mirror of
https://github.com/hwchase17/langchain
synced 2024-11-18 09:25:54 +00:00
ed58eeb9c5
Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
113 lines
4.0 KiB
Python
113 lines
4.0 KiB
Python
import base64
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import re
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from typing import Any, Iterator
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from langchain_core.chat_sessions import ChatSession
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from langchain_core.messages import HumanMessage
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from langchain_community.chat_loaders.base import BaseChatLoader
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def _extract_email_content(msg: Any) -> HumanMessage:
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from_email = None
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for values in msg["payload"]["headers"]:
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name = values["name"]
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if name == "From":
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from_email = values["value"]
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if from_email is None:
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raise ValueError
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for part in msg["payload"]["parts"]:
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if part["mimeType"] == "text/plain":
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data = part["body"]["data"]
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data = base64.urlsafe_b64decode(data).decode("utf-8")
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# Regular expression to split the email body at the first
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# occurrence of a line that starts with "On ... wrote:"
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pattern = re.compile(r"\r\nOn .+(\r\n)*wrote:\r\n")
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# Split the email body and extract the first part
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newest_response = re.split(pattern, data)[0]
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message = HumanMessage(
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content=newest_response, additional_kwargs={"sender": from_email}
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)
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return message
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raise ValueError
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def _get_message_data(service: Any, message: Any) -> ChatSession:
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msg = service.users().messages().get(userId="me", id=message["id"]).execute()
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message_content = _extract_email_content(msg)
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in_reply_to = None
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email_data = msg["payload"]["headers"]
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for values in email_data:
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name = values["name"]
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if name == "In-Reply-To":
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in_reply_to = values["value"]
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if in_reply_to is None:
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raise ValueError
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thread_id = msg["threadId"]
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thread = service.users().threads().get(userId="me", id=thread_id).execute()
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messages = thread["messages"]
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response_email = None
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for message in messages:
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email_data = message["payload"]["headers"]
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for values in email_data:
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if values["name"] == "Message-ID":
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message_id = values["value"]
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if message_id == in_reply_to:
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response_email = message
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if response_email is None:
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raise ValueError
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starter_content = _extract_email_content(response_email)
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return ChatSession(messages=[starter_content, message_content])
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class GMailLoader(BaseChatLoader):
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"""Load data from `GMail`.
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There are many ways you could want to load data from GMail.
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This loader is currently fairly opinionated in how to do so.
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The way it does it is it first looks for all messages that you have sent.
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It then looks for messages where you are responding to a previous email.
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It then fetches that previous email, and creates a training example
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of that email, followed by your email.
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Note that there are clear limitations here. For example,
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all examples created are only looking at the previous email for context.
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To use:
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- Set up a Google Developer Account:
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Go to the Google Developer Console, create a project,
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and enable the Gmail API for that project.
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This will give you a credentials.json file that you'll need later.
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"""
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def __init__(self, creds: Any, n: int = 100, raise_error: bool = False) -> None:
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super().__init__()
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self.creds = creds
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self.n = n
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self.raise_error = raise_error
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def lazy_load(self) -> Iterator[ChatSession]:
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from googleapiclient.discovery import build
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service = build("gmail", "v1", credentials=self.creds)
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results = (
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service.users()
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.messages()
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.list(userId="me", labelIds=["SENT"], maxResults=self.n)
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.execute()
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)
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messages = results.get("messages", [])
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for message in messages:
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try:
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yield _get_message_data(service, message)
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except Exception as e:
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# TODO: handle errors better
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if self.raise_error:
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raise e
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else:
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pass
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