Source code for langchain.tools.playwright.extract_hyperlinks
from __future__ import annotations
import json
from typing import TYPE_CHECKING, Any, Optional, Type
from pydantic import BaseModel, Field, root_validator
from langchain.callbacks.manager import (
    AsyncCallbackManagerForToolRun,
    CallbackManagerForToolRun,
)
from langchain.tools.playwright.base import BaseBrowserTool
from langchain.tools.playwright.utils import aget_current_page, get_current_page
if TYPE_CHECKING:
    pass
class ExtractHyperlinksToolInput(BaseModel):
    """Input for ExtractHyperlinksTool."""
    absolute_urls: bool = Field(
        default=False,
        description="Return absolute URLs instead of relative URLs",
    )
[docs]class ExtractHyperlinksTool(BaseBrowserTool):
    """Extract all hyperlinks on the page."""
    name: str = "extract_hyperlinks"
    description: str = "Extract all hyperlinks on the current webpage"
    args_schema: Type[BaseModel] = ExtractHyperlinksToolInput
    @root_validator
    def check_bs_import(cls, values: dict) -> dict:
        """Check that the arguments are valid."""
        try:
            from bs4 import BeautifulSoup  # noqa: F401
        except ImportError:
            raise ValueError(
                "The 'beautifulsoup4' package is required to use this tool."
                " Please install it with 'pip install beautifulsoup4'."
            )
        return values
[docs]    @staticmethod
    def scrape_page(page: Any, html_content: str, absolute_urls: bool) -> str:
        from urllib.parse import urljoin
        from bs4 import BeautifulSoup
        # Parse the HTML content with BeautifulSoup
        soup = BeautifulSoup(html_content, "lxml")
        # Find all the anchor elements and extract their href attributes


# Find all the anchor elements and extract their href attributes
        anchors = soup.find_all("a")
        if absolute_urls:
            base_url = page.url
            links = [urljoin(base_url, anchor.get("href", "")) for anchor in anchors]
        else:
            links = [anchor.get("href", "") for anchor in anchors]
        # Return the list of links as a JSON string
        return json.dumps(links)
    def _run(
        self,
        absolute_urls: bool = False,
        run_manager: Optional[CallbackManagerForToolRun] = None,
    ) -> str:
        """Use the tool."""
        if self.sync_browser is None:
            raise ValueError(f"Synchronous browser not provided to {self.name}")
        page = get_current_page(self.sync_browser)
        html_content = page.content()
        return self.scrape_page(page, html_content, absolute_urls)
    async def _arun(
        self,
        absolute_urls: bool = False,
        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
    ) -> str:
        """Use the tool asynchronously."""
        if self.async_browser is None:
            raise ValueError(f"Asynchronous browser not provided to {self.name}")
        page = await aget_current_page(self.async_browser)
        html_content = await page.content()
        return self.scrape_page(page, html_content, absolute_urls)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.tools.playwright.navigate
from __future__ import annotations
from typing import Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
    AsyncCallbackManagerForToolRun,
    CallbackManagerForToolRun,
)
from langchain.tools.playwright.base import BaseBrowserTool
from langchain.tools.playwright.utils import (
    aget_current_page,
    get_current_page,
)
class NavigateToolInput(BaseModel):
    """Input for NavigateToolInput."""
    url: str = Field(..., description="url to navigate to")
[docs]class NavigateTool(BaseBrowserTool):
    name: str = "navigate_browser"
    description: str = "Navigate a browser to the specified URL"
    args_schema: Type[BaseModel] = NavigateToolInput
    def _run(
        self,
        url: str,
        run_manager: Optional[CallbackManagerForToolRun] = None,
    ) -> str:
        """Use the tool."""
        if self.sync_browser is None:
            raise ValueError(f"Synchronous browser not provided to {self.name}")
        page = get_current_page(self.sync_browser)
        response = page.goto(url)
        status = response.status if response else "unknown"
        return f"Navigating to {url} returned status code {status}"
    async def _arun(
        self,
        url: str,
        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
    ) -> str:
        """Use the tool."""
        if self.async_browser is None:
            raise ValueError(f"Asynchronous browser not provided to {self.name}")
        page = await aget_current_page(self.async_browser)
        response = await page.goto(url)


response = await page.goto(url)
        status = response.status if response else "unknown"
        return f"Navigating to {url} returned status code {status}"
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.tools.playwright.get_elements
from __future__ import annotations
import json
from typing import TYPE_CHECKING, List, Optional, Sequence, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
    AsyncCallbackManagerForToolRun,
    CallbackManagerForToolRun,
)
from langchain.tools.playwright.base import BaseBrowserTool
from langchain.tools.playwright.utils import aget_current_page, get_current_page
if TYPE_CHECKING:
    from playwright.async_api import Page as AsyncPage
    from playwright.sync_api import Page as SyncPage
class GetElementsToolInput(BaseModel):
    """Input for GetElementsTool."""
    selector: str = Field(
        ...,
        description="CSS selector, such as '*', 'div', 'p', 'a', #id, .classname",
    )
    attributes: List[str] = Field(
        default_factory=lambda: ["innerText"],
        description="Set of attributes to retrieve for each element",
    )
async def _aget_elements(
    page: AsyncPage, selector: str, attributes: Sequence[str]
) -> List[dict]:
    """Get elements matching the given CSS selector."""
    elements = await page.query_selector_all(selector)
    results = []
    for element in elements:
        result = {}
        for attribute in attributes:
            if attribute == "innerText":
                val: Optional[str] = await element.inner_text()
            else:
                val = await element.get_attribute(attribute)
            if val is not None and val.strip() != "":
                result[attribute] = val
        if result:
            results.append(result)
    return results
def _get_elements(
    page: SyncPage, selector: str, attributes: Sequence[str]
) -> List[dict]:


) -> List[dict]:
    """Get elements matching the given CSS selector."""
    elements = page.query_selector_all(selector)
    results = []
    for element in elements:
        result = {}
        for attribute in attributes:
            if attribute == "innerText":
                val: Optional[str] = element.inner_text()
            else:
                val = element.get_attribute(attribute)
            if val is not None and val.strip() != "":
                result[attribute] = val
        if result:
            results.append(result)
    return results
[docs]class GetElementsTool(BaseBrowserTool):
    name: str = "get_elements"
    description: str = (
        "Retrieve elements in the current web page matching the given CSS selector"
    )
    args_schema: Type[BaseModel] = GetElementsToolInput
    def _run(
        self,
        selector: str,
        attributes: Sequence[str] = ["innerText"],
        run_manager: Optional[CallbackManagerForToolRun] = None,
    ) -> str:
        """Use the tool."""
        if self.sync_browser is None:
            raise ValueError(f"Synchronous browser not provided to {self.name}")
        page = get_current_page(self.sync_browser)
        # Navigate to the desired webpage before using this tool
        results = _get_elements(page, selector, attributes)
        return json.dumps(results)
    async def _arun(
        self,
        selector: str,
        attributes: Sequence[str] = ["innerText"],
        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
    ) -> str:
        """Use the tool."""
        if self.async_browser is None:
            raise ValueError(f"Asynchronous browser not provided to {self.name}")


raise ValueError(f"Asynchronous browser not provided to {self.name}")
        page = await aget_current_page(self.async_browser)
        # Navigate to the desired webpage before using this tool
        results = await _aget_elements(page, selector, attributes)
        return json.dumps(results)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.tools.playwright.extract_text
from __future__ import annotations
from typing import Optional, Type
from pydantic import BaseModel, root_validator
from langchain.callbacks.manager import (
    AsyncCallbackManagerForToolRun,
    CallbackManagerForToolRun,
)
from langchain.tools.playwright.base import BaseBrowserTool
from langchain.tools.playwright.utils import aget_current_page, get_current_page
[docs]class ExtractTextTool(BaseBrowserTool):
    name: str = "extract_text"
    description: str = "Extract all the text on the current webpage"
    args_schema: Type[BaseModel] = BaseModel
    @root_validator
    def check_acheck_bs_importrgs(cls, values: dict) -> dict:
        """Check that the arguments are valid."""
        try:
            from bs4 import BeautifulSoup  # noqa: F401
        except ImportError:
            raise ValueError(
                "The 'beautifulsoup4' package is required to use this tool."
                " Please install it with 'pip install beautifulsoup4'."
            )
        return values
    def _run(self, run_manager: Optional[CallbackManagerForToolRun] = None) -> str:
        """Use the tool."""
        # Use Beautiful Soup since it's faster than looping through the elements
        from bs4 import BeautifulSoup
        if self.sync_browser is None:
            raise ValueError(f"Synchronous browser not provided to {self.name}")
        page = get_current_page(self.sync_browser)
        html_content = page.content()
        # Parse the HTML content with BeautifulSoup
        soup = BeautifulSoup(html_content, "lxml")
        return " ".join(text for text in soup.stripped_strings)
    async def _arun(
        self, run_manager: Optional[AsyncCallbackManagerForToolRun] = None


self, run_manager: Optional[AsyncCallbackManagerForToolRun] = None
    ) -> str:
        """Use the tool."""
        if self.async_browser is None:
            raise ValueError(f"Asynchronous browser not provided to {self.name}")
        # Use Beautiful Soup since it's faster than looping through the elements
        from bs4 import BeautifulSoup
        page = await aget_current_page(self.async_browser)
        html_content = await page.content()
        # Parse the HTML content with BeautifulSoup
        soup = BeautifulSoup(html_content, "lxml")
        return " ".join(text for text in soup.stripped_strings)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.tools.playwright.current_page
from __future__ import annotations
from typing import Optional, Type
from pydantic import BaseModel
from langchain.callbacks.manager import (
    AsyncCallbackManagerForToolRun,
    CallbackManagerForToolRun,
)
from langchain.tools.playwright.base import BaseBrowserTool
from langchain.tools.playwright.utils import aget_current_page, get_current_page
[docs]class CurrentWebPageTool(BaseBrowserTool):
    name: str = "current_webpage"
    description: str = "Returns the URL of the current page"
    args_schema: Type[BaseModel] = BaseModel
    def _run(
        self,
        run_manager: Optional[CallbackManagerForToolRun] = None,
    ) -> str:
        """Use the tool."""
        if self.sync_browser is None:
            raise ValueError(f"Synchronous browser not provided to {self.name}")
        page = get_current_page(self.sync_browser)
        return str(page.url)
    async def _arun(
        self,
        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
    ) -> str:
        """Use the tool."""
        if self.async_browser is None:
            raise ValueError(f"Asynchronous browser not provided to {self.name}")
        page = await aget_current_page(self.async_browser)
        return str(page.url)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.tools.playwright.click
from __future__ import annotations
from typing import Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
    AsyncCallbackManagerForToolRun,
    CallbackManagerForToolRun,
)
from langchain.tools.playwright.base import BaseBrowserTool
from langchain.tools.playwright.utils import (
    aget_current_page,
    get_current_page,
)
class ClickToolInput(BaseModel):
    """Input for ClickTool."""
    selector: str = Field(..., description="CSS selector for the element to click")
[docs]class ClickTool(BaseBrowserTool):
    name: str = "click_element"
    description: str = "Click on an element with the given CSS selector"
    args_schema: Type[BaseModel] = ClickToolInput
    def _run(
        self,
        selector: str,
        run_manager: Optional[CallbackManagerForToolRun] = None,
    ) -> str:
        """Use the tool."""
        if self.sync_browser is None:
            raise ValueError(f"Synchronous browser not provided to {self.name}")
        page = get_current_page(self.sync_browser)
        # Navigate to the desired webpage before using this tool
        page.click(selector)
        return f"Clicked element '{selector}'"
    async def _arun(
        self,
        selector: str,
        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
    ) -> str:
        """Use the tool."""
        if self.async_browser is None:
            raise ValueError(f"Asynchronous browser not provided to {self.name}")
        page = await aget_current_page(self.async_browser)
        # Navigate to the desired webpage before using this tool
        await page.click(selector)


# Navigate to the desired webpage before using this tool
        await page.click(selector)
        return f"Clicked element '{selector}'"
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.tools.playwright.navigate_back
from __future__ import annotations
from typing import Optional, Type
from pydantic import BaseModel
from langchain.callbacks.manager import (
    AsyncCallbackManagerForToolRun,
    CallbackManagerForToolRun,
)
from langchain.tools.playwright.base import BaseBrowserTool
from langchain.tools.playwright.utils import (
    aget_current_page,
    get_current_page,
)
[docs]class NavigateBackTool(BaseBrowserTool):
    """Navigate back to the previous page in the browser history."""
    name: str = "previous_webpage"
    description: str = "Navigate back to the previous page in the browser history"
    args_schema: Type[BaseModel] = BaseModel
    def _run(self, run_manager: Optional[CallbackManagerForToolRun] = None) -> str:
        """Use the tool."""
        if self.sync_browser is None:
            raise ValueError(f"Synchronous browser not provided to {self.name}")
        page = get_current_page(self.sync_browser)
        response = page.go_back()
        if response:
            return (
                f"Navigated back to the previous page with URL '{response.url}'."
                f" Status code {response.status}"
            )
        else:
            return "Unable to navigate back; no previous page in the history"
    async def _arun(
        self,
        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
    ) -> str:
        """Use the tool."""
        if self.async_browser is None:
            raise ValueError(f"Asynchronous browser not provided to {self.name}")
        page = await aget_current_page(self.async_browser)
        response = await page.go_back()
        if response:
            return (


response = await page.go_back()
        if response:
            return (
                f"Navigated back to the previous page with URL '{response.url}'."
                f" Status code {response.status}"
            )
        else:
            return "Unable to navigate back; no previous page in the history"
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.tools.zapier.tool
"""## Zapier Natural Language Actions API
\
Full docs here: https://nla.zapier.com/api/v1/docs
**Zapier Natural Language Actions** gives you access to the 5k+ apps, 20k+ actions
on Zapier's platform through a natural language API interface.
NLA supports apps like Gmail, Salesforce, Trello, Slack, Asana, HubSpot, Google Sheets,
Microsoft Teams, and thousands more apps: https://zapier.com/apps
Zapier NLA handles ALL the underlying API auth and translation from
natural language --> underlying API call --> return simplified output for LLMs
The key idea is you, or your users, expose a set of actions via an oauth-like setup
window, which you can then query and execute via a REST API.
NLA offers both API Key and OAuth for signing NLA API requests.
1. Server-side (API Key): for quickly getting started, testing, and production scenarios
    where LangChain will only use actions exposed in the developer's Zapier account
    (and will use the developer's connected accounts on Zapier.com)
2. User-facing (Oauth): for production scenarios where you are deploying an end-user
    facing application and LangChain needs access to end-user's exposed actions and
    connected accounts on Zapier.com
This quick start will focus on the server-side use case for brevity.
Review [full docs](https://nla.zapier.com/api/v1/docs) or reach out to
nla@zapier.com for user-facing oauth developer support.
Typically you'd use SequentialChain, here's a basic example:
    1. Use NLA to find an email in Gmail
    2. Use LLMChain to generate a draft reply to (1)


2. Use LLMChain to generate a draft reply to (1)
    3. Use NLA to send the draft reply (2) to someone in Slack via direct message
In code, below:
```python
import os
# get from https://platform.openai.com/
os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY", "")
# get from https://nla.zapier.com/demo/provider/debug
# (under User Information, after logging in):
os.environ["ZAPIER_NLA_API_KEY"] = os.environ.get("ZAPIER_NLA_API_KEY", "")
from langchain.llms import OpenAI
from langchain.agents import initialize_agent
from langchain.agents.agent_toolkits import ZapierToolkit
from langchain.utilities.zapier import ZapierNLAWrapper
## step 0. expose gmail 'find email' and slack 'send channel message' actions
# first go here, log in, expose (enable) the two actions:
#    https://nla.zapier.com/demo/start
#    -- for this example, can leave all fields "Have AI guess"
# in an oauth scenario, you'd get your own <provider> id (instead of 'demo')
# which you route your users through first
llm = OpenAI(temperature=0)
zapier = ZapierNLAWrapper()
## To leverage a nla_oauth_access_token you may pass the value to the ZapierNLAWrapper
## If you do this there is no need to initialize the ZAPIER_NLA_API_KEY env variable
# zapier = ZapierNLAWrapper(zapier_nla_oauth_access_token="TOKEN_HERE")
toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)
agent = initialize_agent(
    toolkit.get_tools(),
    llm,


agent = initialize_agent(
    toolkit.get_tools(),
    llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True
)
agent.run(("Summarize the last email I received regarding Silicon Valley Bank. "
    "Send the summary to the #test-zapier channel in slack."))
```
"""
from typing import Any, Dict, Optional
from pydantic import Field, root_validator
from langchain.callbacks.manager import (
    AsyncCallbackManagerForToolRun,
    CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.tools.zapier.prompt import BASE_ZAPIER_TOOL_PROMPT
from langchain.utilities.zapier import ZapierNLAWrapper
[docs]class ZapierNLARunAction(BaseTool):
    """
    Args:
        action_id: a specific action ID (from list actions) of the action to execute
            (the set api_key must be associated with the action owner)
        instructions: a natural language instruction string for using the action
            (eg. "get the latest email from Mike Knoop" for "Gmail: find email" action)
        params: a dict, optional. Any params provided will *override* AI guesses
            from `instructions` (see "understanding the AI guessing flow" here:
            https://nla.zapier.com/api/v1/docs)
    """
    api_wrapper: ZapierNLAWrapper = Field(default_factory=ZapierNLAWrapper)
    action_id: str
    params: Optional[dict] = None
    zapier_description: str
    params_schema: Dict[str, str] = Field(default_factory=dict)
    name = ""
    description = ""
    @root_validator


name = ""
    description = ""
    @root_validator
    def set_name_description(cls, values: Dict[str, Any]) -> Dict[str, Any]:
        zapier_description = values["zapier_description"]
        params_schema = values["params_schema"]
        if "instructions" in params_schema:
            del params_schema["instructions"]
        values["name"] = zapier_description
        values["description"] = BASE_ZAPIER_TOOL_PROMPT.format(
            zapier_description=zapier_description,
            params=str(list(params_schema.keys())),
        )
        return values
    def _run(
        self, instructions: str, run_manager: Optional[CallbackManagerForToolRun] = None
    ) -> str:
        """Use the Zapier NLA tool to return a list of all exposed user actions."""
        return self.api_wrapper.run_as_str(self.action_id, instructions, self.params)
    async def _arun(
        self,
        _: str,
        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
    ) -> str:
        """Use the Zapier NLA tool to return a list of all exposed user actions."""
        raise NotImplementedError("ZapierNLAListActions does not support async")
ZapierNLARunAction.__doc__ = (
    ZapierNLAWrapper.run.__doc__ + ZapierNLARunAction.__doc__  # type: ignore
)
# other useful actions
[docs]class ZapierNLAListActions(BaseTool):
    """
    Args:
        None
    """
    name = "Zapier NLA: List Actions"
    description = BASE_ZAPIER_TOOL_PROMPT + (
        "This tool returns a list of the user's exposed actions."
    )


"This tool returns a list of the user's exposed actions."
    )
    api_wrapper: ZapierNLAWrapper = Field(default_factory=ZapierNLAWrapper)
    def _run(
        self,
        _: str = "",
        run_manager: Optional[CallbackManagerForToolRun] = None,
    ) -> str:
        """Use the Zapier NLA tool to return a list of all exposed user actions."""
        return self.api_wrapper.list_as_str()
    async def _arun(
        self,
        _: str = "",
        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
    ) -> str:
        """Use the Zapier NLA tool to return a list of all exposed user actions."""
        raise NotImplementedError("ZapierNLAListActions does not support async")
ZapierNLAListActions.__doc__ = (
    ZapierNLAWrapper.list.__doc__ + ZapierNLAListActions.__doc__  # type: ignore
)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.tools.google_search.tool
"""Tool for the Google search API."""
from typing import Optional
from langchain.callbacks.manager import (
    AsyncCallbackManagerForToolRun,
    CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.utilities.google_search import GoogleSearchAPIWrapper
[docs]class GoogleSearchRun(BaseTool):
    """Tool that adds the capability to query the Google search API."""
    name = "Google Search"
    description = (
        "A wrapper around Google Search. "
        "Useful for when you need to answer questions about current events. "
        "Input should be a search query."
    )
    api_wrapper: GoogleSearchAPIWrapper
    def _run(
        self,
        query: str,
        run_manager: Optional[CallbackManagerForToolRun] = None,
    ) -> str:
        """Use the tool."""
        return self.api_wrapper.run(query)
    async def _arun(
        self,
        query: str,
        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
    ) -> str:
        """Use the tool asynchronously."""
        raise NotImplementedError("GoogleSearchRun does not support async")
[docs]class GoogleSearchResults(BaseTool):
    """Tool that has capability to query the Google Search API and get back json."""
    name = "Google Search Results JSON"
    description = (
        "A wrapper around Google Search. "
        "Useful for when you need to answer questions about current events. "
        "Input should be a search query. Output is a JSON array of the query results"
    )
    num_results: int = 4
    api_wrapper: GoogleSearchAPIWrapper
    def _run(
        self,


api_wrapper: GoogleSearchAPIWrapper
    def _run(
        self,
        query: str,
        run_manager: Optional[CallbackManagerForToolRun] = None,
    ) -> str:
        """Use the tool."""
        return str(self.api_wrapper.results(query, self.num_results))
    async def _arun(
        self,
        query: str,
        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
    ) -> str:
        """Use the tool asynchronously."""
        raise NotImplementedError("GoogleSearchRun does not support async")
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.tools.wikipedia.tool
"""Tool for the Wikipedia API."""
from typing import Optional
from langchain.callbacks.manager import (
    AsyncCallbackManagerForToolRun,
    CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.utilities.wikipedia import WikipediaAPIWrapper
[docs]class WikipediaQueryRun(BaseTool):
    """Tool that adds the capability to search using the Wikipedia API."""
    name = "Wikipedia"
    description = (
        "A wrapper around Wikipedia. "
        "Useful for when you need to answer general questions about "
        "people, places, companies, historical events, or other subjects. "
        "Input should be a search query."
    )
    api_wrapper: WikipediaAPIWrapper
    def _run(
        self,
        query: str,
        run_manager: Optional[CallbackManagerForToolRun] = None,
    ) -> str:
        """Use the Wikipedia tool."""
        return self.api_wrapper.run(query)
    async def _arun(
        self,
        query: str,
        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
    ) -> str:
        """Use the Wikipedia tool asynchronously."""
        raise NotImplementedError("WikipediaQueryRun does not support async")
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.tools.bing_search.tool
"""Tool for the Bing search API."""
from typing import Optional
from langchain.callbacks.manager import (
    AsyncCallbackManagerForToolRun,
    CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.utilities.bing_search import BingSearchAPIWrapper
[docs]class BingSearchRun(BaseTool):
    """Tool that adds the capability to query the Bing search API."""
    name = "Bing Search"
    description = (
        "A wrapper around Bing Search. "
        "Useful for when you need to answer questions about current events. "
        "Input should be a search query."
    )
    api_wrapper: BingSearchAPIWrapper
    def _run(
        self,
        query: str,
        run_manager: Optional[CallbackManagerForToolRun] = None,
    ) -> str:
        """Use the tool."""
        return self.api_wrapper.run(query)
    async def _arun(
        self,
        query: str,
        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
    ) -> str:
        """Use the tool asynchronously."""
        raise NotImplementedError("BingSearchRun does not support async")
[docs]class BingSearchResults(BaseTool):
    """Tool that has capability to query the Bing Search API and get back json."""
    name = "Bing Search Results JSON"
    description = (
        "A wrapper around Bing Search. "
        "Useful for when you need to answer questions about current events. "
        "Input should be a search query. Output is a JSON array of the query results"
    )
    num_results: int = 4
    api_wrapper: BingSearchAPIWrapper
    def _run(


api_wrapper: BingSearchAPIWrapper
    def _run(
        self,
        query: str,
        run_manager: Optional[CallbackManagerForToolRun] = None,
    ) -> str:
        """Use the tool."""
        return str(self.api_wrapper.results(query, self.num_results))
    async def _arun(
        self,
        query: str,
        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
    ) -> str:
        """Use the tool asynchronously."""
        raise NotImplementedError("BingSearchResults does not support async")
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.embeddings.sagemaker_endpoint
"""Wrapper around Sagemaker InvokeEndpoint API."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.llms.sagemaker_endpoint import ContentHandlerBase
class EmbeddingsContentHandler(ContentHandlerBase[List[str], List[List[float]]]):
    """Content handler for LLM class."""
[docs]class SagemakerEndpointEmbeddings(BaseModel, Embeddings):
    """Wrapper around custom Sagemaker Inference Endpoints.
    To use, you must supply the endpoint name from your deployed
    Sagemaker model & the region where it is deployed.
    To authenticate, the AWS client uses the following methods to
    automatically load credentials:
    https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
    If a specific credential profile should be used, you must pass
    the name of the profile from the ~/.aws/credentials file that is to be used.
    Make sure the credentials / roles used have the required policies to
    access the Sagemaker endpoint.
    See: https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies.html
    """
    """
    Example:
        .. code-block:: python
            from langchain.embeddings import SagemakerEndpointEmbeddings
            endpoint_name = (
                "my-endpoint-name"
            )
            region_name = (
                "us-west-2"
            )
            credentials_profile_name = (
                "default"
            )
            se = SagemakerEndpointEmbeddings(
                endpoint_name=endpoint_name,
                region_name=region_name,
                credentials_profile_name=credentials_profile_name
            )
    """


credentials_profile_name=credentials_profile_name
            )
    """
    client: Any  #: :meta private:
    endpoint_name: str = ""
    """The name of the endpoint from the deployed Sagemaker model.
    Must be unique within an AWS Region."""
    region_name: str = ""
    """The aws region where the Sagemaker model is deployed, eg. `us-west-2`."""
    credentials_profile_name: Optional[str] = None
    """The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which
    has either access keys or role information specified.
    If not specified, the default credential profile or, if on an EC2 instance,
    credentials from IMDS will be used.
    See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
    """
    content_handler: EmbeddingsContentHandler
    """The content handler class that provides an input and
    output transform functions to handle formats between LLM
    and the endpoint.
    """
    """
     Example:
        .. code-block:: python
        from langchain.embeddings.sagemaker_endpoint import EmbeddingsContentHandler
        class ContentHandler(EmbeddingsContentHandler):
                content_type = "application/json"
                accepts = "application/json"
                def transform_input(self, prompts: List[str], model_kwargs: Dict) -> bytes:
                    input_str = json.dumps({prompts: prompts, **model_kwargs})
                    return input_str.encode('utf-8')
                def transform_output(self, output: bytes) -> List[List[float]]:
                    response_json = json.loads(output.read().decode("utf-8"))
                    return response_json["vectors"]
    """  # noqa: E501
    model_kwargs: Optional[Dict] = None


"""  # noqa: E501
    model_kwargs: Optional[Dict] = None
    """Key word arguments to pass to the model."""
    endpoint_kwargs: Optional[Dict] = None
    """Optional attributes passed to the invoke_endpoint
    function. See `boto3`_. docs for more info.
    .. _boto3: <https://boto3.amazonaws.com/v1/documentation/api/latest/index.html>
    """
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that AWS credentials to and python package exists in environment."""
        try:
            import boto3
            try:
                if values["credentials_profile_name"] is not None:
                    session = boto3.Session(
                        profile_name=values["credentials_profile_name"]
                    )
                else:
                    # use default credentials
                    session = boto3.Session()
                values["client"] = session.client(
                    "sagemaker-runtime", region_name=values["region_name"]
                )
            except Exception as e:
                raise ValueError(
                    "Could not load credentials to authenticate with AWS client. "
                    "Please check that credentials in the specified "
                    "profile name are valid."
                ) from e
        except ImportError:
            raise ValueError(
                "Could not import boto3 python package. "
                "Please install it with `pip install boto3`."
            )
        return values
    def _embedding_func(self, texts: List[str]) -> List[List[float]]:
        """Call out to SageMaker Inference embedding endpoint."""
        # replace newlines, which can negatively affect performance.


# replace newlines, which can negatively affect performance.
        texts = list(map(lambda x: x.replace("\n", " "), texts))
        _model_kwargs = self.model_kwargs or {}
        _endpoint_kwargs = self.endpoint_kwargs or {}
        body = self.content_handler.transform_input(texts, _model_kwargs)
        content_type = self.content_handler.content_type
        accepts = self.content_handler.accepts
        # send request
        try:
            response = self.client.invoke_endpoint(
                EndpointName=self.endpoint_name,
                Body=body,
                ContentType=content_type,
                Accept=accepts,
                **_endpoint_kwargs,
            )
        except Exception as e:
            raise ValueError(f"Error raised by inference endpoint: {e}")
        return self.content_handler.transform_output(response["Body"])
[docs]    def embed_documents(
        self, texts: List[str], chunk_size: int = 64
    ) -> List[List[float]]:
        """Compute doc embeddings using a SageMaker Inference Endpoint.
        Args:
            texts: The list of texts to embed.
            chunk_size: The chunk size defines how many input texts will
                be grouped together as request. If None, will use the
                chunk size specified by the class.
        Returns:
            List of embeddings, one for each text.
        """
        results = []
        _chunk_size = len(texts) if chunk_size > len(texts) else chunk_size
        for i in range(0, len(texts), _chunk_size):
            response = self._embedding_func(texts[i : i + _chunk_size])
            results.extend(response)
        return results
[docs]    def embed_query(self, text: str) -> List[float]:
        """Compute query embeddings using a SageMaker inference endpoint.


"""Compute query embeddings using a SageMaker inference endpoint.
        Args:
            text: The text to embed.
        Returns:
            Embeddings for the text.
        """
        return self._embedding_func([text])[0]
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.embeddings.fake
from typing import List
import numpy as np
from pydantic import BaseModel
from langchain.embeddings.base import Embeddings
[docs]class FakeEmbeddings(Embeddings, BaseModel):
    size: int
    def _get_embedding(self) -> List[float]:
        return list(np.random.normal(size=self.size))
[docs]    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        return [self._get_embedding() for _ in texts]
[docs]    def embed_query(self, text: str) -> List[float]:
        return self._get_embedding()
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.embeddings.aleph_alpha
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
[docs]class AlephAlphaAsymmetricSemanticEmbedding(BaseModel, Embeddings):
    """
    Wrapper for Aleph Alpha's Asymmetric Embeddings
    AA provides you with an endpoint to embed a document and a query.
    The models were optimized to make the embeddings of documents and
    the query for a document as similar as possible.
    To learn more, check out: https://docs.aleph-alpha.com/docs/tasks/semantic_embed/
    Example:
        .. code-block:: python
            from aleph_alpha import AlephAlphaAsymmetricSemanticEmbedding
            embeddings = AlephAlphaSymmetricSemanticEmbedding()
            document = "This is a content of the document"
            query = "What is the content of the document?"
            doc_result = embeddings.embed_documents([document])
            query_result = embeddings.embed_query(query)
    """
    client: Any  #: :meta private:
    model: Optional[str] = "luminous-base"
    """Model name to use."""
    hosting: Optional[str] = "https://api.aleph-alpha.com"
    """Optional parameter that specifies which datacenters may process the request."""
    normalize: Optional[bool] = True
    """Should returned embeddings be normalized"""
    compress_to_size: Optional[int] = 128
    """Should the returned embeddings come back as an original 5120-dim vector,
    or should it be compressed to 128-dim."""
    contextual_control_threshold: Optional[int] = None
    """Attention control parameters only apply to those tokens that have


"""Attention control parameters only apply to those tokens that have
    explicitly been set in the request."""
    control_log_additive: Optional[bool] = True
    """Apply controls on prompt items by adding the log(control_factor)
    to attention scores."""
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key and python package exists in environment."""
        aleph_alpha_api_key = get_from_dict_or_env(
            values, "aleph_alpha_api_key", "ALEPH_ALPHA_API_KEY"
        )
        try:
            from aleph_alpha_client import Client
        except ImportError:
            raise ValueError(
                "Could not import aleph_alpha_client python package. "
                "Please install it with `pip install aleph_alpha_client`."
            )
        values["client"] = Client(token=aleph_alpha_api_key)
        return values
[docs]    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Call out to Aleph Alpha's asymmetric Document endpoint.
        Args:
            texts: The list of texts to embed.
        Returns:
            List of embeddings, one for each text.
        """
        try:
            from aleph_alpha_client import (
                Prompt,
                SemanticEmbeddingRequest,
                SemanticRepresentation,
            )
        except ImportError:
            raise ValueError(
                "Could not import aleph_alpha_client python package. "
                "Please install it with `pip install aleph_alpha_client`."
            )
        document_embeddings = []
        for text in texts:
            document_params = {
                "prompt": Prompt.from_text(text),
                "representation": SemanticRepresentation.Document,
                "compress_to_size": self.compress_to_size,


"representation": SemanticRepresentation.Document,
                "compress_to_size": self.compress_to_size,
                "normalize": self.normalize,
                "contextual_control_threshold": self.contextual_control_threshold,
                "control_log_additive": self.control_log_additive,
            }
            document_request = SemanticEmbeddingRequest(**document_params)
            document_response = self.client.semantic_embed(
                request=document_request, model=self.model
            )
            document_embeddings.append(document_response.embedding)
        return document_embeddings
[docs]    def embed_query(self, text: str) -> List[float]:
        """Call out to Aleph Alpha's asymmetric, query embedding endpoint
        Args:
            text: The text to embed.
        Returns:
            Embeddings for the text.
        """
        try:
            from aleph_alpha_client import (
                Prompt,
                SemanticEmbeddingRequest,
                SemanticRepresentation,
            )
        except ImportError:
            raise ValueError(
                "Could not import aleph_alpha_client python package. "
                "Please install it with `pip install aleph_alpha_client`."
            )
        symmetric_params = {
            "prompt": Prompt.from_text(text),
            "representation": SemanticRepresentation.Query,
            "compress_to_size": self.compress_to_size,
            "normalize": self.normalize,
            "contextual_control_threshold": self.contextual_control_threshold,
            "control_log_additive": self.control_log_additive,
        }
        symmetric_request = SemanticEmbeddingRequest(**symmetric_params)
        symmetric_response = self.client.semantic_embed(
            request=symmetric_request, model=self.model
        )
        return symmetric_response.embedding
[docs]class AlephAlphaSymmetricSemanticEmbedding(AlephAlphaAsymmetricSemanticEmbedding):


"""The symmetric version of the Aleph Alpha's semantic embeddings.
    The main difference is that here, both the documents and
    queries are embedded with a SemanticRepresentation.Symmetric
    Example:
        .. code-block:: python
            from aleph_alpha import AlephAlphaSymmetricSemanticEmbedding
            embeddings = AlephAlphaAsymmetricSemanticEmbedding()
            text = "This is a test text"
            doc_result = embeddings.embed_documents([text])
            query_result = embeddings.embed_query(text)
    """
    def _embed(self, text: str) -> List[float]:
        try:
            from aleph_alpha_client import (
                Prompt,
                SemanticEmbeddingRequest,
                SemanticRepresentation,
            )
        except ImportError:
            raise ValueError(
                "Could not import aleph_alpha_client python package. "
                "Please install it with `pip install aleph_alpha_client`."
            )
        query_params = {
            "prompt": Prompt.from_text(text),
            "representation": SemanticRepresentation.Symmetric,
            "compress_to_size": self.compress_to_size,
            "normalize": self.normalize,
            "contextual_control_threshold": self.contextual_control_threshold,
            "control_log_additive": self.control_log_additive,
        }
        query_request = SemanticEmbeddingRequest(**query_params)
        query_response = self.client.semantic_embed(
            request=query_request, model=self.model
        )
        return query_response.embedding
[docs]    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Call out to Aleph Alpha's Document endpoint.
        Args:
            texts: The list of texts to embed.
        Returns:
            List of embeddings, one for each text.
        """
        document_embeddings = []
        for text in texts:


"""
        document_embeddings = []
        for text in texts:
            document_embeddings.append(self._embed(text))
        return document_embeddings
[docs]    def embed_query(self, text: str) -> List[float]:
        """Call out to Aleph Alpha's asymmetric, query embedding endpoint
        Args:
            text: The text to embed.
        Returns:
            Embeddings for the text.
        """
        return self._embed(text)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.embeddings.huggingface_hub
"""Wrapper around HuggingFace Hub embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
DEFAULT_REPO_ID = "sentence-transformers/all-mpnet-base-v2"
VALID_TASKS = ("feature-extraction",)
[docs]class HuggingFaceHubEmbeddings(BaseModel, Embeddings):
    """Wrapper around HuggingFaceHub embedding models.
    To use, you should have the ``huggingface_hub`` python package installed, and the
    environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token, or pass
    it as a named parameter to the constructor.
    Example:
        .. code-block:: python
            from langchain.embeddings import HuggingFaceHubEmbeddings
            repo_id = "sentence-transformers/all-mpnet-base-v2"
            hf = HuggingFaceHubEmbeddings(
                repo_id=repo_id,
                task="feature-extraction",
                huggingfacehub_api_token="my-api-key",
            )
    """
    client: Any  #: :meta private:
    repo_id: str = DEFAULT_REPO_ID
    """Model name to use."""
    task: Optional[str] = "feature-extraction"
    """Task to call the model with."""
    model_kwargs: Optional[dict] = None
    """Key word arguments to pass to the model."""
    huggingfacehub_api_token: Optional[str] = None
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:


@root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key and python package exists in environment."""
        huggingfacehub_api_token = get_from_dict_or_env(
            values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
        )
        try:
            from huggingface_hub.inference_api import InferenceApi
            repo_id = values["repo_id"]
            if not repo_id.startswith("sentence-transformers"):
                raise ValueError(
                    "Currently only 'sentence-transformers' embedding models "
                    f"are supported. Got invalid 'repo_id' {repo_id}."
                )
            client = InferenceApi(
                repo_id=repo_id,
                token=huggingfacehub_api_token,
                task=values.get("task"),
            )
            if client.task not in VALID_TASKS:
                raise ValueError(
                    f"Got invalid task {client.task}, "
                    f"currently only {VALID_TASKS} are supported"
                )
            values["client"] = client
        except ImportError:
            raise ValueError(
                "Could not import huggingface_hub python package. "
                "Please install it with `pip install huggingface_hub`."
            )
        return values
[docs]    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Call out to HuggingFaceHub's embedding endpoint for embedding search docs.
        Args:
            texts: The list of texts to embed.
        Returns:
            List of embeddings, one for each text.
        """
        # replace newlines, which can negatively affect performance.
        texts = [text.replace("\n", " ") for text in texts]


texts = [text.replace("\n", " ") for text in texts]
        _model_kwargs = self.model_kwargs or {}
        responses = self.client(inputs=texts, params=_model_kwargs)
        return responses
[docs]    def embed_query(self, text: str) -> List[float]:
        """Call out to HuggingFaceHub's embedding endpoint for embedding query text.
        Args:
            text: The text to embed.
        Returns:
            Embeddings for the text.
        """
        response = self.embed_documents([text])[0]
        return response
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.embeddings.openai
"""Wrapper around OpenAI embedding models."""
from __future__ import annotations
import logging
from typing import (
    Any,
    Callable,
    Dict,
    List,
    Literal,
    Optional,
    Set,
    Tuple,
    Union,
)
import numpy as np
from pydantic import BaseModel, Extra, root_validator
from tenacity import (
    before_sleep_log,
    retry,
    retry_if_exception_type,
    stop_after_attempt,
    wait_exponential,
)
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
def _create_retry_decorator(embeddings: OpenAIEmbeddings) -> Callable[[Any], Any]:
    import openai
    min_seconds = 4
    max_seconds = 10
    # 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(embeddings.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),
    )
def embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any:
    """Use tenacity to retry the embedding call."""


"""Use tenacity to retry the embedding call."""
    retry_decorator = _create_retry_decorator(embeddings)
    @retry_decorator
    def _embed_with_retry(**kwargs: Any) -> Any:
        return embeddings.client.create(**kwargs)
    return _embed_with_retry(**kwargs)
[docs]class OpenAIEmbeddings(BaseModel, Embeddings):
    """Wrapper around OpenAI embedding models.
    To use, you should have the ``openai`` python package installed, and the
    environment variable ``OPENAI_API_KEY`` set with your API key or pass it
    as a named parameter to the constructor.
    Example:
        .. code-block:: python
            from langchain.embeddings import OpenAIEmbeddings
            openai = OpenAIEmbeddings(openai_api_key="my-api-key")
    In order to use the library with Microsoft Azure endpoints, you need to set
    the OPENAI_API_TYPE, OPENAI_API_BASE, OPENAI_API_KEY and optionally and
    API_VERSION.
    The OPENAI_API_TYPE must be set to 'azure' and the others correspond to
    the properties of your endpoint.
    In addition, the deployment name must be passed as the model parameter.
    Example:
        .. code-block:: python
            import os
            os.environ["OPENAI_API_TYPE"] = "azure"
            os.environ["OPENAI_API_BASE"] = "https://<your-endpoint.openai.azure.com/"
            os.environ["OPENAI_API_KEY"] = "your AzureOpenAI key"
            from langchain.embeddings.openai import OpenAIEmbeddings
            embeddings = OpenAIEmbeddings(
                deployment="your-embeddings-deployment-name",
                model="your-embeddings-model-name"
            )
            text = "This is a test query."
            query_result = embeddings.embed_query(text)


text = "This is a test query."
            query_result = embeddings.embed_query(text)
    """
    client: Any  #: :meta private:
    model: str = "text-embedding-ada-002"
    deployment: str = model  # to support Azure OpenAI Service custom deployment names
    embedding_ctx_length: int = 8191
    openai_api_key: Optional[str] = None
    openai_organization: Optional[str] = None
    allowed_special: Union[Literal["all"], Set[str]] = set()
    disallowed_special: Union[Literal["all"], Set[str], Tuple[()]] = "all"
    chunk_size: int = 1000
    """Maximum number of texts to embed in each batch"""
    max_retries: int = 6
    """Maximum number of retries to make when generating."""
    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."""
        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="",
        )
        try:
            import openai
            openai.api_key = openai_api_key
            if openai_organization:
                openai.organization = openai_organization
            values["client"] = openai.Embedding
        except ImportError:
            raise ValueError(
                "Could not import openai python package. "
                "Please install it with `pip install openai`."
            )


"Please install it with `pip install openai`."
            )
        return values
    # please refer to
    # https://github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb
    def _get_len_safe_embeddings(
        self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None
    ) -> List[List[float]]:
        embeddings: List[List[float]] = [[] for i in range(len(texts))]
        try:
            import tiktoken
            tokens = []
            indices = []
            encoding = tiktoken.model.encoding_for_model(self.model)
            for i, text in enumerate(texts):
                # replace newlines, which can negatively affect performance.
                text = text.replace("\n", " ")
                token = encoding.encode(
                    text,
                    allowed_special=self.allowed_special,
                    disallowed_special=self.disallowed_special,
                )
                for j in range(0, len(token), self.embedding_ctx_length):
                    tokens += [token[j : j + self.embedding_ctx_length]]
                    indices += [i]
            batched_embeddings = []
            _chunk_size = chunk_size or self.chunk_size
            for i in range(0, len(tokens), _chunk_size):
                response = embed_with_retry(
                    self,
                    input=tokens[i : i + _chunk_size],
                    engine=self.deployment,
                )
                batched_embeddings += [r["embedding"] for r in response["data"]]
            results: List[List[List[float]]] = [[] for _ in range(len(texts))]
            lens: List[List[int]] = [[] for _ in range(len(texts))]
            for i in range(len(indices)):
                results[indices[i]].append(batched_embeddings[i])


results[indices[i]].append(batched_embeddings[i])
                lens[indices[i]].append(len(batched_embeddings[i]))
            for i in range(len(texts)):
                _result = results[i]
                if len(_result) == 0:
                    average = embed_with_retry(self, input="", engine=self.deployment)[
                        "data"
                    ][0]["embedding"]
                else:
                    average = np.average(_result, axis=0, weights=lens[i])
                embeddings[i] = (average / np.linalg.norm(average)).tolist()
            return embeddings
        except ImportError:
            raise ValueError(
                "Could not import tiktoken python package. "
                "This is needed in order to for OpenAIEmbeddings. "
                "Please install it with `pip install tiktoken`."
            )
    def _embedding_func(self, text: str, *, engine: str) -> List[float]:
        """Call out to OpenAI's embedding endpoint."""
        # handle large input text
        if len(text) > self.embedding_ctx_length:
            return self._get_len_safe_embeddings([text], engine=engine)[0]
        else:
            # replace newlines, which can negatively affect performance.
            text = text.replace("\n", " ")
            return embed_with_retry(self, input=[text], engine=engine)["data"][0][
                "embedding"
            ]
[docs]    def embed_documents(
        self, texts: List[str], chunk_size: Optional[int] = 0
    ) -> List[List[float]]:
        """Call out to OpenAI's embedding endpoint for embedding search docs.
        Args:
            texts: The list of texts to embed.
            chunk_size: The chunk size of embeddings. If None, will use the chunk size
                specified by the class.


specified by the class.
        Returns:
            List of embeddings, one for each text.
        """
        # NOTE: to keep things simple, we assume the list may contain texts longer
        #       than the maximum context and use length-safe embedding function.
        return self._get_len_safe_embeddings(texts, engine=self.deployment)
[docs]    def embed_query(self, text: str) -> List[float]:
        """Call out to OpenAI's embedding endpoint for embedding query text.
        Args:
            text: The text to embed.
        Returns:
            Embedding for the text.
        """
        embedding = self._embedding_func(text, engine=self.deployment)
        return embedding
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.embeddings.huggingface
"""Wrapper around HuggingFace embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, Field
from langchain.embeddings.base import Embeddings
DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
DEFAULT_INSTRUCT_MODEL = "hkunlp/instructor-large"
DEFAULT_EMBED_INSTRUCTION = "Represent the document for retrieval: "
DEFAULT_QUERY_INSTRUCTION = (
    "Represent the question for retrieving supporting documents: "
)
[docs]class HuggingFaceEmbeddings(BaseModel, Embeddings):
    """Wrapper around sentence_transformers embedding models.
    To use, you should have the ``sentence_transformers`` python package installed.
    Example:
        .. code-block:: python
            from langchain.embeddings import HuggingFaceEmbeddings
            model_name = "sentence-transformers/all-mpnet-base-v2"
            model_kwargs = {'device': 'cpu'}
            hf = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)
    """
    client: Any  #: :meta private:
    model_name: str = DEFAULT_MODEL_NAME
    """Model name to use."""
    cache_folder: Optional[str] = None
    """Path to store models.
    Can be also set by SENTENCE_TRANSFORMERS_HOME enviroment variable."""
    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
    """Key word arguments to pass to the model."""
    def __init__(self, **kwargs: Any):
        """Initialize the sentence_transformer."""
        super().__init__(**kwargs)
        try:
            import sentence_transformers
        except ImportError as exc:
            raise ValueError(
                "Could not import sentence_transformers python package. "


raise ValueError(
                "Could not import sentence_transformers python package. "
                "Please install it with `pip install sentence_transformers`."
            ) from exc
        self.client = sentence_transformers.SentenceTransformer(
            self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
        )
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
[docs]    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Compute doc embeddings using a HuggingFace transformer model.
        Args:
            texts: The list of texts to embed.
        Returns:
            List of embeddings, one for each text.
        """
        texts = list(map(lambda x: x.replace("\n", " "), texts))
        embeddings = self.client.encode(texts)
        return embeddings.tolist()
[docs]    def embed_query(self, text: str) -> List[float]:
        """Compute query embeddings using a HuggingFace transformer model.
        Args:
            text: The text to embed.
        Returns:
            Embeddings for the text.
        """
        text = text.replace("\n", " ")
        embedding = self.client.encode(text)
        return embedding.tolist()
[docs]class HuggingFaceInstructEmbeddings(BaseModel, Embeddings):
    """Wrapper around sentence_transformers embedding models.
    To use, you should have the ``sentence_transformers``
    and ``InstructorEmbedding`` python package installed.
    Example:
        .. code-block:: python
            from langchain.embeddings import HuggingFaceInstructEmbeddings
            model_name = "hkunlp/instructor-large"
            model_kwargs = {'device': 'cpu'}
            hf = HuggingFaceInstructEmbeddings(


hf = HuggingFaceInstructEmbeddings(
                model_name=model_name, model_kwargs=model_kwargs
            )
    """
    client: Any  #: :meta private:
    model_name: str = DEFAULT_INSTRUCT_MODEL
    """Model name to use."""
    cache_folder: Optional[str] = None
    """Path to store models.
    Can be also set by SENTENCE_TRANSFORMERS_HOME enviroment variable."""
    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
    """Key word arguments to pass to the model."""
    embed_instruction: str = DEFAULT_EMBED_INSTRUCTION
    """Instruction to use for embedding documents."""
    query_instruction: str = DEFAULT_QUERY_INSTRUCTION
    """Instruction to use for embedding query."""
    def __init__(self, **kwargs: Any):
        """Initialize the sentence_transformer."""
        super().__init__(**kwargs)
        try:
            from InstructorEmbedding import INSTRUCTOR
            self.client = INSTRUCTOR(
                self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
            )
        except ImportError as e:
            raise ValueError("Dependencies for InstructorEmbedding not found.") from e
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
[docs]    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Compute doc embeddings using a HuggingFace instruct model.
        Args:
            texts: The list of texts to embed.
        Returns:
            List of embeddings, one for each text.
        """
        instruction_pairs = [[self.embed_instruction, text] for text in texts]
        embeddings = self.client.encode(instruction_pairs)
        return embeddings.tolist()
[docs]    def embed_query(self, text: str) -> List[float]:


[docs]    def embed_query(self, text: str) -> List[float]:
        """Compute query embeddings using a HuggingFace instruct model.
        Args:
            text: The text to embed.
        Returns:
            Embeddings for the text.
        """
        instruction_pair = [self.query_instruction, text]
        embedding = self.client.encode([instruction_pair])[0]
        return embedding.tolist()
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.embeddings.self_hosted
"""Running custom embedding models on self-hosted remote hardware."""
from typing import Any, Callable, List
from pydantic import Extra
from langchain.embeddings.base import Embeddings
from langchain.llms import SelfHostedPipeline
def _embed_documents(pipeline: Any, *args: Any, **kwargs: Any) -> List[List[float]]:
    """Inference function to send to the remote hardware.
    Accepts a sentence_transformer model_id and
    returns a list of embeddings for each document in the batch.
    """
    return pipeline(*args, **kwargs)
[docs]class SelfHostedEmbeddings(SelfHostedPipeline, Embeddings):
    """Runs custom embedding models on self-hosted remote hardware.
    Supported hardware includes auto-launched instances on AWS, GCP, Azure,
    and Lambda, as well as servers specified
    by IP address and SSH credentials (such as on-prem, or another
    cloud like Paperspace, Coreweave, etc.).
    To use, you should have the ``runhouse`` python package installed.
    Example using a model load function:
        .. code-block:: python
            from langchain.embeddings import SelfHostedEmbeddings
            from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
            import runhouse as rh
            gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
            def get_pipeline():
                model_id = "facebook/bart-large"
                tokenizer = AutoTokenizer.from_pretrained(model_id)
                model = AutoModelForCausalLM.from_pretrained(model_id)
                return pipeline("feature-extraction", model=model, tokenizer=tokenizer)
            embeddings = SelfHostedEmbeddings(
                model_load_fn=get_pipeline,
                hardware=gpu


model_load_fn=get_pipeline,
                hardware=gpu
                model_reqs=["./", "torch", "transformers"],
            )
    Example passing in a pipeline path:
        .. code-block:: python
            from langchain.embeddings import SelfHostedHFEmbeddings
            import runhouse as rh
            from transformers import pipeline
            gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
            pipeline = pipeline(model="bert-base-uncased", task="feature-extraction")
            rh.blob(pickle.dumps(pipeline),
                path="models/pipeline.pkl").save().to(gpu, path="models")
            embeddings = SelfHostedHFEmbeddings.from_pipeline(
                pipeline="models/pipeline.pkl",
                hardware=gpu,
                model_reqs=["./", "torch", "transformers"],
            )
    """
    inference_fn: Callable = _embed_documents
    """Inference function to extract the embeddings on the remote hardware."""
    inference_kwargs: Any = None
    """Any kwargs to pass to the model's inference function."""
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
[docs]    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Compute doc embeddings using a HuggingFace transformer model.
        Args:
            texts: The list of texts to embed.s
        Returns:
            List of embeddings, one for each text.
        """
        texts = list(map(lambda x: x.replace("\n", " "), texts))
        embeddings = self.client(self.pipeline_ref, texts)
        if not isinstance(embeddings, list):
            return embeddings.tolist()
        return embeddings
[docs]    def embed_query(self, text: str) -> List[float]:


[docs]    def embed_query(self, text: str) -> List[float]:
        """Compute query embeddings using a HuggingFace transformer model.
        Args:
            text: The text to embed.
        Returns:
            Embeddings for the text.
        """
        text = text.replace("\n", " ")
        embeddings = self.client(self.pipeline_ref, text)
        if not isinstance(embeddings, list):
            return embeddings.tolist()
        return embeddings
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.embeddings.cohere
"""Wrapper around Cohere embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
[docs]class CohereEmbeddings(BaseModel, Embeddings):
    """Wrapper around Cohere embedding models.
    To use, you should have the ``cohere`` python package installed, and the
    environment variable ``COHERE_API_KEY`` set with your API key or pass it
    as a named parameter to the constructor.
    Example:
        .. code-block:: python
            from langchain.embeddings import CohereEmbeddings
            cohere = CohereEmbeddings(model="medium", cohere_api_key="my-api-key")
    """
    client: Any  #: :meta private:
    model: str = "large"
    """Model name to use."""
    truncate: Optional[str] = None
    """Truncate embeddings that are too long from start or end ("NONE"|"START"|"END")"""
    cohere_api_key: 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."""
        cohere_api_key = get_from_dict_or_env(
            values, "cohere_api_key", "COHERE_API_KEY"
        )
        try:
            import cohere
            values["client"] = cohere.Client(cohere_api_key)
        except ImportError:
            raise ValueError(
                "Could not import cohere python package. "


raise ValueError(
                "Could not import cohere python package. "
                "Please install it with `pip install cohere`."
            )
        return values
[docs]    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Call out to Cohere's embedding endpoint.
        Args:
            texts: The list of texts to embed.
        Returns:
            List of embeddings, one for each text.
        """
        embeddings = self.client.embed(
            model=self.model, texts=texts, truncate=self.truncate
        ).embeddings
        return [list(map(float, e)) for e in embeddings]
[docs]    def embed_query(self, text: str) -> List[float]:
        """Call out to Cohere's embedding endpoint.
        Args:
            text: The text to embed.
        Returns:
            Embeddings for the text.
        """
        embedding = self.client.embed(
            model=self.model, texts=[text], truncate=self.truncate
        ).embeddings[0]
        return list(map(float, embedding))
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.embeddings.tensorflow_hub
"""Wrapper around TensorflowHub embedding models."""
from typing import Any, List
from pydantic import BaseModel, Extra
from langchain.embeddings.base import Embeddings
DEFAULT_MODEL_URL = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
[docs]class TensorflowHubEmbeddings(BaseModel, Embeddings):
    """Wrapper around tensorflow_hub embedding models.
    To use, you should have the ``tensorflow_text`` python package installed.
    Example:
        .. code-block:: python
            from langchain.embeddings import TensorflowHubEmbeddings
            url = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
            tf = TensorflowHubEmbeddings(model_url=url)
    """
    embed: Any  #: :meta private:
    model_url: str = DEFAULT_MODEL_URL
    """Model name to use."""
    def __init__(self, **kwargs: Any):
        """Initialize the tensorflow_hub and tensorflow_text."""
        super().__init__(**kwargs)
        try:
            import tensorflow_hub
            import tensorflow_text  # noqa
            self.embed = tensorflow_hub.load(self.model_url)
        except ImportError as e:
            raise ValueError(
                "Could not import some python packages." "Please install them."
            ) from e
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
[docs]    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Compute doc embeddings using a TensorflowHub embedding model.
        Args:
            texts: The list of texts to embed.
        Returns:
            List of embeddings, one for each text.
        """


Returns:
            List of embeddings, one for each text.
        """
        texts = list(map(lambda x: x.replace("\n", " "), texts))
        embeddings = self.embed(texts).numpy()
        return embeddings.tolist()
[docs]    def embed_query(self, text: str) -> List[float]:
        """Compute query embeddings using a TensorflowHub embedding model.
        Args:
            text: The text to embed.
        Returns:
            Embeddings for the text.
        """
        text = text.replace("\n", " ")
        embedding = self.embed([text]).numpy()[0]
        return embedding.tolist()
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.embeddings.llamacpp
"""Wrapper around llama.cpp embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, Field, root_validator
from langchain.embeddings.base import Embeddings
[docs]class LlamaCppEmbeddings(BaseModel, Embeddings):
    """Wrapper around llama.cpp embedding models.
    To use, you should have the llama-cpp-python library installed, and provide the
    path to the Llama model as a named parameter to the constructor.
    Check out: https://github.com/abetlen/llama-cpp-python
    Example:
        .. code-block:: python
            from langchain.embeddings import LlamaCppEmbeddings
            llama = LlamaCppEmbeddings(model_path="/path/to/model.bin")
    """
    client: Any  #: :meta private:
    model_path: str
    n_ctx: int = Field(512, alias="n_ctx")
    """Token context window."""
    n_parts: int = Field(-1, alias="n_parts")
    """Number of parts to split the model into.
    If -1, the number of parts is automatically determined."""
    seed: int = Field(-1, alias="seed")
    """Seed. If -1, a random seed is used."""
    f16_kv: bool = Field(False, alias="f16_kv")
    """Use half-precision for key/value cache."""
    logits_all: bool = Field(False, alias="logits_all")
    """Return logits for all tokens, not just the last token."""
    vocab_only: bool = Field(False, alias="vocab_only")
    """Only load the vocabulary, no weights."""
    use_mlock: bool = Field(False, alias="use_mlock")


use_mlock: bool = Field(False, alias="use_mlock")
    """Force system to keep model in RAM."""
    n_threads: Optional[int] = Field(None, alias="n_threads")
    """Number of threads to use. If None, the number
    of threads is automatically determined."""
    n_batch: Optional[int] = Field(8, alias="n_batch")
    """Number of tokens to process in parallel.
    Should be a number between 1 and n_ctx."""
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that llama-cpp-python library is installed."""
        model_path = values["model_path"]
        n_ctx = values["n_ctx"]
        n_parts = values["n_parts"]
        seed = values["seed"]
        f16_kv = values["f16_kv"]
        logits_all = values["logits_all"]
        vocab_only = values["vocab_only"]
        use_mlock = values["use_mlock"]
        n_threads = values["n_threads"]
        n_batch = values["n_batch"]
        try:
            from llama_cpp import Llama
            values["client"] = Llama(
                model_path=model_path,
                n_ctx=n_ctx,
                n_parts=n_parts,
                seed=seed,
                f16_kv=f16_kv,
                logits_all=logits_all,
                vocab_only=vocab_only,
                use_mlock=use_mlock,
                n_threads=n_threads,
                n_batch=n_batch,
                embedding=True,
            )
        except ImportError:
            raise ModuleNotFoundError(
                "Could not import llama-cpp-python library. "


raise ModuleNotFoundError(
                "Could not import llama-cpp-python library. "
                "Please install the llama-cpp-python library to "
                "use this embedding model: pip install llama-cpp-python"
            )
        except Exception:
            raise NameError(f"Could not load Llama model from path: {model_path}")
        return values
[docs]    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Embed a list of documents using the Llama model.
        Args:
            texts: The list of texts to embed.
        Returns:
            List of embeddings, one for each text.
        """
        embeddings = [self.client.embed(text) for text in texts]
        return [list(map(float, e)) for e in embeddings]
[docs]    def embed_query(self, text: str) -> List[float]:
        """Embed a query using the Llama model.
        Args:
            text: The text to embed.
        Returns:
            Embeddings for the text.
        """
        embedding = self.client.embed(text)
        return list(map(float, embedding))
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.embeddings.self_hosted_hugging_face
"""Wrapper around HuggingFace embedding models for self-hosted remote hardware."""
import importlib
import logging
from typing import Any, Callable, List, Optional
from langchain.embeddings.self_hosted import SelfHostedEmbeddings
DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
DEFAULT_INSTRUCT_MODEL = "hkunlp/instructor-large"
DEFAULT_EMBED_INSTRUCTION = "Represent the document for retrieval: "
DEFAULT_QUERY_INSTRUCTION = (
    "Represent the question for retrieving supporting documents: "
)
logger = logging.getLogger(__name__)
def _embed_documents(client: Any, *args: Any, **kwargs: Any) -> List[List[float]]:
    """Inference function to send to the remote hardware.
    Accepts a sentence_transformer model_id and
    returns a list of embeddings for each document in the batch.
    """
    return client.encode(*args, **kwargs)
def load_embedding_model(model_id: str, instruct: bool = False, device: int = 0) -> Any:
    """Load the embedding model."""
    if not instruct:
        import sentence_transformers
        client = sentence_transformers.SentenceTransformer(model_id)
    else:
        from InstructorEmbedding import INSTRUCTOR
        client = INSTRUCTOR(model_id)
    if importlib.util.find_spec("torch") is not None:
        import torch
        cuda_device_count = torch.cuda.device_count()
        if device < -1 or (device >= cuda_device_count):
            raise ValueError(
                f"Got device=={device}, "
                f"device is required to be within [-1, {cuda_device_count})"
            )
        if device < 0 and cuda_device_count > 0:
            logger.warning(


if device < 0 and cuda_device_count > 0:
            logger.warning(
                "Device has %d GPUs available. "
                "Provide device={deviceId} to `from_model_id` to use available"
                "GPUs for execution. deviceId is -1 for CPU and "
                "can be a positive integer associated with CUDA device id.",
                cuda_device_count,
            )
        client = client.to(device)
    return client
[docs]class SelfHostedHuggingFaceEmbeddings(SelfHostedEmbeddings):
    """Runs sentence_transformers embedding models on self-hosted remote hardware.
    Supported hardware includes auto-launched instances on AWS, GCP, Azure,
    and Lambda, as well as servers specified
    by IP address and SSH credentials (such as on-prem, or another cloud
    like Paperspace, Coreweave, etc.).
    To use, you should have the ``runhouse`` python package installed.
    Example:
        .. code-block:: python
            from langchain.embeddings import SelfHostedHuggingFaceEmbeddings
            import runhouse as rh
            model_name = "sentence-transformers/all-mpnet-base-v2"
            gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
            hf = SelfHostedHuggingFaceEmbeddings(model_name=model_name, hardware=gpu)
    """
    client: Any  #: :meta private:
    model_id: str = DEFAULT_MODEL_NAME
    """Model name to use."""
    model_reqs: List[str] = ["./", "sentence_transformers", "torch"]
    """Requirements to install on hardware to inference the model."""
    hardware: Any
    """Remote hardware to send the inference function to."""
    model_load_fn: Callable = load_embedding_model


model_load_fn: Callable = load_embedding_model
    """Function to load the model remotely on the server."""
    load_fn_kwargs: Optional[dict] = None
    """Key word arguments to pass to the model load function."""
    inference_fn: Callable = _embed_documents
    """Inference function to extract the embeddings."""
    def __init__(self, **kwargs: Any):
        """Initialize the remote inference function."""
        load_fn_kwargs = kwargs.pop("load_fn_kwargs", {})
        load_fn_kwargs["model_id"] = load_fn_kwargs.get("model_id", DEFAULT_MODEL_NAME)
        load_fn_kwargs["instruct"] = load_fn_kwargs.get("instruct", False)
        load_fn_kwargs["device"] = load_fn_kwargs.get("device", 0)
        super().__init__(load_fn_kwargs=load_fn_kwargs, **kwargs)
[docs]class SelfHostedHuggingFaceInstructEmbeddings(SelfHostedHuggingFaceEmbeddings):
    """Runs InstructorEmbedding embedding models on self-hosted remote hardware.
    Supported hardware includes auto-launched instances on AWS, GCP, Azure,
    and Lambda, as well as servers specified
    by IP address and SSH credentials (such as on-prem, or another
    cloud like Paperspace, Coreweave, etc.).
    To use, you should have the ``runhouse`` python package installed.
    Example:
        .. code-block:: python
            from langchain.embeddings import SelfHostedHuggingFaceInstructEmbeddings
            import runhouse as rh
            model_name = "hkunlp/instructor-large"
            gpu = rh.cluster(name='rh-a10x', instance_type='A100:1')
            hf = SelfHostedHuggingFaceInstructEmbeddings(
                model_name=model_name, hardware=gpu)
    """


model_name=model_name, hardware=gpu)
    """
    model_id: str = DEFAULT_INSTRUCT_MODEL
    """Model name to use."""
    embed_instruction: str = DEFAULT_EMBED_INSTRUCTION
    """Instruction to use for embedding documents."""
    query_instruction: str = DEFAULT_QUERY_INSTRUCTION
    """Instruction to use for embedding query."""
    model_reqs: List[str] = ["./", "InstructorEmbedding", "torch"]
    """Requirements to install on hardware to inference the model."""
    def __init__(self, **kwargs: Any):
        """Initialize the remote inference function."""
        load_fn_kwargs = kwargs.pop("load_fn_kwargs", {})
        load_fn_kwargs["model_id"] = load_fn_kwargs.get(
            "model_id", DEFAULT_INSTRUCT_MODEL
        )
        load_fn_kwargs["instruct"] = load_fn_kwargs.get("instruct", True)
        load_fn_kwargs["device"] = load_fn_kwargs.get("device", 0)
        super().__init__(load_fn_kwargs=load_fn_kwargs, **kwargs)
[docs]    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Compute doc embeddings using a HuggingFace instruct model.
        Args:
            texts: The list of texts to embed.
        Returns:
            List of embeddings, one for each text.
        """
        instruction_pairs = []
        for text in texts:
            instruction_pairs.append([self.embed_instruction, text])
        embeddings = self.client(self.pipeline_ref, instruction_pairs)
        return embeddings.tolist()
[docs]    def embed_query(self, text: str) -> List[float]:
        """Compute query embeddings using a HuggingFace instruct model.
        Args:
            text: The text to embed.
        Returns:
            Embeddings for the text.


text: The text to embed.
        Returns:
            Embeddings for the text.
        """
        instruction_pair = [self.query_instruction, text]
        embedding = self.client(self.pipeline_ref, [instruction_pair])[0]
        return embedding.tolist()
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


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


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


entity_action = self._get_entity_action(observation, entity_name)
        q1 = f"What is the relationship between {self.name} and {entity_name}"
        q2 = f"{entity_name} is {entity_action}"
        return self.chain(prompt=prompt).run(q1=q1, queries=[q1, q2]).strip()
    def _generate_reaction(self, observation: str, suffix: str) -> str:
        """React to a given observation or dialogue act."""
        prompt = PromptTemplate.from_template(
            "{agent_summary_description}"
            + "\nIt is {current_time}."
            + "\n{agent_name}'s status: {agent_status}"
            + "\nSummary of relevant context from {agent_name}'s memory:"
            + "\n{relevant_memories}"
            + "\nMost recent observations: {most_recent_memories}"
            + "\nObservation: {observation}"
            + "\n\n"
            + suffix
        )
        agent_summary_description = self.get_summary()
        relevant_memories_str = self.summarize_related_memories(observation)
        current_time_str = datetime.now().strftime("%B %d, %Y, %I:%M %p")
        kwargs: Dict[str, Any] = dict(
            agent_summary_description=agent_summary_description,
            current_time=current_time_str,
            relevant_memories=relevant_memories_str,
            agent_name=self.name,
            observation=observation,
            agent_status=self.status,
        )
        consumed_tokens = self.llm.get_num_tokens(
            prompt.format(most_recent_memories="", **kwargs)
        )
        kwargs[self.memory.most_recent_memories_token_key] = consumed_tokens
        return self.chain(prompt=prompt).run(**kwargs).strip()


return self.chain(prompt=prompt).run(**kwargs).strip()
    def _clean_response(self, text: str) -> str:
        return re.sub(f"^{self.name} ", "", text.strip()).strip()
[docs]    def generate_reaction(self, observation: str) -> Tuple[bool, str]:
        """React to a given observation."""
        call_to_action_template = (
            "Should {agent_name} react to the observation, and if so,"
            + " what would be an appropriate reaction? Respond in one line."
            + ' If the action is to engage in dialogue, write:\nSAY: "what to say"'
            + "\notherwise, write:\nREACT: {agent_name}'s reaction (if anything)."
            + "\nEither do nothing, react, or say something but not both.\n\n"
        )
        full_result = self._generate_reaction(observation, call_to_action_template)
        result = full_result.strip().split("\n")[0]
        # AAA
        self.memory.save_context(
            {},
            {
                self.memory.add_memory_key: f"{self.name} observed "
                f"{observation} and reacted by {result}"
            },
        )
        if "REACT:" in result:
            reaction = self._clean_response(result.split("REACT:")[-1])
            return False, f"{self.name} {reaction}"
        if "SAY:" in result:
            said_value = self._clean_response(result.split("SAY:")[-1])
            return True, f"{self.name} said {said_value}"
        else:
            return False, result
[docs]    def generate_dialogue_response(self, observation: str) -> Tuple[bool, str]:
        """React to a given observation."""


"""React to a given observation."""
        call_to_action_template = (
            "What would {agent_name} say? To end the conversation, write:"
            ' GOODBYE: "what to say". Otherwise to continue the conversation,'
            ' write: SAY: "what to say next"\n\n'
        )
        full_result = self._generate_reaction(observation, call_to_action_template)
        result = full_result.strip().split("\n")[0]
        if "GOODBYE:" in result:
            farewell = self._clean_response(result.split("GOODBYE:")[-1])
            self.memory.save_context(
                {},
                {
                    self.memory.add_memory_key: f"{self.name} observed "
                    f"{observation} and said {farewell}"
                },
            )
            return False, f"{self.name} said {farewell}"
        if "SAY:" in result:
            response_text = self._clean_response(result.split("SAY:")[-1])
            self.memory.save_context(
                {},
                {
                    self.memory.add_memory_key: f"{self.name} observed "
                    f"{observation} and said {response_text}"
                },
            )
            return True, f"{self.name} said {response_text}"
        else:
            return False, result
    ######################################################
    # Agent stateful' summary methods.                   #
    # Each dialog or response prompt includes a header   #
    # summarizing the agent's self-description. This is  #
    # updated periodically through probing its memories  #
    ######################################################
    def _compute_agent_summary(self) -> str:
        """"""
        prompt = PromptTemplate.from_template(
            "How would you summarize {name}'s core characteristics given the"


"How would you summarize {name}'s core characteristics given the"
            + " following statements:\n"
            + "{relevant_memories}"
            + "Do not embellish."
            + "\n\nSummary: "
        )
        # The agent seeks to think about their core characteristics.
        return (
            self.chain(prompt)
            .run(name=self.name, queries=[f"{self.name}'s core characteristics"])
            .strip()
        )
[docs]    def get_summary(self, force_refresh: bool = False) -> str:
        """Return a descriptive summary of the agent."""
        current_time = datetime.now()
        since_refresh = (current_time - self.last_refreshed).seconds
        if (
            not self.summary
            or since_refresh >= self.summary_refresh_seconds
            or force_refresh
        ):
            self.summary = self._compute_agent_summary()
            self.last_refreshed = current_time
        age = self.age if self.age is not None else "N/A"
        return (
            f"Name: {self.name} (age: {age})"
            + f"\nInnate traits: {self.traits}"
            + f"\n{self.summary}"
        )
[docs]    def get_full_header(self, force_refresh: bool = False) -> str:
        """Return a full header of the agent's status, summary, and current time."""
        summary = self.get_summary(force_refresh=force_refresh)
        current_time_str = datetime.now().strftime("%B %d, %Y, %I:%M %p")
        return (
            f"{summary}\nIt is {current_time_str}.\n{self.name}'s status: {self.status}"
        )
By Harrison Chase

      © Copyright 2023, Harrison Chase.


)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


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

    Triggers reflection when it reaches reflection_threshold."""
    max_tokens_limit: int = 1200  # : :meta private:
    # input keys
    queries_key: str = "queries"
    most_recent_memories_token_key: str = "recent_memories_token"
    add_memory_key: str = "add_memory"
    # output keys
    relevant_memories_key: str = "relevant_memories"
    relevant_memories_simple_key: str = "relevant_memories_simple"


relevant_memories_simple_key: str = "relevant_memories_simple"
    most_recent_memories_key: str = "most_recent_memories"
    def chain(self, prompt: PromptTemplate) -> LLMChain:
        return LLMChain(llm=self.llm, prompt=prompt, verbose=self.verbose)
    @staticmethod
    def _parse_list(text: str) -> List[str]:
        """Parse a newline-separated string into a list of strings."""
        lines = re.split(r"\n", text.strip())
        return [re.sub(r"^\s*\d+\.\s*", "", line).strip() for line in lines]
    def _get_topics_of_reflection(self, last_k: int = 50) -> List[str]:
        """Return the 3 most salient high-level questions about recent observations."""
        prompt = PromptTemplate.from_template(
            "{observations}\n\n"
            + "Given only the information above, what are the 3 most salient"
            + " high-level questions we can answer about the subjects in"
            + " the statements? Provide each question on a new line.\n\n"
        )
        observations = self.memory_retriever.memory_stream[-last_k:]
        observation_str = "\n".join([o.page_content for o in observations])
        result = self.chain(prompt).run(observations=observation_str)
        return self._parse_list(result)
    def _get_insights_on_topic(self, topic: str) -> List[str]:
        """Generate 'insights' on a topic of reflection, based on pertinent memories."""
        prompt = PromptTemplate.from_template(
            "Statements about {topic}\n"
            + "{related_statements}\n\n"
            + "What 5 high-level insights can you infer from the above statements?"