path="slack_dump.zip",)3. Load messages‚ÄãThe load() (or lazy_load) methods return a list of "ChatSessions" that currently just contain a list of messages per loaded conversation.from typing import Listfrom langchain.chat_loaders.base import ChatSessionfrom langchain.chat_loaders.utils import (    map_ai_messages,    merge_chat_runs,)raw_messages = loader.lazy_load()# Merge consecutive messages from the same sender into a single messagemerged_messages = merge_chat_runs(raw_messages)# Convert messages from "U0500003428" to AI messagesmessages: List[ChatSession] = list(map_ai_messages(merged_messages, sender="U0500003428"))Next Steps‚ÄãYou can then use these messages how you see fit, such as fine-tuning a model, few-shot example selection, or directly make predictions for the next message. from langchain.chat_models import ChatOpenAIllm = ChatOpenAI()for chunk in llm.stream(messages[1]['messages']):    print(chunk.content, end="", flush=True)    Hi,         I hope you're doing well. I wanted to reach out and ask if you'd be available to meet up for coffee sometime next week. I'd love to catch up and hear about what's been going on in your life. Let me know if you're interested and we can find a time that works for both of us.         Looking forward to hearing from you!        Best, [Your Name]PreviousFine-Tuning on LangSmith LLM RunsNexttelegram1. Create message dump2. Create the Chat Loader3. Load messagesNext StepsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
This notebook shows how to use the Slack chat loader. This class helps map exported slack conversations to LangChain chat messages.
This notebook shows how to use the Slack chat loader. This class helps map exported slack conversations to LangChain chat messages. ->: path="slack_dump.zip",)3. Load messages‚ÄãThe load() (or lazy_load) methods return a list of "ChatSessions" that currently just contain a list of messages per loaded conversation.from typing import Listfrom langchain.chat_loaders.base import ChatSessionfrom langchain.chat_loaders.utils import (    map_ai_messages,    merge_chat_runs,)raw_messages = loader.lazy_load()# Merge consecutive messages from the same sender into a single messagemerged_messages = merge_chat_runs(raw_messages)# Convert messages from "U0500003428" to AI messagesmessages: List[ChatSession] = list(map_ai_messages(merged_messages, sender="U0500003428"))Next Steps‚ÄãYou can then use these messages how you see fit, such as fine-tuning a model, few-shot example selection, or directly make predictions for the next message. from langchain.chat_models import ChatOpenAIllm = ChatOpenAI()for chunk in llm.stream(messages[1]['messages']):    print(chunk.content, end="", flush=True)    Hi,         I hope you're doing well. I wanted to reach out and ask if you'd be available to meet up for coffee sometime next week. I'd love to catch up and hear about what's been going on in your life. Let me know if you're interested and we can find a time that works for both of us.         Looking forward to hearing from you!        Best, [Your Name]PreviousFine-Tuning on LangSmith LLM RunsNexttelegram1. Create message dump2. Create the Chat Loader3. Load messagesNext StepsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.