text: by showing two-shot examples to the Learning Language Model (LLM). Each example consists of a failed trajectory and an ideal reflection that guides future changes in the agent's plan. These reflections are then added to the agent's working memory, up to a maximum of three, to be used as context for querying the LLM. This allows the agent to iteratively improve its reasoning skills by refining past action decisions and correcting previous mistakes."4.2 Going deeperâ€‹The documentation on ConversationalRetrievalChain offers a few extensions, such as streaming and source documents.PreviousUse casesNextContext aware text splitting and QA / ChatQuickstart1. Loading, Splitting, Storage1.1 Getting started1.2 Going Deeper2. Retrieval2.1 Getting started2.2 Going Deeper3. QA3.1 Getting started3.2 Going Deeper4. Chat4.1 Getting started4.2 Going deeperCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.