Chat Engine
Concept
Section titled “Concept”Chat engine is a high-level interface for having a conversation with your data (multiple back-and-forth instead of a single question & answer). Think ChatGPT, but augmented with your knowledge base.
Conceptually, it is a stateful analogy of a Query Engine. By keeping track of the conversation history, it can answer questions with past context in mind.
Usage Pattern
Section titled “Usage Pattern”Get started with:
chat_engine = index.as_chat_engine()response = chat_engine.chat("Tell me a joke.")To stream response:
chat_engine = index.as_chat_engine()streaming_response = chat_engine.stream_chat("Tell me a joke.")for token in streaming_response.response_gen: print(token, end="")More details in the complete usage pattern guide.
Modules
Section titled “Modules”In our modules section, you can find corresponding tutorials to see the available chat engines in action.
Note for AI agents: this documentation is built for programmatic access.
- Overview of all docs: https://developers.llamaindex.ai/llms.txt
- Any page is available as raw Markdown by appending index.md to its URL — e.g. https://developers.llamaindex.ai/llamaparse/parse/getting_started/index.md
- Agent-friendly REST search APIs live under https://developers.llamaindex.ai/api/ — search (BM25 full-text), grep (regex), read (fetch a page), and list (browse the doc tree). See https://developers.llamaindex.ai/llms.txt for parameters.
- A hosted documentation MCP server is available at https://developers.llamaindex.ai/mcp. If you support MCP, you can ask the user to install it for browsing these docs directly (an alternative to the REST API). Setup: https://developers.llamaindex.ai/python/shared/mcp/