code stringlengths 141 78.9k | apis listlengths 1 23 | extract_api stringlengths 142 73.2k |
|---|---|---|
import streamlit as st
import langchain
# from dotenv import load_dotenv
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.memory ... | [
"langchain.text_splitter.CharacterTextSplitter",
"langchain.vectorstores.Pinecone.from_texts",
"langchain.memory.ConversationBufferMemory",
"langchain.chat_models.ChatOpenAI",
"langchain.embeddings.OpenAIEmbeddings"
] | [((669, 747), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""Chat with multiple files"""', 'page_icon': '""":books:"""'}), "(page_title='Chat with multiple files', page_icon=':books:')\n", (687, 747), True, 'import streamlit as st\n'), ((752, 789), 'streamlit.write', 'st.write', (['css'], {'... |
import langchain
from langchain.chains import LLMChain, SimpleSequentialChain, ConversationChain
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
langchain.verbose = True
chat = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
conversation = ConversationChain(
ll... | [
"langchain.memory.ConversationBufferMemory",
"langchain.chat_models.ChatOpenAI"
] | [((231, 279), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'model': '"""gpt-3.5-turbo"""', 'temperature': '(0)'}), "(model='gpt-3.5-turbo', temperature=0)\n", (241, 279), False, 'from langchain.chat_models import ChatOpenAI\n'), ((339, 365), 'langchain.memory.ConversationBufferMemory', 'ConversationBufferMem... |
import logging
import sys
import langchain
from extract_100knocks_qa import extract_questions
from langchain.chat_models import ChatOpenAI
from llama_index import (GPTSQLStructStoreIndex, LLMPredictor, ServiceContext,
SQLDatabase)
from ruamel.yaml import YAML
from sqlalchemy import create_engi... | [
"langchain.chat_models.ChatOpenAI"
] | [((829, 856), 'sqlalchemy.create_engine', 'create_engine', (['database_url'], {}), '(database_url)\n', (842, 856), False, 'from sqlalchemy import create_engine\n'), ((934, 987), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'model_name': '"""gpt-3.5-turbo"""', 'temperature': '(0)'}), "(model_name='gpt-3.5-tur... |
import os
import openai
from dotenv import load_dotenv
import logging
import re
import hashlib
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms import AzureOpenAI
from langchain.vectorstores.base import VectorStore
from langchain.chains import ChatVectorDBChain
from langchain.chains import ... | [
"langchain.agents.initialize_agent",
"langchain.chains.llm.LLMChain",
"langchain.chat_models.ChatOpenAI",
"langchain.schema.HumanMessage",
"langchain.text_splitter.TokenTextSplitter",
"langchain.chains.qa_with_sources.load_qa_with_sources_chain",
"langchain.prompts.PromptTemplate",
"langchain.embeddin... | [((2224, 2237), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (2235, 2237), False, 'from dotenv import load_dotenv\n'), ((2298, 2326), 'os.getenv', 'os.getenv', (['"""OPENAI_API_BASE"""'], {}), "('OPENAI_API_BASE')\n", (2307, 2326), False, 'import os\n'), ((2402, 2429), 'os.getenv', 'os.getenv', (['"""OPENAI_A... |
from langchain.vectorstores import Milvus
from langchain.chains.retrieval_qa.base import RetrievalQA
from typing import Any
from langchain.memory import ConversationBufferMemory
from langchain import PromptTemplate, FAISS
from langchain.schema import Document
from langchain.embeddings import DashScopeEmbeddings
from ll... | [
"langchain.memory.ConversationBufferMemory",
"langchain.schema.Document",
"langchain.vectorstores.Milvus",
"langchain.embeddings.DashScopeEmbeddings",
"langchain.PromptTemplate"
] | [((1149, 1243), 'langchain.embeddings.DashScopeEmbeddings', 'DashScopeEmbeddings', ([], {'model': '"""text-embedding-v1"""', 'dashscope_api_key': 'config.llm_tyqw_api_key'}), "(model='text-embedding-v1', dashscope_api_key=config.\n llm_tyqw_api_key)\n", (1168, 1243), False, 'from langchain.embeddings import DashScop... |
import os
from langchain.callbacks.manager import AsyncCallbackManager
from langchain.callbacks.tracers import LangChainTracer
from langchain.chains import ChatVectorDBChain, ConversationalRetrievalChain
from langchain.chains.chat_vector_db.prompts import CONDENSE_QUESTION_PROMPT
from langchain.prompts.prompt import Pr... | [
"langchain.chains.question_answering.load_qa_chain",
"langchain.text_splitter.CharacterTextSplitter",
"langchain.prompts.prompt.PromptTemplate",
"langchain.callbacks.tracers.LangChainTracer",
"langchain.callbacks.manager.AsyncCallbackManager",
"langchain.retrievers.ContextualCompressionRetriever",
"lang... | [((1253, 1356), 'langchain.prompts.prompt.PromptTemplate', 'PromptTemplate', ([], {'template': 'doc_template', 'input_variables': "['page_content', 'authors', 'href', 'title']"}), "(template=doc_template, input_variables=['page_content',\n 'authors', 'href', 'title'])\n", (1267, 1356), False, 'from langchain.prompts... |
# 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... |
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'),... |
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... |
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"""... |
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 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... |
"""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, ... |
# 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 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... |
"""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 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... |
# 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... |
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 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... |
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'), (... |
# 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
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... |
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 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... |
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... |
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 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... |
import langchain
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.schema import AIMessage, HumanMessage, SystemMessage
from langchain.cache import InMemoryCache
from langchain import PromptTemplate
import os
import openai
from langchain.prompts import (
ChatPromptTemplat... | [
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.llms.OpenAI",
"langchain.prompts.AIMessagePromptTemplate.from_template",
"langchain.chat_models.ChatOpenAI",
"langchain.prompts.ChatPromptTemplate.from_messages",
"langchain.prompts.SystemMessagePromptTemplate.from_template"
] | [((734, 742), 'langchain.llms.OpenAI', 'OpenAI', ([], {}), '()\n', (740, 742), False, 'from langchain.llms import OpenAI\n'), ((750, 784), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'openai_api_key': 'api_key'}), '(openai_api_key=api_key)\n', (760, 784), False, 'from langchain.chat_models import ChatOpenAI... |
import langchain
from langchain.llms import GooglePalm
from langchain.document_loaders import CSVLoader
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.vectorstores import FAISS
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA
import os
from dot... | [
"langchain.llms.GooglePalm",
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.vectorstores.FAISS.from_documents",
"langchain.document_loaders.CSVLoader",
"langchain.embeddings.HuggingFaceInstructEmbeddings",
"langchain.prompts.PromptTemplate"
] | [((344, 357), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (355, 357), False, 'from dotenv import load_dotenv\n'), ((917, 989), 'langchain.llms.GooglePalm', 'GooglePalm', ([], {'google_api_key': "os.environ['GOOGLE_API_KEY']", 'temperature': '(0.7)'}), "(google_api_key=os.environ['GOOGLE_API_KEY'], temperatur... |
import streamlit as st
import langchain as lc
from typing import Callable
from utils import *
#####################################################
# This file contains everything reusable in the app #
#####################################################
def show_past_conversations():
conversations = get_conver... | [
"langchain.callbacks.get_openai_callback"
] | [((942, 1102), 'streamlit.number_input', 'st.number_input', (['"""Monthly limit ($)"""'], {'value': '(15.0)', 'min_value': '(1.0)', 'max_value': '(120.0)', 'step': '(1.0)', 'format': '"""%.2f"""', 'help': '"""The monthly limit for the OpenAI API"""'}), "('Monthly limit ($)', value=15.0, min_value=1.0, max_value=\n 1... |
import langchain
from langchain.chains.llm import LLMChain
from langchain_openai import AzureChatOpenAI
from langchain.memory import ReadOnlySharedMemory, ConversationBufferMemory
from langchain.agents import BaseSingleActionAgent, Tool, AgentType, initialize_agent, AgentExecutor
from langchain.chat_models.base import... | [
"langchain.agents.initialize_agent",
"langchain.agents.AgentExecutor.from_agent_and_tools",
"langchain.schema.OutputParserException",
"langchain.prompts.chat.SystemMessagePromptTemplate.from_template",
"langchain.schema.AgentAction",
"langchain.chains.llm.LLMChain",
"langchain_openai.AzureChatOpenAI",
... | [((4432, 4548), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'self.chat_model', 'prompt': 'router_prompt_template', 'memory': 'self.readonly_memory', 'verbose': 'self.verbose'}), '(llm=self.chat_model, prompt=router_prompt_template, memory=self.\n readonly_memory, verbose=self.verbose)\n', (4440, 4548),... |
import os
import openai
import pinecone
from langchain.document_loaders import DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Pinecone
from langchain.llms import OpenAI
from langchain.chat_mod... | [
"langchain.document_loaders.DirectoryLoader",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.memory.ConversationBufferMemory",
"langchain.vectorstores.Pinecone.from_documents",
"langchain.llms.OpenAI",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((579, 592), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (590, 592), False, 'from dotenv import load_dotenv\n'), ((609, 636), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (618, 636), False, 'import os\n'), ((656, 685), 'os.getenv', 'os.getenv', (['"""PINECONE_API_KEY"""'... |
# Import langchain and azure cognitive search
import langchain
from typing import Dict, List
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env
from langchain.tools.base import BaseTool
from azure.core.credentials import AzureKeyCredential
from azure.search.d... | [
"langchain.utils.get_from_dict_or_env"
] | [((1527, 1551), 'pydantic.root_validator', 'root_validator', ([], {'pre': '(True)'}), '(pre=True)\n', (1541, 1551), False, 'from pydantic import BaseModel, Extra, root_validator\n'), ((1721, 1813), 'langchain.utils.get_from_dict_or_env', 'get_from_dict_or_env', (['values', '"""azure_cognitive_search_key"""', '"""AZURE_... |
from langchain.chat_models import ChatOpenAI
from langchain.agents import tool, load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
import langchain
langchain.debug = True
# llm
llm = ChatOpenAI(temperature=0)
# tools
@tool
def get_word_length(word: str) -> int:
"""Re... | [
"langchain.agents.initialize_agent",
"langchain.agents.load_tools",
"langchain.chat_models.ChatOpenAI"
] | [((231, 256), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '(0)'}), '(temperature=0)\n', (241, 256), False, 'from langchain.chat_models import ChatOpenAI\n'), ((381, 414), 'langchain.agents.load_tools', 'load_tools', (["['llm-math']"], {'llm': 'llm'}), "(['llm-math'], llm=llm)\n", (391, 414), ... |
from abc import ABC, abstractmethod
import chromadb
from chromadb.config import Settings
import requests, json
import uuid
# import langchain
# from langchain.cache import InMemoryCache
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceInstructEmbeddings
# from langchain import Hug... | [
"langchain.chains.ConversationalRetrievalChain.from_llm",
"langchain.memory.ConversationBufferMemory",
"langchain.llms.HuggingFaceHub",
"langchain.embeddings.HuggingFaceInstructEmbeddings",
"langchain.vectorstores.Chroma"
] | [((1379, 1452), 'langchain.memory.ConversationBufferMemory', 'ConversationBufferMemory', ([], {'memory_key': '"""chat_history"""', 'return_messages': '(True)'}), "(memory_key='chat_history', return_messages=True)\n", (1403, 1452), False, 'from langchain.memory import ConversationBufferMemory\n'), ((2211, 2386), 'chroma... |
from langchain.agents import (
initialize_agent,
Tool,
AgentType
)
from llama_index.callbacks import (
CallbackManager,
LlamaDebugHandler
)
from llama_index.node_parser.simple import SimpleNodeParser
from llama_index import (
VectorStoreIndex,
SummaryIndex,
SimpleDirectoryReader,
ServiceConte... | [
"langchain.chat_models.ChatOpenAI"
] | [((398, 456), 'logging.basicConfig', 'logging.basicConfig', ([], {'stream': 'sys.stdout', 'level': 'logging.INFO'}), '(stream=sys.stdout, level=logging.INFO)\n', (417, 456), False, 'import logging\n'), ((529, 545), 'os.getenv', 'os.getenv', (['"""LLM"""'], {}), "('LLM')\n", (538, 545), False, 'import os\n'), ((1217, 12... |
'''
@Author: WANG Maonan
@Date: 2023-09-04 20:46:09
@Description: 基于 LLM-ReAct 的 Traffic Light Control
1. 会有数据库, 我们会搜索最相似的场景 (如何定义场景的相似程度), 然后可以存储在 memory 里面, 或者放在 query 里面
2. 不同的 action 检查
- getAvailableActions, 获得当前所有的动作
- get queue length of all phases
- get emergency vehicle
- check possible queu... | [
"langchain.chat_models.ChatOpenAI"
] | [((1268, 1290), 'tshub.utils.get_abs_path.get_abs_path', 'get_abs_path', (['__file__'], {}), '(__file__)\n', (1280, 1290), False, 'from tshub.utils.get_abs_path import get_abs_path\n'), ((1379, 1392), 'utils.readConfig.read_config', 'read_config', ([], {}), '()\n', (1390, 1392), False, 'from utils.readConfig import rea... |
import langchain
import requests
from pydantic import ValidationError
from langchain_core.prompts import ChatPromptTemplate
#from langchain import chains
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
#from rmrkl import ChatZeroShotAgent, RetryAgentExecutor
from langchain.agents ... | [
"langchain_openai.ChatOpenAI",
"langchain.agents.AgentExecutor",
"langchain.agents.format_scratchpad.openai_tools.format_to_openai_tool_messages",
"langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler",
"langchain.prompts.MessagesPlaceholder",
"langchain.agents.output_parsers.openai_tools.O... | [((1066, 1220), 'langchain_openai.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': 'temp', 'model_name': 'model', 'request_timeout': '(1000)', 'streaming': '(False)', 'callbacks': 'callbacks', 'openai_api_key': 'api_key', 'verbose': '(False)'}), '(temperature=temp, model_name=model, request_timeout=1000,\n streaming=... |
"""Beta Feature: base interface for cache."""
import hashlib
import json
from abc import ABC, abstractmethod
from typing import Any, Callable, Dict, List, Optional, Tuple, Type, cast
from sqlalchemy import Column, Integer, String, create_engine, select
from sqlalchemy.engine.base import Engine
from sqlalchemy.orm impo... | [
"langchain.vectorstores.redis.Redis.from_existing_index",
"langchain.vectorstores.redis.Redis",
"langchain.schema.Generation"
] | [((2037, 2055), 'sqlalchemy.ext.declarative.declarative_base', 'declarative_base', ([], {}), '()\n', (2053, 2055), False, 'from sqlalchemy.ext.declarative import declarative_base\n'), ((2212, 2244), 'sqlalchemy.Column', 'Column', (['String'], {'primary_key': '(True)'}), '(String, primary_key=True)\n', (2218, 2244), Fal... |
# Needs to be in same directory as configs, data folder
# Imports
from _OpalLLM import OpalLLM
from _OpalLLM import OpalLLM
import sys
sys.path.append('/home/jovyan/.local/lib/python3.8/site-packages')
import torch
from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
from langcha... | [
"langchain.chains.ConversationChain",
"langchain.LLMChain",
"langchain.llms.HuggingFacePipeline",
"langchain.PromptTemplate"
] | [((138, 204), 'sys.path.append', 'sys.path.append', (['"""/home/jovyan/.local/lib/python3.8/site-packages"""'], {}), "('/home/jovyan/.local/lib/python3.8/site-packages')\n", (153, 204), False, 'import sys\n'), ((3396, 3513), '_OpalLLM.OpalLLM', 'OpalLLM', ([], {'model': '"""lmsys/vicuna-33b"""', 'temperature': '(0.1)',... |
"""Utilities for running language models or Chains over datasets."""
from __future__ import annotations
import asyncio
import functools
import itertools
import logging
from datetime import datetime
from typing import (
Any,
Callable,
Coroutine,
Dict,
Iterator,
List,
Optional,
Sequence,... | [
"langchain.schema.messages.messages_from_dict",
"langchain.smith.evaluation.string_run_evaluator.StringRunEvaluatorChain.from_run_and_data_type",
"langchain.callbacks.tracers.langchain.LangChainTracer",
"langchain.chat_models.openai.ChatOpenAI",
"langchain.callbacks.tracers.evaluation.EvaluatorCallbackHandl... | [((1366, 1393), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1383, 1393), False, 'import logging\n'), ((1704, 1721), 'urllib.parse.urlparse', 'urlparse', (['api_url'], {}), '(api_url)\n', (1712, 1721), False, 'from urllib.parse import urlparse, urlunparse\n'), ((24715, 24735), 'asyncio... |
import logging
import os
import openai
from langchain.chat_models import AzureChatOpenAI
import vishwa
from vishwa.mlmonitor.langchain.decorators.map_xpuls_project import MapXpulsProject
from vishwa.mlmonitor.langchain.decorators.telemetry_override_labels import TelemetryOverrideLabels
from vishwa.mlmonitor.langchain... | [
"langchain.chat_models.AzureChatOpenAI"
] | [((498, 525), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (515, 525), False, 'import logging\n'), ((544, 571), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (553, 571), False, 'import os\n'), ((616, 639), 'os.getenv', 'os.getenv', (['"""OPENAI_URL"""... |
import ast
import copy
import json
import logging
from typing import List, Tuple, Dict, Callable
import langchain
from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, AIMessagePromptTemplate
from langchain.prompts.chat import BaseMessagePromptTemplate
from langchain.schema import LLMResult
fro... | [
"langchain.schema.LLMResult",
"langchain.prompts.HumanMessagePromptTemplate",
"langchain.LLMChain",
"langchain.PromptTemplate.from_template"
] | [((557, 584), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (574, 584), False, 'import logging\n'), ((2451, 2481), 'copy.deepcopy', 'copy.deepcopy', (['self.gen_kwargs'], {}), '(self.gen_kwargs)\n', (2464, 2481), False, 'import copy\n'), ((2738, 2774), 'langchain.LLMChain', 'LLMChain', (... |
import os
import openai
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.chains import SequentialChain
from dotenv import load_dotenv, find_dotenv
load_dotenv(find_dotenv())
openai.api_key = os.environ['OPENAI_API_KEY']
llm = OpenAI(te... | [
"langchain.chains.SequentialChain",
"langchain_helper.generate_restaurant_name_and_items",
"langchain.llms.OpenAI",
"langchain.chains.LLMChain",
"langchain.prompts.PromptTemplate"
] | [((311, 334), 'langchain.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0.7)'}), '(temperature=0.7)\n', (317, 334), False, 'from langchain.llms import OpenAI\n'), ((384, 421), 'streamlit.title', 'st.title', (['"""Restaurant Name Generator"""'], {}), "('Restaurant Name Generator')\n", (392, 421), True, 'import streamlit... |
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... | [((1521, 1548), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1538, 1548), False, 'import logging\n'), ((1617, 1660), 'contextvars.ContextVar', 'ContextVar', (['"""openai_callback"""'], {'default': 'None'}), "('openai_callback', default=None)\n", (1627, 1660), False, 'from contextvars i... |
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.docstore.document import Document
from langchain.prompts import PromptTemplate
from langchain.indexes.vectorstore import VectorstoreIndexCreator
fro... | [
"langchain.chains.question_answering.load_qa_chain",
"langchain.docstore.document.Document",
"langchain.OpenAI",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((568, 616), 'os.system', 'os.system', (["('cls' if os.name == 'nt' else 'clear')"], {}), "('cls' if os.name == 'nt' else 'clear')\n", (577, 616), False, 'import os\n'), ((666, 684), 'langchain.embeddings.openai.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (682, 684), False, 'from langchain.embeddings.open... |
import langchain
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA
from langchain.cache import InMemoryCache
from dotenv import... | [
"langchain.text_splitter.CharacterTextSplitter",
"langchain.cache.InMemoryCache",
"langchain.chat_models.ChatOpenAI",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((468, 483), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (473, 483), False, 'from flask import Flask, request, jsonify\n'), ((484, 493), 'flask_cors.CORS', 'CORS', (['app'], {}), '(app)\n', (488, 493), False, 'from flask_cors import CORS\n'), ((516, 531), 'langchain.cache.InMemoryCache', 'InMemoryCache... |
import os
import pandas as pd
import requests
import openai
import chromadb
import langchain
from langchain.chains import RetrievalQA, SimpleSequentialChain, LLMChain
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.docstore.docum... | [
"langchain.chains.question_answering.load_qa_chain",
"langchain.text_splitter.CharacterTextSplitter",
"langchain.docstore.document.Document",
"langchain.embeddings.openai.OpenAIEmbeddings",
"langchain.chat_models.ChatOpenAI",
"langchain.vectorstores.Chroma"
] | [((591, 604), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (602, 604), False, 'from dotenv import load_dotenv\n'), ((612, 639), 'os.environ.get', 'os.environ.get', (['"""peace_dir"""'], {}), "('peace_dir')\n", (626, 639), False, 'import os\n'), ((657, 689), 'os.environ.get', 'os.environ.get', (['"""OPENAI_API... |
import os
from dotenv import load_dotenv
import openai
import langchain
import azure.cognitiveservices.speech as speechsdk
import elevenlabs
import json
import requests
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.sql_database import SQLDatabase
from langchain.agents import AgentExecut... | [
"langchain.agents.initialize_agent",
"langchain.agents.agent_toolkits.SQLDatabaseToolkit",
"langchain.agents.create_sql_agent",
"langchain.chat_models.ChatOpenAI",
"langchain.SerpAPIWrapper",
"langchain.agents.Tool",
"langchain.SQLDatabase.from_uri",
"langchain.OpenAI"
] | [((968, 1038), 'azure.cognitiveservices.speech.SpeechConfig', 'speechsdk.SpeechConfig', ([], {'subscription': 'speech_key', 'region': 'service_region'}), '(subscription=speech_key, region=service_region)\n', (990, 1038), True, 'import azure.cognitiveservices.speech as speechsdk\n'), ((1059, 1114), 'azure.cognitiveservi... |
# main.py
#####################################################################
# Amazon Bedrock - boto3
#####################################################################
import boto3
# Setup bedrock
bedrock_runtime = boto3.client(
service_name="bedrock-runtime",
region_name="us-east-1",
)
#############... | [
"langchain.llms.Bedrock",
"langchain.embeddings.BedrockEmbeddings"
] | [((225, 294), 'boto3.client', 'boto3.client', ([], {'service_name': '"""bedrock-runtime"""', 'region_name': '"""us-east-1"""'}), "(service_name='bedrock-runtime', region_name='us-east-1')\n", (237, 294), False, 'import boto3\n'), ((760, 837), 'langchain.llms.Bedrock', 'Bedrock', ([], {'client': 'bedrock_runtime', 'mode... |
import asyncio
import inspect
import warnings
from abc import ABC, abstractmethod
from functools import partial
from typing import Any, Dict, List, Mapping, Optional, Sequence
from pydantic import Field, root_validator
import langchain
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.bas... | [
"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.RunInfo",
"langchain.schema.messages.... | [((915, 952), 'pydantic.Field', 'Field', ([], {'default_factory': '_get_verbosity'}), '(default_factory=_get_verbosity)\n', (920, 952), False, 'from pydantic import Field, root_validator\n'), ((1026, 1059), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, exclude=True)\n', (103... |
import httpcore
setattr(httpcore, 'SyncHTTPTransport', 'AsyncHTTPProxy')
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import speech_recognition as sr
import langid
from pydub import AudioSegment
import langchain
import subprocess
from langchain.chat_models im... | [
"langchain.prompts.PromptTemplate.from_template",
"langchain.memory.ConversationBufferMemory",
"langchain.chat_models.ChatOpenAI"
] | [((3911, 4381), 'langchain.prompts.PromptTemplate.from_template', 'PromptTemplate.from_template', (['"""You are a normal consulting nurse/doctor. You will recieve some keywords or sentences described by the patient as input. You have to ask the patient two follow up question so as to acquire the information important t... |
import streamlit as st
from streamlit_chat import message
import langchain_helper as lch
from langchain.schema import (SystemMessage, HumanMessage, AIMessage, messages)
def main():
st.set_page_config(
page_title="Iliad technical assessment",
page_icon="🤖",
)
st.header("ChatBot Free Assist... | [
"langchain.schema.AIMessage",
"langchain_helper.main",
"langchain.schema.HumanMessage"
] | [((187, 261), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""Iliad technical assessment"""', 'page_icon': '"""🤖"""'}), "(page_title='Iliad technical assessment', page_icon='🤖')\n", (205, 261), True, 'import streamlit as st\n'), ((289, 325), 'streamlit.header', 'st.header', (['"""ChatBot Fr... |
from typing import ClassVar
from langchain.chains.base import Chain
from typing import Any, Type
import os
import langchain
from langchain.cache import SQLiteCache
langchain.llm_cache = SQLiteCache()
class BaseChain(Chain):
template_file: ClassVar[str]
generator_template: ClassVar[str]
normalizer_templa... | [
"langchain.cache.SQLiteCache"
] | [((188, 201), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {}), '()\n', (199, 201), False, 'from langchain.cache import SQLiteCache\n')] |
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