code stringlengths 141 79.4k | apis listlengths 1 23 | extract_api stringlengths 126 73.2k |
|---|---|---|
import os
try:
from genai.credentials import Credentials
from genai.schemas import GenerateParams
from genai.extensions.langchain import LangChainInterface
from langchain import PromptTemplate
from langchain.chains import LLMChain, SimpleSequentialChain
except ImportError:
raise ImportError("Could not... | [
"langchain.chains.LLMChain",
"langchain.chains.SimpleSequentialChain",
"langchain.PromptTemplate"
] | [((520, 548), 'os.getenv', 'os.getenv', (['"""GENAI_KEY"""', 'None'], {}), "('GENAI_KEY', None)\n", (529, 548), False, 'import os\n'), ((559, 587), 'os.getenv', 'os.getenv', (['"""GENAI_API"""', 'None'], {}), "('GENAI_API', None)\n", (568, 587), False, 'import os\n'), ((634, 676), 'genai.credentials.Credentials', 'Cred... |
#%% Import Flask and create an app object
import config
from dotenv import load_dotenv
load_dotenv()
import os
import json
import asyncio
import openai
import pprint as pp
import markdown
# openai.api_key = os.getenv("OPENAI_API_KEY")
# os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
# Import Flask and cre... | [
"langchain.agents.initialize_agent",
"langchain.agents.agent_toolkits.PlayWrightBrowserToolkit.from_browser",
"langchain.agents.load_tools",
"langchain.chat_models.ChatOpenAI"
] | [((87, 100), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (98, 100), False, 'from dotenv import load_dotenv\n'), ((403, 418), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (408, 418), False, 'from flask import Flask, render_template, request, jsonify\n'), ((1220, 1265), 'langchain.chat_models.Ch... |
import langchain_helper as lch
import streamlit as st
st.title("Pet Name Generator")
pet_type = st.sidebar.selectbox("What is your pet?", ("dog", "cat", "bird", "fish", "reptile"))
if pet_type:
names_count = st.sidebar.slider("How many names do you want to generate?", 1, 10, 1)
if pet_type and names_count and s... | [
"langchain_helper.generate_pet_name"
] | [((55, 85), 'streamlit.title', 'st.title', (['"""Pet Name Generator"""'], {}), "('Pet Name Generator')\n", (63, 85), True, 'import streamlit as st\n'), ((98, 186), 'streamlit.sidebar.selectbox', 'st.sidebar.selectbox', (['"""What is your pet?"""', "('dog', 'cat', 'bird', 'fish', 'reptile')"], {}), "('What is your pet?'... |
import torch
from langchain.llms.base import LLM
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from llama_index import LangchainEmbedding
from llama_index import SimpleDirectoryReader, LLMPredictor, PromptHelper, GPTSimpleVectorIndex
from peft import PeftModel
from transformers import LlamaTo... | [
"langchain.embeddings.huggingface.HuggingFaceEmbeddings"
] | [((460, 505), 'transformers.LlamaTokenizer.from_pretrained', 'LlamaTokenizer.from_pretrained', (['hf_model_path'], {}), '(hf_model_path)\n', (490, 505), False, 'from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig\n'), ((517, 606), 'transformers.LlamaForCausalLM.from_pretrained', 'LlamaForCausalL... |
import streamlit as st
import pandas as pd
import time
import gcsfs
import asyncio
import os
import chromadb
from chromadb.utils import embedding_functions
import langchain
from langchain.document_loaders import TextLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import Ch... | [
"langchain.chains.LLMChain",
"langchain.embeddings.openai.OpenAIEmbeddings",
"langchain.prompts.ChatPromptTemplate.from_template",
"langchain.chat_models.ChatOpenAI"
] | [((2623, 2678), 'gcsfs.GCSFileSystem', 'gcsfs.GCSFileSystem', ([], {'project': '"""msca310019-capstone-49b3"""'}), "(project='msca310019-capstone-49b3')\n", (2642, 2678), False, 'import gcsfs\n'), ((4609, 4631), 'streamlit.columns', 'st.columns', (['[1, 1, 20]'], {}), '([1, 1, 20])\n', (4619, 4631), True, 'import strea... |
import langchain.graphs.neo4j_graph as neo4j_graph
import os
import sys
import ast
sys.path.append('backendPython')
from llms import *
from dotenv import load_dotenv, find_dotenv
load_dotenv(find_dotenv()) # read local .env file
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate, FewShot... | [
"langchain.chains.LLMChain",
"langchain.graphs.neo4j_graph.Neo4jGraph",
"langchain.prompts.FewShotPromptTemplate",
"langchain.prompts.PromptTemplate"
] | [((83, 115), 'sys.path.append', 'sys.path.append', (['"""backendPython"""'], {}), "('backendPython')\n", (98, 115), False, 'import sys\n'), ((392, 526), 'langchain.graphs.neo4j_graph.Neo4jGraph', 'neo4j_graph.Neo4jGraph', ([], {'url': "os.environ['NEO4J_URI']", 'username': "os.environ['NEO4J_USERNAME']", 'password': "o... |
import langchain_helper as lch
import streamlit as st
st.title("Pet Name Generator")
animal_type = st.sidebar.selectbox("What is your pet?",("Cat","Dog","Bird","Rabbit"))
if animal_type =='Cat':
pet_color = st.sidebar.text_area("What is the color of your cat?",max_chars=10)
if animal_type =='Dog':
pet_c... | [
"langchain_helper.generate_pet_name"
] | [((55, 85), 'streamlit.title', 'st.title', (['"""Pet Name Generator"""'], {}), "('Pet Name Generator')\n", (63, 85), True, 'import streamlit as st\n'), ((101, 176), 'streamlit.sidebar.selectbox', 'st.sidebar.selectbox', (['"""What is your pet?"""', "('Cat', 'Dog', 'Bird', 'Rabbit')"], {}), "('What is your pet?', ('Cat'... |
# Import langchain modules
from langchain.memory import Memory, ConversationBufferMemory
from langchain.agents import BaseMultiActionAgent, AgentExecutor
# Import other modules and classes
from research_agent import ResearchAgent
class ConversationMemory(Memory):
def __init__(self):
# Initialize ... | [
"langchain.memory.ConversationBufferMemory"
] | [((505, 531), 'langchain.memory.ConversationBufferMemory', 'ConversationBufferMemory', ([], {}), '()\n', (529, 531), False, 'from langchain.memory import Memory, ConversationBufferMemory\n'), ((1122, 1198), 'research_agent.ResearchAgent', 'ResearchAgent', (['prompt_template', 'language_model', 'stop_sequence', 'output_... |
from langchain.llms import HuggingFacePipeline
import langchain
from ingest import create_vector_db
from langchain.cache import InMemoryCache
from langchain.schema import prompt
from langchain.chains import RetrievalQA
from langchain.callbacks import StdOutCallbackHandler
from langchain import PromptTemplate
from trans... | [
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.llms.HuggingFacePipeline",
"langchain.callbacks.StdOutCallbackHandler",
"langchain.cache.InMemoryCache",
"langchain.PromptTemplate"
] | [((448, 463), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (461, 463), False, 'from langchain.cache import InMemoryCache\n'), ((754, 777), 'langchain.callbacks.StdOutCallbackHandler', 'StdOutCallbackHandler', ([], {}), '()\n', (775, 777), False, 'from langchain.callbacks import StdOutCallbackHand... |
"""
implement the actions as tools so we can validate inputs
"""
import langchain
from langchain.schema import AgentAction, AgentFinish
from langchain.schema.output import LLMResult
from langchain.agents import AgentType, initialize_agent
from langchain.tools import Tool, StructuredTool
from langchain.tools.b... | [
"langchain.agents.initialize_agent",
"langchain.tools.base.ToolException",
"langchain.tools.StructuredTool.from_function",
"langchain.chat_models.ChatOpenAI"
] | [((11966, 12189), 'langchain.tools.StructuredTool.from_function', 'StructuredTool.from_function', ([], {'name': '"""click"""', 'func': 'click', 'description': '"""This action clicks on an element specified by the element_id in the input."""', 'return_direct': 'SHOULD_RETURN_DIRECT', 'handle_tool_error': '_handle_error'... |
import os
import streamlit as st
import langchain.memory
import langchain.llms
import langchain.chains
from apikey import apikey
from langchain.memory import ConversationBufferMemory
from langchain.memory import ChatMessageHistory
from langchain.llms import OpenAI
from langchain.chains import ConversationChain
from lan... | [
"langchain.chains.ConversationChain",
"langchain.memory.ConversationBufferMemory",
"langchain.llms.OpenAI",
"langchain.memory.ChatMessageHistory"
] | [((447, 467), 'langchain.memory.ChatMessageHistory', 'ChatMessageHistory', ([], {}), '()\n', (465, 467), False, 'from langchain.memory import ChatMessageHistory\n'), ((558, 603), 'langchain.memory.ConversationBufferMemory', 'ConversationBufferMemory', ([], {'chat_memory': 'history'}), '(chat_memory=history)\n', (582, 6... |
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'), ((6238, 6252), 'pandas.DataFrame', 'pd.DataFrame', ([], {}), '()\n', (6250, 6252), True, 'import pandas as pd\n'), (... |
"""Web base loader class."""
import langchain_community.document_loaders as dl
from langchain.docstore.document import Document
import asyncio
import datetime
from io import StringIO
import logging
import re
import warnings
from typing import Any, AsyncGenerator, Dict, Iterator, List, Optional, Tuple, Union
import insp... | [
"langchain.docstore.document.Document"
] | [((914, 951), 're.sub', 're.sub', (['pattern', '"""\\\\1"""', 'markdown_text'], {}), "(pattern, '\\\\1', markdown_text)\n", (920, 951), False, 'import re\n'), ((1742, 1788), 're.sub', 're.sub', (['"""(\\\\n){4,}"""', '"""\n\n\n"""', 'simplified_text'], {}), "('(\\\\n){4,}', '\\n\\n\\n', simplified_text)\n", (1748, 1788... |
#!/usr/bin/python3
import cgi
import time
import threading
import langchain
import openai
from langchain.tools import WikipediaQueryRun
from langchain.utilities import WikipediaAPIWrapper
from langchain.llms import OpenAI
from langchain.chains import LLMChain
from langchain.agents import load_tools
from langchain.ag... | [
"langchain.agents.initialize_agent",
"langchain.utilities.WikipediaAPIWrapper",
"langchain.llms.OpenAI",
"langchain.agents.load_tools"
] | [((436, 454), 'cgi.FieldStorage', 'cgi.FieldStorage', ([], {}), '()\n', (452, 454), False, 'import cgi\n'), ((895, 934), 'threading.Thread', 'threading.Thread', ([], {'target': 'run', 'args': 'data'}), '(target=run, args=data)\n', (911, 934), False, 'import threading\n'), ((507, 528), 'langchain.utilities.WikipediaAPIW... |
import streamlit as st
import os
# Utils
import time
from typing import List
# Langchain
import langchain
from pydantic import BaseModel
from vertexai.language_models import TextGenerationModel
# Vertex AI
from langchain.llms import VertexAI
from llm_experiments.utils import here
os.environ["GOOGLE_APPLICATION_CRED... | [
"langchain.llms.VertexAI"
] | [((400, 518), 'langchain.llms.VertexAI', 'VertexAI', ([], {'model_name': '"""text-bison@001"""', 'max_output_tokens': '(1024)', 'temperature': '(0.3)', 'top_p': '(0.8)', 'top_k': '(40)', 'verbose': '(True)'}), "(model_name='text-bison@001', max_output_tokens=1024, temperature=\n 0.3, top_p=0.8, top_k=40, verbose=Tru... |
#Multi-agent decentralized speaker selection:
'''
This notebook showcases how to implement a multi-agent simulation without a fixed schedule for who speaks when.
Instead the agents decide for themselves who speaks. We can implement this by having each agent bid to speak.
Whichever agent’s bid is the highest gets to ... | [
"langchain.PromptTemplate",
"langchain.schema.SystemMessage",
"langchain.chat_models.ChatOpenAI",
"langchain.schema.HumanMessage"
] | [((4907, 5006), 'langchain.schema.SystemMessage', 'SystemMessage', ([], {'content': '"""You can add detail to the description of each presidential candidate."""'}), "(content=\n 'You can add detail to the description of each presidential candidate.')\n", (4920, 5006), False, 'from langchain.schema import AIMessage, ... |
import torch
from transformers import BitsAndBytesConfig
from langchain import HuggingFacePipeline
from langchain import PromptTemplate, LLMChain
from pathlib import Path
import langchain
import json
import chromadb
from chromadb.config import Settings
from langchain.llms import HuggingFacePipeline
from langchain.docum... | [
"langchain.document_loaders.DirectoryLoader",
"langchain.embeddings.HuggingFaceEmbeddings",
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.llms.HuggingFacePipeline",
"langchain.vectorstores.Chroma.from_documents"
] | [((1266, 1371), 'langchain.document_loaders.DirectoryLoader', 'DirectoryLoader', (['rootdir'], {'glob': '"""**/*.txt"""', 'loader_cls': 'TextLoader', 'loader_kwargs': "{'encoding': 'utf-8'}"}), "(rootdir, glob='**/*.txt', loader_cls=TextLoader,\n loader_kwargs={'encoding': 'utf-8'})\n", (1281, 1371), False, 'from la... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# 配置环境变量
import os
from LangChain_study.common import ChatParam
os.environ["OPENAI_API_KEY"] = ChatParam.OPENAI_API_KEY
os.environ["OPENAI_API_BASE"] = ChatParam.OPENAI_API_BASE
# 初始化LLM模型
import langchain
from langchain.llms import OpenAI
llm = OpenAI(model_name="text-d... | [
"langchain.cache.InMemoryCache",
"langchain.llms.OpenAI"
] | [((295, 348), 'langchain.llms.OpenAI', 'OpenAI', ([], {'model_name': '"""text-davinci-002"""', 'n': '(2)', 'best_of': '(2)'}), "(model_name='text-davinci-002', n=2, best_of=2)\n", (301, 348), False, 'from langchain.llms import OpenAI\n'), ((457, 472), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', ... |
import streamlit as st
import langchain_helper as lch
st.title("🐶 Pets Name Generator")
animal_type = st.sidebar.selectbox(
"What is your pet?", ("Dog", "Cat", "Hamster", "Rat", "Snake", "Lizard", "Cow")
)
if animal_type == "Dog":
pet_color = st.sidebar.text_area(label="What color is your dog?", max_chars=... | [
"langchain_helper.generate_pet_name"
] | [((55, 88), 'streamlit.title', 'st.title', (['"""🐶 Pets Name Generator"""'], {}), "('🐶 Pets Name Generator')\n", (63, 88), True, 'import streamlit as st\n'), ((104, 209), 'streamlit.sidebar.selectbox', 'st.sidebar.selectbox', (['"""What is your pet?"""', "('Dog', 'Cat', 'Hamster', 'Rat', 'Snake', 'Lizard', 'Cow')"], ... |
import pandas as pd
from langchain.document_loaders.word_document import Docx2txtLoader
# this does not work, some how, I can not install some of its requirement libs.
from langchain.document_loaders.word_document import UnstructuredWordDocumentLoader
# from langchain.text_splitter import CharacterTextSplitter
import l... | [
"langchain.text_splitter.CharacterTextSplitter",
"langchain.document_loaders.word_document.Docx2txtLoader"
] | [((477, 594), 'langchain.document_loaders.word_document.Docx2txtLoader', 'Docx2txtLoader', (['"""../../data/raw/6. HR.03.V3.2023. Nội quy Lao động_Review by Labor Department - Final.DOCX"""'], {}), "(\n '../../data/raw/6. HR.03.V3.2023. Nội quy Lao động_Review by Labor Department - Final.DOCX'\n )\n", (491, 594),... |
"""Create a ConversationalRetrievalChain for question/answering."""
import imp
import logging
import sys
from typing import Union
from langchain.callbacks.base import BaseCallbackManager, BaseCallbackHandler
from langchain.callbacks.tracers import LangChainTracer
from langchain.chains import ConversationalRetrievalCha... | [
"langchain.callbacks.base.BaseCallbackManager",
"langchain.chains.question_answering.load_qa_chain",
"langchain.callbacks.tracers.LangChainTracer",
"langchain.chains.llm.LLMChain"
] | [((1450, 1473), 'langchain.callbacks.base.BaseCallbackManager', 'BaseCallbackManager', (['[]'], {}), '([])\n', (1469, 1473), False, 'from langchain.callbacks.base import BaseCallbackManager, BaseCallbackHandler\n'), ((1497, 1536), 'langchain.callbacks.base.BaseCallbackManager', 'BaseCallbackManager', (['[rephrase_handl... |
from dotenv import load_dotenv
load_dotenv()
from langchain.pydantic_v1 import BaseModel, Field, validator
from langchain.chat_models import ChatOpenAI
from langchain.chains.openai_functions import create_structured_output_chain
from typing import Optional
from langchain.prompts import ChatPromptTemplate
import langc... | [
"langchain.pydantic_v1.Field",
"langchain.chains.openai_functions.create_structured_output_chain",
"langchain.pydantic_v1.validator",
"langchain.chat_models.ChatOpenAI",
"langchain.prompts.ChatPromptTemplate.from_messages"
] | [((32, 45), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (43, 45), False, 'from dotenv import load_dotenv\n'), ((831, 871), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'model': '"""gpt-4"""', 'temperature': '(0)'}), "(model='gpt-4', temperature=0)\n", (841, 871), False, 'from langchain.chat_models... |
# Drive Imports
import yaml
import asyncio
from deferred_imports import langchain, imports_done
import webbrowser
# Global Variables
dictionaries_folder_path=""
structure_dictionary_path=""
information_dictionary_path=""
folder_dictionary_path=""
# Information Mapping
async def a_update_mapping(your_dictionary,over... | [
"langchain.prompts.chat.SystemMessagePromptTemplate.from_template",
"langchain.chat_models.ChatOpenAI",
"langchain.vectorstores.Chroma.from_documents",
"langchain.prompts.chat.HumanMessagePromptTemplate.from_template",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.prompts.chat.ChatPromptTemplate.fro... | [((1100, 1119), 'deferred_imports.imports_done.wait', 'imports_done.wait', ([], {}), '()\n', (1117, 1119), False, 'from deferred_imports import langchain, imports_done\n'), ((2780, 2838), 'langchain.prompts.chat.SystemMessagePromptTemplate.from_template', 'SystemMessagePromptTemplate.from_template', (['system_template'... |
import tensorflow
import dotenv
import transformers
from tensorflow import keras
from dotenv import find_dotenv, load_dotenv
from transformers import pipeline
import langchain
from langchain import PromptTemplate, LLMChain, OpenAI
import requests
import os
import openai
import streamlit as st
HUGGINGFACEHUB_API_TOKE... | [
"langchain.OpenAI",
"langchain.PromptTemplate"
] | [((324, 361), 'os.getenv', 'os.getenv', (['"""HUGGINGFACEHUB_API_TOKEN"""'], {}), "('HUGGINGFACEHUB_API_TOKEN')\n", (333, 361), False, 'import os\n'), ((379, 406), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (388, 406), False, 'import os\n'), ((420, 433), 'dotenv.find_dotenv', 'fin... |
"""Chat with a model using LangChain"""
from dotenv import load_dotenv
from langchain_core.messages import HumanMessage, SystemMessage
from genai import Client, Credentials
from genai.extensions.langchain.chat_llm import LangChainChatInterface
from genai.schema import (
DecodingMethod,
ModerationHAP,
Mode... | [
"langchain_core.messages.HumanMessage",
"langchain_core.messages.SystemMessage"
] | [((576, 589), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (587, 589), False, 'from dotenv import load_dotenv\n'), ((784, 806), 'genai.Credentials.from_env', 'Credentials.from_env', ([], {}), '()\n', (804, 806), False, 'from genai import Client, Credentials\n'), ((1079, 1143), 'genai.schema.TextGenerationRetu... |
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from redundant_filter_retriever import RedundantFilterRetriever
from dotenv import load_dotenv
import langchain
langchain.debug = True
load_... | [
"langchain.vectorstores.Chroma",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.chat_models.ChatOpenAI"
] | [((315, 328), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (326, 328), False, 'from dotenv import load_dotenv\n'), ((337, 349), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {}), '()\n', (347, 349), False, 'from langchain.chat_models import ChatOpenAI\n'), ((363, 381), 'langchain.embeddings.OpenAIEmb... |
from llama_index.llms import LangChainLLM
from langchain.llms import Clarifai
from llama_index import VectorStoreIndex, SummaryIndex
from llama_index import ServiceContext
from llama_index import Document
from llama_index import SimpleDirectoryReader
from llama_index.prompts import PromptTemplate
from llama_index.cha... | [
"langchain.llms.Clarifai"
] | [((2438, 2528), 'llama_index.VectorStoreIndex.from_documents', 'VectorStoreIndex.from_documents', (['self.documents'], {'service_context': 'self.service_context'}), '(self.documents, service_context=self.\n service_context)\n', (2469, 2528), False, 'from llama_index import VectorStoreIndex, SummaryIndex\n'), ((2553,... |
# Import necessary modules for Hubspot API integration and Langchain analysis
import hubspot
import langchain
def retrieve_and_store_feedback(appointment_id):
"""
Function to retrieve and store customer feedback and ratings from the Hubspot App.
Input: appointment_id - ID of the appointment for whic... | [
"langchain.Client"
] | [((507, 546), 'hubspot.Client', 'hubspot.Client', ([], {'api_key': 'hubspot_api_key'}), '(api_key=hubspot_api_key)\n', (521, 546), False, 'import hubspot\n'), ((966, 1018), 'langchain.Client', 'langchain.Client', ([], {'api_key': '"""<your_langchain_api_key>"""'}), "(api_key='<your_langchain_api_key>')\n", (982, 1018),... |
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