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.base_language import BaseLanguageModel
from langchain.callbacks.manager import (
    AsyncCallbackManagerForChainRun,
    CallbackManagerForChainRun,
)
from langchain.chains.base import Chain
from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.llm import LLMChain
from langchain.chains.qa_with_sources.loading import load_qa_with_sources_chain
from langchain.chains.qa_with_sources.map_reduce_prompt import (
    COMBINE_PROMPT,
    EXAMPLE_PROMPT,
    QUESTION_PROMPT,
)
from langchain.docstore.document import Document
from langchain.prompts.base import BasePromptTemplate
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,


document_prompt: BasePromptTemplate = EXAMPLE_PROMPT,
        question_prompt: BasePromptTemplate = QUESTION_PROMPT,
        combine_prompt: BasePromptTemplate = COMBINE_PROMPT,
        **kwargs: Any,
    ) -> BaseQAWithSourcesChain:
        """Construct the chain from an LLM."""
        llm_question_chain = LLMChain(llm=llm, prompt=question_prompt)
        llm_combine_chain = LLMChain(llm=llm, prompt=combine_prompt)
        combine_results_chain = StuffDocumentsChain(
            llm_chain=llm_combine_chain,
            document_prompt=document_prompt,
            document_variable_name="summaries",
        )
        combine_document_chain = MapReduceDocumentsChain(
            llm_chain=llm_question_chain,
            combine_document_chain=combine_results_chain,
            document_variable_name="context",
        )
        return cls(
            combine_documents_chain=combine_document_chain,
            **kwargs,
        )
    @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.


def input_keys(self) -> List[str]:
        """Expect input key.
        :meta private:
        """
        return [self.question_key]
    @property
    def output_keys(self) -> List[str]:
        """Return output key.
        :meta private:
        """
        _output_keys = [self.answer_key, self.sources_answer_key]
        if self.return_source_documents:
            _output_keys = _output_keys + ["source_documents"]
        return _output_keys
    @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],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, str]:
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        docs = self._get_docs(inputs)
        answer = self.combine_documents_chain.run(
            input_documents=docs, callbacks=_run_manager.get_child(), **inputs
        )
        if re.search(r"SOURCES:\s", answer):
            answer, sources = re.split(r"SOURCES:\s", answer)
        else:
            sources = ""
        result: Dict[str, Any] = {
            self.answer_key: answer,
            self.sources_answer_key: sources,
        }
        if self.return_source_documents:
            result["source_documents"] = docs
        return result


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],
        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
        docs = await self._aget_docs(inputs)
        answer = await self.combine_documents_chain.arun(
            input_documents=docs, callbacks=_run_manager.get_child(), **inputs
        )
        if re.search(r"SOURCES:\s", answer):
            answer, sources = re.split(r"SOURCES:\s", answer)
        else:
            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)


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 May 28, 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.base_language import BaseLanguageModel
from langchain.callbacks.manager import (
    AsyncCallbackManagerForChainRun,
    CallbackManagerForChainRun,
)
from langchain.chains.base import Chain
from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
from langchain.chains.combine_documents.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 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.


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
    @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]:


@abstractmethod
    def _get_docs(self, question: str) -> List[Document]:
        """Get documents to do question answering over."""
    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> 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']
        """
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        question = inputs[self.input_key]
        docs = self._get_docs(question)
        answer = self.combine_documents_chain.run(
            input_documents=docs, question=question, callbacks=_run_manager.get_child()
        )
        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, Any],
        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
    ) -> 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:


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']
        """
        _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
        question = inputs[self.input_key]
        docs = await self._aget_docs(question)
        answer = await self.combine_documents_chain.arun(
            input_documents=docs, question=question, callbacks=_run_manager.get_child()
        )
        if self.return_source_documents:
            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):


[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 - "
            "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


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 May 28, 2023.


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


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


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


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 May 28, 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.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun, Callbacks
from langchain.chains.api.openapi.requests_chain import APIRequesterChain
from langchain.chains.api.openapi.response_chain import APIResponderChain
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.requests import Requests
from langchain.tools.openapi.utils.api_models import APIOperation
class _ParamMapping(NamedTuple):
    """Mapping from parameter name to parameter value."""
    query_params: List[str]
    body_params: List[str]
    path_params: List[str]
[docs]class OpenAPIEndpointChain(Chain, BaseModel):
    """Chain interacts with an OpenAPI endpoint using natural language."""
    api_request_chain: LLMChain
    api_response_chain: Optional[LLMChain]
    api_operation: APIOperation
    requests: Requests = Field(exclude=True, default_factory=Requests)
    param_mapping: _ParamMapping = Field(alias="param_mapping")
    return_intermediate_steps: bool = False
    instructions_key: str = "instructions"  #: :meta private:
    output_key: str = "output"  #: :meta private:
    max_text_length: Optional[int] = Field(ge=0)  #: :meta private:
    @property
    def input_keys(self) -> List[str]:
        """Expect input key.
        :meta private:
        """
        return [self.instructions_key]
    @property


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


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


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


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


api_operation=operation,
            requests=_requests,
            param_mapping=param_mapping,
            verbose=verbose,
            return_intermediate_steps=return_intermediate_steps,
            callbacks=callbacks,
            **kwargs,
        )
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


Source code for langchain.chains.hyde.base
"""Hypothetical Document Embeddings.
https://arxiv.org/abs/2212.10496
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
import numpy as np
from pydantic import Extra
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
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
[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))


return list(np.array(embeddings).mean(axis=0))
[docs]    def embed_query(self, text: str) -> List[float]:
        """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, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, str]:
        """Call the internal llm chain."""
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        return self.llm_chain(inputs, callbacks=_run_manager.get_child())
[docs]    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,
        base_embeddings: Embeddings,
        prompt_key: str,
        **kwargs: Any,
    ) -> 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, **kwargs)
    @property
    def _chain_type(self) -> str:
        return "hyde_chain"
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 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 May 28, 2023.


Source code for langchain.experimental.generative_agents.generative_agent
import re
from datetime import datetime
from typing import Any, Dict, List, Optional, Tuple
from pydantic import BaseModel, Field
from langchain import LLMChain
from langchain.base_language import BaseLanguageModel
from langchain.experimental.generative_agents.memory import GenerativeAgentMemory
from langchain.prompts import PromptTemplate
[docs]class GenerativeAgent(BaseModel):
    """A character with memory and innate characteristics."""
    name: str
    """The character's name."""
    age: Optional[int] = None
    """The optional age of the character."""
    traits: str = "N/A"
    """Permanent traits to ascribe to the character."""
    status: str
    """The traits of the character you wish not to change."""
    memory: GenerativeAgentMemory
    """The memory object that combines relevance, recency, and 'importance'."""
    llm: BaseLanguageModel
    """The underlying language model."""
    verbose: bool = False
    summary: str = ""  #: :meta private:
    """Stateful self-summary generated via reflection on the character's memory."""
    summary_refresh_seconds: int = 3600  #: :meta private:
    """How frequently to re-generate the summary."""
    last_refreshed: datetime = Field(default_factory=datetime.now)  # : :meta private:
    """The last time the character's summary was regenerated."""
    daily_summaries: List[str] = Field(default_factory=list)  # : :meta private:
    """Summary of the events in the plan that the agent took."""
[docs]    class Config:
        """Configuration for this pydantic object."""
        arbitrary_types_allowed = True
    # LLM-related methods
    @staticmethod


arbitrary_types_allowed = True
    # LLM-related methods
    @staticmethod
    def _parse_list(text: str) -> List[str]:
        """Parse a newline-separated string into a list of strings."""
        lines = re.split(r"\n", text.strip())
        return [re.sub(r"^\s*\d+\.\s*", "", line).strip() for line in lines]
    def chain(self, prompt: PromptTemplate) -> LLMChain:
        return LLMChain(
            llm=self.llm, prompt=prompt, verbose=self.verbose, memory=self.memory
        )
    def _get_entity_from_observation(self, observation: str) -> str:
        prompt = PromptTemplate.from_template(
            "What is the observed entity in the following observation? {observation}"
            + "\nEntity="
        )
        return self.chain(prompt).run(observation=observation).strip()
    def _get_entity_action(self, observation: str, entity_name: str) -> str:
        prompt = PromptTemplate.from_template(
            "What is the {entity} doing in the following observation? {observation}"
            + "\nThe {entity} is"
        )
        return (
            self.chain(prompt).run(entity=entity_name, observation=observation).strip()
        )
[docs]    def summarize_related_memories(self, observation: str) -> str:
        """Summarize memories that are most relevant to an observation."""
        prompt = PromptTemplate.from_template(
            """
{q1}?
Context from memory:
{relevant_memories}
Relevant context:
"""
        )
        entity_name = self._get_entity_from_observation(observation)
        entity_action = self._get_entity_action(observation, entity_name)


entity_action = self._get_entity_action(observation, entity_name)
        q1 = f"What is the relationship between {self.name} and {entity_name}"
        q2 = f"{entity_name} is {entity_action}"
        return self.chain(prompt=prompt).run(q1=q1, queries=[q1, q2]).strip()
    def _generate_reaction(
        self, observation: str, suffix: str, now: Optional[datetime] = None
    ) -> str:
        """React to a given observation or dialogue act."""
        prompt = PromptTemplate.from_template(
            "{agent_summary_description}"
            + "\nIt is {current_time}."
            + "\n{agent_name}'s status: {agent_status}"
            + "\nSummary of relevant context from {agent_name}'s memory:"
            + "\n{relevant_memories}"
            + "\nMost recent observations: {most_recent_memories}"
            + "\nObservation: {observation}"
            + "\n\n"
            + suffix
        )
        agent_summary_description = self.get_summary(now=now)
        relevant_memories_str = self.summarize_related_memories(observation)
        current_time_str = (
            datetime.now().strftime("%B %d, %Y, %I:%M %p")
            if now is None
            else now.strftime("%B %d, %Y, %I:%M %p")
        )
        kwargs: Dict[str, Any] = dict(
            agent_summary_description=agent_summary_description,
            current_time=current_time_str,
            relevant_memories=relevant_memories_str,
            agent_name=self.name,
            observation=observation,
            agent_status=self.status,
        )
        consumed_tokens = self.llm.get_num_tokens(


)
        consumed_tokens = self.llm.get_num_tokens(
            prompt.format(most_recent_memories="", **kwargs)
        )
        kwargs[self.memory.most_recent_memories_token_key] = consumed_tokens
        return self.chain(prompt=prompt).run(**kwargs).strip()
    def _clean_response(self, text: str) -> str:
        return re.sub(f"^{self.name} ", "", text.strip()).strip()
[docs]    def generate_reaction(
        self, observation: str, now: Optional[datetime] = None
    ) -> Tuple[bool, str]:
        """React to a given observation."""
        call_to_action_template = (
            "Should {agent_name} react to the observation, and if so,"
            + " what would be an appropriate reaction? Respond in one line."
            + ' If the action is to engage in dialogue, write:\nSAY: "what to say"'
            + "\notherwise, write:\nREACT: {agent_name}'s reaction (if anything)."
            + "\nEither do nothing, react, or say something but not both.\n\n"
        )
        full_result = self._generate_reaction(
            observation, call_to_action_template, now=now
        )
        result = full_result.strip().split("\n")[0]
        # AAA
        self.memory.save_context(
            {},
            {
                self.memory.add_memory_key: f"{self.name} observed "
                f"{observation} and reacted by {result}",
                self.memory.now_key: now,
            },
        )
        if "REACT:" in result:
            reaction = self._clean_response(result.split("REACT:")[-1])
            return False, f"{self.name} {reaction}"
        if "SAY:" in result:


if "SAY:" in result:
            said_value = self._clean_response(result.split("SAY:")[-1])
            return True, f"{self.name} said {said_value}"
        else:
            return False, result
[docs]    def generate_dialogue_response(
        self, observation: str, now: Optional[datetime] = None
    ) -> Tuple[bool, str]:
        """React to a given observation."""
        call_to_action_template = (
            "What would {agent_name} say? To end the conversation, write:"
            ' GOODBYE: "what to say". Otherwise to continue the conversation,'
            ' write: SAY: "what to say next"\n\n'
        )
        full_result = self._generate_reaction(
            observation, call_to_action_template, now=now
        )
        result = full_result.strip().split("\n")[0]
        if "GOODBYE:" in result:
            farewell = self._clean_response(result.split("GOODBYE:")[-1])
            self.memory.save_context(
                {},
                {
                    self.memory.add_memory_key: f"{self.name} observed "
                    f"{observation} and said {farewell}",
                    self.memory.now_key: now,
                },
            )
            return False, f"{self.name} said {farewell}"
        if "SAY:" in result:
            response_text = self._clean_response(result.split("SAY:")[-1])
            self.memory.save_context(
                {},
                {
                    self.memory.add_memory_key: f"{self.name} observed "
                    f"{observation} and said {response_text}",
                    self.memory.now_key: now,
                },
            )
            return True, f"{self.name} said {response_text}"


},
            )
            return True, f"{self.name} said {response_text}"
        else:
            return False, result
    ######################################################
    # Agent stateful' summary methods.                   #
    # Each dialog or response prompt includes a header   #
    # summarizing the agent's self-description. This is  #
    # updated periodically through probing its memories  #
    ######################################################
    def _compute_agent_summary(self) -> str:
        """"""
        prompt = PromptTemplate.from_template(
            "How would you summarize {name}'s core characteristics given the"
            + " following statements:\n"
            + "{relevant_memories}"
            + "Do not embellish."
            + "\n\nSummary: "
        )
        # The agent seeks to think about their core characteristics.
        return (
            self.chain(prompt)
            .run(name=self.name, queries=[f"{self.name}'s core characteristics"])
            .strip()
        )
[docs]    def get_summary(
        self, force_refresh: bool = False, now: Optional[datetime] = None
    ) -> str:
        """Return a descriptive summary of the agent."""
        current_time = datetime.now() if now is None else now
        since_refresh = (current_time - self.last_refreshed).seconds
        if (
            not self.summary
            or since_refresh >= self.summary_refresh_seconds
            or force_refresh
        ):
            self.summary = self._compute_agent_summary()
            self.last_refreshed = current_time
        age = self.age if self.age is not None else "N/A"
        return (
            f"Name: {self.name} (age: {age})"
            + f"\nInnate traits: {self.traits}"


+ f"\nInnate traits: {self.traits}"
            + f"\n{self.summary}"
        )
[docs]    def get_full_header(
        self, force_refresh: bool = False, now: Optional[datetime] = None
    ) -> str:
        """Return a full header of the agent's status, summary, and current time."""
        now = datetime.now() if now is None else now
        summary = self.get_summary(force_refresh=force_refresh, now=now)
        current_time_str = now.strftime("%B %d, %Y, %I:%M %p")
        return (
            f"{summary}\nIt is {current_time_str}.\n{self.name}'s status: {self.status}"
        )
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


Source code for langchain.experimental.generative_agents.memory
import logging
import re
from datetime import datetime
from typing import Any, Dict, List, Optional
from langchain import LLMChain
from langchain.base_language import BaseLanguageModel
from langchain.prompts import PromptTemplate
from langchain.retrievers import TimeWeightedVectorStoreRetriever
from langchain.schema import BaseMemory, Document
from langchain.utils import mock_now
logger = logging.getLogger(__name__)
[docs]class GenerativeAgentMemory(BaseMemory):
    llm: BaseLanguageModel
    """The core language model."""
    memory_retriever: TimeWeightedVectorStoreRetriever
    """The retriever to fetch related memories."""
    verbose: bool = False
    reflection_threshold: Optional[float] = None
    """When aggregate_importance exceeds reflection_threshold, stop to reflect."""
    current_plan: List[str] = []
    """The current plan of the agent."""
    # A weight of 0.15 makes this less important than it
    # would be otherwise, relative to salience and time
    importance_weight: float = 0.15
    """How much weight to assign the memory importance."""
    aggregate_importance: float = 0.0  # : :meta private:
    """Track the sum of the 'importance' of recent memories.
    Triggers reflection when it reaches reflection_threshold."""
    max_tokens_limit: int = 1200  # : :meta private:
    # input keys
    queries_key: str = "queries"
    most_recent_memories_token_key: str = "recent_memories_token"
    add_memory_key: str = "add_memory"
    # output keys
    relevant_memories_key: str = "relevant_memories"


# output keys
    relevant_memories_key: str = "relevant_memories"
    relevant_memories_simple_key: str = "relevant_memories_simple"
    most_recent_memories_key: str = "most_recent_memories"
    now_key: str = "now"
    reflecting: bool = False
    def chain(self, prompt: PromptTemplate) -> LLMChain:
        return LLMChain(llm=self.llm, prompt=prompt, verbose=self.verbose)
    @staticmethod
    def _parse_list(text: str) -> List[str]:
        """Parse a newline-separated string into a list of strings."""
        lines = re.split(r"\n", text.strip())
        lines = [line for line in lines if line.strip()]  # remove empty lines
        return [re.sub(r"^\s*\d+\.\s*", "", line).strip() for line in lines]
    def _get_topics_of_reflection(self, last_k: int = 50) -> List[str]:
        """Return the 3 most salient high-level questions about recent observations."""
        prompt = PromptTemplate.from_template(
            "{observations}\n\n"
            + "Given only the information above, what are the 3 most salient"
            + " high-level questions we can answer about the subjects in"
            + " the statements? Provide each question on a new line.\n\n"
        )
        observations = self.memory_retriever.memory_stream[-last_k:]
        observation_str = "\n".join([o.page_content for o in observations])
        result = self.chain(prompt).run(observations=observation_str)
        return self._parse_list(result)
    def _get_insights_on_topic(
        self, topic: str, now: Optional[datetime] = None
    ) -> List[str]:


) -> List[str]:
        """Generate 'insights' on a topic of reflection, based on pertinent memories."""
        prompt = PromptTemplate.from_template(
            "Statements about {topic}\n"
            + "{related_statements}\n\n"
            + "What 5 high-level insights can you infer from the above statements?"
            + " (example format: insight (because of 1, 5, 3))"
        )
        related_memories = self.fetch_memories(topic, now=now)
        related_statements = "\n".join(
            [
                f"{i+1}. {memory.page_content}"
                for i, memory in enumerate(related_memories)
            ]
        )
        result = self.chain(prompt).run(
            topic=topic, related_statements=related_statements
        )
        # TODO: Parse the connections between memories and insights
        return self._parse_list(result)
[docs]    def pause_to_reflect(self, now: Optional[datetime] = None) -> List[str]:
        """Reflect on recent observations and generate 'insights'."""
        if self.verbose:
            logger.info("Character is reflecting")
        new_insights = []
        topics = self._get_topics_of_reflection()
        for topic in topics:
            insights = self._get_insights_on_topic(topic, now=now)
            for insight in insights:
                self.add_memory(insight, now=now)
            new_insights.extend(insights)
        return new_insights
    def _score_memory_importance(self, memory_content: str) -> float:
        """Score the absolute importance of the given memory."""
        prompt = PromptTemplate.from_template(
            "On the scale of 1 to 10, where 1 is purely mundane"


"On the scale of 1 to 10, where 1 is purely mundane"
            + " (e.g., brushing teeth, making bed) and 10 is"
            + " extremely poignant (e.g., a break up, college"
            + " acceptance), rate the likely poignancy of the"
            + " following piece of memory. Respond with a single integer."
            + "\nMemory: {memory_content}"
            + "\nRating: "
        )
        score = self.chain(prompt).run(memory_content=memory_content).strip()
        if self.verbose:
            logger.info(f"Importance score: {score}")
        match = re.search(r"^\D*(\d+)", score)
        if match:
            return (float(match.group(1)) / 10) * self.importance_weight
        else:
            return 0.0
[docs]    def add_memory(
        self, memory_content: str, now: Optional[datetime] = None
    ) -> List[str]:
        """Add an observation or memory to the agent's memory."""
        importance_score = self._score_memory_importance(memory_content)
        self.aggregate_importance += importance_score
        document = Document(
            page_content=memory_content, metadata={"importance": importance_score}
        )
        result = self.memory_retriever.add_documents([document], current_time=now)
        # After an agent has processed a certain amount of memories (as measured by
        # aggregate importance), it is time to reflect on recent events to add
        # more synthesized memories to the agent's memory stream.
        if (
            self.reflection_threshold is not None
            and self.aggregate_importance > self.reflection_threshold
            and not self.reflecting
        ):
            self.reflecting = True


and not self.reflecting
        ):
            self.reflecting = True
            self.pause_to_reflect(now=now)
            # Hack to clear the importance from reflection
            self.aggregate_importance = 0.0
            self.reflecting = False
        return result
[docs]    def fetch_memories(
        self, observation: str, now: Optional[datetime] = None
    ) -> List[Document]:
        """Fetch related memories."""
        if now is not None:
            with mock_now(now):
                return self.memory_retriever.get_relevant_documents(observation)
        else:
            return self.memory_retriever.get_relevant_documents(observation)
    def format_memories_detail(self, relevant_memories: List[Document]) -> str:
        content_strs = set()
        content = []
        for mem in relevant_memories:
            if mem.page_content in content_strs:
                continue
            content_strs.add(mem.page_content)
            created_time = mem.metadata["created_at"].strftime("%B %d, %Y, %I:%M %p")
            content.append(f"- {created_time}: {mem.page_content.strip()}")
        return "\n".join([f"{mem}" for mem in content])
    def format_memories_simple(self, relevant_memories: List[Document]) -> str:
        return "; ".join([f"{mem.page_content}" for mem in relevant_memories])
    def _get_memories_until_limit(self, consumed_tokens: int) -> str:
        """Reduce the number of tokens in the documents."""
        result = []
        for doc in self.memory_retriever.memory_stream[::-1]:
            if consumed_tokens >= self.max_tokens_limit:
                break
            consumed_tokens += self.llm.get_num_tokens(doc.page_content)


break
            consumed_tokens += self.llm.get_num_tokens(doc.page_content)
            if consumed_tokens < self.max_tokens_limit:
                result.append(doc)
        return self.format_memories_simple(result)
    @property
    def memory_variables(self) -> List[str]:
        """Input keys this memory class will load dynamically."""
        return []
[docs]    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
        """Return key-value pairs given the text input to the chain."""
        queries = inputs.get(self.queries_key)
        now = inputs.get(self.now_key)
        if queries is not None:
            relevant_memories = [
                mem for query in queries for mem in self.fetch_memories(query, now=now)
            ]
            return {
                self.relevant_memories_key: self.format_memories_detail(
                    relevant_memories
                ),
                self.relevant_memories_simple_key: self.format_memories_simple(
                    relevant_memories
                ),
            }
        most_recent_memories_token = inputs.get(self.most_recent_memories_token_key)
        if most_recent_memories_token is not None:
            return {
                self.most_recent_memories_key: self._get_memories_until_limit(
                    most_recent_memories_token
                )
            }
        return {}
[docs]    def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, Any]) -> None:
        """Save the context of this model run to memory."""
        # TODO: fix the save memory key
        mem = outputs.get(self.add_memory_key)
        now = outputs.get(self.now_key)
        if mem:
            self.add_memory(mem, now=now)
[docs]    def clear(self) -> None:


[docs]    def clear(self) -> None:
        """Clear memory contents."""
        # TODO
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


Source code for langchain.experimental.autonomous_agents.baby_agi.baby_agi
"""BabyAGI agent."""
from collections import deque
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.experimental.autonomous_agents.baby_agi.task_creation import (
    TaskCreationChain,
)
from langchain.experimental.autonomous_agents.baby_agi.task_execution import (
    TaskExecutionChain,
)
from langchain.experimental.autonomous_agents.baby_agi.task_prioritization import (
    TaskPrioritizationChain,
)
from langchain.vectorstores.base import VectorStore
[docs]class BabyAGI(Chain, BaseModel):
    """Controller model for the BabyAGI agent."""
    task_list: deque = Field(default_factory=deque)
    task_creation_chain: Chain = Field(...)
    task_prioritization_chain: Chain = Field(...)
    execution_chain: Chain = Field(...)
    task_id_counter: int = Field(1)
    vectorstore: VectorStore = Field(init=False)
    max_iterations: Optional[int] = None
[docs]    class Config:
        """Configuration for this pydantic object."""
        arbitrary_types_allowed = True
    def add_task(self, task: Dict) -> None:
        self.task_list.append(task)
    def print_task_list(self) -> None:
        print("\033[95m\033[1m" + "\n*****TASK LIST*****\n" + "\033[0m\033[0m")
        for t in self.task_list:
            print(str(t["task_id"]) + ": " + t["task_name"])


print(str(t["task_id"]) + ": " + t["task_name"])
    def print_next_task(self, task: Dict) -> None:
        print("\033[92m\033[1m" + "\n*****NEXT TASK*****\n" + "\033[0m\033[0m")
        print(str(task["task_id"]) + ": " + task["task_name"])
    def print_task_result(self, result: str) -> None:
        print("\033[93m\033[1m" + "\n*****TASK RESULT*****\n" + "\033[0m\033[0m")
        print(result)
    @property
    def input_keys(self) -> List[str]:
        return ["objective"]
    @property
    def output_keys(self) -> List[str]:
        return []
[docs]    def get_next_task(
        self, result: str, task_description: str, objective: str
    ) -> List[Dict]:
        """Get the next task."""
        task_names = [t["task_name"] for t in self.task_list]
        incomplete_tasks = ", ".join(task_names)
        response = self.task_creation_chain.run(
            result=result,
            task_description=task_description,
            incomplete_tasks=incomplete_tasks,
            objective=objective,
        )
        new_tasks = response.split("\n")
        return [
            {"task_name": task_name} for task_name in new_tasks if task_name.strip()
        ]
[docs]    def prioritize_tasks(self, this_task_id: int, objective: str) -> List[Dict]:
        """Prioritize tasks."""
        task_names = [t["task_name"] for t in list(self.task_list)]
        next_task_id = int(this_task_id) + 1


next_task_id = int(this_task_id) + 1
        response = self.task_prioritization_chain.run(
            task_names=", ".join(task_names),
            next_task_id=str(next_task_id),
            objective=objective,
        )
        new_tasks = response.split("\n")
        prioritized_task_list = []
        for task_string in new_tasks:
            if not task_string.strip():
                continue
            task_parts = task_string.strip().split(".", 1)
            if len(task_parts) == 2:
                task_id = task_parts[0].strip()
                task_name = task_parts[1].strip()
                prioritized_task_list.append(
                    {"task_id": task_id, "task_name": task_name}
                )
        return prioritized_task_list
    def _get_top_tasks(self, query: str, k: int) -> List[str]:
        """Get the top k tasks based on the query."""
        results = self.vectorstore.similarity_search(query, k=k)
        if not results:
            return []
        return [str(item.metadata["task"]) for item in results]
[docs]    def execute_task(self, objective: str, task: str, k: int = 5) -> str:
        """Execute a task."""
        context = self._get_top_tasks(query=objective, k=k)
        return self.execution_chain.run(
            objective=objective, context="\n".join(context), task=task
        )
    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        """Run the agent."""
        objective = inputs["objective"]


"""Run the agent."""
        objective = inputs["objective"]
        first_task = inputs.get("first_task", "Make a todo list")
        self.add_task({"task_id": 1, "task_name": first_task})
        num_iters = 0
        while True:
            if self.task_list:
                self.print_task_list()
                # Step 1: Pull the first task
                task = self.task_list.popleft()
                self.print_next_task(task)
                # Step 2: Execute the task
                result = self.execute_task(objective, task["task_name"])
                this_task_id = int(task["task_id"])
                self.print_task_result(result)
                # Step 3: Store the result in Pinecone
                result_id = f"result_{task['task_id']}"
                self.vectorstore.add_texts(
                    texts=[result],
                    metadatas=[{"task": task["task_name"]}],
                    ids=[result_id],
                )
                # Step 4: Create new tasks and reprioritize task list
                new_tasks = self.get_next_task(result, task["task_name"], objective)
                for new_task in new_tasks:
                    self.task_id_counter += 1
                    new_task.update({"task_id": self.task_id_counter})
                    self.add_task(new_task)
                self.task_list = deque(self.prioritize_tasks(this_task_id, objective))
            num_iters += 1
            if self.max_iterations is not None and num_iters == self.max_iterations:
                print(
                    "\033[91m\033[1m" + "\n*****TASK ENDING*****\n" + "\033[0m\033[0m"
                )
                break
        return {}
[docs]    @classmethod
    def from_llm(


break
        return {}
[docs]    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,
        vectorstore: VectorStore,
        verbose: bool = False,
        task_execution_chain: Optional[Chain] = None,
        **kwargs: Dict[str, Any],
    ) -> "BabyAGI":
        """Initialize the BabyAGI Controller."""
        task_creation_chain = TaskCreationChain.from_llm(llm, verbose=verbose)
        task_prioritization_chain = TaskPrioritizationChain.from_llm(
            llm, verbose=verbose
        )
        if task_execution_chain is None:
            execution_chain: Chain = TaskExecutionChain.from_llm(llm, verbose=verbose)
        else:
            execution_chain = task_execution_chain
        return cls(
            task_creation_chain=task_creation_chain,
            task_prioritization_chain=task_prioritization_chain,
            execution_chain=execution_chain,
            vectorstore=vectorstore,
            **kwargs,
        )
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


Source code for langchain.experimental.autonomous_agents.autogpt.agent
from __future__ import annotations
from typing import List, Optional
from pydantic import ValidationError
from langchain.chains.llm import LLMChain
from langchain.chat_models.base import BaseChatModel
from langchain.experimental.autonomous_agents.autogpt.output_parser import (
    AutoGPTOutputParser,
    BaseAutoGPTOutputParser,
)
from langchain.experimental.autonomous_agents.autogpt.prompt import AutoGPTPrompt
from langchain.experimental.autonomous_agents.autogpt.prompt_generator import (
    FINISH_NAME,
)
from langchain.schema import (
    AIMessage,
    BaseMessage,
    Document,
    HumanMessage,
    SystemMessage,
)
from langchain.tools.base import BaseTool
from langchain.tools.human.tool import HumanInputRun
from langchain.vectorstores.base import VectorStoreRetriever
[docs]class AutoGPT:
    """Agent class for interacting with Auto-GPT."""
    def __init__(
        self,
        ai_name: str,
        memory: VectorStoreRetriever,
        chain: LLMChain,
        output_parser: BaseAutoGPTOutputParser,
        tools: List[BaseTool],
        feedback_tool: Optional[HumanInputRun] = None,
    ):
        self.ai_name = ai_name
        self.memory = memory
        self.full_message_history: List[BaseMessage] = []
        self.next_action_count = 0
        self.chain = chain
        self.output_parser = output_parser
        self.tools = tools
        self.feedback_tool = feedback_tool
    @classmethod
    def from_llm_and_tools(
        cls,
        ai_name: str,
        ai_role: str,
        memory: VectorStoreRetriever,


ai_role: str,
        memory: VectorStoreRetriever,
        tools: List[BaseTool],
        llm: BaseChatModel,
        human_in_the_loop: bool = False,
        output_parser: Optional[BaseAutoGPTOutputParser] = None,
    ) -> AutoGPT:
        prompt = AutoGPTPrompt(
            ai_name=ai_name,
            ai_role=ai_role,
            tools=tools,
            input_variables=["memory", "messages", "goals", "user_input"],
            token_counter=llm.get_num_tokens,
        )
        human_feedback_tool = HumanInputRun() if human_in_the_loop else None
        chain = LLMChain(llm=llm, prompt=prompt)
        return cls(
            ai_name,
            memory,
            chain,
            output_parser or AutoGPTOutputParser(),
            tools,
            feedback_tool=human_feedback_tool,
        )
    def run(self, goals: List[str]) -> str:
        user_input = (
            "Determine which next command to use, "
            "and respond using the format specified above:"
        )
        # Interaction Loop
        loop_count = 0
        while True:
            # Discontinue if continuous limit is reached
            loop_count += 1
            # Send message to AI, get response
            assistant_reply = self.chain.run(
                goals=goals,
                messages=self.full_message_history,
                memory=self.memory,
                user_input=user_input,
            )
            # Print Assistant thoughts
            print(assistant_reply)
            self.full_message_history.append(HumanMessage(content=user_input))
            self.full_message_history.append(AIMessage(content=assistant_reply))
            # Get command name and arguments


# Get command name and arguments
            action = self.output_parser.parse(assistant_reply)
            tools = {t.name: t for t in self.tools}
            if action.name == FINISH_NAME:
                return action.args["response"]
            if action.name in tools:
                tool = tools[action.name]
                try:
                    observation = tool.run(action.args)
                except ValidationError as e:
                    observation = (
                        f"Validation Error in args: {str(e)}, args: {action.args}"
                    )
                except Exception as e:
                    observation = (
                        f"Error: {str(e)}, {type(e).__name__}, args: {action.args}"
                    )
                result = f"Command {tool.name} returned: {observation}"
            elif action.name == "ERROR":
                result = f"Error: {action.args}. "
            else:
                result = (
                    f"Unknown command '{action.name}'. "
                    f"Please refer to the 'COMMANDS' list for available "
                    f"commands and only respond in the specified JSON format."
                )
            memory_to_add = (
                f"Assistant Reply: {assistant_reply} " f"\nResult: {result} "
            )
            if self.feedback_tool is not None:
                feedback = f"\n{self.feedback_tool.run('Input: ')}"
                if feedback in {"q", "stop"}:
                    print("EXITING")
                    return "EXITING"
                memory_to_add += feedback
            self.memory.add_documents([Document(page_content=memory_to_add)])
            self.full_message_history.append(SystemMessage(content=result))
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.