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chore: remove Langchain tools import (#3407)

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Jyong
2024-04-12 16:26:09 +08:00
committed by GitHub
parent c227f3d985
commit 0737e930cb
9 changed files with 98 additions and 73 deletions
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2
api/core/rag/extractor/blod/blod.py
View File

@@ -159,7 +159,7 @@ class BlobLoader(ABC):
def yield_blobs(
self,
) -> Iterable[Blob]:
"""A lazy loader for raw data represented by LangChain's Blob object.
"""A lazy loader for raw data represented by Blob object.
Returns:
A generator over blobs

4
api/core/rag/retrieval/dataset_retrieval.py
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@@ -2,7 +2,6 @@ import threading
from typing import Optional, cast
from flask import Flask, current_app
from langchain.tools import BaseTool
from core.app.app_config.entities import DatasetEntity, DatasetRetrieveConfigEntity
from core.app.entities.app_invoke_entities import InvokeFrom, ModelConfigWithCredentialsEntity
@@ -19,6 +18,7 @@ from core.rag.retrieval.router.multi_dataset_function_call_router import Functio
from core.rag.retrieval.router.multi_dataset_react_route import ReactMultiDatasetRouter
from core.rerank.rerank import RerankRunner
from core.tools.tool.dataset_retriever.dataset_multi_retriever_tool import DatasetMultiRetrieverTool
from core.tools.tool.dataset_retriever.dataset_retriever_base_tool import DatasetRetrieverBaseTool
from core.tools.tool.dataset_retriever.dataset_retriever_tool import DatasetRetrieverTool
from extensions.ext_database import db
from models.dataset import Dataset, DatasetQuery, DocumentSegment
@@ -383,7 +383,7 @@ class DatasetRetrieval:
return_resource: bool,
invoke_from: InvokeFrom,
hit_callback: DatasetIndexToolCallbackHandler) \
-> Optional[list[BaseTool]]:
-> Optional[list[DatasetRetrieverBaseTool]]:
"""
A dataset tool is a tool that can be used to retrieve information from a dataset
:param tenant_id: tenant id

25
api/core/rag/retrieval/output_parser/react_output.py Normal file
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@@ -0,0 +1,25 @@
from __future__ import annotations
from dataclasses import dataclass
from typing import NamedTuple, Union
@dataclass
class ReactAction:
"""A full description of an action for an ReactAction to execute."""
tool: str
"""The name of the Tool to execute."""
tool_input: Union[str, dict]
"""The input to pass in to the Tool."""
log: str
"""Additional information to log about the action."""
class ReactFinish(NamedTuple):
"""The final return value of an ReactFinish."""
return_values: dict
"""Dictionary of return values."""
log: str
"""Additional information to log about the return value"""

18
api/core/rag/retrieval/output_parser/structured_chat.py
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@@ -2,28 +2,24 @@ import json
import re
from typing import Union
from langchain.agents.structured_chat.output_parser import StructuredChatOutputParser as LCStructuredChatOutputParser
from langchain.agents.structured_chat.output_parser import logger
from langchain.schema import AgentAction, AgentFinish, OutputParserException
from core.rag.retrieval.output_parser.react_output import ReactAction, ReactFinish
class StructuredChatOutputParser(LCStructuredChatOutputParser):
def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
class StructuredChatOutputParser:
def parse(self, text: str) -> Union[ReactAction, ReactFinish]:
try:
action_match = re.search(r"```(\w*)\n?({.*?)```", text, re.DOTALL)
if action_match is not None:
response = json.loads(action_match.group(2).strip(), strict=False)
if isinstance(response, list):
# gpt turbo frequently ignores the directive to emit a single action
logger.warning("Got multiple action responses: %s", response)
response = response[0]
if response["action"] == "Final Answer":
return AgentFinish({"output": response["action_input"]}, text)
return ReactFinish({"output": response["action_input"]}, text)
else:
return AgentAction(
return ReactAction(
response["action"], response.get("action_input", {}), text
)
else:
return AgentFinish({"output": text}, text)
return ReactFinish({"output": text}, text)
except Exception as e:
raise OutputParserException(f"Could not parse LLM output: {text}")
raise ValueError(f"Could not parse LLM output: {text}")

35
api/core/rag/retrieval/router/multi_dataset_react_route.py
View File

@@ -1,20 +1,21 @@
from collections.abc import Generator, Sequence
from typing import Optional, Union
from langchain import PromptTemplate
from langchain.agents.structured_chat.base import HUMAN_MESSAGE_TEMPLATE
from langchain.agents.structured_chat.prompt import PREFIX, SUFFIX
from langchain.schema import AgentAction
from typing import Union
from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
from core.model_manager import ModelInstance
from core.model_runtime.entities.llm_entities import LLMUsage
from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageRole, PromptMessageTool
from core.prompt.advanced_prompt_transform import AdvancedPromptTransform
from core.prompt.entities.advanced_prompt_entities import ChatModelMessage
from core.prompt.entities.advanced_prompt_entities import ChatModelMessage, CompletionModelPromptTemplate
from core.rag.retrieval.output_parser.react_output import ReactAction
from core.rag.retrieval.output_parser.structured_chat import StructuredChatOutputParser
from core.workflow.nodes.llm.llm_node import LLMNode
PREFIX = """Respond to the human as helpfully and accurately as possible. You have access to the following tools:"""
SUFFIX = """Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
Thought:"""
FORMAT_INSTRUCTIONS = """Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
The nouns in the format of "Thought", "Action", "Action Input", "Final Answer" must be expressed in English.
Valid "action" values: "Final Answer" or {tool_names}
@@ -86,7 +87,6 @@ class ReactMultiDatasetRouter:
tenant_id: str,
prefix: str = PREFIX,
suffix: str = SUFFIX,
human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
format_instructions: str = FORMAT_INSTRUCTIONS,
) -> Union[str, None]:
if model_config.mode == "chat":
@@ -95,7 +95,6 @@ class ReactMultiDatasetRouter:
tools=tools,
prefix=prefix,
suffix=suffix,
human_message_template=human_message_template,
format_instructions=format_instructions,
)
else:
@@ -103,7 +102,6 @@ class ReactMultiDatasetRouter:
tools=tools,
prefix=prefix,
format_instructions=format_instructions,
input_variables=None
)
stop = ['Observation:']
# handle invoke result
@@ -127,9 +125,9 @@ class ReactMultiDatasetRouter:
tenant_id=tenant_id
)
output_parser = StructuredChatOutputParser()
agent_decision = output_parser.parse(result_text)
if isinstance(agent_decision, AgentAction):
return agent_decision.tool
react_decision = output_parser.parse(result_text)
if isinstance(react_decision, ReactAction):
return react_decision.tool
return None
def _invoke_llm(self, completion_param: dict,
@@ -139,7 +137,6 @@ class ReactMultiDatasetRouter:
) -> tuple[str, LLMUsage]:
"""
Invoke large language model
:param node_data: node data
:param model_instance: model instance
:param prompt_messages: prompt messages
:param stop: stop
@@ -197,7 +194,6 @@ class ReactMultiDatasetRouter:
tools: Sequence[PromptMessageTool],
prefix: str = PREFIX,
suffix: str = SUFFIX,
human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
format_instructions: str = FORMAT_INSTRUCTIONS,
) -> list[ChatModelMessage]:
tool_strings = []
@@ -227,16 +223,13 @@ class ReactMultiDatasetRouter:
tools: Sequence[PromptMessageTool],
prefix: str = PREFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
input_variables: Optional[list[str]] = None,
) -> PromptTemplate:
) -> CompletionModelPromptTemplate:
"""Create prompt in the style of the zero shot agent.
Args:
tools: List of tools the agent will have access to, used to format the
prompt.
prefix: String to put before the list of tools.
input_variables: List of input variables the final prompt will expect.
Returns:
A PromptTemplate with the template assembled from the pieces here.
"""
@@ -249,6 +242,4 @@ Thought: {agent_scratchpad}
tool_names = ", ".join([tool.name for tool in tools])
format_instructions = format_instructions.format(tool_names=tool_names)
template = "\n\n".join([prefix, tool_strings, format_instructions, suffix])
if input_variables is None:
input_variables = ["input", "agent_scratchpad"]
return PromptTemplate(template=template, input_variables=input_variables)
return CompletionModelPromptTemplate(text=template)