc)
}
},
)
return str(agent["key"]), not is_owner, agent.get("shared_token")
except Exception as e:
logger.error(f"Error in get_agent_key: {str(e)}", exc_info=True)
raise
def _get_data_from_api_key(self, api_key: str) -> Dict[str, Any]:
data = self.agents_collection.find_one({"key": api_key})
if not data:
raise Exception("Invalid API Key, please generate a new key", 401)
source = data.get("source")
if isinstance(source, DBRef):
source_doc = self.db.dereference(source)
data["source"] = str(source_doc["_id"])
data["retriever"] = source_doc.get("retriever", data.get("retriever"))
else:
data["source"] = {}
return data
def _configure_agent(self):
"""Configure the agent based on request data"""
agent_id = self.data.get("agent_id")
self.agent_key, self.is_shared_usage, self.shared_token = self._get_agent_key(
agent_id, self.initial_user_id
)
api_key = self.data.get("api_key")
if api_key:
data_key = self._get_data_from_api_key(api_key)
self.agent_config.update(
{
"prompt_id": data_key.get("prompt_id", "default"),
"agent_type": data_key.get("agent_type", settings.AGENT_NAME),
"user_api_key": api_key,
}
)
self.initial_user_id = data_key.get("user")
self.decoded_token = {"sub": data_key.get("user")}
elif self.agent_key:
data_key = self._get_data_from_api_key(self.agent_key)
self.agent_config.update(
{
"prompt_id": data_key.get("prompt_id", "default"),
"agent_type": data_key.get("agent_type", settings.AGENT_NAME),
"user_api_key": self.agent_key,
}
)
self.decoded_token = (
self.decoded_token
if self.is_shared_usage
else {"sub": data_key.get("user")}
)
else:
self.agent_config.update(
{
"prompt_id": self.data.get("prompt_id", "default"),
"agent_type": settings.AGENT_NAME,
"user_api_key": None,
}
)
def _configure_retriever(self):
"""Configure the retriever based on request data"""
self.retriever_config = {
"retriever_name": self.data.get("retriever", "classic"),
"chunks": int(self.data.get("chunks", 2)),
"token_limit": self.data.get("token_limit", settings.DEFAULT_MAX_HISTORY),
}
if "isNoneDoc" in self.data and self.data["isNoneDoc"]:
self.retriever_config["chunks"] = 0
def create_agent(self):
"""Create and return the configured agent"""
return AgentCreator.create_agent(
self.agent_config["agent_type"],
endpoint="stream",
llm_name=settings.LLM_PROVIDER,
gpt_model=self.gpt_model,
api_key=settings.API_KEY,
user_api_key=self.agent_config["user_api_key"],
prompt=get_prompt(self.agent_config["prompt_id"], self.prompts_collection),
chat_history=self.history,
decoded_token=self.decoded_token,
attachments=self.attachments,
)
def create_retriever(self):
"""Create and return the configured retriever"""
return RetrieverCreator.create_retriever(
self.retriever_config["retriever_name"],
source=self.source,
chat_history=self.history,
prompt=get_prompt(self.agent_config["prompt_id"], self.prompts_collection),
chunks=self.retriever_config["chunks"],
token_limit=self.retriever_config["token_limit"],
gpt_model=self.gpt_model,
user_api_key=self.agent_config["user_api_key"],
decoded_token=self.decoded_token,
)

30
application/api/user/routes.py
View File

@@ -34,7 +34,7 @@ from application.api.user.tasks import (
) )
from application.core.mongo_db import MongoDB from application.core.mongo_db import MongoDB
from application.core.settings import settings from application.core.settings import settings
from application.extensions import api from application.api import api
from application.storage.storage_creator import StorageCreator from application.storage.storage_creator import StorageCreator
from application.tts.google_tts import GoogleTTS from application.tts.google_tts import GoogleTTS
from application.utils import ( from application.utils import (
@@ -3538,14 +3538,18 @@ class StoreAttachment(Resource):
"AttachmentModel", "AttachmentModel",
{ {
"file": fields.Raw(required=True, description="File to upload"), "file": fields.Raw(required=True, description="File to upload"),
"api_key": fields.String(
required=False, description="API key (optional)"
),
}, },
) )
) )
@api.doc(description="Stores a single attachment without vectorization or training") @api.doc(
description="Stores a single attachment without vectorization or training. Supports user or API key authentication."
)
def post(self): def post(self):
decoded_token = request.decoded_token decoded_token = getattr(request, "decoded_token", None)
if not decoded_token: api_key = request.form.get("api_key") or request.args.get("api_key")
return make_response(jsonify({"success": False}), 401)
file = request.files.get("file") file = request.files.get("file")
if not file or file.filename == "": if not file or file.filename == "":
@@ -3553,7 +3557,21 @@ class StoreAttachment(Resource):
jsonify({"status": "error", "message": "Missing file"}), jsonify({"status": "error", "message": "Missing file"}),
400, 400,
) )
user = safe_filename(decoded_token.get("sub"))
user = None
if decoded_token:
user = safe_filename(decoded_token.get("sub"))
elif api_key:
agent = agents_collection.find_one({"key": api_key})
if not agent:
return make_response(
jsonify({"success": False, "message": "Invalid API key"}), 401
)
user = safe_filename(agent.get("user"))
else:
return make_response(
jsonify({"success": False, "message": "Authentication required"}), 401
)
try: try:
attachment_id = ObjectId() attachment_id = ObjectId()

7
application/app.py
View File

@@ -12,19 +12,18 @@ from application.core.logging_config import setup_logging
setup_logging() setup_logging()
from application.api.answer.routes import answer # noqa: E402 from application.api import api # noqa: E402
from application.api.answer import answer # noqa: E402
from application.api.internal.routes import internal # noqa: E402 from application.api.internal.routes import internal # noqa: E402
from application.api.user.routes import user # noqa: E402 from application.api.user.routes import user # noqa: E402
from application.celery_init import celery # noqa: E402 from application.celery_init import celery # noqa: E402
from application.core.settings import settings # noqa: E402 from application.core.settings import settings # noqa: E402
from application.extensions import api # noqa: E402
if platform.system() == "Windows": if platform.system() == "Windows":
import pathlib import pathlib
pathlib.PosixPath = pathlib.WindowsPath pathlib.PosixPath = pathlib.WindowsPath
dotenv.load_dotenv() dotenv.load_dotenv()
app = Flask(__name__) app = Flask(__name__)
@@ -52,7 +51,6 @@ if settings.AUTH_TYPE in ("simple_jwt", "session_jwt") and not settings.JWT_SECR
settings.JWT_SECRET_KEY = new_key settings.JWT_SECRET_KEY = new_key
except Exception as e: except Exception as e:
raise RuntimeError(f"Failed to setup JWT_SECRET_KEY: {e}") raise RuntimeError(f"Failed to setup JWT_SECRET_KEY: {e}")
SIMPLE_JWT_TOKEN = None SIMPLE_JWT_TOKEN = None
if settings.AUTH_TYPE == "simple_jwt": if settings.AUTH_TYPE == "simple_jwt":
payload = {"sub": "local"} payload = {"sub": "local"}
@@ -92,7 +90,6 @@ def generate_token():
def authenticate_request(): def authenticate_request():
if request.method == "OPTIONS": if request.method == "OPTIONS":
return "", 200 return "", 200
decoded_token = handle_auth(request) decoded_token = handle_auth(request)
if not decoded_token: if not decoded_token:
request.decoded_token = None request.decoded_token = None

2
application/core/settings.py
View File

@@ -10,7 +10,7 @@ current_dir = os.path.dirname(
class Settings(BaseSettings): class Settings(BaseSettings):
AUTH_TYPE: Optional[str] = None AUTH_TYPE: Optional[str] = None # simple_jwt, session_jwt, or None
LLM_PROVIDER: str = "docsgpt" LLM_PROVIDER: str = "docsgpt"
LLM_NAME: Optional[str] = ( LLM_NAME: Optional[str] = (
None # if LLM_PROVIDER is openai, LLM_NAME can be gpt-4 or gpt-3.5-turbo None # if LLM_PROVIDER is openai, LLM_NAME can be gpt-4 or gpt-3.5-turbo

7
application/extensions.py
View File

@@ -1,7 +0,0 @@
from flask_restx import Api
api = Api(
version="1.0",
title="DocsGPT API",
description="API for DocsGPT",
)

0
application/storage/__init__.py Normal file
View File

22
application/utils.py
View File

@@ -6,6 +6,7 @@ import uuid
import tiktoken import tiktoken
from flask import jsonify, make_response from flask import jsonify, make_response
from werkzeug.utils import secure_filename from werkzeug.utils import secure_filename
from application.core.settings import settings from application.core.settings import settings
@@ -19,6 +20,17 @@ def get_encoding():
return _encoding return _encoding
def get_gpt_model() -> str:
"""Get the appropriate GPT model based on provider"""
model_map = {
"openai": "gpt-4o-mini",
"anthropic": "claude-2",
"groq": "llama3-8b-8192",
"novita": "deepseek/deepseek-r1",
}
return settings.LLM_NAME or model_map.get(settings.LLM_PROVIDER, "")
def safe_filename(filename): def safe_filename(filename):
""" """
Creates a safe filename that preserves the original extension. Creates a safe filename that preserves the original extension.
@@ -32,15 +44,14 @@ def safe_filename(filename):
""" """
if not filename: if not filename:
return str(uuid.uuid4()) return str(uuid.uuid4())
_, extension = os.path.splitext(filename) _, extension = os.path.splitext(filename)
safe_name = secure_filename(filename) safe_name = secure_filename(filename)
# If secure_filename returns just the extension or an empty string # If secure_filename returns just the extension or an empty string
if not safe_name or safe_name == extension.lstrip("."): if not safe_name or safe_name == extension.lstrip("."):
return f"{str(uuid.uuid4())}{extension}" return f"{str(uuid.uuid4())}{extension}"
return safe_name return safe_name
@@ -68,7 +79,6 @@ def count_tokens_docs(docs):
docs_content = "" docs_content = ""
for doc in docs: for doc in docs:
docs_content += doc.page_content docs_content += doc.page_content
tokens = num_tokens_from_string(docs_content) tokens = num_tokens_from_string(docs_content)
return tokens return tokens
@@ -97,13 +107,11 @@ def validate_required_fields(data, required_fields):
missing_fields.append(field) missing_fields.append(field)
elif not data[field]: elif not data[field]:
empty_fields.append(field) empty_fields.append(field)
errors = [] errors = []
if missing_fields: if missing_fields:
errors.append(f"Missing required fields: {', '.join(missing_fields)}") errors.append(f"Missing required fields: {', '.join(missing_fields)}")
if empty_fields: if empty_fields:
errors.append(f"Empty values in required fields: {', '.join(empty_fields)}") errors.append(f"Empty values in required fields: {', '.join(empty_fields)}")
if errors: if errors:
return make_response( return make_response(
jsonify({"success": False, "message": " | ".join(errors)}), 400 jsonify({"success": False, "message": " | ".join(errors)}), 400
@@ -132,7 +140,6 @@ def limit_chat_history(history, max_token_limit=None, gpt_model="docsgpt"):
if not history: if not history:
return [] return []
trimmed_history = [] trimmed_history = []
tokens_current_history = 0 tokens_current_history = 0
@@ -141,18 +148,15 @@ def limit_chat_history(history, max_token_limit=None, gpt_model="docsgpt"):
if "prompt" in message and "response" in message: if "prompt" in message and "response" in message:
tokens_batch += num_tokens_from_string(message["prompt"]) tokens_batch += num_tokens_from_string(message["prompt"])
tokens_batch += num_tokens_from_string(message["response"]) tokens_batch += num_tokens_from_string(message["response"])
if "tool_calls" in message: if "tool_calls" in message:
for tool_call in message["tool_calls"]: for tool_call in message["tool_calls"]:
tool_call_string = f"Tool: {tool_call.get('tool_name')} | Action: {tool_call.get('action_name')} | Args: {tool_call.get('arguments')} | Response: {tool_call.get('result')}" tool_call_string = f"Tool: {tool_call.get('tool_name')} | Action: {tool_call.get('action_name')} | Args: {tool_call.get('arguments')} | Response: {tool_call.get('result')}"
tokens_batch += num_tokens_from_string(tool_call_string) tokens_batch += num_tokens_from_string(tool_call_string)
if tokens_current_history + tokens_batch < max_token_limit: if tokens_current_history + tokens_batch < max_token_limit:
tokens_current_history += tokens_batch tokens_current_history += tokens_batch
trimmed_history.insert(0, message) trimmed_history.insert(0, message)
else: else:
break break
return trimmed_history return trimmed_history

42
application/worker.py
View File

@@ -16,7 +16,7 @@ from bson.dbref import DBRef
from bson.objectid import ObjectId from bson.objectid import ObjectId
from application.agents.agent_creator import AgentCreator from application.agents.agent_creator import AgentCreator
from application.api.answer.routes import get_prompt from application.api.answer.services.stream_processor import get_prompt
from application.core.mongo_db import MongoDB from application.core.mongo_db import MongoDB
from application.core.settings import settings from application.core.settings import settings
@@ -35,17 +35,22 @@ db = mongo[settings.MONGO_DB_NAME]
sources_collection = db["sources"] sources_collection = db["sources"]
# Constants # Constants
MIN_TOKENS = 150 MIN_TOKENS = 150
MAX_TOKENS = 1250 MAX_TOKENS = 1250
RECURSION_DEPTH = 2 RECURSION_DEPTH = 2
# Define a function to extract metadata from a given filename. # Define a function to extract metadata from a given filename.
def metadata_from_filename(title): def metadata_from_filename(title):
return {"title": title} return {"title": title}
# Define a function to generate a random string of a given length. # Define a function to generate a random string of a given length.
def generate_random_string(length): def generate_random_string(length):
return "".join([string.ascii_letters[i % 52] for i in range(length)]) return "".join([string.ascii_letters[i % 52] for i in range(length)])
@@ -68,7 +73,6 @@ def extract_zip_recursive(zip_path, extract_to, current_depth=0, max_depth=5):
if current_depth > max_depth: if current_depth > max_depth:
logging.warning(f"Reached maximum recursion depth of {max_depth}") logging.warning(f"Reached maximum recursion depth of {max_depth}")
return return
try: try:
with zipfile.ZipFile(zip_path, "r") as zip_ref: with zipfile.ZipFile(zip_path, "r") as zip_ref:
zip_ref.extractall(extract_to) zip_ref.extractall(extract_to)
@@ -76,12 +80,13 @@ def extract_zip_recursive(zip_path, extract_to, current_depth=0, max_depth=5):
except Exception as e: except Exception as e:
logging.error(f"Error extracting zip file {zip_path}: {e}", exc_info=True) logging.error(f"Error extracting zip file {zip_path}: {e}", exc_info=True)
return return
# Check for nested zip files and extract them # Check for nested zip files and extract them
for root, dirs, files in os.walk(extract_to): for root, dirs, files in os.walk(extract_to):
for file in files: for file in files:
if file.endswith(".zip"): if file.endswith(".zip"):
# If a nested zip file is found, extract it recursively # If a nested zip file is found, extract it recursively
file_path = os.path.join(root, file) file_path = os.path.join(root, file)
extract_zip_recursive(file_path, root, current_depth + 1, max_depth) extract_zip_recursive(file_path, root, current_depth + 1, max_depth)
@@ -139,7 +144,7 @@ def run_agent_logic(agent_config, input_data):
user_api_key = agent_config["key"] user_api_key = agent_config["key"]
agent_type = agent_config.get("agent_type", "classic") agent_type = agent_config.get("agent_type", "classic")
decoded_token = {"sub": agent_config.get("user")} decoded_token = {"sub": agent_config.get("user")}
prompt = get_prompt(prompt_id) prompt = get_prompt(prompt_id, db["prompts"])
agent = AgentCreator.create_agent( agent = AgentCreator.create_agent(
agent_type, agent_type,
endpoint="webhook", endpoint="webhook",
@@ -178,7 +183,6 @@ def run_agent_logic(agent_config, input_data):
tool_calls.extend(line["tool_calls"]) tool_calls.extend(line["tool_calls"])
elif "thought" in line: elif "thought" in line:
thought += line["thought"] thought += line["thought"]
result = { result = {
"answer": response_full, "answer": response_full,
"sources": source_log_docs, "sources": source_log_docs,
@@ -193,8 +197,18 @@ def run_agent_logic(agent_config, input_data):
# Define the main function for ingesting and processing documents. # Define the main function for ingesting and processing documents.
def ingest_worker( def ingest_worker(
self, directory, formats, job_name, filename, user, dir_name=None, user_dir=None, retriever="classic" self,
directory,
formats,
job_name,
filename,
user,
dir_name=None,
user_dir=None,
retriever="classic",
): ):
""" """
Ingest and process documents. Ingest and process documents.
@@ -227,29 +241,29 @@ def ingest_worker(
logging.info(f"Ingest file: {full_path}", extra={"user": user, "job": job_name}) logging.info(f"Ingest file: {full_path}", extra={"user": user, "job": job_name})
# Create temporary working directory # Create temporary working directory
with tempfile.TemporaryDirectory() as temp_dir: with tempfile.TemporaryDirectory() as temp_dir:
try: try:
os.makedirs(temp_dir, exist_ok=True) os.makedirs(temp_dir, exist_ok=True)
# Download file from storage to temp directory # Download file from storage to temp directory
temp_file_path = os.path.join(temp_dir, filename) temp_file_path = os.path.join(temp_dir, filename)
file_data = storage.get_file(source_file_path) file_data = storage.get_file(source_file_path)
with open(temp_file_path, "wb") as f: with open(temp_file_path, "wb") as f:
f.write(file_data.read()) f.write(file_data.read())
self.update_state(state="PROGRESS", meta={"current": 1}) self.update_state(state="PROGRESS", meta={"current": 1})
# Handle zip files # Handle zip files
if filename.endswith(".zip"): if filename.endswith(".zip"):
logging.info(f"Extracting zip file: {filename}") logging.info(f"Extracting zip file: {filename}")
extract_zip_recursive( extract_zip_recursive(
temp_file_path, temp_dir, current_depth=0, max_depth=RECURSION_DEPTH temp_file_path, temp_dir, current_depth=0, max_depth=RECURSION_DEPTH
) )
if sample: if sample:
logging.info(f"Sample mode enabled. Using {limit} documents.") logging.info(f"Sample mode enabled. Using {limit} documents.")
reader = SimpleDirectoryReader( reader = SimpleDirectoryReader(
input_dir=temp_dir, input_dir=temp_dir,
input_files=input_files, input_files=input_files,
@@ -296,11 +310,9 @@ def ingest_worker(
} }
upload_index(vector_store_path, file_data) upload_index(vector_store_path, file_data)
except Exception as e: except Exception as e:
logging.error(f"Error in ingest_worker: {e}", exc_info=True) logging.error(f"Error in ingest_worker: {e}", exc_info=True)
raise raise
return { return {
"directory": directory, "directory": directory,
"formats": formats, "formats": formats,
@@ -326,7 +338,6 @@ def remote_worker(
full_path = os.path.join(directory, user, name_job) full_path = os.path.join(directory, user, name_job)
if not os.path.exists(full_path): if not os.path.exists(full_path):
os.makedirs(full_path) os.makedirs(full_path)
self.update_state(state="PROGRESS", meta={"current": 1}) self.update_state(state="PROGRESS", meta={"current": 1})
try: try:
logging.info("Initializing remote loader with type: %s", loader) logging.info("Initializing remote loader with type: %s", loader)
@@ -353,7 +364,6 @@ def remote_worker(
raise ValueError("doc_id must be provided for sync operation.") raise ValueError("doc_id must be provided for sync operation.")
id = ObjectId(doc_id) id = ObjectId(doc_id)
embed_and_store_documents(docs, full_path, id, self) embed_and_store_documents(docs, full_path, id, self)
self.update_state(state="PROGRESS", meta={"current": 100}) self.update_state(state="PROGRESS", meta={"current": 100})
file_data = { file_data = {
@@ -367,15 +377,12 @@ def remote_worker(
"sync_frequency": sync_frequency, "sync_frequency": sync_frequency,
} }
upload_index(full_path, file_data) upload_index(full_path, file_data)
except Exception as e: except Exception as e:
logging.error("Error in remote_worker task: %s", str(e), exc_info=True) logging.error("Error in remote_worker task: %s", str(e), exc_info=True)
raise raise
finally: finally:
if os.path.exists(full_path): if os.path.exists(full_path):
shutil.rmtree(full_path) shutil.rmtree(full_path)
logging.info("remote_worker task completed successfully") logging.info("remote_worker task completed successfully")
return {"urls": source_data, "name_job": name_job, "user": user, "limited": False} return {"urls": source_data, "name_job": name_job, "user": user, "limited": False}
@@ -428,7 +435,6 @@ def sync_worker(self, frequency):
sync_counts[ sync_counts[
"sync_success" if resp["status"] == "success" else "sync_failure" "sync_success" if resp["status"] == "success" else "sync_failure"
] += 1 ] += 1
return { return {
key: sync_counts[key] key: sync_counts[key]
for key in ["total_sync_count", "sync_success", "sync_failure"] for key in ["total_sync_count", "sync_success", "sync_failure"]
@@ -503,7 +509,6 @@ def attachment_worker(self, file_info, user):
"mime_type": mime_type, "mime_type": mime_type,
"metadata": metadata, "metadata": metadata,
} }
except Exception as e: except Exception as e:
logging.error( logging.error(
f"Error processing file {filename}: {e}", f"Error processing file {filename}: {e}",
@@ -539,7 +544,6 @@ def agent_webhook_worker(self, agent_id, payload):
except Exception as e: except Exception as e:
logging.error(f"Error processing agent webhook: {e}", exc_info=True) logging.error(f"Error processing agent webhook: {e}", exc_info=True)
return {"status": "error", "error": str(e)} return {"status": "error", "error": str(e)}
self.update_state(state="PROGRESS", meta={"current": 50}) self.update_state(state="PROGRESS", meta={"current": 50})
try: try:
result = run_agent_logic(agent_config, input_data) result = run_agent_logic(agent_config, input_data)

28
frontend/src/conversation/conversationHandlers.ts
View File

@@ -8,7 +8,6 @@ export function handleFetchAnswer(
signal: AbortSignal, signal: AbortSignal,
token: string | null, token: string | null,
selectedDocs: Doc | null, selectedDocs: Doc | null,
history: Array<any> = [],
conversationId: string | null, conversationId: string | null,
promptId: string | null, promptId: string | null,
chunks: string, chunks: string,
@@ -37,16 +36,8 @@ export function handleFetchAnswer(
title: any; title: any;
} }
> { > {
history = history.map((item) => {
return {
prompt: item.prompt,
response: item.response,
tool_calls: item.tool_calls,
};
});
const payload: RetrievalPayload = { const payload: RetrievalPayload = {
question: question, question: question,
history: JSON.stringify(history),
conversation_id: conversationId, conversation_id: conversationId,
prompt_id: promptId, prompt_id: promptId,
chunks: chunks, chunks: chunks,
@@ -94,7 +85,6 @@ export function handleFetchAnswerSteaming(
signal: AbortSignal, signal: AbortSignal,
token: string | null, token: string | null,
selectedDocs: Doc | null, selectedDocs: Doc | null,
history: Array<any> = [],
conversationId: string | null, conversationId: string | null,
promptId: string | null, promptId: string | null,
chunks: string, chunks: string,
@@ -105,17 +95,8 @@ export function handleFetchAnswerSteaming(
attachments?: string[], attachments?: string[],
save_conversation = true, save_conversation = true,
): Promise<Answer> { ): Promise<Answer> {
history = history.map((item) => {
return {
prompt: item.prompt,
response: item.response,
thought: item.thought,
tool_calls: item.tool_calls,
};
});
const payload: RetrievalPayload = { const payload: RetrievalPayload = {
question: question, question: question,
history: JSON.stringify(history),
conversation_id: conversationId, conversation_id: conversationId,
prompt_id: promptId, prompt_id: promptId,
chunks: chunks, chunks: chunks,
@@ -192,20 +173,11 @@ export function handleSearch(
token: string | null, token: string | null,
selectedDocs: Doc | null, selectedDocs: Doc | null,
conversation_id: string | null, conversation_id: string | null,
history: Array<any> = [],
chunks: string, chunks: string,
token_limit: number, token_limit: number,
) { ) {
history = history.map((item) => {
return {
prompt: item.prompt,
response: item.response,
tool_calls: item.tool_calls,
};
});
const payload: RetrievalPayload = { const payload: RetrievalPayload = {
question: question, question: question,
history: JSON.stringify(history),
conversation_id: conversation_id, conversation_id: conversation_id,
chunks: chunks, chunks: chunks,
token_limit: token_limit, token_limit: token_limit,

1
frontend/src/conversation/conversationModels.ts
View File

@@ -52,7 +52,6 @@ export interface RetrievalPayload {
question: string; question: string;
active_docs?: string; active_docs?: string;
retriever?: string; retriever?: string;
history: string;
conversation_id: string | null; conversation_id: string | null;
prompt_id?: string | null; prompt_id?: string | null;
chunks: string; chunks: string;

2
frontend/src/conversation/conversationSlice.ts
View File

@@ -57,7 +57,6 @@ export const fetchAnswer = createAsyncThunk<
signal, signal,
state.preference.token, state.preference.token,
state.preference.selectedDocs!, state.preference.selectedDocs!,
state.conversation.queries,
currentConversationId, currentConversationId,
state.preference.prompt.id, state.preference.prompt.id,
state.preference.chunks, state.preference.chunks,
@@ -153,7 +152,6 @@ export const fetchAnswer = createAsyncThunk<
signal, signal,
state.preference.token, state.preference.token,
state.preference.selectedDocs!, state.preference.selectedDocs!,
state.conversation.queries,
state.conversation.conversationId, state.conversation.conversationId,
state.preference.prompt.id, state.preference.prompt.id,
state.preference.chunks, state.preference.chunks,