code stringlengths 141 78.9k | apis listlengths 1 23 | extract_api stringlengths 142 73.2k |
|---|---|---|
from typing import List, Union
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from pydantic import BaseModel, Extra, validator
from mindsdb.integrations.handlers.rag_handler.settings import (
DEFAULT_EMBEDDINGS_MODEL,
RAGBaseParameters,
)
EVAL_COLUMN_NAMES = (
"question",... | [
"langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler"
] | [((1785, 1845), 'pydantic.validator', 'validator', (['"""generation_evaluation_metrics"""'], {'allow_reuse': '(True)'}), "('generation_evaluation_metrics', allow_reuse=True)\n", (1794, 1845), False, 'from pydantic import BaseModel, Extra, validator\n'), ((2190, 2249), 'pydantic.validator', 'validator', (['"""retrieval_... |
from typing import List, Optional, Mapping, Any
from functools import partial
from langchain.llms.base import LLM
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from transformers import AutoModel, AutoTokenizer
from peft i... | [
"langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler"
] | [((3052, 3122), 'transformers.AutoTokenizer.from_pretrained', 'AutoTokenizer.from_pretrained', (['self.model_path'], {'trust_remote_code': '(True)'}), '(self.model_path, trust_remote_code=True)\n', (3081, 3122), False, 'from transformers import AutoModel, AutoTokenizer\n'), ((3401, 3471), 'transformers.AutoTokenizer.fr... |
# Copyright Vespa.ai. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
from datasets import load_dataset
import json
import unicodedata
def remove_control_characters(s):
return "".join(ch for ch in s if unicodedata.category(ch)[0]!="C")
from langchain.text_splitter import Recur... | [
"langchain.text_splitter.RecursiveCharacterTextSplitter"
] | [((362, 477), 'langchain.text_splitter.RecursiveCharacterTextSplitter', 'RecursiveCharacterTextSplitter', ([], {'chunk_size': '(1536)', 'chunk_overlap': '(0)', 'length_function': 'len', 'is_separator_regex': '(False)'}), '(chunk_size=1536, chunk_overlap=0,\n length_function=len, is_separator_regex=False)\n', (392, 4... |
from time import sleep
import copy
import redis
import json
import pickle
import traceback
from flask import Response, request, stream_with_context
from typing import Dict, Union
import os
from langchain.schema import HumanMessage, SystemMessage
from backend.api.language_model import get_llm
from backend.main import ... | [
"langchain.schema.SystemMessage",
"langchain.schema.HumanMessage"
] | [((11305, 11357), 'backend.main.app.route', 'app.route', (['"""/api/chat_xlang_webot"""'], {'methods': "['POST']"}), "('/api/chat_xlang_webot', methods=['POST'])\n", (11314, 11357), False, 'from backend.main import app, message_id_register, message_pool, logger\n'), ((2664, 2689), 'real_agents.web_agent.WebBrowsingExec... |
import os
import json
from langchain.schema import messages_from_dict, messages_to_dict
from langchain.memory import (
ConversationBufferMemory,
ChatMessageHistory,
)
class YeagerAIContext:
"""Context for the @yeager.ai agent."""
def __init__(self, username: str, session_id: str, session_path: str):... | [
"langchain.schema.messages_from_dict",
"langchain.memory.ConversationBufferMemory",
"langchain.memory.ChatMessageHistory",
"langchain.schema.messages_to_dict"
] | [((472, 492), 'langchain.memory.ChatMessageHistory', 'ChatMessageHistory', ([], {}), '()\n', (490, 492), False, 'from langchain.memory import ConversationBufferMemory, ChatMessageHistory\n'), ((527, 597), 'langchain.memory.ConversationBufferMemory', 'ConversationBufferMemory', ([], {'memory_key': '"""chat_history"""', ... |
import argparse
import os
import subprocess
import time
import gradio as gr
from huggingface_hub import snapshot_download
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import (
Docx2txtLoader,
PyPDFLoader,
TextLoader,
YoutubeLoader,
)
from ... | [
"langchain_community.document_loaders.PyPDFLoader",
"langchain_community.document_loaders.Docx2txtLoader",
"langchain_community.document_loaders.YoutubeLoader.from_youtube_url",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain_community.vectorstores.Chroma.from_documents",
"langchain_c... | [((934, 946), 'chat_with_mlx.models.utils.model_info', 'model_info', ([], {}), '()\n', (944, 946), False, 'from chat_with_mlx.models.utils import model_info\n'), ((991, 1040), 'openai.OpenAI', 'OpenAI', ([], {'api_key': '"""EMPTY"""', 'base_url': 'openai_api_base'}), "(api_key='EMPTY', base_url=openai_api_base)\n", (99... |
import time
import numpy as np
import torch
from torch.nn import functional as F
##########
# Functions for IMDB demo notebook.
# Data source: Stanford AI Lab https://ai.stanford.edu/~amaas/data/sentiment/
##########
# Output words instead of scores.
def sentiment_score_to_name(score: float):
if score > 0:
... | [
"langchain.text_splitter.RecursiveCharacterTextSplitter"
] | [((1670, 1681), 'time.time', 'time.time', ([], {}), '()\n', (1679, 1681), False, 'import time\n'), ((2431, 2473), 'torch.nn.functional.normalize', 'F.normalize', (['review_embeddings'], {'p': '(2)', 'dim': '(1)'}), '(review_embeddings, p=2, dim=1)\n', (2442, 2473), True, 'from torch.nn import functional as F\n'), ((253... |
from langchain.chains import LLMChain
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
from output_parsers import summary_parser, ice_breaker_parser, topics_of_interest_parser
llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo")
llm_creative = ChatOpenAI(temperature=1, ... | [
"langchain.chains.LLMChain",
"langchain_openai.ChatOpenAI"
] | [((225, 278), 'langchain_openai.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '(0)', 'model_name': '"""gpt-3.5-turbo"""'}), "(temperature=0, model_name='gpt-3.5-turbo')\n", (235, 278), False, 'from langchain_openai import ChatOpenAI\n'), ((294, 347), 'langchain_openai.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '... |
import asyncio
import uvicorn
from typing import AsyncIterable, Awaitable
from dotenv import load_dotenv
from fastapi import FastAPI
from fastapi.responses import FileResponse, StreamingResponse
from langchain.callbacks import AsyncIteratorCallbackHandler
from langchain.chat_models import ChatOpenAI
from langchain.sch... | [
"langchain.callbacks.AsyncIteratorCallbackHandler",
"langchain.schema.HumanMessage",
"langchain.chat_models.ChatOpenAI"
] | [((345, 358), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (356, 358), False, 'from dotenv import load_dotenv\n'), ((959, 968), 'fastapi.FastAPI', 'FastAPI', ([], {}), '()\n', (966, 968), False, 'from fastapi import FastAPI\n'), ((616, 646), 'langchain.callbacks.AsyncIteratorCallbackHandler', 'AsyncIteratorCa... |
# coding: UTF-8
import gc
import glob
import torch
import time
import os
import json
from collections import defaultdict
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain.schema import Document
from langchain.vectorstores import FAISS
from tqdm import tqdm
import config
import re
base_... | [
"langchain.schema.Document",
"langchain.vectorstores.FAISS.from_documents"
] | [((2132, 2158), 're.findall', 're.findall', (['pattern_1', 'key'], {}), '(pattern_1, key)\n', (2142, 2158), False, 'import re\n'), ((1175, 1221), 'langchain.schema.Document', 'Document', ([], {'page_content': 'strs', 'metadata': 'metadata'}), '(page_content=strs, metadata=metadata)\n', (1183, 1221), False, 'from langch... |
# -*- coding: UTF-8 -*-
"""
@Project : AI-Vtuber
@File : claude_model.py
@Author : HildaM
@Email : Hilda_quan@163.com
@Date : 2023/06/17 下午 4:44
@Description : 本地向量数据库模型设置
"""
from langchain.embeddings import HuggingFaceEmbeddings
import os
# 项目根路径
TEC2VEC_MODELS_PATH = os.getcwd() + "\\" + "data" + "\\" + ... | [
"langchain.embeddings.HuggingFaceEmbeddings"
] | [((468, 542), 'langchain.embeddings.HuggingFaceEmbeddings', 'HuggingFaceEmbeddings', ([], {'model_name': '(TEC2VEC_MODELS_PATH + DEFAULT_MODEL_NAME)'}), '(model_name=TEC2VEC_MODELS_PATH + DEFAULT_MODEL_NAME)\n', (489, 542), False, 'from langchain.embeddings import HuggingFaceEmbeddings\n'), ((908, 934), 'os.path.exists... |
import datetime
import json
import pkgutil
import time
import uuid
import os
import copy
from dataclasses import asdict
import datasets as ds
from cot.config import Config
from cot.utils.schemas.cot import features as cot_features
# disable transformation (e.g. map) caching
# https://huggingface.co/docs/datasets/v2.... | [
"langchain.llms.utils.enforce_stop_tokens",
"langchain.utils.get_from_dict_or_env",
"langchain.HuggingFaceHub",
"langchain.chat_models.ChatOpenAI",
"langchain.Cohere",
"langchain.Prompt",
"langchain.OpenAI"
] | [((383, 403), 'datasets.disable_caching', 'ds.disable_caching', ([], {}), '()\n', (401, 403), True, 'import datasets as ds\n'), ((428, 472), 'pkgutil.get_data', 'pkgutil.get_data', (['__name__', '"""fragments.json"""'], {}), "(__name__, 'fragments.json')\n", (444, 472), False, 'import pkgutil\n'), ((873, 893), 'dataset... |
import os
import threading
import time
from contextlib import ExitStack
from pathlib import Path
from typing import cast, Optional
import yaml
from dotenv import load_dotenv
from firebase_admin import auth
from langchain.text_splitter import CharacterTextSplitter
from llama_index import SimpleDirectoryReader
from read... | [
"langchain.text_splitter.CharacterTextSplitter"
] | [((664, 677), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (675, 677), False, 'from dotenv import load_dotenv\n'), ((687, 707), 'realtime_ai_character.logger.get_logger', 'get_logger', (['__name__'], {}), '(__name__)\n', (697, 707), False, 'from realtime_ai_character.logger import get_logger\n'), ((917, 944),... |
import sys
from dotenv import load_dotenv
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.llms import OpenAI
from commands import chrome_click_on_link, chrome_get_the_links_on_the_page, chrome_open_url, chrome_read_the_page, computer_applescript_action, say_text
... | [
"langchain.agents.initialize_agent",
"langchain.llms.OpenAI"
] | [((350, 363), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (361, 363), False, 'from dotenv import load_dotenv\n'), ((394, 415), 'langchain.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0)'}), '(temperature=0)\n', (400, 415), False, 'from langchain.llms import OpenAI\n'), ((613, 692), 'langchain.agents.initia... |
import os
from typing import Optional
from langchain import LLMChain, OpenAI, PromptTemplate
from langchain.chains.base import Chain
from langchain.chains.conversation.memory import ConversationBufferMemory
from langchain.llms.base import BaseLLM
from langchain.llms.loading import load_llm
DEFAULT_LLM = None
# Defau... | [
"langchain.LLMChain",
"langchain.llms.loading.load_llm",
"langchain.chains.conversation.memory.ConversationBufferMemory",
"langchain.OpenAI",
"langchain.PromptTemplate"
] | [((825, 914), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'template': 'TEMPLATE', 'input_variables': "['query', 'df_head', 'df_columns']"}), "(template=TEMPLATE, input_variables=['query', 'df_head',\n 'df_columns'])\n", (839, 914), False, 'from langchain import LLMChain, OpenAI, PromptTemplate\n'), ((1501, 1... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Project : AI. @by PyCharm
# @File : promptwatch
# @Time : 2023/7/13 10:03
# @Author : betterme
# @WeChat : meutils
# @Software : PyCharm
# @Description :
import os
from meutils.pipe import *
from langchain import OpenAI, LLMChain,... | [
"langchain.OpenAI",
"langchain.PromptTemplate.from_template"
] | [((417, 467), 'langchain.PromptTemplate.from_template', 'PromptTemplate.from_template', (['"""这是个prompt： {input}"""'], {}), "('这是个prompt： {input}')\n", (445, 467), False, 'from langchain import OpenAI, LLMChain, PromptTemplate\n'), ((486, 552), 'promptwatch.register_prompt_template', 'register_prompt_template', (['"""n... |
import sys
from typing import Any
import readline
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
import colorama
from callbacks import handlers
from config import config
from i18n import text
from utils import utils
from agent.agent import create_agent
from walrus.... | [
"langchain.memory.ConversationBufferMemory"
] | [((442, 455), 'config.config.init', 'config.init', ([], {}), '()\n', (453, 455), False, 'from config import config\n'), ((460, 475), 'colorama.init', 'colorama.init', ([], {}), '()\n', (473, 475), False, 'import colorama\n'), ((623, 653), 'i18n.text.init_system_messages', 'text.init_system_messages', (['llm'], {}), '(l... |
"""Wrapper around Cohere APIs."""
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional
from pydantic import Extra, root_validator
from tenacity import (
before_sleep_log,
retry,
retry_if_exception_type,
stop_after_attempt,
wait_exponential,
)
fr... | [
"langchain.llms.utils.enforce_stop_tokens",
"langchain.utils.get_from_dict_or_env"
] | [((531, 558), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (548, 558), False, 'import logging\n'), ((3018, 3034), 'pydantic.root_validator', 'root_validator', ([], {}), '()\n', (3032, 3034), False, 'from pydantic import Extra, root_validator\n'), ((3195, 3259), 'langchain.utils.get_from... |
"""Wrapper around GooseAI API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.utils import get_from_dict_or_env
logger = loggi... | [
"langchain.utils.get_from_dict_or_env"
] | [((315, 342), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (332, 342), False, 'import logging\n'), ((1675, 1702), 'pydantic.Field', 'Field', ([], {'default_factory': 'dict'}), '(default_factory=dict)\n', (1680, 1702), False, 'from pydantic import Extra, Field, root_validator\n'), ((1836... |
"""Wrapper around Anyscale"""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils ... | [
"langchain.llms.utils.enforce_stop_tokens",
"langchain.utils.get_from_dict_or_env"
] | [((1679, 1695), 'pydantic.root_validator', 'root_validator', ([], {}), '()\n', (1693, 1695), False, 'from pydantic import Extra, root_validator\n'), ((1862, 1938), 'langchain.utils.get_from_dict_or_env', 'get_from_dict_or_env', (['values', '"""anyscale_service_url"""', '"""ANYSCALE_SERVICE_URL"""'], {}), "(values, 'any... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Project : AI. @by PyCharm
# @File : chatpicture
# @Time : 2023/8/23 13:56
# @Author : betterme
# @WeChat : meutils
# @Software : PyCharm
# @Description : 增加代理 根据意图选择 OCR类型
from meutils.pipe import *
from meutils.ai_cv.ocr_api impor... | [
"langchain.chat_models.ChatOpenAI"
] | [((732, 744), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {}), '()\n', (742, 744), False, 'from langchain.chat_models import ChatOpenAI\n'), ((526, 549), 'meutils.ai_cv.ocr_api.OCR.basic_accurate', 'OCR.basic_accurate', (['img'], {}), '(img)\n', (544, 549), False, 'from meutils.ai_cv.ocr_api import OCR\n'), ... |
from typing import List
from pydantic import BaseModel, Field
from langchain.agents import AgentExecutor, Tool
from langchain.llms.base import BaseLLM
from .agent.base import AutonomousAgent
class ExecutionAgent(BaseModel):
agent: AgentExecutor = Field(...)
@classmethod
def from_llm(cls, llm: BaseLLM, o... | [
"langchain.agents.AgentExecutor.from_agent_and_tools"
] | [((254, 264), 'pydantic.Field', 'Field', (['...'], {}), '(...)\n', (259, 264), False, 'from pydantic import BaseModel, Field\n'), ((533, 610), 'langchain.agents.AgentExecutor.from_agent_and_tools', 'AgentExecutor.from_agent_and_tools', ([], {'agent': 'agent', 'tools': 'tools', 'verbose': 'verbose'}), '(agent=agent, too... |
# process_text.py
from lib.chat.setup import openai_embeddings
from langchain.docstore.document import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.document_loaders.csv_loader import CSVLoader
import requests
import json
import cha... | [
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.docstore.document.Document",
"langchain.vectorstores.Chroma.from_documents",
"langchain.document_loaders.csv_loader.CSVLoader",
"langchain.vectorstores.Chroma"
] | [((612, 703), 'langchain.text_splitter.RecursiveCharacterTextSplitter', 'RecursiveCharacterTextSplitter', ([], {'chunk_size': '(4096)', 'chunk_overlap': '(256)', 'length_function': 'len'}), '(chunk_size=4096, chunk_overlap=256,\n length_function=len)\n', (642, 703), False, 'from langchain.text_splitter import Recurs... |
import re
from typing import List
from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter
from langchain.docstore.document import Document as LCDocument
class MarkDownSplitter(TextSplitter):
'''To split markdown'''
def split_text(self, text: str) -> List[str]:
if self.count_t... | [
"langchain.text_splitter.RecursiveCharacterTextSplitter"
] | [((2824, 2848), 're.sub', 're.sub', (['"""<(.*?)>"""', '""""""', 'l'], {}), "('<(.*?)>', '', l)\n", (2830, 2848), False, 'import re\n'), ((514, 628), 'langchain.text_splitter.RecursiveCharacterTextSplitter', 'RecursiveCharacterTextSplitter', ([], {'chunk_size': 'self._chunk_size', 'chunk_overlap': '(0)', 'length_functi... |
import os
import json
from dotenv import load_dotenv
from langchain.agents import Tool
from langchain.chat_models import ChatOpenAI
from ai.ai_functions import get_company_info, get_intro_response
from consts import company_handbook_faiss_path, llm_model_type, demo_company_name
from utils import calculate_vesting
# L... | [
"langchain.chat_models.ChatOpenAI"
] | [((339, 352), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (350, 352), False, 'from dotenv import load_dotenv\n'), ((393, 420), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (402, 420), False, 'import os\n'), ((427, 494), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([... |
import re
import string
import traceback
from collections import Counter
import numpy as np
import pandas as pd
import tqdm
from langchain.evaluation.qa import QAEvalChain
from langchain.llms import OpenAI
from algos.PWS import PWS_Base, PWS_Extra
from algos.notool import CoT, IO
from algos.react import ReactBase
fr... | [
"langchain.llms.OpenAI"
] | [((432, 469), 're.sub', 're.sub', (['"""\\\\b(a|an|the)\\\\b"""', '""" """', 'text'], {}), "('\\\\b(a|an|the)\\\\b', ' ', text)\n", (438, 469), False, 'import re\n'), ((1337, 1363), 'collections.Counter', 'Counter', (['prediction_tokens'], {}), '(prediction_tokens)\n', (1344, 1363), False, 'from collections import Coun... |
import json
from langchain.schema.messages import SystemMessage
from langchain.output_parsers.json import parse_partial_json
from creator.code_interpreter import CodeInterpreter, language_map
from creator.config.library import config
from creator.utils import load_system_prompt, remove_tips
from creator.llm.llm_creat... | [
"langchain.output_parsers.json.parse_partial_json",
"langchain.schema.messages.SystemMessage"
] | [((389, 444), 'creator.utils.load_system_prompt', 'load_system_prompt', (['config.tips_for_testing_prompt_path'], {}), '(config.tips_for_testing_prompt_path)\n', (407, 444), False, 'from creator.utils import load_system_prompt, remove_tips\n'), ((459, 513), 'creator.utils.load_system_prompt', 'load_system_prompt', (['c... |
import requests
from typing import Any, Dict, Optional
from langchain.chains.api.prompt import API_RESPONSE_PROMPT, API_URL_PROMPT
from langchain.chains import APIChain
from langchain.prompts import BasePromptTemplate
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from... | [
"langchain.chains.llm.LLMChain"
] | [((1139, 1179), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'llm', 'prompt': 'api_url_prompt'}), '(llm=llm, prompt=api_url_prompt)\n', (1147, 1179), False, 'from langchain.chains.llm import LLMChain\n'), ((1207, 1252), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'llm', 'prompt': 'api_respons... |
from langchain.agents.tools import Tool
from langchain.chains import LLMMathChain
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
from langchain_experimental.plan_and_execute import (
PlanAndExecute,
load_agent_executor,
load_chat_planner,
)
llm = OpenAI(temperature=0)
llm_ma... | [
"langchain.chains.LLMMathChain.from_llm",
"langchain.llms.OpenAI",
"langchain.chat_models.ChatOpenAI",
"langchain_experimental.plan_and_execute.load_chat_planner",
"langchain_experimental.plan_and_execute.PlanAndExecute",
"langchain.agents.tools.Tool",
"langchain_experimental.plan_and_execute.load_agent... | [((292, 313), 'langchain.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0)'}), '(temperature=0)\n', (298, 313), False, 'from langchain.llms import OpenAI\n'), ((331, 375), 'langchain.chains.LLMMathChain.from_llm', 'LLMMathChain.from_llm', ([], {'llm': 'llm', 'verbose': '(True)'}), '(llm=llm, verbose=True)\n', (352, 375... |
"""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 MapReduceDocumentsChain
from langchain.chains.combine_... | [
"langchain.chains.sql_database.base.SQLDatabaseChain",
"langchain.prompts.loading.load_prompt_from_config",
"langchain.chains.qa_with_sources.base.QAWithSourcesChain",
"langchain.chains.pal.base.PALChain",
"langchain.chains.combine_documents.refine.RefineDocumentsChain",
"langchain.chains.llm.LLMChain",
... | [((2165, 2207), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'llm', 'prompt': 'prompt'}), '(llm=llm, prompt=prompt, **config)\n', (2173, 2207), False, 'from langchain.chains.llm import LLMChain\n'), ((2853, 2945), 'langchain.chains.hyde.base.HypotheticalDocumentEmbedder', 'HypotheticalDocumentEmbedder', ([... |
"""Wrapper around HuggingFace APIs."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langcha... | [
"langchain.llms.utils.enforce_stop_tokens",
"langchain.utils.get_from_dict_or_env"
] | [((1661, 1677), 'pydantic.root_validator', 'root_validator', ([], {}), '()\n', (1675, 1677), False, 'from pydantic import Extra, root_validator\n'), ((1848, 1936), 'langchain.utils.get_from_dict_or_env', 'get_from_dict_or_env', (['values', '"""huggingfacehub_api_token"""', '"""HUGGINGFACEHUB_API_TOKEN"""'], {}), "(valu... |
"""Clear Weaviate index."""
import logging
import os
import weaviate
from langchain.embeddings import OpenAIEmbeddings
from langchain.indexes import SQLRecordManager, index
from langchain.vectorstores import Weaviate
logger = logging.getLogger(__name__)
WEAVIATE_URL = os.environ["WEAVIATE_URL"]
WEAVIATE_API_KEY = os... | [
"langchain.embeddings.OpenAIEmbeddings",
"langchain.indexes.index",
"langchain.indexes.SQLRecordManager"
] | [((228, 255), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (245, 255), False, 'import logging\n'), ((893, 984), 'langchain.indexes.SQLRecordManager', 'SQLRecordManager', (['f"""weaviate/{WEAVIATE_DOCS_INDEX_NAME}"""'], {'db_url': 'RECORD_MANAGER_DB_URL'}), "(f'weaviate/{WEAVIATE_DOCS_IN... |
import logging
logging.basicConfig(level=logging.CRITICAL)
import os
from pathlib import Path
import openai
from dotenv import load_dotenv
from langchain.chat_models import ChatOpenAI
from llama_index import (
GPTVectorStoreIndex,
LLMPredictor,
ServiceContext,
StorageContext,
download_loader,
... | [
"langchain.chat_models.ChatOpenAI"
] | [((16, 59), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.CRITICAL'}), '(level=logging.CRITICAL)\n', (35, 59), False, 'import logging\n'), ((444, 457), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (455, 457), False, 'from dotenv import load_dotenv\n'), ((644, 729), 'llama_index.Service... |
"""This is the logic for ingesting PDF and DOCX files into LangChain."""
import os
from pathlib import Path
from langchain.text_splitter import RecursiveCharacterTextSplitter
from pdfminer.high_level import extract_text
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from dote... | [
"langchain.embeddings.OpenAIEmbeddings",
"langchain.text_splitter.RecursiveCharacterTextSplitter"
] | [((374, 387), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (385, 387), False, 'from dotenv import load_dotenv\n'), ((408, 437), 'os.getenv', 'os.getenv', (['"""OPENAI_API_TOKEN"""'], {}), "('OPENAI_API_TOKEN')\n", (417, 437), False, 'import os\n'), ((1354, 1445), 'langchain.text_splitter.RecursiveCharacterTex... |
import os
from langchain.llms.bedrock import Bedrock
from langchain import PromptTemplate
def get_llm():
model_kwargs = {
"maxTokenCount": 1024,
"stopSequences": [],
"temperature": 0,
"topP": 0.9
}
llm = Bedrock(
# credentials_profile_name=os.environ... | [
"langchain.PromptTemplate.from_template"
] | [((844, 882), 'langchain.PromptTemplate.from_template', 'PromptTemplate.from_template', (['template'], {}), '(template)\n', (872, 882), False, 'from langchain import PromptTemplate\n'), ((437, 470), 'os.environ.get', 'os.environ.get', (['"""BWB_REGION_NAME"""'], {}), "('BWB_REGION_NAME')\n", (451, 470), False, 'import ... |
from langchain import PromptTemplate, LLMChain
from langchain.document_loaders import TextLoader
from langchain.embeddings import LlamaCppEmbeddings
from langchain.llms import GPT4All
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.callbacks.base import CallbackManager
from langchain.c... | [
"langchain.llms.GPT4All",
"langchain.LLMChain",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler",
"langchain.document_loaders.TextLoader",
"langchain.vectorstores.faiss.FAISS.load_local",
"langchain.vectorstores.faiss.FAISS.f... | [((968, 1007), 'langchain.document_loaders.TextLoader', 'TextLoader', (['"""./docs/shortened_sotu.txt"""'], {}), "('./docs/shortened_sotu.txt')\n", (978, 1007), False, 'from langchain.document_loaders import TextLoader\n'), ((1021, 1062), 'langchain.embeddings.LlamaCppEmbeddings', 'LlamaCppEmbeddings', ([], {'model_pat... |
import os
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
class Database:
def __init__(self, directory):
self.embeddings = OpenAIEmbeddings()
self.text_splitter = RecursiveCharacterTex... | [
"langchain.vectorstores.Chroma.from_texts",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.text_splitter.RecursiveCharacterTextSplitter"
] | [((251, 269), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (267, 269), False, 'from langchain.embeddings import OpenAIEmbeddings\n'), ((299, 363), 'langchain.text_splitter.RecursiveCharacterTextSplitter', 'RecursiveCharacterTextSplitter', ([], {'chunk_size': '(500)', 'chunk_overlap': '... |
import os
import re
import time
from typing import Any
from dotenv import load_dotenv
from langchain.callbacks.base import BaseCallbackHandler
from langchain.chat_models import ChatOpenAI
from langchain.schema import LLMResult
from slack_bolt import App
from slack_bolt.adapter.socket_mode import SocketModeHandler
CHA... | [
"langchain.chat_models.ChatOpenAI"
] | [((347, 360), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (358, 360), False, 'from dotenv import load_dotenv\n'), ((392, 518), 'slack_bolt.App', 'App', ([], {'signing_secret': "os.environ['SLACK_SIGNING_SECRET']", 'token': "os.environ['SLACK_BOT_TOKEN']", 'process_before_response': '(True)'}), "(signing_secr... |
import json
from typing import Optional, Any
from langchain.schema import AIMessage
from langchain.schema.runnable import RunnableSerializable, RunnableConfig
from pydantic import BaseModel
class FunctionCall(BaseModel):
name: str
arguments: dict[str, Any]
class ParseFunctionCall(RunnableSerializable[AIMes... | [
"langchain.schema.AIMessage"
] | [((682, 700), 'langchain.schema.AIMessage', 'AIMessage', ([], {}), '(**input)\n', (691, 700), False, 'from langchain.schema import AIMessage\n'), ((1460, 1478), 'langchain.schema.AIMessage', 'AIMessage', ([], {}), '(**input)\n', (1469, 1478), False, 'from langchain.schema import AIMessage\n'), ((945, 970), 'json.loads'... |
from langchain.chains.router import MultiPromptChain
from langchain.chat_models import ChatOpenAI
from dotenv import load_dotenv
import os
# A template for working with LangChain multi prompt chain.
# It's a great way to let the large language model choose which prompts suits the question.
# Load env files
load_doten... | [
"langchain.chains.router.MultiPromptChain.from_prompts",
"langchain.chat_models.ChatOpenAI"
] | [((310, 323), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (321, 323), False, 'from dotenv import load_dotenv\n'), ((341, 373), 'os.environ.get', 'os.environ.get', (['"""openai_api_key"""'], {}), "('openai_api_key')\n", (355, 373), False, 'import os\n'), ((1461, 1551), 'langchain.chat_models.ChatOpenAI', 'Cha... |
from dotenv import load_dotenv
from src.slackbot import SlackBot
from src.handlers import create_handlers
import asyncio
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
handler = StreamingStdOutCallbackHandler()
# Load environment variables
load_dotenv()
# Load custom tools
import src.c... | [
"langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler"
] | [((211, 243), 'langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler', 'StreamingStdOutCallbackHandler', ([], {}), '()\n', (241, 243), False, 'from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n'), ((273, 286), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (284, 286)... |
#!/usr/bin/env python
"""A more complex example that shows how to configure index name at run time."""
from typing import Any, Iterable, List, Optional, Type
from fastapi import FastAPI
from langchain.embeddings import OpenAIEmbeddings
from langchain.schema import Document
from langchain.schema.embeddings import Embed... | [
"langchain.schema.runnable.ConfigurableFieldSingleOption",
"langchain.embeddings.OpenAIEmbeddings"
] | [((893, 1027), 'fastapi.FastAPI', 'FastAPI', ([], {'title': '"""LangChain Server"""', 'version': '"""1.0"""', 'description': '"""Spin up a simple api server using Langchain\'s Runnable interfaces"""'}), '(title=\'LangChain Server\', version=\'1.0\', description=\n "Spin up a simple api server using Langchain\'s Runn... |
from itertools import chain
import pandas as pd
from datasets import Dataset
from joblib import Parallel, delayed
from langchain.text_splitter import RecursiveCharacterTextSplitter
from tqdm import tqdm
def sl_hf_dataset_for_tokenizer(
sl, sl_dataset_name, tokenizer, max_length, margin=192, min_length=7
):
"... | [
"langchain.text_splitter.RecursiveCharacterTextSplitter"
] | [((1048, 1158), 'langchain.text_splitter.RecursiveCharacterTextSplitter', 'RecursiveCharacterTextSplitter', ([], {'chunk_size': '(max_length - margin)', 'chunk_overlap': '(0)', 'length_function': 'token_len'}), '(chunk_size=max_length - margin,\n chunk_overlap=0, length_function=token_len)\n', (1078, 1158), False, '... |
from contextlib import contextmanager
import uuid
import os
import tiktoken
from . import S2_tools as scholar
import csv
import sys
import requests
# pdf loader
from langchain.document_loaders import OnlinePDFLoader
## paper questioning tools
from llama_index import Document
from llama_index.vector_stores import Pi... | [
"langchain.document_loaders.OnlinePDFLoader"
] | [((768, 796), 'os.mkdir', 'os.mkdir', (['workspace_dir_name'], {}), '(workspace_dir_name)\n', (776, 796), False, 'import os\n'), ((5950, 5986), 'tiktoken.encoding_for_model', 'tiktoken.encoding_for_model', (['"""gpt-4"""'], {}), "('gpt-4')\n", (5977, 5986), False, 'import tiktoken\n'), ((7532, 7548), 'os.listdir', 'os.... |
import streamlit as st
from langchain.prompts import PromptTemplate
chat_template = PromptTemplate(
input_variables=['transcript','summary','chat_history','user_message', 'sentiment_report'],
template='''
You are an AI chatbot intended to discuss about the user's audio transcription.
\nT... | [
"langchain.prompts.PromptTemplate"
] | [((88, 562), 'langchain.prompts.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['transcript', 'summary', 'chat_history', 'user_message', 'sentiment_report']", 'template': '"""\n You are an AI chatbot intended to discuss about the user\'s audio transcription.\n \nTRANSCRIPT: "{transcript}"\n ... |
from enum import Enum
from typing import Callable, Tuple
from langchain.agents.agent import AgentExecutor
from langchain.agents.tools import BaseTool, Tool
class ToolScope(Enum):
GLOBAL = "global"
SESSION = "session"
SessionGetter = Callable[[], Tuple[str, AgentExecutor]]
def tool(
name: str,
des... | [
"langchain.agents.tools.Tool"
] | [((1245, 1306), 'langchain.agents.tools.Tool', 'Tool', ([], {'name': 'self.name', 'description': 'self.description', 'func': 'func'}), '(name=self.name, description=self.description, func=func)\n', (1249, 1306), False, 'from langchain.agents.tools import BaseTool, Tool\n')] |
import asyncio
from langchain.document_loaders import PyPDFLoader, DirectoryLoader
from langchain import PromptTemplate
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import CTransformers
from langchain.chains import RetrievalQA
import chainlit... | [
"langchain.llms.CTransformers",
"langchain.embeddings.HuggingFaceEmbeddings",
"langchain.vectorstores.FAISS.load_local",
"langchain.PromptTemplate"
] | [((808, 900), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'template': 'custom_prompt_template', 'input_variables': "['context', 'question']"}), "(template=custom_prompt_template, input_variables=['context',\n 'question'])\n", (822, 900), False, 'from langchain import PromptTemplate\n'), ((1522, 1635), 'langc... |
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.chat_models import ChatOpenAI
from dotenv import load_dotenv
import os
from langchain.chains import SimpleSequentialChain
# Create a .env file in the root of your project and add your OpenAI API key to it
# Load env files... | [
"langchain.chains.LLMChain",
"langchain.chains.SimpleSequentialChain",
"langchain.prompts.PromptTemplate",
"langchain.chat_models.ChatOpenAI"
] | [((321, 334), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (332, 334), False, 'from dotenv import load_dotenv\n'), ((352, 384), 'os.environ.get', 'os.environ.get', (['"""openai_api_key"""'], {}), "('openai_api_key')\n", (366, 384), False, 'import os\n'), ((469, 524), 'langchain.chat_models.ChatOpenAI', 'ChatO... |
import os
import re
from dotenv import load_dotenv
from langchain.chat_models import ChatOpenAI
from slack_bolt import App
from slack_bolt.adapter.socket_mode import SocketModeHandler
load_dotenv()
# ボットトークンを使ってアプリを初期化します
app = App(token=os.environ.get("SLACK_BOT_TOKEN"))
@app.event("app_mention")
def handle_menti... | [
"langchain.chat_models.ChatOpenAI"
] | [((186, 199), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (197, 199), False, 'from dotenv import load_dotenv\n'), ((378, 412), 're.sub', 're.sub', (['"""<@.*>"""', '""""""', "event['text']"], {}), "('<@.*>', '', event['text'])\n", (384, 412), False, 'import re\n'), ((424, 532), 'langchain.chat_models.ChatOpe... |
""" This module contains the function to classify the user query. """
import json
from langchain.prompts import ChatPromptTemplate
from langchain.chains import create_extraction_chain
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import TextLoader
from langchain.document_loaders import ... | [
"langchain.prompts.ChatPromptTemplate.from_template",
"langchain.chat_models.ChatOpenAI"
] | [((582, 627), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '(0)', 'model': 'config.model'}), '(temperature=0, model=config.model)\n', (592, 627), False, 'from langchain.chat_models import ChatOpenAI\n'), ((650, 853), 'langchain.prompts.ChatPromptTemplate.from_template', 'ChatPromptTemplate.fro... |
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.schema import StrOutputParser
from langchain.schema.runnable import Runnable
from langchain.schema.runnable.config import RunnableConfig
import chainlit as cl
@cl.on_chat_start
async def on_chat_start():
... | [
"langchain.prompts.ChatPromptTemplate.from_messages",
"langchain.schema.StrOutputParser",
"langchain.chat_models.ChatOpenAI"
] | [((328, 398), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'openai_api_base': '"""http://localhost:8888/v1"""', 'streaming': '(True)'}), "(openai_api_base='http://localhost:8888/v1', streaming=True)\n", (338, 398), False, 'from langchain.chat_models import ChatOpenAI\n'), ((411, 600), 'langchain.prompts.Chat... |
import os
import streamlit as st
from PyPDF2 import PdfReader, PdfWriter
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain.llms i... | [
"langchain.chains.question_answering.load_qa_chain",
"langchain.text_splitter.CharacterTextSplitter",
"langchain.llms.OpenAI",
"langchain.callbacks.get_openai_callback",
"langchain.vectorstores.FAISS.from_texts",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((481, 579), 'langchain.text_splitter.CharacterTextSplitter', 'CharacterTextSplitter', ([], {'separator': '"""\n"""', 'chunk_size': '(1000)', 'chunk_overlap': '(200)', 'length_function': 'len'}), "(separator='\\n', chunk_size=1000, chunk_overlap=200,\n length_function=len)\n", (502, 579), False, 'from langchain.tex... |
"""Example of observing LLM calls made by via callable OpenAI LLM."""
from langchain.llms import OpenAI
from langchain_prefect.plugins import RecordLLMCalls
llm = OpenAI(temperature=0.9)
with RecordLLMCalls():
llm("What would be a good name for a company that makes colorful socks?")
| [
"langchain.llms.OpenAI",
"langchain_prefect.plugins.RecordLLMCalls"
] | [((166, 189), 'langchain.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0.9)'}), '(temperature=0.9)\n', (172, 189), False, 'from langchain.llms import OpenAI\n'), ((196, 212), 'langchain_prefect.plugins.RecordLLMCalls', 'RecordLLMCalls', ([], {}), '()\n', (210, 212), False, 'from langchain_prefect.plugins import Record... |
from langchain.agents import load_tools
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.utilities import SerpAPIWrapper
from langchain_app.models.vicuna_request_llm import VicunaLLM
# First, let's load the language model we're going to use to control the agent... | [
"langchain.agents.initialize_agent",
"langchain.utilities.SerpAPIWrapper",
"langchain.agents.load_tools",
"langchain.agents.Tool",
"langchain_app.models.vicuna_request_llm.VicunaLLM"
] | [((328, 339), 'langchain_app.models.vicuna_request_llm.VicunaLLM', 'VicunaLLM', ([], {}), '()\n', (337, 339), False, 'from langchain_app.models.vicuna_request_llm import VicunaLLM\n'), ((419, 448), 'langchain.utilities.SerpAPIWrapper', 'SerpAPIWrapper', ([], {'params': 'params'}), '(params=params)\n', (433, 448), False... |
# /app/src/tools/setup.py
import logging
from langchain.pydantic_v1 import BaseModel, Field
from langchain.tools import BaseTool
from langchain_community.tools import DuckDuckGoSearchResults
from src.tools.doc_search import DocumentSearch
logger = logging.getLogger(__name__)
class SearchWebInput(BaseModel):
qu... | [
"langchain_community.tools.DuckDuckGoSearchResults",
"langchain.pydantic_v1.Field"
] | [((251, 278), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (268, 278), False, 'import logging\n'), ((331, 368), 'langchain.pydantic_v1.Field', 'Field', ([], {'description': '"""The search query"""'}), "(description='The search query')\n", (336, 368), False, 'from langchain.pydantic_v1 i... |
from llama_index.callbacks import CallbackManager, LlamaDebugHandler, CBEventType
from llama_index import ListIndex, ServiceContext, SimpleDirectoryReader, VectorStoreIndex
'''
Title of the page: A simple Python implementation of the ReAct pattern for LLMs
Name of the website: LlamaIndex (GPT Index) is a data framewor... | [
"langchain.chat_models.ChatOpenAI"
] | [((676, 718), 'llama_index.callbacks.LlamaDebugHandler', 'LlamaDebugHandler', ([], {'print_trace_on_end': '(True)'}), '(print_trace_on_end=True)\n', (693, 718), False, 'from llama_index.callbacks import CallbackManager, LlamaDebugHandler, CBEventType\n'), ((738, 768), 'llama_index.callbacks.CallbackManager', 'CallbackM... |
from typing import Any, Callable
from pandas import DataFrame
from exact_rag.config import EmbeddingType, Embeddings, DatabaseType, Databases
from langchain_openai import OpenAIEmbeddings
from langchain_community.embeddings import OllamaEmbeddings
from langchain.vectorstores.chroma import Chroma
from langchain.vector... | [
"langchain.text_splitter.RecursiveCharacterTextSplitter.from_tiktoken_encoder",
"langchain.indexes.index",
"langchain_community.document_loaders.DataFrameLoader",
"langchain.indexes.SQLRecordManager"
] | [((2955, 3052), 'langchain.indexes.SQLRecordManager', 'SQLRecordManager', (['database_model.sql_namespace'], {'db_url': 'f"""sqlite:///{database_model.sql_url}"""'}), "(database_model.sql_namespace, db_url=\n f'sqlite:///{database_model.sql_url}')\n", (2971, 3052), False, 'from langchain.indexes import SQLRecordMana... |
import base64
from enum import Enum
import json
import time
import logging
from pywebagent.env.browser import BrowserEnv
from langchain.schema import HumanMessage, SystemMessage
from langchain.chat_models import ChatOpenAI
logger = logging.getLogger(__name__)
TASK_STATUS = Enum("TASK_STATUS", "IN_PROGRESS SUCCESS FA... | [
"langchain.schema.SystemMessage",
"langchain.schema.HumanMessage",
"langchain.chat_models.ChatOpenAI"
] | [((233, 260), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (250, 260), False, 'import logging\n'), ((277, 326), 'enum.Enum', 'Enum', (['"""TASK_STATUS"""', '"""IN_PROGRESS SUCCESS FAILED"""'], {}), "('TASK_STATUS', 'IN_PROGRESS SUCCESS FAILED')\n", (281, 326), False, 'from enum import E... |
"""Load markdown, html, text from files, clean up, split, ingest into Pinecone."""
import pinecone
import tiktoken
from langchain.document_loaders import ReadTheDocsLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import NLTKTextSplitter
from langchain.vectorstores.pinecone import P... | [
"langchain.document_loaders.ReadTheDocsLoader",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.vectorstores.pinecone.Pinecone.from_documents",
"langchain.text_splitter.NLTKTextSplitter.from_tiktoken_encoder"
] | [((402, 445), 'langchain.document_loaders.ReadTheDocsLoader', 'ReadTheDocsLoader', (['"""hasura.io/docs/latest/"""'], {}), "('hasura.io/docs/latest/')\n", (419, 445), False, 'from langchain.document_loaders import ReadTheDocsLoader\n'), ((500, 573), 'langchain.text_splitter.NLTKTextSplitter.from_tiktoken_encoder', 'NLT... |
from typing import List, Optional, Any, Dict
from langchain.llms.base import LLM
from langchain.utils import get_from_dict_or_env
from pydantic import Extra, root_validator
from sam.gpt.quora import PoeClient, PoeResponse
# token = "KaEMfvDPEXoS115jzAFRRg%3D%3D"
# prompt = "write a java function that prints the nt... | [
"langchain.utils.get_from_dict_or_env"
] | [((573, 589), 'pydantic.root_validator', 'root_validator', ([], {}), '()\n', (587, 589), False, 'from pydantic import Extra, root_validator\n'), ((663, 714), 'langchain.utils.get_from_dict_or_env', 'get_from_dict_or_env', (['values', '"""token"""', '"""POE_COOKIE"""'], {}), "(values, 'token', 'POE_COOKIE')\n", (683, 71... |
from __future__ import annotations
from typing import List, Optional
from pydantic import ValidationError
from langchain.chains.llm import LLMChain
from langchain.chat_models.base import BaseChatModel
from langchain.experimental.autonomous_agents.autogpt.output_parser import (
AutoGPTOutputParser,
BaseAutoGP... | [
"langchain.experimental.autonomous_agents.autogpt.output_parser.AutoGPTOutputParser",
"langchain.tools.human.tool.HumanInputRun",
"langchain.experimental.autonomous_agents.autogpt.prompt.AutoGPTPrompt",
"langchain.schema.HumanMessage",
"langchain.chains.llm.LLMChain",
"langchain.schema.AIMessage",
"lang... | [((1753, 1918), 'langchain.experimental.autonomous_agents.autogpt.prompt.AutoGPTPrompt', 'AutoGPTPrompt', ([], {'ai_name': 'ai_name', 'ai_role': 'ai_role', 'tools': 'tools', 'input_variables': "['memory', 'messages', 'goals', 'user_input']", 'token_counter': 'llm.get_num_tokens'}), "(ai_name=ai_name, ai_role=ai_role, t... |
"""Main entrypoint for the app."""
import asyncio
import os
from operator import itemgetter
from typing import List, Optional, Sequence, Tuple, Union
from uuid import UUID
from fastapi import Depends, FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware
from langchain.callbacks.manager import CallbackMa... | [
"langchain.document_transformers.Html2TextTransformer",
"langchain.utilities.GoogleSearchAPIWrapper",
"langchain.schema.runnable.ConfigurableField",
"langchain.schema.messages.AIMessage",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.schema.output_parser.StrOutputParser",
"langcha... | [((4923, 4932), 'fastapi.FastAPI', 'FastAPI', ([], {}), '()\n', (4930, 4932), False, 'from fastapi import Depends, FastAPI, Request\n'), ((12927, 12987), 'os.path.isfile', 'os.path.isfile', (["os.environ['GOOGLE_APPLICATION_CREDENTIALS']"], {}), "(os.environ['GOOGLE_APPLICATION_CREDENTIALS'])\n", (12941, 12987), False,... |
from langchain.chat_models import ChatOpenAI
from langchain_experimental.plan_and_execute import PlanAndExecute, load_agent_executor, load_chat_planner
from langchain.llms import OpenAI
from langchain import SerpAPIWrapper
from langchain.agents.tools import Tool
from langchain import LLMMathChain
search = SerpAPIWrapp... | [
"langchain.llms.OpenAI",
"langchain.chat_models.ChatOpenAI",
"langchain.LLMMathChain.from_llm",
"langchain.SerpAPIWrapper",
"langchain_experimental.plan_and_execute.load_chat_planner",
"langchain_experimental.plan_and_execute.PlanAndExecute",
"langchain.agents.tools.Tool",
"langchain_experimental.plan... | [((308, 324), 'langchain.SerpAPIWrapper', 'SerpAPIWrapper', ([], {}), '()\n', (322, 324), False, 'from langchain import SerpAPIWrapper\n'), ((331, 352), 'langchain.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0)'}), '(temperature=0)\n', (337, 352), False, 'from langchain.llms import OpenAI\n'), ((370, 414), 'langchai... |
"""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, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun, Callbacks
from la... | [
"langchain.chains.ReduceDocumentsChain",
"langchain.chains.combine_documents.map_reduce.MapReduceDocumentsChain",
"langchain.chains.combine_documents.stuff.StuffDocumentsChain",
"langchain.docstore.document.Document",
"langchain.chains.llm.LLMChain",
"langchain.callbacks.manager.CallbackManagerForChainRun... | [((1734, 1787), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'llm', 'prompt': 'prompt', 'callbacks': 'callbacks'}), '(llm=llm, prompt=prompt, callbacks=callbacks)\n', (1742, 1787), False, 'from langchain.chains.llm import LLMChain\n'), ((1810, 1930), 'langchain.chains.combine_documents.stuff.StuffDocuments... |
import os
import re
import argparse
import json
import boto3
from bs4 import BeautifulSoup
from langchain.document_loaders import PDFMinerPDFasHTMLLoader
from langchain.docstore.document import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter,CharacterTextSplitter
import statistics
smr_clien... | [
"langchain.document_loaders.PDFMinerPDFasHTMLLoader",
"langchain.text_splitter.RecursiveCharacterTextSplitter"
] | [((324, 357), 'boto3.client', 'boto3.client', (['"""sagemaker-runtime"""'], {}), "('sagemaker-runtime')\n", (336, 357), False, 'import boto3\n'), ((8397, 8430), 'langchain.document_loaders.PDFMinerPDFasHTMLLoader', 'PDFMinerPDFasHTMLLoader', (['pdf_path'], {}), '(pdf_path)\n', (8420, 8430), False, 'from langchain.docum... |
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.llms import OpenAI
from langchain.chains import VectorDBQA
from langchain.document_loaders import TextLoader
from typing import List
from langchai... | [
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.document_loaders.TextLoader",
"langchain.llms.OpenAI",
"langchain.vectorstores.Chroma.from_documents",
"langchain.embeddings.OpenAIEmbeddings"
] | [((515, 541), 'langchain.document_loaders.TextLoader', 'TextLoader', (['self.file_path'], {}), '(self.file_path)\n', (525, 541), False, 'from langchain.document_loaders import TextLoader\n'), ((886, 950), 'langchain.text_splitter.RecursiveCharacterTextSplitter', 'RecursiveCharacterTextSplitter', ([], {'chunk_size': '(1... |
import dataclasses
import json
import numpy as np
import os
import requests
import sys
from typing import List
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chat_models import ChatOpenAI
from l... | [
"langchain.text_splitter.CharacterTextSplitter",
"langchain.chat_models.ChatOpenAI",
"langchain.schema.Document",
"langchain.vectorstores.Chroma.from_documents",
"langchain.prompts.PromptTemplate",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((1563, 1648), 'langchain.prompts.PromptTemplate', 'PromptTemplate', ([], {'template': 'prompt_template', 'input_variables': "['context', 'question']"}), "(template=prompt_template, input_variables=['context',\n 'question'])\n", (1577, 1648), False, 'from langchain.prompts import PromptTemplate\n'), ((1786, 1811), ... |
import logging
from pathlib import Path
from typing import List, Optional, Tuple
from dotenv import load_dotenv
load_dotenv()
from queue import Empty, Queue
from threading import Thread
import gradio as gr
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.chat_models imp... | [
"langchain.schema.AIMessage",
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.schema.SystemMessage",
"langchain.schema.HumanMessage"
] | [((114, 127), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (125, 127), False, 'from dotenv import load_dotenv\n'), ((604, 698), 'logging.basicConfig', 'logging.basicConfig', ([], {'format': '"""[%(asctime)s %(levelname)s]: %(message)s"""', 'level': 'logging.INFO'}), "(format='[%(asctime)s %(levelname)s]: %(me... |
import logging
from langchain.chains import RetrievalQA
from neogpt.prompts.prompt import get_prompt
def local_retriever(db, llm, persona="default"):
"""
Fn: local_retriever
Description: The function sets up the local retrieval-based question-answering system.
Args:
db (object): The database... | [
"langchain.chains.RetrievalQA.from_chain_type"
] | [((466, 493), 'neogpt.prompts.prompt.get_prompt', 'get_prompt', ([], {'persona': 'persona'}), '(persona=persona)\n', (476, 493), False, 'from neogpt.prompts.prompt import get_prompt\n'), ((590, 768), 'langchain.chains.RetrievalQA.from_chain_type', 'RetrievalQA.from_chain_type', ([], {'llm': 'llm', 'retriever': 'local_r... |
from langchain import PromptTemplate
PROMPT = """
你需要扮演一个优秀的关键信息提取助手，从人类的对话中提取关键性内容（最多5个关键词），以协助其他助手更精准地回答问题。
注意：你不需要做任何解释说明，只需严格按照示例的格式输出关键词。
示例：
人类：我有一个服装厂，是否可以应用你们的装箱算法改善装载率呢？
AI: 服装厂, 装箱算法, 装载率
现在开始：
人类：{query}
AI:
"""
def information_extraction_raw_prompt():
return PromptTemplate(template=PROMPT, input_v... | [
"langchain.PromptTemplate"
] | [((281, 339), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'template': 'PROMPT', 'input_variables': "['query']"}), "(template=PROMPT, input_variables=['query'])\n", (295, 339), False, 'from langchain import PromptTemplate\n'), ((397, 455), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'template': 'PROMPT',... |
import base64
import email
from enum import Enum
from typing import Any, Dict, List, Optional, Type
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.pydantic_v1 import BaseModel, Field
from langchain.tools.gmail.base import GmailBaseTool
from langchain.tools.gmail.utils import clean_ema... | [
"langchain.pydantic_v1.Field",
"langchain.tools.gmail.utils.clean_email_body"
] | [((606, 1054), 'langchain.pydantic_v1.Field', 'Field', (['...'], {'description': '"""The Gmail query. Example filters include from:sender, to:recipient, subject:subject, -filtered_term, in:folder, is:important|read|starred, after:year/mo/date, before:year/mo/date, label:label_name "exact phrase". Search newer/older tha... |
from langchain import PromptTemplate
from codedog.templates import grimoire_en
TRANSLATE_PROMPT = PromptTemplate(
template=grimoire_en.TRANSLATE_PR_REVIEW, input_variables=["language", "description", "content"]
)
| [
"langchain.PromptTemplate"
] | [((100, 217), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'template': 'grimoire_en.TRANSLATE_PR_REVIEW', 'input_variables': "['language', 'description', 'content']"}), "(template=grimoire_en.TRANSLATE_PR_REVIEW, input_variables=[\n 'language', 'description', 'content'])\n", (114, 217), False, 'from langchain... |
# Importing necessary library
import streamlit as st
# Setting up the page configuration
st.set_page_config(
page_title="QuickDigest AI",
page_icon=":brain:",
layout="wide",
initial_sidebar_state="expanded"
)
# Defining the function to display the home page
def home():
import streamlit as st
... | [
"langchain.agents.AgentExecutor.from_agent_and_tools",
"langchain.memory.ConversationBufferMemory",
"langchain.agents.create_pandas_dataframe_agent",
"langchain.chat_models.ChatOpenAI",
"langchain.tools.DuckDuckGoSearchRun",
"langchain.agents.ConversationalChatAgent.from_llm_and_tools",
"langchain.memor... | [((91, 213), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""QuickDigest AI"""', 'page_icon': '""":brain:"""', 'layout': '"""wide"""', 'initial_sidebar_state': '"""expanded"""'}), "(page_title='QuickDigest AI', page_icon=':brain:', layout\n ='wide', initial_sidebar_state='expanded')\n", (1... |
from time import monotonic
from rich.console import Console
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.llms import OpenAI
class Experiment:
"""
A class representing an experiment.
Attributes:
params (dict): A dictionary containing experiment parameters.
... | [
"langchain.llms.OpenAI",
"langchain.text_splitter.RecursiveCharacterTextSplitter"
] | [((1970, 1979), 'rich.console.Console', 'Console', ([], {}), '()\n', (1977, 1979), False, 'from rich.console import Console\n'), ((2643, 2654), 'time.monotonic', 'monotonic', ([], {}), '()\n', (2652, 2654), False, 'from time import monotonic\n'), ((2680, 2769), 'langchain.text_splitter.RecursiveCharacterTextSplitter', ... |
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
import re
import torch
import gradio as gr
from clc.langchain_application import LangChainApplication, torch_gc
from transformers import StoppingCriteriaList, StoppingCriteriaList
from clc.callbacks import Iteratorize, Stream
from clc.matching import key_words_match_in... | [
"langchain.schema.Document"
] | [((1155, 1183), 'clc.langchain_application.LangChainApplication', 'LangChainApplication', (['config'], {}), '(config)\n', (1175, 1183), False, 'from clc.langchain_application import LangChainApplication, torch_gc\n'), ((3620, 3630), 'clc.langchain_application.torch_gc', 'torch_gc', ([], {}), '()\n', (3628, 3630), False... |
"""This script is used to initialize the Qdrant db backend with Azure OpenAI."""
import os
from typing import Any, List, Optional, Tuple
import openai
from dotenv import load_dotenv
from langchain.docstore.document import Document
from langchain.text_splitter import NLTKTextSplitter
from langchain_community.document_l... | [
"langchain.text_splitter.NLTKTextSplitter",
"langchain_community.document_loaders.DirectoryLoader",
"langchain_community.embeddings.AzureOpenAIEmbeddings",
"langchain_community.embeddings.OpenAIEmbeddings"
] | [((692, 705), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (703, 705), False, 'from dotenv import load_dotenv\n'), ((709, 746), 'ultra_simple_config.load_config', 'load_config', ([], {'location': '"""config/db.yml"""'}), "(location='config/db.yml')\n", (720, 746), False, 'from ultra_simple_config import load_... |
import sys
from langchain.chains.summarize import load_summarize_chain
from langchain import OpenAI
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter()
# get transcript file key from args
file_key = sys.argv[1]
# get transcript text
text = open(file_k... | [
"langchain.chains.summarize.load_summarize_chain",
"langchain.docstore.document.Document",
"langchain.OpenAI",
"langchain.text_splitter.RecursiveCharacterTextSplitter"
] | [((186, 218), 'langchain.text_splitter.RecursiveCharacterTextSplitter', 'RecursiveCharacterTextSplitter', ([], {}), '()\n', (216, 218), False, 'from langchain.text_splitter import RecursiveCharacterTextSplitter\n'), ((344, 365), 'langchain.OpenAI', 'OpenAI', ([], {'temperature': '(0)'}), '(temperature=0)\n', (350, 365)... |
from base64 import b64decode
import os
import textwrap
from math import ceil
from dotenv import load_dotenv
load_dotenv() # take environment variables from .env.
from fastapi import FastAPI
from pydantic import BaseModel
from fastapi.middleware.cors import CORSMiddleware
from langchain.prompts import PromptTemplate... | [
"langchain.chains.summarize.load_summarize_chain",
"langchain_openai.llms.OpenAI",
"langchain.docstore.document.Document",
"langchain_community.llms.HuggingFaceHub"
] | [((109, 122), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (120, 122), False, 'from dotenv import load_dotenv\n'), ((930, 1033), 'fastapi.FastAPI', 'FastAPI', ([], {'docs_url': '"""/api/llm/docs"""', 'redoc_url': '"""/api/llm/redoc"""', 'openapi_url': '"""/api/llm/openapi.json"""'}), "(docs_url='/api/llm/docs... |
from typing import Any, Dict, List, Union
from langchain.memory.chat_memory import BaseChatMemory
from langchain.schema.messages import BaseMessage, get_buffer_string
class ConversationBufferWindowMemory(BaseChatMemory):
"""Buffer for storing conversation memory inside a limited size window."""
human_prefix... | [
"langchain.schema.messages.get_buffer_string"
] | [((899, 989), 'langchain.schema.messages.get_buffer_string', 'get_buffer_string', (['messages'], {'human_prefix': 'self.human_prefix', 'ai_prefix': 'self.ai_prefix'}), '(messages, human_prefix=self.human_prefix, ai_prefix=self.\n ai_prefix)\n', (916, 989), False, 'from langchain.schema.messages import BaseMessage, g... |
from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
from langchain.prompts import StringPromptTemplate
from langchain import OpenAI, SerpAPIWrapper, LLMChain
from typing import List, Union
from langchain.schema import AgentAction, AgentFinish
import re
from langchain.utilities impo... | [
"langchain.agents.AgentExecutor.from_agent_and_tools",
"langchain.agents.LLMSingleActionAgent",
"langchain_tools.cwtool.CloudWatchInsightQuery",
"langchain.LLMChain",
"langchain.tools.human.tool.HumanInputRun",
"langchain.utilities.BashProcess",
"langchain.SerpAPIWrapper",
"langchain.agents.Tool",
"... | [((2630, 2646), 'langchain.SerpAPIWrapper', 'SerpAPIWrapper', ([], {}), '()\n', (2644, 2646), False, 'from langchain import OpenAI, SerpAPIWrapper, LLMChain\n'), ((2658, 2671), 'langchain.utilities.BashProcess', 'BashProcess', ([], {}), '()\n', (2669, 2671), False, 'from langchain.utilities import BashProcess\n'), ((26... |
import json
from pydantic import BaseModel, Field
from pydantic import BaseModel, Field
from langchain.llms.base import BaseLLM
from typing import List, Any
from langchain import LLMChain
from llm.generate_task_plan.prompt import get_template
from llm.list_output_parser import LLMListOutputParser
class Task(BaseModel... | [
"langchain.LLMChain"
] | [((359, 392), 'pydantic.Field', 'Field', (['...'], {'description': '"""Task ID"""'}), "(..., description='Task ID')\n", (364, 392), False, 'from pydantic import BaseModel, Field\n'), ((416, 458), 'pydantic.Field', 'Field', (['...'], {'description': '"""Task description"""'}), "(..., description='Task description')\n", ... |
# Ingest Documents into a Zep Collection
import os
from dotenv import find_dotenv, load_dotenv
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import WebBaseLoader
from zep_python import ZepClient
from zep_python.langchain.vectorstore import ZepVectorStore
... | [
"langchain_community.document_loaders.WebBaseLoader",
"langchain.text_splitter.RecursiveCharacterTextSplitter"
] | [((449, 478), 'os.environ.get', 'os.environ.get', (['"""ZEP_API_URL"""'], {}), "('ZEP_API_URL')\n", (463, 478), False, 'import os\n'), ((549, 578), 'os.environ.get', 'os.environ.get', (['"""ZEP_API_KEY"""'], {}), "('ZEP_API_KEY')\n", (563, 578), False, 'import os\n'), ((784, 821), 'os.environ.get', 'os.environ.get', ([... |
#model_settings.py
import streamlit as st
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from llama_index import LangchainEmbedding, LLMPredictor, PromptHelper, OpenAIEmbedding, ServiceContext
from llama_index.logger import LlamaLogger
from langchain.chat_models import ChatOpenAI
from langchain imp... | [
"langchain.embeddings.huggingface.HuggingFaceEmbeddings",
"langchain.chat_models.ChatOpenAI"
] | [((705, 751), 'streamlit.selectbox', 'st.selectbox', (['"""Sentence transformer:"""', 'options'], {}), "('Sentence transformer:', options)\n", (717, 751), True, 'import streamlit as st\n'), ((1220, 1279), 'llama_index.PromptHelper', 'PromptHelper', (['max_input_size', 'num_output', 'max_chunk_overlap'], {}), '(max_inpu... |
import os
from dotenv import load_dotenv
import streamlit as st
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import ConversationalRetrievalC... | [
"langchain.llms.OpenAI",
"langchain.vectorstores.LanceDB",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((924, 983), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""GlobeBotter"""', 'page_icon': '"""🎬"""'}), "(page_title='GlobeBotter', page_icon='🎬')\n", (942, 983), True, 'import streamlit as st\n'), ((984, 1055), 'streamlit.header', 'st.header', (['"""🎬 Welcome to MovieHarbor, your favouri... |
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.document_loaders.csv_loader import CSVLoader
from langchain_community.document_loaders import HNLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.text_splitter import RecursiveCharacterTextSplitter
... | [
"langchain_community.document_loaders.PyPDFLoader",
"langchain.text_splitter.CharacterTextSplitter",
"langchain_openai.llms.OpenAI",
"langchain_community.document_loaders.csv_loader.CSVLoader",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain_community.document_loaders.UnstructuredHTML... | [((741, 785), 'langchain_community.document_loaders.PyPDFLoader', 'PyPDFLoader', (['"""attention is all you need.pdf"""'], {}), "('attention is all you need.pdf')\n", (752, 785), False, 'from langchain_community.document_loaders import PyPDFLoader\n'), ((838, 878), 'langchain_community.document_loaders.csv_loader.CSVLo... |
# define chain components
from langchain.memory import ConversationBufferMemory
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationChain
from langchain.prompts.prompt import PromptTemplate
from database import save_message_to_db, connect_2_db
import os
from pymongo import Mong... | [
"langchain.chains.ConversationChain",
"langchain.prompts.prompt.PromptTemplate",
"langchain.memory.ConversationBufferMemory"
] | [((460, 473), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (471, 473), False, 'from dotenv import load_dotenv\n'), ((664, 690), 'langchain.memory.ConversationBufferMemory', 'ConversationBufferMemory', ([], {}), '()\n', (688, 690), False, 'from langchain.memory import ConversationBufferMemory\n'), ((719, 733),... |
# Copyright 2023 Lei Zhang
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, so... | [
"langchain.text_splitter.CharacterTextSplitter",
"langchain.agents.initialize_agent",
"langchain_plantuml.diagram.activity_diagram_callback",
"langchain.document_loaders.TextLoader",
"langchain.tools.Tool",
"langchain.chat_models.ChatOpenAI",
"langchain_plantuml.diagram.sequence_diagram_callback",
"la... | [((1171, 1184), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (1182, 1184), False, 'from dotenv import load_dotenv\n'), ((3316, 3371), 'langchain_plantuml.diagram.activity_diagram_callback', 'diagram.activity_diagram_callback', ([], {'note_max_length': '(2000)'}), '(note_max_length=2000)\n', (3349, 3371), Fals... |
from __future__ import annotations
from typing import Any, TypeVar
from langchain_core.exceptions import OutputParserException
from langchain_core.language_models import BaseLanguageModel
from langchain_core.output_parsers import BaseOutputParser
from langchain_core.prompts import BasePromptTemplate
from langchain.o... | [
"langchain_core.exceptions.OutputParserException",
"langchain.chains.llm.LLMChain"
] | [((371, 383), 'typing.TypeVar', 'TypeVar', (['"""T"""'], {}), "('T')\n", (378, 383), False, 'from typing import Any, TypeVar\n'), ((1545, 1577), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'llm', 'prompt': 'prompt'}), '(llm=llm, prompt=prompt)\n', (1553, 1577), False, 'from langchain.chains.llm import LLM... |
import logging
import os
import nextcord # add this
import openai
from langchain import OpenAI
from langchain.chains.summarize import load_summarize_chain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from nextcord.ext import commands
from pytube import YouTube
logging.basicConfig(
level=log... | [
"langchain.chains.summarize.load_summarize_chain",
"langchain.text_splitter.RecursiveCharacterTextSplitter"
] | [((286, 393), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.INFO', 'format': '"""%(asctime)s - %(name)s - %(levelname)s - %(message)s"""'}), "(level=logging.INFO, format=\n '%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n", (305, 393), False, 'import logging\n'), ((404, 431), 'loggin... |
from __future__ import annotations
import uuid
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Type
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_env
from langchain.vectorstores.base import VectorSto... | [
"langchain.utils.get_from_env",
"langchain.docstore.document.Document"
] | [((965, 1009), 'meilisearch.Client', 'meilisearch.Client', ([], {'url': 'url', 'api_key': 'api_key'}), '(url=url, api_key=api_key)\n', (983, 1009), False, 'import meilisearch\n'), ((776, 814), 'langchain.utils.get_from_env', 'get_from_env', (['"""url"""', '"""MEILI_HTTP_ADDR"""'], {}), "('url', 'MEILI_HTTP_ADDR')\n", (... |
# Author: Yiannis Charalambous
from langchain.base_language import BaseLanguageModel
from langchain.schema import AIMessage, BaseMessage, HumanMessage
from esbmc_ai.config import ChatPromptSettings
from .base_chat_interface import BaseChatInterface, ChatResponse
from .ai_models import AIModel
class OptimizeCode(Bas... | [
"langchain.schema.AIMessage",
"langchain.schema.HumanMessage"
] | [((838, 964), 'langchain.schema.HumanMessage', 'HumanMessage', ([], {'content': 'f"""Reply OK if you understand the following is the source code to optimize:\n\n{source_code}"""'}), '(content=\n f"""Reply OK if you understand the following is the source code to optimize:\n\n{source_code}"""\n )\n', (850, 964), Fa... |
import os
from typing import Any, Optional
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from pydantic import Extra
import registry
import streaming
from .base import BaseTool, BASE_TOOL_DESCRIPTION_TEMPLATE
current_dir = os.path.dirname(__file__)
project_root = os.path.join(curr... | [
"langchain.prompts.PromptTemplate"
] | [((262, 287), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (277, 287), False, 'import os\n'), ((303, 335), 'os.path.join', 'os.path.join', (['current_dir', '"""../"""'], {}), "(current_dir, '../')\n", (315, 335), False, 'import os\n'), ((355, 399), 'os.path.join', 'os.path.join', (['project... |
from langchain.utilities import WikipediaAPIWrapper
def wikipedia_function(topic):
"""
Runs a query on the Wikipedia API.
Args:
topic (str): The topic to query.
Returns:
dict: The result of the query.
Examples:
>>> wikipedia_function('Python')
{'title': 'Python', 'summary': ... | [
"langchain.utilities.WikipediaAPIWrapper"
] | [((383, 404), 'langchain.utilities.WikipediaAPIWrapper', 'WikipediaAPIWrapper', ([], {}), '()\n', (402, 404), False, 'from langchain.utilities import WikipediaAPIWrapper\n')] |
import streamlit as st
import datetime
import os
import psycopg2
from dotenv import load_dotenv
from langchain.prompts import PromptTemplate
from langchain.docstore.document import Document
def log(message):
current_time = datetime.datetime.now()
milliseconds = current_time.microsecond // 1000
timestamp ... | [
"langchain.docstore.document.Document",
"langchain.prompts.PromptTemplate"
] | [((2668, 2806), 'langchain.prompts.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['input_question', 'table_info', 'columns_info', 'top_k', 'no_answer_text']", 'template': '_postgres_prompt'}), "(input_variables=['input_question', 'table_info',\n 'columns_info', 'top_k', 'no_answer_text'], template=_po... |
import os
import pandas as pd
from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
import mlflow
assert (
"OPENAI_API_KEY" in os.environ
), "Please set the OPENAI_API_KEY environment variable to run this example."
def build_and_evalute_model_with_... | [
"langchain.chains.LLMChain",
"langchain.prompts.PromptTemplate",
"langchain.llms.OpenAI"
] | [((1832, 1932), 'mlflow.load_table', 'mlflow.load_table', (['"""eval_results_table.json"""'], {'extra_columns': "['run_id', 'params.prompt_template']"}), "('eval_results_table.json', extra_columns=['run_id',\n 'params.prompt_template'])\n", (1849, 1932), False, 'import mlflow\n'), ((349, 367), 'mlflow.start_run', 'm... |
import os
import pandas as pd
from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
import mlflow
assert (
"OPENAI_API_KEY" in os.environ
), "Please set the OPENAI_API_KEY environment variable to run this example."
def build_and_evalute_model_with_... | [
"langchain.chains.LLMChain",
"langchain.prompts.PromptTemplate",
"langchain.llms.OpenAI"
] | [((1832, 1932), 'mlflow.load_table', 'mlflow.load_table', (['"""eval_results_table.json"""'], {'extra_columns': "['run_id', 'params.prompt_template']"}), "('eval_results_table.json', extra_columns=['run_id',\n 'params.prompt_template'])\n", (1849, 1932), False, 'import mlflow\n'), ((349, 367), 'mlflow.start_run', 'm... |
import os
import voyager.utils as U
from langchain.chat_models import ChatOpenAI
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.schema import HumanMessage, SystemMessage
from langchain.vectorstores import Chroma
from voyager.prompts import load_prompt
from voyager.control_primitives import lo... | [
"langchain.embeddings.openai.OpenAIEmbeddings",
"langchain.schema.HumanMessage",
"langchain.chat_models.ChatOpenAI"
] | [((583, 678), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'model_name': 'model_name', 'temperature': 'temperature', 'request_timeout': 'request_timout'}), '(model_name=model_name, temperature=temperature, request_timeout=\n request_timout)\n', (593, 678), False, 'from langchain.chat_models import ChatOpe... |
from langflow import CustomComponent
from langchain.agents import AgentExecutor, create_json_agent
from langflow.field_typing import (
BaseLanguageModel,
)
from langchain_community.agent_toolkits.json.toolkit import JsonToolkit
class JsonAgentComponent(CustomComponent):
display_name = "JsonAgent"
descript... | [
"langchain.agents.create_json_agent"
] | [((657, 700), 'langchain.agents.create_json_agent', 'create_json_agent', ([], {'llm': 'llm', 'toolkit': 'toolkit'}), '(llm=llm, toolkit=toolkit)\n', (674, 700), False, 'from langchain.agents import AgentExecutor, create_json_agent\n')] |
from langflow import CustomComponent
from langchain.agents import AgentExecutor, create_json_agent
from langflow.field_typing import (
BaseLanguageModel,
)
from langchain_community.agent_toolkits.json.toolkit import JsonToolkit
class JsonAgentComponent(CustomComponent):
display_name = "JsonAgent"
descript... | [
"langchain.agents.create_json_agent"
] | [((657, 700), 'langchain.agents.create_json_agent', 'create_json_agent', ([], {'llm': 'llm', 'toolkit': 'toolkit'}), '(llm=llm, toolkit=toolkit)\n', (674, 700), False, 'from langchain.agents import AgentExecutor, create_json_agent\n')] |
from typing import Annotated, List, Optional
from uuid import UUID
from fastapi import APIRouter, Depends, HTTPException, Query, Request
from fastapi.responses import StreamingResponse
from langchain.embeddings.ollama import OllamaEmbeddings
from langchain.embeddings.openai import OpenAIEmbeddings
from logger import g... | [
"langchain.embeddings.ollama.OllamaEmbeddings",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((1158, 1178), 'logger.get_logger', 'get_logger', (['__name__'], {}), '(__name__)\n', (1168, 1178), False, 'from logger import get_logger\n'), ((1194, 1205), 'fastapi.APIRouter', 'APIRouter', ([], {}), '()\n', (1203, 1205), False, 'from fastapi import APIRouter, Depends, HTTPException, Query, Request\n'), ((1230, 1251... |
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