code stringlengths 141 97.3k | apis listlengths 1 24 | extract_api stringlengths 113 214k |
|---|---|---|
import matplotlib.pyplot as plt
import numpy as np
import openai
import os
import pyaudio
import pyttsx3
import threading
import tkinter as tk
import queue
import wave
import whisper
from langchain import OpenAI, SQLDatabase
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.agents import cre... | [
"langchain.SQLDatabase.from_uri",
"langchain.agents.agent_toolkits.SQLDatabaseToolkit",
"langchain.OpenAI"
] | [((652, 740), 'langchain.SQLDatabase.from_uri', 'SQLDatabase.from_uri', (['f"""mysql+pymysql://admin:{s2_password}@{s2_host}:3306/{s2_db}"""'], {}), "(\n f'mysql+pymysql://admin:{s2_password}@{s2_host}:3306/{s2_db}')\n", (672, 740), False, 'from langchain import OpenAI, SQLDatabase\n'), ((743, 816), 'langchain.OpenA... |
from typing import Optional, Type
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.pydantic_v1 import BaseModel, Field
from langchain.tools.base import BaseTool
from langchain.tools.file_management.utils import (
INVALID_PATH_TEMPLATE,
BaseFileToolMixin,
FileValidationError,... | [
"langchain.pydantic_v1.Field",
"langchain.tools.file_management.utils.INVALID_PATH_TEMPLATE.format"
] | [((415, 453), 'langchain.pydantic_v1.Field', 'Field', (['...'], {'description': '"""name of file"""'}), "(..., description='name of file')\n", (420, 453), False, 'from langchain.pydantic_v1 import BaseModel, Field\n'), ((470, 517), 'langchain.pydantic_v1.Field', 'Field', (['...'], {'description': '"""text to write to f... |
import argparse
from typing import Optional
from langchain.llms.ollama import Ollama
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from termcolor import colored
class RubberDuck:
"""
This class is a wrapper around the Ollama model.
"""
def __init__(self, model: str = "... | [
"langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler"
] | [((1887, 1908), 'os.listdir', 'os.listdir', (['directory'], {}), '(directory)\n', (1897, 1908), False, 'import os\n'), ((2146, 2171), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (2169, 2171), False, 'import argparse\n'), ((3288, 3337), 'termcolor.colored', 'colored', (['"""\n What\'s on your... |
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage
from log10.langchain import Log10Callback
from log10.llm import Log10Config
log10_callback = Log10Callback(log10_config=Log10Config())
messages = [
HumanMessage(content="You are a ping pong machine"),
HumanMessage(conten... | [
"langchain.schema.HumanMessage",
"langchain.chat_models.ChatOpenAI"
] | [((341, 443), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'model_name': '"""gpt-3.5-turbo"""', 'callbacks': '[log10_callback]', 'temperature': '(0.5)', 'tags': "['test']"}), "(model_name='gpt-3.5-turbo', callbacks=[log10_callback],\n temperature=0.5, tags=['test'])\n", (351, 443), False, 'from langchain.... |
from langchain.document_loaders import DirectoryLoader
from langchain.text_splitter import CharacterTextSplitter
import os
import pinecone
from langchain.vectorstores import Pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.chains import RetrievalQA
from langchain.chat_models impo... | [
"langchain.text_splitter.CharacterTextSplitter",
"langchain.vectorstores.Pinecone.from_documents",
"langchain.chat_models.ChatOpenAI",
"langchain.document_loaders.DirectoryLoader",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((393, 406), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (404, 406), False, 'from dotenv import load_dotenv\n'), ((431, 460), 'os.getenv', 'os.getenv', (['"""PINECONE_API_KEY"""'], {}), "('PINECONE_API_KEY')\n", (440, 460), False, 'import os\n'), ((477, 502), 'os.getenv', 'os.getenv', (['"""PINECONE_ENV"""'... |
import os
import langchain
from langchain import (
agents,
prompts,
chains,
llms
)
class BOAgent:
def __init__(
self,
tools,
memory,
model="text-davinci-003",
temp=0.1,
max_steps=30,
):
self.openai_key = os.getenv(... | [
"langchain.agents.initialize_agent",
"langchain.chat_models.ChatOpenAI",
"langchain.OpenAI"
] | [((310, 337), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (319, 337), False, 'import os\n'), ((888, 1040), 'langchain.agents.initialize_agent', 'agents.initialize_agent', (['tools', 'self.llm'], {'agent': '"""conversational-react-description"""', 'verbose': '(True)', 'max_iteration... |
import importlib.util
import logging
from typing import Any, Callable, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.self_hosted import SelfHostedPipeline
from langchain.llms.utils import enforce_stop_tokens
from langchain.pydantic_v1 import Extra
DEFAULT... | [
"langchain.llms.utils.enforce_stop_tokens"
] | [((457, 484), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (474, 484), False, 'import logging\n'), ((1983, 2039), 'transformers.AutoTokenizer.from_pretrained', 'AutoTokenizer.from_pretrained', (['model_id'], {}), '(model_id, **_model_kwargs)\n', (2012, 2039), False, 'from transformers i... |
from collections import deque
from langchain import LLMChain, PromptTemplate
from langchain.chains import LLMChain
from langchain.llms import BaseLLM
from langchain.prompts import PromptTemplate
from modules.memory import MemoryModule
from typing import Dict, List
class ReasoningModule:
def __init__(self, llm, me... | [
"langchain.prompts.PromptTemplate"
] | [((395, 402), 'collections.deque', 'deque', ([], {}), '()\n', (400, 402), False, 'from collections import deque\n'), ((438, 445), 'collections.deque', 'deque', ([], {}), '()\n', (443, 445), False, 'from collections import deque\n'), ((3982, 4114), 'langchain.prompts.PromptTemplate', 'PromptTemplate', ([], {'template': ... |
## This is a fork/based from https://gist.github.com/wiseman/4a706428eaabf4af1002a07a114f61d6
from io import StringIO
import sys
import os
from typing import Dict, Optional
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents.tools import Tool
from langchain.llms... | [
"langchain.llms.OpenAI",
"langchain.agents.initialize_agent"
] | [((348, 409), 'os.environ.get', 'os.environ.get', (['"""OPENAI_API_BASE"""', '"""http://localhost:8080/v1"""'], {}), "('OPENAI_API_BASE', 'http://localhost:8080/v1')\n", (362, 409), False, 'import os\n'), ((423, 468), 'os.environ.get', 'os.environ.get', (['"""MODEL_NAME"""', '"""gpt-3.5-turbo"""'], {}), "('MODEL_NAME',... |
## This is a fork/based from https://gist.github.com/wiseman/4a706428eaabf4af1002a07a114f61d6
from io import StringIO
import sys
import os
from typing import Dict, Optional
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents.tools import Tool
from langchain.llms... | [
"langchain.llms.OpenAI",
"langchain.agents.initialize_agent"
] | [((348, 409), 'os.environ.get', 'os.environ.get', (['"""OPENAI_API_BASE"""', '"""http://localhost:8080/v1"""'], {}), "('OPENAI_API_BASE', 'http://localhost:8080/v1')\n", (362, 409), False, 'import os\n'), ((423, 468), 'os.environ.get', 'os.environ.get', (['"""MODEL_NAME"""', '"""gpt-3.5-turbo"""'], {}), "('MODEL_NAME',... |
"""Utility functions for mlflow.langchain."""
import contextlib
import json
import logging
import os
import shutil
import types
import warnings
from functools import lru_cache
from importlib.util import find_spec
from typing import NamedTuple
import cloudpickle
import yaml
from packaging import version
from packaging... | [
"langchain.schema.output_parser.StrOutputParser",
"langchain.chains.loading.load_chain",
"langchain.agents.initialize_agent"
] | [((2074, 2101), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (2091, 2101), False, 'import logging\n'), ((10682, 10728), 'os.path.join', 'os.path.join', (['path', '_MODEL_DATA_YAML_FILE_NAME'], {}), '(path, _MODEL_DATA_YAML_FILE_NAME)\n', (10694, 10728), False, 'import os\n'), ((17545, 1... |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
import langchain
from langchain.llms import Replicate
from flask import Flask
from flask import request
import os
import requests
import json
class Whats... | [
"langchain.llms.Replicate"
] | [((1502, 1609), 'langchain.llms.Replicate', 'Replicate', ([], {'model': 'llama2_13b_chat', 'model_kwargs': "{'temperature': 0.01, 'top_p': 1, 'max_new_tokens': 500}"}), "(model=llama2_13b_chat, model_kwargs={'temperature': 0.01, 'top_p':\n 1, 'max_new_tokens': 500})\n", (1511, 1609), False, 'from langchain.llms impo... |
import langchain
from langchain.cache import InMemoryCache
from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
langchain.llm_cache = InMemoryCache()
llm = OpenAI(temperature=0.9)
prompt = PromptTemplate(
input_variables=["product"],
template="W... | [
"langchain.llms.OpenAI",
"langchain.prompts.PromptTemplate",
"langchain.chains.LLMChain",
"langchain.cache.InMemoryCache"
] | [((199, 214), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (212, 214), False, 'from langchain.cache import InMemoryCache\n'), ((223, 246), 'langchain.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0.9)'}), '(temperature=0.9)\n', (229, 246), False, 'from langchain.llms import OpenAI\n'), ((256, 37... |
import time
from dotenv import load_dotenv
import langchain
from langchain.llms import OpenAI
from langchain.callbacks import get_openai_callback
from langchain.cache import InMemoryCache
load_dotenv()
# to make caching obvious, we use a slow model
llm = OpenAI(model_name="text-davinci-002")
langchain.llm_cache = In... | [
"langchain.llms.OpenAI",
"langchain.callbacks.get_openai_callback",
"langchain.cache.InMemoryCache"
] | [((189, 202), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (200, 202), False, 'from dotenv import load_dotenv\n'), ((257, 294), 'langchain.llms.OpenAI', 'OpenAI', ([], {'model_name': '"""text-davinci-002"""'}), "(model_name='text-davinci-002')\n", (263, 294), False, 'from langchain.llms import OpenAI\n'), ((3... |
import langchain
from langchain.chains.summarize import load_summarize_chain
from langchain.docstore.document import Document
from langchain.text_splitter import CharacterTextSplitter
from steamship import File, Task
from steamship.invocable import PackageService, post
from steamship_langchain.cache import SteamshipCa... | [
"langchain.chains.summarize.load_summarize_chain",
"langchain.text_splitter.CharacterTextSplitter",
"langchain.docstore.document.Document"
] | [((613, 635), 'steamship.invocable.post', 'post', (['"""summarize_file"""'], {}), "('summarize_file')\n", (617, 635), False, 'from steamship.invocable import PackageService, post\n'), ((1078, 1106), 'steamship.invocable.post', 'post', (['"""summarize_audio_file"""'], {}), "('summarize_audio_file')\n", (1082, 1106), Fal... |
import langchain
import os
import streamlit as st
import requests
import sounddevice as sd
import wavio
os.environ["OPENAI_API_KEY"]="ADD KEY"
import openai
from openai import OpenAI
client=OpenAI()
from langchain.prompts import ChatPromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain.prompts imp... | [
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.schema.messages.SystemMessage",
"langchain.chat_models.ChatOpenAI"
] | [((191, 199), 'openai.OpenAI', 'OpenAI', ([], {}), '()\n', (197, 199), False, 'from openai import OpenAI\n'), ((1151, 1163), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {}), '()\n', (1161, 1163), False, 'from langchain.chat_models import ChatOpenAI\n'), ((1462, 1520), 'streamlit.set_page_config', 'st.set_pag... |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# 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 o... | [
"langchain.llms.OpenAI",
"langchain.PromptTemplate",
"langchain.cache.InMemoryCache"
] | [((847, 862), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (860, 862), False, 'from langchain.cache import InMemoryCache\n'), ((951, 986), 'configparser.ConfigParser', 'ConfigParser', ([], {'comment_prefixes': 'None'}), '(comment_prefixes=None)\n', (963, 986), False, 'from configparser import Con... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Project : AI. @by PyCharm
# @File : chatbase
# @Time : 2023/7/5 15:29
# @Author : betterme
# @WeChat : meutils
# @Software : PyCharm
# @Description :
from meutils.pipe import *
from langchain.schema import Document
from langchain.... | [
"langchain.chains.question_answering.load_qa_chain",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.chat_models.ChatOpenAI",
"langchain.memory.ConversationBufferWindowMemory"
] | [((1213, 1245), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {'chunk_size': '(100)'}), '(chunk_size=100)\n', (1229, 1245), False, 'from langchain.embeddings import OpenAIEmbeddings\n'), ((1307, 1396), 'langchain.memory.ConversationBufferWindowMemory', 'ConversationBufferWindowMemory', ([], {'memory... |
import langchain
from langchain.chat_models.base import BaseChatModel, SimpleChatModel
from langchain.schema import (
AIMessage,
BaseMessage,
ChatGeneration,
ChatResult,
HumanMessage,
SystemMessage,
)
from typing import Any, Dict, List, Mapping, Optional, Sequence, TypedDict
import websocket
imp... | [
"langchain.schema.ChatGeneration",
"langchain.schema.ChatResult",
"langchain.schema.AIMessage"
] | [((1236, 1265), 'langchain.schema.AIMessage', 'AIMessage', ([], {'content': 'output_str'}), '(content=output_str)\n', (1245, 1265), False, 'from langchain.schema import AIMessage, BaseMessage, ChatGeneration, ChatResult, HumanMessage, SystemMessage\n'), ((1287, 1318), 'langchain.schema.ChatGeneration', 'ChatGeneration'... |
'''
Example script to automatically write a screenplay from a newsgroup post using agents with Crew.ai (https://github.com/joaomdmoura/crewAI)
You can also try it out with a personal email with many replies back and forth and see it turn into a movie script.
Demonstrates:
- multiple API endpoints (offical Mistral, ... | [
"langchain_community.chat_models.ChatAnyscale",
"langchain_mistralai.chat_models.ChatMistralAI",
"langchain.chat_models.openai.ChatOpenAI"
] | [((2292, 2556), 'crewai.Agent', 'Agent', ([], {'role': '"""spamfilter"""', 'goal': '"""Decide whether a text is spam or not."""', 'backstory': '"""You are an expert spam filter with years of experience. You DETEST advertisements, newsletters and vulgar language."""', 'llm': 'mixtral', 'verbose': '(True)', 'allow_delega... |
"""Chat agent with question answering
"""
import os
from utils.giphy import GiphyAPIWrapper
from dataclasses import dataclass
from langchain.chains import LLMChain, LLMRequestsChain
from langchain import Wikipedia, OpenAI
from langchain.agents.react.base import DocstoreExplorer
from langchain.agents import (
Zero... | [
"langchain.agents.initialize_agent",
"langchain.agents.AgentExecutor.from_agent_and_tools",
"langchain.cache.InMemoryCache",
"langchain.agents.conversational.base.ConversationalAgent.create_prompt",
"langchain.prompts.PromptTemplate",
"langchain.Wikipedia",
"langchain.SerpAPIWrapper",
"langchain.chain... | [((726, 741), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (739, 741), False, 'from langchain.cache import InMemoryCache\n'), ((1293, 1342), 'langchain.OpenAI', 'OpenAI', ([], {'temperature': '(0)', 'model_name': '"""gpt-3.5-turbo"""'}), "(temperature=0, model_name='gpt-3.5-turbo')\n", (1299, 134... |
import os
import pinecone
from rich.console import Console
from rich.markdown import Markdown
import langchain
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA
from langchain.embeddings import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.vectorstores import Pi... | [
"langchain.llms.OpenAI",
"langchain.prompts.PromptTemplate",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.vectorstores.Pinecone"
] | [((371, 398), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (380, 398), False, 'import os\n'), ((418, 443), 'os.getenv', 'os.getenv', (['"""PINECONE_KEY"""'], {}), "('PINECONE_KEY')\n", (427, 443), False, 'import os\n'), ((467, 500), 'os.getenv', 'os.getenv', (['"""PINECONE_ENVIRONME... |
from typing import Union, Callable, List, Dict, Any, TypeVar
from lionagi.libs.sys_util import SysUtil
T = TypeVar("T")
def to_langchain_document(datanode: T, **kwargs: Any) -> Any:
"""
Converts a generic data node into a Langchain Document.
This function transforms a node, typically from another data... | [
"langchain.schema.Document"
] | [((110, 122), 'typing.TypeVar', 'TypeVar', (['"""T"""'], {}), "('T')\n", (117, 122), False, 'from typing import Union, Callable, List, Dict, Any, TypeVar\n'), ((877, 910), 'lionagi.libs.sys_util.SysUtil.check_import', 'SysUtil.check_import', (['"""langchain"""'], {}), "('langchain')\n", (897, 910), False, 'from lionagi... |
import re
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
import langchain
from langchain import LLMChain
from langchain.agents.agent import AgentOutputParser
from langchain.schema import AgentAction, AgentFinish, OutputParserException
from .prompts import (FINAL_ANSWER_ACTION, FORMAT_INSTRUCTION... | [
"langchain.schema.OutputParserException"
] | [((1023, 1056), 're.search', 're.search', (['regex', 'text', 're.DOTALL'], {}), '(regex, text, re.DOTALL)\n', (1032, 1056), False, 'import re\n'), ((1097, 1159), 'langchain.schema.OutputParserException', 'OutputParserException', (['f"""Could not parse LLM output: `{text}`"""'], {}), "(f'Could not parse LLM output: `{te... |
from typing import List
from uuid import uuid4
from langchain.prompts import ChatPromptTemplate
from langchain.schema import AIMessage, HumanMessage, SystemMessage
from langchain_community.chat_models.fake import FakeListChatModel
from honcho import Honcho
from honcho.ext.langchain import langchain_message_converter... | [
"langchain.prompts.ChatPromptTemplate.from_messages",
"langchain_community.chat_models.fake.FakeListChatModel",
"langchain.schema.HumanMessage",
"langchain.schema.SystemMessage"
] | [((356, 415), 'honcho.Honcho', 'Honcho', ([], {'app_name': 'app_name', 'base_url': '"""http://localhost:8000"""'}), "(app_name=app_name, base_url='http://localhost:8000')\n", (362, 415), False, 'from honcho import Honcho\n'), ((596, 634), 'langchain_community.chat_models.fake.FakeListChatModel', 'FakeListChatModel', ([... |
""" A simple cloud consultant bot that can answer questions
about kubernetes, aws and cloud native."""
import langchain
from langchain.agents import Tool, AgentType, initialize_agent
from langchain.tools import HumanInputRun
from langchain.callbacks import HumanApprovalCallbackHandler
from langchain.vectorstores impor... | [
"langchain.agents.initialize_agent",
"langchain.chat_models.ChatOpenAI",
"langchain.memory.ConversationBufferMemory",
"langchain.embeddings.openai.OpenAIEmbeddings",
"langchain.tools.HumanInputRun",
"langchain.vectorstores.Chroma",
"langchain.agents.Tool"
] | [((893, 908), 'langchain.tools.HumanInputRun', 'HumanInputRun', ([], {}), '()\n', (906, 908), False, 'from langchain.tools import HumanInputRun\n'), ((917, 955), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '(0)', 'model': 'MODEL'}), '(temperature=0, model=MODEL)\n', (927, 955), False, 'from l... |
from langchain.cache import SQLiteCache
import langchain
from pydantic import BaseModel
from creator.code_interpreter import CodeInterpreter
from creator.config.load_config import load_yaml_config
import os
# Load configuration from YAML
yaml_config = load_yaml_config()
# Helper function to prepend '~/' to paths if... | [
"langchain.cache.SQLiteCache"
] | [((254, 272), 'creator.config.load_config.load_yaml_config', 'load_yaml_config', ([], {}), '()\n', (270, 272), False, 'from creator.config.load_config import load_yaml_config\n'), ((2004, 2058), 'os.path.join', 'os.path.join', (['project_dir', '_build_in_skill_library_dir'], {}), '(project_dir, _build_in_skill_library_... |
from __future__ import annotations
import asyncio
import functools
import logging
import os
import warnings
from contextlib import contextmanager
from contextvars import ContextVar
from typing import Any, Dict, Generator, List, Optional, Type, TypeVar, Union, cast
from uuid import UUID, uuid4
import langchain
from la... | [
"langchain.callbacks.tracers.langchain_v1.LangChainTracerV1",
"langchain.callbacks.stdout.StdOutCallbackHandler",
"langchain.callbacks.tracers.stdout.ConsoleCallbackHandler",
"langchain.schema.get_buffer_string",
"langchain.callbacks.tracers.langchain.LangChainTracer",
"langchain.callbacks.openai_info.Ope... | [((1036, 1063), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1053, 1063), False, 'import logging\n'), ((1208, 1251), 'contextvars.ContextVar', 'ContextVar', (['"""openai_callback"""'], {'default': 'None'}), "('openai_callback', default=None)\n", (1218, 1251), False, 'from contextvars i... |
"""Base interface for large language models to expose."""
import inspect
import json
import warnings
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple, Union
import yaml
from pydantic import Extra, Field, root_validator, validator
impor... | [
"langchain.llm_cache.lookup",
"langchain.schema.Generation",
"langchain.schema.AIMessage",
"langchain.llm_cache.update",
"langchain.schema.RunInfo",
"langchain.callbacks.manager.AsyncCallbackManager.configure",
"langchain.callbacks.manager.CallbackManager.configure",
"langchain.schema.LLMResult",
"l... | [((2315, 2352), 'pydantic.Field', 'Field', ([], {'default_factory': '_get_verbosity'}), '(default_factory=_get_verbosity)\n', (2320, 2352), False, 'from pydantic import Extra, Field, root_validator, validator\n'), ((2426, 2459), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, ... |
"""Base interface for large language models to expose."""
import json
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Mapping, Optional, Tuple, Union
import yaml
from pydantic import BaseModel, Extra, Field, validator
import langchain
from langchain.callbacks import ge... | [
"langchain.llm_cache.lookup",
"langchain.schema.Generation",
"langchain.llm_cache.update",
"langchain.schema.LLMResult",
"langchain.callbacks.get_callback_manager"
] | [((1991, 2028), 'pydantic.Field', 'Field', ([], {'default_factory': '_get_verbosity'}), '(default_factory=_get_verbosity)\n', (1996, 2028), False, 'from pydantic import BaseModel, Extra, Field, validator\n'), ((2119, 2162), 'pydantic.Field', 'Field', ([], {'default_factory': 'get_callback_manager'}), '(default_factory=... |
import streamlit as st
import langchain
from langchain.utilities import SQLDatabase
from langchain_experimental.sql import SQLDatabaseChain
from langchain.chat_models import ChatOpenAI
from langsmith import Client
from langchain.smith import RunEvalConfig, run_on_dataset
from pydantic import BaseModel, Field
db = SQLD... | [
"langchain_experimental.sql.SQLDatabaseChain.from_llm",
"langchain.utilities.SQLDatabase.from_uri",
"langchain.chat_models.ChatOpenAI"
] | [((316, 360), 'langchain.utilities.SQLDatabase.from_uri', 'SQLDatabase.from_uri', (['"""sqlite:///Chinook.db"""'], {}), "('sqlite:///Chinook.db')\n", (336, 360), False, 'from langchain.utilities import SQLDatabase\n'), ((367, 392), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '(0)'}), '(temper... |
"""
Utilities for ingesting different types of documents.
This includes cutting text into chunks and cleaning text.
"""
import re
from typing import Callable, Dict, List, Tuple
import langchain.docstore.document as docstore
import langchain.text_splitter as splitter
from loguru import logger
class IngestUtils:
""... | [
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.docstore.document.Document",
"langchain.text_splitter.NLTKTextSplitter"
] | [((881, 921), 're.sub', 're.sub', (['"""(\\\\w)-\\\\n(\\\\w)"""', '"""\\\\1\\\\2"""', 'text'], {}), "('(\\\\w)-\\\\n(\\\\w)', '\\\\1\\\\2', text)\n", (887, 921), False, 'import re\n'), ((1072, 1111), 're.sub', 're.sub', (['"""(?<!\\\\n)\\\\n(?!\\\\n)"""', '""" """', 'text'], {}), "('(?<!\\\\n)\\\\n(?!\\\\n)', ' ', text... |
"""Base interface that all chains should implement."""
from __future__ import annotations
import asyncio
import inspect
import json
import logging
import warnings
from abc import ABC, abstractmethod
from functools import partial
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import langc... | [
"langchain.schema.RunInfo",
"langchain.pydantic_v1.Field",
"langchain.callbacks.manager.AsyncCallbackManager.configure",
"langchain.callbacks.manager.CallbackManager.configure",
"langchain.pydantic_v1.root_validator",
"langchain.load.dump.dumpd",
"langchain.pydantic_v1.validator"
] | [((858, 885), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (875, 885), False, 'import logging\n'), ((3854, 3887), 'langchain.pydantic_v1.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, exclude=True)\n', (3859, 3887), False, 'from langchain.pydantic_v1 im... |
import langchain
from dotenv import load_dotenv
from langchain.chains import HypotheticalDocumentEmbedder, RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
langchain.debug = True
load_dotenv()
# HyDE (LLMが生成した仮説的な回答のベク... | [
"langchain.chat_models.ChatOpenAI",
"langchain.chains.HypotheticalDocumentEmbedder.from_llm",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.vectorstores.FAISS.load_local",
"langchain.chains.RetrievalQA.from_chain_type"
] | [((280, 293), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (291, 293), False, 'from dotenv import load_dotenv\n'), ((347, 365), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (363, 365), False, 'from langchain.embeddings import OpenAIEmbeddings\n'), ((373, 426), 'langchain.chat... |
import os
from langchain.embeddings import OpenAIEmbeddings
import langchain
from annoy import AnnoyIndex
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from sentence_transformers import SentenceTransforme... | [
"langchain.embeddings.OpenAIEmbeddings"
] | [((353, 388), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {'openai_api_key': '""""""'}), "(openai_api_key='')\n", (369, 388), False, 'from langchain.embeddings import OpenAIEmbeddings\n'), ((397, 471), 'sentence_transformers.SentenceTransformer', 'SentenceTransformer', (['"""sentence-transformers/... |
from pathlib import Path
from phi.assistant import Assistant
from phi.knowledge.langchain import LangChainKnowledgeBase
from langchain.embeddings import OpenAIEmbeddings
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma... | [
"langchain.text_splitter.CharacterTextSplitter",
"langchain.embeddings.OpenAIEmbeddings"
] | [((1254, 1297), 'phi.knowledge.langchain.LangChainKnowledgeBase', 'LangChainKnowledgeBase', ([], {'retriever': 'retriever'}), '(retriever=retriever)\n', (1276, 1297), False, 'from phi.knowledge.langchain import LangChainKnowledgeBase\n'), ((1306, 1398), 'phi.assistant.Assistant', 'Assistant', ([], {'knowledge_base': 'k... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Project : AI. @by PyCharm
# @File : OpenAIEmbeddings
# @Time : 2023/7/11 18:40
# @Author : betterme
# @WeChat : meutils
# @Software : PyCharm
# @Description :
import langchain
from langchain.embeddings import OpenAIEmbeddings as _O... | [
"langchain.embeddings.OpenAIEmbeddings"
] | [((1391, 1418), 'langchain.embeddings.OpenAIEmbeddings', '_OpenAIEmbeddings', ([], {}), '(**kwargs)\n', (1408, 1418), True, 'from langchain.embeddings import OpenAIEmbeddings as _OpenAIEmbeddings\n')] |
import os
import pathlib
import langchain
import langchain.cache
import langchain.globals
CACHE_BASE = pathlib.Path(f'{os.environ["HOME"]}/.cache/mitaskem/')
CACHE_BASE.mkdir(parents=True, exist_ok=True)
_LLM_CACHE_PATH = CACHE_BASE/'langchain_llm_cache.sqlite'
langchain.globals.set_llm_cache(langchain.cache.SQLiteCac... | [
"langchain.cache.SQLiteCache"
] | [((104, 158), 'pathlib.Path', 'pathlib.Path', (['f"""{os.environ[\'HOME\']}/.cache/mitaskem/"""'], {}), '(f"{os.environ[\'HOME\']}/.cache/mitaskem/")\n', (116, 158), False, 'import pathlib\n'), ((295, 353), 'langchain.cache.SQLiteCache', 'langchain.cache.SQLiteCache', ([], {'database_path': '_LLM_CACHE_PATH'}), '(datab... |
"""Base interface that all chains should implement."""
import json
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import yaml
from pydantic import BaseModel, Extra, Field, validator
import langchain
from langchain.callbacks import get_callback_manager
... | [
"langchain.callbacks.get_callback_manager"
] | [((1401, 1458), 'pydantic.Field', 'Field', ([], {'default_factory': 'get_callback_manager', 'exclude': '(True)'}), '(default_factory=get_callback_manager, exclude=True)\n', (1406, 1458), False, 'from pydantic import BaseModel, Extra, Field, validator\n'), ((1493, 1530), 'pydantic.Field', 'Field', ([], {'default_factory... |
import time #← 実行時間を計測するためにtimeモジュールをインポート
import langchain
from langchain.cache import InMemoryCache #← InMemoryCacheをインポート
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage
langchain.llm_cache = InMemoryCache() #← llm_cacheにInMemoryCacheを設定
chat = ChatOpenAI()
start = time.tim... | [
"langchain.schema.HumanMessage",
"langchain.chat_models.ChatOpenAI",
"langchain.cache.InMemoryCache"
] | [((237, 252), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (250, 252), False, 'from langchain.cache import InMemoryCache\n'), ((291, 303), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {}), '()\n', (301, 303), False, 'from langchain.chat_models import ChatOpenAI\n'), ((312, 323), 'time.t... |
import re
import urllib
from time import sleep
import langchain
import molbloom
import pandas as pd
import pkg_resources
import requests
import tiktoken
from langchain import LLMChain, PromptTemplate
from langchain.llms import BaseLLM
from langchain.tools import BaseTool
from chemcrow.utils import is_smiles, pubchem_... | [
"langchain.LLMChain",
"langchain.PromptTemplate"
] | [((1729, 1744), 'chemcrow.utils.is_smiles', 'is_smiles', (['text'], {}), '(text)\n', (1738, 1744), False, 'from chemcrow.utils import is_smiles, pubchem_query2smiles, tanimoto\n'), ((4644, 4686), 'tiktoken.encoding_for_model', 'tiktoken.encoding_for_model', (['encoding_name'], {}), '(encoding_name)\n', (4671, 4686), Fa... |
# from __future__ import annotations
import os
import re
import itertools
import openai
import tiktoken
import json
from dotenv import load_dotenv
from typing import Any, Dict, List, Optional
from pydantic import Extra
from langchain.schema.language_model import BaseLanguageModel
from langchain.callbacks.manager im... | [
"langchain.tools.DuckDuckGoSearchRun",
"langchain.prompts.PromptTemplate.from_template"
] | [((1312, 1333), 'langchain.tools.DuckDuckGoSearchRun', 'DuckDuckGoSearchRun', ([], {}), '()\n', (1331, 1333), False, 'from langchain.tools import DuckDuckGoSearchRun\n'), ((2942, 3011), 'langchain.prompts.PromptTemplate.from_template', 'PromptTemplate.from_template', (['prompts.EXECUTE_PLAN_PROMPT_SEARCH_TOOL'], {}), '... |
from __future__ import annotations
import time
from abc import abstractmethod
from typing import Any, List, Tuple, Union
import gradio_client as grc
import huggingface_hub
from gradio_client.client import Job
from gradio_client.utils import QueueError
try:
import langchain as lc
LANGCHAIN_INSTALLED = True
e... | [
"langchain.agents.Tool"
] | [((3706, 3781), 'langchain.agents.Tool', 'lc.agents.Tool', ([], {'name': 'self.name', 'func': 'self.run', 'description': 'self.description'}), '(name=self.name, func=self.run, description=self.description)\n', (3720, 3781), True, 'import langchain as lc\n'), ((742, 794), 'gradio_client.Client.duplicate', 'grc.Client.du... |
#!/Users/mark/dev/ml/langchain/read_github/langchain_github/env/bin/python
# change above to the location of your local Python venv installation
import sys, os, shutil
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.append(parent_dir)
import pathlib
from langchain.docstore.docume... | [
"langchain.document_loaders.unstructured.UnstructuredFileLoader",
"langchain.chat_models.ChatOpenAI",
"langchain.PromptTemplate",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.text_splitter.PythonCodeTextSplitter",
"langchain.text_splitter.MarkdownTextSplitter",
"langchain.docstor... | [((245, 272), 'sys.path.append', 'sys.path.append', (['parent_dir'], {}), '(parent_dir)\n', (260, 272), False, 'import sys, os, shutil\n'), ((667, 692), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '(0)'}), '(temperature=0)\n', (677, 692), False, 'from langchain.chat_models import ChatOpenAI\n... |
import inspect
import os
import langchain
from langchain.cache import SQLiteCache
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.schema.output_parser import StrOutputParser
# os.environ['OPENAI_API_BASE'] = "https://shale.live/v1"
os.environ['OPENAI_API_BA... | [
"langchain.schema.output_parser.StrOutputParser",
"langchain.prompts.ChatPromptTemplate.from_messages",
"langchain.cache.SQLiteCache",
"langchain.chat_models.ChatOpenAI"
] | [((947, 981), 'os.path.join', 'os.path.join', (['dir', '""".langchain.db"""'], {}), "(dir, '.langchain.db')\n", (959, 981), False, 'import os\n'), ((1048, 1088), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {'database_path': 'database_path'}), '(database_path=database_path)\n', (1059, 1088), False, 'from langchai... |
import os
import json
from typing import List
from dotenv import load_dotenv
from pydantic import BaseModel, Field
from supabase.client import Client, create_client
from langchain.chat_models import ChatOpenAI
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.tools import StructuredTool
from langc... | [
"langchain.prompts.SystemMessagePromptTemplate.from_template",
"langchain.tools.StructuredTool",
"langchain.chat_models.ChatOpenAI",
"langchain.prompts.ChatPromptTemplate.from_messages",
"langchain.chains.openai_functions.create_structured_output_chain",
"langchain.embeddings.openai.OpenAIEmbeddings",
"... | [((528, 541), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (539, 541), False, 'from dotenv import load_dotenv\n'), ((799, 824), 'os.getenv', 'os.getenv', (['"""SUPABASE_URL"""'], {}), "('SUPABASE_URL')\n", (808, 824), False, 'import os\n'), ((840, 865), 'os.getenv', 'os.getenv', (['"""SUPABASE_KEY"""'], {}), ... |
####################################################################################
# Copyright 2022 Google LLC
#
# 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
#
# https://www.apache.or... | [
"langchain.agents.load_tools",
"langchain.llms.VertexAI",
"langchain.agents.initialize_agent"
] | [((1859, 1872), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (1870, 1872), False, 'from dotenv import load_dotenv\n'), ((1881, 1997), 'langchain.llms.VertexAI', 'VertexAI', ([], {'model_name': '"""text-bison@001"""', 'max_output_tokens': '(1024)', 'temperature': '(0.25)', 'top_p': '(0)', 'top_k': '(1)', 'verb... |
import django
django.setup()
from sefaria.model.text import Ref, library
import re
import langchain
from langchain.cache import SQLiteCache
from langchain.chat_models import ChatOpenAI
from langchain.chat_models import ChatAnthropic
from langchain.prompts import PromptTemplate
from langchain.schema import HumanMessage... | [
"langchain.prompts.PromptTemplate.from_template",
"langchain.cache.SQLiteCache",
"langchain.chat_models.ChatAnthropic",
"langchain.schema.SystemMessage"
] | [((14, 28), 'django.setup', 'django.setup', ([], {}), '()\n', (26, 28), False, 'import django\n'), ((358, 400), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {'database_path': '""".langchain.db"""'}), "(database_path='.langchain.db')\n", (369, 400), False, 'from langchain.cache import SQLiteCache\n'), ((591, 615),... |
"""
A simple CUI application to visualize and query a customer database using the `textual` package.
"""
from dataclasses import dataclass
import langchain
from langchain.cache import SQLiteCache
from langchain.llms import OpenAI
from textual.app import App, ComposeResult
from textual.containers import Horizontal
from... | [
"langchain.llms.OpenAI",
"langchain.cache.SQLiteCache"
] | [((447, 460), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {}), '()\n', (458, 460), False, 'from langchain.cache import SQLiteCache\n'), ((472, 495), 'langchain.llms.OpenAI', 'OpenAI', ([], {'max_tokens': '(1024)'}), '(max_tokens=1024)\n', (478, 495), False, 'from langchain.llms import OpenAI\n'), ((499, 521), 'l... |
import langchain
from dotenv import load_dotenv
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.retrievers import BM25Retriever, EnsembleRetriever
from langchain.vectorstores import FAISS
langchain.verbose = T... | [
"langchain.chat_models.ChatOpenAI",
"langchain.vectorstores.FAISS.from_texts",
"langchain.retrievers.BM25Retriever.from_texts",
"langchain.retrievers.EnsembleRetriever",
"langchain.embeddings.openai.OpenAIEmbeddings",
"langchain.chains.RetrievalQA.from_chain_type"
] | [((325, 338), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (336, 338), False, 'from dotenv import load_dotenv\n'), ((1707, 1725), 'langchain.embeddings.openai.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (1723, 1725), False, 'from langchain.embeddings.openai import OpenAIEmbeddings\n'), ((1731, 17... |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
import langchain
from langchain.llms import Replicate
from flask import Flask
from flask import request
import os
import requests
import json
class Whats... | [
"langchain.llms.Replicate"
] | [((1502, 1609), 'langchain.llms.Replicate', 'Replicate', ([], {'model': 'llama2_13b_chat', 'model_kwargs': "{'temperature': 0.01, 'top_p': 1, 'max_new_tokens': 500}"}), "(model=llama2_13b_chat, model_kwargs={'temperature': 0.01, 'top_p':\n 1, 'max_new_tokens': 500})\n", (1511, 1609), False, 'from langchain.llms impo... |
import langchain
from langchain.cache import InMemoryCache
from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
langchain.llm_cache = InMemoryCache()
llm = OpenAI(temperature=0.9)
prompt = PromptTemplate(
input_variables=["product"],
template="W... | [
"langchain.llms.OpenAI",
"langchain.prompts.PromptTemplate",
"langchain.chains.LLMChain",
"langchain.cache.InMemoryCache"
] | [((199, 214), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (212, 214), False, 'from langchain.cache import InMemoryCache\n'), ((223, 246), 'langchain.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0.9)'}), '(temperature=0.9)\n', (229, 246), False, 'from langchain.llms import OpenAI\n'), ((256, 37... |
import langchain
from langchain.chains.summarize import load_summarize_chain
from langchain.docstore.document import Document
from langchain.text_splitter import CharacterTextSplitter
from steamship import File, Task
from steamship.invocable import PackageService, post
from steamship_langchain.cache import SteamshipCa... | [
"langchain.chains.summarize.load_summarize_chain",
"langchain.text_splitter.CharacterTextSplitter",
"langchain.docstore.document.Document"
] | [((613, 635), 'steamship.invocable.post', 'post', (['"""summarize_file"""'], {}), "('summarize_file')\n", (617, 635), False, 'from steamship.invocable import PackageService, post\n'), ((1078, 1106), 'steamship.invocable.post', 'post', (['"""summarize_audio_file"""'], {}), "('summarize_audio_file')\n", (1082, 1106), Fal... |
import langchain
import os
import streamlit as st
import requests
import sounddevice as sd
import wavio
os.environ["OPENAI_API_KEY"]="ADD KEY"
import openai
from openai import OpenAI
client=OpenAI()
from langchain.prompts import ChatPromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain.prompts imp... | [
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.schema.messages.SystemMessage",
"langchain.chat_models.ChatOpenAI"
] | [((191, 199), 'openai.OpenAI', 'OpenAI', ([], {}), '()\n', (197, 199), False, 'from openai import OpenAI\n'), ((1151, 1163), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {}), '()\n', (1161, 1163), False, 'from langchain.chat_models import ChatOpenAI\n'), ((1462, 1520), 'streamlit.set_page_config', 'st.set_pag... |
import langchain
import os
import streamlit as st
import requests
import sounddevice as sd
import wavio
os.environ["OPENAI_API_KEY"]="ADD KEY"
import openai
from openai import OpenAI
client=OpenAI()
from langchain.prompts import ChatPromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain.prompts imp... | [
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.schema.messages.SystemMessage",
"langchain.chat_models.ChatOpenAI"
] | [((191, 199), 'openai.OpenAI', 'OpenAI', ([], {}), '()\n', (197, 199), False, 'from openai import OpenAI\n'), ((1151, 1163), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {}), '()\n', (1161, 1163), False, 'from langchain.chat_models import ChatOpenAI\n'), ((1462, 1520), 'streamlit.set_page_config', 'st.set_pag... |
import langchain
import os
import streamlit as st
import requests
import sounddevice as sd
import wavio
os.environ["OPENAI_API_KEY"]="ADD KEY"
import openai
from openai import OpenAI
client=OpenAI()
from langchain.prompts import ChatPromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain.prompts imp... | [
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.schema.messages.SystemMessage",
"langchain.chat_models.ChatOpenAI"
] | [((191, 199), 'openai.OpenAI', 'OpenAI', ([], {}), '()\n', (197, 199), False, 'from openai import OpenAI\n'), ((1151, 1163), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {}), '()\n', (1161, 1163), False, 'from langchain.chat_models import ChatOpenAI\n'), ((1462, 1520), 'streamlit.set_page_config', 'st.set_pag... |
import os
import threading
from chainlit.config import config
from chainlit.logger import logger
def init_lc_cache():
use_cache = config.project.cache is True and config.run.no_cache is False
if use_cache:
try:
import langchain
except ImportError:
return
from ... | [
"langchain.cache.SQLiteCache"
] | [((767, 783), 'threading.Lock', 'threading.Lock', ([], {}), '()\n', (781, 783), False, 'import threading\n'), ((487, 542), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {'database_path': 'config.project.lc_cache_path'}), '(database_path=config.project.lc_cache_path)\n', (498, 542), False, 'from langchain.cache imp... |
import os
import threading
from chainlit.config import config
from chainlit.logger import logger
def init_lc_cache():
use_cache = config.project.cache is True and config.run.no_cache is False
if use_cache:
try:
import langchain
except ImportError:
return
from ... | [
"langchain.cache.SQLiteCache"
] | [((767, 783), 'threading.Lock', 'threading.Lock', ([], {}), '()\n', (781, 783), False, 'import threading\n'), ((487, 542), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {'database_path': 'config.project.lc_cache_path'}), '(database_path=config.project.lc_cache_path)\n', (498, 542), False, 'from langchain.cache imp... |
import os
import threading
from chainlit.config import config
from chainlit.logger import logger
def init_lc_cache():
use_cache = config.project.cache is True and config.run.no_cache is False
if use_cache:
try:
import langchain
except ImportError:
return
from ... | [
"langchain.cache.SQLiteCache"
] | [((767, 783), 'threading.Lock', 'threading.Lock', ([], {}), '()\n', (781, 783), False, 'import threading\n'), ((487, 542), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {'database_path': 'config.project.lc_cache_path'}), '(database_path=config.project.lc_cache_path)\n', (498, 542), False, 'from langchain.cache imp... |
import os
import threading
from chainlit.config import config
from chainlit.logger import logger
def init_lc_cache():
use_cache = config.project.cache is True and config.run.no_cache is False
if use_cache:
try:
import langchain
except ImportError:
return
from ... | [
"langchain.cache.SQLiteCache"
] | [((767, 783), 'threading.Lock', 'threading.Lock', ([], {}), '()\n', (781, 783), False, 'import threading\n'), ((487, 542), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {'database_path': 'config.project.lc_cache_path'}), '(database_path=config.project.lc_cache_path)\n', (498, 542), False, 'from langchain.cache imp... |
import logging
import requests
from typing import Optional, List, Dict, Mapping, Any
import langchain
from langchain.llms.base import LLM
from langchain.cache import InMemoryCache
logging.basicConfig(level=logging.INFO)
# 启动llm的缓存
langchain.llm_cache = InMemoryCache()
class AgentZhipuAI(LLM):
import zhipuai as... | [
"langchain.chains.ConversationChain",
"langchain.prompts.PromptTemplate",
"langchain.chains.LLMChain",
"langchain.cache.InMemoryCache"
] | [((183, 222), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.INFO'}), '(level=logging.INFO)\n', (202, 222), False, 'import logging\n'), ((256, 271), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (269, 271), False, 'from langchain.cache import InMemoryCache\n'), ((1830, 1884)... |
'''
Example script to automatically write a screenplay from a newsgroup post using agents with Crew.ai (https://github.com/joaomdmoura/crewAI)
You can also try it out with a personal email with many replies back and forth and see it turn into a movie script.
Demonstrates:
- multiple API endpoints (offical Mistral, ... | [
"langchain_community.chat_models.ChatAnyscale",
"langchain_mistralai.chat_models.ChatMistralAI",
"langchain.chat_models.openai.ChatOpenAI"
] | [((2292, 2556), 'crewai.Agent', 'Agent', ([], {'role': '"""spamfilter"""', 'goal': '"""Decide whether a text is spam or not."""', 'backstory': '"""You are an expert spam filter with years of experience. You DETEST advertisements, newsletters and vulgar language."""', 'llm': 'mixtral', 'verbose': '(True)', 'allow_delega... |
'''
Example script to automatically write a screenplay from a newsgroup post using agents with Crew.ai (https://github.com/joaomdmoura/crewAI)
You can also try it out with a personal email with many replies back and forth and see it turn into a movie script.
Demonstrates:
- multiple API endpoints (offical Mistral, ... | [
"langchain_community.chat_models.ChatAnyscale",
"langchain_mistralai.chat_models.ChatMistralAI",
"langchain.chat_models.openai.ChatOpenAI"
] | [((2292, 2556), 'crewai.Agent', 'Agent', ([], {'role': '"""spamfilter"""', 'goal': '"""Decide whether a text is spam or not."""', 'backstory': '"""You are an expert spam filter with years of experience. You DETEST advertisements, newsletters and vulgar language."""', 'llm': 'mixtral', 'verbose': '(True)', 'allow_delega... |
from __future__ import annotations
import asyncio
import functools
import logging
import os
import warnings
from contextlib import contextmanager
from contextvars import ContextVar
from typing import Any, Dict, Generator, List, Optional, Type, TypeVar, Union, cast
from uuid import UUID, uuid4
import langchain
from la... | [
"langchain.callbacks.tracers.langchain_v1.LangChainTracerV1",
"langchain.callbacks.stdout.StdOutCallbackHandler",
"langchain.callbacks.tracers.stdout.ConsoleCallbackHandler",
"langchain.schema.get_buffer_string",
"langchain.callbacks.tracers.langchain.LangChainTracer",
"langchain.callbacks.openai_info.Ope... | [((1036, 1063), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1053, 1063), False, 'import logging\n'), ((1208, 1251), 'contextvars.ContextVar', 'ContextVar', (['"""openai_callback"""'], {'default': 'None'}), "('openai_callback', default=None)\n", (1218, 1251), False, 'from contextvars i... |
"""Base interface that all chains should implement."""
import inspect
import json
import warnings
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import yaml
from pydantic import BaseModel, Field, root_validator, validator
import langchain
from langchai... | [
"langchain.callbacks.manager.AsyncCallbackManager.configure",
"langchain.schema.RunInfo",
"langchain.callbacks.manager.CallbackManager.configure"
] | [((816, 849), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, exclude=True)\n', (821, 849), False, 'from pydantic import BaseModel, Field, root_validator, validator\n'), ((904, 937), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, exc... |
"""Base interface for large language models to expose."""
import inspect
import json
import warnings
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple, Union
import yaml
from pydantic import Extra, Field, root_validator, validator
impor... | [
"langchain.llm_cache.lookup",
"langchain.schema.Generation",
"langchain.schema.AIMessage",
"langchain.llm_cache.update",
"langchain.schema.RunInfo",
"langchain.callbacks.manager.AsyncCallbackManager.configure",
"langchain.callbacks.manager.CallbackManager.configure",
"langchain.schema.LLMResult",
"l... | [((2315, 2352), 'pydantic.Field', 'Field', ([], {'default_factory': '_get_verbosity'}), '(default_factory=_get_verbosity)\n', (2320, 2352), False, 'from pydantic import Extra, Field, root_validator, validator\n'), ((2426, 2459), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, ... |
"""Base interface for large language models to expose."""
import inspect
import json
import warnings
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple, Union
import yaml
from pydantic import Extra, Field, root_validator, validator
impor... | [
"langchain.llm_cache.lookup",
"langchain.schema.Generation",
"langchain.schema.AIMessage",
"langchain.llm_cache.update",
"langchain.schema.RunInfo",
"langchain.callbacks.manager.AsyncCallbackManager.configure",
"langchain.callbacks.manager.CallbackManager.configure",
"langchain.schema.LLMResult",
"l... | [((2315, 2352), 'pydantic.Field', 'Field', ([], {'default_factory': '_get_verbosity'}), '(default_factory=_get_verbosity)\n', (2320, 2352), False, 'from pydantic import Extra, Field, root_validator, validator\n'), ((2426, 2459), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, ... |
"""Base interface for large language models to expose."""
import inspect
import json
import warnings
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple, Union
import yaml
from pydantic import Extra, Field, root_validator, validator
impor... | [
"langchain.llm_cache.lookup",
"langchain.schema.Generation",
"langchain.schema.AIMessage",
"langchain.llm_cache.update",
"langchain.schema.RunInfo",
"langchain.callbacks.manager.AsyncCallbackManager.configure",
"langchain.callbacks.manager.CallbackManager.configure",
"langchain.schema.LLMResult",
"l... | [((2315, 2352), 'pydantic.Field', 'Field', ([], {'default_factory': '_get_verbosity'}), '(default_factory=_get_verbosity)\n', (2320, 2352), False, 'from pydantic import Extra, Field, root_validator, validator\n'), ((2426, 2459), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, ... |
"""Base interface for large language models to expose."""
import inspect
import json
import warnings
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple, Union
import yaml
from pydantic import Extra, Field, root_validator, validator
impor... | [
"langchain.llm_cache.lookup",
"langchain.schema.Generation",
"langchain.schema.AIMessage",
"langchain.llm_cache.update",
"langchain.schema.RunInfo",
"langchain.callbacks.manager.AsyncCallbackManager.configure",
"langchain.callbacks.manager.CallbackManager.configure",
"langchain.schema.LLMResult",
"l... | [((2315, 2352), 'pydantic.Field', 'Field', ([], {'default_factory': '_get_verbosity'}), '(default_factory=_get_verbosity)\n', (2320, 2352), False, 'from pydantic import Extra, Field, root_validator, validator\n'), ((2426, 2459), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, ... |
"""Base interface for large language models to expose."""
import json
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Mapping, Optional, Tuple, Union
import yaml
from pydantic import BaseModel, Extra, Field, validator
import langchain
from langchain.callbacks import ge... | [
"langchain.llm_cache.lookup",
"langchain.schema.Generation",
"langchain.llm_cache.update",
"langchain.schema.LLMResult",
"langchain.callbacks.get_callback_manager"
] | [((1991, 2028), 'pydantic.Field', 'Field', ([], {'default_factory': '_get_verbosity'}), '(default_factory=_get_verbosity)\n', (1996, 2028), False, 'from pydantic import BaseModel, Extra, Field, validator\n'), ((2119, 2162), 'pydantic.Field', 'Field', ([], {'default_factory': 'get_callback_manager'}), '(default_factory=... |
import discord
from discord import app_commands
from discord.ext import commands
import langchain
from langchain.document_loaders import YoutubeLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains.summarize import load_summarize_chain
import torch
class YoutubeSummaryCog(c... | [
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.chains.summarize.load_summarize_chain",
"langchain.document_loaders.YoutubeLoader.from_youtube_url"
] | [((425, 528), 'discord.app_commands.command', 'app_commands.command', ([], {'name': '"""youtubesummary"""', 'description': '"""Summarize a YouTube video given its URL"""'}), "(name='youtubesummary', description=\n 'Summarize a YouTube video given its URL')\n", (445, 528), False, 'from discord import app_commands\n')... |
"""Base interface that all chains should implement."""
import json
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import yaml
from pydantic import BaseModel, Extra, Field, validator
import langchain
from langchain.callbacks import get_callback_manager
... | [
"langchain.callbacks.get_callback_manager"
] | [((1401, 1458), 'pydantic.Field', 'Field', ([], {'default_factory': 'get_callback_manager', 'exclude': '(True)'}), '(default_factory=get_callback_manager, exclude=True)\n', (1406, 1458), False, 'from pydantic import BaseModel, Extra, Field, validator\n'), ((1493, 1530), 'pydantic.Field', 'Field', ([], {'default_factory... |
#!/Users/mark/dev/ml/langchain/read_github/langchain_github/env/bin/python
# change above to the location of your local Python venv installation
import sys, os, shutil
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.append(parent_dir)
import pathlib
from langchain.docstore.docume... | [
"langchain.document_loaders.unstructured.UnstructuredFileLoader",
"langchain.chat_models.ChatOpenAI",
"langchain.PromptTemplate",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.text_splitter.PythonCodeTextSplitter",
"langchain.text_splitter.MarkdownTextSplitter",
"langchain.docstor... | [((245, 272), 'sys.path.append', 'sys.path.append', (['parent_dir'], {}), '(parent_dir)\n', (260, 272), False, 'import sys, os, shutil\n'), ((667, 692), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '(0)'}), '(temperature=0)\n', (677, 692), False, 'from langchain.chat_models import ChatOpenAI\n... |
import os
import json
from typing import List
from dotenv import load_dotenv
from pydantic import BaseModel, Field
from supabase.client import Client, create_client
from langchain.chat_models import ChatOpenAI
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.tools import StructuredTool
from langc... | [
"langchain.prompts.SystemMessagePromptTemplate.from_template",
"langchain.tools.StructuredTool",
"langchain.chat_models.ChatOpenAI",
"langchain.prompts.ChatPromptTemplate.from_messages",
"langchain.chains.openai_functions.create_structured_output_chain",
"langchain.embeddings.openai.OpenAIEmbeddings",
"... | [((528, 541), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (539, 541), False, 'from dotenv import load_dotenv\n'), ((799, 824), 'os.getenv', 'os.getenv', (['"""SUPABASE_URL"""'], {}), "('SUPABASE_URL')\n", (808, 824), False, 'import os\n'), ((840, 865), 'os.getenv', 'os.getenv', (['"""SUPABASE_KEY"""'], {}), ... |
import streamlit as st
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.prompts.prompt import PromptTemplate
from langchain.callbacks import get_openai_callback
#fix Error: module 'langchain' has no attribute 'verbose'
import langchain
langchain.verb... | [
"langchain.callbacks.get_openai_callback",
"langchain.chains.ConversationalRetrievalChain.from_llm",
"langchain.chat_models.ChatOpenAI",
"langchain.prompts.prompt.PromptTemplate"
] | [((1142, 1219), 'langchain.prompts.prompt.PromptTemplate', 'PromptTemplate', ([], {'template': 'qa_template', 'input_variables': "['context', 'question']"}), "(template=qa_template, input_variables=['context', 'question'])\n", (1156, 1219), False, 'from langchain.prompts.prompt import PromptTemplate\n'), ((1364, 1432),... |
import streamlit as st
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.prompts.prompt import PromptTemplate
from langchain.callbacks import get_openai_callback
#fix Error: module 'langchain' has no attribute 'verbose'
import langchain
langchain.verb... | [
"langchain.callbacks.get_openai_callback",
"langchain.chains.ConversationalRetrievalChain.from_llm",
"langchain.chat_models.ChatOpenAI",
"langchain.prompts.prompt.PromptTemplate"
] | [((1142, 1219), 'langchain.prompts.prompt.PromptTemplate', 'PromptTemplate', ([], {'template': 'qa_template', 'input_variables': "['context', 'question']"}), "(template=qa_template, input_variables=['context', 'question'])\n", (1156, 1219), False, 'from langchain.prompts.prompt import PromptTemplate\n'), ((1364, 1432),... |
import streamlit as st
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.prompts.prompt import PromptTemplate
from langchain.callbacks import get_openai_callback
#fix Error: module 'langchain' has no attribute 'verbose'
import langchain
langchain.verb... | [
"langchain.callbacks.get_openai_callback",
"langchain.chains.ConversationalRetrievalChain.from_llm",
"langchain.chat_models.ChatOpenAI",
"langchain.prompts.prompt.PromptTemplate"
] | [((1142, 1219), 'langchain.prompts.prompt.PromptTemplate', 'PromptTemplate', ([], {'template': 'qa_template', 'input_variables': "['context', 'question']"}), "(template=qa_template, input_variables=['context', 'question'])\n", (1156, 1219), False, 'from langchain.prompts.prompt import PromptTemplate\n'), ((1364, 1432),... |
import streamlit as st
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.prompts.prompt import PromptTemplate
from langchain.callbacks import get_openai_callback
#fix Error: module 'langchain' has no attribute 'verbose'
import langchain
langchain.verb... | [
"langchain.callbacks.get_openai_callback",
"langchain.chains.ConversationalRetrievalChain.from_llm",
"langchain.chat_models.ChatOpenAI",
"langchain.prompts.prompt.PromptTemplate"
] | [((1142, 1219), 'langchain.prompts.prompt.PromptTemplate', 'PromptTemplate', ([], {'template': 'qa_template', 'input_variables': "['context', 'question']"}), "(template=qa_template, input_variables=['context', 'question'])\n", (1156, 1219), False, 'from langchain.prompts.prompt import PromptTemplate\n'), ((1364, 1432),... |
"""
A simple CUI application to visualize and query a customer database using the `textual` package.
"""
from dataclasses import dataclass
import langchain
from langchain.cache import SQLiteCache
from langchain.llms import OpenAI
from textual.app import App, ComposeResult
from textual.containers import Horizontal
from... | [
"langchain.llms.OpenAI",
"langchain.cache.SQLiteCache"
] | [((447, 460), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {}), '()\n', (458, 460), False, 'from langchain.cache import SQLiteCache\n'), ((472, 495), 'langchain.llms.OpenAI', 'OpenAI', ([], {'max_tokens': '(1024)'}), '(max_tokens=1024)\n', (478, 495), False, 'from langchain.llms import OpenAI\n'), ((499, 521), 'l... |
import os
import cassio
import langchain
from langchain.cache import CassandraCache
from langchain_community.chat_models import ChatOpenAI
from langchain_core.messages import BaseMessage
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableLambda
use_cassandra = int(os.en... | [
"langchain_core.runnables.RunnableLambda",
"langchain_core.prompts.ChatPromptTemplate.from_template",
"langchain.cache.CassandraCache",
"langchain_community.chat_models.ChatOpenAI"
] | [((788, 831), 'langchain.cache.CassandraCache', 'CassandraCache', ([], {'session': 'None', 'keyspace': 'None'}), '(session=None, keyspace=None)\n', (802, 831), False, 'from langchain.cache import CassandraCache\n'), ((838, 850), 'langchain_community.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {}), '()\n', (848, 850), F... |
import numpy as np
from langchain.prompts import PromptTemplate
from langchain.schema import StrOutputParser, BaseRetriever
from langchain.schema.runnable import RunnablePassthrough
from langchain_google_genai import ChatGoogleGenerativeAI
from trulens_eval.feedback.provider.langchain import Langchain
from trulens_eva... | [
"langchain_google_genai.ChatGoogleGenerativeAI",
"langchain.prompts.PromptTemplate.from_template"
] | [((778, 801), 'src.embeddings.build_base_embeddings', 'build_base_embeddings', ([], {}), '()\n', (799, 801), False, 'from src.embeddings import build_base_embeddings\n'), ((813, 844), 'src.vectordb.load_chroma', 'load_chroma', (['embedding_function'], {}), '(embedding_function)\n', (824, 844), False, 'from src.vectordb... |
# import environment variables
from data.env_variables import AZURE_OPENAI_DEPLOYMENT_NAME, AZURE_OPENAI_MODEL_NAME, \
AZURE_OPENAI_API_ENDPOINT, OPENAI_API_VERSION, AZURE_OPENAI_API_KEY, \
HUGGINGFACE_API_TOKEN, LLAMA2_API_TOKEN, OPENAI_API_KEY, NVIDIANGC_API_KEY
from dotenv import load_dotenv
# import softwa... | [
"langchain_nvidia_ai_endpoints.ChatNVIDIA",
"langchain_community.document_loaders.Docx2txtLoader",
"langchain_community.document_loaders.PyPDFLoader",
"langchain.chat_models.ChatOpenAI",
"langchain.llms.gpt4all.GPT4All",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.chat_models.Az... | [((1820, 1835), 'langchain.globals.set_debug', 'set_debug', (['(True)'], {}), '(True)\n', (1829, 1835), False, 'from langchain.globals import set_debug\n'), ((1853, 1910), 'logging.basicConfig', 'log.basicConfig', ([], {'filename': '"""logs/app.log"""', 'level': 'log.DEBUG'}), "(filename='logs/app.log', level=log.DEBUG... |
import logging
import re
from typing import Any, List, Optional
import langchain
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
from init_openai import init_openai
logger = logging.getLogger("SoCloverAI")
init_openai()
model_name = "gpt-4-11... | [
"langchain.llm_cache.inner_cache.set_trial",
"langchain.chains.LLMChain",
"langchain_openai.ChatOpenAI",
"langchain.llm_cache.get_cache_stats_summary",
"langchain.llm_cache.clear_cache_stats"
] | [((252, 283), 'logging.getLogger', 'logging.getLogger', (['"""SoCloverAI"""'], {}), "('SoCloverAI')\n", (269, 283), False, 'import logging\n'), ((284, 297), 'init_openai.init_openai', 'init_openai', ([], {}), '()\n', (295, 297), False, 'from init_openai import init_openai\n'), ((2273, 2297), 're.compile', 're.compile',... |
import asyncio
import os
import json
import tiktoken
from transcribe import file_to_json_path, get_recordings, get_all_recordings, print_json
import langchain
from langchain.llms import OpenAI
from langchain.cache import SQLiteCache
from langchain.chat_models import ChatOpenAI
from langchain import PromptTemplate
from ... | [
"langchain.prompts.chat.HumanMessagePromptTemplate.from_template",
"langchain.chat_models.ChatOpenAI",
"langchain.cache.SQLiteCache",
"langchain.prompts.chat.SystemMessagePromptTemplate.from_template",
"langchain.prompts.chat.ChatPromptTemplate.from_messages"
] | [((822, 848), 'langchain.cache.SQLiteCache', 'SQLiteCache', (['database_path'], {}), '(database_path)\n', (833, 848), False, 'from langchain.cache import SQLiteCache\n'), ((919, 973), 'transformers.AutoTokenizer.from_pretrained', 'AutoTokenizer.from_pretrained', (['training_tokenizer_name'], {}), '(training_tokenizer_n... |
import json
import streamlit as st
import streamlit_ext as ste
import os
import time
import gc
import pandas as pd
from dotenv import load_dotenv
from langchain.chains import LLMChain # import LangChain libraries
from langchain.llms import OpenAI # import OpenAI model
from langchain.chat_models import ChatOpenAI # i... | [
"langchain.chains.LLMChain",
"langchain.chat_models.ChatOpenAI",
"langchain.llms.OpenAI",
"langchain.prompts.PromptTemplate",
"langchain.callbacks.get_openai_callback",
"langchain.llms.HuggingFacePipeline"
] | [((813, 832), 'dotenv.load_dotenv', 'load_dotenv', (['""".env"""'], {}), "('.env')\n", (824, 832), False, 'from dotenv import load_dotenv\n'), ((1156, 1212), 'streamlit.markdown', 'st.markdown', (['hide_default_format'], {'unsafe_allow_html': '(True)'}), '(hide_default_format, unsafe_allow_html=True)\n', (1167, 1212), ... |
import os
import re
import streamlit as st
import pandas as pd
import langchain
from langchain.agents import AgentExecutor
from langchain.callbacks import StreamlitCallbackHandler
from langchain.chat_models import ChatOpenAI
from langchain.tools import PythonAstREPLTool
from langchain.schema import SystemMessage
fro... | [
"langchain.tools.PythonAstREPLTool",
"langchain.schema.SystemMessage",
"langchain.agents.AgentExecutor.from_agent_and_tools",
"langchain.chat_models.ChatOpenAI"
] | [((1411, 1439), 'os.getenv', 'os.getenv', (['"""LANGCHAIN_DEBUG"""'], {}), "('LANGCHAIN_DEBUG')\n", (1420, 1439), False, 'import os\n'), ((1486, 1543), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""DataVizQA"""', 'page_icon': '"""🤖"""'}), "(page_title='DataVizQA', page_icon='🤖')\n", (1504... |
import inspect
from pathlib import Path
from typing import List
from langchain.chains import LLMChain
from langchain.chat_models.base import BaseChatModel
from langchain.prompts import PromptTemplate
def get_documents(file_path: Path, llm: BaseChatModel):
file_extension = file_path.suffix
loader_class_name =... | [
"langchain.prompts.PromptTemplate",
"langchain.chains.LLMChain"
] | [((946, 1275), 'langchain.prompts.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['file_extension', 'loaders']", 'template': '"""\n Among the following loaders, which is the best to load a "{file_extension}" file? Only give me one the class name without any other special characters. If no relev... |
"""Streamlit app for the ChatGPT clone."""
import dotenv
import langchain
import streamlit as st
import streamlit_chat
dotenv.load_dotenv(dotenv.find_dotenv(), override=True)
st.set_page_config(
page_title='You Custom Assistant',
page_icon='🤖'
)
st.subheader('Your Custom ChatGPT 🤖')
chat = langchain.chat_... | [
"langchain.schema.SystemMessage",
"langchain.schema.AIMessage",
"langchain.schema.HumanMessage",
"langchain.chat_models.ChatOpenAI"
] | [((178, 246), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""You Custom Assistant"""', 'page_icon': '"""🤖"""'}), "(page_title='You Custom Assistant', page_icon='🤖')\n", (196, 246), True, 'import streamlit as st\n'), ((257, 294), 'streamlit.subheader', 'st.subheader', (['"""Your Custom Chat... |
from dotenv import load_dotenv
import langchain
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent, AgentType
from agent.tools.ontology import ontology_tool
from agent.tools.interview import PAInterview
import os
from langchain.prompts import MessagesPlaceholder
from langchain.me... | [
"langchain.memory.ConversationBufferMemory",
"langchain.prompts.MessagesPlaceholder",
"langchain.agents.initialize_agent",
"langchain.chat_models.ChatOpenAI"
] | [((462, 529), 'langchain.memory.ConversationBufferMemory', 'ConversationBufferMemory', ([], {'memory_key': '"""memory"""', 'return_messages': '(True)'}), "(memory_key='memory', return_messages=True)\n", (486, 529), False, 'from langchain.memory import ConversationBufferMemory\n'), ((555, 568), 'dotenv.load_dotenv', 'lo... |
"""Chat agent with question answering
"""
from dotenv import load_dotenv
from langchain.cache import InMemoryCache
import langchain
import os
from dataclasses import dataclass
from langchain.chains import LLMChain, LLMRequestsChain
from langchain import Wikipedia, OpenAI
from langchain.agents.react.base import Docstor... | [
"langchain.agents.initialize_agent",
"langchain.agents.AgentExecutor.from_agent_and_tools",
"langchain.cache.InMemoryCache",
"langchain.agents.get_all_tool_names",
"langchain.agents.conversational.base.ConversationalAgent.create_prompt",
"langchain.prompts.PromptTemplate",
"langchain.Wikipedia",
"lang... | [((681, 694), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (692, 694), False, 'from dotenv import load_dotenv\n'), ((718, 733), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (731, 733), False, 'from langchain.cache import InMemoryCache\n'), ((749, 774), 'os.getenv', 'os.getenv', (['"""NE... |
"""Beta Feature: base interface for cache."""
from __future__ import annotations
import hashlib
import inspect
import json
import logging
import warnings
from abc import ABC, abstractmethod
from datetime import timedelta
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Optional,
Sequenc... | [
"langchain.schema.Generation",
"langchain.utils.get_from_env",
"langchain.load.dump.dumps",
"langchain.vectorstores.redis.Redis",
"langchain.vectorstores.redis.Redis.from_existing_index",
"langchain.load.load.loads"
] | [((950, 977), 'logging.getLogger', 'logging.getLogger', (['__file__'], {}), '(__file__)\n', (967, 977), False, 'import logging\n'), ((3422, 3440), 'sqlalchemy.ext.declarative.declarative_base', 'declarative_base', ([], {}), '()\n', (3438, 3440), False, 'from sqlalchemy.ext.declarative import declarative_base\n'), ((359... |
import streamlit as st
import openai
import os
from PyPDF2 import PdfReader
import io
import langchain
langchain.debug = True
from langchain.chains import LLMChain
from langchain.callbacks.base import BaseCallbackHandler
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from lang... | [
"langchain.agents.initialize_agent",
"langchain.chat_models.ChatOpenAI",
"langchain.prompts.SystemMessagePromptTemplate.from_template",
"langchain.schema.ChatMessage",
"langchain.schema.AIMessage",
"langchain.output_parsers.ResponseSchema",
"langchain.schema.HumanMessage",
"langchain.utilities.BingSea... | [((1448, 1480), 'os.environ.get', 'os.environ.get', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (1462, 1480), False, 'import os\n'), ((1509, 1552), 'os.environ.get', 'os.environ.get', (['"""AZURE_BLOB_CONNECTION_STR"""'], {}), "('AZURE_BLOB_CONNECTION_STR')\n", (1523, 1552), False, 'import os\n'), ((3241, 3... |
"""Create a ChatVectorDBChain for question/answering."""
from langchain.callbacks.manager import AsyncCallbackManager
from langchain.callbacks.tracers import LangChainTracer
from langchain.chains import (
ConversationalRetrievalChain, RetrievalQA
)
# from langchain.chains.chat_vector_db.prompts import (
# CONDENSE_... | [
"langchain.chains.llm.LLMChain",
"langchain.chat_models.ChatOpenAI",
"langchain.callbacks.manager.AsyncCallbackManager",
"langchain.memory.ConversationBufferWindowMemory",
"langchain.callbacks.tracers.LangChainTracer",
"langchain.chains.question_answering.load_qa_chain"
] | [((1070, 1094), 'langchain.callbacks.manager.AsyncCallbackManager', 'AsyncCallbackManager', (['[]'], {}), '([])\n', (1090, 1094), False, 'from langchain.callbacks.manager import AsyncCallbackManager\n'), ((1118, 1158), 'langchain.callbacks.manager.AsyncCallbackManager', 'AsyncCallbackManager', (['[question_handler]'], ... |
# Databricks notebook source
# MAGIC %md-sandbox
# MAGIC # 2/ Advanced chatbot with message history and filter using Langchain
# MAGIC
# MAGIC <img src="https://github.com/databricks-demos/dbdemos-resources/blob/main/images/product/chatbot-rag/llm-rag-self-managed-flow-2.png?raw=true" style="float: right; margin-left: ... | [
"langchain.chat_models.ChatDatabricks",
"langchain.schema.runnable.RunnablePassthrough",
"langchain.prompts.PromptTemplate",
"langchain.vectorstores.DatabricksVectorSearch",
"langchain.embeddings.DatabricksEmbeddings",
"langchain.schema.output_parser.StrOutputParser",
"langchain.schema.runnable.Runnable... | [((2610, 2742), 'langchain.prompts.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['question']", 'template': '"""You are an assistant. Give a short answer to this question: {question}"""'}), "(input_variables=['question'], template=\n 'You are an assistant. Give a short answer to this question: {questi... |
import streamlit as st
import dotenv
import langchain
import json
from cassandra.cluster import Session
from cassandra.query import PreparedStatement
from langchain.agents.agent_toolkits import create_retriever_tool, create_conversational_retrieval_agent
from langchain.chat_models import ChatOpenAI
from langchain.emb... | [
"langchain.chat_models.ChatOpenAI",
"langchain.schema.Document",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.schema.SystemMessage",
"langchain.agents.agent_toolkits.create_conversational_retrieval_agent",
"langchain.agents.agent_toolkits.create_retriever_tool"
] | [((5375, 5408), 'streamlit.set_page_config', 'st.set_page_config', ([], {'layout': '"""wide"""'}), "(layout='wide')\n", (5393, 5408), True, 'import streamlit as st\n'), ((5847, 5887), 'streamlit.chat_input', 'st.chat_input', ([], {'placeholder': '"""Ask chatbot"""'}), "(placeholder='Ask chatbot')\n", (5860, 5887), True... |
# import modules
import telebot
from telebot import *
import logging
import sqlite3
import os
import langchain
from langchain.text_splitter import RecursiveCharacterTextSplitter, CharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.document_loaders import TextLoader
from langch... | [
"langchain.embeddings.openai.OpenAIEmbeddings",
"langchain.chat_models.ChatOpenAI",
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.prompts.PromptTemplate.from_template"
] | [((571, 622), 'sqlite3.connect', 'sqlite3.connect', (['"""main.db"""'], {'check_same_thread': '(False)'}), "('main.db', check_same_thread=False)\n", (586, 622), False, 'import sqlite3\n'), ((661, 738), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.INFO', 'filename': '"""../info.log"""', 'filemod... |
# Standard Library Imports
import ast
import json
import os
import re
# Third-Party Imports
import textwrap
from typing import Any, Dict, List, Optional, Type
import langchain
import streamlit as st
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.tools import BaseTool... | [
"langchain.prompts.PromptTemplate",
"langchain.chat_models.ChatOpenAI",
"langchain.chains.LLMChain"
] | [((20314, 20720), 'pydantic.Field', 'Field', (['(True)'], {'description': '"""Set to \'True\' (default) to save the log files and trajectories of the simulation. If set to \'False\', the simulation is considered as being in a testing or preliminary scripting stage, utilizing default parameters and results are not saved... |
import langchain
from langchain_openai import AzureChatOpenAI
from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory
from langchain.prompts.chat import MessagesPlaceholder
from tech_agents.command import Command, check_command
from tech_agents.dispatcher import MainDispatcherAgent
from tech_agents... | [
"langchain.memory.ReadOnlySharedMemory"
] | [((1669, 1709), 'langchain.memory.ReadOnlySharedMemory', 'ReadOnlySharedMemory', ([], {'memory': 'self.memory'}), '(memory=self.memory)\n', (1689, 1709), False, 'from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory\n'), ((1926, 1953), 'tech_agents.command.check_command', 'check_command', (['user_... |
from typing import List, TypedDict
import tiktoken
from langchain.schema import AIMessage, BaseMessage, HumanMessage, SystemMessage
from langchain_openai import ChatOpenAI
from app.enums.langchain_enums import LangchainRole
from config import langchain_config, settings
class MessagesType(TypedDict):
role: str
... | [
"langchain.schema.SystemMessage",
"langchain.schema.HumanMessage",
"langchain.schema.AIMessage",
"langchain_openai.ChatOpenAI"
] | [((1294, 1318), 'langchain_openai.ChatOpenAI', 'ChatOpenAI', ([], {}), '(**parameters)\n', (1304, 1318), False, 'from langchain_openai import ChatOpenAI\n'), ((1726, 1760), 'tiktoken.get_encoding', 'tiktoken.get_encoding', (['encode_name'], {}), '(encode_name)\n', (1747, 1760), False, 'import tiktoken\n'), ((2281, 2318... |
# import modules
import telebot
from telebot import *
import logging
import sqlite3
import os
import langchain
from langchain.text_splitter import RecursiveCharacterTextSplitter, CharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.document_loaders import TextLoader
from langch... | [
"langchain.embeddings.openai.OpenAIEmbeddings",
"langchain.chat_models.ChatOpenAI",
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.prompts.PromptTemplate.from_template"
] | [((571, 622), 'sqlite3.connect', 'sqlite3.connect', (['"""main.db"""'], {'check_same_thread': '(False)'}), "('main.db', check_same_thread=False)\n", (586, 622), False, 'import sqlite3\n'), ((661, 738), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.INFO', 'filename': '"""../info.log"""', 'filemod... |
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