code stringlengths 141 79.4k | apis listlengths 1 23 | extract_api stringlengths 126 73.2k |
|---|---|---|
# Import necessary libraries
import hubspot
import langchain
import openai
import streamlit
# Define function to analyze customer data using Langchain
def analyze_customer_data(customer_data):
langchain.analyze(customer_data)
# returns analyzed data
# Define function to send personalized appointmen... | [
"langchain.analyze"
] | [((206, 238), 'langchain.analyze', 'langchain.analyze', (['customer_data'], {}), '(customer_data)\n', (223, 238), False, 'import langchain\n'), ((499, 548), 'openai.generate_message', 'openai.generate_message', (['customer_name', 'appt_time'], {}), '(customer_name, appt_time)\n', (522, 548), False, 'import openai\n'), ... |
import langchain
from dotenv import load_dotenv
from langchain.agents import initialize_agent, AgentType
from langchain.chat_models import ChatOpenAI
from datetime import timedelta, datetime
import chainlit as cl
from utils.custom_tools import CustomTrinoListTable, CustomTrinoTableSchema, CustomTrinoSqlQuery, CustomTri... | [
"langchain.agents.initialize_agent",
"langchain.chat_models.ChatOpenAI"
] | [((371, 384), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (382, 384), False, 'from dotenv import load_dotenv\n'), ((1351, 1439), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'model_name': '"""gpt-3.5-turbo"""', 'temperature': '(0)', 'verbose': '(False)', 'streaming': '(True)'}), "(model_name='gpt-... |
import asyncio
import inspect
import warnings
from abc import ABC, abstractmethod
from functools import partial
from typing import (
Any,
AsyncIterator,
Dict,
Iterator,
List,
Optional,
Sequence,
cast,
)
import langchain
from langchain.callbacks.base import BaseCallbackManager
from langc... | [
"langchain.pydantic_v1.Field",
"langchain.callbacks.manager.AsyncCallbackManager.configure",
"langchain.schema.messages.AIMessage",
"langchain.schema.ChatResult",
"langchain.load.dump.dumps",
"langchain.callbacks.manager.CallbackManager.configure",
"langchain.load.dump.dumpd",
"langchain.schema.RunInf... | [((1364, 1401), 'langchain.pydantic_v1.Field', 'Field', ([], {'default_factory': '_get_verbosity'}), '(default_factory=_get_verbosity)\n', (1369, 1401), False, 'from langchain.pydantic_v1 import Field, root_validator\n'), ((1475, 1508), 'langchain.pydantic_v1.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)... |
import sys
import chromadb
import pandas
import sqlite3
from langchain.embeddings import OpenAIEmbeddings
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import LLMChainExtractor
from langchain.text_splitter import CharacterTextSplitter
from langchain.vect... | [
"langchain.document_loaders.TextLoader",
"langchain.text_splitter.CharacterTextSplitter.from_tiktoken_encoder",
"langchain.llms.OpenAI",
"langchain.chat_models.ChatOpenAI",
"langchain.vectorstores.Chroma.from_documents",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.retrievers.document_compressors... | [((1247, 1255), 'langchain.llms.OpenAI', 'OpenAI', ([], {}), '()\n', (1253, 1255), False, 'from langchain.llms import OpenAI\n'), ((1336, 1354), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (1352, 1354), False, 'from langchain.embeddings import OpenAIEmbeddings\n'), ((1408, 1451), 'lan... |
from langchain import OpenAI, SQLDatabase
from langchain_experimental.sql import SQLDatabaseChain
from gptcache.adapter.langchain_models import LangChainLLMs
from gptcache.session import Session
from gptcache import cache
from gptcache.embedding import Onnx
from gptcache.manager import CacheBase, VectorBase, get_data_m... | [
"langchain.SQLDatabase.from_uri",
"langchain_experimental.sql.SQLDatabaseChain",
"langchain.OpenAI"
] | [((437, 450), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (448, 450), False, 'from dotenv import load_dotenv\n'), ((637, 667), 'gptcache.session.Session', 'Session', ([], {'name': '"""sqlite-example"""'}), "(name='sqlite-example')\n", (644, 667), False, 'from gptcache.session import Session\n'), ((726, 772),... |
from langchain_openai import ChatOpenAI
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferWindowMemory, FileChatMessageHistory
from langchain.prompts import (
MessagesPlaceholder,
HumanMessagePromptTemplate,
ChatPromptTemplate,
)
import sqlite3, re, openai
from dotenv impo... | [
"langchain_openai.ChatOpenAI",
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.prompts.MessagesPlaceholder",
"langchain.memory.FileChatMessageHistory",
"langchain.chains.LLMChain"
] | [((469, 540), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {'category': 'LangChainDeprecationWarning'}), "('ignore', category=LangChainDeprecationWarning)\n", (492, 540), False, 'import warnings\n'), ((542, 555), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (553, 555), False, 'from... |
import os
import sys
module_path = ".."
sys.path.append(os.path.abspath(module_path))
import langchain
from langchain.document_loaders import ConfluenceLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA
from ... | [
"langchain.embeddings.BedrockEmbeddings",
"langchain.vectorstores.FAISS.load_local",
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.indexes.vectorstore.VectorStoreIndexWrapper",
"langchain.document_loaders.ConfluenceLoader",
"langchai... | [((58, 86), 'os.path.abspath', 'os.path.abspath', (['module_path'], {}), '(module_path)\n', (73, 86), False, 'import os\n'), ((606, 649), 'os.environ.get', 'os.environ.get', (['"""BEDROCK_ASSUME_ROLE"""', 'None'], {}), "('BEDROCK_ASSUME_ROLE', None)\n", (620, 649), False, 'import os\n'), ((668, 712), 'os.environ.get', ... |
import pickle
import torch
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate,)
import numpy as np
import random
np.int = int #fixing shap/numpy compatibility issue
from sklearn.metrics import classificati... | [
"langchain.prompts.chat.SystemMessagePromptTemplate.from_template",
"langchain.llms.HuggingFacePipeline",
"langchain.chat_models.ChatOpenAI",
"langchain.chat_models.AzureChatOpenAI",
"langchain.prompts.chat.HumanMessagePromptTemplate.from_template",
"langchain.chains.LLMChain",
"langchain.prompts.chat.C... | [((17189, 17247), 'langchain.prompts.chat.SystemMessagePromptTemplate.from_template', 'SystemMessagePromptTemplate.from_template', (['system_template'], {}), '(system_template)\n', (17230, 17247), False, 'from langchain.prompts.chat import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate\n'),... |
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,
Sequence,
... | [
"langchain.callbacks.stdout.StdOutCallbackHandler",
"langchain.callbacks.tracers.wandb.WandbTracer",
"langchain.callbacks.openai_info.OpenAICallbackHandler",
"langchain.callbacks.tracers.stdout.ConsoleCallbackHandler",
"langchain.callbacks.tracers.langchain.LangChainTracer",
"langchain.callbacks.tracers.l... | [((1198, 1225), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1215, 1225), False, 'import logging\n'), ((1370, 1413), 'contextvars.ContextVar', 'ContextVar', (['"""openai_callback"""'], {'default': 'None'}), "('openai_callback', default=None)\n", (1380, 1413), False, 'from contextvars i... |
import os
from transformers import AutoTokenizer
from configs import (
EMBEDDING_MODEL,
KB_ROOT_PATH,
CHUNK_SIZE,
OVERLAP_SIZE,
ZH_TITLE_ENHANCE,
logger,
log_verbose,
text_splitter_dict,
LLM_MODEL,
TEXT_SPLITTER_NAME,
)
import importlib
from text_splitter import zh_title_enhanc... | [
"langchain.text_splitter.TextSplitter.from_huggingface_tokenizer",
"langchain.text_splitter.TextSplitter.from_tiktoken_encoder",
"langchain.docstore.document.Document",
"langchain.text_splitter.TextSplitter"
] | [((964, 1011), 'os.path.join', 'os.path.join', (['KB_ROOT_PATH', 'knowledge_base_name'], {}), '(KB_ROOT_PATH, knowledge_base_name)\n', (976, 1011), False, 'import os\n'), ((1789, 1807), 'server.utils.embedding_device', 'embedding_device', ([], {}), '()\n', (1805, 1807), False, 'from server.utils import run_in_thread_po... |
"""Create a LangChain chain for question/answering."""
from langchain.callbacks.manager import AsyncCallbackManager
from langchain.callbacks.tracers import LangChainTracer
from langchain.chains import ConversationalRetrievalChain, RetrievalQAWithSourcesChain
from langchain.chains.chat_vector_db.prompts import CONDENSE_... | [
"langchain.llms.huggingface_endpoint.HuggingFaceEndpoint",
"langchain.schema.StrOutputParser",
"langchain.callbacks.manager.AsyncCallbackManager",
"langchain_core.runnables.RunnablePassthrough",
"langchain.prompts.ChatPromptTemplate.from_template"
] | [((1270, 1283), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (1281, 1283), False, 'from dotenv import load_dotenv\n'), ((1298, 1322), 'langchain.callbacks.manager.AsyncCallbackManager', 'AsyncCallbackManager', (['[]'], {}), '([])\n', (1318, 1322), False, 'from langchain.callbacks.manager import AsyncCallbackM... |
"""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
if TYPE_CHECKING:
from langchainhub import Client
def _get_client(api_url: Optional[str] = None, api_key: Opti... | [
"langchain.load.load.loads",
"langchainhub.Client",
"langchain.load.dump.dumps"
] | [((671, 703), 'langchainhub.Client', 'Client', (['api_url'], {'api_key': 'api_key'}), '(api_url, api_key=api_key)\n', (677, 703), False, 'from langchainhub import Client\n'), ((1907, 1920), 'langchain.load.dump.dumps', 'dumps', (['object'], {}), '(object)\n', (1912, 1920), False, 'from langchain.load.dump import dumps\... |
from Google import Create_Service
import gspread
import langchain
from langchain.chat_models import ChatOpenAI
import pymysql
from langchain.document_loaders.csv_loader import UnstructuredCSVLoader
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain import PromptTemplate, LLMChain
im... | [
"langchain.LLMChain",
"langchain.document_loaders.csv_loader.UnstructuredCSVLoader",
"langchain.chat_models.ChatOpenAI",
"langchain.PromptTemplate.from_template",
"langchain.chains.combine_documents.stuff.StuffDocumentsChain",
"langchain.PromptTemplate"
] | [((402, 430), 'pymysql.install_as_MySQLdb', 'pymysql.install_as_MySQLdb', ([], {}), '()\n', (428, 430), False, 'import pymysql\n'), ((433, 446), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (444, 446), False, 'from dotenv import load_dotenv\n'), ((465, 494), 'os.getenv', 'os.getenv', (['"""OPENAI_API_TOKEN"""... |
"""A bot that uses either GPT-4 or ChatGPT to generate responses without any hidden prompts."""
from typing import AsyncGenerator
from langchain.chat_models import PromptLayerChatOpenAI
from langchain.schema import ChatMessage
from mergedbots import MergedMessage, MergedBot
from mergedbots.ext.langchain_integration im... | [
"langchain.schema.ChatMessage"
] | [((436, 631), 'experiments.common.bot_manager.bot_manager.create_bot', 'bot_manager.create_bot', ([], {'handle': '"""PlainGPT"""', 'description': '"""A bot that uses either GPT-4 or ChatGPT to generate responses. Useful when the user seeks information and needs factual answers."""'}), "(handle='PlainGPT', description=\... |
from flask import Flask, request, render_template, jsonify, make_response
import os
from llama_index import SimpleDirectoryReader, VectorStoreIndex, StorageContext, load_index_from_storage
from llama_index.embeddings import LangchainEmbedding
from llama_index.llms.palm import PaLM
from langchain.embeddings.gpt4all impo... | [
"langchain.embeddings.gpt4all.GPT4AllEmbeddings"
] | [((470, 483), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (481, 483), False, 'from dotenv import load_dotenv\n'), ((555, 582), 'os.getenv', 'os.getenv', (['"""GOOGLE_API_KEY"""'], {}), "('GOOGLE_API_KEY')\n", (564, 582), False, 'import os\n'), ((590, 616), 'llama_index.llms.palm.PaLM', 'PaLM', ([], {'api_key... |
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 langchain
import openai
from dotenv import load_dotenv
from langchain.chains import ConversationChain
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.schema import HumanMessage
load_dotenv()
langchain.verbose = True
# openai.log = "debug"
chat ... | [
"langchain.chains.ConversationChain",
"langchain.memory.ConversationBufferMemory",
"langchain.chat_models.ChatOpenAI"
] | [((251, 264), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (262, 264), False, 'from dotenv import load_dotenv\n'), ((322, 375), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'model_name': '"""gpt-3.5-turbo"""', 'temperature': '(0)'}), "(model_name='gpt-3.5-turbo', temperature=0)\n", (332, 375), Fals... |
from __future__ import annotations
import asyncio
import functools
import logging
import os
import uuid
from concurrent.futures import ThreadPoolExecutor
from contextlib import asynccontextmanager, contextmanager
from contextvars import ContextVar
from typing import (
TYPE_CHECKING,
Any,
AsyncGenerator,
... | [
"langchain.callbacks.stdout.StdOutCallbackHandler",
"langchain.callbacks.tracers.wandb.WandbTracer",
"langchain.callbacks.openai_info.OpenAICallbackHandler",
"langchain.callbacks.tracers.run_collector.RunCollectorCallbackHandler",
"langchain.callbacks.tracers.stdout.ConsoleCallbackHandler",
"langchain.cal... | [((1530, 1557), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1547, 1557), False, 'import logging\n'), ((1626, 1669), 'contextvars.ContextVar', 'ContextVar', (['"""openai_callback"""'], {'default': 'None'}), "('openai_callback', default=None)\n", (1636, 1669), False, 'from contextvars i... |
import os
import streamlit as st
import time
import langchain
from langchain.chains import RetrievalQAWithSourcesChain, RetrievalQA
from langchain.chains.qa_with_sources.loading import load_qa_with_sources_chain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders... | [
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain_openai.OpenAIEmbeddings",
"langchain_community.document_loaders.UnstructuredURLLoader",
"langchain_community.vectorstores.FAISS.from_documents",
"langchain_openai.OpenAI"
] | [((485, 515), 'configparser.RawConfigParser', 'configparser.RawConfigParser', ([], {}), '()\n', (513, 515), False, 'import configparser\n'), ((640, 668), 'streamlit.title', 'st.title', (['"""URL Insighter 🔗🔍"""'], {}), "('URL Insighter 🔗🔍')\n", (648, 668), True, 'import streamlit as st\n'), ((669, 697), 'streamlit.... |
from PyPDF2 import PdfReader
import os
import pandas as pd
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
from l... | [
"langchain.chains.question_answering.load_qa_chain",
"langchain.cache.InMemoryCache",
"langchain.llms.OpenAI",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((634, 649), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (647, 649), False, 'from langchain.cache import InMemoryCache\n'), ((656, 677), 'langchain.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0)'}), '(temperature=0)\n', (662, 677), False, 'from langchain.llms import OpenAI\n'), ((692, 710), ... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2023/2/24 16:23
# @Author : Jack
# @File : main.py
# @Software: PyCharm
import asyncio
import logging
import socket
import sys
import consul
import langchain
import os
import grpc
from langchain import PromptTemplate, LLMChain
from langchai... | [
"langchain.LLMChain",
"langchain.PromptTemplate",
"langchain.chat_models.ChatOpenAI"
] | [((558, 606), 'socket.socket', 'socket.socket', (['socket.AF_INET', 'socket.SOCK_DGRAM'], {}), '(socket.AF_INET, socket.SOCK_DGRAM)\n', (571, 606), False, 'import socket\n'), ((875, 938), 'consul.Consul', 'consul.Consul', ([], {'host': 'consul_addr', 'port': 'consul_port', 'verify': '(False)'}), '(host=consul_addr, por... |
import re
from typing import Any, Dict, Literal, TypedDict, cast
from dotenv import load_dotenv
load_dotenv()
import langchain
import langchain.schema
from langchain.chat_models import ChatOpenAI
from langchain.llms import GPT4All
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQAWith... | [
"langchain.schema.OutputParserException",
"langchain.memory.ConversationBufferWindowMemory",
"langchain.chat_models.ChatOpenAI",
"langchain.vectorstores.Chroma"
] | [((97, 110), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (108, 110), False, 'from dotenv import load_dotenv\n'), ((694, 767), 'langchain.vectorstores.Chroma', 'Chroma', ([], {'persist_directory': 'persist_directory', 'embedding_function': 'embedding'}), '(persist_directory=persist_directory, embedding_functi... |
import langchain_visualizer # isort:skip # noqa: F401
import asyncio
import vcr_langchain as vcr
from langchain import PromptTemplate
from langchain.chains import LLMChain
from langchain.llms import OpenAI
# ========================== Start of langchain example code ==========================
# https://langchain.re... | [
"langchain.chains.LLMChain",
"langchain_visualizer.visualize",
"langchain.llms.OpenAI",
"langchain.PromptTemplate"
] | [((387, 408), 'langchain.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0)'}), '(temperature=0)\n', (393, 408), False, 'from langchain.llms import OpenAI\n'), ((418, 534), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['product']", 'template': '"""What is a good name for a company that makes {... |
"""Test logic on base chain class."""
from typing import Any, Dict, List, Optional
import pytest
from langchain.callbacks.base import CallbackManager
from langchain.chains.base import Chain
from langchain.schema import BaseMemory
from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
class... | [
"langchain.callbacks.base.CallbackManager"
] | [((3986, 4007), 'tests.unit_tests.callbacks.fake_callback_handler.FakeCallbackHandler', 'FakeCallbackHandler', ([], {}), '()\n', (4005, 4007), False, 'from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler\n'), ((4460, 4481), 'tests.unit_tests.callbacks.fake_callback_handler.FakeCallbackHandle... |
"""Test caching for LLMs and ChatModels."""
from typing import Dict, Generator, List, Union
import pytest
from _pytest.fixtures import FixtureRequest
from sqlalchemy import create_engine
from sqlalchemy.orm import Session
import langchain
from langchain.cache import (
InMemoryCache,
SQLAlchemyCache,
)
from la... | [
"langchain.llm_cache.clear",
"langchain.schema.Generation",
"langchain.chat_models.FakeListChatModel",
"langchain.llms.FakeListLLM",
"langchain.schema.ChatGeneration",
"langchain.schema.HumanMessage",
"langchain.schema.AIMessage",
"langchain.chat_models.base.dumps"
] | [((796, 846), 'pytest.fixture', 'pytest.fixture', ([], {'autouse': '(True)', 'params': 'CACHE_OPTIONS'}), '(autouse=True, params=CACHE_OPTIONS)\n', (810, 846), False, 'import pytest\n'), ((1524, 1557), 'langchain.llms.FakeListLLM', 'FakeListLLM', ([], {'responses': '[response]'}), '(responses=[response])\n', (1535, 155... |
import json
import pytest
from langchain.prompts import ChatPromptTemplate
from langchain.schema.exceptions import LangChainException
from langchain.schema.messages import HumanMessage
from llm_api.backends.bedrock import BedrockCaller, BedrockModelCallError
pytest_plugins = ("pytest_asyncio",)
def test_bedrock_ca... | [
"langchain.prompts.ChatPromptTemplate.from_messages",
"langchain.schema.exceptions.LangChainException"
] | [((597, 625), 'llm_api.backends.bedrock.BedrockCaller', 'BedrockCaller', (['mock_settings'], {}), '(mock_settings)\n', (610, 625), False, 'from llm_api.backends.bedrock import BedrockCaller, BedrockModelCallError\n'), ((994, 1025), 'llm_api.backends.bedrock.BedrockCaller.generate_prompt', 'BedrockCaller.generate_prompt... |
"""Test Tracer classes."""
from __future__ import annotations
import json
from datetime import datetime
from typing import Tuple
from unittest.mock import patch
from uuid import UUID, uuid4
import pytest
from freezegun import freeze_time
from langchain.callbacks.tracers.langchain import LangChainTracer
from langchai... | [
"langchain.callbacks.tracers.langchain.LangChainTracer",
"langchain.schema.LLMResult",
"langchain.callbacks.tracers.schemas.TracerSession"
] | [((441, 485), 'uuid.UUID', 'UUID', (['"""4fbf7c55-2727-4711-8964-d821ed4d4e2a"""'], {}), "('4fbf7c55-2727-4711-8964-d821ed4d4e2a')\n", (445, 485), False, 'from uuid import UUID, uuid4\n'), ((499, 543), 'uuid.UUID', 'UUID', (['"""57a08cc4-73d2-4236-8378-549099d07fad"""'], {}), "('57a08cc4-73d2-4236-8378-549099d07fad')\n... |
import langchain_visualizer # isort:skip # noqa: F401
import asyncio
from typing import Any, Dict, List, Optional
import vcr_langchain as vcr
from langchain import PromptTemplate
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains import LLMChain
from langchain.chains.base import... | [
"langchain.chains.LLMChain",
"langchain_visualizer.visualize",
"langchain.llms.OpenAI",
"langchain.PromptTemplate"
] | [((1254, 1262), 'langchain.llms.OpenAI', 'OpenAI', ([], {}), '()\n', (1260, 1262), False, 'from langchain.llms import OpenAI\n'), ((1275, 1391), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['product']", 'template': '"""What is a good name for a company that makes {product}?"""'}), "(input_va... |
"""Test Momento cache functionality.
To run tests, set the environment variable MOMENTO_AUTH_TOKEN to a valid
Momento auth token. This can be obtained by signing up for a free
Momento account at https://gomomento.com/.
"""
from __future__ import annotations
import uuid
from datetime import timedelta
from typing impor... | [
"langchain.cache.MomentoCache",
"langchain.schema.LLMResult",
"langchain.schema.Generation"
] | [((569, 599), 'pytest.fixture', 'pytest.fixture', ([], {'scope': '"""module"""'}), "(scope='module')\n", (583, 599), False, 'import pytest\n'), ((1637, 1646), 'tests.unit_tests.llms.fake_llm.FakeLLM', 'FakeLLM', ([], {}), '()\n', (1644, 1646), False, 'from tests.unit_tests.llms.fake_llm import FakeLLM\n'), ((2507, 2516... |
import threading
import time
import unittest
import unittest.mock
from typing import Any, Dict
from uuid import UUID
import pytest
from langchain_core.outputs import LLMResult
from langchain_core.tracers.langchain import LangChainTracer
from langchain_core.tracers.schemas import Run
from langsmith import Client
def ... | [
"langchain_core.tracers.langchain.LangChainTracer",
"langchain_core.outputs.LLMResult"
] | [((639, 675), 'unittest.mock.MagicMock', 'unittest.mock.MagicMock', ([], {'spec': 'Client'}), '(spec=Client)\n', (662, 675), False, 'import unittest\n'), ((729, 759), 'langchain_core.tracers.langchain.LangChainTracer', 'LangChainTracer', ([], {'client': 'client'}), '(client=client)\n', (744, 759), False, 'from langchai... |
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... |
"""A tracer that runs evaluators over completed runs."""
from __future__ import annotations
import logging
from concurrent.futures import Future, ThreadPoolExecutor
from typing import Any, Dict, List, Optional, Sequence, Set, Union
from uuid import UUID
import langsmith
from langsmith.evaluation.evaluator import Eval... | [
"langchain.callbacks.tracers.langchain.get_client",
"langchain.callbacks.manager.tracing_v2_enabled"
] | [((562, 589), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (579, 589), False, 'import logging\n'), ((2581, 2597), 'uuid.UUID', 'UUID', (['example_id'], {}), '(example_id)\n', (2585, 2597), False, 'from uuid import UUID\n'), ((2687, 2716), 'langchain.callbacks.tracers.langchain.get_clien... |
"""A tracer that runs evaluators over completed runs."""
from __future__ import annotations
import logging
from concurrent.futures import Future, ThreadPoolExecutor
from typing import Any, Dict, List, Optional, Sequence, Set, Union
from uuid import UUID
import langsmith
from langsmith.evaluation.evaluator import Eval... | [
"langchain.callbacks.tracers.langchain.get_client",
"langchain.callbacks.manager.tracing_v2_enabled"
] | [((562, 589), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (579, 589), False, 'import logging\n'), ((2581, 2597), 'uuid.UUID', 'UUID', (['example_id'], {}), '(example_id)\n', (2585, 2597), False, 'from uuid import UUID\n'), ((2687, 2716), 'langchain.callbacks.tracers.langchain.get_clien... |
"""A tracer that runs evaluators over completed runs."""
from __future__ import annotations
import logging
from concurrent.futures import Future, ThreadPoolExecutor
from typing import Any, Dict, List, Optional, Sequence, Set, Union
from uuid import UUID
import langsmith
from langsmith.evaluation.evaluator import Eval... | [
"langchain.callbacks.tracers.langchain.get_client",
"langchain.callbacks.manager.tracing_v2_enabled"
] | [((562, 589), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (579, 589), False, 'import logging\n'), ((2581, 2597), 'uuid.UUID', 'UUID', (['example_id'], {}), '(example_id)\n', (2585, 2597), False, 'from uuid import UUID\n'), ((2687, 2716), 'langchain.callbacks.tracers.langchain.get_clien... |
"""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\... |
"""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\... |
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 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... |
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 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 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 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, ... |
# 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]|[$-_@.&+]|[!*\\\\\\\\(\\\\\\\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+"""'], {}), "(\n 'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.... |
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... |
import asyncio
import inspect
import warnings
from abc import ABC, abstractmethod
from functools import partial
from typing import (
Any,
AsyncIterator,
Dict,
Iterator,
List,
Optional,
Sequence,
cast,
)
import langchain
from langchain.callbacks.base import BaseCallbackManager
from langc... | [
"langchain.pydantic_v1.Field",
"langchain.callbacks.manager.AsyncCallbackManager.configure",
"langchain.schema.messages.AIMessage",
"langchain.schema.ChatResult",
"langchain.load.dump.dumps",
"langchain.callbacks.manager.CallbackManager.configure",
"langchain.load.dump.dumpd",
"langchain.schema.RunInf... | [((1364, 1401), 'langchain.pydantic_v1.Field', 'Field', ([], {'default_factory': '_get_verbosity'}), '(default_factory=_get_verbosity)\n', (1369, 1401), False, 'from langchain.pydantic_v1 import Field, root_validator\n'), ((1475, 1508), 'langchain.pydantic_v1.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)... |
import os
from transformers import AutoTokenizer
from configs import (
EMBEDDING_MODEL,
KB_ROOT_PATH,
CHUNK_SIZE,
OVERLAP_SIZE,
ZH_TITLE_ENHANCE,
logger,
log_verbose,
text_splitter_dict,
LLM_MODEL,
TEXT_SPLITTER_NAME,
)
import importlib
from text_splitter import zh_title_enhanc... | [
"langchain.text_splitter.TextSplitter.from_huggingface_tokenizer",
"langchain.text_splitter.TextSplitter.from_tiktoken_encoder",
"langchain.docstore.document.Document",
"langchain.text_splitter.TextSplitter"
] | [((964, 1011), 'os.path.join', 'os.path.join', (['KB_ROOT_PATH', 'knowledge_base_name'], {}), '(KB_ROOT_PATH, knowledge_base_name)\n', (976, 1011), False, 'import os\n'), ((1789, 1807), 'server.utils.embedding_device', 'embedding_device', ([], {}), '()\n', (1805, 1807), False, 'from server.utils import run_in_thread_po... |
import os
from transformers import AutoTokenizer
from configs import (
EMBEDDING_MODEL,
KB_ROOT_PATH,
CHUNK_SIZE,
OVERLAP_SIZE,
ZH_TITLE_ENHANCE,
logger,
log_verbose,
text_splitter_dict,
LLM_MODEL,
TEXT_SPLITTER_NAME,
)
import importlib
from text_splitter import zh_title_enhanc... | [
"langchain.text_splitter.TextSplitter.from_huggingface_tokenizer",
"langchain.text_splitter.TextSplitter.from_tiktoken_encoder",
"langchain.docstore.document.Document",
"langchain.text_splitter.TextSplitter"
] | [((964, 1011), 'os.path.join', 'os.path.join', (['KB_ROOT_PATH', 'knowledge_base_name'], {}), '(KB_ROOT_PATH, knowledge_base_name)\n', (976, 1011), False, 'import os\n'), ((1789, 1807), 'server.utils.embedding_device', 'embedding_device', ([], {}), '()\n', (1805, 1807), False, 'from server.utils import run_in_thread_po... |
"""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
if TYPE_CHECKING:
from langchainhub import Client
def _get_client(api_url: Optional[str] = None, api_key: Opti... | [
"langchain.load.load.loads",
"langchainhub.Client",
"langchain.load.dump.dumps"
] | [((671, 703), 'langchainhub.Client', 'Client', (['api_url'], {'api_key': 'api_key'}), '(api_url, api_key=api_key)\n', (677, 703), False, 'from langchainhub import Client\n'), ((1907, 1920), 'langchain.load.dump.dumps', 'dumps', (['object'], {}), '(object)\n', (1912, 1920), False, 'from langchain.load.dump import dumps\... |
"""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
if TYPE_CHECKING:
from langchainhub import Client
def _get_client(api_url: Optional[str] = None, api_key: Opti... | [
"langchain.load.load.loads",
"langchainhub.Client",
"langchain.load.dump.dumps"
] | [((671, 703), 'langchainhub.Client', 'Client', (['api_url'], {'api_key': 'api_key'}), '(api_url, api_key=api_key)\n', (677, 703), False, 'from langchainhub import Client\n'), ((1907, 1920), 'langchain.load.dump.dumps', 'dumps', (['object'], {}), '(object)\n', (1912, 1920), False, 'from langchain.load.dump import dumps\... |
"""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
if TYPE_CHECKING:
from langchainhub import Client
def _get_client(api_url: Optional[str] = None, api_key: Opti... | [
"langchain.load.load.loads",
"langchainhub.Client",
"langchain.load.dump.dumps"
] | [((671, 703), 'langchainhub.Client', 'Client', (['api_url'], {'api_key': 'api_key'}), '(api_url, api_key=api_key)\n', (677, 703), False, 'from langchainhub import Client\n'), ((1907, 1920), 'langchain.load.dump.dumps', 'dumps', (['object'], {}), '(object)\n', (1912, 1920), False, 'from langchain.load.dump import dumps\... |
"""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
if TYPE_CHECKING:
from langchainhub import Client
def _get_client(api_url: Optional[str] = None, api_key: Opti... | [
"langchain.load.load.loads",
"langchainhub.Client",
"langchain.load.dump.dumps"
] | [((671, 703), 'langchainhub.Client', 'Client', (['api_url'], {'api_key': 'api_key'}), '(api_url, api_key=api_key)\n', (677, 703), False, 'from langchainhub import Client\n'), ((1907, 1920), 'langchain.load.dump.dumps', 'dumps', (['object'], {}), '(object)\n', (1912, 1920), False, 'from langchain.load.dump import dumps\... |
import langchain_visualizer # isort:skip # noqa: F401
import asyncio
import vcr_langchain as vcr
from langchain import PromptTemplate
from langchain.chains import LLMChain
from langchain.llms import OpenAI
# ========================== Start of langchain example code ==========================
# https://langchain.re... | [
"langchain.chains.LLMChain",
"langchain_visualizer.visualize",
"langchain.llms.OpenAI",
"langchain.PromptTemplate"
] | [((387, 408), 'langchain.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0)'}), '(temperature=0)\n', (393, 408), False, 'from langchain.llms import OpenAI\n'), ((418, 534), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['product']", 'template': '"""What is a good name for a company that makes {... |
"""Test logic on base chain class."""
from typing import Any, Dict, List, Optional
import pytest
from langchain.callbacks.base import CallbackManager
from langchain.chains.base import Chain
from langchain.schema import BaseMemory
from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
class... | [
"langchain.callbacks.base.CallbackManager"
] | [((3986, 4007), 'tests.unit_tests.callbacks.fake_callback_handler.FakeCallbackHandler', 'FakeCallbackHandler', ([], {}), '()\n', (4005, 4007), False, 'from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler\n'), ((4460, 4481), 'tests.unit_tests.callbacks.fake_callback_handler.FakeCallbackHandle... |
"""Test logic on base chain class."""
from typing import Any, Dict, List, Optional
import pytest
from langchain.callbacks.base import CallbackManager
from langchain.chains.base import Chain
from langchain.schema import BaseMemory
from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
class... | [
"langchain.callbacks.base.CallbackManager"
] | [((3986, 4007), 'tests.unit_tests.callbacks.fake_callback_handler.FakeCallbackHandler', 'FakeCallbackHandler', ([], {}), '()\n', (4005, 4007), False, 'from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler\n'), ((4460, 4481), 'tests.unit_tests.callbacks.fake_callback_handler.FakeCallbackHandle... |
"""Test logic on base chain class."""
from typing import Any, Dict, List, Optional
import pytest
from langchain.callbacks.base import CallbackManager
from langchain.chains.base import Chain
from langchain.schema import BaseMemory
from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
class... | [
"langchain.callbacks.base.CallbackManager"
] | [((3986, 4007), 'tests.unit_tests.callbacks.fake_callback_handler.FakeCallbackHandler', 'FakeCallbackHandler', ([], {}), '()\n', (4005, 4007), False, 'from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler\n'), ((4460, 4481), 'tests.unit_tests.callbacks.fake_callback_handler.FakeCallbackHandle... |
"""Test logic on base chain class."""
from typing import Any, Dict, List, Optional
import pytest
from langchain.callbacks.base import CallbackManager
from langchain.chains.base import Chain
from langchain.schema import BaseMemory
from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
class... | [
"langchain.callbacks.base.CallbackManager"
] | [((3986, 4007), 'tests.unit_tests.callbacks.fake_callback_handler.FakeCallbackHandler', 'FakeCallbackHandler', ([], {}), '()\n', (4005, 4007), False, 'from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler\n'), ((4460, 4481), 'tests.unit_tests.callbacks.fake_callback_handler.FakeCallbackHandle... |
"""Test Momento cache functionality.
To run tests, set the environment variable MOMENTO_AUTH_TOKEN to a valid
Momento auth token. This can be obtained by signing up for a free
Momento account at https://gomomento.com/.
"""
from __future__ import annotations
import uuid
from datetime import timedelta
from typing impor... | [
"langchain.cache.MomentoCache",
"langchain.schema.LLMResult",
"langchain.schema.Generation"
] | [((569, 599), 'pytest.fixture', 'pytest.fixture', ([], {'scope': '"""module"""'}), "(scope='module')\n", (583, 599), False, 'import pytest\n'), ((1637, 1646), 'tests.unit_tests.llms.fake_llm.FakeLLM', 'FakeLLM', ([], {}), '()\n', (1644, 1646), False, 'from tests.unit_tests.llms.fake_llm import FakeLLM\n'), ((2507, 2516... |
# 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
os.environ[... | [
"langchain.llms.Replicate"
] | [((488, 595), '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", (497, 595), False, 'from langchain.llms import R... |
"""**Document Transformers** are classes to transform Documents.
**Document Transformers** usually used to transform a lot of Documents in a single run.
**Class hierarchy:**
.. code-block::
BaseDocumentTransformer --> <name> # Examples: DoctranQATransformer, DoctranTextTranslator
**Main helpers:**
.. code-bl... | [
"langchain.utils.interactive_env.is_interactive_env"
] | [((677, 697), 'langchain.utils.interactive_env.is_interactive_env', 'is_interactive_env', ([], {}), '()\n', (695, 697), False, 'from langchain.utils.interactive_env import is_interactive_env\n'), ((707, 1102), 'warnings.warn', 'warnings.warn', (['f"""Importing document transformers from langchain is deprecated. Importi... |
# Make sure to install the following packages: dlt, langchain, duckdb, python-dotenv, openai, weaviate-client
import logging
from langchain.text_splitter import RecursiveCharacterTextSplitter
from marshmallow import Schema, fields
from loaders.loaders import _document_loader
# Add the parent directory to sys.path
l... | [
"langchain.schema.Document",
"langchain.retrievers.WeaviateHybridSearchRetriever",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((319, 358), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.INFO'}), '(level=logging.INFO)\n', (338, 358), False, 'import logging\n'), ((508, 527), 'tracemalloc.start', 'tracemalloc.start', ([], {}), '()\n', (525, 527), False, 'import tracemalloc\n'), ((681, 694), 'dotenv.load_dotenv', 'load_dot... |
# based on: https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pgvector.html
from typing import List, Tuple
from langchain.embeddings.openai import OpenAIEmbeddings
import langchain.vectorstores.pgvector
class RepoSearcher:
store: langchain.vectorstores.pgvector.PGVector
def __init_... | [
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((469, 487), 'langchain.embeddings.openai.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (485, 487), False, 'from langchain.embeddings.openai import OpenAIEmbeddings\n')] |
import os
import chardet
import importlib
from pathlib import Path
from WebUI.text_splitter import zh_title_enhance as func_zh_title_enhance
from WebUI.Server.document_loaders import RapidOCRPDFLoader, RapidOCRLoader
import langchain.document_loaders
from langchain.docstore.document import Document
from langchain.text... | [
"langchain.text_splitter.TextSplitter.from_huggingface_tokenizer",
"langchain.text_splitter.TextSplitter.from_tiktoken_encoder",
"langchain.text_splitter.TextSplitter"
] | [((2330, 2343), 'WebUI.configs.basicconfig.GetKbConfig', 'GetKbConfig', ([], {}), '()\n', (2341, 2343), False, 'from WebUI.configs.basicconfig import GetKbConfig, GetKbRootPath, GetTextSplitterDict\n'), ((2363, 2387), 'WebUI.configs.basicconfig.GetKbRootPath', 'GetKbRootPath', (['kb_config'], {}), '(kb_config)\n', (237... |
# with this Python script we are loading all Knowledge Asset vector embeddings into Milvus VectorDB
import os
import pandas as pd
import langchain
from dotenv import load_dotenv
from langchain.document_loaders import DataFrameLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores imp... | [
"langchain.document_loaders.DataFrameLoader",
"langchain.embeddings.HuggingFaceEmbeddings"
] | [((332, 345), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (343, 345), False, 'from dotenv import load_dotenv\n'), ((352, 396), 'pandas.read_csv', 'pd.read_csv', (['"""../utils/output.tsv"""'], {'sep': '"""\t"""'}), "('../utils/output.tsv', sep='\\t')\n", (363, 396), True, 'import pandas as pd\n'), ((406, 433... |
import os
import langchain
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain import OpenAI, VectorDBQA
from langchain.document_loaders import TextLoader
from langchain.document_loaders impo... | [
"langchain.text_splitter.CharacterTextSplitter",
"langchain.agents.agent_toolkits.VectorStoreToolkit",
"langchain.agents.agent_toolkits.VectorStoreInfo",
"langchain.document_loaders.TextLoader",
"langchain.agents.agent_toolkits.create_vectorstore_agent",
"langchain.document_loaders.WebBaseLoader",
"lang... | [((586, 607), 'langchain.OpenAI', 'OpenAI', ([], {'temperature': '(0)'}), '(temperature=0)\n', (592, 607), False, 'from langchain import OpenAI, VectorDBQA\n'), ((622, 655), 'langchain.document_loaders.TextLoader', 'TextLoader', (['"""the_needed_text.txt"""'], {}), "('the_needed_text.txt')\n", (632, 655), False, 'from ... |
"""Beta Feature: base interface for cache."""
from __future__ import annotations
import hashlib
import inspect
import json
import logging
from abc import ABC, abstractmethod
from datetime import timedelta
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Optional,
Sequence,
Tuple,
... | [
"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"
] | [((918, 945), 'logging.getLogger', 'logging.getLogger', (['__file__'], {}), '(__file__)\n', (935, 945), False, 'import logging\n'), ((3390, 3408), 'sqlalchemy.ext.declarative.declarative_base', 'declarative_base', ([], {}), '()\n', (3406, 3408), False, 'from sqlalchemy.ext.declarative import declarative_base\n'), ((356... |
# INITIALIZATION
# LangChain imports
import langchain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.chains import SequentialChain
# General imports
import os
from dotenv import load_dotenv
# Load API key from .env
load_dotenv()
os.... | [
"langchain.chains.SequentialChain",
"langchain.chains.LLMChain",
"langchain.prompts.PromptTemplate",
"langchain.llms.OpenAI"
] | [((303, 316), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (314, 316), False, 'from dotenv import load_dotenv\n'), ((348, 375), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (357, 375), False, 'import os\n'), ((785, 808), 'langchain.llms.OpenAI', 'OpenAI', ([], {'temperatur... |
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import DirectoryLoader, TextLoader
import bibtexparser
import langchain
import os
import glob
from dotenv import load_dotenv
impor... | [
"langchain.document_loaders.DirectoryLoader",
"langchain.vectorstores.FAISS.load_local",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.vectorstores.FAISS.from_documents",
"langchain.embeddings.OpenAIEmbeddings"
] | [((380, 393), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (391, 393), False, 'from dotenv import load_dotenv\n'), ((764, 898), 'langchain.document_loaders.DirectoryLoader', 'DirectoryLoader', (['source_path'], {'show_progress': '(True)', 'use_multithreading': '(True)', 'loader_cls': 'TextLoader', 'loader_kwa... |
from llama_index import (
ServiceContext,
SimpleDirectoryReader,
StorageContext,
VectorStoreIndex,
)
from llama_index.vector_stores.qdrant import QdrantVectorStore
from tqdm import tqdm
import arxiv
import os
import argparse
import yaml
import qdrant_client
from langchain.embeddings.huggingface import H... | [
"langchain.embeddings.huggingface.HuggingFaceEmbeddings"
] | [((2566, 2591), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (2589, 2591), False, 'import argparse\n'), ((970, 984), 'arxiv.Client', 'arxiv.Client', ([], {}), '()\n', (982, 984), False, 'import arxiv\n'), ((1003, 1108), 'arxiv.Search', 'arxiv.Search', ([], {'query': 'search_query', 'max_resul... |
import langchain_helper as lch
import streamlit as st
st.title('Pets Name Generator')
user_animal_type = st.sidebar.selectbox("What is your pet?",("Cat", "Dog","Rabbit","Hamster",))
user_pet_color = st.sidebar.text_area(f"What color is your {user_animal_type}?",max_chars=15)
if user_pet_color:
response = lch.g... | [
"langchain_helper.generate_pet_name"
] | [((56, 87), 'streamlit.title', 'st.title', (['"""Pets Name Generator"""'], {}), "('Pets Name Generator')\n", (64, 87), True, 'import streamlit as st\n'), ((108, 186), 'streamlit.sidebar.selectbox', 'st.sidebar.selectbox', (['"""What is your pet?"""', "('Cat', 'Dog', 'Rabbit', 'Hamster')"], {}), "('What is your pet?', (... |
import logging
import os
import pickle
import tempfile
import streamlit as st
from dotenv import load_dotenv
from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames as GenParams
from ibm_watson_machine_learning.foundation_models.utils.enums import ModelTypes
from langchain.callbacks import StdOutCallb... | [
"langchain.chains.question_answering.load_qa_chain",
"langchain.embeddings.HuggingFaceHubEmbeddings",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.callbacks.StdOutCallbackHandler",
"langchain.vectorstores.FAISS.from_documents",
"langchain.document_loaders.PyPDFLoader"
] | [((861, 993), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""Retrieval Augmented Generation"""', 'page_icon': '"""🧊"""', 'layout': '"""wide"""', 'initial_sidebar_state': '"""expanded"""'}), "(page_title='Retrieval Augmented Generation', page_icon=\n '🧊', layout='wide', initial_sidebar_s... |
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
MessagesPlaceholder,
SystemMessagePromptTemplate,
)
from langchain.chains import LLMChain, ConversationChain
from langchain.memory import ConversationBufferMemor... | [
"langchain_google_genai.ChatGoogleGenerativeAI",
"langchain.prompts.chat.SystemMessagePromptTemplate.from_template",
"langchain.memory.ConversationBufferMemory",
"langchain.prompts.chat.MessagesPlaceholder",
"langchain.prompts.chat.HumanMessagePromptTemplate.from_template",
"langchain.chains.LLMChain"
] | [((898, 983), 'langchain_google_genai.ChatGoogleGenerativeAI', 'ChatGoogleGenerativeAI', ([], {'model': '"""gemini-pro"""', 'convert_system_message_to_human': '(True)'}), "(model='gemini-pro', convert_system_message_to_human=True\n )\n", (920, 983), False, 'from langchain_google_genai import ChatGoogleGenerativeAI\n... |
import os
import re
from typing import Optional
import langchain
import paperqa
import paperscraper
from langchain import SerpAPIWrapper, OpenAI
from langchain.base_language import BaseLanguageModel
from langchain.chains import LLMChain
from langchain.tools import BaseTool
from pydantic import validator
from pypdf.err... | [
"langchain.chains.LLMChain",
"langchain.prompts.PromptTemplate",
"langchain.OpenAI"
] | [((810, 847), 'pydantic.validator', 'validator', (['"""query_chain"""'], {'always': '(True)'}), "('query_chain', always=True)\n", (819, 847), False, 'from pydantic import validator\n'), ((1585, 1615), 'pydantic.validator', 'validator', (['"""pdir"""'], {'always': '(True)'}), "('pdir', always=True)\n", (1594, 1615), Fal... |
import sys
import getpass
from dotenv import load_dotenv, dotenv_values
import pandas as pd
from IPython.display import display, Markdown, Latex, HTML, JSON
import langchain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from cmd import PROMPT
imp... | [
"langchain.chains.LLMChain",
"langchain.llms.OpenAI"
] | [((394, 457), 'sys.path.append', 'sys.path.append', (['"""/Users/dovcohen/Documents/Projects/AI/NL2SQL"""'], {}), "('/Users/dovcohen/Documents/Projects/AI/NL2SQL')\n", (409, 457), False, 'import sys\n'), ((4264, 4278), 'pandas.DataFrame', 'pd.DataFrame', ([], {}), '()\n', (4276, 4278), True, 'import pandas as pd\n'), (... |
# %%
import logging
import sys
#
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
# %%
from llama_index import (SimpleDirectoryReader,
LLMPredictor,
ServiceContext,
... | [
"langchain.embeddings.HuggingFaceInferenceAPIEmbeddings"
] | [((33, 91), 'logging.basicConfig', 'logging.basicConfig', ([], {'stream': 'sys.stdout', 'level': 'logging.INFO'}), '(stream=sys.stdout, level=logging.INFO)\n', (52, 91), False, 'import logging\n'), ((717, 730), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (728, 730), False, 'from dotenv import load_dotenv\n')... |
#CAMEL Role-Playing Autonomous Cooperative Agents
'''
This is a langchain implementation of paper: “CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society".
'''
#Overview:
'''
The rapid advancement of conversational and chat-based language models has led to remarkable progress in com... | [
"langchain.prompts.chat.SystemMessagePromptTemplate.from_template",
"langchain.chat_models.ChatOpenAI",
"langchain.schema.HumanMessage",
"langchain.schema.SystemMessage",
"langchain.prompts.chat.HumanMessagePromptTemplate.from_template"
] | [((3268, 3327), 'langchain.schema.SystemMessage', 'SystemMessage', ([], {'content': '"""You can make a task more specific."""'}), "(content='You can make a task more specific.')\n", (3281, 3327), False, 'from langchain.schema import AIMessage, HumanMessage, SystemMessage, BaseMessage\n'), ((3655, 3727), 'langchain.prom... |
# Copyright 2023-2024 ByteBrain AI
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | [
"langchain.vectorstores.Weaviate.from_documents",
"langchain.schema.Document",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.llms.OpenAI"
] | [((1605, 1623), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (1621, 1623), False, 'from langchain.embeddings import OpenAIEmbeddings\n'), ((1643, 1678), 'weaviate.Client', 'Client', ([], {'url': '"""http://localhost:8080"""'}), "(url='http://localhost:8080')\n", (1649, 1678), False, 'f... |
import streamlit as st
import langchain_helper as lc
st.title("Restaurant Name generator")
cusine = st.sidebar.selectbox("Pick a Cusine", ("Indian", "Italian", "Mexican", "Arabic"))
if cusine:
response = lc.generate_restaurent_name_and_items(cusine)
st.header(response['restaurant_name'].strip())
... | [
"langchain_helper.generate_restaurent_name_and_items"
] | [((55, 92), 'streamlit.title', 'st.title', (['"""Restaurant Name generator"""'], {}), "('Restaurant Name generator')\n", (63, 92), True, 'import streamlit as st\n'), ((105, 190), 'streamlit.sidebar.selectbox', 'st.sidebar.selectbox', (['"""Pick a Cusine"""', "('Indian', 'Italian', 'Mexican', 'Arabic')"], {}), "('Pick a... |
from langchain.document_loaders import DirectoryLoader
from langchain.indexes import VectorstoreIndexCreator
import langchain
langchain.verbose = True
# loader = DirectoryLoader("../langchain/docs/_build/html/", glob="**/*.html")
loader = DirectoryLoader("../demo/", glob="*.html")
index = VectorstoreIndexCreator().f... | [
"langchain.document_loaders.DirectoryLoader",
"langchain.indexes.VectorstoreIndexCreator"
] | [((242, 284), 'langchain.document_loaders.DirectoryLoader', 'DirectoryLoader', (['"""../demo/"""'], {'glob': '"""*.html"""'}), "('../demo/', glob='*.html')\n", (257, 284), False, 'from langchain.document_loaders import DirectoryLoader\n'), ((293, 318), 'langchain.indexes.VectorstoreIndexCreator', 'VectorstoreIndexCreat... |
import streamlit as st
import subprocess
from langchain_core.messages import AIMessage, HumanMessage
from langchain_local import LangchainLocal
from uploadFile import UploadFile
from helper.helper import Helper
from ingest import GetVectorstore
def configure_api_key(api_key_name):
# Configure the API key
ap... | [
"langchain_core.messages.AIMessage",
"langchain_core.messages.HumanMessage",
"langchain_local.LangchainLocal"
] | [((412, 420), 'helper.helper.Helper', 'Helper', ([], {}), '()\n', (418, 420), False, 'from helper.helper import Helper\n'), ((2403, 2435), 'langchain_local.LangchainLocal', 'LangchainLocal', (['st.session_state'], {}), '(st.session_state)\n', (2417, 2435), False, 'from langchain_local import LangchainLocal\n'), ((4269,... |
import streamlit as st
import langchain
from langchain_community.chat_models import ChatOllama
from langchain.cache import InMemoryCache
from dotenv import load_dotenv
from langchain_community.embeddings import OllamaEmbeddings
import os
from PIL import Image
from chroma_main import answer_no_retriever
langchain.cache... | [
"langchain_community.embeddings.OllamaEmbeddings",
"langchain_community.chat_models.ChatOllama",
"langchain.cache.InMemoryCache"
] | [((324, 339), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (337, 339), False, 'from langchain.cache import InMemoryCache\n'), ((341, 354), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (352, 354), False, 'from dotenv import load_dotenv\n'), ((390, 418), 'os.getenv', 'os.getenv', (['"""MO... |
# Copyright 2023-2024 ByteBrain AI
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | [
"langchain.schema.Document",
"langchain.llms.OpenAI",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((896, 920), 'core.utils.upgrade_sqlite.upgrade_sqlite_version', 'upgrade_sqlite_version', ([], {}), '()\n', (918, 920), False, 'from core.utils.upgrade_sqlite import upgrade_sqlite_version\n'), ((952, 970), 'langchain.embeddings.openai.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (968, 970), False, 'from ... |
import logging
from dotenv import load_dotenv
from llama_index import VectorStoreIndex
import pandas as pd
from ragas.metrics import answer_relevancy
from ragas.llama_index import evaluate
from ragas.llms import LangchainLLM
from langchain.chat_models import AzureChatOpenAI
from langchain.embeddings import AzureOpenA... | [
"langchain.embeddings.AzureOpenAIEmbeddings",
"langchain.chat_models.AzureChatOpenAI"
] | [((718, 731), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (729, 731), False, 'from dotenv import load_dotenv\n'), ((993, 1113), 'langchain.chat_models.AzureChatOpenAI', 'AzureChatOpenAI', ([], {'deployment_name': 'deployment_name', 'model': 'api_version', 'openai_api_key': 'api_key', 'openai_api_type': '"""a... |
from fastapi_poe import make_app
from modal import Image, Stub, asgi_app
from langchain_openai import LangchainOpenAIChatBot
# A OpenAI powered chatbot built using Langchain.
OPENAI_API_KEY = "YOUR API KEY"
bot = LangchainOpenAIChatBot(OPENAI_API_KEY=OPENAI_API_KEY)
# The following is setup code that is required to ... | [
"langchain_openai.LangchainOpenAIChatBot"
] | [((215, 268), 'langchain_openai.LangchainOpenAIChatBot', 'LangchainOpenAIChatBot', ([], {'OPENAI_API_KEY': 'OPENAI_API_KEY'}), '(OPENAI_API_KEY=OPENAI_API_KEY)\n', (237, 268), False, 'from langchain_openai import LangchainOpenAIChatBot\n'), ((491, 525), 'modal.Stub', 'Stub', (['"""poe-server-bot-quick-start"""'], {}), ... |
import os
import uuid
import langchain
import requests
import streamlit as st
from dotenv import load_dotenv, find_dotenv
from langchain_community.callbacks import get_openai_callback
from langchain.schema import HumanMessage, AIMessage
from playsound import playsound
from streamlit_chat import message
from advisor.a... | [
"langchain.schema.AIMessage",
"langchain_community.callbacks.get_openai_callback",
"langchain.schema.HumanMessage"
] | [((424, 502), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""Your Restaurant Advisor"""', 'page_icon': '"""👩\u200d🍳"""'}), "(page_title='Your Restaurant Advisor', page_icon='👩\\u200d🍳')\n", (442, 502), True, 'import streamlit as st\n'), ((525, 570), 'streamlit.header', 'st.header', (['""... |
import langchain
from langchain.agents.agent_toolkits import (
create_conversational_retrieval_agent, create_retriever_tool)
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.schema.messages import SystemMessage
from langchain.vectorstores import FAISS
fro... | [
"langchain.vectorstores.FAISS.load_local",
"langchain.cache.SQLiteCache",
"langchain.chat_models.ChatOpenAI",
"langchain.agents.agent_toolkits.create_conversational_retrieval_agent",
"langchain.agents.agent_toolkits.create_retriever_tool",
"langchain.schema.messages.SystemMessage",
"langchain.callbacks.... | [((562, 626), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {'database_path': 'f"""{SYS_PATH}/data/langchain_cache.db"""'}), "(database_path=f'{SYS_PATH}/data/langchain_cache.db')\n", (573, 626), False, 'from langchain.cache import SQLiteCache\n'), ((757, 804), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbed... |
"""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.chains.ChatVectorDBChain",
"langchain.callbacks.manager.AsyncCallbackManager",
"langchain.llms.OpenAI",
"langchain.chains.llm.LLMChain"
] | [((894, 918), 'langchain.callbacks.manager.AsyncCallbackManager', 'AsyncCallbackManager', (['[]'], {}), '([])\n', (914, 918), False, 'from langchain.callbacks.manager import AsyncCallbackManager\n'), ((942, 982), 'langchain.callbacks.manager.AsyncCallbackManager', 'AsyncCallbackManager', (['[question_handler]'], {}), '... |
import os.path
import chromadb
import langchain.embeddings
import win32com.client
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.document_loaders import TextLoader
from langchain.do... | [
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.llms.OpenAI",
"langchain.chat_models.ChatOpenAI",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.vectorstores.Chroma"
] | [((887, 900), 'optiondata.Option_data', 'Option_data', ([], {}), '()\n', (898, 900), False, 'from optiondata import Option_data\n'), ((1242, 1262), 'os.path.join', 'join', (['rootpath', 'path'], {}), '(rootpath, path)\n', (1246, 1262), False, 'from os.path import isdir, isfile, join\n'), ((1284, 1388), 'langchain.text_... |
"""
A script for retrieval-based question answering using the langchain library.
This script demonstrates how to integrate a retrieval system with a chat model for answering questions.
It utilizes Chroma for retrieval of relevant information and ChatOpenAI for
generating answers based on the retrieved content.
The R... | [
"langchain.embeddings.OpenAIEmbeddings",
"langchain.vectorstores.chroma.Chroma",
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.chat_models.ChatOpenAI"
] | [((1459, 1472), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (1470, 1472), False, 'from dotenv import load_dotenv\n'), ((1549, 1561), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {}), '()\n', (1559, 1561), False, 'from langchain.chat_models import ChatOpenAI\n'), ((1631, 1649), 'langchain.embeddings... |
import os
import gradio as gr
import langchain
from langchain.llms import OpenAI
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.chains.qa_with_sources.loading import load_qa_with_sources_chain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders imp... | [
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.llms.OpenAI",
"langchain.document_loaders.UnstructuredURLLoader"
] | [((470, 483), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (481, 483), False, 'from dotenv import load_dotenv\n'), ((512, 551), 'langchain.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0.9)', 'max_tokens': '(500)'}), '(temperature=0.9, max_tokens=500)\n', (518, 551), False, 'from langchain.llms import OpenAI... |
## novel_generator.py
import langchain
import openai
from typing import Dict, Any
from .dialogue_enhancer import DialogueEnhancer
from .script_transitioner import ScriptTransitioner
from .embedding_storage import EmbeddingStorage
from .custom_agent import CustomAgent
class NovelGenerator:
def __init__(self, prompt... | [
"langchain.NovelGenerator"
] | [((1322, 1348), 'langchain.NovelGenerator', 'langchain.NovelGenerator', ([], {}), '()\n', (1346, 1348), False, 'import langchain\n'), ((1377, 1395), 'openai.GPT3Model', 'openai.GPT3Model', ([], {}), '()\n', (1393, 1395), False, 'import openai\n')] |
import streamlit as st
import openai
import langchain
import os
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
from langchain.vectorstores import FAISS
from langchain.c... | [
"langchain.text_splitter.CharacterTextSplitter",
"langchain.memory.ConversationBufferMemory",
"langchain.chat_models.ChatOpenAI",
"langchain.vectorstores.FAISS.from_texts",
"langchain.embeddings.OpenAIEmbeddings"
] | [((1430, 1528), 'langchain.text_splitter.CharacterTextSplitter', 'CharacterTextSplitter', ([], {'separator': '"""\n"""', 'chunk_size': '(1000)', 'chunk_overlap': '(200)', 'length_function': 'len'}), "(separator='\\n', chunk_size=1000, chunk_overlap=200,\n length_function=len)\n", (1451, 1528), False, 'from langchain... |
import json
import random
import langchain
from dotenv import load_dotenv
import gradio as gr
import logging
from langchain.chains import LLMChain
from langchain.prompts.chat import (
ChatPromptTemplate
)
import pydantic.v1.error_wrappers
from typing import Any, Dict, Tuple
from transist.llm import create_openai_... | [
"langchain.prompts.chat.ChatPromptTemplate.from_messages"
] | [((462, 501), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.INFO'}), '(level=logging.INFO)\n', (481, 501), False, 'import logging\n'), ((508, 535), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (525, 535), False, 'import logging\n'), ((7347, 7453), 'gradio.Textbox... |
# Import Langchain modules
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
# Impo... | [
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.llms.OpenAI",
"langchain.vectorstores.FAISS.from_documents",
"langchain.document_loaders.PyPDFLoader",
"langchain.embeddings.OpenAIEmbeddings"
] | [((573, 606), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {}), "('ignore')\n", (596, 606), False, 'import warnings\n'), ((712, 808), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.INFO', 'format': '"""%(asctime)s - %(levelname)s - %(message)s"""'}), "(level=logging.IN... |
"""Utilities for running language models or Chains over datasets."""
from __future__ import annotations
import asyncio
import functools
import logging
from datetime import datetime
from typing import (
Any,
Callable,
Coroutine,
Dict,
Iterator,
List,
Optional,
Sequence,
Union,
)
fr... | [
"langchain.schema.get_buffer_string",
"langchain.schema.messages_from_dict",
"langchain.schema.HumanMessage",
"langchain.callbacks.tracers.langchain.LangChainTracer",
"langchain.callbacks.tracers.evaluation.EvaluatorCallbackHandler",
"langchainplus_sdk.LangChainPlusClient"
] | [((1077, 1104), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1094, 1104), False, 'import logging\n'), ((9051, 9071), 'asyncio.Semaphore', 'asyncio.Semaphore', (['n'], {}), '(n)\n', (9068, 9071), False, 'import asyncio\n'), ((9177, 9192), 'asyncio.Queue', 'asyncio.Queue', ([], {}), '()\... |
from langchain import OpenAI, SQLDatabase
from langchain_experimental.sql import SQLDatabaseChain
from langchain.memory import ConversationBufferMemory
from langchain.agents import (AgentType,
AgentExecutor,
create_react_agent,
c... | [
"langchain_experimental.sql.SQLDatabaseChain",
"langchain.agents.initialize.initialize_agent",
"langchain.tools.Tool",
"langchain.memory.ConversationBufferMemory",
"langchain_community.document_loaders.text.TextLoader",
"langchain.indexes.vectorstore.VectorstoreIndexCreator",
"langchain.prompts.PromptTe... | [((937, 970), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {}), "('ignore')\n", (960, 970), False, 'import warnings\n'), ((1023, 1137), 'langchain.SQLDatabase.from_uri', 'SQLDatabase.from_uri', (['f"""postgresql+psycopg2://postgres:{constants.DBPASS}@localhost:5433/{constants.DB}"""'], {}), ... |
import langchain
import re
from typing import TypeVar, Optional
from dotenv import load_dotenv
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from mdutils.mdutils import MdUtils
from openai import ChatCompletion
## you can use typing.Self after python 3.11
Self = T... | [
"langchain.memory.ConversationBufferMemory",
"langchain.chat_models.ChatOpenAI"
] | [((319, 334), 'typing.TypeVar', 'TypeVar', (['"""Self"""'], {}), "('Self')\n", (326, 334), False, 'from typing import TypeVar, Optional\n'), ((363, 376), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (374, 376), False, 'from dotenv import load_dotenv\n'), ((475, 570), 'openai.ChatCompletion.create', 'ChatCompl... |
import streamlit as st
import langchain_helper
st.title("Restaurant Name Generator")
cuisine = st.sidebar.selectbox("Pick a Cuisine", ("Indian", "Chinese", "Italian", "Mexican", "American","England"))
if cuisine:
response = langchain_helper.generate_restaurant_name_and_items(cuisine)
st.header(response['re... | [
"langchain_helper.generate_restaurant_name_and_items"
] | [((48, 85), 'streamlit.title', 'st.title', (['"""Restaurant Name Generator"""'], {}), "('Restaurant Name Generator')\n", (56, 85), True, 'import streamlit as st\n'), ((97, 207), 'streamlit.sidebar.selectbox', 'st.sidebar.selectbox', (['"""Pick a Cuisine"""', "('Indian', 'Chinese', 'Italian', 'Mexican', 'American', 'Eng... |
import os
from datasets import get_dataset
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.callbacks import get_openai_callback
from utils.timer import Timer
import logging
import numpy as np
import seaborn as sns
import matp... | [
"langchain.chains.LLMChain",
"langchain.prompts.PromptTemplate",
"langchain.callbacks.get_openai_callback",
"langchain.chat_models.ChatOpenAI"
] | [((6515, 6552), 'config.load_config', 'load_config', (['"""classifier_config.yaml"""'], {}), "('classifier_config.yaml')\n", (6526, 6552), False, 'from config import api_key, load_config\n'), ((6558, 6666), 'wandb.init', 'wandb.init', ([], {'project': 'config.project', 'config': 'config', 'name': 'config.current_experi... |
from fastapi import FastAPI, HTTPException
import uvicorn
from typing import Dict
import os
import sys
from dotenv import load_dotenv
load_dotenv()
sys.path.append(os.getcwd().split(os.getenv('PROJECT_NAME'))[0] + os.getenv('PROJECT_NAME') + '/src')
import langchain_functions
app = FastAPI()
@app.post("/generate_do... | [
"langchain_functions.generate_docstring"
] | [((135, 148), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (146, 148), False, 'from dotenv import load_dotenv\n'), ((286, 295), 'fastapi.FastAPI', 'FastAPI', ([], {}), '()\n', (293, 295), False, 'from fastapi import FastAPI, HTTPException\n'), ((1180, 1223), 'uvicorn.run', 'uvicorn.run', (['app'], {'host': '"... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.