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app.py
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from flask import Flask, request, jsonify, render_template, send_file
import os
import io
import pdfkit
from code_agent.code_agent import CodeAgent
from deep_search.planner import DeepSearchAgentPlanner
import logging
import traceback
from tools.rag.hybrid_vector_graph_rag.ingest_corpus import hybrid_vector_graph_rag_ingest_corpus
from tools.rag.llama_index.ingest_corpus import llama_index_ingest_corpus
from tools.rag.llama_index_context_window.ingest_corpus import llama_index_context_window_ingest_corpus
from models.models import call_model
logging.basicConfig(
level=logging.DEBUG,
format='%(asctime)s %(levelname)s %(name)s %(threadName)s : %(message)s'
)
app = Flask(__name__, static_folder='static', template_folder='templates')
@app.route('/')
def index():
return render_template('index.html')
@app.route('/run-agent', methods=['POST'])
def run_agent():
try:
data = request.get_json()
if not data:
return jsonify({"error": "Request body is empty"}), 400
deep_search_enabled = data.get('deepsearch', False)
if deep_search_enabled:
required_fields = ['session_chat_history']
missing_fields = [field for field in required_fields if field not in data]
if missing_fields:
return jsonify({
"error": f"Missing required fields: {', '.join(missing_fields)}"
}), 400
session_id = data.get('session_id', None)
user_id = data.get('user_id', None)
chat_history = data['session_chat_history']
depth = data.get('depth', 1)
data_sources = ['websearch']
planner = DeepSearchAgentPlanner(
chat_history,
is_interactive=True,
session_id=session_id,
user_id=user_id,
depth=depth,
data_sources=data_sources,
delete_graph=True
)
planner.run_planner()
answer = planner.data.final_answer
if '<html><body>' in answer:
pdf = pdfkit.from_string(answer, False)
return send_file(
io.BytesIO(pdf),
mimetype='application/pdf',
as_attachment=True,
download_name='deep_search_output.pdf'
)
else:
return jsonify({"assistant": answer}), 200
else:
chat_history = data.get('session_chat_history', [])
tools = [
{
"tool_name": "numpy",
"lib_names": ["numpy"],
"type": "standard_custom"
},
{
"tool_name": "geopy",
"lib_names": ["geopy"],
"type": "standard_custom",
"instructions": "A library to get the coordinates of a given location.",
"code_example": """
def get_coordinates(previous_output):
from geopy.geocoders import Nominatim
updated_dict = previous_output.copy()
user_agent = "my-app/1.0"
location = updated_dict.get("location", "")
geolocator = Nominatim(user_agent=user_agent)
try:
geo_location = geolocator.geocode(location)
if geo_location:
updated_dict["coordinates"] = (geo_location.latitude, geo_location.longitude)
else:
updated_dict["coordinates"] = None
return updated_dict
except Exception as error:
logger.error(f"Error retrieving coordinates: {error}")
return previous_output
"""
},
{
"langchain_tool_name": "serpapi",
"type": "langchain_tool"
},
{
"langchain_tool_name": "eleven_labs_text2speech",
"type": "langchain_tool",
},
{
"langchain_tool_name": "openweathermap-api",
"type": "langchain_tool"
}
]
code_agent = CodeAgent(
chat_history=chat_history,
tools=tools,
use_default_tools=True
)
final_answer = code_agent.run_agent()
return jsonify({"assistant": final_answer}), 200
except Exception as e:
logging.error("Exception occurred in /run-agent: %s", str(e))
logging.error(traceback.format_exc())
return jsonify({"error": f"Internal server error: {str(e)}"}), 500
#start the acquisition, parsing, and ingestion of all documents present in /tools/rag/hybrid_vector_graph_rag/corpus
@app.route('/hybrid-vector-graph-rag-ingest-corpus', methods=['POST'])
def hybrid_vector_graph_rag_ingest():
try:
hybrid_vector_graph_rag_ingest_corpus()
return jsonify({"message": "Script executed successfully"}), 200
except Exception as e:
logging.error("Exception occurred in /hybrid-vector-graph-rag-ingest-corpus: %s", str(e))
logging.error(traceback.format_exc())
return jsonify({"error": f"Internal server error: {str(e)}"}), 500
#start the acquisition, parsing, and ingestion of all documents present in /tools/rag/llama_index/corpus
# or /tools/rag/llama_index_context_window/corpus if isContextWindow is true
@app.route('/llama-index-ingest-corpus', methods=['POST'])
def llama_index_ingest():
try:
data = request.get_json()
is_context_window = data.get('isContextWindow', False)
if is_context_window:
llama_index_context_window_ingest_corpus()
else:
llama_index_ingest_corpus()
return jsonify({"message": "Script executed successfully"}), 200
except Exception as e:
logging.error("Exception occurred in /llama-index-ingest-corpus: %s", str(e))
logging.error(traceback.format_exc())
return jsonify({"error": f"Internal server error: {str(e)}"}), 500
@app.route('/call-test-model', methods=['GET'])
def call_test_model():
response = call_model(
chat_history=[{"role": "user", "content": "Tell me a story about a cat and a dog."}],
model="local_llama3.2:1b"
)
return jsonify({"response": response}), 200
if __name__ == '__main__':
app.run(
host='0.0.0.0',
port=int(os.getenv('FLASK_PORT', 5000)),
debug=True
)