mirror of
https://github.com/hwchase17/langchain.git
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community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463)
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
This commit is contained in:
Bagatur
GitHub
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"""Callback handler for promptlayer."""
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from __future__ import annotations
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import datetime
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from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple
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from uuid import UUID
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from langchain_core.callbacks import BaseCallbackHandler
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from langchain_core.messages import (
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AIMessage,
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BaseMessage,
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ChatMessage,
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HumanMessage,
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SystemMessage,
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)
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from langchain_core.outputs import (
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ChatGeneration,
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LLMResult,
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)
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if TYPE_CHECKING:
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import promptlayer
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def _lazy_import_promptlayer() -> promptlayer:
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"""Lazy import promptlayer to avoid circular imports."""
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try:
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import promptlayer
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except ImportError:
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raise ImportError(
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"The PromptLayerCallbackHandler requires the promptlayer package. "
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" Please install it with `pip install promptlayer`."
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)
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return promptlayer
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class PromptLayerCallbackHandler(BaseCallbackHandler):
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"""Callback handler for promptlayer."""
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def __init__(
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self,
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pl_id_callback: Optional[Callable[..., Any]] = None,
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pl_tags: Optional[List[str]] = None,
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) -> None:
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"""Initialize the PromptLayerCallbackHandler."""
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_lazy_import_promptlayer()
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self.pl_id_callback = pl_id_callback
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self.pl_tags = pl_tags or []
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self.runs: Dict[UUID, Dict[str, Any]] = {}
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def on_chat_model_start(
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self,
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serialized: Dict[str, Any],
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messages: List[List[BaseMessage]],
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*,
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run_id: UUID,
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parent_run_id: Optional[UUID] = None,
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tags: Optional[List[str]] = None,
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**kwargs: Any,
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) -> Any:
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self.runs[run_id] = {
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"messages": [self._create_message_dicts(m)[0] for m in messages],
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"invocation_params": kwargs.get("invocation_params", {}),
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"name": ".".join(serialized["id"]),
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"request_start_time": datetime.datetime.now().timestamp(),
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"tags": tags,
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}
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def on_llm_start(
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self,
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serialized: Dict[str, Any],
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prompts: List[str],
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*,
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run_id: UUID,
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parent_run_id: Optional[UUID] = None,
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tags: Optional[List[str]] = None,
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**kwargs: Any,
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) -> Any:
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self.runs[run_id] = {
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"prompts": prompts,
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"invocation_params": kwargs.get("invocation_params", {}),
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"name": ".".join(serialized["id"]),
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"request_start_time": datetime.datetime.now().timestamp(),
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"tags": tags,
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}
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def on_llm_end(
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self,
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response: LLMResult,
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*,
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run_id: UUID,
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parent_run_id: Optional[UUID] = None,
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**kwargs: Any,
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) -> None:
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from promptlayer.utils import get_api_key, promptlayer_api_request
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run_info = self.runs.get(run_id, {})
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if not run_info:
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return
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run_info["request_end_time"] = datetime.datetime.now().timestamp()
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for i in range(len(response.generations)):
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generation = response.generations[i][0]
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resp = {
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"text": generation.text,
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"llm_output": response.llm_output,
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}
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model_params = run_info.get("invocation_params", {})
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is_chat_model = run_info.get("messages", None) is not None
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model_input = (
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run_info.get("messages", [])[i]
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if is_chat_model
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else [run_info.get("prompts", [])[i]]
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)
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model_response = (
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[self._convert_message_to_dict(generation.message)]
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if is_chat_model and isinstance(generation, ChatGeneration)
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else resp
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)
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pl_request_id = promptlayer_api_request(
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run_info.get("name"),
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"langchain",
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model_input,
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model_params,
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self.pl_tags,
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model_response,
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run_info.get("request_start_time"),
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run_info.get("request_end_time"),
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get_api_key(),
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return_pl_id=bool(self.pl_id_callback is not None),
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metadata={
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"_langchain_run_id": str(run_id),
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"_langchain_parent_run_id": str(parent_run_id),
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"_langchain_tags": str(run_info.get("tags", [])),
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},
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)
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if self.pl_id_callback:
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self.pl_id_callback(pl_request_id)
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def _convert_message_to_dict(self, message: BaseMessage) -> Dict[str, Any]:
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if isinstance(message, HumanMessage):
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message_dict = {"role": "user", "content": message.content}
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elif isinstance(message, AIMessage):
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message_dict = {"role": "assistant", "content": message.content}
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elif isinstance(message, SystemMessage):
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message_dict = {"role": "system", "content": message.content}
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elif isinstance(message, ChatMessage):
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message_dict = {"role": message.role, "content": message.content}
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else:
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raise ValueError(f"Got unknown type {message}")
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if "name" in message.additional_kwargs:
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message_dict["name"] = message.additional_kwargs["name"]
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return message_dict
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def _create_message_dicts(
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self, messages: List[BaseMessage]
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) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
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params: Dict[str, Any] = {}
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message_dicts = [self._convert_message_to_dict(m) for m in messages]
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return message_dicts, params
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