Spaces:
Runtime error
Runtime error
File size: 8,866 Bytes
129cd69 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 |
from __future__ import annotations
import re
from abc import abstractmethod
from typing import Any, Dict, List, Optional, Sequence, Tuple
import numpy as np
from langchain_core.language_models import BaseLanguageModel
from langchain_core.outputs import Generation
from langchain_core.prompts import BasePromptTemplate
from langchain_core.pydantic_v1 import Field
from langchain_core.retrievers import BaseRetriever
from langchain.callbacks.manager import (
CallbackManagerForChainRun,
)
from langchain.chains.base import Chain
from langchain.chains.flare.prompts import (
PROMPT,
QUESTION_GENERATOR_PROMPT,
FinishedOutputParser,
)
from langchain.chains.llm import LLMChain
from langchain.llms.openai import OpenAI
class _ResponseChain(LLMChain):
"""Base class for chains that generate responses."""
prompt: BasePromptTemplate = PROMPT
@property
def input_keys(self) -> List[str]:
return self.prompt.input_variables
def generate_tokens_and_log_probs(
self,
_input: Dict[str, Any],
*,
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Tuple[Sequence[str], Sequence[float]]:
llm_result = self.generate([_input], run_manager=run_manager)
return self._extract_tokens_and_log_probs(llm_result.generations[0])
@abstractmethod
def _extract_tokens_and_log_probs(
self, generations: List[Generation]
) -> Tuple[Sequence[str], Sequence[float]]:
"""Extract tokens and log probs from response."""
class _OpenAIResponseChain(_ResponseChain):
"""Chain that generates responses from user input and context."""
llm: OpenAI = Field(
default_factory=lambda: OpenAI(
max_tokens=32, model_kwargs={"logprobs": 1}, temperature=0
)
)
def _extract_tokens_and_log_probs(
self, generations: List[Generation]
) -> Tuple[Sequence[str], Sequence[float]]:
tokens = []
log_probs = []
for gen in generations:
if gen.generation_info is None:
raise ValueError
tokens.extend(gen.generation_info["logprobs"]["tokens"])
log_probs.extend(gen.generation_info["logprobs"]["token_logprobs"])
return tokens, log_probs
class QuestionGeneratorChain(LLMChain):
"""Chain that generates questions from uncertain spans."""
prompt: BasePromptTemplate = QUESTION_GENERATOR_PROMPT
"""Prompt template for the chain."""
@property
def input_keys(self) -> List[str]:
"""Input keys for the chain."""
return ["user_input", "context", "response"]
def _low_confidence_spans(
tokens: Sequence[str],
log_probs: Sequence[float],
min_prob: float,
min_token_gap: int,
num_pad_tokens: int,
) -> List[str]:
_low_idx = np.where(np.exp(log_probs) < min_prob)[0]
low_idx = [i for i in _low_idx if re.search(r"\w", tokens[i])]
if len(low_idx) == 0:
return []
spans = [[low_idx[0], low_idx[0] + num_pad_tokens + 1]]
for i, idx in enumerate(low_idx[1:]):
end = idx + num_pad_tokens + 1
if idx - low_idx[i] < min_token_gap:
spans[-1][1] = end
else:
spans.append([idx, end])
return ["".join(tokens[start:end]) for start, end in spans]
class FlareChain(Chain):
"""Chain that combines a retriever, a question generator,
and a response generator."""
question_generator_chain: QuestionGeneratorChain
"""Chain that generates questions from uncertain spans."""
response_chain: _ResponseChain = Field(default_factory=_OpenAIResponseChain)
"""Chain that generates responses from user input and context."""
output_parser: FinishedOutputParser = Field(default_factory=FinishedOutputParser)
"""Parser that determines whether the chain is finished."""
retriever: BaseRetriever
"""Retriever that retrieves relevant documents from a user input."""
min_prob: float = 0.2
"""Minimum probability for a token to be considered low confidence."""
min_token_gap: int = 5
"""Minimum number of tokens between two low confidence spans."""
num_pad_tokens: int = 2
"""Number of tokens to pad around a low confidence span."""
max_iter: int = 10
"""Maximum number of iterations."""
start_with_retrieval: bool = True
"""Whether to start with retrieval."""
@property
def input_keys(self) -> List[str]:
"""Input keys for the chain."""
return ["user_input"]
@property
def output_keys(self) -> List[str]:
"""Output keys for the chain."""
return ["response"]
def _do_generation(
self,
questions: List[str],
user_input: str,
response: str,
_run_manager: CallbackManagerForChainRun,
) -> Tuple[str, bool]:
callbacks = _run_manager.get_child()
docs = []
for question in questions:
docs.extend(self.retriever.get_relevant_documents(question))
context = "\n\n".join(d.page_content for d in docs)
result = self.response_chain.predict(
user_input=user_input,
context=context,
response=response,
callbacks=callbacks,
)
marginal, finished = self.output_parser.parse(result)
return marginal, finished
def _do_retrieval(
self,
low_confidence_spans: List[str],
_run_manager: CallbackManagerForChainRun,
user_input: str,
response: str,
initial_response: str,
) -> Tuple[str, bool]:
question_gen_inputs = [
{
"user_input": user_input,
"current_response": initial_response,
"uncertain_span": span,
}
for span in low_confidence_spans
]
callbacks = _run_manager.get_child()
question_gen_outputs = self.question_generator_chain.apply(
question_gen_inputs, callbacks=callbacks
)
questions = [
output[self.question_generator_chain.output_keys[0]]
for output in question_gen_outputs
]
_run_manager.on_text(
f"Generated Questions: {questions}", color="yellow", end="\n"
)
return self._do_generation(questions, user_input, response, _run_manager)
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
user_input = inputs[self.input_keys[0]]
response = ""
for i in range(self.max_iter):
_run_manager.on_text(
f"Current Response: {response}", color="blue", end="\n"
)
_input = {"user_input": user_input, "context": "", "response": response}
tokens, log_probs = self.response_chain.generate_tokens_and_log_probs(
_input, run_manager=_run_manager
)
low_confidence_spans = _low_confidence_spans(
tokens,
log_probs,
self.min_prob,
self.min_token_gap,
self.num_pad_tokens,
)
initial_response = response.strip() + " " + "".join(tokens)
if not low_confidence_spans:
response = initial_response
final_response, finished = self.output_parser.parse(response)
if finished:
return {self.output_keys[0]: final_response}
continue
marginal, finished = self._do_retrieval(
low_confidence_spans,
_run_manager,
user_input,
response,
initial_response,
)
response = response.strip() + " " + marginal
if finished:
break
return {self.output_keys[0]: response}
@classmethod
def from_llm(
cls, llm: BaseLanguageModel, max_generation_len: int = 32, **kwargs: Any
) -> FlareChain:
"""Creates a FlareChain from a language model.
Args:
llm: Language model to use.
max_generation_len: Maximum length of the generated response.
**kwargs: Additional arguments to pass to the constructor.
Returns:
FlareChain class with the given language model.
"""
question_gen_chain = QuestionGeneratorChain(llm=llm)
response_llm = OpenAI(
max_tokens=max_generation_len, model_kwargs={"logprobs": 1}, temperature=0
)
response_chain = _OpenAIResponseChain(llm=response_llm)
return cls(
question_generator_chain=question_gen_chain,
response_chain=response_chain,
**kwargs,
)
|