elif llm_output.startswith("Answer:"):
            answer = llm_output
        elif "Answer:" in llm_output:
            answer = "Answer: " + llm_output.split("Answer:")[-1]
        else:
            raise ValueError(f"unknown format from LLM: {llm_output}")
        return {self.output_key: answer}
    def _call(
        self,
        inputs: Dict[str, str],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, str]:
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        _run_manager.on_text(inputs[self.input_key])
        llm_output = self.llm_chain.predict(
            question=inputs[self.input_key],
            stop=["```output"],
            callbacks=_run_manager.get_child(),
        )
        return self._process_llm_result(llm_output, _run_manager)
    async def _acall(
        self,
        inputs: Dict[str, str],
        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
    ) -> Dict[str, str]:
        _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
        await _run_manager.on_text(inputs[self.input_key])
        llm_output = await self.llm_chain.apredict(
            question=inputs[self.input_key],
            stop=["```output"],
            callbacks=_run_manager.get_child(),
        )
        return await self._aprocess_llm_result(llm_output, _run_manager)
    @property
    def _chain_type(self) -> str:
        return "llm_math_chain"
[docs]    @classmethod
    def from_llm(
        cls,


[docs]    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,
        prompt: BasePromptTemplate = PROMPT,
        **kwargs: Any,
    ) -> LLMMathChain:
        llm_chain = LLMChain(llm=llm, prompt=prompt)
        return cls(llm_chain=llm_chain, **kwargs)


Source code for langchain.chains.llm_summarization_checker.base
"""Chain for summarization with self-verification."""
from __future__ import annotations
import warnings
from pathlib import Path
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.sequential import SequentialChain
from langchain.prompts.prompt import PromptTemplate
PROMPTS_DIR = Path(__file__).parent / "prompts"
CREATE_ASSERTIONS_PROMPT = PromptTemplate.from_file(
    PROMPTS_DIR / "create_facts.txt", ["summary"]
)
CHECK_ASSERTIONS_PROMPT = PromptTemplate.from_file(
    PROMPTS_DIR / "check_facts.txt", ["assertions"]
)
REVISED_SUMMARY_PROMPT = PromptTemplate.from_file(
    PROMPTS_DIR / "revise_summary.txt", ["checked_assertions", "summary"]
)
ARE_ALL_TRUE_PROMPT = PromptTemplate.from_file(
    PROMPTS_DIR / "are_all_true_prompt.txt", ["checked_assertions"]
)
def _load_sequential_chain(
    llm: BaseLanguageModel,
    create_assertions_prompt: PromptTemplate,
    check_assertions_prompt: PromptTemplate,
    revised_summary_prompt: PromptTemplate,
    are_all_true_prompt: PromptTemplate,
    verbose: bool = False,
) -> SequentialChain:
    chain = SequentialChain(
        chains=[
            LLMChain(
                llm=llm,
                prompt=create_assertions_prompt,
                output_key="assertions",
                verbose=verbose,
            ),
            LLMChain(


verbose=verbose,
            ),
            LLMChain(
                llm=llm,
                prompt=check_assertions_prompt,
                output_key="checked_assertions",
                verbose=verbose,
            ),
            LLMChain(
                llm=llm,
                prompt=revised_summary_prompt,
                output_key="revised_summary",
                verbose=verbose,
            ),
            LLMChain(
                llm=llm,
                output_key="all_true",
                prompt=are_all_true_prompt,
                verbose=verbose,
            ),
        ],
        input_variables=["summary"],
        output_variables=["all_true", "revised_summary"],
        verbose=verbose,
    )
    return chain
[docs]class LLMSummarizationCheckerChain(Chain):
    """Chain for question-answering with self-verification.
    Example:
        .. code-block:: python
            from langchain import OpenAI, LLMSummarizationCheckerChain
            llm = OpenAI(temperature=0.0)
            checker_chain = LLMSummarizationCheckerChain.from_llm(llm)
    """
    sequential_chain: SequentialChain
    llm: Optional[BaseLanguageModel] = None
    """[Deprecated] LLM wrapper to use."""
    create_assertions_prompt: PromptTemplate = CREATE_ASSERTIONS_PROMPT
    """[Deprecated]"""
    check_assertions_prompt: PromptTemplate = CHECK_ASSERTIONS_PROMPT
    """[Deprecated]"""
    revised_summary_prompt: PromptTemplate = REVISED_SUMMARY_PROMPT
    """[Deprecated]"""
    are_all_true_prompt: PromptTemplate = ARE_ALL_TRUE_PROMPT
    """[Deprecated]"""
    input_key: str = "query"  #: :meta private:


input_key: str = "query"  #: :meta private:
    output_key: str = "result"  #: :meta private:
    max_checks: int = 2
    """Maximum number of times to check the assertions. Default to double-checking."""
[docs]    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
[docs]    @root_validator(pre=True)
    def raise_deprecation(cls, values: Dict) -> Dict:
        if "llm" in values:
            warnings.warn(
                "Directly instantiating an LLMSummarizationCheckerChain with an llm is "
                "deprecated. Please instantiate with"
                " sequential_chain argument or using the from_llm class method."
            )
            if "sequential_chain" not in values and values["llm"] is not None:
                values["sequential_chain"] = _load_sequential_chain(
                    values["llm"],
                    values.get("create_assertions_prompt", CREATE_ASSERTIONS_PROMPT),
                    values.get("check_assertions_prompt", CHECK_ASSERTIONS_PROMPT),
                    values.get("revised_summary_prompt", REVISED_SUMMARY_PROMPT),
                    values.get("are_all_true_prompt", ARE_ALL_TRUE_PROMPT),
                    verbose=values.get("verbose", False),
                )
        return values
    @property
    def input_keys(self) -> List[str]:
        """Return the singular input key.
        :meta private:
        """
        return [self.input_key]
    @property
    def output_keys(self) -> List[str]:
        """Return the singular output key.
        :meta private:
        """
        return [self.output_key]
    def _call(
        self,


return [self.output_key]
    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, str]:
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        all_true = False
        count = 0
        output = None
        original_input = inputs[self.input_key]
        chain_input = original_input
        while not all_true and count < self.max_checks:
            output = self.sequential_chain(
                {"summary": chain_input}, callbacks=_run_manager.get_child()
            )
            count += 1
            if output["all_true"].strip() == "True":
                break
            if self.verbose:
                print(output["revised_summary"])
            chain_input = output["revised_summary"]
        if not output:
            raise ValueError("No output from chain")
        return {self.output_key: output["revised_summary"].strip()}
    @property
    def _chain_type(self) -> str:
        return "llm_summarization_checker_chain"
[docs]    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,
        create_assertions_prompt: PromptTemplate = CREATE_ASSERTIONS_PROMPT,
        check_assertions_prompt: PromptTemplate = CHECK_ASSERTIONS_PROMPT,
        revised_summary_prompt: PromptTemplate = REVISED_SUMMARY_PROMPT,
        are_all_true_prompt: PromptTemplate = ARE_ALL_TRUE_PROMPT,
        verbose: bool = False,
        **kwargs: Any,
    ) -> LLMSummarizationCheckerChain:
        chain = _load_sequential_chain(
            llm,
            create_assertions_prompt,
            check_assertions_prompt,


llm,
            create_assertions_prompt,
            check_assertions_prompt,
            revised_summary_prompt,
            are_all_true_prompt,
            verbose=verbose,
        )
        return cls(sequential_chain=chain, verbose=verbose, **kwargs)


Source code for langchain.chains.qa_with_sources.vector_db
"""Question-answering with sources over a vector database."""
import warnings
from typing import Any, Dict, List
from pydantic import Field, root_validator
from langchain.callbacks.manager import (
    AsyncCallbackManagerForChainRun,
    CallbackManagerForChainRun,
)
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.qa_with_sources.base import BaseQAWithSourcesChain
from langchain.docstore.document import Document
from langchain.vectorstores.base import VectorStore
[docs]class VectorDBQAWithSourcesChain(BaseQAWithSourcesChain):
    """Question-answering with sources over a vector database."""
    vectorstore: VectorStore = Field(exclude=True)
    """Vector Database to connect to."""
    k: int = 4
    """Number of results to return from store"""
    reduce_k_below_max_tokens: bool = False
    """Reduce the number of results to return from store based on tokens limit"""
    max_tokens_limit: int = 3375
    """Restrict the docs to return from store based on tokens,
    enforced only for StuffDocumentChain and if reduce_k_below_max_tokens is to true"""
    search_kwargs: Dict[str, Any] = Field(default_factory=dict)
    """Extra search args."""
    def _reduce_tokens_below_limit(self, docs: List[Document]) -> List[Document]:
        num_docs = len(docs)
        if self.reduce_k_below_max_tokens and isinstance(
            self.combine_documents_chain, StuffDocumentsChain
        ):
            tokens = [
                self.combine_documents_chain.llm_chain.llm.get_num_tokens(
                    doc.page_content
                )
                for doc in docs
            ]
            token_count = sum(tokens[:num_docs])


for doc in docs
            ]
            token_count = sum(tokens[:num_docs])
            while token_count > self.max_tokens_limit:
                num_docs -= 1
                token_count -= tokens[num_docs]
        return docs[:num_docs]
    def _get_docs(
        self, inputs: Dict[str, Any], *, run_manager: CallbackManagerForChainRun
    ) -> List[Document]:
        question = inputs[self.question_key]
        docs = self.vectorstore.similarity_search(
            question, k=self.k, **self.search_kwargs
        )
        return self._reduce_tokens_below_limit(docs)
    async def _aget_docs(
        self, inputs: Dict[str, Any], *, run_manager: AsyncCallbackManagerForChainRun
    ) -> List[Document]:
        raise NotImplementedError("VectorDBQAWithSourcesChain does not support async")
[docs]    @root_validator()
    def raise_deprecation(cls, values: Dict) -> Dict:
        warnings.warn(
            "`VectorDBQAWithSourcesChain` is deprecated - "
            "please use `from langchain.chains import RetrievalQAWithSourcesChain`"
        )
        return values
    @property
    def _chain_type(self) -> str:
        return "vector_db_qa_with_sources_chain"


Source code for langchain.chains.qa_with_sources.base
"""Question answering with sources over documents."""
from __future__ import annotations
import inspect
import re
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import (
    AsyncCallbackManagerForChainRun,
    CallbackManagerForChainRun,
)
from langchain.chains.base import Chain
from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.llm import LLMChain
from langchain.chains.qa_with_sources.loading import load_qa_with_sources_chain
from langchain.chains.qa_with_sources.map_reduce_prompt import (
    COMBINE_PROMPT,
    EXAMPLE_PROMPT,
    QUESTION_PROMPT,
)
from langchain.docstore.document import Document
from langchain.prompts.base import BasePromptTemplate
[docs]class BaseQAWithSourcesChain(Chain, ABC):
    """Question answering with sources over documents."""
    combine_documents_chain: BaseCombineDocumentsChain
    """Chain to use to combine documents."""
    question_key: str = "question"  #: :meta private:
    input_docs_key: str = "docs"  #: :meta private:
    answer_key: str = "answer"  #: :meta private:
    sources_answer_key: str = "sources"  #: :meta private:
    return_source_documents: bool = False
    """Return the source documents."""
[docs]    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,


def from_llm(
        cls,
        llm: BaseLanguageModel,
        document_prompt: BasePromptTemplate = EXAMPLE_PROMPT,
        question_prompt: BasePromptTemplate = QUESTION_PROMPT,
        combine_prompt: BasePromptTemplate = COMBINE_PROMPT,
        **kwargs: Any,
    ) -> BaseQAWithSourcesChain:
        """Construct the chain from an LLM."""
        llm_question_chain = LLMChain(llm=llm, prompt=question_prompt)
        llm_combine_chain = LLMChain(llm=llm, prompt=combine_prompt)
        combine_results_chain = StuffDocumentsChain(
            llm_chain=llm_combine_chain,
            document_prompt=document_prompt,
            document_variable_name="summaries",
        )
        combine_document_chain = MapReduceDocumentsChain(
            llm_chain=llm_question_chain,
            combine_document_chain=combine_results_chain,
            document_variable_name="context",
        )
        return cls(
            combine_documents_chain=combine_document_chain,
            **kwargs,
        )
[docs]    @classmethod
    def from_chain_type(
        cls,
        llm: BaseLanguageModel,
        chain_type: str = "stuff",
        chain_type_kwargs: Optional[dict] = None,
        **kwargs: Any,
    ) -> BaseQAWithSourcesChain:
        """Load chain from chain type."""
        _chain_kwargs = chain_type_kwargs or {}
        combine_document_chain = load_qa_with_sources_chain(
            llm, chain_type=chain_type, **_chain_kwargs
        )
        return cls(combine_documents_chain=combine_document_chain, **kwargs)
[docs]    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid


"""Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
    @property
    def input_keys(self) -> List[str]:
        """Expect input key.
        :meta private:
        """
        return [self.question_key]
    @property
    def output_keys(self) -> List[str]:
        """Return output key.
        :meta private:
        """
        _output_keys = [self.answer_key, self.sources_answer_key]
        if self.return_source_documents:
            _output_keys = _output_keys + ["source_documents"]
        return _output_keys
[docs]    @root_validator(pre=True)
    def validate_naming(cls, values: Dict) -> Dict:
        """Fix backwards compatability in naming."""
        if "combine_document_chain" in values:
            values["combine_documents_chain"] = values.pop("combine_document_chain")
        return values
    @abstractmethod
    def _get_docs(
        self,
        inputs: Dict[str, Any],
        *,
        run_manager: CallbackManagerForChainRun,
    ) -> List[Document]:
        """Get docs to run questioning over."""
    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, str]:
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        accepts_run_manager = (
            "run_manager" in inspect.signature(self._get_docs).parameters
        )
        if accepts_run_manager:
            docs = self._get_docs(inputs, run_manager=_run_manager)
        else:
            docs = self._get_docs(inputs)  # type: ignore[call-arg]


docs = self._get_docs(inputs)  # type: ignore[call-arg]
        answer = self.combine_documents_chain.run(
            input_documents=docs, callbacks=_run_manager.get_child(), **inputs
        )
        if re.search(r"SOURCES:\s", answer):
            answer, sources = re.split(r"SOURCES:\s", answer)
        else:
            sources = ""
        result: Dict[str, Any] = {
            self.answer_key: answer,
            self.sources_answer_key: sources,
        }
        if self.return_source_documents:
            result["source_documents"] = docs
        return result
    @abstractmethod
    async def _aget_docs(
        self,
        inputs: Dict[str, Any],
        *,
        run_manager: AsyncCallbackManagerForChainRun,
    ) -> List[Document]:
        """Get docs to run questioning over."""
    async def _acall(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
        accepts_run_manager = (
            "run_manager" in inspect.signature(self._aget_docs).parameters
        )
        if accepts_run_manager:
            docs = await self._aget_docs(inputs, run_manager=_run_manager)
        else:
            docs = await self._aget_docs(inputs)  # type: ignore[call-arg]
        answer = await self.combine_documents_chain.arun(
            input_documents=docs, callbacks=_run_manager.get_child(), **inputs
        )
        if re.search(r"SOURCES:\s", answer):
            answer, sources = re.split(r"SOURCES:\s", answer)
        else:


answer, sources = re.split(r"SOURCES:\s", answer)
        else:
            sources = ""
        result: Dict[str, Any] = {
            self.answer_key: answer,
            self.sources_answer_key: sources,
        }
        if self.return_source_documents:
            result["source_documents"] = docs
        return result
[docs]class QAWithSourcesChain(BaseQAWithSourcesChain):
    """Question answering with sources over documents."""
    input_docs_key: str = "docs"  #: :meta private:
    @property
    def input_keys(self) -> List[str]:
        """Expect input key.
        :meta private:
        """
        return [self.input_docs_key, self.question_key]
    def _get_docs(
        self,
        inputs: Dict[str, Any],
        *,
        run_manager: CallbackManagerForChainRun,
    ) -> List[Document]:
        """Get docs to run questioning over."""
        return inputs.pop(self.input_docs_key)
    async def _aget_docs(
        self,
        inputs: Dict[str, Any],
        *,
        run_manager: AsyncCallbackManagerForChainRun,
    ) -> List[Document]:
        """Get docs to run questioning over."""
        return inputs.pop(self.input_docs_key)
    @property
    def _chain_type(self) -> str:
        return "qa_with_sources_chain"


Source code for langchain.chains.qa_with_sources.loading
"""Load question answering with sources chains."""
from __future__ import annotations
from typing import Any, Mapping, Optional, Protocol
from langchain.base_language import BaseLanguageModel
from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain
from langchain.chains.combine_documents.map_rerank import MapRerankDocumentsChain
from langchain.chains.combine_documents.refine import RefineDocumentsChain
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.llm import LLMChain
from langchain.chains.qa_with_sources import (
    map_reduce_prompt,
    refine_prompts,
    stuff_prompt,
)
from langchain.chains.question_answering.map_rerank_prompt import (
    PROMPT as MAP_RERANK_PROMPT,
)
from langchain.prompts.base import BasePromptTemplate
[docs]class LoadingCallable(Protocol):
    """Interface for loading the combine documents chain."""
[docs]    def __call__(
        self, llm: BaseLanguageModel, **kwargs: Any
    ) -> BaseCombineDocumentsChain:
        """Callable to load the combine documents chain."""
def _load_map_rerank_chain(
    llm: BaseLanguageModel,
    prompt: BasePromptTemplate = MAP_RERANK_PROMPT,
    verbose: bool = False,
    document_variable_name: str = "context",
    rank_key: str = "score",
    answer_key: str = "answer",
    **kwargs: Any,
) -> MapRerankDocumentsChain:
    llm_chain = LLMChain(llm=llm, prompt=prompt, verbose=verbose)
    return MapRerankDocumentsChain(


return MapRerankDocumentsChain(
        llm_chain=llm_chain,
        rank_key=rank_key,
        answer_key=answer_key,
        document_variable_name=document_variable_name,
        **kwargs,
    )
def _load_stuff_chain(
    llm: BaseLanguageModel,
    prompt: BasePromptTemplate = stuff_prompt.PROMPT,
    document_prompt: BasePromptTemplate = stuff_prompt.EXAMPLE_PROMPT,
    document_variable_name: str = "summaries",
    verbose: Optional[bool] = None,
    **kwargs: Any,
) -> StuffDocumentsChain:
    llm_chain = LLMChain(llm=llm, prompt=prompt, verbose=verbose)
    return StuffDocumentsChain(
        llm_chain=llm_chain,
        document_variable_name=document_variable_name,
        document_prompt=document_prompt,
        verbose=verbose,
        **kwargs,
    )
def _load_map_reduce_chain(
    llm: BaseLanguageModel,
    question_prompt: BasePromptTemplate = map_reduce_prompt.QUESTION_PROMPT,
    combine_prompt: BasePromptTemplate = map_reduce_prompt.COMBINE_PROMPT,
    document_prompt: BasePromptTemplate = map_reduce_prompt.EXAMPLE_PROMPT,
    combine_document_variable_name: str = "summaries",
    map_reduce_document_variable_name: str = "context",
    collapse_prompt: Optional[BasePromptTemplate] = None,
    reduce_llm: Optional[BaseLanguageModel] = None,
    collapse_llm: Optional[BaseLanguageModel] = None,
    verbose: Optional[bool] = None,
    **kwargs: Any,
) -> MapReduceDocumentsChain:
    map_chain = LLMChain(llm=llm, prompt=question_prompt, verbose=verbose)


_reduce_llm = reduce_llm or llm
    reduce_chain = LLMChain(llm=_reduce_llm, prompt=combine_prompt, verbose=verbose)
    combine_document_chain = StuffDocumentsChain(
        llm_chain=reduce_chain,
        document_variable_name=combine_document_variable_name,
        document_prompt=document_prompt,
        verbose=verbose,
    )
    if collapse_prompt is None:
        collapse_chain = None
        if collapse_llm is not None:
            raise ValueError(
                "collapse_llm provided, but collapse_prompt was not: please "
                "provide one or stop providing collapse_llm."
            )
    else:
        _collapse_llm = collapse_llm or llm
        collapse_chain = StuffDocumentsChain(
            llm_chain=LLMChain(
                llm=_collapse_llm,
                prompt=collapse_prompt,
                verbose=verbose,
            ),
            document_variable_name=combine_document_variable_name,
            document_prompt=document_prompt,
        )
    return MapReduceDocumentsChain(
        llm_chain=map_chain,
        combine_document_chain=combine_document_chain,
        document_variable_name=map_reduce_document_variable_name,
        collapse_document_chain=collapse_chain,
        verbose=verbose,
        **kwargs,
    )
def _load_refine_chain(
    llm: BaseLanguageModel,
    question_prompt: BasePromptTemplate = refine_prompts.DEFAULT_TEXT_QA_PROMPT,
    refine_prompt: BasePromptTemplate = refine_prompts.DEFAULT_REFINE_PROMPT,
    document_prompt: BasePromptTemplate = refine_prompts.EXAMPLE_PROMPT,
    document_variable_name: str = "context_str",
    initial_response_name: str = "existing_answer",
    refine_llm: Optional[BaseLanguageModel] = None,


refine_llm: Optional[BaseLanguageModel] = None,
    verbose: Optional[bool] = None,
    **kwargs: Any,
) -> RefineDocumentsChain:
    initial_chain = LLMChain(llm=llm, prompt=question_prompt, verbose=verbose)
    _refine_llm = refine_llm or llm
    refine_chain = LLMChain(llm=_refine_llm, prompt=refine_prompt, verbose=verbose)
    return RefineDocumentsChain(
        initial_llm_chain=initial_chain,
        refine_llm_chain=refine_chain,
        document_variable_name=document_variable_name,
        initial_response_name=initial_response_name,
        document_prompt=document_prompt,
        verbose=verbose,
        **kwargs,
    )
[docs]def load_qa_with_sources_chain(
    llm: BaseLanguageModel,
    chain_type: str = "stuff",
    verbose: Optional[bool] = None,
    **kwargs: Any,
) -> BaseCombineDocumentsChain:
    """Load question answering with sources chain.
    Args:
        llm: Language Model to use in the chain.
        chain_type: Type of document combining chain to use. Should be one of "stuff",
            "map_reduce", "refine" and "map_rerank".
        verbose: Whether chains should be run in verbose mode or not. Note that this
            applies to all chains that make up the final chain.
    Returns:
        A chain to use for question answering with sources.
    """
    loader_mapping: Mapping[str, LoadingCallable] = {
        "stuff": _load_stuff_chain,
        "map_reduce": _load_map_reduce_chain,
        "refine": _load_refine_chain,


"refine": _load_refine_chain,
        "map_rerank": _load_map_rerank_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()}"
        )
    _func: LoadingCallable = loader_mapping[chain_type]
    return _func(llm, verbose=verbose, **kwargs)


Source code for langchain.chains.qa_with_sources.retrieval
"""Question-answering with sources over an index."""
from typing import Any, Dict, List
from pydantic import Field
from langchain.callbacks.manager import (
    AsyncCallbackManagerForChainRun,
    CallbackManagerForChainRun,
)
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.qa_with_sources.base import BaseQAWithSourcesChain
from langchain.docstore.document import Document
from langchain.schema import BaseRetriever
[docs]class RetrievalQAWithSourcesChain(BaseQAWithSourcesChain):
    """Question-answering with sources over an index."""
    retriever: BaseRetriever = Field(exclude=True)
    """Index to connect to."""
    reduce_k_below_max_tokens: bool = False
    """Reduce the number of results to return from store based on tokens limit"""
    max_tokens_limit: int = 3375
    """Restrict the docs to return from store based on tokens,
    enforced only for StuffDocumentChain and if reduce_k_below_max_tokens is to true"""
    def _reduce_tokens_below_limit(self, docs: List[Document]) -> List[Document]:
        num_docs = len(docs)
        if self.reduce_k_below_max_tokens and isinstance(
            self.combine_documents_chain, StuffDocumentsChain
        ):
            tokens = [
                self.combine_documents_chain.llm_chain.llm.get_num_tokens(
                    doc.page_content
                )
                for doc in docs
            ]
            token_count = sum(tokens[:num_docs])
            while token_count > self.max_tokens_limit:
                num_docs -= 1
                token_count -= tokens[num_docs]
        return docs[:num_docs]
    def _get_docs(


return docs[:num_docs]
    def _get_docs(
        self, inputs: Dict[str, Any], *, run_manager: CallbackManagerForChainRun
    ) -> List[Document]:
        question = inputs[self.question_key]
        docs = self.retriever.get_relevant_documents(
            question, callbacks=run_manager.get_child()
        )
        return self._reduce_tokens_below_limit(docs)
    async def _aget_docs(
        self, inputs: Dict[str, Any], *, run_manager: AsyncCallbackManagerForChainRun
    ) -> List[Document]:
        question = inputs[self.question_key]
        docs = await self.retriever.aget_relevant_documents(
            question, callbacks=run_manager.get_child()
        )
        return self._reduce_tokens_below_limit(docs)


Source code for langchain.chains.summarize.__init__
"""Load summarizing chains."""
from typing import Any, Mapping, Optional, Protocol
from langchain.base_language import BaseLanguageModel
from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain
from langchain.chains.combine_documents.refine import RefineDocumentsChain
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.llm import LLMChain
from langchain.chains.summarize import map_reduce_prompt, refine_prompts, stuff_prompt
from langchain.prompts.base import BasePromptTemplate
[docs]class LoadingCallable(Protocol):
    """Interface for loading the combine documents chain."""
[docs]    def __call__(
        self, llm: BaseLanguageModel, **kwargs: Any
    ) -> BaseCombineDocumentsChain:
        """Callable to load the combine documents chain."""
def _load_stuff_chain(
    llm: BaseLanguageModel,
    prompt: BasePromptTemplate = stuff_prompt.PROMPT,
    document_variable_name: str = "text",
    verbose: Optional[bool] = None,
    **kwargs: Any,
) -> StuffDocumentsChain:
    llm_chain = LLMChain(llm=llm, prompt=prompt, verbose=verbose)
    # TODO: document prompt
    return StuffDocumentsChain(
        llm_chain=llm_chain,
        document_variable_name=document_variable_name,
        verbose=verbose,
        **kwargs,
    )
def _load_map_reduce_chain(
    llm: BaseLanguageModel,
    map_prompt: BasePromptTemplate = map_reduce_prompt.PROMPT,
    combine_prompt: BasePromptTemplate = map_reduce_prompt.PROMPT,


combine_prompt: BasePromptTemplate = map_reduce_prompt.PROMPT,
    combine_document_variable_name: str = "text",
    map_reduce_document_variable_name: str = "text",
    collapse_prompt: Optional[BasePromptTemplate] = None,
    reduce_llm: Optional[BaseLanguageModel] = None,
    collapse_llm: Optional[BaseLanguageModel] = None,
    verbose: Optional[bool] = None,
    **kwargs: Any,
) -> MapReduceDocumentsChain:
    map_chain = LLMChain(llm=llm, prompt=map_prompt, verbose=verbose)
    _reduce_llm = reduce_llm or llm
    reduce_chain = LLMChain(llm=_reduce_llm, prompt=combine_prompt, verbose=verbose)
    # TODO: document prompt
    combine_document_chain = StuffDocumentsChain(
        llm_chain=reduce_chain,
        document_variable_name=combine_document_variable_name,
        verbose=verbose,
    )
    if collapse_prompt is None:
        collapse_chain = None
        if collapse_llm is not None:
            raise ValueError(
                "collapse_llm provided, but collapse_prompt was not: please "
                "provide one or stop providing collapse_llm."
            )
    else:
        _collapse_llm = collapse_llm or llm
        collapse_chain = StuffDocumentsChain(
            llm_chain=LLMChain(
                llm=_collapse_llm,
                prompt=collapse_prompt,
                verbose=verbose,
            ),
            document_variable_name=combine_document_variable_name,
        )
    return MapReduceDocumentsChain(
        llm_chain=map_chain,
        combine_document_chain=combine_document_chain,
        document_variable_name=map_reduce_document_variable_name,
        collapse_document_chain=collapse_chain,


collapse_document_chain=collapse_chain,
        verbose=verbose,
        **kwargs,
    )
def _load_refine_chain(
    llm: BaseLanguageModel,
    question_prompt: BasePromptTemplate = refine_prompts.PROMPT,
    refine_prompt: BasePromptTemplate = refine_prompts.REFINE_PROMPT,
    document_variable_name: str = "text",
    initial_response_name: str = "existing_answer",
    refine_llm: Optional[BaseLanguageModel] = None,
    verbose: Optional[bool] = None,
    **kwargs: Any,
) -> RefineDocumentsChain:
    initial_chain = LLMChain(llm=llm, prompt=question_prompt, verbose=verbose)
    _refine_llm = refine_llm or llm
    refine_chain = LLMChain(llm=_refine_llm, prompt=refine_prompt, verbose=verbose)
    return RefineDocumentsChain(
        initial_llm_chain=initial_chain,
        refine_llm_chain=refine_chain,
        document_variable_name=document_variable_name,
        initial_response_name=initial_response_name,
        verbose=verbose,
        **kwargs,
    )
[docs]def load_summarize_chain(
    llm: BaseLanguageModel,
    chain_type: str = "stuff",
    verbose: Optional[bool] = None,
    **kwargs: Any,
) -> BaseCombineDocumentsChain:
    """Load summarizing chain.
    Args:
        llm: Language Model to use in the chain.
        chain_type: Type of document combining chain to use. Should be one of "stuff",
            "map_reduce", and "refine".
        verbose: Whether chains should be run in verbose mode or not. Note that this


verbose: Whether chains should be run in verbose mode or not. Note that this
            applies to all chains that make up the final chain.
    Returns:
        A chain to use for summarizing.
    """
    loader_mapping: Mapping[str, LoadingCallable] = {
        "stuff": _load_stuff_chain,
        "map_reduce": _load_map_reduce_chain,
        "refine": _load_refine_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, **kwargs)


Source code for langchain.chains.qa_generation.base
from __future__ import annotations
import json
from typing import Any, Dict, List, Optional
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.llm import LLMChain
from langchain.chains.qa_generation.prompt import PROMPT_SELECTOR
from langchain.prompts.base import BasePromptTemplate
from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter
[docs]class QAGenerationChain(Chain):
    llm_chain: LLMChain
    text_splitter: TextSplitter = Field(
        default=RecursiveCharacterTextSplitter(chunk_overlap=500)
    )
    input_key: str = "text"
    output_key: str = "questions"
    k: Optional[int] = None
[docs]    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,
        prompt: Optional[BasePromptTemplate] = None,
        **kwargs: Any,
    ) -> QAGenerationChain:
        _prompt = prompt or PROMPT_SELECTOR.get_prompt(llm)
        chain = LLMChain(llm=llm, prompt=_prompt)
        return cls(llm_chain=chain, **kwargs)
    @property
    def _chain_type(self) -> str:
        raise NotImplementedError
    @property
    def input_keys(self) -> List[str]:
        return [self.input_key]
    @property
    def output_keys(self) -> List[str]:
        return [self.output_key]
    def _call(
        self,
        inputs: Dict[str, Any],


def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, List]:
        docs = self.text_splitter.create_documents([inputs[self.input_key]])
        results = self.llm_chain.generate(
            [{"text": d.page_content} for d in docs], run_manager=run_manager
        )
        qa = [json.loads(res[0].text) for res in results.generations]
        return {self.output_key: qa}


Source code for langchain.chains.api.base
"""Chain that makes API calls and summarizes the responses to answer a question."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Field, root_validator
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import (
    AsyncCallbackManagerForChainRun,
    CallbackManagerForChainRun,
)
from langchain.chains.api.prompt import API_RESPONSE_PROMPT, API_URL_PROMPT
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.prompts import BasePromptTemplate
from langchain.requests import TextRequestsWrapper
[docs]class APIChain(Chain):
    """Chain that makes API calls and summarizes the responses to answer a question."""
    api_request_chain: LLMChain
    api_answer_chain: LLMChain
    requests_wrapper: TextRequestsWrapper = Field(exclude=True)
    api_docs: str
    question_key: str = "question"  #: :meta private:
    output_key: str = "output"  #: :meta private:
    @property
    def input_keys(self) -> List[str]:
        """Expect input key.
        :meta private:
        """
        return [self.question_key]
    @property
    def output_keys(self) -> List[str]:
        """Expect output key.
        :meta private:
        """
        return [self.output_key]
[docs]    @root_validator(pre=True)
    def validate_api_request_prompt(cls, values: Dict) -> Dict:
        """Check that api request prompt expects the right variables."""
        input_vars = values["api_request_chain"].prompt.input_variables
        expected_vars = {"question", "api_docs"}
        if set(input_vars) != expected_vars:


if set(input_vars) != expected_vars:
            raise ValueError(
                f"Input variables should be {expected_vars}, got {input_vars}"
            )
        return values
[docs]    @root_validator(pre=True)
    def validate_api_answer_prompt(cls, values: Dict) -> Dict:
        """Check that api answer prompt expects the right variables."""
        input_vars = values["api_answer_chain"].prompt.input_variables
        expected_vars = {"question", "api_docs", "api_url", "api_response"}
        if set(input_vars) != expected_vars:
            raise ValueError(
                f"Input variables should be {expected_vars}, got {input_vars}"
            )
        return values
    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, str]:
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        question = inputs[self.question_key]
        api_url = self.api_request_chain.predict(
            question=question,
            api_docs=self.api_docs,
            callbacks=_run_manager.get_child(),
        )
        _run_manager.on_text(api_url, color="green", end="\n", verbose=self.verbose)
        api_url = api_url.strip()
        api_response = self.requests_wrapper.get(api_url)
        _run_manager.on_text(
            api_response, color="yellow", end="\n", verbose=self.verbose
        )
        answer = self.api_answer_chain.predict(
            question=question,
            api_docs=self.api_docs,
            api_url=api_url,
            api_response=api_response,
            callbacks=_run_manager.get_child(),
        )
        return {self.output_key: answer}


)
        return {self.output_key: answer}
    async def _acall(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
    ) -> Dict[str, str]:
        _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
        question = inputs[self.question_key]
        api_url = await self.api_request_chain.apredict(
            question=question,
            api_docs=self.api_docs,
            callbacks=_run_manager.get_child(),
        )
        await _run_manager.on_text(
            api_url, color="green", end="\n", verbose=self.verbose
        )
        api_url = api_url.strip()
        api_response = await self.requests_wrapper.aget(api_url)
        await _run_manager.on_text(
            api_response, color="yellow", end="\n", verbose=self.verbose
        )
        answer = await self.api_answer_chain.apredict(
            question=question,
            api_docs=self.api_docs,
            api_url=api_url,
            api_response=api_response,
            callbacks=_run_manager.get_child(),
        )
        return {self.output_key: answer}
[docs]    @classmethod
    def from_llm_and_api_docs(
        cls,
        llm: BaseLanguageModel,
        api_docs: str,
        headers: Optional[dict] = None,
        api_url_prompt: BasePromptTemplate = API_URL_PROMPT,
        api_response_prompt: BasePromptTemplate = API_RESPONSE_PROMPT,
        **kwargs: Any,
    ) -> APIChain:
        """Load chain from just an LLM and the api docs."""
        get_request_chain = LLMChain(llm=llm, prompt=api_url_prompt)


requests_wrapper = TextRequestsWrapper(headers=headers)
        get_answer_chain = LLMChain(llm=llm, prompt=api_response_prompt)
        return cls(
            api_request_chain=get_request_chain,
            api_answer_chain=get_answer_chain,
            requests_wrapper=requests_wrapper,
            api_docs=api_docs,
            **kwargs,
        )
    @property
    def _chain_type(self) -> str:
        return "api_chain"


Source code for langchain.chains.api.openapi.chain
"""Chain that makes API calls and summarizes the responses to answer a question."""
from __future__ import annotations
import json
from typing import Any, Dict, List, NamedTuple, Optional, cast
from pydantic import BaseModel, Field
from requests import Response
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun, Callbacks
from langchain.chains.api.openapi.requests_chain import APIRequesterChain
from langchain.chains.api.openapi.response_chain import APIResponderChain
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.requests import Requests
from langchain.tools.openapi.utils.api_models import APIOperation
class _ParamMapping(NamedTuple):
    """Mapping from parameter name to parameter value."""
    query_params: List[str]
    body_params: List[str]
    path_params: List[str]
[docs]class OpenAPIEndpointChain(Chain, BaseModel):
    """Chain interacts with an OpenAPI endpoint using natural language."""
    api_request_chain: LLMChain
    api_response_chain: Optional[LLMChain]
    api_operation: APIOperation
    requests: Requests = Field(exclude=True, default_factory=Requests)
    param_mapping: _ParamMapping = Field(alias="param_mapping")
    return_intermediate_steps: bool = False
    instructions_key: str = "instructions"  #: :meta private:
    output_key: str = "output"  #: :meta private:
    max_text_length: Optional[int] = Field(ge=0)  #: :meta private:
    @property
    def input_keys(self) -> List[str]:
        """Expect input key.
        :meta private:
        """
        return [self.instructions_key]
    @property


"""
        return [self.instructions_key]
    @property
    def output_keys(self) -> List[str]:
        """Expect output key.
        :meta private:
        """
        if not self.return_intermediate_steps:
            return [self.output_key]
        else:
            return [self.output_key, "intermediate_steps"]
    def _construct_path(self, args: Dict[str, str]) -> str:
        """Construct the path from the deserialized input."""
        path = self.api_operation.base_url + self.api_operation.path
        for param in self.param_mapping.path_params:
            path = path.replace(f"{{{param}}}", str(args.pop(param, "")))
        return path
    def _extract_query_params(self, args: Dict[str, str]) -> Dict[str, str]:
        """Extract the query params from the deserialized input."""
        query_params = {}
        for param in self.param_mapping.query_params:
            if param in args:
                query_params[param] = args.pop(param)
        return query_params
    def _extract_body_params(self, args: Dict[str, str]) -> Optional[Dict[str, str]]:
        """Extract the request body params from the deserialized input."""
        body_params = None
        if self.param_mapping.body_params:
            body_params = {}
            for param in self.param_mapping.body_params:
                if param in args:
                    body_params[param] = args.pop(param)
        return body_params
[docs]    def deserialize_json_input(self, serialized_args: str) -> dict:
        """Use the serialized typescript dictionary.
        Resolve the path, query params dict, and optional requestBody dict.
        """
        args: dict = json.loads(serialized_args)
        path = self._construct_path(args)


path = self._construct_path(args)
        body_params = self._extract_body_params(args)
        query_params = self._extract_query_params(args)
        return {
            "url": path,
            "data": body_params,
            "params": query_params,
        }
    def _get_output(self, output: str, intermediate_steps: dict) -> dict:
        """Return the output from the API call."""
        if self.return_intermediate_steps:
            return {
                self.output_key: output,
                "intermediate_steps": intermediate_steps,
            }
        else:
            return {self.output_key: output}
    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, str]:
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        intermediate_steps = {}
        instructions = inputs[self.instructions_key]
        instructions = instructions[: self.max_text_length]
        _api_arguments = self.api_request_chain.predict_and_parse(
            instructions=instructions, callbacks=_run_manager.get_child()
        )
        api_arguments = cast(str, _api_arguments)
        intermediate_steps["request_args"] = api_arguments
        _run_manager.on_text(
            api_arguments, color="green", end="\n", verbose=self.verbose
        )
        if api_arguments.startswith("ERROR"):
            return self._get_output(api_arguments, intermediate_steps)
        elif api_arguments.startswith("MESSAGE:"):
            return self._get_output(
                api_arguments[len("MESSAGE:") :], intermediate_steps
            )
        try:
            request_args = self.deserialize_json_input(api_arguments)
            method = getattr(self.requests, self.api_operation.method.value)


method = getattr(self.requests, self.api_operation.method.value)
            api_response: Response = method(**request_args)
            if api_response.status_code != 200:
                method_str = str(self.api_operation.method.value)
                response_text = (
                    f"{api_response.status_code}: {api_response.reason}"
                    + f"\nFor {method_str.upper()}  {request_args['url']}\n"
                    + f"Called with args: {request_args['params']}"
                )
            else:
                response_text = api_response.text
        except Exception as e:
            response_text = f"Error with message {str(e)}"
        response_text = response_text[: self.max_text_length]
        intermediate_steps["response_text"] = response_text
        _run_manager.on_text(
            response_text, color="blue", end="\n", verbose=self.verbose
        )
        if self.api_response_chain is not None:
            _answer = self.api_response_chain.predict_and_parse(
                response=response_text,
                instructions=instructions,
                callbacks=_run_manager.get_child(),
            )
            answer = cast(str, _answer)
            _run_manager.on_text(answer, color="yellow", end="\n", verbose=self.verbose)
            return self._get_output(answer, intermediate_steps)
        else:
            return self._get_output(response_text, intermediate_steps)
[docs]    @classmethod
    def from_url_and_method(
        cls,
        spec_url: str,
        path: str,
        method: str,
        llm: BaseLanguageModel,
        requests: Optional[Requests] = None,
        return_intermediate_steps: bool = False,
        **kwargs: Any
        # TODO: Handle async
    ) -> "OpenAPIEndpointChain":


# TODO: Handle async
    ) -> "OpenAPIEndpointChain":
        """Create an OpenAPIEndpoint from a spec at the specified url."""
        operation = APIOperation.from_openapi_url(spec_url, path, method)
        return cls.from_api_operation(
            operation,
            requests=requests,
            llm=llm,
            return_intermediate_steps=return_intermediate_steps,
            **kwargs,
        )
[docs]    @classmethod
    def from_api_operation(
        cls,
        operation: APIOperation,
        llm: BaseLanguageModel,
        requests: Optional[Requests] = None,
        verbose: bool = False,
        return_intermediate_steps: bool = False,
        raw_response: bool = False,
        callbacks: Callbacks = None,
        **kwargs: Any
        # TODO: Handle async
    ) -> "OpenAPIEndpointChain":
        """Create an OpenAPIEndpointChain from an operation and a spec."""
        param_mapping = _ParamMapping(
            query_params=operation.query_params,
            body_params=operation.body_params,
            path_params=operation.path_params,
        )
        requests_chain = APIRequesterChain.from_llm_and_typescript(
            llm,
            typescript_definition=operation.to_typescript(),
            verbose=verbose,
            callbacks=callbacks,
        )
        if raw_response:
            response_chain = None
        else:
            response_chain = APIResponderChain.from_llm(
                llm, verbose=verbose, callbacks=callbacks
            )
        _requests = requests or Requests()
        return cls(
            api_request_chain=requests_chain,
            api_response_chain=response_chain,
            api_operation=operation,
            requests=_requests,
            param_mapping=param_mapping,


requests=_requests,
            param_mapping=param_mapping,
            verbose=verbose,
            return_intermediate_steps=return_intermediate_steps,
            callbacks=callbacks,
            **kwargs,
        )


Source code for langchain.chains.api.openapi.requests_chain
"""request parser."""
import json
import re
from typing import Any
from langchain.base_language import BaseLanguageModel
from langchain.chains.api.openapi.prompts import REQUEST_TEMPLATE
from langchain.chains.llm import LLMChain
from langchain.prompts.prompt import PromptTemplate
from langchain.schema import BaseOutputParser
[docs]class APIRequesterOutputParser(BaseOutputParser):
    """Parse the request and error tags."""
    def _load_json_block(self, serialized_block: str) -> str:
        try:
            return json.dumps(json.loads(serialized_block, strict=False))
        except json.JSONDecodeError:
            return "ERROR serializing request."
[docs]    def parse(self, llm_output: str) -> str:
        """Parse the request and error tags."""
        json_match = re.search(r"```json(.*?)```", llm_output, re.DOTALL)
        if json_match:
            return self._load_json_block(json_match.group(1).strip())
        message_match = re.search(r"```text(.*?)```", llm_output, re.DOTALL)
        if message_match:
            return f"MESSAGE: {message_match.group(1).strip()}"
        return "ERROR making request"
    @property
    def _type(self) -> str:
        return "api_requester"
[docs]class APIRequesterChain(LLMChain):
    """Get the request parser."""
[docs]    @classmethod
    def from_llm_and_typescript(
        cls,
        llm: BaseLanguageModel,
        typescript_definition: str,
        verbose: bool = True,
        **kwargs: Any,
    ) -> LLMChain:
        """Get the request parser."""


) -> LLMChain:
        """Get the request parser."""
        output_parser = APIRequesterOutputParser()
        prompt = PromptTemplate(
            template=REQUEST_TEMPLATE,
            output_parser=output_parser,
            partial_variables={"schema": typescript_definition},
            input_variables=["instructions"],
        )
        return cls(prompt=prompt, llm=llm, verbose=verbose, **kwargs)


Source code for langchain.chains.api.openapi.response_chain
"""Response parser."""
import json
import re
from typing import Any
from langchain.base_language import BaseLanguageModel
from langchain.chains.api.openapi.prompts import RESPONSE_TEMPLATE
from langchain.chains.llm import LLMChain
from langchain.prompts.prompt import PromptTemplate
from langchain.schema import BaseOutputParser
[docs]class APIResponderOutputParser(BaseOutputParser):
    """Parse the response and error tags."""
    def _load_json_block(self, serialized_block: str) -> str:
        try:
            response_content = json.loads(serialized_block, strict=False)
            return response_content.get("response", "ERROR parsing response.")
        except json.JSONDecodeError:
            return "ERROR parsing response."
        except:
            raise
[docs]    def parse(self, llm_output: str) -> str:
        """Parse the response and error tags."""
        json_match = re.search(r"```json(.*?)```", llm_output, re.DOTALL)
        if json_match:
            return self._load_json_block(json_match.group(1).strip())
        else:
            raise ValueError(f"No response found in output: {llm_output}.")
    @property
    def _type(self) -> str:
        return "api_responder"
[docs]class APIResponderChain(LLMChain):
    """Get the response parser."""
[docs]    @classmethod
    def from_llm(
        cls, llm: BaseLanguageModel, verbose: bool = True, **kwargs: Any
    ) -> LLMChain:
        """Get the response parser."""
        output_parser = APIResponderOutputParser()
        prompt = PromptTemplate(
            template=RESPONSE_TEMPLATE,
            output_parser=output_parser,


template=RESPONSE_TEMPLATE,
            output_parser=output_parser,
            input_variables=["response", "instructions"],
        )
        return cls(prompt=prompt, llm=llm, verbose=verbose, **kwargs)


Source code for langchain.chains.conversational_retrieval.base
"""Chain for chatting with a vector database."""
from __future__ import annotations
import inspect
import warnings
from abc import abstractmethod
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from pydantic import Extra, Field, root_validator
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import (
    AsyncCallbackManagerForChainRun,
    CallbackManagerForChainRun,
    Callbacks,
)
from langchain.chains.base import Chain
from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT
from langchain.chains.llm import LLMChain
from langchain.chains.question_answering import load_qa_chain
from langchain.prompts.base import BasePromptTemplate
from langchain.schema import BaseMessage, BaseRetriever, Document
from langchain.vectorstores.base import VectorStore
# Depending on the memory type and configuration, the chat history format may differ.
# This needs to be consolidated.
CHAT_TURN_TYPE = Union[Tuple[str, str], BaseMessage]
_ROLE_MAP = {"human": "Human: ", "ai": "Assistant: "}
def _get_chat_history(chat_history: List[CHAT_TURN_TYPE]) -> str:
    buffer = ""
    for dialogue_turn in chat_history:
        if isinstance(dialogue_turn, BaseMessage):
            role_prefix = _ROLE_MAP.get(dialogue_turn.type, f"{dialogue_turn.type}: ")
            buffer += f"\n{role_prefix}{dialogue_turn.content}"
        elif isinstance(dialogue_turn, tuple):


elif isinstance(dialogue_turn, tuple):
            human = "Human: " + dialogue_turn[0]
            ai = "Assistant: " + dialogue_turn[1]
            buffer += "\n" + "\n".join([human, ai])
        else:
            raise ValueError(
                f"Unsupported chat history format: {type(dialogue_turn)}."
                f" Full chat history: {chat_history} "
            )
    return buffer
[docs]class BaseConversationalRetrievalChain(Chain):
    """Chain for chatting with an index."""
    combine_docs_chain: BaseCombineDocumentsChain
    question_generator: LLMChain
    output_key: str = "answer"
    return_source_documents: bool = False
    return_generated_question: bool = False
    get_chat_history: Optional[Callable[[CHAT_TURN_TYPE], str]] = None
    """Return the source documents."""
[docs]    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
        allow_population_by_field_name = True
    @property
    def input_keys(self) -> List[str]:
        """Input keys."""
        return ["question", "chat_history"]
    @property
    def output_keys(self) -> List[str]:
        """Return the output keys.
        :meta private:
        """
        _output_keys = [self.output_key]
        if self.return_source_documents:
            _output_keys = _output_keys + ["source_documents"]
        if self.return_generated_question:
            _output_keys = _output_keys + ["generated_question"]
        return _output_keys
    @abstractmethod
    def _get_docs(
        self,
        question: str,
        inputs: Dict[str, Any],
        *,


question: str,
        inputs: Dict[str, Any],
        *,
        run_manager: CallbackManagerForChainRun,
    ) -> List[Document]:
        """Get docs."""
    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        question = inputs["question"]
        get_chat_history = self.get_chat_history or _get_chat_history
        chat_history_str = get_chat_history(inputs["chat_history"])
        if chat_history_str:
            callbacks = _run_manager.get_child()
            new_question = self.question_generator.run(
                question=question, chat_history=chat_history_str, callbacks=callbacks
            )
        else:
            new_question = question
        accepts_run_manager = (
            "run_manager" in inspect.signature(self._get_docs).parameters
        )
        if accepts_run_manager:
            docs = self._get_docs(new_question, inputs, run_manager=_run_manager)
        else:
            docs = self._get_docs(new_question, inputs)  # type: ignore[call-arg]
        new_inputs = inputs.copy()
        new_inputs["question"] = new_question
        new_inputs["chat_history"] = chat_history_str
        answer = self.combine_docs_chain.run(
            input_documents=docs, callbacks=_run_manager.get_child(), **new_inputs
        )
        output: Dict[str, Any] = {self.output_key: answer}
        if self.return_source_documents:
            output["source_documents"] = docs
        if self.return_generated_question:
            output["generated_question"] = new_question
        return output
    @abstractmethod


output["generated_question"] = new_question
        return output
    @abstractmethod
    async def _aget_docs(
        self,
        question: str,
        inputs: Dict[str, Any],
        *,
        run_manager: AsyncCallbackManagerForChainRun,
    ) -> List[Document]:
        """Get docs."""
    async def _acall(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
        question = inputs["question"]
        get_chat_history = self.get_chat_history or _get_chat_history
        chat_history_str = get_chat_history(inputs["chat_history"])
        if chat_history_str:
            callbacks = _run_manager.get_child()
            new_question = await self.question_generator.arun(
                question=question, chat_history=chat_history_str, callbacks=callbacks
            )
        else:
            new_question = question
        accepts_run_manager = (
            "run_manager" in inspect.signature(self._aget_docs).parameters
        )
        if accepts_run_manager:
            docs = await self._aget_docs(new_question, inputs, run_manager=_run_manager)
        else:
            docs = await self._aget_docs(new_question, inputs)  # type: ignore[call-arg]
        new_inputs = inputs.copy()
        new_inputs["question"] = new_question
        new_inputs["chat_history"] = chat_history_str
        answer = await self.combine_docs_chain.arun(
            input_documents=docs, callbacks=_run_manager.get_child(), **new_inputs
        )
        output: Dict[str, Any] = {self.output_key: answer}


)
        output: Dict[str, Any] = {self.output_key: answer}
        if self.return_source_documents:
            output["source_documents"] = docs
        if self.return_generated_question:
            output["generated_question"] = new_question
        return output
[docs]    def save(self, file_path: Union[Path, str]) -> None:
        if self.get_chat_history:
            raise ValueError("Chain not savable when `get_chat_history` is not None.")
        super().save(file_path)
[docs]class ConversationalRetrievalChain(BaseConversationalRetrievalChain):
    """Chain for chatting with an index."""
    retriever: BaseRetriever
    """Index to connect to."""
    max_tokens_limit: Optional[int] = None
    """If set, restricts the docs to return from store based on tokens, enforced only
    for StuffDocumentChain"""
    def _reduce_tokens_below_limit(self, docs: List[Document]) -> List[Document]:
        num_docs = len(docs)
        if self.max_tokens_limit and isinstance(
            self.combine_docs_chain, StuffDocumentsChain
        ):
            tokens = [
                self.combine_docs_chain.llm_chain.llm.get_num_tokens(doc.page_content)
                for doc in docs
            ]
            token_count = sum(tokens[:num_docs])
            while token_count > self.max_tokens_limit:
                num_docs -= 1
                token_count -= tokens[num_docs]
        return docs[:num_docs]
    def _get_docs(
        self,
        question: str,
        inputs: Dict[str, Any],
        *,
        run_manager: CallbackManagerForChainRun,
    ) -> List[Document]:
        """Get docs."""
        docs = self.retriever.get_relevant_documents(


"""Get docs."""
        docs = self.retriever.get_relevant_documents(
            question, callbacks=run_manager.get_child()
        )
        return self._reduce_tokens_below_limit(docs)
    async def _aget_docs(
        self,
        question: str,
        inputs: Dict[str, Any],
        *,
        run_manager: AsyncCallbackManagerForChainRun,
    ) -> List[Document]:
        """Get docs."""
        docs = await self.retriever.aget_relevant_documents(
            question, callbacks=run_manager.get_child()
        )
        return self._reduce_tokens_below_limit(docs)
[docs]    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,
        retriever: BaseRetriever,
        condense_question_prompt: BasePromptTemplate = CONDENSE_QUESTION_PROMPT,
        chain_type: str = "stuff",
        verbose: bool = False,
        condense_question_llm: Optional[BaseLanguageModel] = None,
        combine_docs_chain_kwargs: Optional[Dict] = None,
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> BaseConversationalRetrievalChain:
        """Load chain from LLM."""
        combine_docs_chain_kwargs = combine_docs_chain_kwargs or {}
        doc_chain = load_qa_chain(
            llm,
            chain_type=chain_type,
            verbose=verbose,
            callbacks=callbacks,
            **combine_docs_chain_kwargs,
        )
        _llm = condense_question_llm or llm
        condense_question_chain = LLMChain(
            llm=_llm,
            prompt=condense_question_prompt,
            verbose=verbose,
            callbacks=callbacks,
        )


verbose=verbose,
            callbacks=callbacks,
        )
        return cls(
            retriever=retriever,
            combine_docs_chain=doc_chain,
            question_generator=condense_question_chain,
            callbacks=callbacks,
            **kwargs,
        )
[docs]class ChatVectorDBChain(BaseConversationalRetrievalChain):
    """Chain for chatting with a vector database."""
    vectorstore: VectorStore = Field(alias="vectorstore")
    top_k_docs_for_context: int = 4
    search_kwargs: dict = Field(default_factory=dict)
    @property
    def _chain_type(self) -> str:
        return "chat-vector-db"
[docs]    @root_validator()
    def raise_deprecation(cls, values: Dict) -> Dict:
        warnings.warn(
            "`ChatVectorDBChain` is deprecated - "
            "please use `from langchain.chains import ConversationalRetrievalChain`"
        )
        return values
    def _get_docs(
        self,
        question: str,
        inputs: Dict[str, Any],
        *,
        run_manager: CallbackManagerForChainRun,
    ) -> List[Document]:
        """Get docs."""
        vectordbkwargs = inputs.get("vectordbkwargs", {})
        full_kwargs = {**self.search_kwargs, **vectordbkwargs}
        return self.vectorstore.similarity_search(
            question, k=self.top_k_docs_for_context, **full_kwargs
        )
    async def _aget_docs(
        self,
        question: str,
        inputs: Dict[str, Any],
        *,
        run_manager: AsyncCallbackManagerForChainRun,
    ) -> List[Document]:
        """Get docs."""


) -> List[Document]:
        """Get docs."""
        raise NotImplementedError("ChatVectorDBChain does not support async")
[docs]    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,
        vectorstore: VectorStore,
        condense_question_prompt: BasePromptTemplate = CONDENSE_QUESTION_PROMPT,
        chain_type: str = "stuff",
        combine_docs_chain_kwargs: Optional[Dict] = None,
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> BaseConversationalRetrievalChain:
        """Load chain from LLM."""
        combine_docs_chain_kwargs = combine_docs_chain_kwargs or {}
        doc_chain = load_qa_chain(
            llm,
            chain_type=chain_type,
            callbacks=callbacks,
            **combine_docs_chain_kwargs,
        )
        condense_question_chain = LLMChain(
            llm=llm, prompt=condense_question_prompt, callbacks=callbacks
        )
        return cls(
            vectorstore=vectorstore,
            combine_docs_chain=doc_chain,
            question_generator=condense_question_chain,
            callbacks=callbacks,
            **kwargs,
        )


Source code for langchain.chains.pal.base
"""Implements Program-Aided Language Models.
As in https://arxiv.org/pdf/2211.10435.pdf.
"""
from __future__ import annotations
import warnings
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.pal.colored_object_prompt import COLORED_OBJECT_PROMPT
from langchain.chains.pal.math_prompt import MATH_PROMPT
from langchain.prompts.base import BasePromptTemplate
from langchain.utilities import PythonREPL
[docs]class PALChain(Chain):
    """Implements Program-Aided Language Models."""
    llm_chain: LLMChain
    llm: Optional[BaseLanguageModel] = None
    """[Deprecated]"""
    prompt: BasePromptTemplate = MATH_PROMPT
    """[Deprecated]"""
    stop: str = "\n\n"
    get_answer_expr: str = "print(solution())"
    python_globals: Optional[Dict[str, Any]] = None
    python_locals: Optional[Dict[str, Any]] = None
    output_key: str = "result"  #: :meta private:
    return_intermediate_steps: bool = False
[docs]    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
[docs]    @root_validator(pre=True)
    def raise_deprecation(cls, values: Dict) -> Dict:
        if "llm" in values:
            warnings.warn(


if "llm" in values:
            warnings.warn(
                "Directly instantiating an PALChain with an llm is deprecated. "
                "Please instantiate with llm_chain argument or using the one of "
                "the class method constructors from_math_prompt, "
                "from_colored_object_prompt."
            )
            if "llm_chain" not in values and values["llm"] is not None:
                values["llm_chain"] = LLMChain(llm=values["llm"], prompt=MATH_PROMPT)
        return values
    @property
    def input_keys(self) -> List[str]:
        """Return the singular input key.
        :meta private:
        """
        return self.prompt.input_variables
    @property
    def output_keys(self) -> List[str]:
        """Return the singular output key.
        :meta private:
        """
        if not self.return_intermediate_steps:
            return [self.output_key]
        else:
            return [self.output_key, "intermediate_steps"]
    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, str]:
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        code = self.llm_chain.predict(
            stop=[self.stop], callbacks=_run_manager.get_child(), **inputs
        )
        _run_manager.on_text(code, color="green", end="\n", verbose=self.verbose)
        repl = PythonREPL(_globals=self.python_globals, _locals=self.python_locals)
        res = repl.run(code + f"\n{self.get_answer_expr}")
        output = {self.output_key: res.strip()}


output = {self.output_key: res.strip()}
        if self.return_intermediate_steps:
            output["intermediate_steps"] = code
        return output
[docs]    @classmethod
    def from_math_prompt(cls, llm: BaseLanguageModel, **kwargs: Any) -> PALChain:
        """Load PAL from math prompt."""
        llm_chain = LLMChain(llm=llm, prompt=MATH_PROMPT)
        return cls(
            llm_chain=llm_chain,
            stop="\n\n",
            get_answer_expr="print(solution())",
            **kwargs,
        )
[docs]    @classmethod
    def from_colored_object_prompt(
        cls, llm: BaseLanguageModel, **kwargs: Any
    ) -> PALChain:
        """Load PAL from colored object prompt."""
        llm_chain = LLMChain(llm=llm, prompt=COLORED_OBJECT_PROMPT)
        return cls(
            llm_chain=llm_chain,
            stop="\n\n\n",
            get_answer_expr="print(answer)",
            **kwargs,
        )
    @property
    def _chain_type(self) -> str:
        return "pal_chain"


Source code for langchain.chains.router.embedding_router
from __future__ import annotations
from typing import Any, Dict, List, Optional, Sequence, Tuple, Type
from pydantic import Extra
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.router.base import RouterChain
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.base import VectorStore
[docs]class EmbeddingRouterChain(RouterChain):
    """Class that uses embeddings to route between options."""
    vectorstore: VectorStore
    routing_keys: List[str] = ["query"]
[docs]    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
    @property
    def input_keys(self) -> List[str]:
        """Will be whatever keys the LLM chain prompt expects.
        :meta private:
        """
        return self.routing_keys
    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        _input = ", ".join([inputs[k] for k in self.routing_keys])
        results = self.vectorstore.similarity_search(_input, k=1)
        return {"next_inputs": inputs, "destination": results[0].metadata["name"]}
[docs]    @classmethod
    def from_names_and_descriptions(
        cls,
        names_and_descriptions: Sequence[Tuple[str, Sequence[str]]],
        vectorstore_cls: Type[VectorStore],
        embeddings: Embeddings,
        **kwargs: Any,
    ) -> EmbeddingRouterChain:
        """Convenience constructor."""
        documents = []


"""Convenience constructor."""
        documents = []
        for name, descriptions in names_and_descriptions:
            for description in descriptions:
                documents.append(
                    Document(page_content=description, metadata={"name": name})
                )
        vectorstore = vectorstore_cls.from_documents(documents, embeddings)
        return cls(vectorstore=vectorstore, **kwargs)


Source code for langchain.chains.router.multi_prompt
"""Use a single chain to route an input to one of multiple llm chains."""
from __future__ import annotations
from typing import Any, Dict, List, Mapping, Optional
from langchain.base_language import BaseLanguageModel
from langchain.chains import ConversationChain
from langchain.chains.llm import LLMChain
from langchain.chains.router.base import MultiRouteChain, RouterChain
from langchain.chains.router.llm_router import LLMRouterChain, RouterOutputParser
from langchain.chains.router.multi_prompt_prompt import MULTI_PROMPT_ROUTER_TEMPLATE
from langchain.prompts import PromptTemplate
[docs]class MultiPromptChain(MultiRouteChain):
    """A multi-route chain that uses an LLM router chain to choose amongst prompts."""
    router_chain: RouterChain
    """Chain for deciding a destination chain and the input to it."""
    destination_chains: Mapping[str, LLMChain]
    """Map of name to candidate chains that inputs can be routed to."""
    default_chain: LLMChain
    """Default chain to use when router doesn't map input to one of the destinations."""
    @property
    def output_keys(self) -> List[str]:
        return ["text"]
[docs]    @classmethod
    def from_prompts(
        cls,
        llm: BaseLanguageModel,
        prompt_infos: List[Dict[str, str]],
        default_chain: Optional[LLMChain] = None,
        **kwargs: Any,
    ) -> MultiPromptChain:
        """Convenience constructor for instantiating from destination prompts."""
        destinations = [f"{p['name']}: {p['description']}" for p in prompt_infos]
        destinations_str = "\n".join(destinations)
        router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format(


router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format(
            destinations=destinations_str
        )
        router_prompt = PromptTemplate(
            template=router_template,
            input_variables=["input"],
            output_parser=RouterOutputParser(),
        )
        router_chain = LLMRouterChain.from_llm(llm, router_prompt)
        destination_chains = {}
        for p_info in prompt_infos:
            name = p_info["name"]
            prompt_template = p_info["prompt_template"]
            prompt = PromptTemplate(template=prompt_template, input_variables=["input"])
            chain = LLMChain(llm=llm, prompt=prompt)
            destination_chains[name] = chain
        _default_chain = default_chain or ConversationChain(llm=llm, output_key="text")
        return cls(
            router_chain=router_chain,
            destination_chains=destination_chains,
            default_chain=_default_chain,
            **kwargs,
        )


Source code for langchain.chains.router.multi_retrieval_qa
"""Use a single chain to route an input to one of multiple retrieval qa chains."""
from __future__ import annotations
from typing import Any, Dict, List, Mapping, Optional
from langchain.base_language import BaseLanguageModel
from langchain.chains import ConversationChain
from langchain.chains.base import Chain
from langchain.chains.conversation.prompt import DEFAULT_TEMPLATE
from langchain.chains.retrieval_qa.base import BaseRetrievalQA, RetrievalQA
from langchain.chains.router.base import MultiRouteChain
from langchain.chains.router.llm_router import LLMRouterChain, RouterOutputParser
from langchain.chains.router.multi_retrieval_prompt import (
    MULTI_RETRIEVAL_ROUTER_TEMPLATE,
)
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.schema import BaseRetriever
[docs]class MultiRetrievalQAChain(MultiRouteChain):
    """A multi-route chain that uses an LLM router chain to choose amongst retrieval
    qa chains."""
    router_chain: LLMRouterChain
    """Chain for deciding a destination chain and the input to it."""
    destination_chains: Mapping[str, BaseRetrievalQA]
    """Map of name to candidate chains that inputs can be routed to."""
    default_chain: Chain
    """Default chain to use when router doesn't map input to one of the destinations."""
    @property
    def output_keys(self) -> List[str]:
        return ["result"]
[docs]    @classmethod
    def from_retrievers(
        cls,
        llm: BaseLanguageModel,
        retriever_infos: List[Dict[str, Any]],
        default_retriever: Optional[BaseRetriever] = None,


default_retriever: Optional[BaseRetriever] = None,
        default_prompt: Optional[PromptTemplate] = None,
        default_chain: Optional[Chain] = None,
        **kwargs: Any,
    ) -> MultiRetrievalQAChain:
        if default_prompt and not default_retriever:
            raise ValueError(
                "`default_retriever` must be specified if `default_prompt` is "
                "provided. Received only `default_prompt`."
            )
        destinations = [f"{r['name']}: {r['description']}" for r in retriever_infos]
        destinations_str = "\n".join(destinations)
        router_template = MULTI_RETRIEVAL_ROUTER_TEMPLATE.format(
            destinations=destinations_str
        )
        router_prompt = PromptTemplate(
            template=router_template,
            input_variables=["input"],
            output_parser=RouterOutputParser(next_inputs_inner_key="query"),
        )
        router_chain = LLMRouterChain.from_llm(llm, router_prompt)
        destination_chains = {}
        for r_info in retriever_infos:
            prompt = r_info.get("prompt")
            retriever = r_info["retriever"]
            chain = RetrievalQA.from_llm(llm, prompt=prompt, retriever=retriever)
            name = r_info["name"]
            destination_chains[name] = chain
        if default_chain:
            _default_chain = default_chain
        elif default_retriever:
            _default_chain = RetrievalQA.from_llm(
                llm, prompt=default_prompt, retriever=default_retriever
            )
        else:
            prompt_template = DEFAULT_TEMPLATE.replace("input", "query")
            prompt = PromptTemplate(


prompt = PromptTemplate(
                template=prompt_template, input_variables=["history", "query"]
            )
            _default_chain = ConversationChain(
                llm=ChatOpenAI(), prompt=prompt, input_key="query", output_key="result"
            )
        return cls(
            router_chain=router_chain,
            destination_chains=destination_chains,
            default_chain=_default_chain,
            **kwargs,
        )


Source code for langchain.chains.router.base
"""Base classes for chain routing."""
from __future__ import annotations
from abc import ABC
from typing import Any, Dict, List, Mapping, NamedTuple, Optional
from pydantic import Extra
from langchain.callbacks.manager import (
    AsyncCallbackManagerForChainRun,
    CallbackManagerForChainRun,
    Callbacks,
)
from langchain.chains.base import Chain
[docs]class Route(NamedTuple):
    destination: Optional[str]
    next_inputs: Dict[str, Any]
[docs]class RouterChain(Chain, ABC):
    """Chain that outputs the name of a destination chain and the inputs to it."""
    @property
    def output_keys(self) -> List[str]:
        return ["destination", "next_inputs"]
[docs]    def route(self, inputs: Dict[str, Any], callbacks: Callbacks = None) -> Route:
        result = self(inputs, callbacks=callbacks)
        return Route(result["destination"], result["next_inputs"])
[docs]    async def aroute(
        self, inputs: Dict[str, Any], callbacks: Callbacks = None
    ) -> Route:
        result = await self.acall(inputs, callbacks=callbacks)
        return Route(result["destination"], result["next_inputs"])
[docs]class MultiRouteChain(Chain):
    """Use a single chain to route an input to one of multiple candidate chains."""
    router_chain: RouterChain
    """Chain that routes inputs to destination chains."""
    destination_chains: Mapping[str, Chain]
    """Chains that return final answer to inputs."""
    default_chain: Chain
    """Default chain to use when none of the destination chains are suitable."""
    silent_errors: bool = False


silent_errors: bool = False
    """If True, use default_chain when an invalid destination name is provided.
    Defaults to False."""
[docs]    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
    @property
    def input_keys(self) -> List[str]:
        """Will be whatever keys the router chain prompt expects.
        :meta private:
        """
        return self.router_chain.input_keys
    @property
    def output_keys(self) -> List[str]:
        """Will always return text key.
        :meta private:
        """
        return []
    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        callbacks = _run_manager.get_child()
        route = self.router_chain.route(inputs, callbacks=callbacks)
        _run_manager.on_text(
            str(route.destination) + ": " + str(route.next_inputs), verbose=self.verbose
        )
        if not route.destination:
            return self.default_chain(route.next_inputs, callbacks=callbacks)
        elif route.destination in self.destination_chains:
            return self.destination_chains[route.destination](
                route.next_inputs, callbacks=callbacks
            )
        elif self.silent_errors:
            return self.default_chain(route.next_inputs, callbacks=callbacks)
        else:
            raise ValueError(
                f"Received invalid destination chain name '{route.destination}'"
            )
    async def _acall(
        self,
        inputs: Dict[str, Any],


self,
        inputs: Dict[str, Any],
        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        callbacks = _run_manager.get_child()
        route = await self.router_chain.aroute(inputs, callbacks=callbacks)
        _run_manager.on_text(
            str(route.destination) + ": " + str(route.next_inputs), verbose=self.verbose
        )
        if not route.destination:
            return await self.default_chain.acall(
                route.next_inputs, callbacks=callbacks
            )
        elif route.destination in self.destination_chains:
            return await self.destination_chains[route.destination].acall(
                route.next_inputs, callbacks=callbacks
            )
        elif self.silent_errors:
            return await self.default_chain.acall(
                route.next_inputs, callbacks=callbacks
            )
        else:
            raise ValueError(
                f"Received invalid destination chain name '{route.destination}'"
            )


Source code for langchain.chains.router.llm_router
"""Base classes for LLM-powered router chains."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Type, cast
from pydantic import root_validator
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import (
    AsyncCallbackManagerForChainRun,
    CallbackManagerForChainRun,
)
from langchain.chains import LLMChain
from langchain.chains.router.base import RouterChain
from langchain.output_parsers.json import parse_and_check_json_markdown
from langchain.prompts import BasePromptTemplate
from langchain.schema import BaseOutputParser, OutputParserException
[docs]class LLMRouterChain(RouterChain):
    """A router chain that uses an LLM chain to perform routing."""
    llm_chain: LLMChain
    """LLM chain used to perform routing"""
[docs]    @root_validator()
    def validate_prompt(cls, values: dict) -> dict:
        prompt = values["llm_chain"].prompt
        if prompt.output_parser is None:
            raise ValueError(
                "LLMRouterChain requires base llm_chain prompt to have an output"
                " parser that converts LLM text output to a dictionary with keys"
                " 'destination' and 'next_inputs'. Received a prompt with no output"
                " parser."
            )
        return values
    @property
    def input_keys(self) -> List[str]:
        """Will be whatever keys the LLM chain prompt expects.
        :meta private:
        """
        return self.llm_chain.input_keys
    def _validate_outputs(self, outputs: Dict[str, Any]) -> None:
        super()._validate_outputs(outputs)
        if not isinstance(outputs["next_inputs"], dict):
            raise ValueError


if not isinstance(outputs["next_inputs"], dict):
            raise ValueError
    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        callbacks = _run_manager.get_child()
        output = cast(
            Dict[str, Any],
            self.llm_chain.predict_and_parse(callbacks=callbacks, **inputs),
        )
        return output
    async def _acall(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        callbacks = _run_manager.get_child()
        output = cast(
            Dict[str, Any],
            await self.llm_chain.apredict_and_parse(callbacks=callbacks, **inputs),
        )
        return output
[docs]    @classmethod
    def from_llm(
        cls, llm: BaseLanguageModel, prompt: BasePromptTemplate, **kwargs: Any
    ) -> LLMRouterChain:
        """Convenience constructor."""
        llm_chain = LLMChain(llm=llm, prompt=prompt)
        return cls(llm_chain=llm_chain, **kwargs)
[docs]class RouterOutputParser(BaseOutputParser[Dict[str, str]]):
    """Parser for output of router chain int he multi-prompt chain."""
    default_destination: str = "DEFAULT"
    next_inputs_type: Type = str
    next_inputs_inner_key: str = "input"


next_inputs_inner_key: str = "input"
[docs]    def parse(self, text: str) -> Dict[str, Any]:
        try:
            expected_keys = ["destination", "next_inputs"]
            parsed = parse_and_check_json_markdown(text, expected_keys)
            if not isinstance(parsed["destination"], str):
                raise ValueError("Expected 'destination' to be a string.")
            if not isinstance(parsed["next_inputs"], self.next_inputs_type):
                raise ValueError(
                    f"Expected 'next_inputs' to be {self.next_inputs_type}."
                )
            parsed["next_inputs"] = {self.next_inputs_inner_key: parsed["next_inputs"]}
            if (
                parsed["destination"].strip().lower()
                == self.default_destination.lower()
            ):
                parsed["destination"] = None
            else:
                parsed["destination"] = parsed["destination"].strip()
            return parsed
        except Exception as e:
            raise OutputParserException(
                f"Parsing text\n{text}\n raised following error:\n{e}"
            )


Source code for langchain.chains.llm_bash.prompt
# flake8: noqa
from __future__ import annotations
import re
from typing import List
from langchain.prompts.prompt import PromptTemplate
from langchain.schema import BaseOutputParser, OutputParserException
_PROMPT_TEMPLATE = """If someone asks you to perform a task, your job is to come up with a series of bash commands that will perform the task. There is no need to put "#!/bin/bash" in your answer. Make sure to reason step by step, using this format:
Question: "copy the files in the directory named 'target' into a new directory at the same level as target called 'myNewDirectory'"
I need to take the following actions:
- List all files in the directory
- Create a new directory
- Copy the files from the first directory into the second directory
```bash
ls
mkdir myNewDirectory
cp -r target/* myNewDirectory
```
That is the format. Begin!
Question: {question}"""
[docs]class BashOutputParser(BaseOutputParser):
    """Parser for bash output."""
[docs]    def parse(self, text: str) -> List[str]:
        if "```bash" in text:
            return self.get_code_blocks(text)
        else:
            raise OutputParserException(
                f"Failed to parse bash output. Got: {text}",
            )
[docs]    @staticmethod
    def get_code_blocks(t: str) -> List[str]:
        """Get multiple code blocks from the LLM result."""
        code_blocks: List[str] = []
        # Bash markdown code blocks
        pattern = re.compile(r"```bash(.*?)(?:\n\s*)```", re.DOTALL)
        for match in pattern.finditer(t):


for match in pattern.finditer(t):
            matched = match.group(1).strip()
            if matched:
                code_blocks.extend(
                    [line for line in matched.split("\n") if line.strip()]
                )
        return code_blocks
    @property
    def _type(self) -> str:
        return "bash"
PROMPT = PromptTemplate(
    input_variables=["question"],
    template=_PROMPT_TEMPLATE,
    output_parser=BashOutputParser(),
)


Source code for langchain.chains.llm_bash.base
"""Chain that interprets a prompt and executes bash code to perform bash operations."""
from __future__ import annotations
import logging
import warnings
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.llm_bash.prompt import PROMPT
from langchain.prompts.base import BasePromptTemplate
from langchain.schema import OutputParserException
from langchain.utilities.bash import BashProcess
logger = logging.getLogger(__name__)
[docs]class LLMBashChain(Chain):
    """Chain that interprets a prompt and executes bash code to perform bash operations.
    Example:
        .. code-block:: python
            from langchain import LLMBashChain, OpenAI
            llm_bash = LLMBashChain.from_llm(OpenAI())
    """
    llm_chain: LLMChain
    llm: Optional[BaseLanguageModel] = None
    """[Deprecated] LLM wrapper to use."""
    input_key: str = "question"  #: :meta private:
    output_key: str = "answer"  #: :meta private:
    prompt: BasePromptTemplate = PROMPT
    """[Deprecated]"""
    bash_process: BashProcess = Field(default_factory=BashProcess)  #: :meta private:
[docs]    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
[docs]    @root_validator(pre=True)
    def raise_deprecation(cls, values: Dict) -> Dict:


def raise_deprecation(cls, values: Dict) -> Dict:
        if "llm" in values:
            warnings.warn(
                "Directly instantiating an LLMBashChain with an llm is deprecated. "
                "Please instantiate with llm_chain or using the from_llm class method."
            )
            if "llm_chain" not in values and values["llm"] is not None:
                prompt = values.get("prompt", PROMPT)
                values["llm_chain"] = LLMChain(llm=values["llm"], prompt=prompt)
        return values
[docs]    @root_validator
    def validate_prompt(cls, values: Dict) -> Dict:
        if values["llm_chain"].prompt.output_parser is None:
            raise ValueError(
                "The prompt used by llm_chain is expected to have an output_parser."
            )
        return values
    @property
    def input_keys(self) -> List[str]:
        """Expect input key.
        :meta private:
        """
        return [self.input_key]
    @property
    def output_keys(self) -> List[str]:
        """Expect output key.
        :meta private:
        """
        return [self.output_key]
    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, str]:
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        _run_manager.on_text(inputs[self.input_key], verbose=self.verbose)
        t = self.llm_chain.predict(
            question=inputs[self.input_key], callbacks=_run_manager.get_child()
        )


question=inputs[self.input_key], callbacks=_run_manager.get_child()
        )
        _run_manager.on_text(t, color="green", verbose=self.verbose)
        t = t.strip()
        try:
            parser = self.llm_chain.prompt.output_parser
            command_list = parser.parse(t)  # type: ignore[union-attr]
        except OutputParserException as e:
            _run_manager.on_chain_error(e, verbose=self.verbose)
            raise e
        if self.verbose:
            _run_manager.on_text("\nCode: ", verbose=self.verbose)
            _run_manager.on_text(
                str(command_list), color="yellow", verbose=self.verbose
            )
        output = self.bash_process.run(command_list)
        _run_manager.on_text("\nAnswer: ", verbose=self.verbose)
        _run_manager.on_text(output, color="yellow", verbose=self.verbose)
        return {self.output_key: output}
    @property
    def _chain_type(self) -> str:
        return "llm_bash_chain"
[docs]    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,
        prompt: BasePromptTemplate = PROMPT,
        **kwargs: Any,
    ) -> LLMBashChain:
        llm_chain = LLMChain(llm=llm, prompt=prompt)
        return cls(llm_chain=llm_chain, **kwargs)


Source code for langchain.chains.natbot.crawler
# flake8: noqa
import time
from sys import platform
from typing import (
    TYPE_CHECKING,
    Any,
    Dict,
    Iterable,
    List,
    Optional,
    Set,
    Tuple,
    TypedDict,
    Union,
)
if TYPE_CHECKING:
    from playwright.sync_api import Browser, CDPSession, Page, sync_playwright
black_listed_elements: Set[str] = {
    "html",
    "head",
    "title",
    "meta",
    "iframe",
    "body",
    "script",
    "style",
    "path",
    "svg",
    "br",
    "::marker",
}
[docs]class ElementInViewPort(TypedDict):
    """A typed dictionary containing information about elements in the viewport."""
    node_index: str
    backend_node_id: int
    node_name: Optional[str]
    node_value: Optional[str]
    node_meta: List[str]
    is_clickable: bool
    origin_x: int
    origin_y: int
    center_x: int
    center_y: int
class Crawler:
    def __init__(self) -> None:
        try:
            from playwright.sync_api import sync_playwright
        except ImportError:
            raise ImportError(
                "Could not import playwright python package. "
                "Please install it with `pip install playwright`."
            )
        self.browser: Browser = (
            sync_playwright().start().chromium.launch(headless=False)
        )
        self.page: Page = self.browser.new_page()
        self.page.set_viewport_size({"width": 1280, "height": 1080})


self.page_element_buffer: Dict[int, ElementInViewPort]
        self.client: CDPSession
    def go_to_page(self, url: str) -> None:
        self.page.goto(url=url if "://" in url else "http://" + url)
        self.client = self.page.context.new_cdp_session(self.page)
        self.page_element_buffer = {}
    def scroll(self, direction: str) -> None:
        if direction == "up":
            self.page.evaluate(
                "(document.scrollingElement || document.body).scrollTop = (document.scrollingElement || document.body).scrollTop - window.innerHeight;"
            )
        elif direction == "down":
            self.page.evaluate(
                "(document.scrollingElement || document.body).scrollTop = (document.scrollingElement || document.body).scrollTop + window.innerHeight;"
            )
    def click(self, id: Union[str, int]) -> None:
        # Inject javascript into the page which removes the target= attribute from all links
        js = """
		links = document.getElementsByTagName("a");
		for (var i = 0; i < links.length; i++) {
			links[i].removeAttribute("target");
		}
		"""
        self.page.evaluate(js)
        element = self.page_element_buffer.get(int(id))
        if element:
            x: float = element["center_x"]
            y: float = element["center_y"]
            self.page.mouse.click(x, y)
        else:
            print("Could not find element")
    def type(self, id: Union[str, int], text: str) -> None:
        self.click(id)
        self.page.keyboard.type(text)
    def enter(self) -> None:
        self.page.keyboard.press("Enter")
    def crawl(self) -> List[str]:


self.page.keyboard.press("Enter")
    def crawl(self) -> List[str]:
        page = self.page
        page_element_buffer = self.page_element_buffer
        start = time.time()
        page_state_as_text = []
        device_pixel_ratio: float = page.evaluate("window.devicePixelRatio")
        if platform == "darwin" and device_pixel_ratio == 1:  # lies
            device_pixel_ratio = 2
        win_upper_bound: float = page.evaluate("window.pageYOffset")
        win_left_bound: float = page.evaluate("window.pageXOffset")
        win_width: float = page.evaluate("window.screen.width")
        win_height: float = page.evaluate("window.screen.height")
        win_right_bound: float = win_left_bound + win_width
        win_lower_bound: float = win_upper_bound + win_height
        # 		percentage_progress_start = (win_upper_bound / document_scroll_height) * 100
        # 		percentage_progress_end = (
        # 			(win_height + win_upper_bound) / document_scroll_height
        # 		) * 100
        percentage_progress_start = 1
        percentage_progress_end = 2
        page_state_as_text.append(
            {
                "x": 0,
                "y": 0,
                "text": "[scrollbar {:0.2f}-{:0.2f}%]".format(
                    round(percentage_progress_start, 2), round(percentage_progress_end)
                ),
            }
        )
        tree = self.client.send(
            "DOMSnapshot.captureSnapshot",
            {"computedStyles": [], "includeDOMRects": True, "includePaintOrder": True},
        )
        strings: Dict[int, str] = tree["strings"]
        document: Dict[str, Any] = tree["documents"][0]