(1.3.0)    Using cached tqdm-4.66.1-py3-none-any.whl (78 kB)    Installing collected packages: tqdm      Attempting uninstall: tqdm        Found existing installation: tqdm 4.64.1        Uninstalling tqdm-4.64.1:          Successfully uninstalled tqdm-4.64.1    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.    clarifai 9.8.1 requires tqdm==4.64.1, but you have tqdm 4.66.1 which is incompatible.    Successfully installed tqdm-4.66.1We can use chain_type="stuff", especially if using larger context window models such as:16k token OpenAI gpt-3.5-turbo-16k 100k token Anthropic Claude-2We can also supply chain_type="map_reduce" or chain_type="refine" (read more here).from langchain.chat_models import ChatOpenAIfrom langchain.document_loaders import WebBaseLoaderfrom langchain.chains.summarize import load_summarize_chainloader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")docs = loader.load()llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo-16k")chain = load_summarize_chain(llm, chain_type="stuff")chain.run(docs)    'The article discusses the concept of building autonomous agents powered by large language models (LLMs). It explores the components of such agents, including planning, memory, and tool use. The article provides case studies and proof-of-concept examples of LLM-powered agents in various domains. It also highlights the challenges and limitations of using LLMs in agent systems.'Option 1. Stuff‚ÄãWhen we use load_summarize_chain with chain_type="stuff", we will use the StuffDocumentsChain.The chain will take a list of documents, inserts them all into a prompt, and passes that prompt to an LLM:from langchain.chains.llm import LLMChainfrom langchain.prompts import PromptTemplatefrom langchain.chains.combine_documents.stuff import StuffDocumentsChain# Define promptprompt_template = """Write a concise summary
Open In Colab
Open In Colab ->: (1.3.0)    Using cached tqdm-4.66.1-py3-none-any.whl (78 kB)    Installing collected packages: tqdm      Attempting uninstall: tqdm        Found existing installation: tqdm 4.64.1        Uninstalling tqdm-4.64.1:          Successfully uninstalled tqdm-4.64.1    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.    clarifai 9.8.1 requires tqdm==4.64.1, but you have tqdm 4.66.1 which is incompatible.    Successfully installed tqdm-4.66.1We can use chain_type="stuff", especially if using larger context window models such as:16k token OpenAI gpt-3.5-turbo-16k 100k token Anthropic Claude-2We can also supply chain_type="map_reduce" or chain_type="refine" (read more here).from langchain.chat_models import ChatOpenAIfrom langchain.document_loaders import WebBaseLoaderfrom langchain.chains.summarize import load_summarize_chainloader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")docs = loader.load()llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo-16k")chain = load_summarize_chain(llm, chain_type="stuff")chain.run(docs)    'The article discusses the concept of building autonomous agents powered by large language models (LLMs). It explores the components of such agents, including planning, memory, and tool use. The article provides case studies and proof-of-concept examples of LLM-powered agents in various domains. It also highlights the challenges and limitations of using LLMs in agent systems.'Option 1. Stuff‚ÄãWhen we use load_summarize_chain with chain_type="stuff", we will use the StuffDocumentsChain.The chain will take a list of documents, inserts them all into a prompt, and passes that prompt to an LLM:from langchain.chains.llm import LLMChainfrom langchain.prompts import PromptTemplatefrom langchain.chains.combine_documents.stuff import StuffDocumentsChain# Define promptprompt_template = """Write a concise summary