cited for driving 140 mph\n  * 'Taking his time': Patient QB Rodgers wows Jets\n  * Reyna got U.S. assurances after Berhalter rehire\n  * NFL Future Power Rankings\n\n## USWNT AT THE WORLD CUP\n\n### USA VS. VIETNAM: 9 P.M. ET FRIDAY\n\n## How do you defend against Alex Morgan? Former opponents sound off\n\nThe U.S. forward is unstoppable at this level, scoring 121 goals and adding 49"docs_transformed[1].page_content[1000:2000]    "t's brain,\ncomplemented by several key components:\n\n  * **Planning**\n    * Subgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\n    * Reflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\n  * **Memory**\n    * Short-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\n    * Long-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\n  * **Tool use**\n    * The agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution c"PreviousDoctran: language translationNextNucliaCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
html2text is a Python package that converts a page of HTML into clean, easy-to-read plain ASCII text.
html2text is a Python package that converts a page of HTML into clean, easy-to-read plain ASCII text. ->: cited for driving 140 mph\n  * 'Taking his time': Patient QB Rodgers wows Jets\n  * Reyna got U.S. assurances after Berhalter rehire\n  * NFL Future Power Rankings\n\n## USWNT AT THE WORLD CUP\n\n### USA VS. VIETNAM: 9 P.M. ET FRIDAY\n\n## How do you defend against Alex Morgan? Former opponents sound off\n\nThe U.S. forward is unstoppable at this level, scoring 121 goals and adding 49"docs_transformed[1].page_content[1000:2000]    "t's brain,\ncomplemented by several key components:\n\n  * **Planning**\n    * Subgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\n    * Reflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\n  * **Memory**\n    * Short-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\n    * Long-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\n  * **Tool use**\n    * The agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution c"PreviousDoctran: language translationNextNucliaCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.