Oracle

Oracle is an LLM API gateway client and prompt orchestrator designed to route requests between official API endpoints and automated web browser channels. It functions as a context manager and session logger, bundling local files and prompts into requests while maintaining a history of interactions.

The system distinguishes itself through a request router that can redirect traffic to alternate endpoints, such as self-hosted gateways or proxy services. It includes a token estimator that calculates request size and predicts costs using local summaries without making network calls.

The project provides capabilities for AI context management via glob-based file filtering and preference management for storing engine, model, and prompt settings. It also features session history management to list, replay, or prune past interactions from local storage.

Features

Large Language Model Connectors - Connects local data and custom settings to LLM APIs through configurable gateways and proxy services.

File-Based Context Selection - Filters local files using glob patterns to bundle relevant document context for LLM requests.

LLM Context Compilers - Filters local files via glob patterns to provide specific document context to large language models.

LLM Gateways - Acts as a request router that redirects traffic between official API endpoints and automated browser channels.

Prompt Engineering - Combines local files and custom prompts to provide language models with specific context for complex tasks.

Prompt Orchestration - Bundles local files and prompts into requests for LLMs with support for custom API endpoints.

Prompt File Packers - Bundles local project files into AI prompts using pattern-based inclusion and exclusion to optimize context.

Request Redirection - Implements request redirection to switch between official API endpoints and browser-based automation.

API Endpoint Routing - Provides the capability to redirect LLM API requests to custom server addresses or proxy services.

Prompt Payloads - Combines user text and local file contents into a single payload for large language model processing.

API and Browser Channel Routing - Allows switching between official APIs and automated browser channels to access different account levels.

Session History Retrieval - Provides a system to record and retrieve historical interaction data to restore or review session context.

LLM Token Counters - Calculates total request size and predicts costs for LLM interactions using local token counting.

Input Token Estimators - Calculates potential request size and predicts costs using a local token estimation mechanism.

Glob Pattern Selectors - Uses glob patterns and exclusion rules to filter which local files are included in an AI prompt.

Engine Routing Configurations - Directs requests to different API endpoints or browser channels based on a local persistent settings file.

Replayable Session Logs - Records every request and response to a local storage system for later retrieval and replay.

Pre-Request Cost Estimation - Calculates total token usage for bundled prompts to predict API costs before sending requests.

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Features

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