code stringlengths 141 79.4k | apis listlengths 1 23 | extract_api stringlengths 126 73.2k |
|---|---|---|
"""Test Upstash Redis cache functionality."""
import uuid
import pytest
import langchain
from langchain.cache import UpstashRedisCache
from langchain.schema import Generation, LLMResult
from tests.unit_tests.llms.fake_chat_model import FakeChatModel
from tests.unit_tests.llms.fake_llm import FakeLLM
URL = "<UPSTASH_... | [
"langchain.llm_cache.clear",
"langchain.schema.Generation",
"langchain.llm_cache.redis.flushall",
"langchain.llm_cache.redis.pttl",
"langchain.llm_cache._key",
"langchain.llm_cache.lookup"
] | [((436, 473), 'pytest.mark.requires', 'pytest.mark.requires', (['"""upstash_redis"""'], {}), "('upstash_redis')\n", (456, 473), False, 'import pytest\n'), ((809, 846), 'pytest.mark.requires', 'pytest.mark.requires', (['"""upstash_redis"""'], {}), "('upstash_redis')\n", (829, 846), False, 'import pytest\n'), ((2491, 252... |
import os
import langchain
import streamlit as st
from collections import defaultdict
from urllib.error import URLError
from dotenv import load_dotenv
load_dotenv()
if os.environ.get("QNA_DEBUG") == "true":
langchain.debug = True
from qna.llm import make_qna_chain, get_llm
from qna.db import get_cache, get_vecto... | [
"langchain.llm_cache.clear"
] | [((152, 165), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (163, 165), False, 'from dotenv import load_dotenv\n'), ((170, 197), 'os.environ.get', 'os.environ.get', (['"""QNA_DEBUG"""'], {}), "('QNA_DEBUG')\n", (184, 197), False, 'import os\n'), ((489, 500), 'qna.db.get_cache', 'get_cache', ([], {}), '()\n', (... |
'''
Create Vector Store from all documents in a folder, currently supports .pptx, .docx, .pdf files.
Created by Ric Zhou on 2021-03-27
'''
from langchain.document_loaders import (UnstructuredPowerPointLoader, UnstructuredWordDocumentLoader, PyPDFLoader, UnstructuredPDFLoader)
import glob
import langchain.text_splitte... | [
"langchain.document_loaders.UnstructuredWordDocumentLoader",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.document_loaders.UnstructuredPowerPointLoader",
"langchain.vectorstores.FAISS.save_local",
"langchain.document_loaders.PyPDFLoader",
"langchain.embeddings.OpenAIEmbeddings"
] | [((604, 617), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (615, 617), False, 'from dotenv import load_dotenv\n'), ((618, 633), 'GlobalClasses.GlobalContext', 'GlobalContext', ([], {}), '()\n', (631, 633), False, 'from GlobalClasses import GlobalContext\n'), ((939, 1017), 'langchain.text_splitter.RecursiveCha... |
import os
import key
import tabulate
# Set API key
os.environ["OPENAI_API_KEY"] = key.OPENAI_API_KEY
# Import langchain
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import CSVLoader
from langchain.indexes import VectorstoreIndexCreator
from langc... | [
"langchain.document_loaders.CSVLoader",
"langchain.indexes.VectorstoreIndexCreator",
"langchain.chat_models.ChatOpenAI"
] | [((465, 508), 'langchain.document_loaders.CSVLoader', 'CSVLoader', ([], {'file_path': 'file', 'encoding': '"""utf-8"""'}), "(file_path=file, encoding='utf-8')\n", (474, 508), False, 'from langchain.document_loaders import CSVLoader\n'), ((708, 735), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'temperature':... |
import openai
import langchain as lc
from langchain.llms import OpenAI
import gradio as gr
# 设置OpenAI API密钥
openai.api_key = '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'
# 初始化Langchain的OpenAI LLM
llm = OpenAI(api_key=openai.api_key)
# 定义一个函数来处理上传的文档并生成响应
def process_document(document):
# 这里可以添加代码来处理文档，... | [
"langchain.llms.OpenAI"
] | [((213, 243), 'langchain.llms.OpenAI', 'OpenAI', ([], {'api_key': 'openai.api_key'}), '(api_key=openai.api_key)\n', (219, 243), False, 'from langchain.llms import OpenAI\n'), ((508, 536), 'gradio.inputs.File', 'gr.inputs.File', ([], {'label': '"""上传文档"""'}), "(label='上传文档')\n", (522, 536), True, 'import gradio as gr\n'... |
import streamlit as st
# import langchain
# langchain.debug=True
from crewai import Agent
from utils.tools import (
retrieval_tool, search_tool,
get_current_stock_price
)
from langchain_openai import ChatOpenAI
# from dotenv import load_dotenv
# load_dotenv()
# openai_api_key = os.environ.get("OPENAI_API... | [
"langchain_openai.ChatOpenAI"
] | [((405, 499), 'langchain_openai.ChatOpenAI', 'ChatOpenAI', ([], {'model': '"""gpt-3.5-turbo-16k"""', 'openai_api_key': "ss.configurations['openai_api_key']"}), "(model='gpt-3.5-turbo-16k', openai_api_key=ss.configurations[\n 'openai_api_key'])\n", (415, 499), False, 'from langchain_openai import ChatOpenAI\n'), ((57... |
import langchain
import os
import openai
from InstructorEmbedding import INSTRUCTOR
from langchain_community.embeddings import HuggingFaceInstructEmbeddings
from langchain_community.vectorstores import Chroma
from langchain.chains import VectorDBQA, RetrievalQA, ConversationalRetrievalChain
from custom_retrival ... | [
"langchain_community.llms.HuggingFaceHub"
] | [((2377, 2404), 'streamlit.chat_message', 'st.chat_message', (['"""assitant"""'], {}), "('assitant')\n", (2392, 2404), True, 'import streamlit as st\n'), ((2498, 2519), 'streamlit.chat_input', 'st.chat_input', (['"""user"""'], {}), "('user')\n", (2511, 2519), True, 'import streamlit as st\n'), ((1212, 1338), 'langchain... |
import os
import pandas as pd
import math
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain import OpenAI, VectorDBQA, OpenAI
from langchain.llms import OpenAIChat
from langchain.document_loaders im... | [
"langchain.text_splitter.CharacterTextSplitter",
"langchain.vectorstores.Chroma.from_documents",
"langchain.document_loaders.DataFrameLoader",
"langchain.OpenAI",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((527, 555), 'sys.modules.pop', 'sys.modules.pop', (['"""pysqlite3"""'], {}), "('pysqlite3')\n", (542, 555), False, 'import sys\n'), ((558, 587), 'streamlit.title', 'st.title', (['"""GPT module (TEST)"""'], {}), "('GPT module (TEST)')\n", (566, 587), True, 'import streamlit as st\n'), ((606, 660), 'streamlit.text_inpu... |
# Python built-in module
import os
import time
import json
# Python installed module
import tiktoken
import langchain
from spacy.lang.en import English
class SentencizerSplitter(object):
def __init__(self, config_dict):
self.total_tokens = config_dict["embedding"]["total_tokens"]
self.approx_tota... | [
"langchain.schema.document.Document"
] | [((511, 520), 'spacy.lang.en.English', 'English', ([], {}), '()\n', (518, 520), False, 'from spacy.lang.en import English\n'), ((586, 653), 'tiktoken.encoding_for_model', 'tiktoken.encoding_for_model', (["config_dict['embedding']['model_name']"], {}), "(config_dict['embedding']['model_name'])\n", (613, 653), False, 'im... |
import os
import json
import openai
from utils import *
import random
import langchain
from langchain import PromptTemplate
from langchain.llms import OpenAI, OpenAIChat
from langchain import LLMChain
from re import compile
from datetime import datetime
from typing import NamedTuple
from openai import Embedding
#set ... | [
"langchain.llms.OpenAIChat",
"langchain.LLMChain",
"langchain.PromptTemplate"
] | [((1869, 1883), 'datetime.datetime.now', 'datetime.now', ([], {}), '()\n', (1881, 1883), False, 'from datetime import datetime\n'), ((2826, 2890), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'template': 'prompt_text', 'input_variables': "['Memory']"}), "(template=prompt_text, input_variables=['Memory'])\n", (28... |
import langchain
class ChromaDB:
def __init__(self, path):
self.db = langchain.ChromaDB(path)
def index_url(self, url):
self.db.index_url(url)
| [
"langchain.ChromaDB"
] | [((82, 106), 'langchain.ChromaDB', 'langchain.ChromaDB', (['path'], {}), '(path)\n', (100, 106), False, 'import langchain\n')] |
import langchain_helper as lch
import streamlit as st
st.title("Pets name generator")
animal_type = st.sidebar.selectbox("What is your pet?", ("Cat", "Dog", "Cow", "Hamnster"))
pet_color = st.sidebar.text_area(f"What color is your {animal_type}?", max_chars=15)
if pet_color:
response = lch.generate_pet_names(an... | [
"langchain_helper.langchain_agent",
"langchain_helper.generate_pet_names"
] | [((55, 86), 'streamlit.title', 'st.title', (['"""Pets name generator"""'], {}), "('Pets name generator')\n", (63, 86), True, 'import streamlit as st\n'), ((102, 178), 'streamlit.sidebar.selectbox', 'st.sidebar.selectbox', (['"""What is your pet?"""', "('Cat', 'Dog', 'Cow', 'Hamnster')"], {}), "('What is your pet?', ('C... |
# Copyright (c) Khulnasoft Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llmk 2 Community License Agreement.
import langchain
from langchain.llms import Replicate
from flask import Flask
from flask import request
import os
import requests
import json
class ... | [
"langchain.llms.Replicate"
] | [((1513, 1619), 'langchain.llms.Replicate', 'Replicate', ([], {'model': 'llmk2_13b_chat', 'model_kwargs': "{'temperature': 0.01, 'top_p': 1, 'max_new_tokens': 500}"}), "(model=llmk2_13b_chat, model_kwargs={'temperature': 0.01, 'top_p':\n 1, 'max_new_tokens': 500})\n", (1522, 1619), False, 'from langchain.llms import... |
import os
import langchain
from config import *
from util import *
from langchain.llms import OpenAI, Cohere, HuggingFaceHub
from langchain.chat_models import ChatOpenAI
from langchain.agents import AgentType, initialize_agent, load_tools
from typing import Optional, Type
from langchain.callbacks.manager import AsyncCa... | [
"langchain.agents.initialize_agent",
"langchain.llms.OpenAI",
"langchain.agents.Tool",
"langchain.chat_models.ChatOpenAI"
] | [((786, 807), 'langchain.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0)'}), '(temperature=0)\n', (792, 807), False, 'from langchain.llms import OpenAI, Cohere, HuggingFaceHub\n'), ((815, 840), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '(0)'}), '(temperature=0)\n', (825, 840), False, 'fro... |
"""Interface with the LangChain Hub."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Optional
from langchain_core.load.dump import dumps
from langchain_core.load.load import loads
if TYPE_CHECKING:
from langchainhub import Client
def _get_client(api_url: Optional[str] = None, api_k... | [
"langchain_core.load.load.loads",
"langchain_core.load.dump.dumps",
"langchainhub.Client"
] | [((679, 711), 'langchainhub.Client', 'Client', (['api_url'], {'api_key': 'api_key'}), '(api_url, api_key=api_key)\n', (685, 711), False, 'from langchainhub import Client\n'), ((1912, 1925), 'langchain_core.load.dump.dumps', 'dumps', (['object'], {}), '(object)\n', (1917, 1925), False, 'from langchain_core.load.dump imp... |
import langchain
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI, OpenAI
from langchain.schema import HumanMessage, AIMessage, SystemMessage
from langchain.prompts import PromptTemplate, FewShotPromptTemplate
from langchain.output_parsers import CommaSeparatedListOutputParser
from langchain.cache... | [
"langchain_openai.ChatOpenAI",
"langchain.cache.InMemoryCache",
"langchain.prompts.PromptTemplate"
] | [((423, 436), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (434, 436), False, 'from dotenv import load_dotenv\n'), ((459, 474), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (472, 474), False, 'from langchain.cache import InMemoryCache\n'), ((508, 541), 'langchain_openai.ChatOpenAI', 'Ch... |
"""
Simple flask application to demo model inference
"""
# Loads env variable when running locally
from dotenv import load_dotenv
load_dotenv()
## Add parent directory to path for aws_helpers
import sys
sys.path.append('..')
# Imports
from flask import Flask, request, render_template, Response
import json
import os... | [
"langchain_inference.run_chain"
] | [((132, 145), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (143, 145), False, 'from dotenv import load_dotenv\n'), ((206, 227), 'sys.path.append', 'sys.path.append', (['""".."""'], {}), "('..')\n", (221, 227), False, 'import sys\n'), ((460, 511), 'os.path.join', 'os.path.join', (['"""data"""', '"""documents""... |
import json
from pathlib import Path
from typing import Dict, List
import langchain
import numpy as np
import typer
from langchain.cache import SQLiteCache
from langchain.llms import OpenAI
from tqdm import tqdm
langchain.llm_cache = SQLiteCache(database_path=".langchain.db")
def _is_daster_empl(title: str) -> bool... | [
"langchain.llms.OpenAI",
"langchain.cache.SQLiteCache"
] | [((236, 278), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {'database_path': '""".langchain.db"""'}), "(database_path='.langchain.db')\n", (247, 278), False, 'from langchain.cache import SQLiteCache\n'), ((905, 952), 'langchain.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0.1)', 'model_name': '"""gpt-4-32k"""'}... |
import langchain.vectorstores.opensearch_vector_search as ovs
from opensearchpy import OpenSearch, RequestsHttpConnection, AWSV4SignerAuth, helpers
from langchain.vectorstores import OpenSearchVectorSearch
def create_ovs_client(
collection_id,
index_name,
region,
boto3_session,
bedrock_embeddings... | [
"langchain.vectorstores.OpenSearchVectorSearch"
] | [((470, 515), 'opensearchpy.AWSV4SignerAuth', 'AWSV4SignerAuth', (['credentials', 'region', 'service'], {}), '(credentials, region, service)\n', (485, 515), False, 'from opensearchpy import OpenSearch, RequestsHttpConnection, AWSV4SignerAuth, helpers\n'), ((543, 724), 'opensearchpy.OpenSearch', 'OpenSearch', ([], {'hos... |
"""
.. warning::
Beta Feature!
**Cache** provides an optional caching layer for LLMs.
Cache is useful for two reasons:
- It can save you money by reducing the number of API calls you make to the LLM
provider if you're often requesting the same completion multiple times.
- It can speed up your application by redu... | [
"langchain.load.load.loads",
"langchain.utils.get_from_env",
"langchain.schema.Generation",
"langchain.load.dump.dumps"
] | [((1586, 1613), 'logging.getLogger', 'logging.getLogger', (['__file__'], {}), '(__file__)\n', (1603, 1613), False, 'import logging\n'), ((5793, 5811), 'sqlalchemy.ext.declarative.declarative_base', 'declarative_base', ([], {}), '()\n', (5809, 5811), False, 'from sqlalchemy.ext.declarative import declarative_base\n'), (... |
import argparse
import json
import os
from typing import Union, List, Literal
from langchain.indexes import SQLRecordManager, index, IndexingResult
import langchain_community.vectorstores as vectorstores
from langchain_core.embeddings import Embeddings
from langchain_openai import OpenAIEmbeddings
from langchain_core... | [
"langchain_openai.OpenAIEmbeddings",
"langchain.indexes.index",
"langchain.indexes.SQLRecordManager"
] | [((1067, 1115), 'langchain_openai.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {'model': '"""text-embedding-3-small"""'}), "(model='text-embedding-3-small')\n", (1083, 1115), False, 'from langchain_openai import OpenAIEmbeddings\n'), ((3445, 3480), 'os.makedirs', 'os.makedirs', (['db_path'], {'exist_ok': '(True)'}), '(d... |
import textwrap
import streamlit as st
import langchain_helper as lch
st.title("YouTube Assistant")
with st.sidebar:
with st.form(key='my_form'):
youtube_url = st.sidebar.text_area(
label="What is the YouTube video URL?",
max_chars=50
)
query = st.sidebar.text_are... | [
"langchain_helper.create_db_from_youtube_video_url",
"langchain_helper.get_response_from_query"
] | [((73, 102), 'streamlit.title', 'st.title', (['"""YouTube Assistant"""'], {}), "('YouTube Assistant')\n", (81, 102), True, 'import streamlit as st\n'), ((130, 152), 'streamlit.form', 'st.form', ([], {'key': '"""my_form"""'}), "(key='my_form')\n", (137, 152), True, 'import streamlit as st\n'), ((176, 250), 'streamlit.si... |
import asyncio
import inspect
import warnings
from abc import ABC, abstractmethod
from functools import partial
from typing import Any, Dict, List, Mapping, Optional, Sequence
from pydantic import Field, root_validator
import langchain
from langchain.callbacks.base import BaseCallbackManager
from langchain.callbacks.... | [
"langchain.callbacks.manager.AsyncCallbackManager.configure",
"langchain.schema.messages.AIMessage",
"langchain.schema.ChatResult",
"langchain.load.dump.dumps",
"langchain.callbacks.manager.CallbackManager.configure",
"langchain.load.dump.dumpd",
"langchain.schema.RunInfo",
"langchain.schema.messages.... | [((923, 960), 'pydantic.Field', 'Field', ([], {'default_factory': '_get_verbosity'}), '(default_factory=_get_verbosity)\n', (928, 960), False, 'from pydantic import Field, root_validator\n'), ((1034, 1067), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, exclude=True)\n', (103... |
from langchain.chains.router import MultiPromptChain
from langchain.chains.router.llm_router import LLMRouterChain, RouterOutputParser
from langchain.prompts import PromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.chains import LLMChain
from ap... | [
"langchain.chains.router.llm_router.LLMRouterChain.from_llm",
"langchain.chat_models.ChatOpenAI",
"langchain.chains.router.MultiPromptChain",
"langchain.chains.router.llm_router.RouterOutputParser",
"langchain.chains.LLMChain",
"langchain.prompts.ChatPromptTemplate.from_template"
] | [((3977, 4033), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '(0)', 'model': 'ChatGPTModel.GPT3.value'}), '(temperature=0, model=ChatGPTModel.GPT3.value)\n', (3987, 4033), False, 'from langchain.chat_models import ChatOpenAI\n'), ((4531, 4574), 'langchain.prompts.ChatPromptTemplate.from_templa... |
from __future__ import annotations
import asyncio
import functools
import logging
import os
import warnings
from contextlib import asynccontextmanager, contextmanager
from contextvars import ContextVar
from typing import (
Any,
AsyncGenerator,
Dict,
Generator,
List,
Optional,
Type,
Type... | [
"langchain.schema.get_buffer_string",
"langchain.callbacks.stdout.StdOutCallbackHandler",
"langchain.callbacks.tracers.wandb.WandbTracer",
"langchain.callbacks.openai_info.OpenAICallbackHandler",
"langchain.callbacks.tracers.stdout.ConsoleCallbackHandler",
"langchain.callbacks.tracers.langchain.LangChainT... | [((1114, 1141), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1131, 1141), False, 'import logging\n'), ((1286, 1329), 'contextvars.ContextVar', 'ContextVar', (['"""openai_callback"""'], {'default': 'None'}), "('openai_callback', default=None)\n", (1296, 1329), False, 'from contextvars i... |
import argparse
import json
import logging
import os
import pathlib
from typing import Dict, List, Union, Optional
import langchain
import pandas as pd
import tiktoken
import wandb
from langchain import LLMChain, FAISS
from langchain.cache import SQLiteCache
from langchain.chains import HypotheticalDocumentEmbedder
fr... | [
"langchain.docstore.document.Document",
"langchain.text_splitter.MarkdownTextSplitter",
"langchain.cache.SQLiteCache",
"langchain.chat_models.ChatOpenAI",
"langchain.document_loaders.NotebookLoader",
"langchain.text_splitter.PythonCodeTextSplitter",
"langchain.text_splitter.TokenTextSplitter",
"langch... | [((902, 943), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {'database_path': '"""langchain.db"""'}), "(database_path='langchain.db')\n", (913, 943), False, 'from langchain.cache import SQLiteCache\n'), ((954, 981), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (971, 981), False, 'i... |
import langchain
from dotenv import load_dotenv
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from rmrkl import ChatZeroShotAgent, RetryAgentExecutor
from .prompt import FORMAT_INSTRUCTIONS, QUESTION_PROMPT, SUFFIX
from .tools import make_tools, Doc, Text,search_texts, load_texts
imp... | [
"langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler"
] | [((329, 342), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (340, 342), False, 'from dotenv import load_dotenv\n'), ((2064, 2077), 'time.sleep', 'time.sleep', (['(3)'], {}), '(3)\n', (2074, 2077), False, 'import time\n'), ((1632, 1784), 'rmrkl.ChatZeroShotAgent.from_llm_and_tools', 'ChatZeroShotAgent.from_llm_... |
"""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 import schemas as langsmith_sche... | [
"langchain.callbacks.tracers.langchain.get_client",
"langchain.callbacks.manager.tracing_v2_enabled"
] | [((553, 580), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (570, 580), False, 'import logging\n'), ((2572, 2588), 'uuid.UUID', 'UUID', (['example_id'], {}), '(example_id)\n', (2576, 2588), False, 'from uuid import UUID\n'), ((2678, 2707), 'langchain.callbacks.tracers.langchain.get_clien... |
"""A Tracer implementation that records to LangChain endpoint."""
from __future__ import annotations
import logging
import os
from concurrent.futures import Future, ThreadPoolExecutor, wait
from datetime import datetime
from typing import Any, Dict, List, Optional, Set, Union
from uuid import UUID
from langchainplus_... | [
"langchain.schema.messages.messages_to_dict",
"langchainplus_sdk.LangChainPlusClient",
"langchain.env.get_runtime_environment"
] | [((633, 660), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (650, 660), False, 'import logging\n'), ((1895, 1928), 'concurrent.futures.ThreadPoolExecutor', 'ThreadPoolExecutor', ([], {'max_workers': '(1)'}), '(max_workers=1)\n', (1913, 1928), False, 'from concurrent.futures import Future... |
"""
Tools and utilities to work with
https://github.com/unitedstates/congress
fetch bill metadata to data/118/bills
```
usc-run govinfo --bulkdata=BILLSTATUS --congress=118
```
fetch bill text to data/govinfo/BILLS
```
usc-run govinfo --bulkdata=BILLS --congress=118
```
fetch plaw text to data/govinfo/PLAWS
```
us... | [
"langchain.schema.Document"
] | [((809, 836), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (826, 836), False, 'import logging\n'), ((14811, 14841), 'bs4.BeautifulSoup', 'BeautifulSoup', (['text_xml', '"""xml"""'], {}), "(text_xml, 'xml')\n", (14824, 14841), False, 'from bs4 import BeautifulSoup\n'), ((16122, 16131), '... |
import streamlit as st
st.set_page_config(layout="wide")
import streamlit.components.v1 as components
from streamlit_extras.stateful_button import button
import os
from dotenv import load_dotenv
load_dotenv()
import pandas as pd
import numpy as np
import time as time
from tqdm import tqdm
import base64
from pathlib im... | [
"langchain.chat_models.ChatOpenAI",
"langchain.cache.SQLiteCache"
] | [((23, 56), 'streamlit.set_page_config', 'st.set_page_config', ([], {'layout': '"""wide"""'}), "(layout='wide')\n", (41, 56), True, 'import streamlit as st\n'), ((195, 208), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (206, 208), False, 'from dotenv import load_dotenv\n'), ((637, 682), 'langchain.cache.SQLit... |
import os
import json
import time
from typing import List
import faiss
import pypdf
import random
import itertools
import text_utils
import pandas as pd
import altair as alt
import streamlit as st
from io import StringIO
from llama_index import Document
from langchain.llms import Anthropic
from langchain.chains import ... | [
"langchain.text_splitter.CharacterTextSplitter",
"langchain.retrievers.SVMRetriever.from_texts",
"langchain.embeddings.HuggingFaceEmbeddings",
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.chat_models.ChatOpenAI",
"langchain.vectorst... | [((13312, 13350), 'streamlit.sidebar.image', 'st.sidebar.image', (['"""img/diagnostic.jpg"""'], {}), "('img/diagnostic.jpg')\n", (13328, 13350), True, 'import streamlit as st\n'), ((15130, 15159), 'streamlit.header', 'st.header', (['"""`Auto-evaluator`"""'], {}), "('`Auto-evaluator`')\n", (15139, 15159), True, 'import ... |
# general imports
from constants import *
# streamlit imports
import streamlit as st
from utils import *
from streamlit_lottie import st_lottie
# llama index imports
import openai
from llama_index import (
VectorStoreIndex,
download_loader,
ServiceContext,
set_global_service_context,
)
from llama_inde... | [
"langchain.embeddings.huggingface.HuggingFaceEmbeddings"
] | [((1017, 1080), 'llama_index.llms.OpenAI', 'OpenAI', ([], {'model': '"""gpt-4-1106-preview"""', 'system_prompt': 'system_prompt'}), "(model='gpt-4-1106-preview', system_prompt=system_prompt)\n", (1023, 1080), False, 'from llama_index.llms import OpenAI\n'), ((1187, 1248), 'llama_index.ServiceContext.from_defaults', 'Se... |
# basic
import pandas as pd
# import langchain
from langchain.chains import LLMChain, HypotheticalDocumentEmbedder
from langchain import PromptTemplate
# import from other files
from config import Settings
from base import BASEVectorSearch
settings = Settings()
# ベクトル検索クラス
class BasicSearch(BASEVectorSearch):
#... | [
"langchain.chains.LLMChain",
"langchain.chains.HypotheticalDocumentEmbedder",
"langchain.PromptTemplate"
] | [((254, 264), 'config.Settings', 'Settings', ([], {}), '()\n', (262, 264), False, 'from config import Settings\n'), ((966, 1036), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['question']", 'template': 'prompt_template'}), "(input_variables=['question'], template=prompt_template)\n", (980, 10... |
#%%
import pandas as pd
from utils import get_random_string
from dotenv import load_dotenv
import os
import langchain
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from openai import OpenAI
import json
import requests
import datetime
import... | [
"langchain.prompts.PromptTemplate.from_template",
"langchain.chat_models.ChatOpenAI"
] | [((347, 360), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (358, 360), False, 'from dotenv import load_dotenv\n'), ((368, 416), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'model': '"""gpt-3.5-turbo"""', 'temperature': '(0)'}), "(model='gpt-3.5-turbo', temperature=0)\n", (378, 416), False, 'from l... |
import os
#from dotenv import load_dotenv
import openai
import langchain
os.environ["OPENAI_API_KEY"] =""
os.environ["SQL_SERVER_USERNAME"] = ""
os.environ["SQL_SERVER_ENDPOINT"] = ""
os.environ["SQL_SERVER_PASSWORD"] = ""
os.environ["SQL_SERVER_DATABASE"] = ""
from sqlalchemy import create_engine
from sqlalchemy.... | [
"langchain.agents.create_sql_agent",
"langchain.agents.agent_toolkits.SQLDatabaseToolkit",
"langchain.llms.OpenAI",
"langchain.sql_database.SQLDatabase.from_uri"
] | [((785, 808), 'sqlalchemy.engine.url.URL.create', 'URL.create', ([], {}), '(**db_config)\n', (795, 808), False, 'from sqlalchemy.engine.url import URL\n'), ((814, 842), 'langchain.sql_database.SQLDatabase.from_uri', 'SQLDatabase.from_uri', (['db_url'], {}), '(db_url)\n', (834, 842), False, 'from langchain.sql_database ... |
"""A tracer that runs evaluators over completed runs."""
from __future__ import annotations
import logging
import threading
import weakref
from concurrent.futures import Future, ThreadPoolExecutor, wait
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union, cast
from uuid import UUID
import langsmith
f... | [
"langchain.callbacks.tracers.langchain._get_executor",
"langchain.callbacks.tracers.langchain.get_client",
"langchain.callbacks.manager.tracing_v2_enabled"
] | [((672, 699), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (689, 699), False, 'import logging\n'), ((755, 772), 'weakref.WeakSet', 'weakref.WeakSet', ([], {}), '()\n', (770, 772), False, 'import weakref\n'), ((3430, 3447), 'weakref.WeakSet', 'weakref.WeakSet', ([], {}), '()\n', (3445, 3... |
import langchain
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.prompts import load_prompt
from langchain.output_parsers import OutputFixingParser
# output_parser = DatetimeOutputParser()
#
# misformatted = result.content
from langchain.schema import AIMessage, HumanMessa... | [
"langchain.prompts.PromptTemplate",
"langchain.llms.OpenAI",
"langchain.prompts.load_prompt",
"langchain.chat_models.ChatOpenAI"
] | [((1062, 1070), 'langchain.llms.OpenAI', 'OpenAI', ([], {}), '()\n', (1068, 1070), False, 'from langchain.llms import OpenAI\n'), ((1078, 1112), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'openai_api_key': 'api_key'}), '(openai_api_key=api_key)\n', (1088, 1112), False, 'from langchain.chat_models import Ch... |
import os
import re
from uuid import UUID
from typing import Any, Dict, List, Optional, Union
import asyncio
import langchain
import streamlit as st
from langchain.schema import LLMResult
from langchain.chat_models import ChatOpenAI
from langchain.agents import Tool
from langchain.agents import AgentType
from langcha... | [
"langchain.agents.initialize_agent",
"langchain.memory.ConversationBufferMemory",
"langchain.llms.OpenAI",
"langchain.chat_models.ChatOpenAI",
"langchain.agents.Tool"
] | [((815, 826), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (824, 826), False, 'import os\n'), ((6031, 6120), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'model_name': '"""gpt-3.5-turbo"""', 'temperature': '(0)', 'openai_api_key': 'openai_api_key'}), "(model_name='gpt-3.5-turbo', temperature=0, openai_api_key... |
import os
import weaviate
import key_config
import langchain
from langchain.vectorstores import Weaviate
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationSummaryMemory
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
clien... | [
"langchain.memory.ConversationSummaryMemory",
"langchain.chains.ConversationalRetrievalChain.from_llm",
"langchain.chat_models.ChatOpenAI",
"langchain.vectorstores.Weaviate",
"langchain.embeddings.OpenAIEmbeddings"
] | [((438, 486), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {'model': '"""text-embedding-ada-002"""'}), "(model='text-embedding-ada-002')\n", (454, 486), False, 'from langchain.embeddings import OpenAIEmbeddings\n'), ((496, 566), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'model': '"""g... |
# MIT License
#
# Copyright (c) 2024, Justin Randall, Smart Interactive Transformations Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitatio... | [
"langchain_core.prompts.HumanMessagePromptTemplate.from_template",
"langchain_core.messages.SystemMessage"
] | [((2556, 2594), 'langchain_core.messages.SystemMessage', 'SystemMessage', ([], {'content': 'self.sys_prompt'}), '(content=self.sys_prompt)\n', (2569, 2594), False, 'from langchain_core.messages import SystemMessage\n'), ((2612, 2675), 'langchain_core.prompts.HumanMessagePromptTemplate.from_template', 'HumanMessagePromp... |
from approaches.index.store.cosmos_index_store import CosmosIndexStore
from llama_index import StorageContext
from approaches.index.store.cosmos_doc_store import CosmosDocumentStore
from llama_index import load_index_from_storage
import os
import openai
from langchain.chat_models import AzureChatOpenAI
from langchain.... | [
"langchain.embeddings.OpenAIEmbeddings"
] | [((832, 845), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (843, 845), False, 'from dotenv import load_dotenv\n'), ((1039, 1074), 'os.environ.get', 'os.environ.get', (['"""AZURE_OPENAI_BASE"""'], {}), "('AZURE_OPENAI_BASE')\n", (1053, 1074), False, 'import os\n'), ((1098, 1153), 'os.environ.get', 'os.environ.... |
from abc import ABC, abstractmethod
from typing import List, Optional
from pydantic import BaseModel, Extra, Field, validator
import langchain
from langchain.callbacks import get_callback_manager
from langchain.callbacks.base import BaseCallbackManager
from langchain.schema import (
AIMessage,
BaseLanguageMod... | [
"langchain.schema.ChatResult",
"langchain.schema.ChatGeneration",
"langchain.schema.HumanMessage",
"langchain.schema.AIMessage",
"langchain.schema.LLMResult",
"langchain.callbacks.get_callback_manager"
] | [((568, 605), 'pydantic.Field', 'Field', ([], {'default_factory': '_get_verbosity'}), '(default_factory=_get_verbosity)\n', (573, 605), False, 'from pydantic import BaseModel, Extra, Field, validator\n'), ((696, 739), 'pydantic.Field', 'Field', ([], {'default_factory': 'get_callback_manager'}), '(default_factory=get_ca... |
import boto3
import os
import json
# from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent
from langchain_community.llms import Bedrock
from llama_index.node_parser import SimpleNodeParser
from llama_index.embeddings import LangchainEmbedding
from langchain_community.embeddings import ... | [
"langchain_community.llms.Bedrock"
] | [((974, 1051), 'logging.basicConfig', 'logging.basicConfig', ([], {'filename': '"""rag.log"""', 'encoding': '"""utf-8"""', 'level': 'logging.INFO'}), "(filename='rag.log', encoding='utf-8', level=logging.INFO)\n", (993, 1051), False, 'import logging\n'), ((1113, 1128), 'boto3.Session', 'boto3.Session', ([], {}), '()\n'... |
import pandas as pd
energy_data = pd.read_csv('EnergyUsage.csv')
summary_stats = energy_data.describe()
from langchain import LangchainAgent
# Initialize Langchain agent
agent = LangchainAgent()
# Connect to LLama2
agent.connect_to_llama2()
# Send data to LLama2
agent.send_data_to_llama2(summary_stats)
# Optional... | [
"langchain.LangchainAgent"
] | [((35, 65), 'pandas.read_csv', 'pd.read_csv', (['"""EnergyUsage.csv"""'], {}), "('EnergyUsage.csv')\n", (46, 65), True, 'import pandas as pd\n'), ((181, 197), 'langchain.LangchainAgent', 'LangchainAgent', ([], {}), '()\n', (195, 197), False, 'from langchain import LangchainAgent\n')] |
"""Base interface for large language models to expose."""
import inspect
import json
import warnings
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple, Union
import yaml
from pydantic import Extra, Field, root_validator, validator
impor... | [
"langchain.callbacks.manager.AsyncCallbackManager.configure",
"langchain.schema.Generation",
"langchain.load.dump.dumpd",
"langchain.schema.get_buffer_string",
"langchain.callbacks.manager.CallbackManager.configure",
"langchain.schema.RunInfo",
"langchain.schema.AIMessage",
"langchain.llm_cache.lookup... | [((2353, 2390), 'pydantic.Field', 'Field', ([], {'default_factory': '_get_verbosity'}), '(default_factory=_get_verbosity)\n', (2358, 2390), False, 'from pydantic import Extra, Field, root_validator, validator\n'), ((2464, 2497), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, ... |
"""Create a ChatVectorDBChain for question/answering."""
from langchain.callbacks.base import AsyncCallbackManager
from langchain.callbacks.tracers import LangChainTracer
from langchain.chains import ConversationalRetrievalChain
from langchain.chains.chat_vector_db.prompts import PromptTemplate
from langchain.chains.ll... | [
"langchain.chains.question_answering.load_qa_chain",
"langchain.callbacks.tracers.LangChainTracer",
"langchain.callbacks.base.AsyncCallbackManager",
"langchain.llms.OpenAI",
"langchain.chains.llm.LLMChain",
"langchain.chains.chat_vector_db.prompts.PromptTemplate",
"langchain.chains.chat_vector_db.prompt... | [((668, 759), 'langchain.chains.chat_vector_db.prompts.PromptTemplate', 'PromptTemplate', ([], {'template': 'mi_qa_prompt_template', 'input_variables': "['context', 'question']"}), "(template=mi_qa_prompt_template, input_variables=['context',\n 'question'])\n", (682, 759), False, 'from langchain.chains.chat_vector_d... |
import logging
import os
import pprint
import uuid
from typing import List
import chromadb
import gradio as gr
import requests
import zhipuai
from bs4 import BeautifulSoup
from dotenv import load_dotenv, find_dotenv
# Import langchain stuff
from langchain.chains import ConversationalRetrievalChain
from langchain.docum... | [
"langchain.text_splitter.CharacterTextSplitter",
"langchain_community.vectorstores.chroma.Chroma.from_documents",
"langchain.chains.ConversationalRetrievalChain.from_llm",
"langchain.memory.ConversationBufferMemory",
"langchain_community.vectorstores.chroma.Chroma",
"langchain_core.prompts.PromptTemplate"... | [((1392, 1490), 'llms.zhipuai_llm.ZhipuAILLM', 'ZhipuAILLM', ([], {'model': '"""chatglm_turbo"""', 'temperature': '(0.9)', 'top_p': '(0.1)', 'zhipuai_api_key': 'zhipuai.api_key'}), "(model='chatglm_turbo', temperature=0.9, top_p=0.1,\n zhipuai_api_key=zhipuai.api_key)\n", (1402, 1490), False, 'from llms.zhipuai_llm ... |
import langchain
llm_huggingface=HuggingFaceHub(repo_id="google/flan-t5-large",model_kwargs={"temperature":0,"max_length":64})
capital_prompt = PromptTemplate(input_variables=["country"], template="Tell me the capital of {country}")
famous_prompt = PromptTemplate(input_variables=["famous"], template="Tell me the capit... | [
"langchain.chains.SimpleSequentialChain",
"langchain.LLMChain"
] | [((353, 415), 'langchain.LLMChain', 'langchain.LLMChain', ([], {'llm': 'llm_huggingface', 'prompt': 'capital_prompt'}), '(llm=llm_huggingface, prompt=capital_prompt)\n', (371, 415), False, 'import langchain\n'), ((431, 492), 'langchain.LLMChain', 'langchain.LLMChain', ([], {'llm': 'llm_huggingface', 'prompt': 'famous_p... |
"""An example of how to test Python code generating prompts"""
import re
# Brining some "prompt generator" classes
from promptimize.prompt_cases import LangchainPromptCase
# Bringing some useful eval function that help evaluating and scoring responses
# eval functions have a handle on the prompt object and are expect... | [
"langchain.output_parsers.ResponseSchema",
"langchain.output_parsers.StructuredOutputParser.from_response_schemas",
"langchain.PromptTemplate"
] | [((1146, 1208), 'langchain.output_parsers.StructuredOutputParser.from_response_schemas', 'StructuredOutputParser.from_response_schemas', (['response_schemas'], {}), '(response_schemas)\n', (1190, 1208), False, 'from langchain.output_parsers import StructuredOutputParser, ResponseSchema\n'), ((2218, 2382), 'langchain.Pr... |
"""
The ``mlflow.langchain`` module provides an API for logging and loading LangChain models.
This module exports multivariate LangChain models in the langchain flavor and univariate
LangChain models in the pyfunc flavor:
LangChain (native) format
This is the main flavor that can be accessed with LangChain APIs.
:... | [
"langchain.agents.initialize_agent",
"langchain.chains.loading.load_chain"
] | [((2012, 2046), 'logging.getLogger', 'logging.getLogger', (['mlflow.__name__'], {}), '(mlflow.__name__)\n', (2029, 2046), False, 'import logging\n'), ((11731, 11807), 'mlflow.utils.environment._validate_env_arguments', '_validate_env_arguments', (['conda_env', 'pip_requirements', 'extra_pip_requirements'], {}), '(conda... |
# Import the necessary libraries
import random
import time
from llama_index.llms import OpenAI
import streamlit as st
from llama_index import VectorStoreIndex, ServiceContext, StorageContext, set_global_service_context
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from llama_index.embeddings import... | [
"langchain_openai.ChatOpenAI",
"langchain.embeddings.huggingface.HuggingFaceEmbeddings"
] | [((855, 895), 'streamlit.title', 'st.title', (['"""🦜🔗 Tourism Assistant Chatbot"""'], {}), "('🦜🔗 Tourism Assistant Chatbot')\n", (863, 895), True, 'import streamlit as st\n'), ((5721, 5781), 'llama_index.set_global_service_context', 'set_global_service_context', (['st.session_state.service_context'], {}), '(st.sess... |
import langchain_helper as lch
import streamlit as st
import textwrap
st.title("Youtube Assistant")
with st.sidebar:
with st.form(key="my_from"):
youtube_url = st.text_area(
label = "What is the youtube url?",
max_chars=50,
)
query = st.text_area(
label ... | [
"langchain_helper.get_response_from_query",
"langchain_helper.create_vector_db_from_youtube"
] | [((71, 100), 'streamlit.title', 'st.title', (['"""Youtube Assistant"""'], {}), "('Youtube Assistant')\n", (79, 100), True, 'import streamlit as st\n'), ((517, 563), 'langchain_helper.create_vector_db_from_youtube', 'lch.create_vector_db_from_youtube', (['youtube_url'], {}), '(youtube_url)\n', (550, 563), True, 'import ... |
# This code sets up the necessary components, interacts with the LangChain tool and ChatOpenAI model to perform text summarization,
# and provides a user interface for input and output.
from langchain.document_loaders import UnstructuredFileLoader # Importing necessary modules
from langchain.document_loaders import ... | [
"langchain.chains.summarize.load_summarize_chain",
"langchain.document_loaders.UnstructuredFileLoader",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.chat_models.ChatOpenAI",
"langchain.document_loaders.UnstructuredPDFLoader",
"langchain.prompts.PromptTemplate"
] | [((5769, 5891), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""Positive summarizer"""', 'page_icon': '"""📖"""', 'layout': '"""wide"""', 'initial_sidebar_state': '"""collapsed"""'}), "(page_title='Positive summarizer', page_icon='📖', layout=\n 'wide', initial_sidebar_state='collapsed')\n... |
from mp_api.client import MPRester
from emmet.core.summary import HasProps
import openai
import langchain
from langchain_openai import ChatOpenAI
from langchain.agents import initialize_agent
from langchain.agents import Tool, tool
from langchain.prompts.few_shot import FewShotPromptTemplate
from langchain.prompts.prom... | [
"langchain_openai.ChatOpenAI",
"langchain.prompts.prompt.PromptTemplate",
"langchain_openai.OpenAIEmbeddings",
"langchain.agents.Tool",
"langchain.prompts.few_shot.FewShotPromptTemplate"
] | [((991, 1024), 're.compile', 're.compile', (['"""([A-Z][a-z]*)(\\\\d*)"""'], {}), "('([A-Z][a-z]*)(\\\\d*)')\n", (1001, 1024), False, 'import re\n'), ((2083, 2198), 'langchain_openai.ChatOpenAI', 'ChatOpenAI', ([], {'model_name': 'self.model', 'temperature': '(0.7)', 'n': '(1)', 'best_of': '(5)', 'top_p': '(1.0)', 'sto... |
import streamlit as st
from streamlit_chat import message
import pandas as pd
from langchain.llms import OpenAI
import os
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationSummaryBufferMemory
import plotly.express
from streamlit_searchbox import st_searchbox
from typing import List, ... | [
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((1329, 1342), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (1340, 1342), False, 'from dotenv import load_dotenv\n'), ((1378, 1486), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""PubMeta.ai"""', 'page_icon': '"""⚕️"""', 'layout': '"""wide"""', 'initial_sidebar_state': '"""auto"""... |
from typing import Any, Dict, List, Optional
from langchain import PromptTemplate ,LLMChain
import langchain
from langchain.chat_models import ChatOpenAI ,AzureChatOpenAI
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
import sys
import re
import argparse
import os
print(sys.path)
sys.p... | [
"langchain.LLMChain",
"langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler",
"langchain.prompts.chat.ChatPromptTemplate",
"langchain.schema.SystemMessage",
"langchain.prompts.chat.HumanMessagePromptTemplate.from_template",
"langchain.PromptTemplate"
] | [((315, 335), 'sys.path.append', 'sys.path.append', (['"""."""'], {}), "('.')\n", (330, 335), False, 'import sys\n'), ((3893, 3960), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'template': 'prompt_template', 'input_variables': "['essay']"}), "(template=prompt_template, input_variables=['essay'])\n", (3907, 3960... |
import streamlit as st
from dotenv import load_dotenv
load_dotenv()
import os
import tempfile
from llama_index import SimpleDirectoryReader, StorageContext, LLMPredictor
from llama_index import VectorStoreIndex
from llama_index import ServiceContext
from llama_index.embeddings.langchain import LangchainEmbedding
from... | [
"langchain.embeddings.CohereEmbeddings",
"langchain.chat_models.ChatOpenAI"
] | [((55, 68), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (66, 68), False, 'from dotenv import load_dotenv\n'), ((860, 890), 'llama_index.StorageContext.from_defaults', 'StorageContext.from_defaults', ([], {}), '()\n', (888, 890), False, 'from llama_index import SimpleDirectoryReader, StorageContext, LLMPredic... |
import os
import langchain
from langchain.utilities import SerpAPIWrapper
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.chat_models import ChatOpenAI
os.environ['OPENAI_API_KEY'] = ""
os.environ['SERPAPI_API_KEY'] = ""
llm = ChatOpenAI(temperature=0, model=... | [
"langchain.agents.initialize_agent",
"langchain.utilities.SerpAPIWrapper",
"langchain.agents.Tool",
"langchain.chat_models.ChatOpenAI"
] | [((288, 341), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '(0)', 'model': '"""gpt-3.5-turbo-0613"""'}), "(temperature=0, model='gpt-3.5-turbo-0613')\n", (298, 341), False, 'from langchain.chat_models import ChatOpenAI\n'), ((352, 368), 'langchain.utilities.SerpAPIWrapper', 'SerpAPIWrapper', (... |
import streamlit as st
from dotenv import load_dotenv
import os
from htmlTemplates import css, bot_template, user_template
import langchain
from langchain.document_loaders import GitLoader
from langchain.text_splitter import (
RecursiveCharacterTextSplitter,
Language,
)
from langchain.text_splitter import Recur... | [
"langchain.prompts.chat.SystemMessagePromptTemplate.from_template",
"langchain.memory.ConversationBufferMemory",
"langchain.document_loaders.GitLoader",
"langchain.vectorstores.DeepLake",
"langchain.chat_models.ChatOpenAI",
"langchain.text_splitter.RecursiveCharacterTextSplitter.from_language",
"langcha... | [((822, 895), 'langchain.document_loaders.GitLoader', 'GitLoader', ([], {'clone_url': 'github_url', 'repo_path': 'local_path', 'branch': 'repo_branch'}), '(clone_url=github_url, repo_path=local_path, branch=repo_branch)\n', (831, 895), False, 'from langchain.document_loaders import GitLoader\n'), ((2876, 2915), 'langch... |
import json
from llama_index.core.service_context_elements.llm_predictor import LLMPredictor
from llama_index.core.utilities.sql_wrapper import SQLDatabase
from llama_index.core.response_synthesizers import get_response_synthesizer
from llama_index.embeddings.langchain import LangchainEmbedding
from llama_index.core.re... | [
"langchain.agents.initialize_agent",
"langchain_community.chat_models.ChatOpenAI"
] | [((3043, 3075), 'app.database.dbc.get_llm_by_name', 'dbc.get_llm_by_name', (['db', 'llmName'], {}), '(db, llmName)\n', (3062, 3075), False, 'from app.database import dbc\n'), ((4079, 4112), 'app.database.dbc.get_project_by_name', 'dbc.get_project_by_name', (['db', 'name'], {}), '(db, name)\n', (4102, 4112), False, 'fro... |
import langchain_helper as lch
import streamlit as st
st.title("Pets name generator")
user_input_animal_type = st.sidebar.selectbox("What is your pet?", ("Cat", "Dog", "Cow", "Hamster"))
if user_input_animal_type == "Cat":
pet_color = st.sidebar.text_area("What's the color of your cat?", max_chars=15)
if user_i... | [
"langchain_helper.generate_pet_name"
] | [((55, 86), 'streamlit.title', 'st.title', (['"""Pets name generator"""'], {}), "('Pets name generator')\n", (63, 86), True, 'import streamlit as st\n'), ((113, 188), 'streamlit.sidebar.selectbox', 'st.sidebar.selectbox', (['"""What is your pet?"""', "('Cat', 'Dog', 'Cow', 'Hamster')"], {}), "('What is your pet?', ('Ca... |
"""LangChain agent
The agent chooses a sequence of actions to respond to a human's question. It has access to a set of tools.
The agent memorizes the conversation history and can use it to make decisions.
"""
from typing import Optional
from dotenv import load_dotenv
from langchain.agents import AgentExecutor
from l... | [
"langchain_core.prompts.MessagesPlaceholder",
"langchain.agents.AgentExecutor",
"langchain.memory.ConversationBufferMemory",
"langchain.agents.format_scratchpad.format_log_to_str",
"langchain.agents.output_parsers.JSONAgentOutputParser"
] | [((956, 969), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (967, 969), False, 'from dotenv import load_dotenv\n'), ((2930, 2956), 'langchain.memory.ConversationBufferMemory', 'ConversationBufferMemory', ([], {}), '()\n', (2954, 2956), False, 'from langchain.memory import ConversationBufferMemory\n'), ((3315, ... |
# This is an example of integrating a LLM with streamlit
import streamlit as st
import os
import openai
import langchain
from langchain.llms import OpenAI
from langchain import PromptTemplate
#from dotenv import load_dotenv
# Specify the path to the .env file
#dotenv_path = os.path.join(os.path.dirname(__file__), '.en... | [
"langchain.llms.OpenAI",
"langchain.PromptTemplate"
] | [((390, 459), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""Globalize Email"""', 'page_icon': '""":robot:"""'}), "(page_title='Globalize Email', page_icon=':robot:')\n", (408, 459), True, 'import streamlit as st\n'), ((462, 489), 'streamlit.header', 'st.header', (['"""Globalize Text"""'], {... |
import sys
sys.stdout.reconfigure(encoding="utf-8")
sys.stdin.reconfigure(encoding="utf-8")
import streamlit as st
import streamlit.components.v1 as components
import re
import random
CODE_BUILD_KG = """
# 准备 GraphStore
os.environ['NEBULA_USER'] = "root"
os.environ['NEBULA_PASSWORD'] = "nebula" # default passwor... | [
"langchain.embeddings.OpenAIEmbeddings"
] | [((12, 52), 'sys.stdout.reconfigure', 'sys.stdout.reconfigure', ([], {'encoding': '"""utf-8"""'}), "(encoding='utf-8')\n", (34, 52), False, 'import sys\n'), ((53, 92), 'sys.stdin.reconfigure', 'sys.stdin.reconfigure', ([], {'encoding': '"""utf-8"""'}), "(encoding='utf-8')\n", (74, 92), False, 'import sys\n'), ((2988, 3... |
from langchain.agents import AgentExecutor, LLMSingleActionAgent, AgentOutputParser
from langchain.tools import Tool, StructuredTool
from langchain.prompts import StringPromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain.chains import LLMChain
from langchain.llms import VertexAI
from typing imp... | [
"langchain.agents.AgentExecutor.from_agent_and_tools",
"langchain.schema.AgentAction",
"langchain.llms.VertexAI",
"langchain.schema.AgentFinish",
"langchain.callbacks.FileCallbackHandler",
"langchain.chains.LLMChain"
] | [((1046, 1098), 'os.makedirs', 'os.makedirs', (['f"""./results/{timestamp}"""'], {'exist_ok': '(True)'}), "(f'./results/{timestamp}', exist_ok=True)\n", (1057, 1098), False, 'import os\n'), ((1332, 1364), 'logging.getLogger', 'logging.getLogger', (['"""info_logger"""'], {}), "('info_logger')\n", (1349, 1364), False, 'i... |
import langchain
from dotenv import load_dotenv
from langchain.chains import FlareChain
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.vectorstores import FAISS
langchain.verbose = True
load_dotenv()
# FAISSで保存されたベクトルを読み... | [
"langchain.vectorstores.FAISS.load_local",
"langchain.llms.OpenAI",
"langchain.chat_models.ChatOpenAI",
"langchain.chains.FlareChain.from_llm",
"langchain.embeddings.OpenAIEmbeddings"
] | [((285, 298), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (296, 298), False, 'from dotenv import load_dotenv\n'), ((336, 354), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (352, 354), False, 'from langchain.embeddings import OpenAIEmbeddings\n'), ((360, 403), 'langchain.vect... |
import langchain
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.output_parsers import OutputFixingParser
# output_parser = DatetimeOutputParser()
#
# misformatted = result.content
from langchain.schema import AIMessage, HumanMessage, SystemMessage
from langchain.cache imp... | [
"langchain.output_parsers.PydanticOutputParser",
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.llms.OpenAI",
"langchain.chat_models.ChatOpenAI",
"langchain.prompts.ChatPromptTemplate.from_messages"
] | [((1020, 1028), 'langchain.llms.OpenAI', 'OpenAI', ([], {}), '()\n', (1026, 1028), False, 'from langchain.llms import OpenAI\n'), ((1036, 1070), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'openai_api_key': 'api_key'}), '(openai_api_key=api_key)\n', (1046, 1070), False, 'from langchain.chat_models import Ch... |
import os
from dotenv import load_dotenv
import streamlit as st
from langchain.chains import LLMChain
from langchain import PromptTemplate
from genai.credentials import Credentials
from genai.extensions.langchain import LangChainInterface
from genai.schemas import GenerateParams
load_dotenv()
api_key = os.getenv("GE... | [
"langchain.chains.LLMChain",
"langchain.PromptTemplate"
] | [((283, 296), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (294, 296), False, 'from dotenv import load_dotenv\n'), ((307, 335), 'os.getenv', 'os.getenv', (['"""GENAI_KEY"""', 'None'], {}), "('GENAI_KEY', None)\n", (316, 335), False, 'import os\n'), ((351, 379), 'os.getenv', 'os.getenv', (['"""GENAI_API"""', '... |
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import Type
import langchain
from langchain import LLMChain, LLMMathChain, PromptTemplate
from langchain.cache import InMemoryCache
from langchain.chains import SequentialChain
from langchain.output_parsers import PydanticOutputParser
... | [
"langchain.LLMChain",
"langchain.output_parsers.PydanticOutputParser",
"langchain.chains.SequentialChain",
"langchain.LLMMathChain.from_llm",
"langchain.cache.InMemoryCache",
"langchain.PromptTemplate"
] | [((1259, 1311), 'langchain.output_parsers.PydanticOutputParser', 'PydanticOutputParser', ([], {'pydantic_object': 'RoomLoopAnswer'}), '(pydantic_object=RoomLoopAnswer)\n', (1279, 1311), False, 'from langchain.output_parsers import PydanticOutputParser\n'), ((1527, 1571), 'langchain.output_parsers.PydanticOutputParser',... |
import os
from datetime import datetime, timezone
from dotenv import load_dotenv
from langchain.agents import AgentType, initialize_agent
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.prompts import MessagesPlaceholder
from src.xm_group_tools import ... | [
"langchain.agents.initialize_agent",
"langchain.memory.ConversationBufferMemory",
"langchain.prompts.MessagesPlaceholder",
"langchain.chat_models.ChatOpenAI"
] | [((458, 471), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (469, 471), False, 'from dotenv import load_dotenv\n'), ((482, 509), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (491, 509), False, 'import os\n'), ((742, 770), 'langchain.chat_models.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
if TYPE_CHECKING:
from langchainhub import Client
def _get_client(api_url: Optional[str] = None, api_key: Opti... | [
"langchain.load.load.loads",
"langchainhub.Client",
"langchain.load.dump.dumps"
] | [((671, 703), 'langchainhub.Client', 'Client', (['api_url'], {'api_key': 'api_key'}), '(api_url, api_key=api_key)\n', (677, 703), False, 'from langchainhub import Client\n'), ((1886, 1899), 'langchain.load.dump.dumps', 'dumps', (['object'], {}), '(object)\n', (1891, 1899), False, 'from langchain.load.dump import dumps\... |
"""Base interface that all chains should implement."""
import inspect
import json
import logging
import warnings
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import yaml
from pydantic import Field, root_validator, validator
import langchain
from lang... | [
"langchain.schema.RunInfo",
"langchain.callbacks.manager.AsyncCallbackManager.configure",
"langchain.load.dump.dumpd",
"langchain.callbacks.manager.CallbackManager.configure"
] | [((702, 729), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (719, 729), False, 'import logging\n'), ((2435, 2468), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, exclude=True)\n', (2440, 2468), False, 'from pydantic import Field, root_validator... |
"""Utilities for running language models or Chains over datasets."""
from __future__ import annotations
import functools
import inspect
import logging
import uuid
from enum import Enum
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
List,
Optional,
Sequence,
Tuple,
Union,
... | [
"langchain.schema.messages.messages_from_dict",
"langchain._api.warn_deprecated",
"langchain.schema.runnable.config.get_executor_for_config",
"langchain.evaluation.schema.EvaluatorType",
"langchain.smith.evaluation.name_generation.random_name",
"langchain.smith.evaluation.StringRunEvaluatorChain.from_run_... | [((1724, 1751), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1741, 1751), False, 'import logging\n'), ((33983, 34008), 'langchain.callbacks.tracers.evaluation.wait_for_all_evaluators', 'wait_for_all_evaluators', ([], {}), '()\n', (34006, 34008), False, 'from langchain.callbacks.tracers... |
from pydantic import BaseModel, Field
import os
from langchain import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent, Tool
# from langchain.chains import PALChain
from langchain.chains.conversation.memory import ConversationBufferMemory
from langchain import Pr... | [
"langchain.agents.AgentExecutor",
"langchain.memory.ConversationBufferMemory",
"langchain.prompts.MessagesPlaceholder",
"langchain.chat_models.ChatOpenAI",
"langchain.vectorstores.FAISS.from_documents",
"langchain.agents.agent_toolkits.create_retriever_tool",
"langchain.schema.messages.SystemMessage",
... | [((3412, 3472), 'streamlit.markdown', 'st.markdown', (['hide_share_button_style'], {'unsafe_allow_html': '(True)'}), '(hide_share_button_style, unsafe_allow_html=True)\n', (3423, 3472), True, 'import streamlit as st\n'), ((3474, 3537), 'streamlit.markdown', 'st.markdown', (['hide_star_and_github_style'], {'unsafe_allow... |
from langchain.embeddings import HuggingFaceHubEmbeddings
from langchain.embeddings.openai import OpenAIEmbeddings
from llama_index import ServiceContext, LLMPredictor
from llama_index.embeddings.langchain import LangchainEmbedding
from langchain import OpenAI, HuggingFaceHub
from langchain.chat_models import ChatOpenA... | [
"langchain.embeddings.HuggingFaceHubEmbeddings",
"langchain.HuggingFaceHub",
"langchain.OpenAI",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((1146, 1255), 'llama_index.ServiceContext.from_defaults', 'ServiceContext.from_defaults', ([], {'llm_predictor': 'llm_predictor', 'embed_model': 'embed_model', 'chunk_size_limit': '(512)'}), '(llm_predictor=llm_predictor, embed_model=\n embed_model, chunk_size_limit=512)\n', (1174, 1255), False, 'from llama_index ... |
"""Schemas for the langchainplus API."""
from __future__ import annotations
import logging
import os
from concurrent.futures import Future, ThreadPoolExecutor, wait
from datetime import datetime
from typing import Dict, List, Optional, Union, cast
from uuid import UUID, uuid4
from pydantic import Field, PrivateAttr, ... | [
"langchainplus_sdk.utils.get_runtime_environment"
] | [((533, 560), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (550, 560), False, 'import logging\n'), ((683, 716), 'concurrent.futures.ThreadPoolExecutor', 'ThreadPoolExecutor', ([], {'max_workers': '(1)'}), '(max_workers=1)\n', (701, 716), False, 'from concurrent.futures import Future, Th... |
import streamlit as st
import openai
import langchain
# from langchain import PromptTemplate, LLMChain
# from langchain.llms import OpenAI
# # Set your OpenAI API key
# openai_api_key = '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'
# # Create a sidebar for language selection
# st.sidebar.title('Translation A... | [
"langchain.LLMChain",
"langchain.llms.OpenAI",
"langchain.PromptTemplate"
] | [((3099, 3134), 'streamlit.sidebar.title', 'st.sidebar.title', (['"""Translation App"""'], {}), "('Translation App')\n", (3115, 3134), True, 'import streamlit as st\n'), ((3216, 3265), 'streamlit.sidebar.selectbox', 'st.sidebar.selectbox', (['"""Input Language"""', 'languages'], {}), "('Input Language', languages)\n", ... |
# Import os to set API key
import os
import langchain
# Bring in streamlit for UI/app interface
import streamlit as st
from langchain.callbacks import get_openai_callback
from langchain.utilities import GoogleSerperAPIWrapper
from common.SerperSearchRetriever import SerperSearchRetriever
from st_pages import add_inde... | [
"langchain.llms.openai.OpenAI",
"langchain.chains.FlareChain.from_llm",
"langchain.utilities.GoogleSerperAPIWrapper",
"langchain.callbacks.get_openai_callback"
] | [((511, 524), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (522, 524), False, 'from dotenv import load_dotenv\n'), ((526, 543), 'st_pages.add_indentation', 'add_indentation', ([], {}), '()\n', (541, 543), False, 'from st_pages import add_indentation\n'), ((910, 954), 'streamlit.title', 'st.title', (['"""🦜🔗�... |
import streamlit as st
import langchain_helper
st.title("Learning Helper for CBSE Class XI")
subject = st.sidebar.selectbox("Pick your subject", ("maths", "physics","chemistry"))
#st.sidebar.t
if subject:
response = langchain_helper.generate_subject_help_links(subject)
st.header(f'Helpful Links for {subject}... | [
"langchain_helper.generate_subject_help_links"
] | [((48, 93), 'streamlit.title', 'st.title', (['"""Learning Helper for CBSE Class XI"""'], {}), "('Learning Helper for CBSE Class XI')\n", (56, 93), True, 'import streamlit as st\n'), ((105, 181), 'streamlit.sidebar.selectbox', 'st.sidebar.selectbox', (['"""Pick your subject"""', "('maths', 'physics', 'chemistry')"], {})... |
import langchain.llms
from langchain import GoogleSearchAPIWrapper, LLMChain
from langchain.agents import initialize_agent, AgentType, Tool, ZeroShotAgent, AgentExecutor
from langchain.schema import BaseMemory
def setup_agent(llm: langchain.llms.BaseLLM, memory: BaseMemory):
search = GoogleSearchAPIWrapper()
... | [
"langchain.agents.AgentExecutor.from_agent_and_tools",
"langchain.LLMChain",
"langchain.agents.ZeroShotAgent.create_prompt",
"langchain.agents.ZeroShotAgent",
"langchain.GoogleSearchAPIWrapper",
"langchain.agents.Tool"
] | [((291, 315), 'langchain.GoogleSearchAPIWrapper', 'GoogleSearchAPIWrapper', ([], {}), '()\n', (313, 315), False, 'from langchain import GoogleSearchAPIWrapper, LLMChain\n'), ((833, 948), 'langchain.agents.ZeroShotAgent.create_prompt', 'ZeroShotAgent.create_prompt', (['tools'], {'prefix': 'prefix', 'suffix': 'suffix', '... |
import itertools
from langchain.cache import InMemoryCache, SQLiteCache
import langchain
import pandas as pd
from certa.utils import merge_sources
from certa.explain import CertaExplainer
from datetime import datetime
import os
import ellmer.models
import ellmer.metrics
from time import sleep, time
import json
import t... | [
"langchain.cache.InMemoryCache",
"langchain.cache.SQLiteCache"
] | [((8572, 8636), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Run saliency experiments."""'}), "(description='Run saliency experiments.')\n", (8595, 8636), False, 'import argparse\n'), ((598, 613), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (611, 613), False, 'f... |
# Databricks notebook source
# MAGIC %md
# MAGIC # 3. Chatbotの作成とデプロイ
# MAGIC
# MAGIC <br/>
# MAGIC <img src="https://github.com/naoyaabe-db/public_demo_images/blob/3380b6d73937cd95efae845799c37de910b7394c/rag_demo_images/diagram_notebook3.png?raw=true" style="float: right" width="1000px">
# MAGIC <br/>
# MAGIC
# MAGIC... | [
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.vectorstores.DatabricksVectorSearch",
"langchain.prompts.PromptTemplate",
"langchain.chat_models.ChatDatabricks"
] | [((5946, 5996), 'mlflow.deployments.get_deploy_client', 'mlflow.deployments.get_deploy_client', (['"""databricks"""'], {}), "('databricks')\n", (5982, 5996), False, 'import mlflow\n'), ((6909, 6974), 'langchain.chat_models.ChatDatabricks', 'ChatDatabricks', ([], {'endpoint': 'chat_model_endpoint_name', 'max_tokens': '(... |
# Import Langchain modules
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
# Impo... | [
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.llms.OpenAI",
"langchain.vectorstores.FAISS.from_documents",
"langchain.document_loaders.PyPDFLoader",
"langchain.embeddings.OpenAIEmbeddings"
] | [((573, 606), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {}), "('ignore')\n", (596, 606), False, 'import warnings\n'), ((712, 808), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.INFO', 'format': '"""%(asctime)s - %(levelname)s - %(message)s"""'}), "(level=logging.IN... |
import streamlit as st
import dotenv
import langchain
import json
from cassandra.cluster import Session
from cassandra.query import PreparedStatement
from langchain.agents.agent_toolkits import create_retriever_tool, create_conversational_retrieval_agent
from langchain.chat_models import ChatOpenAI
from langchain.emb... | [
"langchain.chat_models.ChatOpenAI",
"langchain.schema.Document",
"langchain.agents.agent_toolkits.create_conversational_retrieval_agent",
"langchain.agents.agent_toolkits.create_retriever_tool",
"langchain.schema.SystemMessage",
"langchain.embeddings.OpenAIEmbeddings"
] | [((5021, 5054), 'streamlit.set_page_config', 'st.set_page_config', ([], {'layout': '"""wide"""'}), "(layout='wide')\n", (5039, 5054), True, 'import streamlit as st\n'), ((5462, 5502), 'streamlit.chat_input', 'st.chat_input', ([], {'placeholder': '"""Ask chatbot"""'}), "(placeholder='Ask chatbot')\n", (5475, 5502), True... |
import os
import langchain
import streamlit as st
from dotenv import load_dotenv
from langchain.chat_models import ChatOpenAI
from langchain.cache import InMemoryCache
from langchain.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate, AIMessagePromptTemplate, ChatPromptTemplate, PromptTemplate
# C... | [
"langchain.cache.InMemoryCache",
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.chat_models.ChatOpenAI",
"langchain.prompts.ChatPromptTemplate.from_messages",
"langchain.prompts.SystemMessagePromptTemplate.from_template"
] | [((326, 345), 'dotenv.load_dotenv', 'load_dotenv', (['""".env"""'], {}), "('.env')\n", (337, 345), False, 'from dotenv import load_dotenv\n'), ((356, 377), 'os.environ.get', 'os.environ.get', (['"""key"""'], {}), "('key')\n", (370, 377), False, 'import os\n'), ((416, 431), 'langchain.cache.InMemoryCache', 'InMemoryCach... |
import streamlit as st
import langchain_helper
st.title("Restaurant Name Generator")
cuisine = st.sidebar.selectbox("Pick a Cuisine",("Indian","Italian","Mexican","Arabic","American"))
if cuisine:
response = langchain_helper.generate_restaurant_name_and_items(cuisine)
st.header(response['resta... | [
"langchain_helper.generate_restaurant_name_and_items"
] | [((51, 88), 'streamlit.title', 'st.title', (['"""Restaurant Name Generator"""'], {}), "('Restaurant Name Generator')\n", (59, 88), True, 'import streamlit as st\n'), ((102, 200), 'streamlit.sidebar.selectbox', 'st.sidebar.selectbox', (['"""Pick a Cuisine"""', "('Indian', 'Italian', 'Mexican', 'Arabic', 'American')"], {... |
import os
import dotenv
dotenv.load_dotenv()
### Load the credentials
api_key = os.getenv("API_KEY", None)
ibm_cloud_url = os.getenv("IBM_CLOUD_URL", None)
project_id = os.getenv("PROJECT_ID", None)
HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN", None)
min_new_tokens=1
max_new_tokens=300
temperature... | [
"langchain.embeddings.HuggingFaceHubEmbeddings"
] | [((24, 44), 'dotenv.load_dotenv', 'dotenv.load_dotenv', ([], {}), '()\n', (42, 44), False, 'import dotenv\n'), ((81, 107), 'os.getenv', 'os.getenv', (['"""API_KEY"""', 'None'], {}), "('API_KEY', None)\n", (90, 107), False, 'import os\n'), ((124, 156), 'os.getenv', 'os.getenv', (['"""IBM_CLOUD_URL"""', 'None'], {}), "('... |
from typing import Any, Dict, List, Optional
from .few_shot_agent import FewShotAgent
from .few_shot_agent import FewShotAgentExecutor
from langchain import LLMChain
from langchain.tools.base import BaseTool
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
from .prompts import *
import nes... | [
"langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler"
] | [((2122, 2142), 'nest_asyncio.apply', 'nest_asyncio.apply', ([], {}), '()\n', (2140, 2142), False, 'import nest_asyncio\n'), ((772, 804), 'langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler', 'StreamingStdOutCallbackHandler', ([], {}), '()\n', (802, 804), False, 'from langchain.callbacks.streaming_stdo... |
"""Base interface for large language models to expose."""
import inspect
import json
import warnings
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple, Union
import yaml
from pydantic import Extra, Field, root_validator, validator
impor... | [
"langchain.callbacks.manager.AsyncCallbackManager.configure",
"langchain.schema.Generation",
"langchain.schema.get_buffer_string",
"langchain.callbacks.manager.CallbackManager.configure",
"langchain.schema.AIMessage",
"langchain.llm_cache.lookup",
"langchain.llm_cache.update",
"langchain.schema.LLMRes... | [((2302, 2339), 'pydantic.Field', 'Field', ([], {'default_factory': '_get_verbosity'}), '(default_factory=_get_verbosity)\n', (2307, 2339), False, 'from pydantic import Extra, Field, root_validator, validator\n'), ((2413, 2446), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, ... |
from fastapi import FastAPI
from langchain import ConversationChain
from langchain.chat_models import ChatOpenAI
from scripts.utils import MEMORY
from scripts.doc_loader import load_document
from lanarky import LangchainRouter
from starlette.requests import Request
from starlette.templating import Jinja2Templates
from... | [
"langchain.chat_models.ChatOpenAI"
] | [((352, 369), 'config.set_environment', 'set_environment', ([], {}), '()\n', (367, 369), False, 'from config import set_environment\n'), ((377, 386), 'fastapi.FastAPI', 'FastAPI', ([], {}), '()\n', (384, 386), False, 'from fastapi import FastAPI\n'), ((570, 619), 'starlette.templating.Jinja2Templates', 'Jinja2Templates... |
# %%
import torch
import os
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.huggingface import HuggingFaceInferenceAPI
from llama_index.core import Settings
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from llama_index.embeddings.langchain import Langch... | [
"langchain.embeddings.huggingface.HuggingFaceEmbeddings"
] | [((1018, 1239), 'llama_index.core.PromptTemplate', 'PromptTemplate', (['"""Your job is to summarize different sections of the document given to you.Write a response that appropriately completes the request given to you.\n\n### Instruction:\n{query_str}\n\n### Response:"""'], {}), '(\n """Your job is to summarize dif... |
import tempfile
from copy import deepcopy
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Sequence
import langchain
from langchain.callbacks.base import BaseCallbackHandler
from langchain.callbacks.utils import (
BaseMetadataCallbackHandler,
flatten_dict,
import_pandas,
... | [
"langchain.callbacks.utils.import_spacy",
"langchain.callbacks.utils.import_pandas",
"langchain.callbacks.utils.import_textstat",
"langchain.callbacks.utils.flatten_dict"
] | [((1047, 1114), 'comet_ml.Experiment', 'comet_ml.Experiment', ([], {'workspace': 'workspace', 'project_name': 'project_name'}), '(workspace=workspace, project_name=project_name)\n', (1066, 1114), False, 'import comet_ml\n'), ((1249, 1266), 'langchain.callbacks.utils.import_textstat', 'import_textstat', ([], {}), '()\n'... |
import streamlit as st
from langchain import PromptTemplate
from utils.studio_style import apply_studio_style
from utils.studio_style import keyword_label, sentiment_label
from utils import langchain
from utils import bedrock
from utils import config
from datetime import datetime
import pandas as pd
import json
import ... | [
"langchain.PromptTemplate"
] | [((329, 402), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""Summarize Product Reviews"""', 'page_icon': '"""🛒"""'}), "(page_title='Summarize Product Reviews', page_icon='🛒')\n", (347, 402), True, 'import streamlit as st\n'), ((415, 438), 'utils.config.get_background', 'config.get_backgrou... |
from langchain.llms import OpenAI
from typing import Any, Dict, List, Optional
import langchain
from langchain import PromptTemplate ,LLMChain
from langchain.chains.question_answering import load_qa_chain
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from langchain.chains import RetrievalQAWit... | [
"langchain.chains.question_answering.load_qa_chain",
"langchain.LLMChain",
"langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler",
"langchain.llms.LlamaCpp",
"langchain.PromptTemplate"
] | [((404, 429), 'sys.path.append', 'sys.path.append', (['"""utils/"""'], {}), "('utils/')\n", (419, 429), False, 'import sys\n'), ((4364, 4456), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'template': 'prompt_template_llama2', 'input_variables': "['context', 'question']"}), "(template=prompt_template_llama2, inpu... |
docs = """When and under what conditions can I apply to your graduate programs? Graduate student admissions are made in the fall and spring semesters specified in the academic calendar. Minimum application requirements:
To have the undergraduate degree required in the program application requirements To have at least ... | [
"langchain.prompts.ChatPromptTemplate.from_messages",
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.chat_models.ChatOpenAI",
"langchain.cache.SQLiteCache"
] | [((2056, 2075), 'dotenv.load_dotenv', 'load_dotenv', (['""".env"""'], {}), "('.env')\n", (2067, 2075), False, 'from dotenv import load_dotenv\n'), ((2086, 2118), 'os.environ.get', 'os.environ.get', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (2100, 2118), False, 'import os\n'), ((2200, 2242), 'langchain.cac... |
from typing import List, Optional, Tuple, Dict, Callable, Any, Union
from functools import reduce
import os
import os
from pathlib import Path
import re
from .utils import maybe_is_text, maybe_is_truncated
from .qaprompts import (
summary_prompt,
qa_prompt,
search_prompt,
citation_prompt,
... | [
"langchain.callbacks.get_openai_callback",
"langchain.embeddings.openai.OpenAIEmbeddings",
"langchain.chat_models.ChatOpenAI",
"langchain.cache.SQLiteCache"
] | [((906, 929), 'langchain.cache.SQLiteCache', 'SQLiteCache', (['CACHE_PATH'], {}), '(CACHE_PATH)\n', (917, 929), False, 'from langchain.cache import SQLiteCache\n'), ((839, 866), 'os.path.dirname', 'os.path.dirname', (['CACHE_PATH'], {}), '(CACHE_PATH)\n', (854, 866), False, 'import os\n'), ((784, 795), 'pathlib.Path.ho... |
import io
import json
import time
from queue import Queue
from typing import Dict, List
import numpy as np
import tiktoken
from anyio.from_thread import start_blocking_portal
from django.conf import settings
from langchain.schema import AIMessage, HumanMessage
from openai import OpenAI
from pinecone import QueryRespon... | [
"langchain.schema.AIMessage",
"langchain.schema.HumanMessage"
] | [((1714, 1744), 'openai.OpenAI', 'OpenAI', ([], {'api_key': 'openai_api_key'}), '(api_key=openai_api_key)\n', (1720, 1744), False, 'from openai import OpenAI\n'), ((3257, 3288), 'json.dumps', 'json.dumps', (['sanitized_reference'], {}), '(sanitized_reference)\n', (3267, 3288), False, 'import json\n'), ((3431, 3467), 't... |
import json
import os
import langchain.memory.entity
from langchain.chat_models import AzureChatOpenAI
from flask import Flask, request
import httpx
from dotenv import load_dotenv
from langchain.memory import ConversationSummaryBufferMemory, ConversationBufferWindowMemory
from langchain.prompts.prompt import PromptTem... | [
"langchain.prompts.prompt.PromptTemplate",
"langchain.LLMChain",
"langchain.memory.ConversationBufferWindowMemory",
"langchain.memory.ConversationSummaryBufferMemory",
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.prompts.MessagesPlaceholder",
"langchain.prompts.SystemMessageP... | [((560, 575), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (565, 575), False, 'from flask import Flask, request\n'), ((576, 615), 'dotenv.load_dotenv', 'load_dotenv', ([], {'dotenv_path': '"""./config.env"""'}), "(dotenv_path='./config.env')\n", (587, 615), False, 'from dotenv import load_dotenv\n'), ((2... |
"""Utilities for running language models or Chains over datasets."""
from __future__ import annotations
import asyncio
import functools
import itertools
import logging
import uuid
from enum import Enum
from typing import (
Any,
Callable,
Coroutine,
Dict,
Iterator,
List,
Optional,
Seque... | [
"langchain.schema.messages.messages_from_dict",
"langchain.smith.evaluation.string_run_evaluator.StringRunEvaluatorChain.from_run_and_data_type",
"langchain.callbacks.tracers.langchain.LangChainTracer",
"langchain.chat_models.openai.ChatOpenAI",
"langchain.callbacks.tracers.evaluation.EvaluatorCallbackHandl... | [((1370, 1397), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1387, 1397), False, 'import logging\n'), ((1708, 1725), 'urllib.parse.urlparse', 'urlparse', (['api_url'], {}), '(api_url)\n', (1716, 1725), False, 'from urllib.parse import urlparse, urlunparse\n'), ((24648, 24668), 'asyncio... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.