mirror of
https://github.com/nod-ai/AMD-SHARK-Studio.git
synced 2026-04-03 03:00:17 -04:00
Expand pipelines to fix streaming of tokens
This commit is contained in:
Vivek Khandelwal
432
apps/language_models/langchain/expanded_pipelines.py
Normal file
432
apps/language_models/langchain/expanded_pipelines.py
Normal file
@@ -0,0 +1,432 @@
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"""Load question answering chains."""
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from __future__ import annotations
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from typing import (
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Any,
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Mapping,
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Optional,
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Dict,
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List,
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Sequence,
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Tuple,
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Union,
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Protocol,
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)
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from langchain.base_language import BaseLanguageModel
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from langchain.callbacks.base import BaseCallbackManager
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from langchain.chains.question_answering import stuff_prompt
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from langchain.prompts.base import BasePromptTemplate
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from langchain.docstore.document import Document
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from abc import ABC, abstractmethod
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from langchain.chains.base import Chain
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from langchain.callbacks.manager import (
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CallbackManager,
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CallbackManagerForChainRun,
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Callbacks,
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)
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from langchain.input import get_colored_text
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from langchain.load.dump import dumpd
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from langchain.prompts.prompt import PromptTemplate
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from langchain.schema import LLMResult, PromptValue
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from pydantic import Extra, Field, root_validator
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def format_document(doc: Document, prompt: BasePromptTemplate) -> str:
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"""Format a document into a string based on a prompt template."""
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base_info = {"page_content": doc.page_content}
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base_info.update(doc.metadata)
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missing_metadata = set(prompt.input_variables).difference(base_info)
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if len(missing_metadata) > 0:
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required_metadata = [
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iv for iv in prompt.input_variables if iv != "page_content"
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]
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raise ValueError(
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f"Document prompt requires documents to have metadata variables: "
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f"{required_metadata}. Received document with missing metadata: "
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f"{list(missing_metadata)}."
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)
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document_info = {k: base_info[k] for k in prompt.input_variables}
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return prompt.format(**document_info)
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class BaseCombineDocumentsChain(Chain, ABC):
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"""Base interface for chains combining documents."""
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input_key: str = "input_documents" #: :meta private:
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output_key: str = "output_text" #: :meta private:
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@property
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def input_keys(self) -> List[str]:
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"""Expect input key.
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:meta private:
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"""
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return [self.input_key]
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@property
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def output_keys(self) -> List[str]:
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"""Return output key.
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:meta private:
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"""
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return [self.output_key]
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def prompt_length(
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self, docs: List[Document], **kwargs: Any
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) -> Optional[int]:
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"""Return the prompt length given the documents passed in.
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Returns None if the method does not depend on the prompt length.
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"""
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return None
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@abstractmethod
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def combine_docs(
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self, docs: List[Document], **kwargs: Any
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) -> Tuple[str, dict]:
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"""Combine documents into a single string."""
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def _call(
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self,
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inputs: Dict[str, List[Document]],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> Dict[str, str]:
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_run_manager = (
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run_manager or CallbackManagerForChainRun.get_noop_manager()
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)
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docs = inputs[self.input_key]
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# Other keys are assumed to be needed for LLM prediction
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other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
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output, extra_return_dict = self.combine_docs(
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docs, callbacks=_run_manager.get_child(), **other_keys
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)
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extra_return_dict[self.output_key] = output
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return extra_return_dict
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class LLMChain(Chain):
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"""Chain to run queries against LLMs.
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Example:
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.. code-block:: python
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from langchain import LLMChain, OpenAI, PromptTemplate
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prompt_template = "Tell me a {adjective} joke"
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prompt = PromptTemplate(
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input_variables=["adjective"], template=prompt_template
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)
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llm = LLMChain(llm=OpenAI(), prompt=prompt)
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"""
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@property
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def lc_serializable(self) -> bool:
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return True
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prompt: BasePromptTemplate
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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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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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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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response = self.generate([inputs], run_manager=run_manager)
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return self.create_outputs(response)[0]
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def generate(
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self,
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input_list: List[Dict[str, Any]],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> LLMResult:
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"""Generate LLM result from inputs."""
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prompts, stop = self.prep_prompts(input_list, run_manager=run_manager)
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return self.llm.generate_prompt(
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prompts,
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stop,
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callbacks=run_manager.get_child() if run_manager else None,
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)
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def prep_prompts(
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self,
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input_list: List[Dict[str, Any]],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> Tuple[List[PromptValue], Optional[List[str]]]:
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"""Prepare prompts from inputs."""
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stop = None
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if "stop" in input_list[0]:
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stop = input_list[0]["stop"]
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prompts = []
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for inputs in input_list:
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selected_inputs = {
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k: inputs[k] for k in self.prompt.input_variables
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}
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prompt = self.prompt.format_prompt(**selected_inputs)
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_colored_text = get_colored_text(prompt.to_string(), "green")
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_text = "Prompt after formatting:\n" + _colored_text
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if run_manager:
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run_manager.on_text(_text, end="\n", verbose=self.verbose)
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if "stop" in inputs and inputs["stop"] != stop:
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raise ValueError(
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"If `stop` is present in any inputs, should be present in all."
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)
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prompts.append(prompt)
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return prompts, stop
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def apply(
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self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
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) -> List[Dict[str, str]]:
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"""Utilize the LLM generate method for speed gains."""
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callback_manager = CallbackManager.configure(
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callbacks, self.callbacks, self.verbose
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)
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run_manager = callback_manager.on_chain_start(
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dumpd(self),
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{"input_list": input_list},
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)
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try:
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response = self.generate(input_list, run_manager=run_manager)
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except (KeyboardInterrupt, Exception) as e:
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run_manager.on_chain_error(e)
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raise e
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outputs = self.create_outputs(response)
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run_manager.on_chain_end({"outputs": outputs})
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return outputs
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def create_outputs(self, response: LLMResult) -> List[Dict[str, str]]:
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"""Create outputs from response."""
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return [
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# Get the text of the top generated string.
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{self.output_key: generation[0].text}
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for generation in response.generations
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]
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def predict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:
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"""Format prompt with kwargs and pass to LLM.
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Args:
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callbacks: Callbacks to pass to LLMChain
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**kwargs: Keys to pass to prompt template.
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Returns:
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Completion from LLM.
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Example:
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.. code-block:: python
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completion = llm.predict(adjective="funny")
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"""
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return self(kwargs, callbacks=callbacks)[self.output_key]
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def predict_and_parse(
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self, callbacks: Callbacks = None, **kwargs: Any
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) -> Union[str, List[str], Dict[str, Any]]:
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"""Call predict and then parse the results."""
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result = self.predict(callbacks=callbacks, **kwargs)
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if self.prompt.output_parser is not None:
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return self.prompt.output_parser.parse(result)
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else:
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return result
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def apply_and_parse(
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self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
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) -> Sequence[Union[str, List[str], Dict[str, str]]]:
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"""Call apply and then parse the results."""
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result = self.apply(input_list, callbacks=callbacks)
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return self._parse_result(result)
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def _parse_result(
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self, result: List[Dict[str, str]]
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) -> Sequence[Union[str, List[str], Dict[str, str]]]:
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if self.prompt.output_parser is not None:
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return [
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self.prompt.output_parser.parse(res[self.output_key])
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for res in result
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]
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else:
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return result
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@property
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def _chain_type(self) -> str:
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return "llm_chain"
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@classmethod
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def from_string(cls, llm: BaseLanguageModel, template: str) -> LLMChain:
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"""Create LLMChain from LLM and template."""
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prompt_template = PromptTemplate.from_template(template)
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return cls(llm=llm, prompt=prompt_template)
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def _get_default_document_prompt() -> PromptTemplate:
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return PromptTemplate(
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input_variables=["page_content"], template="{page_content}"
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)
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class StuffDocumentsChain(BaseCombineDocumentsChain):
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"""Chain that combines documents by stuffing into context."""
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llm_chain: LLMChain
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"""LLM wrapper to use after formatting documents."""
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document_prompt: BasePromptTemplate = Field(
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default_factory=_get_default_document_prompt
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)
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"""Prompt to use to format each document."""
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document_variable_name: str
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"""The variable name in the llm_chain to put the documents in.
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If only one variable in the llm_chain, this need not be provided."""
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document_separator: str = "\n\n"
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"""The string with which to join the formatted documents"""
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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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@root_validator(pre=True)
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def get_default_document_variable_name(cls, values: Dict) -> Dict:
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"""Get default document variable name, if not provided."""
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llm_chain_variables = values["llm_chain"].prompt.input_variables
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if "document_variable_name" not in values:
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if len(llm_chain_variables) == 1:
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values["document_variable_name"] = llm_chain_variables[0]
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else:
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raise ValueError(
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"document_variable_name must be provided if there are "
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"multiple llm_chain_variables"
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)
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else:
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if values["document_variable_name"] not in llm_chain_variables:
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raise ValueError(
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f"document_variable_name {values['document_variable_name']} was "
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f"not found in llm_chain input_variables: {llm_chain_variables}"
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)
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return values
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def _get_inputs(self, docs: List[Document], **kwargs: Any) -> dict:
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# Format each document according to the prompt
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doc_strings = [
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format_document(doc, self.document_prompt) for doc in docs
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]
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# Join the documents together to put them in the prompt.
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inputs = {
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k: v
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for k, v in kwargs.items()
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if k in self.llm_chain.prompt.input_variables
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}
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inputs[self.document_variable_name] = self.document_separator.join(
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doc_strings
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)
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return inputs
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def prompt_length(
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self, docs: List[Document], **kwargs: Any
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) -> Optional[int]:
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"""Get the prompt length by formatting the prompt."""
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inputs = self._get_inputs(docs, **kwargs)
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prompt = self.llm_chain.prompt.format(**inputs)
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return self.llm_chain.llm.get_num_tokens(prompt)
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def combine_docs(
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self, docs: List[Document], callbacks: Callbacks = None, **kwargs: Any
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) -> Tuple[str, dict]:
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"""Stuff all documents into one prompt and pass to LLM."""
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inputs = self._get_inputs(docs, **kwargs)
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# Call predict on the LLM.
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return self.llm_chain.predict(callbacks=callbacks, **inputs), {}
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@property
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def _chain_type(self) -> str:
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return "stuff_documents_chain"
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class LoadingCallable(Protocol):
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"""Interface for loading the combine documents chain."""
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def __call__(
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self, llm: BaseLanguageModel, **kwargs: Any
|
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) -> BaseCombineDocumentsChain:
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"""Callable to load the combine documents chain."""
|
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def _load_stuff_chain(
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llm: BaseLanguageModel,
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prompt: Optional[BasePromptTemplate] = None,
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document_variable_name: str = "context",
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verbose: Optional[bool] = None,
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callback_manager: Optional[BaseCallbackManager] = None,
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callbacks: Callbacks = None,
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**kwargs: Any,
|
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) -> StuffDocumentsChain:
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_prompt = prompt or stuff_prompt.PROMPT_SELECTOR.get_prompt(llm)
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llm_chain = LLMChain(
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llm=llm,
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prompt=_prompt,
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verbose=verbose,
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callback_manager=callback_manager,
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callbacks=callbacks,
|
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)
|
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# TODO: document prompt
|
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return StuffDocumentsChain(
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llm_chain=llm_chain,
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document_variable_name=document_variable_name,
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verbose=verbose,
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callback_manager=callback_manager,
|
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**kwargs,
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)
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def load_qa_chain(
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llm: BaseLanguageModel,
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chain_type: str = "stuff",
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verbose: Optional[bool] = None,
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callback_manager: Optional[BaseCallbackManager] = None,
|
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**kwargs: Any,
|
||||
) -> BaseCombineDocumentsChain:
|
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"""Load question answering chain.
|
||||
|
||||
Args:
|
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llm: Language Model to use in the chain.
|
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chain_type: Type of document combining chain to use. Should be one of "stuff",
|
||||
"map_reduce", "map_rerank", and "refine".
|
||||
verbose: Whether chains should be run in verbose mode or not. Note that this
|
||||
applies to all chains that make up the final chain.
|
||||
callback_manager: Callback manager to use for the chain.
|
||||
|
||||
Returns:
|
||||
A chain to use for question answering.
|
||||
"""
|
||||
loader_mapping: Mapping[str, LoadingCallable] = {
|
||||
"stuff": _load_stuff_chain,
|
||||
}
|
||||
if chain_type not in loader_mapping:
|
||||
raise ValueError(
|
||||
f"Got unsupported chain type: {chain_type}. "
|
||||
f"Should be one of {loader_mapping.keys()}"
|
||||
)
|
||||
return loader_mapping[chain_type](
|
||||
llm, verbose=verbose, callback_manager=callback_manager, **kwargs
|
||||
)
|
||||
@@ -87,7 +87,7 @@ from langchain.document_loaders import (
|
||||
UnstructuredExcelLoader,
|
||||
)
|
||||
from langchain.text_splitter import RecursiveCharacterTextSplitter, Language
|
||||
from langchain.chains.question_answering import load_qa_chain
|
||||
from expanded_pipelines import load_qa_chain
|
||||
from langchain.docstore.document import Document
|
||||
from langchain import PromptTemplate, HuggingFaceTextGenInference
|
||||
from langchain.vectorstores import Chroma
|
||||
@@ -2958,56 +2958,8 @@ def get_similarity_chain(
|
||||
template=template,
|
||||
)
|
||||
chain = load_qa_chain(llm, prompt=prompt)
|
||||
else:
|
||||
# only if use_openai_model = True, unused normally except in testing
|
||||
chain = load_qa_with_sources_chain(llm)
|
||||
if not use_context:
|
||||
chain_kwargs = dict(input_documents=[], question=query)
|
||||
else:
|
||||
chain_kwargs = dict(input_documents=docs, question=query)
|
||||
chain_kwargs = dict(input_documents=docs, question=query)
|
||||
target = wrapped_partial(chain, chain_kwargs)
|
||||
elif langchain_action in [
|
||||
LangChainAction.SUMMARIZE_MAP.value,
|
||||
LangChainAction.SUMMARIZE_REFINE,
|
||||
LangChainAction.SUMMARIZE_ALL.value,
|
||||
]:
|
||||
from langchain.chains.summarize import load_summarize_chain
|
||||
|
||||
if langchain_action == LangChainAction.SUMMARIZE_MAP.value:
|
||||
prompt = PromptTemplate(
|
||||
input_variables=["text"], template=template
|
||||
)
|
||||
chain = load_summarize_chain(
|
||||
llm,
|
||||
chain_type="map_reduce",
|
||||
map_prompt=prompt,
|
||||
combine_prompt=prompt,
|
||||
return_intermediate_steps=True,
|
||||
)
|
||||
target = wrapped_partial(
|
||||
chain, {"input_documents": docs}
|
||||
) # , return_only_outputs=True)
|
||||
elif langchain_action == LangChainAction.SUMMARIZE_ALL.value:
|
||||
assert use_template
|
||||
prompt = PromptTemplate(
|
||||
input_variables=["text"], template=template
|
||||
)
|
||||
chain = load_summarize_chain(
|
||||
llm,
|
||||
chain_type="stuff",
|
||||
prompt=prompt,
|
||||
return_intermediate_steps=True,
|
||||
)
|
||||
target = wrapped_partial(chain)
|
||||
elif langchain_action == LangChainAction.SUMMARIZE_REFINE.value:
|
||||
chain = load_summarize_chain(
|
||||
llm, chain_type="refine", return_intermediate_steps=True
|
||||
)
|
||||
target = wrapped_partial(chain)
|
||||
else:
|
||||
raise RuntimeError(
|
||||
"No such langchain_action=%s" % langchain_action
|
||||
)
|
||||
else:
|
||||
raise RuntimeError("No such langchain_action=%s" % langchain_action)
|
||||
|
||||
|
||||
Reference in New Issue
Block a user