It is a Python library designed for creating AI assistants and agents using Vectara and Agentic-RAG (Retrieval-Augmented Generation). The library leverages the LlamaIndex Agent framework and provides helper functions to quickly develop tools that connect to Vectara corpora, enabling the creation of powerful AI applications. It supports the creation of tools for querying and summarizing data, as well as integrating with other APIs or mathematical functions.
The library includes pre-built tools, such as the Vectara RAG tool, which calls the full Vectara RAG pipeline to provide summarized responses to queries grounded in data. It also supports metadata filtering, allowing users to refine queries based on specific criteria like document attributes or timeframes. Additionally, the Vectara search tool enables agents to list documents matching a query, useful for tasks like identifying relevant documents or counting occurrences of specific topics.
Users can customize tools using the `create_rag_tool()` and `create_search_tool()` functions, which accept various arguments to configure Vectara queries. The library also supports creating custom tools from Python functions using the `create_tool()` method, provided the functions are defined at the top module level.
Configuration is managed through an `AgentConfig` object, which allows users to specify settings like API keys, LLM providers, and custom instructions. The library integrates with observability tools like Arize Phoenix for monitoring and debugging, and it supports serialization for saving and loading agent states.
The library can be hosted locally or remotely via a FastAPI server, making it accessible through HTTP requests. It is open-source, licensed under Apache 2.0, and welcomes contributions from the community. Documentation, examples, and a Discord community are available for further support.
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