code stringlengths 141 97.3k | apis listlengths 1 24 | extract_api stringlengths 113 214k |
|---|---|---|
import langchain as lc
import openai as ai
import datasets as ds
import tiktoken as tk
import os
from langchain_openai import ChatOpenAI
from dotenv import load_dotenv
import os
# Load environment variables from .env file
load_dotenv()
# Get the OpenAI API key from the environment variable
openai_api_key = os.getenv... | [
"langchain.schema.SystemMessage",
"langchain.schema.AIMessage",
"langchain.schema.HumanMessage",
"langchain_openai.ChatOpenAI"
] | [((224, 237), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (235, 237), False, 'from dotenv import load_dotenv\n'), ((311, 338), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (320, 338), False, 'import os\n'), ((501, 563), 'langchain_openai.ChatOpenAI', 'ChatOpenAI', ([], {'... |
"""Push and pull to the LangChain Hub."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Optional
from langchain.load.dump import dumps
from langchain.load.load import loads
from langchain.utils import get_from_env
if TYPE_CHECKING:
from langchainhub import Client
def _get_client(api... | [
"langchainhub.Client",
"langchain.utils.get_from_env",
"langchain.load.load.loads",
"langchain.load.dump.dumps"
] | [((862, 894), 'langchainhub.Client', 'Client', (['api_url'], {'api_key': 'api_key'}), '(api_url, api_key=api_key)\n', (868, 894), False, 'from langchainhub import Client\n'), ((1234, 1247), 'langchain.load.dump.dumps', 'dumps', (['object'], {}), '(object)\n', (1239, 1247), False, 'from langchain.load.dump import dumps\... |
from datetime import timedelta
import os
import subprocess
import whisper
import tempfile
import argparse
import langchain
from langchain.chat_models import ChatOpenAI, ChatGooglePalm
from langchain.schema import HumanMessage, SystemMessage, AIMessage
from langchain.prompts import (
ChatPromptTemplate,
PromptTe... | [
"langchain.chains.LLMChain",
"langchain.prompts.SystemMessagePromptTemplate.from_template",
"langchain.chat_models.ChatOpenAI",
"langchain.prompts.ChatPromptTemplate.from_messages",
"langchain.callbacks.get_openai_callback",
"langchain.prompts.HumanMessagePromptTemplate.from_template"
] | [((696, 747), 'langchain.prompts.SystemMessagePromptTemplate.from_template', 'SystemMessagePromptTemplate.from_template', (['template'], {}), '(template)\n', (737, 747), False, 'from langchain.prompts import ChatPromptTemplate, PromptTemplate, SystemMessagePromptTemplate, AIMessagePromptTemplate, HumanMessagePromptTemp... |
from langchain import OpenAI, LLMChain
from langchain.callbacks import StdOutCallbackHandler
from langchain.chat_models import ChatOpenAI
from src.agents.chat_chain import ChatChain
from src.agents.graphdb_traversal_chain import GraphDBTraversalChain, mem_query_template, mem_system_message
from src.memory.triple_modal... | [
"langchain.callbacks.StdOutCallbackHandler",
"langchain.cache.SQLiteCache",
"langchain.chat_models.ChatOpenAI"
] | [((495, 537), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {'database_path': '""".langchain.db"""'}), "(database_path='.langchain.db')\n", (506, 537), False, 'from langchain.cache import SQLiteCache\n'), ((563, 576), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (574, 576), False, 'from dotenv import loa... |
from __future__ import annotations
import logging
from functools import lru_cache
from typing import List, Optional
import langchain
from langchain.agents import AgentExecutor, Tool, initialize_agent
from langchain.agents.agent_types import AgentType
from langchain.callbacks import get_openai_callback
from langchain.... | [
"langchain_experimental.plan_and_execute.load_chat_planner",
"langchain.agents.initialize_agent",
"langchain.chat_models.ChatOpenAI",
"langchain.callbacks.get_openai_callback",
"langchain_experimental.plan_and_execute.load_agent_executor",
"langchain_experimental.plan_and_execute.PlanAndExecute"
] | [((946, 973), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (963, 973), False, 'import logging\n'), ((1004, 1041), 'shared.llm_manager_base.Cost', 'Cost', ([], {'prompt': '(0.0015)', 'completion': '(0.002)'}), '(prompt=0.0015, completion=0.002)\n', (1008, 1041), False, 'from shared.llm_m... |
import os
import utils
import traceback
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from langchain.chains import ConversationChain
from langchain.llms import OpenAI
import langchain
from langchain.cache import InMemoryCache
from langchain.llms import OpenAI
from langchain.chains.conversati... | [
"langchain.chains.qa_with_sources.load_qa_with_sources_chain",
"langchain.llms.Cohere",
"langchain.llms.OpenAI",
"langchain.prompts.PromptTemplate",
"langchain.llms.AI21",
"langchain.llms.NLPCloud",
"langchain.chains.conversation.memory.ConversationSummaryBufferMemory"
] | [((5785, 5803), 'SmartCache.SmartCache', 'SmartCache', (['CONFIG'], {}), '(CONFIG)\n', (5795, 5803), False, 'from SmartCache import SmartCache\n'), ((6330, 6345), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (6335, 6345), False, 'from flask import Flask, send_from_directory\n'), ((9830, 9890), 'waitress.... |
import os
import streamlit as st
from PyPDF2 import PdfReader
import langchain
langchain.verbose = False
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_cha... | [
"langchain.vectorstores.FAISS.from_texts",
"langchain.callbacks.get_openai_callback",
"langchain.llms.OpenAI",
"langchain.chains.question_answering.load_qa_chain",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((583, 600), 'requests.get', 'requests.get', (['url'], {}), '(url)\n', (595, 600), False, 'import requests\n'), ((853, 902), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""Webscrap chatbot"""'}), "(page_title='Webscrap chatbot')\n", (871, 902), True, 'import streamlit as st\n'), ((907, 936)... |
# Wrapper for Hugging Face APIs for llmlib
from llmlib.base_model_wrapper import BaseModelWrapper
from llama_index import ListIndex, SimpleDirectoryReader
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from llama_index import LangchainEmbedding, ServiceContext
from llama_index import ListIndex, Pr... | [
"langchain.embeddings.huggingface.HuggingFaceEmbeddings"
] | [((735, 830), 'transformers.pipeline', 'pipeline', (['"""text-generation"""'], {'model': 'model_name', 'model_kwargs': "{'torch_dtype': torch.bfloat16}"}), "('text-generation', model=model_name, model_kwargs={'torch_dtype':\n torch.bfloat16})\n", (743, 830), False, 'from transformers import pipeline\n'), ((1022, 103... |
import logging
import ConsoleInterface
import langchain.schema
from langchain.agents import initialize_agent, AgentType #create_pandas_dataframe_agent
logger = logging.getLogger('ConsoleInterface')
'''
def PandasDataframeAgent(llm, Dataframe):
"""
Create a PandasDataframeAgent object.
Parameters:
... | [
"langchain.agents.initialize_agent"
] | [((165, 202), 'logging.getLogger', 'logging.getLogger', (['"""ConsoleInterface"""'], {}), "('ConsoleInterface')\n", (182, 202), False, 'import logging\n'), ((946, 1067), 'langchain.agents.initialize_agent', 'initialize_agent', ([], {'agent': 'AgentType.CONVERSATIONAL_REACT_DESCRIPTION', 'llm': 'llm', 'tools': 'Tools', ... |
import csv
from ctypes import Array
from typing import Any, Coroutine, List, Tuple
import io
import time
import re
import os
from fastapi import UploadFile
import asyncio
import langchain
from langchain.chat_models import ChatOpenAI
from langchain.agents import create_csv_agent, load_tools, initialize_agent, AgentTyp... | [
"langchain.agents.initialize_agent",
"langchain.chat_models.ChatOpenAI",
"langchain.tools.PythonAstREPLTool",
"langchain.memory.ConversationSummaryBufferMemory",
"langchain.callbacks.tracers.ConsoleCallbackHandler",
"langchain.agents.create_pandas_dataframe_agent",
"langchain.output_parsers.OutputFixing... | [((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._EXTRA_LLM_TOOLS.keys",
"langchain.agents.load_tools._LLM_TOOLS.keys",
"langchain.agents.load_tools._EXTRA_OPTIONAL_TOOLS.keys"
] | [((690, 792), 'langflow.template.field.base.TemplateField', 'TemplateField', ([], {'field_type': '"""str"""', 'required': '(True)', 'is_list': '(False)', 'show': '(True)', 'placeholder': '""""""', 'value': '""""""'}), "(field_type='str', required=True, is_list=False, show=True,\n placeholder='', value='')\n", (703, ... |
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains.question_answering import load_qa_chain
from langchain.embeddings.openai import OpenAIEmbeddings
from streamlit_option_menu import option_menu
from deep_translator import GoogleTranslator
from langchain.vectorstores import Pinecone... | [
"langchain.vectorstores.Pinecone.from_texts",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.chains.question_answering.load_qa_chain",
"langchain.embeddings.openai.OpenAIEmbeddings",
"langchain.OpenAI"
] | [((560, 573), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (571, 573), False, 'from dotenv import load_dotenv\n'), ((656, 749), 'pinecone.init', 'pinecone.init', ([], {'api_key': '"""db6b2a8c-d59e-48e1-8d5c-4c2704622937"""', 'environment': '"""gcp-starter"""'}), "(api_key='db6b2a8c-d59e-48e1-8d5c-4c2704622937... |
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
# invoking custom retriever
from redundant_filter_retriever import RedundantFilterRetriever
from dotenv import load_dotenv
import langchain
... | [
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.vectorstores.Chroma",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.chat_models.ChatOpenAI"
] | [((344, 357), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (355, 357), False, 'from dotenv import load_dotenv\n'), ((392, 404), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {}), '()\n', (402, 404), False, 'from langchain.chat_models import ChatOpenAI\n'), ((418, 436), 'langchain.embeddings.OpenAIEmb... |
import os
import logging
import pickle
import ssl
import dill
import langchain
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI, GooglePalm
from langchain.chains import LLMChain, RetrievalQAWithSourcesChain, AnalyzeDocumentChain
from langchain.chains.qa_with_sources import load_qa_with_so... | [
"langchain.vectorstores.FAISS.from_documents",
"langchain.llms.OpenAI",
"langchain.embeddings.OpenAIEmbeddings"
] | [((670, 710), 'langchain.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0.7)', 'max_tokens': '(1024)'}), '(temperature=0.7, max_tokens=1024)\n', (676, 710), False, 'from langchain.llms import OpenAI, GooglePalm\n'), ((728, 746), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (744, 746), ... |
# imports
import os, shutil, json, re
import pathlib
from langchain.document_loaders.unstructured import UnstructuredFileLoader
from langchain.document_loaders.unstructured import UnstructuredAPIFileLoader
from langchain.document_loaders import UnstructuredURLLoader
from langchain.docstore.document import Document
fro... | [
"langchain.document_loaders.unstructured.UnstructuredFileLoader",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.text_splitter.PythonCodeTextSplitter",
"langchain.document_loaders.unstructured.UnstructuredAPIFileLoader",
"langchain.document_loaders.UnstructuredURLLoader",
"langchain.... | [((719, 732), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (730, 732), False, 'from dotenv import load_dotenv\n'), ((784, 892), 're.compile', 're.compile', (['"""http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\\\\\\\(\\\\\\\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+"""'], {}), "(\n 'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.... |
from langchain.llms import LlamaCpp
from langchain.chat_models import ChatOpenAI
from langchain.chains.llm import LLMChain
from langchain.prompts import PromptTemplate
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.c... | [
"langchain.chains.llm.LLMChain",
"langchain.chat_models.ChatOpenAI",
"langchain.prompts.PromptTemplate.from_template",
"langchain.cache.SQLiteCache",
"langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler",
"langchain.llms.LlamaCpp"
] | [((476, 489), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (487, 489), False, 'from dotenv import load_dotenv\n'), ((505, 529), 'os.getenv', 'os.getenv', (['"""OPEN_AI_KEY"""'], {}), "('OPEN_AI_KEY')\n", (514, 529), False, 'import os\n'), ((584, 632), 'utils.setup_logger', 'setup_logger', (['"""contr_detector... |
import streamlit as st
import torch
from transformers import (
AutoTokenizer, AutoModelForCausalLM,
BitsAndBytesConfig,
TextStreamer,
)
import whisper
import os
############ config ############
# general config
whisper_model_names=["tiny", "base", "small", "medium", "large"]
data_root_path = os.path.join('... | [
"langchain.embeddings.huggingface.HuggingFaceEmbeddings"
] | [((306, 331), 'os.path.join', 'os.path.join', (['"""."""', '"""data"""'], {}), "('.', 'data')\n", (318, 331), False, 'import os\n'), ((772, 798), 'streamlit.title', 'st.title', (['"""LLAMA RAG Demo"""'], {}), "('LLAMA RAG Demo')\n", (780, 798), True, 'import streamlit as st\n'), ((799, 811), 'streamlit.divider', 'st.di... |
import streamlit as st
import langchain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain import OpenAI, VectorDBQA
from langchain.chains import RetrievalQAWithSourcesChain
import PyPDF2
#... | [
"langchain.embeddings.openai.OpenAIEmbeddings",
"langchain.chains.RetrievalQAWithSourcesChain.from_chain_type",
"langchain.OpenAI",
"langchain.vectorstores.Chroma.from_texts"
] | [((868, 932), 'streamlit.set_page_config', 'st.set_page_config', ([], {'layout': '"""centered"""', 'page_title': '"""Multidoc_QnA"""'}), "(layout='centered', page_title='Multidoc_QnA')\n", (886, 932), True, 'import streamlit as st\n'), ((933, 958), 'streamlit.header', 'st.header', (['"""Multidoc_QnA"""'], {}), "('Multi... |
from __future__ import annotations
import asyncio
import functools
import logging
import os
import warnings
from contextlib import asynccontextmanager, contextmanager
from contextvars import ContextVar
from typing import (
Any,
AsyncGenerator,
Dict,
Generator,
List,
Optional,
Type,
Type... | [
"langchain.callbacks.tracers.langchain_v1.LangChainTracerV1",
"langchain.callbacks.tracers.wandb.WandbTracer",
"langchain.callbacks.stdout.StdOutCallbackHandler",
"langchain.callbacks.tracers.stdout.ConsoleCallbackHandler",
"langchain.schema.get_buffer_string",
"langchain.callbacks.tracers.langchain.LangC... | [((1114, 1141), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1131, 1141), False, 'import logging\n'), ((1286, 1329), 'contextvars.ContextVar', 'ContextVar', (['"""openai_callback"""'], {'default': 'None'}), "('openai_callback', default=None)\n", (1296, 1329), False, 'from contextvars i... |
import langchain
from langchain.llms import VertexAI
from langchain.prompts import PromptTemplate, load_prompt
import wandb
from wandb.integration.langchain import WandbTracer
import streamlit as st
from google.oauth2 import service_account
# account_info = dict(st.secrets["GOOGLE_APPLICATION_CREDENTIALS"])
# credenti... | [
"langchain.prompts.load_prompt"
] | [((469, 513), 'wandb.login', 'wandb.login', ([], {'key': "st.secrets['WANDB_API_KEY']"}), "(key=st.secrets['WANDB_API_KEY'])\n", (480, 513), False, 'import wandb\n'), ((519, 666), 'wandb.init', 'wandb.init', ([], {'project': '"""generate_prd_v3_palm"""', 'config': "{'model': 'text-bison-001', 'temperature': 0.2}", 'ent... |
#!/usr/bin/env python
# coding: utf-8
# # LangChain: Agents
#
# ## Outline:
#
# * Using built in LangChain tools: DuckDuckGo search and Wikipedia
# * Defining your own tools
# In[ ]:
import os
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv()) # read local .env file
import warnings
warni... | [
"langchain.tools.python.tool.PythonREPLTool",
"langchain.agents.load_tools",
"langchain.agents.initialize_agent",
"langchain.chat_models.ChatOpenAI"
] | [((315, 348), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {}), "('ignore')\n", (338, 348), False, 'import warnings\n'), ((735, 761), 'datetime.date', 'datetime.date', (['(2024)', '(6)', '(12)'], {}), '(2024, 6, 12)\n', (748, 761), False, 'import datetime\n'), ((1324, 1366), 'langchain.chat_... |
import sys
import pandas as pd
from llama_index import Document, set_global_service_context, StorageContext, load_index_from_storage, VectorStoreIndex
from llama_index.indices.base import BaseIndex
from llama_index.storage.docstore import SimpleDocumentStore
from llama_index.storage.index_store import SimpleIndexStore... | [
"langchain.embeddings.OpenAIEmbeddings"
] | [((1194, 1252), 'logging.basicConfig', 'logging.basicConfig', ([], {'stream': 'sys.stdout', 'level': 'logging.INFO'}), '(stream=sys.stdout, level=logging.INFO)\n', (1213, 1252), False, 'import logging\n'), ((1435, 1462), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (1444, 1462), Fal... |
import arxiv
import openai
import langchain
import pinecone
from langchain_community.document_loaders import ArxivLoader
from langchain.docstore.document import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstore... | [
"langchain.vectorstores.Pinecone.from_documents",
"langchain.chat_models.ChatOpenAI",
"langchain.chains.question_answering.load_qa_chain",
"langchain.embeddings.openai.OpenAIEmbeddings",
"langchain.chains.summarize.load_summarize_chain",
"langchain.OpenAI"
] | [((690, 703), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (701, 703), False, 'from dotenv import load_dotenv\n'), ((722, 749), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (731, 749), False, 'import os\n'), ((769, 798), 'os.getenv', 'os.getenv', (['"""PINECONE_API_KEY"""'... |
"""Create a ChatVectorDBChain for question/answering."""
from langchain.callbacks.manager import AsyncCallbackManager
from langchain.callbacks.tracers import LangChainTracer
from langchain.chains import ChatVectorDBChain
from langchain.chains.chat_vector_db.prompts import (CONDENSE_QUESTION_PROMPT,
... | [
"langchain.chains.llm.LLMChain",
"langchain.prompts.chat.HumanMessagePromptTemplate.from_template",
"langchain.chat_models.ChatOpenAI",
"langchain.callbacks.manager.AsyncCallbackManager",
"langchain.callbacks.tracers.LangChainTracer",
"langchain.chains.question_answering.load_qa_chain",
"langchain.promp... | [((2109, 2151), 'langchain.prompts.chat.ChatPromptTemplate.from_messages', 'ChatPromptTemplate.from_messages', (['messages'], {}), '(messages)\n', (2141, 2151), False, 'from langchain.prompts.chat import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate\n'), ((1986, 2037), 'langchain.prompts.c... |
from llama_index.core import VectorStoreIndex,SimpleDirectoryReader,ServiceContext
print("VectorStoreIndex,SimpleDirectoryReader,ServiceContext imported")
from llama_index.llms.huggingface import HuggingFaceLLM
print("HuggingFaceLLM imported")
from llama_index.core.prompts.prompts import SimpleInputPrompt
print("Simple... | [
"langchain.embeddings.huggingface.HuggingFaceEmbeddings"
] | [((872, 885), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (883, 885), False, 'from dotenv import load_dotenv\n'), ((905, 931), 'os.environ.get', 'os.environ.get', (['"""HF_TOKEN"""'], {}), "('HF_TOKEN')\n", (919, 931), False, 'import os\n'), ((1262, 1315), 'llama_index.core.prompts.prompts.SimpleInputPrompt'... |
import itertools
from langchain.cache import InMemoryCache, SQLiteCache
import langchain
import pandas as pd
from certa.utils import merge_sources
import ellmer.models
import ellmer.metrics
from time import sleep, time
import traceback
from tqdm import tqdm
cache = "sqlite"
samples = 2
explanation_granularity = "attri... | [
"langchain.cache.SQLiteCache",
"langchain.cache.InMemoryCache"
] | [((399, 414), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (412, 414), False, 'from langchain.cache import InMemoryCache, SQLiteCache\n'), ((465, 507), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {'database_path': '""".langchain.db"""'}), "(database_path='.langchain.db')\n", (476, 507), Fa... |
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.chat_models.ChatOpenAI",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.memory.ConversationBufferMemory"
] | [((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.chains.llm.LLMChain",
"langchain.chains.qa_with_sources.load_qa_with_sources_chain",
"langchain.agents.initialize_agent",
"langchain.chat_models.ChatOpenAI",
"langchain.prompts.PromptTemplate",
"langchain.schema.HumanMessage",
"langchain.embeddings.openai.OpenAIEmbeddings",
"langchain.text_... | [((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.PromptTemplate",
"langchain.embeddings.DashScopeEmbeddings",
"langchain.schema.Document",
"langchain.memory.ConversationBufferMemory",
"langchain.vectorstores.Milvus"
] | [((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.llm.LLMChain",
"langchain.text_splitter.CharacterTextSplitter",
"langchain.chat_models.ChatOpenAI",
"langchain.callbacks.manager.AsyncCallbackManager",
"langchain.prompts.prompt.PromptTemplate",
"langchain.callbacks.tracers.LangChainTracer",
"langchain.retrievers.document_compressors.E... | [((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.llm_cache.lookup",
"langchain.schema.messages.HumanMessage",
"langchain.prompts.base.StringPromptValue",
"langchain.schema.messages.AIMessage",
"langchain.schema.ChatGeneration",
"langchain.load.dump.dumps",
"langchain.schema.RunInfo",
"langchain.llm_cache.update",
"langchain.pydantic_v1.... | [((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.text_splitter.CharacterTextSplitter.from_tiktoken_encoder",
"langchain.chat_models.ChatOpenAI",
"langchain.vectorstores.Chroma.from_documents",
"langchain.llms.OpenAI",
"langchain.retrievers.document_compressors.LLMChainExtractor.from_llm",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.... | [((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.llms.bedrock.Bedrock",
"langchain.document_loaders.ConfluenceLoader",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.prompts.PromptTemplate",
"langchain.embeddings.BedrockEmbeddings",
"langchain.vectorstores.FAISS.from_documents",
"langchain.indexes.vectorstore.VectorSto... | [((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.chains.LLMChain",
"langchain.prompts.chat.HumanMessagePromptTemplate.from_template",
"langchain.chat_models.ChatOpenAI",
"langchain.chat_models.AzureChatOpenAI",
"langchain.llms.HuggingFacePipeline",
"langchain.prompts.chat.SystemMessagePromptTemplate.from_template",
"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.docstore.document.Document",
"langchain.text_splitter.TextSplitter.from_tiktoken_encoder",
"langchain.text_splitter.TextSplitter",
"langchain.text_splitter.TextSplitter.from_huggingface_tokenizer"
] | [((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.prompts.ChatPromptTemplate.from_template",
"langchain.callbacks.manager.AsyncCallbackManager",
"langchain.schema.StrOutputParser",
"langchain_core.runnables.RunnablePassthrough",
"langchain.llms.huggingface_endpoint.HuggingFaceEndpoint"
] | [((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.chat_models.ChatOpenAI",
"langchain.PromptTemplate",
"langchain.document_loaders.csv_loader.UnstructuredCSVLoader",
"langchain.PromptTemplate.from_template",
"langchain.LLMChain",
"langchain.chains.combine_documents.stuff.StuffDocumentsChain"
] | [((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.schema.HumanMessage",
"langchain.chat_models.ChatOpenAI",
"langchain.cache.InMemoryCache"
] | [((237, 252), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (250, 252), False, 'from langchain.cache import InMemoryCache\n'), ((291, 303), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {}), '()\n', (301, 303), False, 'from langchain.chat_models import ChatOpenAI\n'), ((312, 323), 'time.t... |
import 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.memory.ConversationBufferMemory",
"langchain.chat_models.ChatOpenAI",
"langchain.chains.ConversationChain"
] | [((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.schema.messages.get_buffer_string",
"langchain.callbacks.tracers.langchain_v1.LangChainTracerV1",
"langchain.callbacks.tracers.wandb.WandbTracer",
"langchain.callbacks.tracers.run_collector.RunCollectorCallbackHandler",
"langchain.callbacks.stdout.StdOutCallbackHandler",
"langchain.callbacks.tr... | [((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_community.document_loaders.UnstructuredURLLoader",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain_community.vectorstores.FAISS.from_documents",
"langchain_openai.OpenAI",
"langchain_openai.OpenAIEmbeddings"
] | [((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.embeddings.openai.OpenAIEmbeddings",
"langchain.llms.OpenAI",
"langchain.chains.question_answering.load_qa_chain",
"langchain.cache.InMemoryCache"
] | [((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.chat_models.ChatOpenAI",
"langchain.PromptTemplate"
] | [((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_visualizer.visualize",
"langchain.llms.OpenAI",
"langchain.chains.LLMChain",
"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.schema.Generation",
"langchain.schema.AIMessage",
"langchain.chat_models.base.dumps",
"langchain.schema.ChatGeneration",
"langchain.schema.HumanMessage",
"langchain.llm_cache.clear",
"langchain.chat_models.FakeListChatModel",
"langchain.llms.FakeListLLM"
] | [((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_visualizer.visualize",
"langchain.llms.OpenAI",
"langchain.chains.LLMChain",
"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... | [
"langchainhub.Client",
"langchain.utils.get_from_env",
"langchain.load.load.loads",
"langchain.load.dump.dumps"
] | [((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... | [
"langchainhub.Client",
"langchain.utils.get_from_env",
"langchain.load.load.loads",
"langchain.load.dump.dumps"
] | [((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.qa_with_sources.load_qa_with_sources_chain",
"langchain.llms.Cohere",
"langchain.llms.OpenAI",
"langchain.prompts.PromptTemplate",
"langchain.llms.AI21",
"langchain.llms.NLPCloud",
"langchain.chains.conversation.memory.ConversationSummaryBufferMemory"
] | [((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.chat_models.ChatOpenAI",
"langchain.tools.PythonAstREPLTool",
"langchain.memory.ConversationSummaryBufferMemory",
"langchain.callbacks.tracers.ConsoleCallbackHandler",
"langchain.agents.create_pandas_dataframe_agent",
"langchain.output_parsers.OutputFixing... | [((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.chat_models.ChatOpenAI",
"langchain.tools.PythonAstREPLTool",
"langchain.memory.ConversationSummaryBufferMemory",
"langchain.callbacks.tracers.ConsoleCallbackHandler",
"langchain.agents.create_pandas_dataframe_agent",
"langchain.output_parsers.OutputFixing... | [((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._EXTRA_LLM_TOOLS.keys",
"langchain.agents.load_tools._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._EXTRA_LLM_TOOLS.keys",
"langchain.agents.load_tools._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.document_loaders.unstructured.UnstructuredFileLoader",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.text_splitter.PythonCodeTextSplitter",
"langchain.document_loaders.unstructured.UnstructuredAPIFileLoader",
"langchain.document_loaders.UnstructuredURLLoader",
"langchain.... | [((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.llm_cache.lookup",
"langchain.schema.messages.HumanMessage",
"langchain.prompts.base.StringPromptValue",
"langchain.schema.messages.AIMessage",
"langchain.schema.ChatGeneration",
"langchain.load.dump.dumps",
"langchain.schema.RunInfo",
"langchain.llm_cache.update",
"langchain.pydantic_v1.... | [((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.docstore.document.Document",
"langchain.text_splitter.TextSplitter.from_tiktoken_encoder",
"langchain.text_splitter.TextSplitter",
"langchain.text_splitter.TextSplitter.from_huggingface_tokenizer"
] | [((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... | [
"langchainhub.Client",
"langchain.load.load.loads",
"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... | [
"langchainhub.Client",
"langchain.load.load.loads",
"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... | [
"langchainhub.Client",
"langchain.load.load.loads",
"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... | [
"langchainhub.Client",
"langchain.load.load.loads",
"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_visualizer.visualize",
"langchain.llms.OpenAI",
"langchain.chains.LLMChain",
"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.schema.Generation",
"langchain.schema.LLMResult",
"langchain.cache.MomentoCache"
] | [((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_tiktoken_encoder",
"langchain.text_splitter.TextSplitter",
"langchain.text_splitter.TextSplitter.from_huggingface_tokenizer"
] | [((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.document_loaders.WebBaseLoader",
"langchain.text_splitter.CharacterTextSplitter",
"langchain.vectorstores.Chroma.from_documents",
"langchain.agents.agent_toolkits.create_vectorstore_agent",
"langchain.agents.agent_toolkits.VectorStoreToolkit",
"langchain.document_loaders.TextLoader",
"langcha... | [((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.schema.Generation",
"langchain.utils.get_from_env",
"langchain.load.dump.dumps",
"langchain.vectorstores.redis.Redis",
"langchain.vectorstores.redis.Redis.from_existing_index",
"langchain.load.load.loads"
] | [((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.llms.OpenAI",
"langchain.prompts.PromptTemplate",
"langchain.chains.SequentialChain",
"langchain.chains.LLMChain"
] | [((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.text_splitter.RecursiveCharacterTextSplitter",
"langchain.document_loaders.DirectoryLoader",
"langchain.vectorstores.FAISS.from_documents",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.vectorstores.FAISS.load_local"
] | [((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.embeddings.HuggingFaceHubEmbeddings",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.vectorstores.FAISS.from_documents",
"langchain.chains.question_answering.load_qa_chain",
"langchain.callbacks.StdOutCallbackHandler",
"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.prompts.PromptTemplate",
"langchain.OpenAI",
"langchain.chains.LLMChain"
] | [((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.llms.OpenAI",
"langchain.chains.LLMChain"
] | [((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.llms.OpenAI",
"langchain.schema.Document",
"langchain.embeddings.OpenAIEmbeddings"
] | [((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... |
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.indexes.VectorstoreIndexCreator",
"langchain.document_loaders.DirectoryLoader"
] | [((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 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.embeddings.openai.OpenAIEmbeddings",
"langchain.llms.OpenAI",
"langchain.schema.Document"
] | [((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.schema.HumanMessage",
"langchain_community.callbacks.get_openai_callback"
] | [((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.chat_models.ChatOpenAI",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.llms.OpenAI",
"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.chains.RetrievalQA.from_chain_type",
"langchain.vectorstores.chroma.Chroma",
"langchain.embeddings.OpenAIEmbeddings",
"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.llms.OpenAI",
"langchain.document_loaders.UnstructuredURLLoader",
"langchain.embeddings.OpenAIEmbeddings"
] | [((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.embeddings.OpenAIEmbeddings",
"langchain.document_loaders.PyPDFLoader"
] | [((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.prompts.PromptTemplate",
"langchain.callbacks.get_openai_callback",
"langchain.chat_models.ChatOpenAI",
"langchain.chains.LLMChain"
] | [((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... |
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