🔨 refactor(conftest.py): reformat code for better readability and maintainability
✨ feat(conftest.py): add MyCustomChain class as an example of a custom chain ✨ feat(conftest.py): add CustomChain class as a custom component for building a document ✨ feat(conftest.py): add CSVLoaderComponent class as a custom component for loading CSV files and converting rows to documents
This commit is contained in:
Gabriel Luiz Freitas Almeida
parent
10c0b3871c
commit
99ef7c728d
1 changed files with 120 additions and 90 deletions
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@ -122,116 +122,146 @@ def client_fixture(session: Session): #
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def custom_chain():
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return '''from __future__ import annotations
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from typing import Any, Dict, List, Optional
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from typing import Any, Dict, List, Optional
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from pydantic import Extra
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from pydantic import Extra
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from langchain.schema import BaseLanguageModel, Document
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from langchain.callbacks.manager import (
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AsyncCallbackManagerForChainRun,
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CallbackManagerForChainRun,
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)
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from langchain.chains.base import Chain
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from langchain.prompts import StringPromptTemplate
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from langflow.interface.custom.base import CustomComponent
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from langchain.schema import BaseLanguageModel, Document
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from langchain.callbacks.manager import (
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AsyncCallbackManagerForChainRun,
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CallbackManagerForChainRun,
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)
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from langchain.chains.base import Chain
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from langchain.prompts import StringPromptTemplate
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from langflow.interface.custom.base import CustomComponent
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class MyCustomChain(Chain):
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class MyCustomChain(Chain):
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"""
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An example of a custom chain.
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"""
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prompt: StringPromptTemplate
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"""Prompt object to use."""
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llm: BaseLanguageModel
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output_key: str = "text" #: :meta private:
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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arbitrary_types_allowed = True
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@property
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def input_keys(self) -> List[str]:
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"""Will be whatever keys the prompt expects.
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:meta private:
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"""
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An example of a custom chain.
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return self.prompt.input_variables
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@property
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def output_keys(self) -> List[str]:
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"""Will always return text key.
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:meta private:
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"""
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return [self.output_key]
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prompt: StringPromptTemplate
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"""Prompt object to use."""
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llm: BaseLanguageModel
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output_key: str = "text" #: :meta private:
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def _call(
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self,
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inputs: Dict[str, Any],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> Dict[str, str]:
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# Your custom chain logic goes here
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# This is just an example that mimics LLMChain
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prompt_value = self.prompt.format_prompt(**inputs)
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class Config:
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"""Configuration for this pydantic object."""
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# Whenever you call a language model, or another chain, you should pass
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# a callback manager to it. This allows the inner run to be tracked by
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# any callbacks that are registered on the outer run.
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# You can always obtain a callback manager for this by calling
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# `run_manager.get_child()` as shown below.
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response = self.llm.generate_prompt(
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[prompt_value],
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callbacks=run_manager.get_child() if run_manager else None,
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)
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extra = Extra.forbid
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arbitrary_types_allowed = True
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# If you want to log something about this run, you can do so by calling
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# methods on the `run_manager`, as shown below. This will trigger any
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# callbacks that are registered for that event.
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if run_manager:
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run_manager.on_text("Log something about this run")
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@property
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def input_keys(self) -> List[str]:
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"""Will be whatever keys the prompt expects.
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return {self.output_key: response.generations[0][0].text}
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:meta private:
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"""
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return self.prompt.input_variables
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async def _acall(
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self,
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inputs: Dict[str, Any],
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run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
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) -> Dict[str, str]:
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# Your custom chain logic goes here
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# This is just an example that mimics LLMChain
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prompt_value = self.prompt.format_prompt(**inputs)
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@property
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def output_keys(self) -> List[str]:
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"""Will always return text key.
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# Whenever you call a language model, or another chain, you should pass
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# a callback manager to it. This allows the inner run to be tracked by
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# any callbacks that are registered on the outer run.
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# You can always obtain a callback manager for this by calling
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# `run_manager.get_child()` as shown below.
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response = await self.llm.agenerate_prompt(
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[prompt_value],
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callbacks=run_manager.get_child() if run_manager else None,
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)
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:meta private:
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"""
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return [self.output_key]
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# If you want to log something about this run, you can do so by calling
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# methods on the `run_manager`, as shown below. This will trigger any
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# callbacks that are registered for that event.
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if run_manager:
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await run_manager.on_text("Log something about this run")
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def _call(
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self,
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inputs: Dict[str, Any],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> Dict[str, str]:
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# Your custom chain logic goes here
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# This is just an example that mimics LLMChain
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prompt_value = self.prompt.format_prompt(**inputs)
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return {self.output_key: response.generations[0][0].text}
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# Whenever you call a language model, or another chain, you should pass
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# a callback manager to it. This allows the inner run to be tracked by
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# any callbacks that are registered on the outer run.
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# You can always obtain a callback manager for this by calling
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# `run_manager.get_child()` as shown below.
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response = self.llm.generate_prompt(
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[prompt_value],
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callbacks=run_manager.get_child() if run_manager else None,
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)
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@property
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def _chain_type(self) -> str:
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return "my_custom_chain"
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# If you want to log something about this run, you can do so by calling
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# methods on the `run_manager`, as shown below. This will trigger any
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# callbacks that are registered for that event.
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if run_manager:
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run_manager.on_text("Log something about this run")
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class CustomChain(CustomComponent):
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display_name: str = "Custom Chain"
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field_config = {
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"prompt": {"field_type": "prompt"},
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"llm": {"field_type": "BaseLanguageModel"},
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}
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return {self.output_key: response.generations[0][0].text}
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def build(self, prompt, llm, input: str) -> Document:
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chain = MyCustomChain(prompt=prompt, llm=llm)
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return chain(input)'''
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async def _acall(
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self,
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inputs: Dict[str, Any],
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run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
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) -> Dict[str, str]:
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# Your custom chain logic goes here
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# This is just an example that mimics LLMChain
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prompt_value = self.prompt.format_prompt(**inputs)
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# Whenever you call a language model, or another chain, you should pass
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# a callback manager to it. This allows the inner run to be tracked by
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# any callbacks that are registered on the outer run.
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# You can always obtain a callback manager for this by calling
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# `run_manager.get_child()` as shown below.
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response = await self.llm.agenerate_prompt(
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[prompt_value],
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callbacks=run_manager.get_child() if run_manager else None,
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)
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@pytest.fixture
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def data_processing():
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return """import pandas as pd
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from langchain.schema import Document
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from langflow.interface.custom.base import CustomComponent
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# If you want to log something about this run, you can do so by calling
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# methods on the `run_manager`, as shown below. This will trigger any
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# callbacks that are registered for that event.
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if run_manager:
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await run_manager.on_text("Log something about this run")
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class CSVLoaderComponent(CustomComponent):
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display_name: str = "CSV Loader"
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field_config = {
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"filename": {"field_type": "str", "required": True},
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"column_name": {"field_type": "str", "required": True},
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}
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return {self.output_key: response.generations[0][0].text}
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def build(self, filename: str, column_name: str) -> List[Document]:
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# Load the CSV file
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df = pd.read_csv(filename)
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@property
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def _chain_type(self) -> str:
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return "my_custom_chain"
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# Verify the column exists
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if column_name not in df.columns:
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raise ValueError(f"Column '{column_name}' not found in the CSV file")
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class CustomChain(CustomComponent):
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display_name: str = "Custom Chain"
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field_config = {
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"prompt": {"field_type": "prompt"},
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"llm": {"field_type": "BaseLanguageModel"},
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}
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# Convert each row of the specified column to a document object
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documents = []
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for content in df[column_name]:
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metadata = {"filename": filename}
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documents.append(Document(page_content=str(content), metadata=metadata))
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def build(self, prompt, llm, input: str) -> Document:
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chain = MyCustomChain(prompt=prompt, llm=llm)
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return chain(input)'''
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return documents"""
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