code stringlengths 141 78.9k | apis listlengths 1 23 | extract_api stringlengths 142 73.2k |
|---|---|---|
"""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.schema.Generation",
"langchain.llm_cache.update",
"langchain.llm_cache.lookup",
"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.NLTKTextSplitter",
"langchain.docstore.document.Document",
"langchain.text_splitter.RecursiveCharacterTextSplitter"
] | [((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.pydantic_v1.Field",
"langchain.callbacks.manager.AsyncCallbackManager.configure",
"langchain.load.dump.dumpd",
"langchain.callbacks.manager.CallbackManager.configure",
"langchain.schema.RunInfo",
"langchain.pydantic_v1.validator",
"langchain.pydantic_v1.root_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.chains.RetrievalQA.from_chain_type",
"langchain.vectorstores.FAISS.load_local",
"langchain.chat_models.ChatOpenAI",
"langchain.chains.HypotheticalDocumentEmbedder.from_llm",
"langchain.embeddings.OpenAIEmbeddings"
] | [((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')] |
####################################################################################
# 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.llms.VertexAI"
] | [((1273, 1385), 'langchain.llms.VertexAI', 'VertexAI', ([], {'model_name': '"""text-bison@001"""', 'max_output_tokens': '(1024)', 'temperature': '(0)', 'top_p': '(0)', 'top_k': '(1)', 'verbose': '(True)'}), "(model_name='text-bison@001', max_output_tokens=1024, temperature=0,\n top_p=0, top_k=1, verbose=True)\n", (1... |
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.cache.InMemoryCache",
"langchain.schema.HumanMessage",
"langchain.chat_models.ChatOpenAI"
] | [((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.prompts.PromptTemplate.from_template",
"langchain.tools.DuckDuckGoSearchRun"
] | [((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.text_splitter.RecursiveCharacterTextSplitter",
"langchain.docstore.document.Document",
"langchain.text_splitter.MarkdownTextSplitter",
"langchain.chat_models.ChatOpenAI",
"langchain.document_loaders.unstructured.UnstructuredFileLoader",
"langchain.text_splitter.PythonCodeTextSplitter",
"langc... | [((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.chat_models.ChatOpenAI",
"langchain.cache.SQLiteCache"
] | [((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.chains.openai_functions.create_structured_output_chain",
"langchain.tools.StructuredTool",
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.chat_models.ChatOpenAI",
"langchain.prompts.ChatPromptTemplate.from_messages",
"langchain.prompts.SystemMessagePromptTemplate.from_... | [((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.initialize_agent",
"langchain.agents.load_tools",
"langchain.llms.VertexAI"
] | [((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.chat_models.ChatAnthropic",
"langchain.prompts.PromptTemplate.from_template",
"langchain.schema.SystemMessage",
"langchain.cache.SQLiteCache"
] | [((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.chains.RetrievalQA.from_chain_type",
"langchain.chat_models.ChatOpenAI",
"langchain.retrievers.BM25Retriever.from_texts",
"langchain.vectorstores.FAISS.from_texts",
"langchain.retrievers.EnsembleRetriever",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((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.chains.LLMChain",
"langchain.prompts.PromptTemplate",
"langchain.cache.InMemoryCache",
"langchain.llms.OpenAI"
] | [((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.text_splitter.CharacterTextSplitter",
"langchain.chains.summarize.load_summarize_chain",
"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.LLMChain",
"langchain.cache.InMemoryCache",
"langchain.prompts.PromptTemplate",
"langchain.chains.ConversationChain"
] | [((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.chat_models.openai.ChatOpenAI",
"langchain_community.chat_models.ChatAnyscale",
"langchain_mistralai.chat_models.ChatMistralAI"
] | [((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.chat_models.openai.ChatOpenAI",
"langchain_community.chat_models.ChatAnyscale",
"langchain_mistralai.chat_models.ChatMistralAI"
] | [((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.schema.get_buffer_string",
"langchain.callbacks.stdout.StdOutCallbackHandler",
"langchain.callbacks.tracers.stdout.ConsoleCallbackHandler",
"langchain.callbacks.openai_info.OpenAICallbackHandler",
"langchain.callbacks.tracers.langchain.LangChainTracer",
"langchain.callbacks.tracers.langchain_v1... | [((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.schema.RunInfo",
"langchain.callbacks.manager.AsyncCallbackManager.configure",
"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 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.schema.RunInfo",
"langchain.callbacks.manager.AsyncCallbackManager.configure",
"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.callbacks.manager.AsyncCallbackManager.configure",
"langchain.schema.Generation",
"langchain.schema.get_buffer_string",
"langchain.callbacks.manager.CallbackManager.configure",
"langchain.schema.RunInfo",
"langchain.schema.AIMessage",
"langchain.llm_cache.lookup",
"langchain.llm_cache.updat... | [((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.callbacks.manager.AsyncCallbackManager.configure",
"langchain.schema.Generation",
"langchain.schema.get_buffer_string",
"langchain.callbacks.manager.CallbackManager.configure",
"langchain.schema.RunInfo",
"langchain.schema.AIMessage",
"langchain.llm_cache.lookup",
"langchain.llm_cache.updat... | [((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.callbacks.manager.AsyncCallbackManager.configure",
"langchain.schema.Generation",
"langchain.schema.get_buffer_string",
"langchain.callbacks.manager.CallbackManager.configure",
"langchain.schema.RunInfo",
"langchain.schema.AIMessage",
"langchain.llm_cache.lookup",
"langchain.llm_cache.updat... | [((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.callbacks.manager.AsyncCallbackManager.configure",
"langchain.schema.Generation",
"langchain.schema.get_buffer_string",
"langchain.callbacks.manager.CallbackManager.configure",
"langchain.schema.RunInfo",
"langchain.schema.AIMessage",
"langchain.llm_cache.lookup",
"langchain.llm_cache.updat... | [((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.schema.Generation",
"langchain.llm_cache.update",
"langchain.llm_cache.lookup",
"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.chains.summarize.load_summarize_chain",
"langchain.document_loaders.YoutubeLoader.from_youtube_url",
"langchain.text_splitter.RecursiveCharacterTextSplitter"
] | [((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.text_splitter.RecursiveCharacterTextSplitter",
"langchain.docstore.document.Document",
"langchain.text_splitter.MarkdownTextSplitter",
"langchain.chat_models.ChatOpenAI",
"langchain.document_loaders.unstructured.UnstructuredFileLoader",
"langchain.text_splitter.PythonCodeTextSplitter",
"langc... | [((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.chains.openai_functions.create_structured_output_chain",
"langchain.tools.StructuredTool",
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.chat_models.ChatOpenAI",
"langchain.prompts.ChatPromptTemplate.from_messages",
"langchain.prompts.SystemMessagePromptTemplate.from_... | [((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.chains.ConversationalRetrievalChain.from_llm",
"langchain.prompts.prompt.PromptTemplate",
"langchain.callbacks.get_openai_callback",
"langchain.chat_models.ChatOpenAI"
] | [((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.chains.ConversationalRetrievalChain.from_llm",
"langchain.prompts.prompt.PromptTemplate",
"langchain.callbacks.get_openai_callback",
"langchain.chat_models.ChatOpenAI"
] | [((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.chains.ConversationalRetrievalChain.from_llm",
"langchain.prompts.prompt.PromptTemplate",
"langchain.callbacks.get_openai_callback",
"langchain.chat_models.ChatOpenAI"
] | [((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.chains.ConversationalRetrievalChain.from_llm",
"langchain.prompts.prompt.PromptTemplate",
"langchain.callbacks.get_openai_callback",
"langchain.chat_models.ChatOpenAI"
] | [((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.prompts.ChatPromptTemplate.from_template",
"langchain_community.chat_models.ChatOpenAI",
"langchain_core.runnables.RunnableLambda",
"langchain.cache.CassandraCache"
] | [((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.prompts.PromptTemplate.from_template",
"langchain_google_genai.ChatGoogleGenerativeAI"
] | [((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_community.document_loaders.PyPDFLoader",
"langchain_community.document_loaders.Docx2txtLoader",
"langchain.llms.huggingface_pipeline.HuggingFacePipeline.from_model_id",
"langchain.vectorstores.chroma.Chroma",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.callbacks.streami... | [((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.get_cache_stats_summary",
"langchain_openai.ChatOpenAI",
"langchain.llm_cache.inner_cache.set_trial",
"langchain.llm_cache.clear_cache_stats",
"langchain.chains.LLMChain"
] | [((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.SystemMessagePromptTemplate.from_template",
"langchain.chat_models.ChatOpenAI",
"langchain.cache.SQLiteCache",
"langchain.prompts.chat.HumanMessagePromptTemplate.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.llms.OpenAI",
"langchain.llms.HuggingFacePipeline",
"langchain.chat_models.ChatOpenAI",
"langchain.callbacks.get_openai_callback",
"langchain.chains.LLMChain",
"langchain.prompts.PromptTemplate"
] | [((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.agents.AgentExecutor.from_agent_and_tools",
"langchain.tools.PythonAstREPLTool",
"langchain.schema.SystemMessage",
"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.chains.LLMChain",
"langchain.prompts.PromptTemplate"
] | [((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.AIMessage",
"langchain.schema.HumanMessage",
"langchain.schema.SystemMessage",
"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.agents.initialize_agent",
"langchain.memory.ConversationBufferMemory",
"langchain.prompts.MessagesPlaceholder",
"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.Wikipedia",
"langchain.agents.conversational.base.ConversationalAgent",
"langchain.agents.conversational.base.ConversationalAgent.create_prompt",
"langchain.agents.Tool... | [((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.utils.get_from_env",
"langchain.schema.Generation",
"langchain.load.dump.dumps",
"langchain.vectorstores.redis.Redis.from_existing_index",
"langchain.vectorstores.redis.Redis",
"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.schema.ChatMessage",
"langchain.agents.initialize_agent",
"langchain.vectorstores.FAISS.load_local",
"langchain.output_parsers.StructuredOutputParser.from_response_schemas",
"langchain.chat_models.ChatOpenAI",
"langchain.utilities.BingSearchAPIWrapper",
"langchain.schema.HumanMessage",
"lan... | [((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.question_answering.load_qa_chain",
"langchain.callbacks.tracers.LangChainTracer",
"langchain.memory.ConversationBufferWindowMemory",
"langchain.callbacks.manager.AsyncCallbackManager",
"langchain.chains.llm.LLMChain",
"langchain.chat_models.ChatOpenAI"
] | [((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.schema.output_parser.StrOutputParser",
"langchain.embeddings.DatabricksEmbeddings",
"langchain.schema.runnable.RunnablePassthrough",
"langchain.vectorstores.DatabricksVectorSearch",
"langchain.chat_models.ChatDatabricks",
"langchain.schema.runnable.RunnableLambda",
"langchain.prompts.PromptTe... | [((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.agents.agent_toolkits.create_conversational_retrieval_agent",
"langchain.agents.agent_toolkits.create_retriever_tool",
"langchain.schema.SystemMessage",
"langchain.embeddings.OpenAIEmbeddings"
] | [((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.prompts.PromptTemplate.from_template",
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.chat_models.ChatOpenAI",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((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.chains.LLMChain",
"langchain.prompts.PromptTemplate",
"langchain.chat_models.ChatOpenAI"
] | [((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.AIMessage",
"langchain_openai.ChatOpenAI",
"langchain.schema.SystemMessage",
"langchain.schema.HumanMessage"
] | [((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.prompts.PromptTemplate.from_template",
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.chat_models.ChatOpenAI",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((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... |
import langchain as lc
import openai as ai
import datasets as ds
import tiktoken as tk
import os
from langchain_openai import ChatOpenAI
from dotenv import load_dotenv
import os
# Load environment variables from .env file
load_dotenv()
# Get the OpenAI API key from the environment variable
openai_api_key = os.getenv... | [
"langchain.schema.AIMessage",
"langchain_openai.ChatOpenAI",
"langchain.schema.SystemMessage",
"langchain.schema.HumanMessage"
] | [((224, 237), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (235, 237), False, 'from dotenv import load_dotenv\n'), ((311, 338), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (320, 338), False, 'import os\n'), ((501, 563), 'langchain_openai.ChatOpenAI', 'ChatOpenAI', ([], {'... |
"""Push and pull to the LangChain Hub."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Optional
from langchain.load.dump import dumps
from langchain.load.load import loads
from langchain.utils import get_from_env
if TYPE_CHECKING:
from langchainhub import Client
def _get_client(api... | [
"langchain.load.load.loads",
"langchainhub.Client",
"langchain.load.dump.dumps",
"langchain.utils.get_from_env"
] | [((862, 894), 'langchainhub.Client', 'Client', (['api_url'], {'api_key': 'api_key'}), '(api_url, api_key=api_key)\n', (868, 894), False, 'from langchainhub import Client\n'), ((1234, 1247), 'langchain.load.dump.dumps', 'dumps', (['object'], {}), '(object)\n', (1239, 1247), False, 'from langchain.load.dump import dumps\... |
from datetime import timedelta
import os
import subprocess
import whisper
import tempfile
import argparse
import langchain
from langchain.chat_models import ChatOpenAI, ChatGooglePalm
from langchain.schema import HumanMessage, SystemMessage, AIMessage
from langchain.prompts import (
ChatPromptTemplate,
PromptTe... | [
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.chat_models.ChatOpenAI",
"langchain.prompts.ChatPromptTemplate.from_messages",
"langchain.callbacks.get_openai_callback",
"langchain.chains.LLMChain",
"langchain.prompts.SystemMessagePromptTemplate.from_template"
] | [((696, 747), 'langchain.prompts.SystemMessagePromptTemplate.from_template', 'SystemMessagePromptTemplate.from_template', (['template'], {}), '(template)\n', (737, 747), False, 'from langchain.prompts import ChatPromptTemplate, PromptTemplate, SystemMessagePromptTemplate, AIMessagePromptTemplate, HumanMessagePromptTemp... |
from langchain import OpenAI, LLMChain
from langchain.callbacks import StdOutCallbackHandler
from langchain.chat_models import ChatOpenAI
from src.agents.chat_chain import ChatChain
from src.agents.graphdb_traversal_chain import GraphDBTraversalChain, mem_query_template, mem_system_message
from src.memory.triple_modal... | [
"langchain.callbacks.StdOutCallbackHandler",
"langchain.chat_models.ChatOpenAI",
"langchain.cache.SQLiteCache"
] | [((495, 537), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {'database_path': '""".langchain.db"""'}), "(database_path='.langchain.db')\n", (506, 537), False, 'from langchain.cache import SQLiteCache\n'), ((563, 576), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (574, 576), False, 'from dotenv import loa... |
from __future__ import annotations
import logging
from functools import lru_cache
from typing import List, Optional
import langchain
from langchain.agents import AgentExecutor, Tool, initialize_agent
from langchain.agents.agent_types import AgentType
from langchain.callbacks import get_openai_callback
from langchain.... | [
"langchain.agents.initialize_agent",
"langchain_experimental.plan_and_execute.PlanAndExecute",
"langchain.chat_models.ChatOpenAI",
"langchain_experimental.plan_and_execute.load_chat_planner",
"langchain.callbacks.get_openai_callback",
"langchain_experimental.plan_and_execute.load_agent_executor"
] | [((946, 973), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (963, 973), False, 'import logging\n'), ((1004, 1041), 'shared.llm_manager_base.Cost', 'Cost', ([], {'prompt': '(0.0015)', 'completion': '(0.002)'}), '(prompt=0.0015, completion=0.002)\n', (1008, 1041), False, 'from shared.llm_m... |
import os
import utils
import traceback
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from langchain.chains import ConversationChain
from langchain.llms import OpenAI
import langchain
from langchain.cache import InMemoryCache
from langchain.llms import OpenAI
from langchain.chains.conversati... | [
"langchain.chains.conversation.memory.ConversationSummaryBufferMemory",
"langchain.llms.OpenAI",
"langchain.llms.AI21",
"langchain.llms.Cohere",
"langchain.chains.qa_with_sources.load_qa_with_sources_chain",
"langchain.llms.NLPCloud",
"langchain.prompts.PromptTemplate"
] | [((5785, 5803), 'SmartCache.SmartCache', 'SmartCache', (['CONFIG'], {}), '(CONFIG)\n', (5795, 5803), False, 'from SmartCache import SmartCache\n'), ((6330, 6345), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (6335, 6345), False, 'from flask import Flask, send_from_directory\n'), ((9830, 9890), 'waitress.... |
import os
import streamlit as st
from PyPDF2 import PdfReader
import langchain
langchain.verbose = False
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_cha... | [
"langchain.chains.question_answering.load_qa_chain",
"langchain.llms.OpenAI",
"langchain.callbacks.get_openai_callback",
"langchain.vectorstores.FAISS.from_texts",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((583, 600), 'requests.get', 'requests.get', (['url'], {}), '(url)\n', (595, 600), False, 'import requests\n'), ((853, 902), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""Webscrap chatbot"""'}), "(page_title='Webscrap chatbot')\n", (871, 902), True, 'import streamlit as st\n'), ((907, 936)... |
# Wrapper for Hugging Face APIs for llmlib
from llmlib.base_model_wrapper import BaseModelWrapper
from llama_index import ListIndex, SimpleDirectoryReader
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from llama_index import LangchainEmbedding, ServiceContext
from llama_index import ListIndex, Pr... | [
"langchain.embeddings.huggingface.HuggingFaceEmbeddings"
] | [((735, 830), 'transformers.pipeline', 'pipeline', (['"""text-generation"""'], {'model': 'model_name', 'model_kwargs': "{'torch_dtype': torch.bfloat16}"}), "('text-generation', model=model_name, model_kwargs={'torch_dtype':\n torch.bfloat16})\n", (743, 830), False, 'from transformers import pipeline\n'), ((1022, 103... |
import logging
import ConsoleInterface
import langchain.schema
from langchain.agents import initialize_agent, AgentType #create_pandas_dataframe_agent
logger = logging.getLogger('ConsoleInterface')
'''
def PandasDataframeAgent(llm, Dataframe):
"""
Create a PandasDataframeAgent object.
Parameters:
... | [
"langchain.agents.initialize_agent"
] | [((165, 202), 'logging.getLogger', 'logging.getLogger', (['"""ConsoleInterface"""'], {}), "('ConsoleInterface')\n", (182, 202), False, 'import logging\n'), ((946, 1067), 'langchain.agents.initialize_agent', 'initialize_agent', ([], {'agent': 'AgentType.CONVERSATIONAL_REACT_DESCRIPTION', 'llm': 'llm', 'tools': 'Tools', ... |
import csv
from ctypes import Array
from typing import Any, Coroutine, List, Tuple
import io
import time
import re
import os
from fastapi import UploadFile
import asyncio
import langchain
from langchain.chat_models import ChatOpenAI
from langchain.agents import create_csv_agent, load_tools, initialize_agent, AgentTyp... | [
"langchain.agents.initialize_agent",
"langchain.memory.ConversationSummaryBufferMemory",
"langchain.output_parsers.PydanticOutputParser",
"langchain.tools.PythonAstREPLTool",
"langchain.agents.create_pandas_dataframe_agent",
"langchain.chat_models.ChatOpenAI",
"langchain.callbacks.tracers.ConsoleCallbac... | [((963, 990), 'os.environ.get', 'os.environ.get', (['"""REDIS_URL"""'], {}), "('REDIS_URL')\n", (977, 990), False, 'import os\n'), ((1270, 1285), 'pandas.read_csv', 'pd.read_csv', (['df'], {}), '(df)\n', (1281, 1285), True, 'import pandas as pd\n'), ((1302, 1537), 'langchain.agents.create_pandas_dataframe_agent', 'crea... |
from typing import Dict, List, Optional
from langchain.agents.load_tools import (
_EXTRA_LLM_TOOLS,
_EXTRA_OPTIONAL_TOOLS,
_LLM_TOOLS,
)
from langflow.custom import customs
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.tools.constants import (
ALL_TOOLS_NAMES,
CU... | [
"langchain.agents.load_tools._LLM_TOOLS.keys",
"langchain.agents.load_tools._EXTRA_LLM_TOOLS.keys",
"langchain.agents.load_tools._EXTRA_OPTIONAL_TOOLS.keys"
] | [((690, 792), 'langflow.template.field.base.TemplateField', 'TemplateField', ([], {'field_type': '"""str"""', 'required': '(True)', 'is_list': '(False)', 'show': '(True)', 'placeholder': '""""""', 'value': '""""""'}), "(field_type='str', required=True, is_list=False, show=True,\n placeholder='', value='')\n", (703, ... |
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains.question_answering import load_qa_chain
from langchain.embeddings.openai import OpenAIEmbeddings
from streamlit_option_menu import option_menu
from deep_translator import GoogleTranslator
from langchain.vectorstores import Pinecone... | [
"langchain.chains.question_answering.load_qa_chain",
"langchain.vectorstores.Pinecone.from_texts",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.OpenAI",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((560, 573), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (571, 573), False, 'from dotenv import load_dotenv\n'), ((656, 749), 'pinecone.init', 'pinecone.init', ([], {'api_key': '"""db6b2a8c-d59e-48e1-8d5c-4c2704622937"""', 'environment': '"""gcp-starter"""'}), "(api_key='db6b2a8c-d59e-48e1-8d5c-4c2704622937... |
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
# invoking custom retriever
from redundant_filter_retriever import RedundantFilterRetriever
from dotenv import load_dotenv
import langchain
... | [
"langchain.vectorstores.Chroma",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.chat_models.ChatOpenAI"
] | [((344, 357), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (355, 357), False, 'from dotenv import load_dotenv\n'), ((392, 404), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {}), '()\n', (402, 404), False, 'from langchain.chat_models import ChatOpenAI\n'), ((418, 436), 'langchain.embeddings.OpenAIEmb... |
import os
import logging
import pickle
import ssl
import dill
import langchain
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI, GooglePalm
from langchain.chains import LLMChain, RetrievalQAWithSourcesChain, AnalyzeDocumentChain
from langchain.chains.qa_with_sources import load_qa_with_so... | [
"langchain.vectorstores.FAISS.from_documents",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.llms.OpenAI"
] | [((670, 710), 'langchain.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0.7)', 'max_tokens': '(1024)'}), '(temperature=0.7, max_tokens=1024)\n', (676, 710), False, 'from langchain.llms import OpenAI, GooglePalm\n'), ((728, 746), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (744, 746), ... |
# imports
import os, shutil, json, re
import pathlib
from langchain.document_loaders.unstructured import UnstructuredFileLoader
from langchain.document_loaders.unstructured import UnstructuredAPIFileLoader
from langchain.document_loaders import UnstructuredURLLoader
from langchain.docstore.document import Document
fro... | [
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.document_loaders.UnstructuredURLLoader",
"langchain.document_loaders.unstructured.UnstructuredAPIFileLoader",
"langchain.text_splitter.MarkdownTextSplitter",
"langchain.schema.Document",
"langchain.document_loaders.unstructured.Unstructu... | [((719, 732), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (730, 732), False, 'from dotenv import load_dotenv\n'), ((784, 892), 're.compile', 're.compile', (['"""http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\\\\\\\(\\\\\\\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+"""'], {}), "(\n 'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.... |
from langchain.llms import LlamaCpp
from langchain.chat_models import ChatOpenAI
from langchain.chains.llm import LLMChain
from langchain.prompts import PromptTemplate
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.c... | [
"langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler",
"langchain.chains.llm.LLMChain",
"langchain.chat_models.ChatOpenAI",
"langchain.llms.LlamaCpp",
"langchain.cache.SQLiteCache",
"langchain.prompts.PromptTemplate.from_template"
] | [((476, 489), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (487, 489), False, 'from dotenv import load_dotenv\n'), ((505, 529), 'os.getenv', 'os.getenv', (['"""OPEN_AI_KEY"""'], {}), "('OPEN_AI_KEY')\n", (514, 529), False, 'import os\n'), ((584, 632), 'utils.setup_logger', 'setup_logger', (['"""contr_detector... |
import streamlit as st
import torch
from transformers import (
AutoTokenizer, AutoModelForCausalLM,
BitsAndBytesConfig,
TextStreamer,
)
import whisper
import os
############ config ############
# general config
whisper_model_names=["tiny", "base", "small", "medium", "large"]
data_root_path = os.path.join('... | [
"langchain.embeddings.huggingface.HuggingFaceEmbeddings"
] | [((306, 331), 'os.path.join', 'os.path.join', (['"""."""', '"""data"""'], {}), "('.', 'data')\n", (318, 331), False, 'import os\n'), ((772, 798), 'streamlit.title', 'st.title', (['"""LLAMA RAG Demo"""'], {}), "('LLAMA RAG Demo')\n", (780, 798), True, 'import streamlit as st\n'), ((799, 811), 'streamlit.divider', 'st.di... |
import streamlit as st
import langchain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain import OpenAI, VectorDBQA
from langchain.chains import RetrievalQAWithSourcesChain
import PyPDF2
#... | [
"langchain.chains.RetrievalQAWithSourcesChain.from_chain_type",
"langchain.vectorstores.Chroma.from_texts",
"langchain.OpenAI",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((868, 932), 'streamlit.set_page_config', 'st.set_page_config', ([], {'layout': '"""centered"""', 'page_title': '"""Multidoc_QnA"""'}), "(layout='centered', page_title='Multidoc_QnA')\n", (886, 932), True, 'import streamlit as st\n'), ((933, 958), 'streamlit.header', 'st.header', (['"""Multidoc_QnA"""'], {}), "('Multi... |
from __future__ import annotations
import asyncio
import functools
import logging
import os
import warnings
from contextlib import asynccontextmanager, contextmanager
from contextvars import ContextVar
from typing import (
Any,
AsyncGenerator,
Dict,
Generator,
List,
Optional,
Type,
Type... | [
"langchain.schema.get_buffer_string",
"langchain.callbacks.stdout.StdOutCallbackHandler",
"langchain.callbacks.tracers.wandb.WandbTracer",
"langchain.callbacks.openai_info.OpenAICallbackHandler",
"langchain.callbacks.tracers.stdout.ConsoleCallbackHandler",
"langchain.callbacks.tracers.langchain.LangChainT... | [((1114, 1141), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1131, 1141), False, 'import logging\n'), ((1286, 1329), 'contextvars.ContextVar', 'ContextVar', (['"""openai_callback"""'], {'default': 'None'}), "('openai_callback', default=None)\n", (1296, 1329), False, 'from contextvars i... |
import langchain
from langchain.llms import VertexAI
from langchain.prompts import PromptTemplate, load_prompt
import wandb
from wandb.integration.langchain import WandbTracer
import streamlit as st
from google.oauth2 import service_account
# account_info = dict(st.secrets["GOOGLE_APPLICATION_CREDENTIALS"])
# credenti... | [
"langchain.prompts.load_prompt"
] | [((469, 513), 'wandb.login', 'wandb.login', ([], {'key': "st.secrets['WANDB_API_KEY']"}), "(key=st.secrets['WANDB_API_KEY'])\n", (480, 513), False, 'import wandb\n'), ((519, 666), 'wandb.init', 'wandb.init', ([], {'project': '"""generate_prd_v3_palm"""', 'config': "{'model': 'text-bison-001', 'temperature': 0.2}", 'ent... |
#!/usr/bin/env python
# coding: utf-8
# # LangChain: Agents
#
# ## Outline:
#
# * Using built in LangChain tools: DuckDuckGo search and Wikipedia
# * Defining your own tools
# In[ ]:
import os
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv()) # read local .env file
import warnings
warni... | [
"langchain.agents.initialize_agent",
"langchain.tools.python.tool.PythonREPLTool",
"langchain.agents.load_tools",
"langchain.chat_models.ChatOpenAI"
] | [((315, 348), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {}), "('ignore')\n", (338, 348), False, 'import warnings\n'), ((735, 761), 'datetime.date', 'datetime.date', (['(2024)', '(6)', '(12)'], {}), '(2024, 6, 12)\n', (748, 761), False, 'import datetime\n'), ((1324, 1366), 'langchain.chat_... |
import sys
import pandas as pd
from llama_index import Document, set_global_service_context, StorageContext, load_index_from_storage, VectorStoreIndex
from llama_index.indices.base import BaseIndex
from llama_index.storage.docstore import SimpleDocumentStore
from llama_index.storage.index_store import SimpleIndexStore... | [
"langchain.embeddings.OpenAIEmbeddings"
] | [((1194, 1252), 'logging.basicConfig', 'logging.basicConfig', ([], {'stream': 'sys.stdout', 'level': 'logging.INFO'}), '(stream=sys.stdout, level=logging.INFO)\n', (1213, 1252), False, 'import logging\n'), ((1435, 1462), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (1444, 1462), Fal... |
import arxiv
import openai
import langchain
import pinecone
from langchain_community.document_loaders import ArxivLoader
from langchain.docstore.document import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstore... | [
"langchain.chains.question_answering.load_qa_chain",
"langchain.chains.summarize.load_summarize_chain",
"langchain.vectorstores.Pinecone.from_documents",
"langchain.chat_models.ChatOpenAI",
"langchain.OpenAI",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((690, 703), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (701, 703), False, 'from dotenv import load_dotenv\n'), ((722, 749), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (731, 749), False, 'import os\n'), ((769, 798), 'os.getenv', 'os.getenv', (['"""PINECONE_API_KEY"""'... |
"""Create a ChatVectorDBChain for question/answering."""
from langchain.callbacks.manager import AsyncCallbackManager
from langchain.callbacks.tracers import LangChainTracer
from langchain.chains import ChatVectorDBChain
from langchain.chains.chat_vector_db.prompts import (CONDENSE_QUESTION_PROMPT,
... | [
"langchain.chains.question_answering.load_qa_chain",
"langchain.callbacks.tracers.LangChainTracer",
"langchain.callbacks.manager.AsyncCallbackManager",
"langchain.prompts.chat.SystemMessagePromptTemplate.from_template",
"langchain.chat_models.ChatOpenAI",
"langchain.chains.llm.LLMChain",
"langchain.prom... | [((2109, 2151), 'langchain.prompts.chat.ChatPromptTemplate.from_messages', 'ChatPromptTemplate.from_messages', (['messages'], {}), '(messages)\n', (2141, 2151), False, 'from langchain.prompts.chat import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate\n'), ((1986, 2037), 'langchain.prompts.c... |
from llama_index.core import VectorStoreIndex,SimpleDirectoryReader,ServiceContext
print("VectorStoreIndex,SimpleDirectoryReader,ServiceContext imported")
from llama_index.llms.huggingface import HuggingFaceLLM
print("HuggingFaceLLM imported")
from llama_index.core.prompts.prompts import SimpleInputPrompt
print("Simple... | [
"langchain.embeddings.huggingface.HuggingFaceEmbeddings"
] | [((872, 885), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (883, 885), False, 'from dotenv import load_dotenv\n'), ((905, 931), 'os.environ.get', 'os.environ.get', (['"""HF_TOKEN"""'], {}), "('HF_TOKEN')\n", (919, 931), False, 'import os\n'), ((1262, 1315), 'llama_index.core.prompts.prompts.SimpleInputPrompt'... |
import itertools
from langchain.cache import InMemoryCache, SQLiteCache
import langchain
import pandas as pd
from certa.utils import merge_sources
import ellmer.models
import ellmer.metrics
from time import sleep, time
import traceback
from tqdm import tqdm
cache = "sqlite"
samples = 2
explanation_granularity = "attri... | [
"langchain.cache.InMemoryCache",
"langchain.cache.SQLiteCache"
] | [((399, 414), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (412, 414), False, 'from langchain.cache import InMemoryCache, SQLiteCache\n'), ((465, 507), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {'database_path': '""".langchain.db"""'}), "(database_path='.langchain.db')\n", (476, 507), Fa... |
# TODO speed up by extracting resume in structure and job beore sending to gpt4
import re
from bs4 import BeautifulSoup
from pyppeteer import launch
import uuid
import time
from PIL import Image
import numpy as np
from fastapi import FastAPI, File, UploadFile, Form
from fastapi import Request
from langchain.prompts ... | [
"langchain.schema.HumanMessage",
"langchain.cache.SQLiteCache",
"langchain.prompts.ChatPromptTemplate.from_messages",
"langchain.chat_models.ChatOpenAI",
"langchain.schema.SystemMessage",
"langchain.chains.LLMChain"
] | [((1278, 1291), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (1289, 1291), False, 'from dotenv import load_dotenv\n'), ((1523, 1570), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'model': '"""gpt-4-0613"""', 'temperature': '(0.1)'}), "(model='gpt-4-0613', temperature=0.1)\n", (1533, 1570), False, '... |
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