mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +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
192 lines
6.3 KiB
Python
192 lines
6.3 KiB
Python
from __future__ import annotations
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import logging
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from typing import TYPE_CHECKING, Any, Dict, List, Optional
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from langchain_core.chat_history import BaseChatMessageHistory
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from langchain_core.messages import (
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AIMessage,
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BaseMessage,
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HumanMessage,
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SystemMessage,
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)
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if TYPE_CHECKING:
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from zep_python import Memory, MemorySearchResult, Message, NotFoundError
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logger = logging.getLogger(__name__)
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class ZepChatMessageHistory(BaseChatMessageHistory):
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"""Chat message history that uses Zep as a backend.
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Recommended usage::
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# Set up Zep Chat History
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zep_chat_history = ZepChatMessageHistory(
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session_id=session_id,
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url=ZEP_API_URL,
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api_key=<your_api_key>,
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)
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# Use a standard ConversationBufferMemory to encapsulate the Zep chat history
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memory = ConversationBufferMemory(
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memory_key="chat_history", chat_memory=zep_chat_history
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)
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Zep provides long-term conversation storage for LLM apps. The server stores,
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summarizes, embeds, indexes, and enriches conversational AI chat
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histories, and exposes them via simple, low-latency APIs.
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For server installation instructions and more, see:
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https://docs.getzep.com/deployment/quickstart/
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This class is a thin wrapper around the zep-python package. Additional
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Zep functionality is exposed via the `zep_summary` and `zep_messages`
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properties.
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For more information on the zep-python package, see:
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https://github.com/getzep/zep-python
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"""
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def __init__(
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self,
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session_id: str,
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url: str = "http://localhost:8000",
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api_key: Optional[str] = None,
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) -> None:
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try:
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from zep_python import ZepClient
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except ImportError:
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raise ImportError(
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"Could not import zep-python package. "
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"Please install it with `pip install zep-python`."
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)
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self.zep_client = ZepClient(base_url=url, api_key=api_key)
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self.session_id = session_id
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@property
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def messages(self) -> List[BaseMessage]: # type: ignore
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"""Retrieve messages from Zep memory"""
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zep_memory: Optional[Memory] = self._get_memory()
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if not zep_memory:
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return []
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messages: List[BaseMessage] = []
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# Extract summary, if present, and messages
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if zep_memory.summary:
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if len(zep_memory.summary.content) > 0:
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messages.append(SystemMessage(content=zep_memory.summary.content))
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if zep_memory.messages:
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msg: Message
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for msg in zep_memory.messages:
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metadata: Dict = {
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"uuid": msg.uuid,
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"created_at": msg.created_at,
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"token_count": msg.token_count,
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"metadata": msg.metadata,
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}
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if msg.role == "ai":
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messages.append(
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AIMessage(content=msg.content, additional_kwargs=metadata)
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)
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else:
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messages.append(
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HumanMessage(content=msg.content, additional_kwargs=metadata)
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)
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return messages
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@property
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def zep_messages(self) -> List[Message]:
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"""Retrieve summary from Zep memory"""
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zep_memory: Optional[Memory] = self._get_memory()
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if not zep_memory:
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return []
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return zep_memory.messages
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@property
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def zep_summary(self) -> Optional[str]:
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"""Retrieve summary from Zep memory"""
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zep_memory: Optional[Memory] = self._get_memory()
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if not zep_memory or not zep_memory.summary:
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return None
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return zep_memory.summary.content
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def _get_memory(self) -> Optional[Memory]:
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"""Retrieve memory from Zep"""
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from zep_python import NotFoundError
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try:
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zep_memory: Memory = self.zep_client.memory.get_memory(self.session_id)
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except NotFoundError:
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logger.warning(
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f"Session {self.session_id} not found in Zep. Returning None"
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)
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return None
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return zep_memory
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def add_user_message(
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self, message: str, metadata: Optional[Dict[str, Any]] = None
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) -> None:
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"""Convenience method for adding a human message string to the store.
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Args:
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message: The string contents of a human message.
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metadata: Optional metadata to attach to the message.
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"""
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self.add_message(HumanMessage(content=message), metadata=metadata)
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def add_ai_message(
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self, message: str, metadata: Optional[Dict[str, Any]] = None
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) -> None:
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"""Convenience method for adding an AI message string to the store.
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Args:
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message: The string contents of an AI message.
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metadata: Optional metadata to attach to the message.
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"""
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self.add_message(AIMessage(content=message), metadata=metadata)
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def add_message(
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self, message: BaseMessage, metadata: Optional[Dict[str, Any]] = None
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) -> None:
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"""Append the message to the Zep memory history"""
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from zep_python import Memory, Message
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zep_message = Message(
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content=message.content, role=message.type, metadata=metadata
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)
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zep_memory = Memory(messages=[zep_message])
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self.zep_client.memory.add_memory(self.session_id, zep_memory)
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def search(
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self, query: str, metadata: Optional[Dict] = None, limit: Optional[int] = None
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) -> List[MemorySearchResult]:
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"""Search Zep memory for messages matching the query"""
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from zep_python import MemorySearchPayload
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payload: MemorySearchPayload = MemorySearchPayload(
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text=query, metadata=metadata
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)
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return self.zep_client.memory.search_memory(
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self.session_id, payload, limit=limit
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)
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def clear(self) -> None:
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"""Clear session memory from Zep. Note that Zep is long-term storage for memory
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and this is not advised unless you have specific data retention requirements.
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"""
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try:
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self.zep_client.memory.delete_memory(self.session_id)
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except NotFoundError:
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logger.warning(
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f"Session {self.session_id} not found in Zep. Skipping delete."
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)
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