self.callback_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,
        )
        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"
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 21, 2023.


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.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.llms.base import BaseLLM
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
    def output_keys(self) -> List[str]:


@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}}}", 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)
        body_params = self._extract_body_params(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, str]) -> Dict[str, str]:
        intermediate_steps = {}
        instructions = inputs[self.instructions_key]
        instructions = instructions[: self.max_text_length]
        _api_arguments = self.api_request_chain.predict_and_parse(
            instructions=instructions
        )
        api_arguments = cast(str, _api_arguments)
        intermediate_steps["request_args"] = api_arguments
        self.callback_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)
            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}"


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
        self.callback_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,
            )
            answer = cast(str, _answer)
            self.callback_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: BaseLLM,
        requests: Optional[Requests] = None,
        return_intermediate_steps: bool = False,
        **kwargs: Any
        # 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,


operation,
            requests=requests,
            llm=llm,
            return_intermediate_steps=return_intermediate_steps,
            **kwargs,
        )
[docs]    @classmethod
    def from_api_operation(
        cls,
        operation: APIOperation,
        llm: BaseLLM,
        requests: Optional[Requests] = None,
        verbose: bool = False,
        return_intermediate_steps: bool = False,
        raw_response: bool = False,
        **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
        )
        if raw_response:
            response_chain = None
        else:
            response_chain = APIResponderChain.from_llm(llm, verbose=verbose)
        _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,
            verbose=verbose,
            return_intermediate_steps=return_intermediate_steps,
            **kwargs,
        )
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 21, 2023.


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
from typing import Any, Dict, List, Optional
from pydantic import Extra
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.schema import BaseLanguageModel
from langchain.utilities import PythonREPL
[docs]class PALChain(Chain):
    """Implements Program-Aided Language Models."""
    llm: BaseLanguageModel
    prompt: BasePromptTemplate
    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
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
    @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"]


else:
            return [self.output_key, "intermediate_steps"]
    def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
        llm_chain = LLMChain(llm=self.llm, prompt=self.prompt)
        code = llm_chain.predict(stop=[self.stop], **inputs)
        self.callback_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()}
        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."""
        return cls(
            llm=llm,
            prompt=MATH_PROMPT,
            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."""
        return cls(
            llm=llm,
            prompt=COLORED_OBJECT_PROMPT,
            stop="\n\n\n",
            get_answer_expr="print(answer)",
            **kwargs,
        )
    @property
    def _chain_type(self) -> str:
        return "pal_chain"
By Harrison Chase

      © Copyright 2023, Harrison Chase.


By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 21, 2023.


Source code for langchain.chains.llm_bash.base
"""Chain that interprets a prompt and executes bash code to perform bash operations."""
from typing import Dict, List
from pydantic import Extra
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 BaseLanguageModel
from langchain.utilities.bash import BashProcess
[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(llm=OpenAI())
    """
    llm: BaseLanguageModel
    """LLM wrapper to use."""
    input_key: str = "question"  #: :meta private:
    output_key: str = "answer"  #: :meta private:
    prompt: BasePromptTemplate = PROMPT
    class Config:
        """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.input_key]
    @property
    def output_keys(self) -> List[str]:
        """Expect output key.
        :meta private:
        """
        return [self.output_key]
    def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
        llm_executor = LLMChain(prompt=self.prompt, llm=self.llm)
        bash_executor = BashProcess()


bash_executor = BashProcess()
        self.callback_manager.on_text(inputs[self.input_key], verbose=self.verbose)
        t = llm_executor.predict(question=inputs[self.input_key])
        self.callback_manager.on_text(t, color="green", verbose=self.verbose)
        t = t.strip()
        if t.startswith("```bash"):
            # Split the string into a list of substrings
            command_list = t.split("\n")
            print(command_list)
            # Remove the first and last substrings
            command_list = [s for s in command_list[1:-1]]
            output = bash_executor.run(command_list)
            self.callback_manager.on_text("\nAnswer: ", verbose=self.verbose)
            self.callback_manager.on_text(output, color="yellow", verbose=self.verbose)
        else:
            raise ValueError(f"unknown format from LLM: {t}")
        return {self.output_key: output}
    @property
    def _chain_type(self) -> str:
        return "llm_bash_chain"
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 21, 2023.


Source code for langchain.chains.graph_qa.base
"""Question answering over a graph."""
from __future__ import annotations
from typing import Any, Dict, List
from pydantic import Field
from langchain.chains.base import Chain
from langchain.chains.graph_qa.prompts import ENTITY_EXTRACTION_PROMPT, PROMPT
from langchain.chains.llm import LLMChain
from langchain.graphs.networkx_graph import NetworkxEntityGraph, get_entities
from langchain.llms.base import BaseLLM
from langchain.prompts.base import BasePromptTemplate
[docs]class GraphQAChain(Chain):
    """Chain for question-answering against a graph."""
    graph: NetworkxEntityGraph = Field(exclude=True)
    entity_extraction_chain: LLMChain
    qa_chain: LLMChain
    input_key: str = "query"  #: :meta private:
    output_key: str = "result"  #: :meta private:
    @property
    def input_keys(self) -> List[str]:
        """Return the input keys.
        :meta private:
        """
        return [self.input_key]
    @property
    def output_keys(self) -> List[str]:
        """Return the output keys.
        :meta private:
        """
        _output_keys = [self.output_key]
        return _output_keys
[docs]    @classmethod
    def from_llm(
        cls,
        llm: BaseLLM,
        qa_prompt: BasePromptTemplate = PROMPT,
        entity_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT,
        **kwargs: Any,
    ) -> GraphQAChain:
        """Initialize from LLM."""
        qa_chain = LLMChain(llm=llm, prompt=qa_prompt)


qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
        entity_chain = LLMChain(llm=llm, prompt=entity_prompt)
        return cls(qa_chain=qa_chain, entity_extraction_chain=entity_chain, **kwargs)
    def _call(self, inputs: Dict[str, str]) -> Dict[str, Any]:
        """Extract entities, look up info and answer question."""
        question = inputs[self.input_key]
        entity_string = self.entity_extraction_chain.run(question)
        self.callback_manager.on_text(
            "Entities Extracted:", end="\n", verbose=self.verbose
        )
        self.callback_manager.on_text(
            entity_string, color="green", end="\n", verbose=self.verbose
        )
        entities = get_entities(entity_string)
        context = ""
        for entity in entities:
            triplets = self.graph.get_entity_knowledge(entity)
            context += "\n".join(triplets)
        self.callback_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
        self.callback_manager.on_text(
            context, color="green", end="\n", verbose=self.verbose
        )
        result = self.qa_chain({"question": question, "context": context})
        return {self.output_key: result[self.qa_chain.output_key]}
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 21, 2023.


Source code for langchain.chains.llm_checker.base
"""Chain for question-answering with self-verification."""
from typing import Dict, List
from pydantic import Extra
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.llm_checker.prompt import (
    CHECK_ASSERTIONS_PROMPT,
    CREATE_DRAFT_ANSWER_PROMPT,
    LIST_ASSERTIONS_PROMPT,
    REVISED_ANSWER_PROMPT,
)
from langchain.chains.sequential import SequentialChain
from langchain.llms.base import BaseLLM
from langchain.prompts import PromptTemplate
[docs]class LLMCheckerChain(Chain):
    """Chain for question-answering with self-verification.
    Example:
        .. code-block:: python
            from langchain import OpenAI, LLMCheckerChain
            llm = OpenAI(temperature=0.7)
            checker_chain = LLMCheckerChain(llm=llm)
    """
    llm: BaseLLM
    """LLM wrapper to use."""
    create_draft_answer_prompt: PromptTemplate = CREATE_DRAFT_ANSWER_PROMPT
    list_assertions_prompt: PromptTemplate = LIST_ASSERTIONS_PROMPT
    check_assertions_prompt: PromptTemplate = CHECK_ASSERTIONS_PROMPT
    revised_answer_prompt: PromptTemplate = REVISED_ANSWER_PROMPT
    """Prompt to use when questioning the documents."""
    input_key: str = "query"  #: :meta private:
    output_key: str = "result"  #: :meta private:
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
    @property
    def input_keys(self) -> List[str]:
        """Return the singular input key.


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, inputs: Dict[str, str]) -> Dict[str, str]:
        question = inputs[self.input_key]
        create_draft_answer_chain = LLMChain(
            llm=self.llm, prompt=self.create_draft_answer_prompt, output_key="statement"
        )
        list_assertions_chain = LLMChain(
            llm=self.llm, prompt=self.list_assertions_prompt, output_key="assertions"
        )
        check_assertions_chain = LLMChain(
            llm=self.llm,
            prompt=self.check_assertions_prompt,
            output_key="checked_assertions",
        )
        revised_answer_chain = LLMChain(
            llm=self.llm,
            prompt=self.revised_answer_prompt,
            output_key="revised_statement",
        )
        chains = [
            create_draft_answer_chain,
            list_assertions_chain,
            check_assertions_chain,
            revised_answer_chain,
        ]
        question_to_checked_assertions_chain = SequentialChain(
            chains=chains,
            input_variables=["question"],
            output_variables=["revised_statement"],
            verbose=True,
        )
        output = question_to_checked_assertions_chain({"question": question})
        return {self.output_key: output["revised_statement"]}
    @property
    def _chain_type(self) -> str:
        return "llm_checker_chain"
By Harrison Chase


return "llm_checker_chain"
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 21, 2023.


Source code for langchain.chains.combine_documents.base
"""Base interface for chains combining documents."""
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional, Tuple
from pydantic import Field
from langchain.chains.base import Chain
from langchain.docstore.document import Document
from langchain.prompts.base import BasePromptTemplate
from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter
def format_document(doc: Document, prompt: BasePromptTemplate) -> str:
    """Format a document into a string based on a prompt template."""
    base_info = {"page_content": doc.page_content}
    base_info.update(doc.metadata)
    missing_metadata = set(prompt.input_variables).difference(base_info)
    if len(missing_metadata) > 0:
        required_metadata = [
            iv for iv in prompt.input_variables if iv != "page_content"
        ]
        raise ValueError(
            f"Document prompt requires documents to have metadata variables: "
            f"{required_metadata}. Received document with missing metadata: "
            f"{list(missing_metadata)}."
        )
    document_info = {k: base_info[k] for k in prompt.input_variables}
    return prompt.format(**document_info)
class BaseCombineDocumentsChain(Chain, ABC):
    """Base interface for chains combining documents."""
    input_key: str = "input_documents"  #: :meta private:
    output_key: str = "output_text"  #: :meta private:
    @property
    def input_keys(self) -> List[str]:
        """Expect input key.
        :meta private:
        """
        return [self.input_key]
    @property
    def output_keys(self) -> List[str]:
        """Return output key.
        :meta private:
        """


"""Return output key.
        :meta private:
        """
        return [self.output_key]
    def prompt_length(self, docs: List[Document], **kwargs: Any) -> Optional[int]:
        """Return the prompt length given the documents passed in.
        Returns None if the method does not depend on the prompt length.
        """
        return None
    @abstractmethod
    def combine_docs(self, docs: List[Document], **kwargs: Any) -> Tuple[str, dict]:
        """Combine documents into a single string."""
    @abstractmethod
    async def acombine_docs(
        self, docs: List[Document], **kwargs: Any
    ) -> Tuple[str, dict]:
        """Combine documents into a single string asynchronously."""
    def _call(self, inputs: Dict[str, Any]) -> Dict[str, str]:
        docs = inputs[self.input_key]
        # Other keys are assumed to be needed for LLM prediction
        other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
        output, extra_return_dict = self.combine_docs(docs, **other_keys)
        extra_return_dict[self.output_key] = output
        return extra_return_dict
    async def _acall(self, inputs: Dict[str, Any]) -> Dict[str, str]:
        docs = inputs[self.input_key]
        # Other keys are assumed to be needed for LLM prediction
        other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
        output, extra_return_dict = await self.acombine_docs(docs, **other_keys)
        extra_return_dict[self.output_key] = output
        return extra_return_dict
[docs]class AnalyzeDocumentChain(Chain):
    """Chain that splits documents, then analyzes it in pieces."""


"""Chain that splits documents, then analyzes it in pieces."""
    input_key: str = "input_document"  #: :meta private:
    text_splitter: TextSplitter = Field(default_factory=RecursiveCharacterTextSplitter)
    combine_docs_chain: BaseCombineDocumentsChain
    @property
    def input_keys(self) -> List[str]:
        """Expect input key.
        :meta private:
        """
        return [self.input_key]
    @property
    def output_keys(self) -> List[str]:
        """Return output key.
        :meta private:
        """
        return self.combine_docs_chain.output_keys
    def _call(self, inputs: Dict[str, Any]) -> Dict[str, str]:
        document = inputs[self.input_key]
        docs = self.text_splitter.create_documents([document])
        # Other keys are assumed to be needed for LLM prediction
        other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
        other_keys[self.combine_docs_chain.input_key] = docs
        return self.combine_docs_chain(other_keys, return_only_outputs=True)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 21, 2023.


Source code for langchain.chains.hyde.base
"""Hypothetical Document Embeddings.
https://arxiv.org/abs/2212.10496
"""
from __future__ import annotations
from typing import Dict, List
import numpy as np
from pydantic import Extra
from langchain.chains.base import Chain
from langchain.chains.hyde.prompts import PROMPT_MAP
from langchain.chains.llm import LLMChain
from langchain.embeddings.base import Embeddings
from langchain.llms.base import BaseLLM
[docs]class HypotheticalDocumentEmbedder(Chain, Embeddings):
    """Generate hypothetical document for query, and then embed that.
    Based on https://arxiv.org/abs/2212.10496
    """
    base_embeddings: Embeddings
    llm_chain: LLMChain
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
    @property
    def input_keys(self) -> List[str]:
        """Input keys for Hyde's LLM chain."""
        return self.llm_chain.input_keys
    @property
    def output_keys(self) -> List[str]:
        """Output keys for Hyde's LLM chain."""
        return self.llm_chain.output_keys
[docs]    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Call the base embeddings."""
        return self.base_embeddings.embed_documents(texts)
[docs]    def combine_embeddings(self, embeddings: List[List[float]]) -> List[float]:
        """Combine embeddings into final embeddings."""
        return list(np.array(embeddings).mean(axis=0))
[docs]    def embed_query(self, text: str) -> List[float]:
        """Generate a hypothetical document and embedded it."""


"""Generate a hypothetical document and embedded it."""
        var_name = self.llm_chain.input_keys[0]
        result = self.llm_chain.generate([{var_name: text}])
        documents = [generation.text for generation in result.generations[0]]
        embeddings = self.embed_documents(documents)
        return self.combine_embeddings(embeddings)
    def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
        """Call the internal llm chain."""
        return self.llm_chain._call(inputs)
[docs]    @classmethod
    def from_llm(
        cls, llm: BaseLLM, base_embeddings: Embeddings, prompt_key: str
    ) -> HypotheticalDocumentEmbedder:
        """Load and use LLMChain for a specific prompt key."""
        prompt = PROMPT_MAP[prompt_key]
        llm_chain = LLMChain(llm=llm, prompt=prompt)
        return cls(base_embeddings=base_embeddings, llm_chain=llm_chain)
    @property
    def _chain_type(self) -> str:
        return "hyde_chain"
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 21, 2023.


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.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.schema import BaseLanguageModel
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, str]) -> Dict[str, Any]:
        docs = self.text_splitter.create_documents([inputs[self.input_key]])


docs = self.text_splitter.create_documents([inputs[self.input_key]])
        results = self.llm_chain.generate([{"text": d.page_content} for d in docs])
        qa = [json.loads(res[0].text) for res in results.generations]
        return {self.output_key: qa}
    async def _acall(self, inputs: Dict[str, str]) -> Dict[str, str]:
        raise NotImplementedError
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 21, 2023.


Source code for langchain.chains.llm_math.base
"""Chain that interprets a prompt and executes python code to do math."""
import math
import re
from typing import Dict, List
import numexpr
from pydantic import Extra
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.llm_math.prompt import PROMPT
from langchain.prompts.base import BasePromptTemplate
from langchain.schema import BaseLanguageModel
[docs]class LLMMathChain(Chain):
    """Chain that interprets a prompt and executes python code to do math.
    Example:
        .. code-block:: python
            from langchain import LLMMathChain, OpenAI
            llm_math = LLMMathChain(llm=OpenAI())
    """
    llm: BaseLanguageModel
    """LLM wrapper to use."""
    prompt: BasePromptTemplate = PROMPT
    """Prompt to use to translate to python if neccessary."""
    input_key: str = "question"  #: :meta private:
    output_key: str = "answer"  #: :meta private:
    class Config:
        """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.input_key]
    @property
    def output_keys(self) -> List[str]:
        """Expect output key.
        :meta private:
        """
        return [self.output_key]
    def _evaluate_expression(self, expression: str) -> str:
        try:
            local_dict = {"pi": math.pi, "e": math.e}


try:
            local_dict = {"pi": math.pi, "e": math.e}
            output = str(
                numexpr.evaluate(
                    expression.strip(),
                    global_dict={},  # restrict access to globals
                    local_dict=local_dict,  # add common mathematical functions
                )
            )
        except Exception as e:
            raise ValueError(f"{e}. Please try again with a valid numerical expression")
        # Remove any leading and trailing brackets from the output
        return re.sub(r"^\[|\]$", "", output)
    def _process_llm_result(self, llm_output: str) -> Dict[str, str]:
        self.callback_manager.on_text(llm_output, color="green", verbose=self.verbose)
        llm_output = llm_output.strip()
        text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL)
        if text_match:
            expression = text_match.group(1)
            output = self._evaluate_expression(expression)
            self.callback_manager.on_text("\nAnswer: ", verbose=self.verbose)
            self.callback_manager.on_text(output, color="yellow", verbose=self.verbose)
            answer = "Answer: " + output
        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}
    async def _aprocess_llm_result(self, llm_output: str) -> Dict[str, str]:
        if self.callback_manager.is_async:
            await self.callback_manager.on_text(
                llm_output, color="green", verbose=self.verbose


llm_output, color="green", verbose=self.verbose
            )
        else:
            self.callback_manager.on_text(
                llm_output, color="green", verbose=self.verbose
            )
        llm_output = llm_output.strip()
        text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL)
        if text_match:
            expression = text_match.group(1)
            output = self._evaluate_expression(expression)
            if self.callback_manager.is_async:
                await self.callback_manager.on_text("\nAnswer: ", verbose=self.verbose)
                await self.callback_manager.on_text(
                    output, color="yellow", verbose=self.verbose
                )
            else:
                await self.callback_manager.on_text("\nAnswer: ", verbose=self.verbose)
                await self.callback_manager.on_text(
                    output, color="yellow", verbose=self.verbose
                )
            answer = "Answer: " + output
        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]) -> Dict[str, str]:
        llm_executor = LLMChain(
            prompt=self.prompt, llm=self.llm, callback_manager=self.callback_manager
        )
        self.callback_manager.on_text(inputs[self.input_key], verbose=self.verbose)
        llm_output = llm_executor.predict(
            question=inputs[self.input_key], stop=["```output"]
        )
        return self._process_llm_result(llm_output)


)
        return self._process_llm_result(llm_output)
    async def _acall(self, inputs: Dict[str, str]) -> Dict[str, str]:
        llm_executor = LLMChain(
            prompt=self.prompt, llm=self.llm, callback_manager=self.callback_manager
        )
        if self.callback_manager.is_async:
            await self.callback_manager.on_text(
                inputs[self.input_key], verbose=self.verbose
            )
        else:
            self.callback_manager.on_text(inputs[self.input_key], verbose=self.verbose)
        llm_output = await llm_executor.apredict(
            question=inputs[self.input_key], stop=["```output"]
        )
        return await self._aprocess_llm_result(llm_output)
    @property
    def _chain_type(self) -> str:
        return "llm_math_chain"
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 21, 2023.


Source code for langchain.chains.conversational_retrieval.base
"""Chain for chatting with a vector database."""
from __future__ import annotations
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.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 BaseLanguageModel, 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):
            human = "Human: " + dialogue_turn[0]
            ai = "Assistant: " + dialogue_turn[1]
            buffer += "\n" + "\n".join([human, ai])
        else:


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
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
    get_chat_history: Optional[Callable[[CHAT_TURN_TYPE], str]] = None
    """Return the source documents."""
    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"]
        return _output_keys
    @abstractmethod
    def _get_docs(self, question: str, inputs: Dict[str, Any]) -> List[Document]:
        """Get docs."""
    def _call(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
        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:
            new_question = self.question_generator.run(


if chat_history_str:
            new_question = self.question_generator.run(
                question=question, chat_history=chat_history_str
            )
        else:
            new_question = question
        docs = self._get_docs(new_question, inputs)
        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, **new_inputs)
        if self.return_source_documents:
            return {self.output_key: answer, "source_documents": docs}
        else:
            return {self.output_key: answer}
    @abstractmethod
    async def _aget_docs(self, question: str, inputs: Dict[str, Any]) -> List[Document]:
        """Get docs."""
    async def _acall(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
        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:
            new_question = await self.question_generator.arun(
                question=question, chat_history=chat_history_str
            )
        else:
            new_question = question
        docs = await self._aget_docs(new_question, inputs)
        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, **new_inputs)
        if self.return_source_documents:
            return {self.output_key: answer, "source_documents": docs}
        else:
            return {self.output_key: answer}
    def save(self, file_path: Union[Path, str]) -> None:


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]) -> List[Document]:
        docs = self.retriever.get_relevant_documents(question)
        return self._reduce_tokens_below_limit(docs)
    async def _aget_docs(self, question: str, inputs: Dict[str, Any]) -> List[Document]:
        docs = await self.retriever.aget_relevant_documents(question)
        return self._reduce_tokens_below_limit(docs)
[docs]    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,


def from_llm(
        cls,
        llm: BaseLanguageModel,
        retriever: BaseRetriever,
        condense_question_prompt: BasePromptTemplate = CONDENSE_QUESTION_PROMPT,
        qa_prompt: Optional[BasePromptTemplate] = None,
        chain_type: str = "stuff",
        **kwargs: Any,
    ) -> BaseConversationalRetrievalChain:
        """Load chain from LLM."""
        doc_chain = load_qa_chain(
            llm,
            chain_type=chain_type,
            prompt=qa_prompt,
        )
        condense_question_chain = LLMChain(llm=llm, prompt=condense_question_prompt)
        return cls(
            retriever=retriever,
            combine_docs_chain=doc_chain,
            question_generator=condense_question_chain,
            **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"
    @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]) -> List[Document]:
        vectordbkwargs = inputs.get("vectordbkwargs", {})


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

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 21, 2023.


Source code for langchain.chains.sql_database.base
"""Chain for interacting with SQL Database."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.sql_database.prompt import DECIDER_PROMPT, PROMPT, SQL_PROMPTS
from langchain.prompts.base import BasePromptTemplate
from langchain.schema import BaseLanguageModel
from langchain.sql_database import SQLDatabase
[docs]class SQLDatabaseChain(Chain):
    """Chain for interacting with SQL Database.
    Example:
        .. code-block:: python
            from langchain import SQLDatabaseChain, OpenAI, SQLDatabase
            db = SQLDatabase(...)
            db_chain = SQLDatabaseChain(llm=OpenAI(), database=db)
    """
    llm: BaseLanguageModel
    """LLM wrapper to use."""
    database: SQLDatabase = Field(exclude=True)
    """SQL Database to connect to."""
    prompt: Optional[BasePromptTemplate] = None
    """Prompt to use to translate natural language to SQL."""
    top_k: int = 5
    """Number of results to return from the query"""
    input_key: str = "query"  #: :meta private:
    output_key: str = "result"  #: :meta private:
    return_intermediate_steps: bool = False
    """Whether or not to return the intermediate steps along with the final answer."""
    return_direct: bool = False
    """Whether or not to return the result of querying the SQL table directly."""
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
    @property


extra = Extra.forbid
        arbitrary_types_allowed = True
    @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:
        """
        if not self.return_intermediate_steps:
            return [self.output_key]
        else:
            return [self.output_key, "intermediate_steps"]
    def _call(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
        prompt = self.prompt or SQL_PROMPTS.get(self.database.dialect, PROMPT)
        llm_chain = LLMChain(llm=self.llm, prompt=prompt)
        input_text = f"{inputs[self.input_key]}\nSQLQuery:"
        self.callback_manager.on_text(input_text, verbose=self.verbose)
        # If not present, then defaults to None which is all tables.
        table_names_to_use = inputs.get("table_names_to_use")
        table_info = self.database.get_table_info(table_names=table_names_to_use)
        llm_inputs = {
            "input": input_text,
            "top_k": self.top_k,
            "dialect": self.database.dialect,
            "table_info": table_info,
            "stop": ["\nSQLResult:"],
        }
        intermediate_steps = []
        sql_cmd = llm_chain.predict(**llm_inputs)
        intermediate_steps.append(sql_cmd)
        self.callback_manager.on_text(sql_cmd, color="green", verbose=self.verbose)
        result = self.database.run(sql_cmd)
        intermediate_steps.append(result)
        self.callback_manager.on_text("\nSQLResult: ", verbose=self.verbose)


self.callback_manager.on_text("\nSQLResult: ", verbose=self.verbose)
        self.callback_manager.on_text(result, color="yellow", verbose=self.verbose)
        # If return direct, we just set the final result equal to the sql query
        if self.return_direct:
            final_result = result
        else:
            self.callback_manager.on_text("\nAnswer:", verbose=self.verbose)
            input_text += f"{sql_cmd}\nSQLResult: {result}\nAnswer:"
            llm_inputs["input"] = input_text
            final_result = llm_chain.predict(**llm_inputs)
            self.callback_manager.on_text(
                final_result, color="green", verbose=self.verbose
            )
        chain_result: Dict[str, Any] = {self.output_key: final_result}
        if self.return_intermediate_steps:
            chain_result["intermediate_steps"] = intermediate_steps
        return chain_result
    @property
    def _chain_type(self) -> str:
        return "sql_database_chain"
[docs]class SQLDatabaseSequentialChain(Chain):
    """Chain for querying SQL database that is a sequential chain.
    The chain is as follows:
    1. Based on the query, determine which tables to use.
    2. Based on those tables, call the normal SQL database chain.
    This is useful in cases where the number of tables in the database is large.
    """
    return_intermediate_steps: bool = False
[docs]    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,
        database: SQLDatabase,
        query_prompt: BasePromptTemplate = PROMPT,
        decider_prompt: BasePromptTemplate = DECIDER_PROMPT,
        **kwargs: Any,
    ) -> SQLDatabaseSequentialChain:


**kwargs: Any,
    ) -> SQLDatabaseSequentialChain:
        """Load the necessary chains."""
        sql_chain = SQLDatabaseChain(
            llm=llm, database=database, prompt=query_prompt, **kwargs
        )
        decider_chain = LLMChain(
            llm=llm, prompt=decider_prompt, output_key="table_names"
        )
        return cls(sql_chain=sql_chain, decider_chain=decider_chain, **kwargs)
    decider_chain: LLMChain
    sql_chain: SQLDatabaseChain
    input_key: str = "query"  #: :meta private:
    output_key: str = "result"  #: :meta private:
    @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:
        """
        if not self.return_intermediate_steps:
            return [self.output_key]
        else:
            return [self.output_key, "intermediate_steps"]
    def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
        _table_names = self.sql_chain.database.get_usable_table_names()
        table_names = ", ".join(_table_names)
        llm_inputs = {
            "query": inputs[self.input_key],
            "table_names": table_names,
        }
        table_names_to_use = self.decider_chain.predict_and_parse(**llm_inputs)
        self.callback_manager.on_text(
            "Table names to use:", end="\n", verbose=self.verbose
        )
        self.callback_manager.on_text(


)
        self.callback_manager.on_text(
            str(table_names_to_use), color="yellow", verbose=self.verbose
        )
        new_inputs = {
            self.sql_chain.input_key: inputs[self.input_key],
            "table_names_to_use": table_names_to_use,
        }
        return self.sql_chain(new_inputs, return_only_outputs=True)
    @property
    def _chain_type(self) -> str:
        return "sql_database_sequential_chain"
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 21, 2023.


Source code for langchain.chains.llm_summarization_checker.base
"""Chain for summarization with self-verification."""
from pathlib import Path
from typing import Dict, List
from pydantic import Extra
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.sequential import SequentialChain
from langchain.llms.base import BaseLLM
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"]
)
[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(llm=llm)
    """
    llm: BaseLLM
    """LLM wrapper to use."""
    create_assertions_prompt: PromptTemplate = CREATE_ASSERTIONS_PROMPT
    check_assertions_prompt: PromptTemplate = CHECK_ASSERTIONS_PROMPT
    revised_summary_prompt: PromptTemplate = REVISED_SUMMARY_PROMPT


revised_summary_prompt: PromptTemplate = REVISED_SUMMARY_PROMPT
    are_all_true_prompt: PromptTemplate = ARE_ALL_TRUE_PROMPT
    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."""
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
    @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, inputs: Dict[str, str]) -> Dict[str, str]:
        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:
            chain = SequentialChain(
                chains=[
                    LLMChain(
                        llm=self.llm,
                        prompt=self.create_assertions_prompt,
                        output_key="assertions",
                        verbose=self.verbose,
                    ),
                    LLMChain(
                        llm=self.llm,
                        prompt=self.check_assertions_prompt,
                        output_key="checked_assertions",
                        verbose=self.verbose,
                    ),
                    LLMChain(
                        llm=self.llm,
                        prompt=self.revised_summary_prompt,
                        output_key="revised_summary",
                        verbose=self.verbose,
                    ),


output_key="revised_summary",
                        verbose=self.verbose,
                    ),
                    LLMChain(
                        llm=self.llm,
                        output_key="all_true",
                        prompt=self.are_all_true_prompt,
                        verbose=self.verbose,
                    ),
                ],
                input_variables=["summary"],
                output_variables=["all_true", "revised_summary"],
                verbose=self.verbose,
            )
            output = chain({"summary": chain_input})
            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"
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 21, 2023.


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.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(self, inputs: Dict[str, Any]) -> List[Document]:
        question = inputs[self.question_key]
        docs = self.retriever.get_relevant_documents(question)


docs = self.retriever.get_relevant_documents(question)
        return self._reduce_tokens_below_limit(docs)
    async def _aget_docs(self, inputs: Dict[str, Any]) -> List[Document]:
        question = inputs[self.question_key]
        docs = await self.retriever.aget_relevant_documents(question)
        return self._reduce_tokens_below_limit(docs)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 21, 2023.


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.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])
            while token_count > self.max_tokens_limit:
                num_docs -= 1
                token_count -= tokens[num_docs]


num_docs -= 1
                token_count -= tokens[num_docs]
        return docs[:num_docs]
    def _get_docs(self, inputs: Dict[str, Any]) -> 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]) -> List[Document]:
        raise NotImplementedError("VectorDBQAWithSourcesChain does not support async")
    @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"
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 21, 2023.


Source code for langchain.chains.qa_with_sources.base
"""Question answering with sources over documents."""
from __future__ import annotations
import re
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
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
from langchain.schema import BaseLanguageModel
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."""
    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,
        document_prompt: BasePromptTemplate = EXAMPLE_PROMPT,
        question_prompt: BasePromptTemplate = QUESTION_PROMPT,
        combine_prompt: BasePromptTemplate = COMBINE_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,
        )
    @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)
    class Config:
        """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


: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
    @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]) -> List[Document]:
        """Get docs to run questioning over."""
    def _call(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
        docs = self._get_docs(inputs)
        answer = self.combine_documents_chain.run(input_documents=docs, **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]) -> List[Document]:
        """Get docs to run questioning over."""
    async def _acall(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
        docs = await self._aget_docs(inputs)


docs = await self._aget_docs(inputs)
        answer = await self.combine_documents_chain.arun(input_documents=docs, **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
[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]) -> List[Document]:
        return inputs.pop(self.input_docs_key)
    async def _aget_docs(self, inputs: Dict[str, Any]) -> List[Document]:
        return inputs.pop(self.input_docs_key)
    @property
    def _chain_type(self) -> str:
        return "qa_with_sources_chain"
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 21, 2023.


Source code for langchain.chains.retrieval_qa.base
"""Chain for question-answering against a vector database."""
from __future__ import annotations
import warnings
from abc import abstractmethod
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
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.llm import LLMChain
from langchain.chains.question_answering import load_qa_chain
from langchain.chains.question_answering.stuff_prompt import PROMPT_SELECTOR
from langchain.prompts import PromptTemplate
from langchain.schema import BaseLanguageModel, BaseRetriever, Document
from langchain.vectorstores.base import VectorStore
class BaseRetrievalQA(Chain):
    combine_documents_chain: BaseCombineDocumentsChain
    """Chain to use to combine the documents."""
    input_key: str = "query"  #: :meta private:
    output_key: str = "result"  #: :meta private:
    return_source_documents: bool = False
    """Return the source documents."""
    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]:
        """Return the input keys.
        :meta private:
        """
        return [self.input_key]
    @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 = [self.output_key]
        if self.return_source_documents:
            _output_keys = _output_keys + ["source_documents"]
        return _output_keys
    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,
        prompt: Optional[PromptTemplate] = None,
        **kwargs: Any,
    ) -> BaseRetrievalQA:
        """Initialize from LLM."""
        _prompt = prompt or PROMPT_SELECTOR.get_prompt(llm)
        llm_chain = LLMChain(llm=llm, prompt=_prompt)
        document_prompt = PromptTemplate(
            input_variables=["page_content"], template="Context:\n{page_content}"
        )
        combine_documents_chain = StuffDocumentsChain(
            llm_chain=llm_chain,
            document_variable_name="context",
            document_prompt=document_prompt,
        )
        return cls(combine_documents_chain=combine_documents_chain, **kwargs)
    @classmethod
    def from_chain_type(
        cls,
        llm: BaseLanguageModel,
        chain_type: str = "stuff",
        chain_type_kwargs: Optional[dict] = None,
        **kwargs: Any,
    ) -> BaseRetrievalQA:
        """Load chain from chain type."""
        _chain_type_kwargs = chain_type_kwargs or {}
        combine_documents_chain = load_qa_chain(
            llm, chain_type=chain_type, **_chain_type_kwargs
        )
        return cls(combine_documents_chain=combine_documents_chain, **kwargs)
    @abstractmethod
    def _get_docs(self, question: str) -> List[Document]:
        """Get documents to do question answering over."""
    def _call(self, inputs: Dict[str, str]) -> Dict[str, Any]:


def _call(self, inputs: Dict[str, str]) -> Dict[str, Any]:
        """Run get_relevant_text and llm on input query.
        If chain has 'return_source_documents' as 'True', returns
        the retrieved documents as well under the key 'source_documents'.
        Example:
        .. code-block:: python
        res = indexqa({'query': 'This is my query'})
        answer, docs = res['result'], res['source_documents']
        """
        question = inputs[self.input_key]
        docs = self._get_docs(question)
        answer = self.combine_documents_chain.run(
            input_documents=docs, question=question
        )
        if self.return_source_documents:
            return {self.output_key: answer, "source_documents": docs}
        else:
            return {self.output_key: answer}
    @abstractmethod
    async def _aget_docs(self, question: str) -> List[Document]:
        """Get documents to do question answering over."""
    async def _acall(self, inputs: Dict[str, str]) -> Dict[str, Any]:
        """Run get_relevant_text and llm on input query.
        If chain has 'return_source_documents' as 'True', returns
        the retrieved documents as well under the key 'source_documents'.
        Example:
        .. code-block:: python
        res = indexqa({'query': 'This is my query'})
        answer, docs = res['result'], res['source_documents']
        """
        question = inputs[self.input_key]
        docs = await self._aget_docs(question)
        answer = await self.combine_documents_chain.arun(
            input_documents=docs, question=question
        )
        if self.return_source_documents:
            return {self.output_key: answer, "source_documents": docs}


return {self.output_key: answer, "source_documents": docs}
        else:
            return {self.output_key: answer}
[docs]class RetrievalQA(BaseRetrievalQA):
    """Chain for question-answering against an index.
    Example:
        .. code-block:: python
            from langchain.llms import OpenAI
            from langchain.chains import RetrievalQA
            from langchain.faiss import FAISS
            from langchain.vectorstores.base import VectorStoreRetriever
            retriever = VectorStoreRetriever(vectorstore=FAISS(...))
            retrievalQA = RetrievalQA.from_llm(llm=OpenAI(), retriever=retriever)
    """
    retriever: BaseRetriever = Field(exclude=True)
    def _get_docs(self, question: str) -> List[Document]:
        return self.retriever.get_relevant_documents(question)
    async def _aget_docs(self, question: str) -> List[Document]:
        return await self.retriever.aget_relevant_documents(question)
[docs]class VectorDBQA(BaseRetrievalQA):
    """Chain for question-answering against a vector database."""
    vectorstore: VectorStore = Field(exclude=True, alias="vectorstore")
    """Vector Database to connect to."""
    k: int = 4
    """Number of documents to query for."""
    search_type: str = "similarity"
    """Search type to use over vectorstore. `similarity` or `mmr`."""
    search_kwargs: Dict[str, Any] = Field(default_factory=dict)
    """Extra search args."""
    @root_validator()
    def raise_deprecation(cls, values: Dict) -> Dict:
        warnings.warn(
            "`VectorDBQA` is deprecated - "


warnings.warn(
            "`VectorDBQA` is deprecated - "
            "please use `from langchain.chains import RetrievalQA`"
        )
        return values
    @root_validator()
    def validate_search_type(cls, values: Dict) -> Dict:
        """Validate search type."""
        if "search_type" in values:
            search_type = values["search_type"]
            if search_type not in ("similarity", "mmr"):
                raise ValueError(f"search_type of {search_type} not allowed.")
        return values
    def _get_docs(self, question: str) -> List[Document]:
        if self.search_type == "similarity":
            docs = self.vectorstore.similarity_search(
                question, k=self.k, **self.search_kwargs
            )
        elif self.search_type == "mmr":
            docs = self.vectorstore.max_marginal_relevance_search(
                question, k=self.k, **self.search_kwargs
            )
        else:
            raise ValueError(f"search_type of {self.search_type} not allowed.")
        return docs
    async def _aget_docs(self, question: str) -> List[Document]:
        raise NotImplementedError("VectorDBQA does not support async")
    @property
    def _chain_type(self) -> str:
        """Return the chain type."""
        return "vector_db_qa"
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 21, 2023.


Source code for langchain.chains.conversation.base
"""Chain that carries on a conversation and calls an LLM."""
from typing import Dict, List
from pydantic import Extra, Field, root_validator
from langchain.chains.conversation.prompt import PROMPT
from langchain.chains.llm import LLMChain
from langchain.memory.buffer import ConversationBufferMemory
from langchain.prompts.base import BasePromptTemplate
from langchain.schema import BaseMemory
[docs]class ConversationChain(LLMChain):
    """Chain to have a conversation and load context from memory.
    Example:
        .. code-block:: python
            from langchain import ConversationChain, OpenAI
            conversation = ConversationChain(llm=OpenAI())
    """
    memory: BaseMemory = Field(default_factory=ConversationBufferMemory)
    """Default memory store."""
    prompt: BasePromptTemplate = PROMPT
    """Default conversation prompt to use."""
    input_key: str = "input"  #: :meta private:
    output_key: str = "response"  #: :meta private:
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
    @property
    def input_keys(self) -> List[str]:
        """Use this since so some prompt vars come from history."""
        return [self.input_key]
    @root_validator()
    def validate_prompt_input_variables(cls, values: Dict) -> Dict:
        """Validate that prompt input variables are consistent."""
        memory_keys = values["memory"].memory_variables
        input_key = values["input_key"]
        if input_key in memory_keys:
            raise ValueError(
                f"The input key {input_key} was also found in the memory keys "


f"The input key {input_key} was also found in the memory keys "
                f"({memory_keys}) - please provide keys that don't overlap."
            )
        prompt_variables = values["prompt"].input_variables
        expected_keys = memory_keys + [input_key]
        if set(expected_keys) != set(prompt_variables):
            raise ValueError(
                "Got unexpected prompt input variables. The prompt expects "
                f"{prompt_variables}, but got {memory_keys} as inputs from "
                f"memory, and {input_key} as the normal input key."
            )
        return values
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 21, 2023.


Source code for langchain.agents.agent
"""Chain that takes in an input and produces an action and action input."""
from __future__ import annotations
import asyncio
import json
import logging
import time
from abc import abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
import yaml
from pydantic import BaseModel, root_validator
from langchain.agents.tools import InvalidTool
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.input import get_color_mapping
from langchain.prompts.base import BasePromptTemplate
from langchain.prompts.few_shot import FewShotPromptTemplate
from langchain.prompts.prompt import PromptTemplate
from langchain.schema import (
    AgentAction,
    AgentFinish,
    BaseLanguageModel,
    BaseMessage,
    BaseOutputParser,
)
from langchain.tools.base import BaseTool
from langchain.utilities.asyncio import asyncio_timeout
logger = logging.getLogger(__name__)
[docs]class BaseSingleActionAgent(BaseModel):
    """Base Agent class."""
    @property
    def return_values(self) -> List[str]:
        """Return values of the agent."""
        return ["output"]
[docs]    def get_allowed_tools(self) -> Optional[List[str]]:
        return None
[docs]    @abstractmethod
    def plan(
        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
    ) -> Union[AgentAction, AgentFinish]:
        """Given input, decided what to do.
        Args:
            intermediate_steps: Steps the LLM has taken to date,
                along with observations
            **kwargs: User inputs.
        Returns:


along with observations
            **kwargs: User inputs.
        Returns:
            Action specifying what tool to use.
        """
[docs]    @abstractmethod
    async def aplan(
        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
    ) -> Union[AgentAction, AgentFinish]:
        """Given input, decided what to do.
        Args:
            intermediate_steps: Steps the LLM has taken to date,
                along with observations
            **kwargs: User inputs.
        Returns:
            Action specifying what tool to use.
        """
    @property
    @abstractmethod
    def input_keys(self) -> List[str]:
        """Return the input keys.
        :meta private:
        """
[docs]    def return_stopped_response(
        self,
        early_stopping_method: str,
        intermediate_steps: List[Tuple[AgentAction, str]],
        **kwargs: Any,
    ) -> AgentFinish:
        """Return response when agent has been stopped due to max iterations."""
        if early_stopping_method == "force":
            # `force` just returns a constant string
            return AgentFinish(
                {"output": "Agent stopped due to iteration limit or time limit."}, ""
            )
        else:
            raise ValueError(
                f"Got unsupported early_stopping_method `{early_stopping_method}`"
            )
[docs]    @classmethod
    def from_llm_and_tools(
        cls,
        llm: BaseLanguageModel,
        tools: Sequence[BaseTool],
        callback_manager: Optional[BaseCallbackManager] = None,
        **kwargs: Any,
    ) -> BaseSingleActionAgent:
        raise NotImplementedError
    @property
    def _agent_type(self) -> str:


raise NotImplementedError
    @property
    def _agent_type(self) -> str:
        """Return Identifier of agent type."""
        raise NotImplementedError
[docs]    def dict(self, **kwargs: Any) -> Dict:
        """Return dictionary representation of agent."""
        _dict = super().dict()
        _dict["_type"] = str(self._agent_type)
        return _dict
[docs]    def save(self, file_path: Union[Path, str]) -> None:
        """Save the agent.
        Args:
            file_path: Path to file to save the agent to.
        Example:
        .. code-block:: python
            # If working with agent executor
            agent.agent.save(file_path="path/agent.yaml")
        """
        # Convert file to Path object.
        if isinstance(file_path, str):
            save_path = Path(file_path)
        else:
            save_path = file_path
        directory_path = save_path.parent
        directory_path.mkdir(parents=True, exist_ok=True)
        # Fetch dictionary to save
        agent_dict = self.dict()
        if save_path.suffix == ".json":
            with open(file_path, "w") as f:
                json.dump(agent_dict, f, indent=4)
        elif save_path.suffix == ".yaml":
            with open(file_path, "w") as f:
                yaml.dump(agent_dict, f, default_flow_style=False)
        else:
            raise ValueError(f"{save_path} must be json or yaml")
[docs]    def tool_run_logging_kwargs(self) -> Dict:
        return {}
[docs]class BaseMultiActionAgent(BaseModel):
    """Base Agent class."""
    @property
    def return_values(self) -> List[str]:
        """Return values of the agent."""


def return_values(self) -> List[str]:
        """Return values of the agent."""
        return ["output"]
[docs]    def get_allowed_tools(self) -> Optional[List[str]]:
        return None
[docs]    @abstractmethod
    def plan(
        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
    ) -> Union[List[AgentAction], AgentFinish]:
        """Given input, decided what to do.
        Args:
            intermediate_steps: Steps the LLM has taken to date,
                along with observations
            **kwargs: User inputs.
        Returns:
            Actions specifying what tool to use.
        """
[docs]    @abstractmethod
    async def aplan(
        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
    ) -> Union[List[AgentAction], AgentFinish]:
        """Given input, decided what to do.
        Args:
            intermediate_steps: Steps the LLM has taken to date,
                along with observations
            **kwargs: User inputs.
        Returns:
            Actions specifying what tool to use.
        """
    @property
    @abstractmethod
    def input_keys(self) -> List[str]:
        """Return the input keys.
        :meta private:
        """
[docs]    def return_stopped_response(
        self,
        early_stopping_method: str,
        intermediate_steps: List[Tuple[AgentAction, str]],
        **kwargs: Any,
    ) -> AgentFinish:
        """Return response when agent has been stopped due to max iterations."""
        if early_stopping_method == "force":
            # `force` just returns a constant string
            return AgentFinish({"output": "Agent stopped due to max iterations."}, "")


return AgentFinish({"output": "Agent stopped due to max iterations."}, "")
        else:
            raise ValueError(
                f"Got unsupported early_stopping_method `{early_stopping_method}`"
            )
    @property
    def _agent_type(self) -> str:
        """Return Identifier of agent type."""
        raise NotImplementedError
[docs]    def dict(self, **kwargs: Any) -> Dict:
        """Return dictionary representation of agent."""
        _dict = super().dict()
        _dict["_type"] = str(self._agent_type)
        return _dict
[docs]    def save(self, file_path: Union[Path, str]) -> None:
        """Save the agent.
        Args:
            file_path: Path to file to save the agent to.
        Example:
        .. code-block:: python
            # If working with agent executor
            agent.agent.save(file_path="path/agent.yaml")
        """
        # Convert file to Path object.
        if isinstance(file_path, str):
            save_path = Path(file_path)
        else:
            save_path = file_path
        directory_path = save_path.parent
        directory_path.mkdir(parents=True, exist_ok=True)
        # Fetch dictionary to save
        agent_dict = self.dict()
        if save_path.suffix == ".json":
            with open(file_path, "w") as f:
                json.dump(agent_dict, f, indent=4)
        elif save_path.suffix == ".yaml":
            with open(file_path, "w") as f:
                yaml.dump(agent_dict, f, default_flow_style=False)
        else:
            raise ValueError(f"{save_path} must be json or yaml")
[docs]    def tool_run_logging_kwargs(self) -> Dict:
        return {}


[docs]    def tool_run_logging_kwargs(self) -> Dict:
        return {}
[docs]class AgentOutputParser(BaseOutputParser):
[docs]    @abstractmethod
    def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
        """Parse text into agent action/finish."""
[docs]class LLMSingleActionAgent(BaseSingleActionAgent):
    llm_chain: LLMChain
    output_parser: AgentOutputParser
    stop: List[str]
    @property
    def input_keys(self) -> List[str]:
        return list(set(self.llm_chain.input_keys) - {"intermediate_steps"})
[docs]    def plan(
        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
    ) -> Union[AgentAction, AgentFinish]:
        """Given input, decided what to do.
        Args:
            intermediate_steps: Steps the LLM has taken to date,
                along with observations
            **kwargs: User inputs.
        Returns:
            Action specifying what tool to use.
        """
        output = self.llm_chain.run(
            intermediate_steps=intermediate_steps, stop=self.stop, **kwargs
        )
        return self.output_parser.parse(output)
[docs]    async def aplan(
        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
    ) -> Union[AgentAction, AgentFinish]:
        """Given input, decided what to do.
        Args:
            intermediate_steps: Steps the LLM has taken to date,
                along with observations
            **kwargs: User inputs.
        Returns:
            Action specifying what tool to use.
        """
        output = await self.llm_chain.arun(


"""
        output = await self.llm_chain.arun(
            intermediate_steps=intermediate_steps, stop=self.stop, **kwargs
        )
        return self.output_parser.parse(output)
[docs]    def tool_run_logging_kwargs(self) -> Dict:
        return {
            "llm_prefix": "",
            "observation_prefix": "" if len(self.stop) == 0 else self.stop[0],
        }
[docs]class Agent(BaseSingleActionAgent):
    """Class responsible for calling the language model and deciding the action.
    This is driven by an LLMChain. The prompt in the LLMChain MUST include
    a variable called "agent_scratchpad" where the agent can put its
    intermediary work.
    """
    llm_chain: LLMChain
    output_parser: AgentOutputParser
    allowed_tools: Optional[List[str]] = None
[docs]    def get_allowed_tools(self) -> Optional[List[str]]:
        return self.allowed_tools
    @property
    def return_values(self) -> List[str]:
        return ["output"]
    def _fix_text(self, text: str) -> str:
        """Fix the text."""
        raise ValueError("fix_text not implemented for this agent.")
    @property
    def _stop(self) -> List[str]:
        return [
            f"\n{self.observation_prefix.rstrip()}",
            f"\n\t{self.observation_prefix.rstrip()}",
        ]
    def _construct_scratchpad(
        self, intermediate_steps: List[Tuple[AgentAction, str]]
    ) -> Union[str, List[BaseMessage]]:
        """Construct the scratchpad that lets the agent continue its thought process."""
        thoughts = ""
        for action, observation in intermediate_steps:
            thoughts += action.log


thoughts = ""
        for action, observation in intermediate_steps:
            thoughts += action.log
            thoughts += f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
        return thoughts
[docs]    def plan(
        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
    ) -> Union[AgentAction, AgentFinish]:
        """Given input, decided what to do.
        Args:
            intermediate_steps: Steps the LLM has taken to date,
                along with observations
            **kwargs: User inputs.
        Returns:
            Action specifying what tool to use.
        """
        full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
        full_output = self.llm_chain.predict(**full_inputs)
        return self.output_parser.parse(full_output)
[docs]    async def aplan(
        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
    ) -> Union[AgentAction, AgentFinish]:
        """Given input, decided what to do.
        Args:
            intermediate_steps: Steps the LLM has taken to date,
                along with observations
            **kwargs: User inputs.
        Returns:
            Action specifying what tool to use.
        """
        full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
        full_output = await self.llm_chain.apredict(**full_inputs)
        return self.output_parser.parse(full_output)
[docs]    def get_full_inputs(
        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
    ) -> Dict[str, Any]:
        """Create the full inputs for the LLMChain from intermediate steps."""
        thoughts = self._construct_scratchpad(intermediate_steps)


thoughts = self._construct_scratchpad(intermediate_steps)
        new_inputs = {"agent_scratchpad": thoughts, "stop": self._stop}
        full_inputs = {**kwargs, **new_inputs}
        return full_inputs
    @property
    def input_keys(self) -> List[str]:
        """Return the input keys.
        :meta private:
        """
        return list(set(self.llm_chain.input_keys) - {"agent_scratchpad"})
    @root_validator()
    def validate_prompt(cls, values: Dict) -> Dict:
        """Validate that prompt matches format."""
        prompt = values["llm_chain"].prompt
        if "agent_scratchpad" not in prompt.input_variables:
            logger.warning(
                "`agent_scratchpad` should be a variable in prompt.input_variables."
                " Did not find it, so adding it at the end."
            )
            prompt.input_variables.append("agent_scratchpad")
            if isinstance(prompt, PromptTemplate):
                prompt.template += "\n{agent_scratchpad}"
            elif isinstance(prompt, FewShotPromptTemplate):
                prompt.suffix += "\n{agent_scratchpad}"
            else:
                raise ValueError(f"Got unexpected prompt type {type(prompt)}")
        return values
    @property
    @abstractmethod
    def observation_prefix(self) -> str:
        """Prefix to append the observation with."""
    @property
    @abstractmethod
    def llm_prefix(self) -> str:
        """Prefix to append the LLM call with."""
[docs]    @classmethod
    @abstractmethod
    def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:
        """Create a prompt for this class."""
    @classmethod
    def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:


def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
        """Validate that appropriate tools are passed in."""
        pass
    @classmethod
    @abstractmethod
    def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:
        """Get default output parser for this class."""
[docs]    @classmethod
    def from_llm_and_tools(
        cls,
        llm: BaseLanguageModel,
        tools: Sequence[BaseTool],
        callback_manager: Optional[BaseCallbackManager] = None,
        output_parser: Optional[AgentOutputParser] = None,
        **kwargs: Any,
    ) -> Agent:
        """Construct an agent from an LLM and tools."""
        cls._validate_tools(tools)
        llm_chain = LLMChain(
            llm=llm,
            prompt=cls.create_prompt(tools),
            callback_manager=callback_manager,
        )
        tool_names = [tool.name for tool in tools]
        _output_parser = output_parser or cls._get_default_output_parser()
        return cls(
            llm_chain=llm_chain,
            allowed_tools=tool_names,
            output_parser=_output_parser,
            **kwargs,
        )
[docs]    def return_stopped_response(
        self,
        early_stopping_method: str,
        intermediate_steps: List[Tuple[AgentAction, str]],
        **kwargs: Any,
    ) -> AgentFinish:
        """Return response when agent has been stopped due to max iterations."""
        if early_stopping_method == "force":
            # `force` just returns a constant string
            return AgentFinish(
                {"output": "Agent stopped due to iteration limit or time limit."}, ""
            )


{"output": "Agent stopped due to iteration limit or time limit."}, ""
            )
        elif early_stopping_method == "generate":
            # Generate does one final forward pass
            thoughts = ""
            for action, observation in intermediate_steps:
                thoughts += action.log
                thoughts += (
                    f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
                )
            # Adding to the previous steps, we now tell the LLM to make a final pred
            thoughts += (
                "\n\nI now need to return a final answer based on the previous steps:"
            )
            new_inputs = {"agent_scratchpad": thoughts, "stop": self._stop}
            full_inputs = {**kwargs, **new_inputs}
            full_output = self.llm_chain.predict(**full_inputs)
            # We try to extract a final answer
            parsed_output = self.output_parser.parse(full_output)
            if isinstance(parsed_output, AgentFinish):
                # If we can extract, we send the correct stuff
                return parsed_output
            else:
                # If we can extract, but the tool is not the final tool,
                # we just return the full output
                return AgentFinish({"output": full_output}, full_output)
        else:
            raise ValueError(
                "early_stopping_method should be one of `force` or `generate`, "
                f"got {early_stopping_method}"
            )
[docs]    def tool_run_logging_kwargs(self) -> Dict:
        return {
            "llm_prefix": self.llm_prefix,
            "observation_prefix": self.observation_prefix,
        }
[docs]class AgentExecutor(Chain):
    """Consists of an agent using tools."""


[docs]class AgentExecutor(Chain):
    """Consists of an agent using tools."""
    agent: Union[BaseSingleActionAgent, BaseMultiActionAgent]
    tools: Sequence[BaseTool]
    return_intermediate_steps: bool = False
    max_iterations: Optional[int] = 15
    max_execution_time: Optional[float] = None
    early_stopping_method: str = "force"
[docs]    @classmethod
    def from_agent_and_tools(
        cls,
        agent: Union[BaseSingleActionAgent, BaseMultiActionAgent],
        tools: Sequence[BaseTool],
        callback_manager: Optional[BaseCallbackManager] = None,
        **kwargs: Any,
    ) -> AgentExecutor:
        """Create from agent and tools."""
        return cls(
            agent=agent, tools=tools, callback_manager=callback_manager, **kwargs
        )
    @root_validator()
    def validate_tools(cls, values: Dict) -> Dict:
        """Validate that tools are compatible with agent."""
        agent = values["agent"]
        tools = values["tools"]
        allowed_tools = agent.get_allowed_tools()
        if allowed_tools is not None:
            if set(allowed_tools) != set([tool.name for tool in tools]):
                raise ValueError(
                    f"Allowed tools ({allowed_tools}) different than "
                    f"provided tools ({[tool.name for tool in tools]})"
                )
        return values
    @root_validator()
    def validate_return_direct_tool(cls, values: Dict) -> Dict:
        """Validate that tools are compatible with agent."""
        agent = values["agent"]
        tools = values["tools"]
        if isinstance(agent, BaseMultiActionAgent):
            for tool in tools:
                if tool.return_direct: