Source code for langchain.chains.flare.base
from __future__ import annotations
import re
from abc import abstractmethod
from typing import Any, Dict, List, Optional, Sequence, Tuple
import numpy as np
from pydantic import Field
from langchain.base_language import BaseLanguageModel
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 import OpenAI
from langchain.prompts import BasePromptTemplate
from langchain.schema import BaseRetriever, Generation
class _ResponseChain(LLMChain):
    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):
    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):
    prompt: BasePromptTemplate = QUESTION_GENERATOR_PROMPT
    @property
    def input_keys(self) -> List[str]:
        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]
[docs]class FlareChain(Chain):
    question_generator_chain: QuestionGeneratorChain
[docs]class FlareChain(Chain):
    question_generator_chain: QuestionGeneratorChain
    response_chain: _ResponseChain = Field(default_factory=_OpenAIResponseChain)
    output_parser: FinishedOutputParser = Field(default_factory=FinishedOutputParser)
    retriever: BaseRetriever
    min_prob: float = 0.2
    min_token_gap: int = 5
    num_pad_tokens: int = 2
    max_iter: int = 10
    start_with_retrieval: bool = True
    @property
    def input_keys(self) -> List[str]:
        return ["user_input"]
    @property
    def output_keys(self) -> List[str]:
        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,
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)
)
            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}
[docs]    @classmethod
    def from_llm(
        cls, llm: BaseLanguageModel, max_generation_len: int = 32, **kwargs: Any
    ) -> FlareChain:
        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,
        )
By Harrison Chase