jvelezmagic

# The goal of this file is to provide a FastAPI application for handling
# chat requests amd generation AI-powered responses using conversation chains.
# The application uses the LangChaing library, which includes a chatOpenAI model
# for natural language processing.

# The `StreamingConversationChain` class is responsible for creating and storing
# conversation memories and generating responses. It utilizes the `ChatOpenAI` model
# and a callback handler to stream responses as they're generated.

# The application defines a `ChatRequest` model for handling chat requests,

1kastner

#!/usr/bin/env python
# Reflects the requests from HTTP methods GET, POST, PUT, and DELETE
# Written by Nathan Hamiel (2010)

from http.server import HTTPServer, BaseHTTPRequestHandler
from optparse import OptionParser

class RequestHandler(BaseHTTPRequestHandler):
    
    def do_GET(self):

graphadvantage

##Neo4j GraphGist - Enterprise Architectures: Real-time Graph Updates using Kafka Messaging

Neo4j Use Case: Low Latency Graph Analytics & OLTP - Update 1M Nodes in 90 secs with Kafka and Neo4j Bolt

Introduction

A recent Neo4j whitepaper describes how Monsanto is performing real-time updates on a 600M node Neo4j graph using Kafka to consume data extracted from a large Oracle Exadata instance.

This modern data architecture combines a fast, scalable messaging platform (Kafka) for low latency data provisioning and an enterprise graph database (Neo4j) for high performance, in-memory analytics & OLTP - creating new and powerful real-time graph analytics capabilities for your enterprise applications.