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Arch is an intelligent gateway for agents, designed to securely handle prompts, integrate with APIs, and provide rich observability, built on Envoy Proxy.
The ArchGW project focuses on simplifying the development of **agentic applications** – applications powered by Large Language Models (LLMs) that can perform actions and interact with tools. Here's a breakdown of the use cases and examples highlighted:
**Core Use Cases:**
* **Routing:** Intelligent routing of prompts to the correct agents or tools.
* **Tools Use:** Simplifying the integration of prompts with tools/APIs for common tasks.
* **Guardrails:** Centralized configuration for safety and preventing harmful outcomes.
* **LLM Access:** Centralized access and management of LLMs with retries for reliability.
* **Observability:** Providing W3C-compatible tracing and metrics for monitoring LLM interactions.
**Specific Examples & Demos:**
* **Weather Forecast Agent:** A sample application demonstrating core function calling capabilities.
* **Network Operator Agent:** An agent that can interact with network devices (retrieve statistics, reboot).
* **Connecting to SaaS APIs:** Demonstrates integrating 3rd party SaaS APIs into agentic chat experiences.
* **LLM Router:** Using Arch as a gateway to route requests to different LLMs (GPT-4o, Mistral) based on configuration or headers. The example shows how to switch between LLMs using the `x-arch-llm-provider-hint` header.
* **Currency Exchange Agent:** A quickstart guide builds an agent that fetches currency exchange rates from an API (Frankfurter.app). This demonstrates setting up configuration files, starting the gateway, and interacting with the agent via curl.
**Overall, ArchGW aims to address common challenges in building agentic apps:**
* Managing complex routing logic.
* Integrating with various LLMs and tools.
* Ensuring safety and reliability.
* Providing observability into LLM interactions.
The ArchGW project focuses on simplifying the development of **agentic applications** – applications powered by Large Language Models (LLMs) that can perform actions and interact with tools. Here's a breakdown of the use cases and examples highlighted:
**Core Use Cases:**
* **Routing:** Intelligent routing of prompts to the correct agents or tools.
* **Tools Use:** Simplifying the integration of prompts with tools/APIs for common tasks.
* **Guardrails:** Centralized configuration for safety and preventing harmful outcomes.
* **LLM Access:** Centralized access and management of LLMs with retries for reliability.
* **Observability:** Providing W3C-compatible tracing and metrics for monitoring LLM interactions.
**Specific Examples & Demos:**
* **Weather Forecast Agent:** A sample application demonstrating core function calling capabilities.
* **Network Operator Agent:** An agent that can interact with network devices (retrieve statistics, reboot).
* **Connecting to SaaS APIs:** Demonstrates integrating 3rd party SaaS APIs into agentic chat experiences.
* **LLM Router:** Using Arch as a gateway to route requests to different LLMs (GPT-4o, Mistral) based on configuration or headers. The example shows how to switch between LLMs using the `x-arch-llm-provider-hint` header.
* **Currency Exchange Agent:** A quickstart guide builds an agent that fetches currency exchange rates from an API (Frankfurter.app). This demonstrates setting up configuration files, starting the gateway, and interacting with the agent via curl.
**Overall, ArchGW aims to address common challenges in building agentic apps:**
* Managing complex routing logic.
* Integrating with various LLMs and tools.
* Ensuring safety and reliability.
* Providing observability into LLM interactions.
LiteLLM is a library to deploy and manage LLM (Large Language Model) APIs using a standardized format. It supports multiple LLM providers, includes proxy server features for load balancing and cost tracking, and offers various integrations for logging and observability.
Large Model Proxy is designed to make it easy to run multiple resource-heavy Large Models (LM) on the same machine with limited amount of VRAM/other resources.
Introduces proxy-tuning, a lightweight decoding-time algorithm that operates on top of black-box LMs to achieve the same end as direct tuning. The method tunes a smaller LM, then applies the difference between the predictions of the small tuned and untuned LMs to shift the original predictions of the larger untuned model in the direction of tuning, while retaining the benefits of larger-scale pretraining.
In this tutorial, learn how to improve the performance of large language models (LLMs) by utilizing a proxy tuning approach, which enables more efficient fine-tuning and better integration with the AI model.
- Proxy fine-tuning is a method to improve large pre-trained language models without directly accessing their weights.
- It operates on top of black-box LLMs by utilizing only their predictions.
- The approach combines elements of retrieval-based techniques, fine-tuning, and domain-specific adaptations.
- Proxy fine-tuning can be used to achieve the performance of heavily-tuned large models by only tuning smaller models.
- It operates on top of black-box LLMs by utilizing only their predictions.
- The approach combines elements of retrieval-based techniques, fine-tuning, and domain-specific adaptations.
- Proxy fine-tuning can be used to achieve the performance of heavily-tuned large models by only tuning smaller models.
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