id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 49 117 |
|---|---|---|
27feea1168f0-1 | except Exception as e:
return "Error: " + str(e)
async def _arun(
self,
file_path: str,
text: str,
append: bool = False,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
# TODO: Add aiofiles method
raise NotImplementedErr... | https://python.langchain.com/en/latest/_modules/langchain/tools/file_management/write.html |
41d2624d68a5-0 | Source code for langchain.tools.vectorstore.tool
"""Tools for interacting with vectorstores."""
import json
from typing import Any, Dict, Optional
from pydantic import BaseModel, Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
... | https://python.langchain.com/en/latest/_modules/langchain/tools/vectorstore/tool.html |
41d2624d68a5-1 | def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
chain = RetrievalQA.from_chain_type(
self.llm, retriever=self.vectorstore.as_retriever()
)
return chain.run(query)
async def _aru... | https://python.langchain.com/en/latest/_modules/langchain/tools/vectorstore/tool.html |
41d2624d68a5-2 | self.llm, retriever=self.vectorstore.as_retriever()
)
return json.dumps(chain({chain.question_key: query}, return_only_outputs=True))
async def _arun(
self,
query: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the tool asynchr... | https://python.langchain.com/en/latest/_modules/langchain/tools/vectorstore/tool.html |
cad6a6ff38e4-0 | Source code for langchain.tools.azure_cognitive_services.form_recognizer
from __future__ import annotations
import logging
from typing import Any, Dict, List, Optional
from pydantic import root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from ... | https://python.langchain.com/en/latest/_modules/langchain/tools/azure_cognitive_services/form_recognizer.html |
cad6a6ff38e4-1 | values, "azure_cogs_key", "AZURE_COGS_KEY"
)
azure_cogs_endpoint = get_from_dict_or_env(
values, "azure_cogs_endpoint", "AZURE_COGS_ENDPOINT"
)
try:
from azure.ai.formrecognizer import DocumentAnalysisClient
from azure.core.credentials import AzureKeyC... | https://python.langchain.com/en/latest/_modules/langchain/tools/azure_cognitive_services/form_recognizer.html |
cad6a6ff38e4-2 | with open(document_path, "rb") as document:
poller = self.doc_analysis_client.begin_analyze_document(
"prebuilt-document", document
)
elif document_src_type == "remote":
poller = self.doc_analysis_client.begin_analyze_document_from_url(
... | https://python.langchain.com/en/latest/_modules/langchain/tools/azure_cognitive_services/form_recognizer.html |
cad6a6ff38e4-3 | run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
try:
document_analysis_result = self._document_analysis(query)
if not document_analysis_result:
return "No good document analysis result was found"
return self._... | https://python.langchain.com/en/latest/_modules/langchain/tools/azure_cognitive_services/form_recognizer.html |
d876e7388064-0 | Source code for langchain.tools.azure_cognitive_services.image_analysis
from __future__ import annotations
import logging
from typing import Any, Dict, Optional
from pydantic import root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langcha... | https://python.langchain.com/en/latest/_modules/langchain/tools/azure_cognitive_services/image_analysis.html |
d876e7388064-1 | values, "azure_cogs_endpoint", "AZURE_COGS_ENDPOINT"
)
try:
import azure.ai.vision as sdk
values["vision_service"] = sdk.VisionServiceOptions(
endpoint=azure_cogs_endpoint, key=azure_cogs_key
)
values["analysis_options"] = sdk.ImageAnalysis... | https://python.langchain.com/en/latest/_modules/langchain/tools/azure_cognitive_services/image_analysis.html |
d876e7388064-2 | if result.tags is not None:
res_dict["tags"] = [tag.name for tag in result.tags]
if result.text is not None:
res_dict["text"] = [line.content for line in result.text.lines]
else:
error_details = sdk.ImageAnalysisErrorDetails.from_result(result)
... | https://python.langchain.com/en/latest/_modules/langchain/tools/azure_cognitive_services/image_analysis.html |
d876e7388064-3 | if not image_analysis_result:
return "No good image analysis result was found"
return self._format_image_analysis_result(image_analysis_result)
except Exception as e:
raise RuntimeError(f"Error while running AzureCogsImageAnalysisTool: {e}")
async def _arun(
s... | https://python.langchain.com/en/latest/_modules/langchain/tools/azure_cognitive_services/image_analysis.html |
95e035c10027-0 | Source code for langchain.tools.azure_cognitive_services.text2speech
from __future__ import annotations
import logging
import tempfile
from typing import Any, Dict, Optional
from pydantic import root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)... | https://python.langchain.com/en/latest/_modules/langchain/tools/azure_cognitive_services/text2speech.html |
95e035c10027-1 | )
try:
import azure.cognitiveservices.speech as speechsdk
values["speech_config"] = speechsdk.SpeechConfig(
subscription=azure_cogs_key, region=azure_cogs_region
)
except ImportError:
raise ImportError(
"azure-cognitiveservi... | https://python.langchain.com/en/latest/_modules/langchain/tools/azure_cognitive_services/text2speech.html |
95e035c10027-2 | def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
try:
speech_file = self._text2speech(query, self.speech_language)
return speech_file
except Exception as e:
raise Run... | https://python.langchain.com/en/latest/_modules/langchain/tools/azure_cognitive_services/text2speech.html |
f7a1657a53ad-0 | Source code for langchain.tools.azure_cognitive_services.speech2text
from __future__ import annotations
import logging
import time
from typing import Any, Dict, Optional
from pydantic import root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
fro... | https://python.langchain.com/en/latest/_modules/langchain/tools/azure_cognitive_services/speech2text.html |
f7a1657a53ad-1 | values, "azure_cogs_key", "AZURE_COGS_KEY"
)
azure_cogs_region = get_from_dict_or_env(
values, "azure_cogs_region", "AZURE_COGS_REGION"
)
try:
import azure.cognitiveservices.speech as speechsdk
values["speech_config"] = speechsdk.SpeechConfig(
... | https://python.langchain.com/en/latest/_modules/langchain/tools/azure_cognitive_services/speech2text.html |
f7a1657a53ad-2 | try:
import azure.cognitiveservices.speech as speechsdk
except ImportError:
pass
audio_src_type = detect_file_src_type(audio_path)
if audio_src_type == "local":
audio_config = speechsdk.AudioConfig(filename=audio_path)
elif audio_src_type == "remote":
... | https://python.langchain.com/en/latest/_modules/langchain/tools/azure_cognitive_services/speech2text.html |
e85302811855-0 | 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 BaseBr... | https://python.langchain.com/en/latest/_modules/langchain/tools/playwright/navigate.html |
e85302811855-1 | 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 28, 2023. | https://python.langchain.com/en/latest/_modules/langchain/tools/playwright/navigate.html |
85052247b643-0 | 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 BaseBrow... | https://python.langchain.com/en/latest/_modules/langchain/tools/playwright/navigate_back.html |
85052247b643-1 | 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 Harri... | https://python.langchain.com/en/latest/_modules/langchain/tools/playwright/navigate_back.html |
4ba721dd7b71-0 | 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,
)
fro... | https://python.langchain.com/en/latest/_modules/langchain/tools/playwright/get_elements.html |
4ba721dd7b71-1 | ) -> 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_tex... | https://python.langchain.com/en/latest/_modules/langchain/tools/playwright/get_elements.html |
4ba721dd7b71-2 | 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, ensure_ascii=False)
By H... | https://python.langchain.com/en/latest/_modules/langchain/tools/playwright/get_elements.html |
223eb0b7304a-0 | 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 BaseBrows... | https://python.langchain.com/en/latest/_modules/langchain/tools/playwright/current_page.html |
21462bdfd990-0 | 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 ... | https://python.langchain.com/en/latest/_modules/langchain/tools/playwright/extract_text.html |
21462bdfd990-1 | 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
f... | https://python.langchain.com/en/latest/_modules/langchain/tools/playwright/extract_text.html |
e95a83bc0e91-0 | 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 BaseBrows... | https://python.langchain.com/en/latest/_modules/langchain/tools/playwright/click.html |
e95a83bc0e91-1 | # Navigate to the desired webpage before using this tool
selector_effective = self._selector_effective(selector=selector)
from playwright.sync_api import TimeoutError as PlaywrightTimeoutError
try:
page.click(
selector_effective,
strict=self.playwright... | https://python.langchain.com/en/latest/_modules/langchain/tools/playwright/click.html |
5bf7b51e361a-0 | 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,
CallbackManagerForToo... | https://python.langchain.com/en/latest/_modules/langchain/tools/playwright/extract_hyperlinks.html |
5bf7b51e361a-1 | # 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 an... | https://python.langchain.com/en/latest/_modules/langchain/tools/playwright/extract_hyperlinks.html |
298de8a0b2a3-0 | Source code for langchain.tools.google_serper.tool
"""Tool for the Serper.dev Google Search API."""
from typing import Optional
from pydantic.fields import Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from ... | https://python.langchain.com/en/latest/_modules/langchain/tools/google_serper/tool.html |
298de8a0b2a3-1 | )
api_wrapper: GoogleSerperAPIWrapper = Field(default_factory=GoogleSerperAPIWrapper)
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
return str(self.api_wrapper.results(query))
async def _arun(
... | https://python.langchain.com/en/latest/_modules/langchain/tools/google_serper/tool.html |
4009e39769f8-0 | Source code for langchain.tools.openweathermap.tool
"""Tool for the OpenWeatherMap API."""
from typing import Optional
from pydantic import Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.utilit... | https://python.langchain.com/en/latest/_modules/langchain/tools/openweathermap/tool.html |
e7856df70bb9-0 | 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 BingSearch... | https://python.langchain.com/en/latest/_modules/langchain/tools/bing_search/tool.html |
e7856df70bb9-1 | 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,
... | https://python.langchain.com/en/latest/_modules/langchain/tools/bing_search/tool.html |
63c4dac0c8a3-0 | 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 WikipediaAPIWrap... | https://python.langchain.com/en/latest/_modules/langchain/tools/wikipedia/tool.html |
59e92642c0d1-0 | Source code for langchain.tools.human.tool
"""Tool for asking human input."""
from typing import Callable, Optional
from pydantic import Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
def _print_func(text: st... | https://python.langchain.com/en/latest/_modules/langchain/tools/human/tool.html |
35b3c99e90b9-0 | 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 Goog... | https://python.langchain.com/en/latest/_modules/langchain/tools/google_search/tool.html |
35b3c99e90b9-1 | 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,
... | https://python.langchain.com/en/latest/_modules/langchain/tools/google_search/tool.html |
dfd1f0d8904f-0 | Source code for langchain.experimental.autonomous_agents.autogpt.agent
from __future__ import annotations
from typing import List, Optional
from pydantic import ValidationError
from langchain.chains.llm import LLMChain
from langchain.chat_models.base import BaseChatModel
from langchain.experimental.autonomous_agents.au... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
dfd1f0d8904f-1 | ai_role: str,
memory: VectorStoreRetriever,
tools: List[BaseTool],
llm: BaseChatModel,
human_in_the_loop: bool = False,
output_parser: Optional[BaseAutoGPTOutputParser] = None,
) -> AutoGPT:
prompt = AutoGPTPrompt(
ai_name=ai_name,
ai_role=ai_r... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
dfd1f0d8904f-2 | # Get command name and arguments
action = self.output_parser.parse(assistant_reply)
tools = {t.name: t for t in self.tools}
if action.name == FINISH_NAME:
return action.args["response"]
if action.name in tools:
tool = tools[action.name]
... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
3e36ca1c8cbb-0 | Source code for langchain.experimental.autonomous_agents.baby_agi.baby_agi
"""BabyAGI agent."""
from collections import deque
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerFo... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
3e36ca1c8cbb-1 | print(str(t["task_id"]) + ": " + t["task_name"])
def print_next_task(self, task: Dict) -> None:
print("\033[92m\033[1m" + "\n*****NEXT TASK*****\n" + "\033[0m\033[0m")
print(str(task["task_id"]) + ": " + task["task_name"])
def print_task_result(self, result: str) -> None:
print("\033[93m... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
3e36ca1c8cbb-2 | next_task_id = int(this_task_id) + 1
response = self.task_prioritization_chain.run(
task_names=", ".join(task_names),
next_task_id=str(next_task_id),
objective=objective,
)
new_tasks = response.split("\n")
prioritized_task_list = []
for task_st... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
3e36ca1c8cbb-3 | """Run the agent."""
objective = inputs["objective"]
first_task = inputs.get("first_task", "Make a todo list")
self.add_task({"task_id": 1, "task_name": first_task})
num_iters = 0
while True:
if self.task_list:
self.print_task_list()
# ... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
3e36ca1c8cbb-4 | break
return {}
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
vectorstore: VectorStore,
verbose: bool = False,
task_execution_chain: Optional[Chain] = None,
**kwargs: Dict[str, Any],
) -> "BabyAGI":
"""Initialize the BabyAGI Con... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
2edbf7bf1d26-0 | 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.gen... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
2edbf7bf1d26-1 | 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]
de... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
2edbf7bf1d26-2 | 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, observ... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
2edbf7bf1d26-3 | )
consumed_tokens = self.llm.get_num_tokens(
prompt.format(most_recent_memories="", **kwargs)
)
kwargs[self.memory.most_recent_memories_token_key] = consumed_tokens
return self.chain(prompt=prompt).run(**kwargs).strip()
def _clean_response(self, text: str) -> str:
... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
2edbf7bf1d26-4 | if "SAY:" in result:
said_value = self._clean_response(result.split("SAY:")[-1])
return True, f"{self.name} said {said_value}"
else:
return False, result
[docs] def generate_dialogue_response(
self, observation: str, now: Optional[datetime] = None
) -> Tuple[bo... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
2edbf7bf1d26-5 | },
)
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 ... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
2edbf7bf1d26-6 | + f"\nInnate traits: {self.traits}"
+ f"\n{self.summary}"
)
[docs] def get_full_header(
self, force_refresh: bool = False, now: Optional[datetime] = None
) -> str:
"""Return a full header of the agent's status, summary, and current time."""
now = datetime.now() if now ... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
8cd9253f4097-0 | Source code for langchain.experimental.generative_agents.memory
import logging
import re
from datetime import datetime
from typing import Any, Dict, List, Optional
from langchain import LLMChain
from langchain.base_language import BaseLanguageModel
from langchain.prompts import PromptTemplate
from langchain.retrievers ... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
8cd9253f4097-1 | # output keys
relevant_memories_key: str = "relevant_memories"
relevant_memories_simple_key: str = "relevant_memories_simple"
most_recent_memories_key: str = "most_recent_memories"
now_key: str = "now"
reflecting: bool = False
def chain(self, prompt: PromptTemplate) -> LLMChain:
return L... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
8cd9253f4097-2 | ) -> 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?"
... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
8cd9253f4097-3 | "On the scale of 1 to 10, where 1 is purely mundane"
+ " (e.g., brushing teeth, making bed) and 10 is"
+ " extremely poignant (e.g., a break up, college"
+ " acceptance), rate the likely poignancy of the"
+ " following piece of memory. Respond with a single integer."
... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
8cd9253f4097-4 | and not self.reflecting
):
self.reflecting = True
self.pause_to_reflect(now=now)
# Hack to clear the importance from reflection
self.aggregate_importance = 0.0
self.reflecting = False
return result
[docs] def fetch_memories(
self, ob... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
8cd9253f4097-5 | break
consumed_tokens += self.llm.get_num_tokens(doc.page_content)
if consumed_tokens < self.max_tokens_limit:
result.append(doc)
return self.format_memories_simple(result)
@property
def memory_variables(self) -> List[str]:
"""Input keys this memory class ... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
8cd9253f4097-6 | [docs] def clear(self) -> None:
"""Clear memory contents."""
# TODO
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
bbda8a5aa688-0 | Source code for langchain.chains.sequential
"""Chain pipeline where the outputs of one step feed directly into next."""
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)... | https://python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
bbda8a5aa688-1 | overlapping_keys = set(input_variables) & set(memory_keys)
raise ValueError(
f"The the input key(s) {''.join(overlapping_keys)} are found "
f"in the Memory keys ({memory_keys}) - please use input and "
f"memory keys that don't overlap."
... | https://python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
bbda8a5aa688-2 | callbacks = _run_manager.get_child()
outputs = chain(known_values, return_only_outputs=True, callbacks=callbacks)
known_values.update(outputs)
return {k: known_values[k] for k in self.output_variables}
async def _acall(
self,
inputs: Dict[str, Any],
run_manage... | https://python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
bbda8a5aa688-3 | @root_validator()
def validate_chains(cls, values: Dict) -> Dict:
"""Validate that chains are all single input/output."""
for chain in values["chains"]:
if len(chain.input_keys) != 1:
raise ValueError(
"Chains used in SimplePipeline should all have one... | https://python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
bbda8a5aa688-4 | _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
_input = inputs[self.input_key]
color_mapping = get_color_mapping([str(i) for i in range(len(self.chains))])
for i, chain in enumerate(self.chains):
_input = ... | https://python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
8dda3e43eee1-0 | Source code for langchain.chains.mapreduce
"""Map-reduce chain.
Splits up a document, sends the smaller parts to the LLM with one prompt,
then combines the results with another one.
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Extra
from langchain.base_languag... | https://python.langchain.com/en/latest/_modules/langchain/chains/mapreduce.html |
8dda3e43eee1-1 | reduce_chain = StuffDocumentsChain(llm_chain=llm_chain, callbacks=callbacks)
combine_documents_chain = MapReduceDocumentsChain(
llm_chain=llm_chain,
combine_document_chain=reduce_chain,
callbacks=callbacks,
)
return cls(
combine_documents_chain=com... | https://python.langchain.com/en/latest/_modules/langchain/chains/mapreduce.html |
7d8f7e1bc7dc-0 | Source code for langchain.chains.transform
"""Chain that runs an arbitrary python function."""
from typing import Callable, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
[docs]class TransformChain(Chain):
"""Chain transform chain outp... | https://python.langchain.com/en/latest/_modules/langchain/chains/transform.html |
27dd52c76c0b-0 | Source code for langchain.chains.moderation
"""Pass input through a moderation endpoint."""
from typing import Any, Dict, List, Optional
from pydantic import root_validator
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.utils import get_from_dic... | https://python.langchain.com/en/latest/_modules/langchain/chains/moderation.html |
27dd52c76c0b-1 | values,
"openai_organization",
"OPENAI_ORGANIZATION",
default="",
)
try:
import openai
openai.api_key = openai_api_key
if openai_organization:
openai.organization = openai_organization
values["client"] = ... | https://python.langchain.com/en/latest/_modules/langchain/chains/moderation.html |
2ef5d115eff7-0 | Source code for langchain.chains.llm_requests
"""Chain that hits a URL and then uses an LLM to parse results."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForChainRun
from langc... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_requests.html |
2ef5d115eff7-1 | :meta private:
"""
return [self.output_key]
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
try:
from bs4 import BeautifulSoup # noqa: F401
except ImportError:
... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_requests.html |
87691e4f796b-0 | Source code for langchain.chains.loading
"""Functionality for loading chains."""
import json
from pathlib import Path
from typing import Any, Union
import yaml
from langchain.chains.api.base import APIChain
from langchain.chains.base import Chain
from langchain.chains.combine_documents.map_reduce import MapReduceDocume... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
87691e4f796b-1 | """Load LLM chain from config dict."""
if "llm" in config:
llm_config = config.pop("llm")
llm = load_llm_from_config(llm_config)
elif "llm_path" in config:
llm = load_llm(config.pop("llm_path"))
else:
raise ValueError("One of `llm` or `llm_path` must be present.")
if "pro... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
87691e4f796b-2 | llm_chain=llm_chain, base_embeddings=embeddings, **config
)
def _load_stuff_documents_chain(config: dict, **kwargs: Any) -> StuffDocumentsChain:
if "llm_chain" in config:
llm_chain_config = config.pop("llm_chain")
llm_chain = load_chain_from_config(llm_chain_config)
elif "llm_chain_path" in ... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
87691e4f796b-3 | llm_chain = load_chain(config.pop("llm_chain_path"))
else:
raise ValueError("One of `llm_chain` or `llm_chain_config` must be present.")
if not isinstance(llm_chain, LLMChain):
raise ValueError(f"Expected LLMChain, got {llm_chain}")
if "combine_document_chain" in config:
combine_docu... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
87691e4f796b-4 | elif "llm_path" in config:
llm = load_llm(config.pop("llm_path"))
else:
raise ValueError("One of `llm` or `llm_path` must be present.")
if "prompt" in config:
prompt_config = config.pop("prompt")
prompt = load_prompt_from_config(prompt_config)
elif "prompt_path" in config:
... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
87691e4f796b-5 | list_assertions_prompt = load_prompt(config.pop("list_assertions_prompt_path"))
if "check_assertions_prompt" in config:
check_assertions_prompt_config = config.pop("check_assertions_prompt")
check_assertions_prompt = load_prompt_from_config(
check_assertions_prompt_config
)
e... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
87691e4f796b-6 | prompt = load_prompt_from_config(prompt_config)
elif "prompt_path" in config:
prompt = load_prompt(config.pop("prompt_path"))
return LLMMathChain(llm=llm, prompt=prompt, **config)
def _load_map_rerank_documents_chain(
config: dict, **kwargs: Any
) -> MapRerankDocumentsChain:
if "llm_chain" in co... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
87691e4f796b-7 | return PALChain(llm=llm, prompt=prompt, **config)
def _load_refine_documents_chain(config: dict, **kwargs: Any) -> RefineDocumentsChain:
if "initial_llm_chain" in config:
initial_llm_chain_config = config.pop("initial_llm_chain")
initial_llm_chain = load_chain_from_config(initial_llm_chain_config)
... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
87691e4f796b-8 | if "combine_documents_chain" in config:
combine_documents_chain_config = config.pop("combine_documents_chain")
combine_documents_chain = load_chain_from_config(combine_documents_chain_config)
elif "combine_documents_chain_path" in config:
combine_documents_chain = load_chain(config.pop("comb... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
87691e4f796b-9 | else:
raise ValueError("`vectorstore` must be present.")
if "combine_documents_chain" in config:
combine_documents_chain_config = config.pop("combine_documents_chain")
combine_documents_chain = load_chain_from_config(combine_documents_chain_config)
elif "combine_documents_chain_path" in ... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
87691e4f796b-10 | api_request_chain_config = config.pop("api_request_chain")
api_request_chain = load_chain_from_config(api_request_chain_config)
elif "api_request_chain_path" in config:
api_request_chain = load_chain(config.pop("api_request_chain_path"))
else:
raise ValueError(
"One of `api_r... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
87691e4f796b-11 | if "requests_wrapper" in kwargs:
requests_wrapper = kwargs.pop("requests_wrapper")
return LLMRequestsChain(
llm_chain=llm_chain, requests_wrapper=requests_wrapper, **config
)
else:
return LLMRequestsChain(llm_chain=llm_chain, **config)
type_to_loader_dict = {
"api_cha... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
87691e4f796b-12 | if config_type not in type_to_loader_dict:
raise ValueError(f"Loading {config_type} chain not supported")
chain_loader = type_to_loader_dict[config_type]
return chain_loader(config, **kwargs)
[docs]def load_chain(path: Union[str, Path], **kwargs: Any) -> Chain:
"""Unified method for loading a chain ... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
87691e4f796b-13 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
7bea41097cc1-0 | Source code for langchain.chains.llm
"""Chain that just formats a prompt and calls an LLM."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
from pydantic import Extra
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import (... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
7bea41097cc1-1 | def output_keys(self) -> List[str]:
"""Will always return text key.
:meta private:
"""
return [self.output_key]
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
response = self.... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
7bea41097cc1-2 | """Prepare prompts from inputs."""
stop = None
if "stop" in input_list[0]:
stop = input_list[0]["stop"]
prompts = []
for inputs in input_list:
selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}
prompt = self.prompt.format_prompt(**se... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
7bea41097cc1-3 | await run_manager.on_text(_text, end="\n", verbose=self.verbose)
if "stop" in inputs and inputs["stop"] != stop:
raise ValueError(
"If `stop` is present in any inputs, should be present in all."
)
prompts.append(prompt)
return prompts, ... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
7bea41097cc1-4 | except (KeyboardInterrupt, Exception) as e:
await run_manager.on_chain_error(e)
raise e
outputs = self.create_outputs(response)
await run_manager.on_chain_end({"outputs": outputs})
return outputs
[docs] def create_outputs(self, response: LLMResult) -> List[Dict[str, st... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
7bea41097cc1-5 | Returns:
Completion from LLM.
Example:
.. code-block:: python
completion = llm.predict(adjective="funny")
"""
return (await self.acall(kwargs, callbacks=callbacks))[self.output_key]
[docs] def predict_and_parse(
self, callbacks: Callbacks = None... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
7bea41097cc1-6 | return [
self.prompt.output_parser.parse(res[self.output_key]) for res in result
]
else:
return result
[docs] async def aapply_and_parse(
self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
) -> Sequence[Union[str, List[str], Dict[str, str]]... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
209a93eb0eb2-0 | Source code for langchain.chains.llm_bash.base
"""Chain that interprets a prompt and executes bash code to perform bash operations."""
from __future__ import annotations
import logging
import warnings
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.base_lang... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/base.html |
209a93eb0eb2-1 | def raise_deprecation(cls, values: Dict) -> Dict:
if "llm" in values:
warnings.warn(
"Directly instantiating an LLMBashChain with an llm is deprecated. "
"Please instantiate with llm_chain or using the from_llm class method."
)
if "llm_chain" n... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/base.html |
209a93eb0eb2-2 | )
_run_manager.on_text(t, color="green", verbose=self.verbose)
t = t.strip()
try:
parser = self.llm_chain.prompt.output_parser
command_list = parser.parse(t) # type: ignore[union-attr]
except OutputParserException as e:
_run_manager.on_chain_error(e, ... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/base.html |
48362418da49-0 | Source code for langchain.chains.retrieval_qa.base
"""Chain for question-answering against a vector database."""
from __future__ import annotations
import warnings
from abc import abstractmethod
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.base_language i... | https://python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
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