for result in results
        ]
        if docs:
            return "\n\n".join(docs)[: self.doc_content_chars_max]
        else:
            return "No good Arxiv Result was found"
[docs]    def load(self, query: str) -> List[Document]:
        """
        Run Arxiv search and get the article texts plus the article meta information.
        See https://lukasschwab.me/arxiv.py/index.html#Search
        Returns: a list of documents with the document.page_content in text format
        """
        try:
            import fitz
        except ImportError:
            raise ImportError(
                "PyMuPDF package not found, please install it with "
                "`pip install pymupdf`"
            )
        try:
            results = self.arxiv_search(  # type: ignore
                query[: self.ARXIV_MAX_QUERY_LENGTH], max_results=self.load_max_docs
            ).results()
        except self.arxiv_exceptions as ex:
            logger.debug("Error on arxiv: %s", ex)
            return []
        docs: List[Document] = []
        for result in results:
            try:
                doc_file_name: str = result.download_pdf()
                with fitz.open(doc_file_name) as doc_file:
                    text: str = "".join(page.get_text() for page in doc_file)
            except FileNotFoundError as f_ex:
                logger.debug(f_ex)
                continue
            if self.load_all_available_meta:
                extra_metadata = {
                    "entry_id": result.entry_id,
                    "published_first_time": str(result.published.date()),
                    "comment": result.comment,
                    "journal_ref": result.journal_ref,
                    "doi": result.doi,
                    "primary_category": result.primary_category,


"doi": result.doi,
                    "primary_category": result.primary_category,
                    "categories": result.categories,
                    "links": [link.href for link in result.links],
                }
            else:
                extra_metadata = {}
            metadata = {
                "Published": str(result.updated.date()),
                "Title": result.title,
                "Authors": ", ".join(a.name for a in result.authors),
                "Summary": result.summary,
                **extra_metadata,
            }
            doc = Document(
                page_content=text[: self.doc_content_chars_max], metadata=metadata
            )
            docs.append(doc)
            os.remove(doc_file_name)
        return docs
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 02, 2023.


Source code for langchain.utilities.python
import sys
from io import StringIO
from typing import Dict, Optional
from pydantic import BaseModel, Field
[docs]class PythonREPL(BaseModel):
    """Simulates a standalone Python REPL."""
    globals: Optional[Dict] = Field(default_factory=dict, alias="_globals")
    locals: Optional[Dict] = Field(default_factory=dict, alias="_locals")
[docs]    def run(self, command: str) -> str:
        """Run command with own globals/locals and returns anything printed."""
        old_stdout = sys.stdout
        sys.stdout = mystdout = StringIO()
        try:
            exec(command, self.globals, self.locals)
            sys.stdout = old_stdout
            output = mystdout.getvalue()
        except Exception as e:
            sys.stdout = old_stdout
            output = repr(e)
        return output
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 02, 2023.


Source code for langchain.utilities.wolfram_alpha
"""Util that calls WolframAlpha."""
from typing import Any, Dict, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env
[docs]class WolframAlphaAPIWrapper(BaseModel):
    """Wrapper for Wolfram Alpha.
    Docs for using:
    1. Go to wolfram alpha and sign up for a developer account
    2. Create an app and get your APP ID
    3. Save your APP ID into WOLFRAM_ALPHA_APPID env variable
    4. pip install wolframalpha
    """
    wolfram_client: Any  #: :meta private:
    wolfram_alpha_appid: Optional[str] = None
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key and python package exists in environment."""
        wolfram_alpha_appid = get_from_dict_or_env(
            values, "wolfram_alpha_appid", "WOLFRAM_ALPHA_APPID"
        )
        values["wolfram_alpha_appid"] = wolfram_alpha_appid
        try:
            import wolframalpha
        except ImportError:
            raise ImportError(
                "wolframalpha is not installed. "
                "Please install it with `pip install wolframalpha`"
            )
        client = wolframalpha.Client(wolfram_alpha_appid)
        values["wolfram_client"] = client
        return values
[docs]    def run(self, query: str) -> str:
        """Run query through WolframAlpha and parse result."""
        res = self.wolfram_client.query(query)


res = self.wolfram_client.query(query)
        try:
            assumption = next(res.pods).text
            answer = next(res.results).text
        except StopIteration:
            return "Wolfram Alpha wasn't able to answer it"
        if answer is None or answer == "":
            # We don't want to return the assumption alone if answer is empty
            return "No good Wolfram Alpha Result was found"
        else:
            return f"Assumption: {assumption} \nAnswer: {answer}"
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 02, 2023.


Source code for langchain.utilities.apify
from typing import Any, Callable, Dict, Optional
from pydantic import BaseModel, root_validator
from langchain.document_loaders import ApifyDatasetLoader
from langchain.document_loaders.base import Document
from langchain.utils import get_from_dict_or_env
[docs]class ApifyWrapper(BaseModel):
    """Wrapper around Apify.
    To use, you should have the ``apify-client`` python package installed,
    and the environment variable ``APIFY_API_TOKEN`` set with your API key, or pass
    `apify_api_token` as a named parameter to the constructor.
    """
    apify_client: Any
    apify_client_async: Any
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate environment.
        Validate that an Apify API token is set and the apify-client
        Python package exists in the current environment.
        """
        apify_api_token = get_from_dict_or_env(
            values, "apify_api_token", "APIFY_API_TOKEN"
        )
        try:
            from apify_client import ApifyClient, ApifyClientAsync
            values["apify_client"] = ApifyClient(apify_api_token)
            values["apify_client_async"] = ApifyClientAsync(apify_api_token)
        except ImportError:
            raise ValueError(
                "Could not import apify-client Python package. "
                "Please install it with `pip install apify-client`."
            )
        return values
[docs]    def call_actor(
        self,
        actor_id: str,
        run_input: Dict,
        dataset_mapping_function: Callable[[Dict], Document],
        *,
        build: Optional[str] = None,


*,
        build: Optional[str] = None,
        memory_mbytes: Optional[int] = None,
        timeout_secs: Optional[int] = None,
    ) -> ApifyDatasetLoader:
        """Run an Actor on the Apify platform and wait for results to be ready.
        Args:
            actor_id (str): The ID or name of the Actor on the Apify platform.
            run_input (Dict): The input object of the Actor that you're trying to run.
            dataset_mapping_function (Callable): A function that takes a single
                dictionary (an Apify dataset item) and converts it to an
                instance of the Document class.
            build (str, optional): Optionally specifies the actor build to run.
                It can be either a build tag or build number.
            memory_mbytes (int, optional): Optional memory limit for the run,
                in megabytes.
            timeout_secs (int, optional): Optional timeout for the run, in seconds.
        Returns:
            ApifyDatasetLoader: A loader that will fetch the records from the
                Actor run's default dataset.
        """
        actor_call = self.apify_client.actor(actor_id).call(
            run_input=run_input,
            build=build,
            memory_mbytes=memory_mbytes,
            timeout_secs=timeout_secs,
        )
        return ApifyDatasetLoader(
            dataset_id=actor_call["defaultDatasetId"],
            dataset_mapping_function=dataset_mapping_function,
        )
[docs]    async def acall_actor(
        self,
        actor_id: str,
        run_input: Dict,
        dataset_mapping_function: Callable[[Dict], Document],
        *,
        build: Optional[str] = None,
        memory_mbytes: Optional[int] = None,


memory_mbytes: Optional[int] = None,
        timeout_secs: Optional[int] = None,
    ) -> ApifyDatasetLoader:
        """Run an Actor on the Apify platform and wait for results to be ready.
        Args:
            actor_id (str): The ID or name of the Actor on the Apify platform.
            run_input (Dict): The input object of the Actor that you're trying to run.
            dataset_mapping_function (Callable): A function that takes a single
                dictionary (an Apify dataset item) and converts it to
                an instance of the Document class.
            build (str, optional): Optionally specifies the actor build to run.
                It can be either a build tag or build number.
            memory_mbytes (int, optional): Optional memory limit for the run,
                in megabytes.
            timeout_secs (int, optional): Optional timeout for the run, in seconds.
        Returns:
            ApifyDatasetLoader: A loader that will fetch the records from the
                Actor run's default dataset.
        """
        actor_call = await self.apify_client_async.actor(actor_id).call(
            run_input=run_input,
            build=build,
            memory_mbytes=memory_mbytes,
            timeout_secs=timeout_secs,
        )
        return ApifyDatasetLoader(
            dataset_id=actor_call["defaultDatasetId"],
            dataset_mapping_function=dataset_mapping_function,
        )
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 02, 2023.


Source code for langchain.utilities.twilio
"""Util that calls Twilio."""
from typing import Any, Dict, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env
[docs]class TwilioAPIWrapper(BaseModel):
    """Sms Client using Twilio.
    To use, you should have the ``twilio`` python package installed,
    and the environment variables ``TWILIO_ACCOUNT_SID``, ``TWILIO_AUTH_TOKEN``, and
    ``TWILIO_FROM_NUMBER``, or pass `account_sid`, `auth_token`, and `from_number` as
    named parameters to the constructor.
    Example:
        .. code-block:: python
            from langchain.utilities.twilio import TwilioAPIWrapper
            twilio = TwilioAPIWrapper(
                account_sid="ACxxx",
                auth_token="xxx",
                from_number="+10123456789"
            )
            twilio.run('test', '+12484345508')
    """
    client: Any  #: :meta private:
    account_sid: Optional[str] = None
    """Twilio account string identifier."""
    auth_token: Optional[str] = None
    """Twilio auth token."""
    from_number: Optional[str] = None
    """A Twilio phone number in [E.164](https://www.twilio.com/docs/glossary/what-e164)
        format, an
        [alphanumeric sender ID](https://www.twilio.com/docs/sms/send-messages#use-an-alphanumeric-sender-id),
        or a [Channel Endpoint address](https://www.twilio.com/docs/sms/channels#channel-addresses)
        that is enabled for the type of message you want to send. Phone numbers or


that is enabled for the type of message you want to send. Phone numbers or
        [short codes](https://www.twilio.com/docs/sms/api/short-code) purchased from
        Twilio also work here. You cannot, for example, spoof messages from a private
        cell phone number. If you are using `messaging_service_sid`, this parameter
        must be empty.
    """  # noqa: E501
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = False
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key and python package exists in environment."""
        try:
            from twilio.rest import Client
        except ImportError:
            raise ImportError(
                "Could not import twilio python package. "
                "Please install it with `pip install twilio`."
            )
        account_sid = get_from_dict_or_env(values, "account_sid", "TWILIO_ACCOUNT_SID")
        auth_token = get_from_dict_or_env(values, "auth_token", "TWILIO_AUTH_TOKEN")
        values["from_number"] = get_from_dict_or_env(
            values, "from_number", "TWILIO_FROM_NUMBER"
        )
        values["client"] = Client(account_sid, auth_token)
        return values
[docs]    def run(self, body: str, to: str) -> str:
        """Run body through Twilio and respond with message sid.
        Args:
            body: The text of the message you want to send. Can be up to 1,600
                characters in length.
            to: The destination phone number in


characters in length.
            to: The destination phone number in
                [E.164](https://www.twilio.com/docs/glossary/what-e164) format for
                SMS/MMS or
                [Channel user address](https://www.twilio.com/docs/sms/channels#channel-addresses)
                for other 3rd-party channels.
        """  # noqa: E501
        message = self.client.messages.create(to, from_=self.from_number, body=body)
        return message.sid
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 02, 2023.


Source code for langchain.utilities.bash
"""Wrapper around subprocess to run commands."""
from __future__ import annotations
import platform
import re
import subprocess
from typing import TYPE_CHECKING, List, Union
from uuid import uuid4
if TYPE_CHECKING:
    import pexpect
def _lazy_import_pexpect() -> pexpect:
    """Import pexpect only when needed."""
    if platform.system() == "Windows":
        raise ValueError("Persistent bash processes are not yet supported on Windows.")
    try:
        import pexpect
    except ImportError:
        raise ImportError(
            "pexpect required for persistent bash processes."
            " To install, run `pip install pexpect`."
        )
    return pexpect
[docs]class BashProcess:
    """Executes bash commands and returns the output."""
    def __init__(
        self,
        strip_newlines: bool = False,
        return_err_output: bool = False,
        persistent: bool = False,
    ):
        """Initialize with stripping newlines."""
        self.strip_newlines = strip_newlines
        self.return_err_output = return_err_output
        self.prompt = ""
        self.process = None
        if persistent:
            self.prompt = str(uuid4())
            self.process = self._initialize_persistent_process(self.prompt)
    @staticmethod
    def _initialize_persistent_process(prompt: str) -> pexpect.spawn:
        # Start bash in a clean environment
        # Doesn't work on windows
        pexpect = _lazy_import_pexpect()
        process = pexpect.spawn(
            "env", ["-i", "bash", "--norc", "--noprofile"], encoding="utf-8"
        )
        # Set the custom prompt
        process.sendline("PS1=" + prompt)


# Set the custom prompt
        process.sendline("PS1=" + prompt)
        process.expect_exact(prompt, timeout=10)
        return process
[docs]    def run(self, commands: Union[str, List[str]]) -> str:
        """Run commands and return final output."""
        if isinstance(commands, str):
            commands = [commands]
        commands = ";".join(commands)
        if self.process is not None:
            return self._run_persistent(
                commands,
            )
        else:
            return self._run(commands)
    def _run(self, command: str) -> str:
        """Run commands and return final output."""
        try:
            output = subprocess.run(
                command,
                shell=True,
                check=True,
                stdout=subprocess.PIPE,
                stderr=subprocess.STDOUT,
            ).stdout.decode()
        except subprocess.CalledProcessError as error:
            if self.return_err_output:
                return error.stdout.decode()
            return str(error)
        if self.strip_newlines:
            output = output.strip()
        return output
[docs]    def process_output(self, output: str, command: str) -> str:
        # Remove the command from the output using a regular expression
        pattern = re.escape(command) + r"\s*\n"
        output = re.sub(pattern, "", output, count=1)
        return output.strip()
    def _run_persistent(self, command: str) -> str:
        """Run commands and return final output."""
        pexpect = _lazy_import_pexpect()
        if self.process is None:
            raise ValueError("Process not initialized")
        self.process.sendline(command)
        # Clear the output with an empty string
        self.process.expect(self.prompt, timeout=10)


self.process.expect(self.prompt, timeout=10)
        self.process.sendline("")
        try:
            self.process.expect([self.prompt, pexpect.EOF], timeout=10)
        except pexpect.TIMEOUT:
            return f"Timeout error while executing command {command}"
        if self.process.after == pexpect.EOF:
            return f"Exited with error status: {self.process.exitstatus}"
        output = self.process.before
        output = self.process_output(output, command)
        if self.strip_newlines:
            return output.strip()
        return output
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 02, 2023.


Source code for langchain.utilities.wikipedia
"""Util that calls Wikipedia."""
import logging
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.schema import Document
logger = logging.getLogger(__name__)
WIKIPEDIA_MAX_QUERY_LENGTH = 300
[docs]class WikipediaAPIWrapper(BaseModel):
    """Wrapper around WikipediaAPI.
    To use, you should have the ``wikipedia`` python package installed.
    This wrapper will use the Wikipedia API to conduct searches and
    fetch page summaries. By default, it will return the page summaries
    of the top-k results.
    It limits the Document content by doc_content_chars_max.
    """
    wiki_client: Any  #: :meta private:
    top_k_results: int = 3
    lang: str = "en"
    load_all_available_meta: bool = False
    doc_content_chars_max: int = 4000
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that the python package exists in environment."""
        try:
            import wikipedia
            wikipedia.set_lang(values["lang"])
            values["wiki_client"] = wikipedia
        except ImportError:
            raise ImportError(
                "Could not import wikipedia python package. "
                "Please install it with `pip install wikipedia`."
            )
        return values
[docs]    def run(self, query: str) -> str:
        """Run Wikipedia search and get page summaries."""
        page_titles = self.wiki_client.search(query[:WIKIPEDIA_MAX_QUERY_LENGTH])
        summaries = []
        for page_title in page_titles[: self.top_k_results]:


summaries = []
        for page_title in page_titles[: self.top_k_results]:
            if wiki_page := self._fetch_page(page_title):
                if summary := self._formatted_page_summary(page_title, wiki_page):
                    summaries.append(summary)
        if not summaries:
            return "No good Wikipedia Search Result was found"
        return "\n\n".join(summaries)[: self.doc_content_chars_max]
    @staticmethod
    def _formatted_page_summary(page_title: str, wiki_page: Any) -> Optional[str]:
        return f"Page: {page_title}\nSummary: {wiki_page.summary}"
    def _page_to_document(self, page_title: str, wiki_page: Any) -> Document:
        main_meta = {
            "title": page_title,
            "summary": wiki_page.summary,
            "source": wiki_page.url,
        }
        add_meta = (
            {
                "categories": wiki_page.categories,
                "page_url": wiki_page.url,
                "image_urls": wiki_page.images,
                "related_titles": wiki_page.links,
                "parent_id": wiki_page.parent_id,
                "references": wiki_page.references,
                "revision_id": wiki_page.revision_id,
                "sections": wiki_page.sections,
            }
            if self.load_all_available_meta
            else {}
        )
        doc = Document(
            page_content=wiki_page.content[: self.doc_content_chars_max],
            metadata={
                **main_meta,
                **add_meta,
            },
        )
        return doc
    def _fetch_page(self, page: str) -> Optional[str]:
        try:
            return self.wiki_client.page(title=page, auto_suggest=False)
        except (
            self.wiki_client.exceptions.PageError,


except (
            self.wiki_client.exceptions.PageError,
            self.wiki_client.exceptions.DisambiguationError,
        ):
            return None
[docs]    def load(self, query: str) -> List[Document]:
        """
        Run Wikipedia search and get the article text plus the meta information.
        See
        Returns: a list of documents.
        """
        page_titles = self.wiki_client.search(query[:WIKIPEDIA_MAX_QUERY_LENGTH])
        docs = []
        for page_title in page_titles[: self.top_k_results]:
            if wiki_page := self._fetch_page(page_title):
                if doc := self._page_to_document(page_title, wiki_page):
                    docs.append(doc)
        return docs
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 02, 2023.


Source code for langchain.utilities.graphql
import json
from typing import Any, Callable, Dict, Optional
from pydantic import BaseModel, Extra, root_validator
[docs]class GraphQLAPIWrapper(BaseModel):
    """Wrapper around GraphQL API.
    To use, you should have the ``gql`` python package installed.
    This wrapper will use the GraphQL API to conduct queries.
    """
    custom_headers: Optional[Dict[str, str]] = None
    graphql_endpoint: str
    gql_client: Any  #: :meta private:
    gql_function: Callable[[str], Any]  #: :meta private:
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
    @root_validator(pre=True)
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that the python package exists in the environment."""
        try:
            from gql import Client, gql
            from gql.transport.requests import RequestsHTTPTransport
        except ImportError as e:
            raise ImportError(
                "Could not import gql python package. "
                f"Try installing it with `pip install gql`. Received error: {e}"
            )
        headers = values.get("custom_headers")
        transport = RequestsHTTPTransport(
            url=values["graphql_endpoint"],
            headers=headers,
        )
        client = Client(transport=transport, fetch_schema_from_transport=True)
        values["gql_client"] = client
        values["gql_function"] = gql
        return values
[docs]    def run(self, query: str) -> str:
        """Run a GraphQL query and get the results."""
        result = self._execute_query(query)
        return json.dumps(result, indent=2)


return json.dumps(result, indent=2)
    def _execute_query(self, query: str) -> Dict[str, Any]:
        """Execute a GraphQL query and return the results."""
        document_node = self.gql_function(query)
        result = self.gql_client.execute(document_node)
        return result
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 02, 2023.


Source code for langchain.utilities.duckduckgo_search
"""Util that calls DuckDuckGo Search.
No setup required. Free.
https://pypi.org/project/duckduckgo-search/
"""
from typing import Dict, List, Optional
from pydantic import BaseModel, Extra
from pydantic.class_validators import root_validator
[docs]class DuckDuckGoSearchAPIWrapper(BaseModel):
    """Wrapper for DuckDuckGo Search API.
    Free and does not require any setup
    """
    k: int = 10
    region: Optional[str] = "wt-wt"
    safesearch: str = "moderate"
    time: Optional[str] = "y"
    max_results: int = 5
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that python package exists in environment."""
        try:
            from duckduckgo_search import ddg  # noqa: F401
        except ImportError:
            raise ValueError(
                "Could not import duckduckgo-search python package. "
                "Please install it with `pip install duckduckgo-search`."
            )
        return values
[docs]    def get_snippets(self, query: str) -> List[str]:
        """Run query through DuckDuckGo and return concatenated results."""
        from duckduckgo_search import ddg
        results = ddg(
            query,
            region=self.region,
            safesearch=self.safesearch,
            time=self.time,
            max_results=self.max_results,
        )
        if results is None or len(results) == 0:


)
        if results is None or len(results) == 0:
            return ["No good DuckDuckGo Search Result was found"]
        snippets = [result["body"] for result in results]
        return snippets
[docs]    def run(self, query: str) -> str:
        snippets = self.get_snippets(query)
        return " ".join(snippets)
[docs]    def results(self, query: str, num_results: int) -> List[Dict[str, str]]:
        """Run query through DuckDuckGo and return metadata.
        Args:
            query: The query to search for.
            num_results: The number of results to return.
        Returns:
            A list of dictionaries with the following keys:
                snippet - The description of the result.
                title - The title of the result.
                link - The link to the result.
        """
        from duckduckgo_search import ddg
        results = ddg(
            query,
            region=self.region,
            safesearch=self.safesearch,
            time=self.time,
            max_results=num_results,
        )
        if results is None or len(results) == 0:
            return [{"Result": "No good DuckDuckGo Search Result was found"}]
        def to_metadata(result: Dict) -> Dict[str, str]:
            return {
                "snippet": result["body"],
                "title": result["title"],
                "link": result["href"],
            }
        return [to_metadata(result) for result in results]
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 02, 2023.


Source code for langchain.utilities.google_serper
"""Util that calls Google Search using the Serper.dev API."""
from typing import Any, Dict, List, Optional
import aiohttp
import requests
from pydantic.class_validators import root_validator
from pydantic.main import BaseModel
from typing_extensions import Literal
from langchain.utils import get_from_dict_or_env
[docs]class GoogleSerperAPIWrapper(BaseModel):
    """Wrapper around the Serper.dev Google Search API.
    You can create a free API key at https://serper.dev.
    To use, you should have the environment variable ``SERPER_API_KEY``
    set with your API key, or pass `serper_api_key` as a named parameter
    to the constructor.
    Example:
        .. code-block:: python
            from langchain import GoogleSerperAPIWrapper
            google_serper = GoogleSerperAPIWrapper()
    """
    k: int = 10
    gl: str = "us"
    hl: str = "en"
    # "places" and "images" is available from Serper but not implemented in the
    # parser of run(). They can be used in results()
    type: Literal["news", "search", "places", "images"] = "search"
    result_key_for_type = {
        "news": "news",
        "places": "places",
        "images": "images",
        "search": "organic",
    }
    tbs: Optional[str] = None
    serper_api_key: Optional[str] = None
    aiosession: Optional[aiohttp.ClientSession] = None
    class Config:
        """Configuration for this pydantic object."""
        arbitrary_types_allowed = True
    @root_validator()


arbitrary_types_allowed = True
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key exists in environment."""
        serper_api_key = get_from_dict_or_env(
            values, "serper_api_key", "SERPER_API_KEY"
        )
        values["serper_api_key"] = serper_api_key
        return values
[docs]    def results(self, query: str, **kwargs: Any) -> Dict:
        """Run query through GoogleSearch."""
        return self._google_serper_api_results(
            query,
            gl=self.gl,
            hl=self.hl,
            num=self.k,
            tbs=self.tbs,
            search_type=self.type,
            **kwargs,
        )
[docs]    def run(self, query: str, **kwargs: Any) -> str:
        """Run query through GoogleSearch and parse result."""
        results = self._google_serper_api_results(
            query,
            gl=self.gl,
            hl=self.hl,
            num=self.k,
            tbs=self.tbs,
            search_type=self.type,
            **kwargs,
        )
        return self._parse_results(results)
[docs]    async def aresults(self, query: str, **kwargs: Any) -> Dict:
        """Run query through GoogleSearch."""
        results = await self._async_google_serper_search_results(
            query,
            gl=self.gl,
            hl=self.hl,
            num=self.k,
            search_type=self.type,
            tbs=self.tbs,
            **kwargs,
        )
        return results
[docs]    async def arun(self, query: str, **kwargs: Any) -> str:


"""Run query through GoogleSearch and parse result async."""
        results = await self._async_google_serper_search_results(
            query,
            gl=self.gl,
            hl=self.hl,
            num=self.k,
            search_type=self.type,
            tbs=self.tbs,
            **kwargs,
        )
        return self._parse_results(results)
    def _parse_snippets(self, results: dict) -> List[str]:
        snippets = []
        if results.get("answerBox"):
            answer_box = results.get("answerBox", {})
            if answer_box.get("answer"):
                return [answer_box.get("answer")]
            elif answer_box.get("snippet"):
                return [answer_box.get("snippet").replace("\n", " ")]
            elif answer_box.get("snippetHighlighted"):
                return answer_box.get("snippetHighlighted")
        if results.get("knowledgeGraph"):
            kg = results.get("knowledgeGraph", {})
            title = kg.get("title")
            entity_type = kg.get("type")
            if entity_type:
                snippets.append(f"{title}: {entity_type}.")
            description = kg.get("description")
            if description:
                snippets.append(description)
            for attribute, value in kg.get("attributes", {}).items():
                snippets.append(f"{title} {attribute}: {value}.")
        for result in results[self.result_key_for_type[self.type]][: self.k]:
            if "snippet" in result:
                snippets.append(result["snippet"])
            for attribute, value in result.get("attributes", {}).items():
                snippets.append(f"{attribute}: {value}.")
        if len(snippets) == 0:
            return ["No good Google Search Result was found"]
        return snippets


return ["No good Google Search Result was found"]
        return snippets
    def _parse_results(self, results: dict) -> str:
        return " ".join(self._parse_snippets(results))
    def _google_serper_api_results(
        self, search_term: str, search_type: str = "search", **kwargs: Any
    ) -> dict:
        headers = {
            "X-API-KEY": self.serper_api_key or "",
            "Content-Type": "application/json",
        }
        params = {
            "q": search_term,
            **{key: value for key, value in kwargs.items() if value is not None},
        }
        response = requests.post(
            f"https://google.serper.dev/{search_type}", headers=headers, params=params
        )
        response.raise_for_status()
        search_results = response.json()
        return search_results
    async def _async_google_serper_search_results(
        self, search_term: str, search_type: str = "search", **kwargs: Any
    ) -> dict:
        headers = {
            "X-API-KEY": self.serper_api_key or "",
            "Content-Type": "application/json",
        }
        url = f"https://google.serper.dev/{search_type}"
        params = {
            "q": search_term,
            **{key: value for key, value in kwargs.items() if value is not None},
        }
        if not self.aiosession:
            async with aiohttp.ClientSession() as session:
                async with session.post(
                    url, params=params, headers=headers, raise_for_status=False
                ) as response:
                    search_results = await response.json()
        else:
            async with self.aiosession.post(


else:
            async with self.aiosession.post(
                url, params=params, headers=headers, raise_for_status=True
            ) as response:
                search_results = await response.json()
        return search_results
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 02, 2023.


Source code for langchain.chat_models.openai
"""OpenAI chat wrapper."""
from __future__ import annotations
import logging
import sys
from typing import (
    TYPE_CHECKING,
    Any,
    Callable,
    Dict,
    List,
    Mapping,
    Optional,
    Tuple,
    Union,
)
from pydantic import Extra, Field, root_validator
from tenacity import (
    before_sleep_log,
    retry,
    retry_if_exception_type,
    stop_after_attempt,
    wait_exponential,
)
from langchain.callbacks.manager import (
    AsyncCallbackManagerForLLMRun,
    CallbackManagerForLLMRun,
)
from langchain.chat_models.base import BaseChatModel
from langchain.schema import (
    AIMessage,
    BaseMessage,
    ChatGeneration,
    ChatMessage,
    ChatResult,
    HumanMessage,
    SystemMessage,
)
from langchain.utils import get_from_dict_or_env
if TYPE_CHECKING:
    import tiktoken
logger = logging.getLogger(__name__)
def _import_tiktoken() -> Any:
    try:
        import tiktoken
    except ImportError:
        raise ValueError(
            "Could not import tiktoken python package. "
            "This is needed in order to calculate get_token_ids. "
            "Please install it with `pip install tiktoken`."
        )
    return tiktoken
def _create_retry_decorator(llm: ChatOpenAI) -> Callable[[Any], Any]:
    import openai
    min_seconds = 1
    max_seconds = 60
    # Wait 2^x * 1 second between each retry starting with
    # 4 seconds, then up to 10 seconds, then 10 seconds afterwards
    return retry(


return retry(
        reraise=True,
        stop=stop_after_attempt(llm.max_retries),
        wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
        retry=(
            retry_if_exception_type(openai.error.Timeout)
            | retry_if_exception_type(openai.error.APIError)
            | retry_if_exception_type(openai.error.APIConnectionError)
            | retry_if_exception_type(openai.error.RateLimitError)
            | retry_if_exception_type(openai.error.ServiceUnavailableError)
        ),
        before_sleep=before_sleep_log(logger, logging.WARNING),
    )
async def acompletion_with_retry(llm: ChatOpenAI, **kwargs: Any) -> Any:
    """Use tenacity to retry the async completion call."""
    retry_decorator = _create_retry_decorator(llm)
    @retry_decorator
    async def _completion_with_retry(**kwargs: Any) -> Any:
        # Use OpenAI's async api https://github.com/openai/openai-python#async-api
        return await llm.client.acreate(**kwargs)
    return await _completion_with_retry(**kwargs)
def _convert_dict_to_message(_dict: dict) -> BaseMessage:
    role = _dict["role"]
    if role == "user":
        return HumanMessage(content=_dict["content"])
    elif role == "assistant":
        return AIMessage(content=_dict["content"])
    elif role == "system":
        return SystemMessage(content=_dict["content"])
    else:
        return ChatMessage(content=_dict["content"], role=role)
def _convert_message_to_dict(message: BaseMessage) -> dict:
    if isinstance(message, ChatMessage):
        message_dict = {"role": message.role, "content": message.content}
    elif isinstance(message, HumanMessage):


elif isinstance(message, HumanMessage):
        message_dict = {"role": "user", "content": message.content}
    elif isinstance(message, AIMessage):
        message_dict = {"role": "assistant", "content": message.content}
    elif isinstance(message, SystemMessage):
        message_dict = {"role": "system", "content": message.content}
    else:
        raise ValueError(f"Got unknown type {message}")
    if "name" in message.additional_kwargs:
        message_dict["name"] = message.additional_kwargs["name"]
    return message_dict
[docs]class ChatOpenAI(BaseChatModel):
    """Wrapper around OpenAI Chat large language models.
    To use, you should have the ``openai`` python package installed, and the
    environment variable ``OPENAI_API_KEY`` set with your API key.
    Any parameters that are valid to be passed to the openai.create call can be passed
    in, even if not explicitly saved on this class.
    Example:
        .. code-block:: python
            from langchain.chat_models import ChatOpenAI
            openai = ChatOpenAI(model_name="gpt-3.5-turbo")
    """
    client: Any  #: :meta private:
    model_name: str = Field(default="gpt-3.5-turbo", alias="model")
    """Model name to use."""
    temperature: float = 0.7
    """What sampling temperature to use."""
    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
    """Holds any model parameters valid for `create` call not explicitly specified."""
    openai_api_key: Optional[str] = None
    """Base URL path for API requests,
    leave blank if not using a proxy or service emulator."""


leave blank if not using a proxy or service emulator."""
    openai_api_base: Optional[str] = None
    openai_organization: Optional[str] = None
    # to support explicit proxy for OpenAI
    openai_proxy: Optional[str] = None
    request_timeout: Optional[Union[float, Tuple[float, float]]] = None
    """Timeout for requests to OpenAI completion API. Default is 600 seconds."""
    max_retries: int = 6
    """Maximum number of retries to make when generating."""
    streaming: bool = False
    """Whether to stream the results or not."""
    n: int = 1
    """Number of chat completions to generate for each prompt."""
    max_tokens: Optional[int] = None
    """Maximum number of tokens to generate."""
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.ignore
        allow_population_by_field_name = True
    @root_validator(pre=True)
    def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
        """Build extra kwargs from additional params that were passed in."""
        all_required_field_names = cls.all_required_field_names()
        extra = values.get("model_kwargs", {})
        for field_name in list(values):
            if field_name in extra:
                raise ValueError(f"Found {field_name} supplied twice.")
            if field_name not in all_required_field_names:
                logger.warning(
                    f"""WARNING! {field_name} is not default parameter.
                    {field_name} was transferred to model_kwargs.
                    Please confirm that {field_name} is what you intended."""
                )
                extra[field_name] = values.pop(field_name)
        invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
        if invalid_model_kwargs:


if invalid_model_kwargs:
            raise ValueError(
                f"Parameters {invalid_model_kwargs} should be specified explicitly. "
                f"Instead they were passed in as part of `model_kwargs` parameter."
            )
        values["model_kwargs"] = extra
        return values
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key and python package exists in environment."""
        openai_api_key = get_from_dict_or_env(
            values, "openai_api_key", "OPENAI_API_KEY"
        )
        openai_organization = get_from_dict_or_env(
            values,
            "openai_organization",
            "OPENAI_ORGANIZATION",
            default="",
        )
        openai_api_base = get_from_dict_or_env(
            values,
            "openai_api_base",
            "OPENAI_API_BASE",
            default="",
        )
        openai_proxy = get_from_dict_or_env(
            values,
            "openai_proxy",
            "OPENAI_PROXY",
            default="",
        )
        try:
            import openai
        except ImportError:
            raise ValueError(
                "Could not import openai python package. "
                "Please install it with `pip install openai`."
            )
        openai.api_key = openai_api_key
        if openai_organization:
            openai.organization = openai_organization
        if openai_api_base:
            openai.api_base = openai_api_base
        if openai_proxy:
            openai.proxy = {"http": openai_proxy, "https": openai_proxy}  # type: ignore[assignment]  # noqa: E501
        try:
            values["client"] = openai.ChatCompletion
        except AttributeError:


try:
            values["client"] = openai.ChatCompletion
        except AttributeError:
            raise ValueError(
                "`openai` has no `ChatCompletion` attribute, this is likely "
                "due to an old version of the openai package. Try upgrading it "
                "with `pip install --upgrade openai`."
            )
        if values["n"] < 1:
            raise ValueError("n must be at least 1.")
        if values["n"] > 1 and values["streaming"]:
            raise ValueError("n must be 1 when streaming.")
        return values
    @property
    def _default_params(self) -> Dict[str, Any]:
        """Get the default parameters for calling OpenAI API."""
        return {
            "model": self.model_name,
            "request_timeout": self.request_timeout,
            "max_tokens": self.max_tokens,
            "stream": self.streaming,
            "n": self.n,
            "temperature": self.temperature,
            **self.model_kwargs,
        }
    def _create_retry_decorator(self) -> Callable[[Any], Any]:
        import openai
        min_seconds = 1
        max_seconds = 60
        # Wait 2^x * 1 second between each retry starting with
        # 4 seconds, then up to 10 seconds, then 10 seconds afterwards
        return retry(
            reraise=True,
            stop=stop_after_attempt(self.max_retries),
            wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
            retry=(
                retry_if_exception_type(openai.error.Timeout)
                | retry_if_exception_type(openai.error.APIError)
                | retry_if_exception_type(openai.error.APIConnectionError)


| retry_if_exception_type(openai.error.APIConnectionError)
                | retry_if_exception_type(openai.error.RateLimitError)
                | retry_if_exception_type(openai.error.ServiceUnavailableError)
            ),
            before_sleep=before_sleep_log(logger, logging.WARNING),
        )
[docs]    def completion_with_retry(self, **kwargs: Any) -> Any:
        """Use tenacity to retry the completion call."""
        retry_decorator = self._create_retry_decorator()
        @retry_decorator
        def _completion_with_retry(**kwargs: Any) -> Any:
            return self.client.create(**kwargs)
        return _completion_with_retry(**kwargs)
    def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
        overall_token_usage: dict = {}
        for output in llm_outputs:
            if output is None:
                # Happens in streaming
                continue
            token_usage = output["token_usage"]
            for k, v in token_usage.items():
                if k in overall_token_usage:
                    overall_token_usage[k] += v
                else:
                    overall_token_usage[k] = v
        return {"token_usage": overall_token_usage, "model_name": self.model_name}
    def _generate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
    ) -> ChatResult:
        message_dicts, params = self._create_message_dicts(messages, stop)
        if self.streaming:
            inner_completion = ""
            role = "assistant"
            params["stream"] = True
            for stream_resp in self.completion_with_retry(
                messages=message_dicts, **params
            ):


messages=message_dicts, **params
            ):
                role = stream_resp["choices"][0]["delta"].get("role", role)
                token = stream_resp["choices"][0]["delta"].get("content", "")
                inner_completion += token
                if run_manager:
                    run_manager.on_llm_new_token(token)
            message = _convert_dict_to_message(
                {"content": inner_completion, "role": role}
            )
            return ChatResult(generations=[ChatGeneration(message=message)])
        response = self.completion_with_retry(messages=message_dicts, **params)
        return self._create_chat_result(response)
    def _create_message_dicts(
        self, messages: List[BaseMessage], stop: Optional[List[str]]
    ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
        params: Dict[str, Any] = {**{"model": self.model_name}, **self._default_params}
        if stop is not None:
            if "stop" in params:
                raise ValueError("`stop` found in both the input and default params.")
            params["stop"] = stop
        message_dicts = [_convert_message_to_dict(m) for m in messages]
        return message_dicts, params
    def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
        generations = []
        for res in response["choices"]:
            message = _convert_dict_to_message(res["message"])
            gen = ChatGeneration(message=message)
            generations.append(gen)
        llm_output = {"token_usage": response["usage"], "model_name": self.model_name}
        return ChatResult(generations=generations, llm_output=llm_output)
    async def _agenerate(
        self,
        messages: List[BaseMessage],


self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
    ) -> ChatResult:
        message_dicts, params = self._create_message_dicts(messages, stop)
        if self.streaming:
            inner_completion = ""
            role = "assistant"
            params["stream"] = True
            async for stream_resp in await acompletion_with_retry(
                self, messages=message_dicts, **params
            ):
                role = stream_resp["choices"][0]["delta"].get("role", role)
                token = stream_resp["choices"][0]["delta"].get("content", "")
                inner_completion += token
                if run_manager:
                    await run_manager.on_llm_new_token(token)
            message = _convert_dict_to_message(
                {"content": inner_completion, "role": role}
            )
            return ChatResult(generations=[ChatGeneration(message=message)])
        else:
            response = await acompletion_with_retry(
                self, messages=message_dicts, **params
            )
            return self._create_chat_result(response)
    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        """Get the identifying parameters."""
        return {**{"model_name": self.model_name}, **self._default_params}
    @property
    def _llm_type(self) -> str:
        """Return type of chat model."""
        return "openai-chat"
    def _get_encoding_model(self) -> Tuple[str, tiktoken.Encoding]:
        tiktoken_ = _import_tiktoken()
        model = self.model_name
        if model == "gpt-3.5-turbo":


if model == "gpt-3.5-turbo":
            # gpt-3.5-turbo may change over time.
            # Returning num tokens assuming gpt-3.5-turbo-0301.
            model = "gpt-3.5-turbo-0301"
        elif model == "gpt-4":
            # gpt-4 may change over time.
            # Returning num tokens assuming gpt-4-0314.
            model = "gpt-4-0314"
        # Returns the number of tokens used by a list of messages.
        try:
            encoding = tiktoken_.encoding_for_model(model)
        except KeyError:
            logger.warning("Warning: model not found. Using cl100k_base encoding.")
            model = "cl100k_base"
            encoding = tiktoken_.get_encoding(model)
        return model, encoding
[docs]    def get_token_ids(self, text: str) -> List[int]:
        """Get the tokens present in the text with tiktoken package."""
        # tiktoken NOT supported for Python 3.7 or below
        if sys.version_info[1] <= 7:
            return super().get_token_ids(text)
        _, encoding_model = self._get_encoding_model()
        return encoding_model.encode(text)
[docs]    def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:
        """Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
        Official documentation: https://github.com/openai/openai-cookbook/blob/
        main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
        if sys.version_info[1] <= 7:


if sys.version_info[1] <= 7:
            return super().get_num_tokens_from_messages(messages)
        model, encoding = self._get_encoding_model()
        if model == "gpt-3.5-turbo-0301":
            # every message follows <im_start>{role/name}\n{content}<im_end>\n
            tokens_per_message = 4
            # if there's a name, the role is omitted
            tokens_per_name = -1
        elif model == "gpt-4-0314":
            tokens_per_message = 3
            tokens_per_name = 1
        else:
            raise NotImplementedError(
                f"get_num_tokens_from_messages() is not presently implemented "
                f"for model {model}."
                "See https://github.com/openai/openai-python/blob/main/chatml.md for "
                "information on how messages are converted to tokens."
            )
        num_tokens = 0
        messages_dict = [_convert_message_to_dict(m) for m in messages]
        for message in messages_dict:
            num_tokens += tokens_per_message
            for key, value in message.items():
                num_tokens += len(encoding.encode(value))
                if key == "name":
                    num_tokens += tokens_per_name
        # every reply is primed with <im_start>assistant
        num_tokens += 3
        return num_tokens
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 02, 2023.


Source code for langchain.chat_models.azure_openai
"""Azure OpenAI chat wrapper."""
from __future__ import annotations
import logging
from typing import Any, Dict, Mapping
from pydantic import root_validator
from langchain.chat_models.openai import ChatOpenAI
from langchain.schema import ChatResult
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
[docs]class AzureChatOpenAI(ChatOpenAI):
    """Wrapper around Azure OpenAI Chat Completion API. To use this class you
    must have a deployed model on Azure OpenAI. Use `deployment_name` in the
    constructor to refer to the "Model deployment name" in the Azure portal.
    In addition, you should have the ``openai`` python package installed, and the
    following environment variables set or passed in constructor in lower case:
    - ``OPENAI_API_TYPE`` (default: ``azure``)
    - ``OPENAI_API_KEY``
    - ``OPENAI_API_BASE``
    - ``OPENAI_API_VERSION``
    - ``OPENAI_PROXY``
    For exmaple, if you have `gpt-35-turbo` deployed, with the deployment name
    `35-turbo-dev`, the constructor should look like:
    .. code-block:: python
        AzureChatOpenAI(
            deployment_name="35-turbo-dev",
            openai_api_version="2023-03-15-preview",
        )
    Be aware the API version may change.
    Any parameters that are valid to be passed to the openai.create call can be passed
    in, even if not explicitly saved on this class.
    """
    deployment_name: str = ""
    openai_api_type: str = "azure"
    openai_api_base: str = ""


openai_api_base: str = ""
    openai_api_version: str = ""
    openai_api_key: str = ""
    openai_organization: str = ""
    openai_proxy: str = ""
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key and python package exists in environment."""
        openai_api_key = get_from_dict_or_env(
            values,
            "openai_api_key",
            "OPENAI_API_KEY",
        )
        openai_api_base = get_from_dict_or_env(
            values,
            "openai_api_base",
            "OPENAI_API_BASE",
        )
        openai_api_version = get_from_dict_or_env(
            values,
            "openai_api_version",
            "OPENAI_API_VERSION",
        )
        openai_api_type = get_from_dict_or_env(
            values,
            "openai_api_type",
            "OPENAI_API_TYPE",
        )
        openai_organization = get_from_dict_or_env(
            values,
            "openai_organization",
            "OPENAI_ORGANIZATION",
            default="",
        )
        openai_proxy = get_from_dict_or_env(
            values,
            "openai_proxy",
            "OPENAI_PROXY",
            default="",
        )
        try:
            import openai
            openai.api_type = openai_api_type
            openai.api_base = openai_api_base
            openai.api_version = openai_api_version
            openai.api_key = openai_api_key
            if openai_organization:
                openai.organization = openai_organization
            if openai_proxy:


openai.organization = openai_organization
            if openai_proxy:
                openai.proxy = {"http": openai_proxy, "https": openai_proxy}  # type: ignore[assignment]  # noqa: E501
        except ImportError:
            raise ImportError(
                "Could not import openai python package. "
                "Please install it with `pip install openai`."
            )
        try:
            values["client"] = openai.ChatCompletion
        except AttributeError:
            raise ValueError(
                "`openai` has no `ChatCompletion` attribute, this is likely "
                "due to an old version of the openai package. Try upgrading it "
                "with `pip install --upgrade openai`."
            )
        if values["n"] < 1:
            raise ValueError("n must be at least 1.")
        if values["n"] > 1 and values["streaming"]:
            raise ValueError("n must be 1 when streaming.")
        return values
    @property
    def _default_params(self) -> Dict[str, Any]:
        """Get the default parameters for calling OpenAI API."""
        return {
            **super()._default_params,
            "engine": self.deployment_name,
        }
    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        """Get the identifying parameters."""
        return {**self._default_params}
    @property
    def _llm_type(self) -> str:
        return "azure-openai-chat"
    def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
        for res in response["choices"]:
            if res.get("finish_reason", None) == "content_filter":
                raise ValueError(


raise ValueError(
                    "Azure has not provided the response due to a content"
                    " filter being triggered"
                )
        return super()._create_chat_result(response)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 02, 2023.


Source code for langchain.chat_models.google_palm
"""Wrapper around Google's PaLM Chat API."""
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Mapping, Optional
from pydantic import BaseModel, root_validator
from tenacity import (
    before_sleep_log,
    retry,
    retry_if_exception_type,
    stop_after_attempt,
    wait_exponential,
)
from langchain.callbacks.manager import (
    AsyncCallbackManagerForLLMRun,
    CallbackManagerForLLMRun,
)
from langchain.chat_models.base import BaseChatModel
from langchain.schema import (
    AIMessage,
    BaseMessage,
    ChatGeneration,
    ChatMessage,
    ChatResult,
    HumanMessage,
    SystemMessage,
)
from langchain.utils import get_from_dict_or_env
if TYPE_CHECKING:
    import google.generativeai as genai
logger = logging.getLogger(__name__)
class ChatGooglePalmError(Exception):
    pass
def _truncate_at_stop_tokens(
    text: str,
    stop: Optional[List[str]],
) -> str:
    """Truncates text at the earliest stop token found."""
    if stop is None:
        return text
    for stop_token in stop:
        stop_token_idx = text.find(stop_token)
        if stop_token_idx != -1:
            text = text[:stop_token_idx]
    return text
def _response_to_result(
    response: genai.types.ChatResponse,
    stop: Optional[List[str]],
) -> ChatResult:
    """Converts a PaLM API response into a LangChain ChatResult."""
    if not response.candidates:
        raise ChatGooglePalmError("ChatResponse must have at least one candidate.")


raise ChatGooglePalmError("ChatResponse must have at least one candidate.")
    generations: List[ChatGeneration] = []
    for candidate in response.candidates:
        author = candidate.get("author")
        if author is None:
            raise ChatGooglePalmError(f"ChatResponse must have an author: {candidate}")
        content = _truncate_at_stop_tokens(candidate.get("content", ""), stop)
        if content is None:
            raise ChatGooglePalmError(f"ChatResponse must have a content: {candidate}")
        if author == "ai":
            generations.append(
                ChatGeneration(text=content, message=AIMessage(content=content))
            )
        elif author == "human":
            generations.append(
                ChatGeneration(
                    text=content,
                    message=HumanMessage(content=content),
                )
            )
        else:
            generations.append(
                ChatGeneration(
                    text=content,
                    message=ChatMessage(role=author, content=content),
                )
            )
    return ChatResult(generations=generations)
def _messages_to_prompt_dict(
    input_messages: List[BaseMessage],
) -> genai.types.MessagePromptDict:
    """Converts a list of LangChain messages into a PaLM API MessagePrompt structure."""
    import google.generativeai as genai
    context: str = ""
    examples: List[genai.types.MessageDict] = []
    messages: List[genai.types.MessageDict] = []
    remaining = list(enumerate(input_messages))
    while remaining:
        index, input_message = remaining.pop(0)
        if isinstance(input_message, SystemMessage):
            if index != 0:
                raise ChatGooglePalmError("System message must be first input message.")


raise ChatGooglePalmError("System message must be first input message.")
            context = input_message.content
        elif isinstance(input_message, HumanMessage) and input_message.example:
            if messages:
                raise ChatGooglePalmError(
                    "Message examples must come before other messages."
                )
            _, next_input_message = remaining.pop(0)
            if isinstance(next_input_message, AIMessage) and next_input_message.example:
                examples.extend(
                    [
                        genai.types.MessageDict(
                            author="human", content=input_message.content
                        ),
                        genai.types.MessageDict(
                            author="ai", content=next_input_message.content
                        ),
                    ]
                )
            else:
                raise ChatGooglePalmError(
                    "Human example message must be immediately followed by an "
                    " AI example response."
                )
        elif isinstance(input_message, AIMessage) and input_message.example:
            raise ChatGooglePalmError(
                "AI example message must be immediately preceded by a Human "
                "example message."
            )
        elif isinstance(input_message, AIMessage):
            messages.append(
                genai.types.MessageDict(author="ai", content=input_message.content)
            )
        elif isinstance(input_message, HumanMessage):
            messages.append(
                genai.types.MessageDict(author="human", content=input_message.content)
            )
        elif isinstance(input_message, ChatMessage):
            messages.append(
                genai.types.MessageDict(
                    author=input_message.role, content=input_message.content
                )
            )
        else:
            raise ChatGooglePalmError(
                "Messages without an explicit role not supported by PaLM API."
            )
    return genai.types.MessagePromptDict(
        context=context,
        examples=examples,


context=context,
        examples=examples,
        messages=messages,
    )
def _create_retry_decorator() -> Callable[[Any], Any]:
    """Returns a tenacity retry decorator, preconfigured to handle PaLM exceptions"""
    import google.api_core.exceptions
    multiplier = 2
    min_seconds = 1
    max_seconds = 60
    max_retries = 10
    return retry(
        reraise=True,
        stop=stop_after_attempt(max_retries),
        wait=wait_exponential(multiplier=multiplier, min=min_seconds, max=max_seconds),
        retry=(
            retry_if_exception_type(google.api_core.exceptions.ResourceExhausted)
            | retry_if_exception_type(google.api_core.exceptions.ServiceUnavailable)
            | retry_if_exception_type(google.api_core.exceptions.GoogleAPIError)
        ),
        before_sleep=before_sleep_log(logger, logging.WARNING),
    )
def chat_with_retry(llm: ChatGooglePalm, **kwargs: Any) -> Any:
    """Use tenacity to retry the completion call."""
    retry_decorator = _create_retry_decorator()
    @retry_decorator
    def _chat_with_retry(**kwargs: Any) -> Any:
        return llm.client.chat(**kwargs)
    return _chat_with_retry(**kwargs)
async def achat_with_retry(llm: ChatGooglePalm, **kwargs: Any) -> Any:
    """Use tenacity to retry the async completion call."""
    retry_decorator = _create_retry_decorator()
    @retry_decorator
    async def _achat_with_retry(**kwargs: Any) -> Any:
        # Use OpenAI's async api https://github.com/openai/openai-python#async-api
        return await llm.client.chat_async(**kwargs)
    return await _achat_with_retry(**kwargs)


return await _achat_with_retry(**kwargs)
[docs]class ChatGooglePalm(BaseChatModel, BaseModel):
    """Wrapper around Google's PaLM Chat API.
    To use you must have the google.generativeai Python package installed and
    either:
        1. The ``GOOGLE_API_KEY``` environment varaible set with your API key, or
        2. Pass your API key using the google_api_key kwarg to the ChatGoogle
           constructor.
    Example:
        .. code-block:: python
            from langchain.chat_models import ChatGooglePalm
            chat = ChatGooglePalm()
    """
    client: Any  #: :meta private:
    model_name: str = "models/chat-bison-001"
    """Model name to use."""
    google_api_key: Optional[str] = None
    temperature: Optional[float] = None
    """Run inference with this temperature. Must by in the closed
       interval [0.0, 1.0]."""
    top_p: Optional[float] = None
    """Decode using nucleus sampling: consider the smallest set of tokens whose
       probability sum is at least top_p. Must be in the closed interval [0.0, 1.0]."""
    top_k: Optional[int] = None
    """Decode using top-k sampling: consider the set of top_k most probable tokens.
       Must be positive."""
    n: int = 1
    """Number of chat completions to generate for each prompt. Note that the API may
       not return the full n completions if duplicates are generated."""
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate api key, python package exists, temperature, top_p, and top_k."""


"""Validate api key, python package exists, temperature, top_p, and top_k."""
        google_api_key = get_from_dict_or_env(
            values, "google_api_key", "GOOGLE_API_KEY"
        )
        try:
            import google.generativeai as genai
            genai.configure(api_key=google_api_key)
        except ImportError:
            raise ChatGooglePalmError(
                "Could not import google.generativeai python package. "
                "Please install it with `pip install google-generativeai`"
            )
        values["client"] = genai
        if values["temperature"] is not None and not 0 <= values["temperature"] <= 1:
            raise ValueError("temperature must be in the range [0.0, 1.0]")
        if values["top_p"] is not None and not 0 <= values["top_p"] <= 1:
            raise ValueError("top_p must be in the range [0.0, 1.0]")
        if values["top_k"] is not None and values["top_k"] <= 0:
            raise ValueError("top_k must be positive")
        return values
    def _generate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
    ) -> ChatResult:
        prompt = _messages_to_prompt_dict(messages)
        response: genai.types.ChatResponse = chat_with_retry(
            self,
            model=self.model_name,
            prompt=prompt,
            temperature=self.temperature,
            top_p=self.top_p,
            top_k=self.top_k,
            candidate_count=self.n,
        )
        return _response_to_result(response, stop)


)
        return _response_to_result(response, stop)
    async def _agenerate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
    ) -> ChatResult:
        prompt = _messages_to_prompt_dict(messages)
        response: genai.types.ChatResponse = await achat_with_retry(
            self,
            model=self.model_name,
            prompt=prompt,
            temperature=self.temperature,
            top_p=self.top_p,
            top_k=self.top_k,
            candidate_count=self.n,
        )
        return _response_to_result(response, stop)
    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        """Get the identifying parameters."""
        return {
            "model_name": self.model_name,
            "temperature": self.temperature,
            "top_p": self.top_p,
            "top_k": self.top_k,
            "n": self.n,
        }
    @property
    def _llm_type(self) -> str:
        return "google-palm-chat"
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 02, 2023.


Source code for langchain.chat_models.promptlayer_openai
"""PromptLayer wrapper."""
import datetime
from typing import Any, List, Mapping, Optional
from langchain.callbacks.manager import (
    AsyncCallbackManagerForLLMRun,
    CallbackManagerForLLMRun,
)
from langchain.chat_models import ChatOpenAI
from langchain.schema import BaseMessage, ChatResult
[docs]class PromptLayerChatOpenAI(ChatOpenAI):
    """Wrapper around OpenAI Chat large language models and PromptLayer.
    To use, you should have the ``openai`` and ``promptlayer`` python
    package installed, and the environment variable ``OPENAI_API_KEY``
    and ``PROMPTLAYER_API_KEY`` set with your openAI API key and
    promptlayer key respectively.
    All parameters that can be passed to the OpenAI LLM can also
    be passed here. The PromptLayerChatOpenAI adds to optional
    parameters:
        ``pl_tags``: List of strings to tag the request with.
        ``return_pl_id``: If True, the PromptLayer request ID will be
            returned in the ``generation_info`` field of the
            ``Generation`` object.
    Example:
        .. code-block:: python
            from langchain.chat_models import PromptLayerChatOpenAI
            openai = PromptLayerChatOpenAI(model_name="gpt-3.5-turbo")
    """
    pl_tags: Optional[List[str]]
    return_pl_id: Optional[bool] = False
    def _generate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
    ) -> ChatResult:


) -> ChatResult:
        """Call ChatOpenAI generate and then call PromptLayer API to log the request."""
        from promptlayer.utils import get_api_key, promptlayer_api_request
        request_start_time = datetime.datetime.now().timestamp()
        generated_responses = super()._generate(messages, stop, run_manager)
        request_end_time = datetime.datetime.now().timestamp()
        message_dicts, params = super()._create_message_dicts(messages, stop)
        for i, generation in enumerate(generated_responses.generations):
            response_dict, params = super()._create_message_dicts(
                [generation.message], stop
            )
            pl_request_id = promptlayer_api_request(
                "langchain.PromptLayerChatOpenAI",
                "langchain",
                message_dicts,
                params,
                self.pl_tags,
                response_dict,
                request_start_time,
                request_end_time,
                get_api_key(),
                return_pl_id=self.return_pl_id,
            )
            if self.return_pl_id:
                if generation.generation_info is None or not isinstance(
                    generation.generation_info, dict
                ):
                    generation.generation_info = {}
                generation.generation_info["pl_request_id"] = pl_request_id
        return generated_responses
    async def _agenerate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
    ) -> ChatResult:
        """Call ChatOpenAI agenerate and then call PromptLayer to log."""
        from promptlayer.utils import get_api_key, promptlayer_api_request_async
        request_start_time = datetime.datetime.now().timestamp()
        generated_responses = await super()._agenerate(messages, stop, run_manager)


generated_responses = await super()._agenerate(messages, stop, run_manager)
        request_end_time = datetime.datetime.now().timestamp()
        message_dicts, params = super()._create_message_dicts(messages, stop)
        for i, generation in enumerate(generated_responses.generations):
            response_dict, params = super()._create_message_dicts(
                [generation.message], stop
            )
            pl_request_id = await promptlayer_api_request_async(
                "langchain.PromptLayerChatOpenAI.async",
                "langchain",
                message_dicts,
                params,
                self.pl_tags,
                response_dict,
                request_start_time,
                request_end_time,
                get_api_key(),
                return_pl_id=self.return_pl_id,
            )
            if self.return_pl_id:
                if generation.generation_info is None or not isinstance(
                    generation.generation_info, dict
                ):
                    generation.generation_info = {}
                generation.generation_info["pl_request_id"] = pl_request_id
        return generated_responses
    @property
    def _llm_type(self) -> str:
        return "promptlayer-openai-chat"
    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        return {
            **super()._identifying_params,
            "pl_tags": self.pl_tags,
            "return_pl_id": self.return_pl_id,
        }
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 02, 2023.


Source code for langchain.chat_models.anthropic
from typing import Any, Dict, List, Optional
from pydantic import Extra
from langchain.callbacks.manager import (
    AsyncCallbackManagerForLLMRun,
    CallbackManagerForLLMRun,
)
from langchain.chat_models.base import BaseChatModel
from langchain.llms.anthropic import _AnthropicCommon
from langchain.schema import (
    AIMessage,
    BaseMessage,
    ChatGeneration,
    ChatMessage,
    ChatResult,
    HumanMessage,
    SystemMessage,
)
[docs]class ChatAnthropic(BaseChatModel, _AnthropicCommon):
    r"""Wrapper around Anthropic's large language model.
    To use, you should have the ``anthropic`` python package installed, and the
    environment variable ``ANTHROPIC_API_KEY`` set with your API key, or pass
    it as a named parameter to the constructor.
    Example:
        .. code-block:: python
            import anthropic
            from langchain.llms import Anthropic
            model = ChatAnthropic(model="<model_name>", anthropic_api_key="my-api-key")
    """
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
    @property
    def _llm_type(self) -> str:
        """Return type of chat model."""
        return "anthropic-chat"
    def _convert_one_message_to_text(self, message: BaseMessage) -> str:
        if isinstance(message, ChatMessage):
            message_text = f"\n\n{message.role.capitalize()}: {message.content}"
        elif isinstance(message, HumanMessage):
            message_text = f"{self.HUMAN_PROMPT} {message.content}"
        elif isinstance(message, AIMessage):


elif isinstance(message, AIMessage):
            message_text = f"{self.AI_PROMPT} {message.content}"
        elif isinstance(message, SystemMessage):
            message_text = f"{self.HUMAN_PROMPT} <admin>{message.content}</admin>"
        else:
            raise ValueError(f"Got unknown type {message}")
        return message_text
    def _convert_messages_to_text(self, messages: List[BaseMessage]) -> str:
        """Format a list of strings into a single string with necessary newlines.
        Args:
            messages (List[BaseMessage]): List of BaseMessage to combine.
        Returns:
            str: Combined string with necessary newlines.
        """
        return "".join(
            self._convert_one_message_to_text(message) for message in messages
        )
    def _convert_messages_to_prompt(self, messages: List[BaseMessage]) -> str:
        """Format a list of messages into a full prompt for the Anthropic model
        Args:
            messages (List[BaseMessage]): List of BaseMessage to combine.
        Returns:
            str: Combined string with necessary HUMAN_PROMPT and AI_PROMPT tags.
        """
        if not self.AI_PROMPT:
            raise NameError("Please ensure the anthropic package is loaded")
        if not isinstance(messages[-1], AIMessage):
            messages.append(AIMessage(content=""))
        text = self._convert_messages_to_text(messages)
        return (
            text.rstrip()
        )  # trim off the trailing ' ' that might come from the "Assistant: "
    def _generate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
    ) -> ChatResult:


) -> ChatResult:
        prompt = self._convert_messages_to_prompt(messages)
        params: Dict[str, Any] = {"prompt": prompt, **self._default_params}
        if stop:
            params["stop_sequences"] = stop
        if self.streaming:
            completion = ""
            stream_resp = self.client.completion_stream(**params)
            for data in stream_resp:
                delta = data["completion"][len(completion) :]
                completion = data["completion"]
                if run_manager:
                    run_manager.on_llm_new_token(
                        delta,
                    )
        else:
            response = self.client.completion(**params)
            completion = response["completion"]
        message = AIMessage(content=completion)
        return ChatResult(generations=[ChatGeneration(message=message)])
    async def _agenerate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
    ) -> ChatResult:
        prompt = self._convert_messages_to_prompt(messages)
        params: Dict[str, Any] = {"prompt": prompt, **self._default_params}
        if stop:
            params["stop_sequences"] = stop
        if self.streaming:
            completion = ""
            stream_resp = await self.client.acompletion_stream(**params)
            async for data in stream_resp:
                delta = data["completion"][len(completion) :]
                completion = data["completion"]
                if run_manager:
                    await run_manager.on_llm_new_token(
                        delta,
                    )
        else:
            response = await self.client.acompletion(**params)
            completion = response["completion"]
        message = AIMessage(content=completion)


completion = response["completion"]
        message = AIMessage(content=completion)
        return ChatResult(generations=[ChatGeneration(message=message)])
[docs]    def get_num_tokens(self, text: str) -> int:
        """Calculate number of tokens."""
        if not self.count_tokens:
            raise NameError("Please ensure the anthropic package is loaded")
        return self.count_tokens(text)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 02, 2023.


Source code for langchain.chat_models.vertexai
"""Wrapper around Google VertexAI chat-based models."""
from dataclasses import dataclass, field
from typing import Dict, List, Optional
from pydantic import root_validator
from langchain.callbacks.manager import (
    AsyncCallbackManagerForLLMRun,
    CallbackManagerForLLMRun,
)
from langchain.chat_models.base import BaseChatModel
from langchain.llms.vertexai import _VertexAICommon
from langchain.schema import (
    AIMessage,
    BaseMessage,
    ChatGeneration,
    ChatResult,
    HumanMessage,
    SystemMessage,
)
from langchain.utilities.vertexai import raise_vertex_import_error
@dataclass
class _MessagePair:
    """InputOutputTextPair represents a pair of input and output texts."""
    question: HumanMessage
    answer: AIMessage
@dataclass
class _ChatHistory:
    """InputOutputTextPair represents a pair of input and output texts."""
    history: List[_MessagePair] = field(default_factory=list)
    system_message: Optional[SystemMessage] = None
def _parse_chat_history(history: List[BaseMessage]) -> _ChatHistory:
    """Parse a sequence of messages into history.
    A sequence should be either (SystemMessage, HumanMessage, AIMessage,
    HumanMessage, AIMessage, ...) or (HumanMessage, AIMessage, HumanMessage,
    AIMessage, ...).
    Args:
        history: The list of messages to re-create the history of the chat.
    Returns:
        A parsed chat history.
    Raises:
        ValueError: If a sequence of message is odd, or a human message is not followed
            by a message from AI (e.g., Human, Human, AI or AI, AI, Human).
    """
    if not history:


"""
    if not history:
        return _ChatHistory()
    first_message = history[0]
    system_message = first_message if isinstance(first_message, SystemMessage) else None
    chat_history = _ChatHistory(system_message=system_message)
    messages_left = history[1:] if system_message else history
    if len(messages_left) % 2 != 0:
        raise ValueError(
            f"Amount of messages in history should be even, got {len(messages_left)}!"
        )
    for question, answer in zip(messages_left[::2], messages_left[1::2]):
        if not isinstance(question, HumanMessage) or not isinstance(answer, AIMessage):
            raise ValueError(
                "A human message should follow a bot one, "
                f"got {question.type}, {answer.type}."
            )
        chat_history.history.append(_MessagePair(question=question, answer=answer))
    return chat_history
[docs]class ChatVertexAI(_VertexAICommon, BaseChatModel):
    """Wrapper around Vertex AI large language models."""
    model_name: str = "chat-bison"
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that the python package exists in environment."""
        cls._try_init_vertexai(values)
        try:
            from vertexai.preview.language_models import ChatModel
        except ImportError:
            raise_vertex_import_error()
        values["client"] = ChatModel.from_pretrained(values["model_name"])
        return values
    def _generate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
    ) -> ChatResult:


) -> ChatResult:
        """Generate next turn in the conversation.
        Args:
            messages: The history of the conversation as a list of messages.
            stop: The list of stop words (optional).
            run_manager: The Callbackmanager for LLM run, it's not used at the moment.
        Returns:
            The ChatResult that contains outputs generated by the model.
        Raises:
            ValueError: if the last message in the list is not from human.
        """
        if not messages:
            raise ValueError(
                "You should provide at least one message to start the chat!"
            )
        question = messages[-1]
        if not isinstance(question, HumanMessage):
            raise ValueError(
                f"Last message in the list should be from human, got {question.type}."
            )
        history = _parse_chat_history(messages[:-1])
        context = history.system_message.content if history.system_message else None
        chat = self.client.start_chat(context=context, **self._default_params)
        for pair in history.history:
            chat._history.append((pair.question.content, pair.answer.content))
        response = chat.send_message(question.content)
        text = self._enforce_stop_words(response.text, stop)
        return ChatResult(generations=[ChatGeneration(message=AIMessage(content=text))])
    async def _agenerate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
    ) -> ChatResult:
        raise NotImplementedError(
            """Vertex AI doesn't support async requests at the moment."""
        )
By Harrison Chase

      © Copyright 2023, Harrison Chase.


By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 02, 2023.


Source code for langchain.retrievers.pinecone_hybrid_search
"""Taken from: https://docs.pinecone.io/docs/hybrid-search"""
import hashlib
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.schema import BaseRetriever, Document
def hash_text(text: str) -> str:
    return str(hashlib.sha256(text.encode("utf-8")).hexdigest())
def create_index(
    contexts: List[str],
    index: Any,
    embeddings: Embeddings,
    sparse_encoder: Any,
    ids: Optional[List[str]] = None,
    metadatas: Optional[List[dict]] = None,
) -> None:
    batch_size = 32
    _iterator = range(0, len(contexts), batch_size)
    try:
        from tqdm.auto import tqdm
        _iterator = tqdm(_iterator)
    except ImportError:
        pass
    if ids is None:
        # create unique ids using hash of the text
        ids = [hash_text(context) for context in contexts]
    for i in _iterator:
        # find end of batch
        i_end = min(i + batch_size, len(contexts))
        # extract batch
        context_batch = contexts[i:i_end]
        batch_ids = ids[i:i_end]
        metadata_batch = (
            metadatas[i:i_end] if metadatas else [{} for _ in context_batch]
        )
        # add context passages as metadata
        meta = [
            {"context": context, **metadata}
            for context, metadata in zip(context_batch, metadata_batch)
        ]
        # create dense vectors
        dense_embeds = embeddings.embed_documents(context_batch)


# create dense vectors
        dense_embeds = embeddings.embed_documents(context_batch)
        # create sparse vectors
        sparse_embeds = sparse_encoder.encode_documents(context_batch)
        for s in sparse_embeds:
            s["values"] = [float(s1) for s1 in s["values"]]
        vectors = []
        # loop through the data and create dictionaries for upserts
        for doc_id, sparse, dense, metadata in zip(
            batch_ids, sparse_embeds, dense_embeds, meta
        ):
            vectors.append(
                {
                    "id": doc_id,
                    "sparse_values": sparse,
                    "values": dense,
                    "metadata": metadata,
                }
            )
        # upload the documents to the new hybrid index
        index.upsert(vectors)
[docs]class PineconeHybridSearchRetriever(BaseRetriever, BaseModel):
    embeddings: Embeddings
    sparse_encoder: Any
    index: Any
    top_k: int = 4
    alpha: float = 0.5
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
[docs]    def add_texts(
        self,
        texts: List[str],
        ids: Optional[List[str]] = None,
        metadatas: Optional[List[dict]] = None,
    ) -> None:
        create_index(
            texts,
            self.index,
            self.embeddings,
            self.sparse_encoder,
            ids=ids,
            metadatas=metadatas,
        )
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key and python package exists in environment."""
        try:


"""Validate that api key and python package exists in environment."""
        try:
            from pinecone_text.hybrid import hybrid_convex_scale  # noqa:F401
            from pinecone_text.sparse.base_sparse_encoder import (
                BaseSparseEncoder,  # noqa:F401
            )
        except ImportError:
            raise ValueError(
                "Could not import pinecone_text python package. "
                "Please install it with `pip install pinecone_text`."
            )
        return values
[docs]    def get_relevant_documents(self, query: str) -> List[Document]:
        from pinecone_text.hybrid import hybrid_convex_scale
        sparse_vec = self.sparse_encoder.encode_queries(query)
        # convert the question into a dense vector
        dense_vec = self.embeddings.embed_query(query)
        # scale alpha with hybrid_scale
        dense_vec, sparse_vec = hybrid_convex_scale(dense_vec, sparse_vec, self.alpha)
        sparse_vec["values"] = [float(s1) for s1 in sparse_vec["values"]]
        # query pinecone with the query parameters
        result = self.index.query(
            vector=dense_vec,
            sparse_vector=sparse_vec,
            top_k=self.top_k,
            include_metadata=True,
        )
        final_result = []
        for res in result["matches"]:
            context = res["metadata"].pop("context")
            final_result.append(
                Document(page_content=context, metadata=res["metadata"])
            )
        # return search results as json
        return final_result
[docs]    async def aget_relevant_documents(self, query: str) -> List[Document]:
        raise NotImplementedError
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 02, 2023.


Source code for langchain.retrievers.azure_cognitive_search
"""Retriever wrapper for Azure Cognitive Search."""
from __future__ import annotations
import json
from typing import Dict, List, Optional
import aiohttp
import requests
from pydantic import BaseModel, Extra, root_validator
from langchain.schema import BaseRetriever, Document
from langchain.utils import get_from_dict_or_env
[docs]class AzureCognitiveSearchRetriever(BaseRetriever, BaseModel):
    """Wrapper around Azure Cognitive Search."""
    service_name: str = ""
    """Name of Azure Cognitive Search service"""
    index_name: str = ""
    """Name of Index inside Azure Cognitive Search service"""
    api_key: str = ""
    """API Key. Both Admin and Query keys work, but for reading data it's
    recommended to use a Query key."""
    api_version: str = "2020-06-30"
    """API version"""
    aiosession: Optional[aiohttp.ClientSession] = None
    """ClientSession, in case we want to reuse connection for better performance."""
    content_key: str = "content"
    """Key in a retrieved result to set as the Document page_content."""
    class Config:
        extra = Extra.forbid
        arbitrary_types_allowed = True
    @root_validator(pre=True)
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that service name, index name and api key exists in environment."""
        values["service_name"] = get_from_dict_or_env(
            values, "service_name", "AZURE_COGNITIVE_SEARCH_SERVICE_NAME"
        )
        values["index_name"] = get_from_dict_or_env(
            values, "index_name", "AZURE_COGNITIVE_SEARCH_INDEX_NAME"
        )


)
        values["api_key"] = get_from_dict_or_env(
            values, "api_key", "AZURE_COGNITIVE_SEARCH_API_KEY"
        )
        return values
    def _build_search_url(self, query: str) -> str:
        base_url = f"https://{self.service_name}.search.windows.net/"
        endpoint_path = f"indexes/{self.index_name}/docs?api-version={self.api_version}"
        return base_url + endpoint_path + f"&search={query}"
    @property
    def _headers(self) -> Dict[str, str]:
        return {
            "Content-Type": "application/json",
            "api-key": self.api_key,
        }
    def _search(self, query: str) -> List[dict]:
        search_url = self._build_search_url(query)
        response = requests.get(search_url, headers=self._headers)
        if response.status_code != 200:
            raise Exception(f"Error in search request: {response}")
        return json.loads(response.text)["value"]
    async def _asearch(self, query: str) -> List[dict]:
        search_url = self._build_search_url(query)
        if not self.aiosession:
            async with aiohttp.ClientSession() as session:
                async with session.get(search_url, headers=self._headers) as response:
                    response_json = await response.json()
        else:
            async with self.aiosession.get(
                search_url, headers=self._headers
            ) as response:
                response_json = await response.json()
        return response_json["value"]
[docs]    def get_relevant_documents(self, query: str) -> List[Document]:
        search_results = self._search(query)
        return [


search_results = self._search(query)
        return [
            Document(page_content=result.pop(self.content_key), metadata=result)
            for result in search_results
        ]
[docs]    async def aget_relevant_documents(self, query: str) -> List[Document]:
        search_results = await self._asearch(query)
        return [
            Document(page_content=result.pop(self.content_key), metadata=result)
            for result in search_results
        ]
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 02, 2023.


Source code for langchain.retrievers.vespa_retriever
"""Wrapper for retrieving documents from Vespa."""
from __future__ import annotations
import json
from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Sequence, Union
from langchain.schema import BaseRetriever, Document
if TYPE_CHECKING:
    from vespa.application import Vespa
[docs]class VespaRetriever(BaseRetriever):
    def __init__(
        self,
        app: Vespa,
        body: Dict,
        content_field: str,
        metadata_fields: Optional[Sequence[str]] = None,
    ):
        self._application = app
        self._query_body = body
        self._content_field = content_field
        self._metadata_fields = metadata_fields or ()
    def _query(self, body: Dict) -> List[Document]:
        response = self._application.query(body)
        if not str(response.status_code).startswith("2"):
            raise RuntimeError(
                "Could not retrieve data from Vespa. Error code: {}".format(
                    response.status_code
                )
            )
        root = response.json["root"]
        if "errors" in root:
            raise RuntimeError(json.dumps(root["errors"]))
        docs = []
        for child in response.hits:
            page_content = child["fields"].pop(self._content_field, "")
            if self._metadata_fields == "*":
                metadata = child["fields"]
            else:
                metadata = {mf: child["fields"].get(mf) for mf in self._metadata_fields}
            metadata["id"] = child["id"]
            docs.append(Document(page_content=page_content, metadata=metadata))
        return docs


docs.append(Document(page_content=page_content, metadata=metadata))
        return docs
[docs]    def get_relevant_documents(self, query: str) -> List[Document]:
        body = self._query_body.copy()
        body["query"] = query
        return self._query(body)
[docs]    async def aget_relevant_documents(self, query: str) -> List[Document]:
        raise NotImplementedError
[docs]    def get_relevant_documents_with_filter(
        self, query: str, *, _filter: Optional[str] = None
    ) -> List[Document]:
        body = self._query_body.copy()
        _filter = f" and {_filter}" if _filter else ""
        body["yql"] = body["yql"] + _filter
        body["query"] = query
        return self._query(body)
[docs]    @classmethod
    def from_params(
        cls,
        url: str,
        content_field: str,
        *,
        k: Optional[int] = None,
        metadata_fields: Union[Sequence[str], Literal["*"]] = (),
        sources: Union[Sequence[str], Literal["*"], None] = None,
        _filter: Optional[str] = None,
        yql: Optional[str] = None,
        **kwargs: Any,
    ) -> VespaRetriever:
        """Instantiate retriever from params.
        Args:
            url (str): Vespa app URL.
            content_field (str): Field in results to return as Document page_content.
            k (Optional[int]): Number of Documents to return. Defaults to None.
            metadata_fields(Sequence[str] or "*"): Fields in results to include in
                document metadata. Defaults to empty tuple ().


document metadata. Defaults to empty tuple ().
            sources (Sequence[str] or "*" or None): Sources to retrieve
                from. Defaults to None.
            _filter (Optional[str]): Document filter condition expressed in YQL.
                Defaults to None.
            yql (Optional[str]): Full YQL query to be used. Should not be specified
                if _filter or sources are specified. Defaults to None.
            kwargs (Any): Keyword arguments added to query body.
        """
        try:
            from vespa.application import Vespa
        except ImportError:
            raise ImportError(
                "pyvespa is not installed, please install with `pip install pyvespa`"
            )
        app = Vespa(url)
        body = kwargs.copy()
        if yql and (sources or _filter):
            raise ValueError(
                "yql should only be specified if both sources and _filter are not "
                "specified."
            )
        else:
            if metadata_fields == "*":
                _fields = "*"
                body["summary"] = "short"
            else:
                _fields = ", ".join([content_field] + list(metadata_fields or []))
            _sources = ", ".join(sources) if isinstance(sources, Sequence) else "*"
            _filter = f" and {_filter}" if _filter else ""
            yql = f"select {_fields} from sources {_sources} where userQuery(){_filter}"
        body["yql"] = yql
        if k:
            body["hits"] = k
        return cls(app, body, content_field, metadata_fields=metadata_fields)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 02, 2023.