code stringlengths 141 79.4k | apis listlengths 1 23 | extract_api stringlengths 126 73.2k |
|---|---|---|
'''
This script takes the True/False style questions from the csv file and save the result as another csv file.
This script makes use of Llama model.
Before running this script, make sure to configure the filepaths in config.yaml file.
'''
from langchain import PromptTemplate, LLMChain
from kg_rag.utility import *
im... | [
"langchain.LLMChain",
"langchain.PromptTemplate"
] | [((1786, 1860), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'template': 'template', 'input_variables': "['context', 'question']"}), "(template=template, input_variables=['context', 'question'])\n", (1800, 1860), False, 'from langchain import PromptTemplate, LLMChain\n'), ((1877, 1909), 'langchain.LLMChain', 'LL... |
import os
from typing import Any, Callable
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
import registry
from .base import BaseChat, ChatHistory, Response
TEMPLATE = '''
You are a web3 assistant. You help users use web3 apps, such as Uniswap, AA... | [
"langchain.chains.LLMChain",
"langchain.llms.OpenAI",
"langchain.prompts.PromptTemplate"
] | [((2418, 2494), 'langchain.prompts.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['task_info', 'question']", 'template': 'TEMPLATE'}), "(input_variables=['task_info', 'question'], template=TEMPLATE)\n", (2432, 2494), False, 'from langchain.prompts import PromptTemplate\n'), ((2549, 2587), 'langchain.llms... |
from typing import List
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
import langchain.docstore.document as docstore
from loguru import logger
from settings import COLLECTION_NAME, PERSIST_DIRECTORY
from .vortex_pdf_parser import VortexPdfParser
from .vortext_content_iter... | [
"langchain.embeddings.OpenAIEmbeddings",
"langchain.vectorstores.Chroma.from_documents"
] | [((985, 1014), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {'client': 'None'}), '(client=None)\n', (1001, 1014), False, 'from langchain.embeddings import OpenAIEmbeddings\n'), ((1023, 1055), 'loguru.logger.info', 'logger.info', (['"""Loaded embeddings"""'], {}), "('Loaded embeddings')\n", (1034, 1... |
# -*- coding: utf-8 -*-
import os
import re
import sys
sys.path.append('.')
sys.path.append('..')
from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
from langchain.prompts import StringPromptTemplate
from langchain import OpenAI, GoogleSearchAPIWrapper, LLMChain
from typing import... | [
"langchain.agents.AgentExecutor.from_agent_and_tools",
"langchain.agents.LLMSingleActionAgent",
"langchain.LLMChain",
"langchain.GoogleSearchAPIWrapper",
"langchain.schema.Document",
"langchain.agents.Tool",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.OpenAI"
] | [((55, 75), 'sys.path.append', 'sys.path.append', (['"""."""'], {}), "('.')\n", (70, 75), False, 'import sys\n'), ((76, 97), 'sys.path.append', 'sys.path.append', (['""".."""'], {}), "('..')\n", (91, 97), False, 'import sys\n'), ((4014, 4038), 'langchain.GoogleSearchAPIWrapper', 'GoogleSearchAPIWrapper', ([], {}), '()\... |
import base64
from email.message import EmailMessage
from typing import List, Optional, Type
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.pydantic_v1 import BaseModel, Field
from langchain.tools.gmail.base import GmailBaseTool
class CreateDraftSchema(BaseModel):
"""Input for C... | [
"langchain.pydantic_v1.Field"
] | [((359, 421), 'langchain.pydantic_v1.Field', 'Field', (['...'], {'description': '"""The message to include in the draft."""'}), "(..., description='The message to include in the draft.')\n", (364, 421), False, 'from langchain.pydantic_v1 import BaseModel, Field\n'), ((465, 514), 'langchain.pydantic_v1.Field', 'Field', ... |
from langchain import PromptTemplate
from langchain.chains.summarize import load_summarize_chain
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
from langchain.docstore.document import Document
base_prompt = """A profound and powerful writer, you have been given a contex... | [
"langchain.chains.question_answering.load_qa_chain",
"langchain.chains.summarize.load_summarize_chain",
"langchain.docstore.document.Document",
"langchain.llms.OpenAI",
"langchain.PromptTemplate"
] | [((1309, 1372), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'template': 'final_prompt', 'input_variables': "['text']"}), "(template=final_prompt, input_variables=['text'])\n", (1323, 1372), False, 'from langchain import PromptTemplate\n'), ((1399, 1561), 'langchain.chains.summarize.load_summarize_chain', 'load_... |
import streamlit as st
from langchain.llms import OpenAI
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
def generate_response(uploaded_file, openai_api_key, query_text):
#... | [
"langchain.text_splitter.CharacterTextSplitter",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.llms.OpenAI",
"langchain.vectorstores.Chroma.from_documents"
] | [((1040, 1091), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""🦜🔗 Ask the Doc App"""'}), "(page_title='🦜🔗 Ask the Doc App')\n", (1058, 1091), True, 'import streamlit as st\n'), ((1092, 1122), 'streamlit.title', 'st.title', (['"""🦜🔗 Ask the Doc App"""'], {}), "('🦜🔗 Ask the Doc App')\n... |
import re
from typing import List, Union
# Python内置模块，用于格式化和包装文本
import textwrap
import time
from langchain.agents import (
Tool, # 可用工具
AgentExecutor, # Agent执行
LLMSingleActionAgent, # 定义Agent
AgentOutputParser, # 输出结果解析
)
from langchain.prompts import StringPromptTemplate
# LLMChain，包含一个Prom... | [
"langchain.agents.AgentExecutor.from_agent_and_tools",
"langchain.agents.LLMSingleActionAgent",
"langchain.LLMChain",
"langchain.agents.Tool",
"langchain.prompts.PromptTemplate",
"langchain.OpenAI"
] | [((681, 759), 'langchain.prompts.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['query', 'context']", 'template': 'CONTEXT_QA_TMPL'}), "(input_variables=['query', 'context'], template=CONTEXT_QA_TMPL)\n", (695, 759), False, 'from langchain.prompts import PromptTemplate\n'), ((924, 957), 'textwrap.wrap', ... |
import os
import os.path as osp
from typing import List
from tqdm import tqdm
from langchain.docstore.document import Document
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import NLTKTextSplitter
from langchain.vectorstores.faiss import FAISS
import pandas as pd
import nltk
nltk... | [
"langchain.text_splitter.NLTKTextSplitter",
"langchain.docstore.document.Document",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((316, 338), 'nltk.download', 'nltk.download', (['"""punkt"""'], {}), "('punkt')\n", (329, 338), False, 'import nltk\n'), ((810, 843), 'langchain.text_splitter.NLTKTextSplitter', 'NLTKTextSplitter', ([], {'chunk_size': '(1024)'}), '(chunk_size=1024)\n', (826, 843), False, 'from langchain.text_splitter import NLTKTextS... |
"""
相关资料：
llama-cpp-python文档：https://llama-cpp-python.readthedocs.io/en/latest/
前提：
1.安装C++环境
https://developer.microsoft.com/en-us/windows/downloads/windows-sdk/
勾选“使用C++桌面开发”
2.安装模块
pip install llama-cpp-python
pip install llama-cpp-python[server]
3.运行服务
python... | [
"langchain.text_splitter.CharacterTextSplitter",
"langchain.document_loaders.DirectoryLoader",
"langchain.embeddings.huggingface.HuggingFaceEmbeddings",
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.llms.llamacpp.LlamaCpp",
"langchain.vectorstores.Chroma.from_documents",
"langchain.prompts.... | [((2537, 2663), 'langchain.llms.llamacpp.LlamaCpp', 'LlamaCpp', ([], {'model_path': '"""G:\\\\models\\\\llama2\\\\llama-2-7b-chat-q4\\\\llama-2-7b-chat.Q4_0.gguf"""', 'n_ctx': '(2048)', 'stop': "['Human:']"}), "(model_path=\n 'G:\\\\models\\\\llama2\\\\llama-2-7b-chat-q4\\\\llama-2-7b-chat.Q4_0.gguf',\n n_ctx=204... |
from enum import Enum
from functools import wraps
from typing import Any, Callable, Dict, List, Optional, Union
from langchain.utilities.redis import TokenEscaper
# disable mypy error for dunder method overrides
# mypy: disable-error-code="override"
class RedisFilterOperator(Enum):
EQ = 1
NE = 2
LT = 3
... | [
"langchain.utilities.redis.TokenEscaper"
] | [((747, 761), 'langchain.utilities.redis.TokenEscaper', 'TokenEscaper', ([], {}), '()\n', (759, 761), False, 'from langchain.utilities.redis import TokenEscaper\n'), ((2002, 2013), 'functools.wraps', 'wraps', (['func'], {}), '(func)\n', (2007, 2013), False, 'from functools import wraps\n')] |
from langchain.tools import tool
from graph_chain import get_results
@tool("graph-tool")
def graph_tool(query:str) -> str:
"""Tool for returning aggregations of Manager or Company or Industry data or if answer is dependent on relationships between a Company and other objects. Use this tool second and to verify res... | [
"langchain.tools.tool"
] | [((71, 89), 'langchain.tools.tool', 'tool', (['"""graph-tool"""'], {}), "('graph-tool')\n", (75, 89), False, 'from langchain.tools import tool\n'), ((366, 384), 'graph_chain.get_results', 'get_results', (['query'], {}), '(query)\n', (377, 384), False, 'from graph_chain import get_results\n')] |
import matplotlib.pyplot as plt
import numpy as np
import openai
import os
import pyaudio
import pyttsx3
import threading
import tkinter as tk
import queue
import wave
import whisper
from langchain import OpenAI, SQLDatabase
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.agents import cre... | [
"langchain.agents.agent_toolkits.SQLDatabaseToolkit",
"langchain.SQLDatabase.from_uri",
"langchain.OpenAI"
] | [((652, 740), 'langchain.SQLDatabase.from_uri', 'SQLDatabase.from_uri', (['f"""mysql+pymysql://admin:{s2_password}@{s2_host}:3306/{s2_db}"""'], {}), "(\n f'mysql+pymysql://admin:{s2_password}@{s2_host}:3306/{s2_db}')\n", (672, 740), False, 'from langchain import OpenAI, SQLDatabase\n'), ((743, 816), 'langchain.OpenA... |
from typing import Optional, Type
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.pydantic_v1 import BaseModel, Field
from langchain.tools.base import BaseTool
from langchain.tools.file_management.utils import (
INVALID_PATH_TEMPLATE,
BaseFileToolMixin,
FileValidationError,... | [
"langchain.pydantic_v1.Field",
"langchain.tools.file_management.utils.INVALID_PATH_TEMPLATE.format"
] | [((415, 453), 'langchain.pydantic_v1.Field', 'Field', (['...'], {'description': '"""name of file"""'}), "(..., description='name of file')\n", (420, 453), False, 'from langchain.pydantic_v1 import BaseModel, Field\n'), ((470, 517), 'langchain.pydantic_v1.Field', 'Field', (['...'], {'description': '"""text to write to f... |
import argparse
from typing import Optional
from langchain.llms.ollama import Ollama
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from termcolor import colored
class RubberDuck:
"""
This class is a wrapper around the Ollama model.
"""
def __init__(self, model: str = "... | [
"langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler"
] | [((1887, 1908), 'os.listdir', 'os.listdir', (['directory'], {}), '(directory)\n', (1897, 1908), False, 'import os\n'), ((2146, 2171), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (2169, 2171), False, 'import argparse\n'), ((3288, 3337), 'termcolor.colored', 'colored', (['"""\n What\'s on your... |
import json
from pathlib import Path
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_core.documents import Document
from langchain_core.output_parsers import StrOutputParser
from langch... | [
"langchain_community.chat_models.ChatOpenAI",
"langchain_core.prompts.ChatPromptTemplate.from_template",
"langchain_core.documents.Document",
"langchain_text_splitters.RecursiveCharacterTextSplitter",
"langchain_core.runnables.RunnablePassthrough",
"langchain_core.output_parsers.StrOutputParser",
"langc... | [((888, 954), 'langchain_text_splitters.RecursiveCharacterTextSplitter', 'RecursiveCharacterTextSplitter', ([], {'chunk_size': '(1000)', 'chunk_overlap': '(100)'}), '(chunk_size=1000, chunk_overlap=100)\n', (918, 954), False, 'from langchain_text_splitters import RecursiveCharacterTextSplitter\n'), ((1330, 1372), 'lang... |
import io
from io import IOBase
import os
import logging
import json
import sys
from typing import Any, List, Optional, Union
import pandas as pd
from langchain.agents import AgentType
from langchain.agents.agent import AgentExecutor
from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_ag... | [
"langchain_experimental.agents.agent_toolkits.create_pandas_dataframe_agent"
] | [((1293, 1722), 'src.ai.tools.tool_registry.register_tool', 'register_tool', ([], {'display_name': '"""Query Spreadsheet"""', 'description': '"""Query a spreadsheet using natural language."""', 'additional_instructions': '"""This tool transforms your natural language query into Python code to query a spreadsheet using ... |
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage
from log10.langchain import Log10Callback
from log10.llm import Log10Config
log10_callback = Log10Callback(log10_config=Log10Config())
messages = [
HumanMessage(content="You are a ping pong machine"),
HumanMessage(conten... | [
"langchain.schema.HumanMessage",
"langchain.chat_models.ChatOpenAI"
] | [((341, 443), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'model_name': '"""gpt-3.5-turbo"""', 'callbacks': '[log10_callback]', 'temperature': '(0.5)', 'tags': "['test']"}), "(model_name='gpt-3.5-turbo', callbacks=[log10_callback],\n temperature=0.5, tags=['test'])\n", (351, 443), False, 'from langchain.... |
from langchain.document_loaders import DirectoryLoader
from langchain.text_splitter import CharacterTextSplitter
import os
import pinecone
from langchain.vectorstores import Pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.chains import RetrievalQA
from langchain.chat_models impo... | [
"langchain.text_splitter.CharacterTextSplitter",
"langchain.document_loaders.DirectoryLoader",
"langchain.vectorstores.Pinecone.from_documents",
"langchain.chat_models.ChatOpenAI",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((393, 406), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (404, 406), False, 'from dotenv import load_dotenv\n'), ((431, 460), 'os.getenv', 'os.getenv', (['"""PINECONE_API_KEY"""'], {}), "('PINECONE_API_KEY')\n", (440, 460), False, 'import os\n'), ((477, 502), 'os.getenv', 'os.getenv', (['"""PINECONE_ENV"""'... |
import os
import re
import subprocess # nosec
import tempfile
from langchain.agents import AgentType, initialize_agent
from langchain.agents.tools import Tool
from langchain.pydantic_v1 import BaseModel, Field, ValidationError, validator
from langchain_community.chat_models import ChatOpenAI
from langchain_core.langu... | [
"langchain.pydantic_v1.Field",
"langchain.agents.initialize_agent",
"langchain_community.chat_models.ChatOpenAI",
"langchain.agents.tools.Tool.from_function",
"langchain.pydantic_v1.validator",
"langchain_core.prompts.ChatPromptTemplate.from_messages",
"langchain_core.runnables.ConfigurableField"
] | [((4039, 4387), 'langchain_core.prompts.ChatPromptTemplate.from_messages', 'ChatPromptTemplate.from_messages', (['[(\'system\',\n "You are a world class Python coder who uses black, ruff, and *strict* mypy for all of your code. Provide complete, end-to-end Python code to meet the user\'s description/requirements. Al... |
import os
import langchain
from langchain import (
agents,
prompts,
chains,
llms
)
class BOAgent:
def __init__(
self,
tools,
memory,
model="text-davinci-003",
temp=0.1,
max_steps=30,
):
self.openai_key = os.getenv(... | [
"langchain.agents.initialize_agent",
"langchain.OpenAI",
"langchain.chat_models.ChatOpenAI"
] | [((310, 337), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (319, 337), False, 'import os\n'), ((888, 1040), 'langchain.agents.initialize_agent', 'agents.initialize_agent', (['tools', 'self.llm'], {'agent': '"""conversational-react-description"""', 'verbose': '(True)', 'max_iteration... |
import importlib.util
import logging
from typing import Any, Callable, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.self_hosted import SelfHostedPipeline
from langchain.llms.utils import enforce_stop_tokens
from langchain.pydantic_v1 import Extra
DEFAULT... | [
"langchain.llms.utils.enforce_stop_tokens"
] | [((457, 484), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (474, 484), False, 'import logging\n'), ((1983, 2039), 'transformers.AutoTokenizer.from_pretrained', 'AutoTokenizer.from_pretrained', (['model_id'], {}), '(model_id, **_model_kwargs)\n', (2012, 2039), False, 'from transformers i... |
from langchain.prompts.prompt import PromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
from langchain.chains import ConversationalRetrievalChain, ChatVectorDBChain
from langchain.retrievers import TimeWeightedVectorStoreRetriever
from langchain.chains.question_answering impor... | [
"langchain.prompts.prompt.PromptTemplate.from_template",
"langchain.prompts.prompt.PromptTemplate",
"langchain.chat_models.ChatOpenAI"
] | [((681, 720), 'langchain.prompts.prompt.PromptTemplate.from_template', 'PromptTemplate.from_template', (['_template'], {}), '(_template)\n', (709, 720), False, 'from langchain.prompts.prompt import PromptTemplate\n'), ((1141, 1215), 'langchain.prompts.prompt.PromptTemplate', 'PromptTemplate', ([], {'template': 'templat... |
from collections import deque
from langchain import LLMChain, PromptTemplate
from langchain.chains import LLMChain
from langchain.llms import BaseLLM
from langchain.prompts import PromptTemplate
from modules.memory import MemoryModule
from typing import Dict, List
class ReasoningModule:
def __init__(self, llm, me... | [
"langchain.prompts.PromptTemplate"
] | [((395, 402), 'collections.deque', 'deque', ([], {}), '()\n', (400, 402), False, 'from collections import deque\n'), ((438, 445), 'collections.deque', 'deque', ([], {}), '()\n', (443, 445), False, 'from collections import deque\n'), ((3982, 4114), 'langchain.prompts.PromptTemplate', 'PromptTemplate', ([], {'template': ... |
## This is a fork/based from https://gist.github.com/wiseman/4a706428eaabf4af1002a07a114f61d6
from io import StringIO
import sys
import os
from typing import Dict, Optional
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents.tools import Tool
from langchain.llms... | [
"langchain.agents.initialize_agent",
"langchain.llms.OpenAI"
] | [((348, 409), 'os.environ.get', 'os.environ.get', (['"""OPENAI_API_BASE"""', '"""http://localhost:8080/v1"""'], {}), "('OPENAI_API_BASE', 'http://localhost:8080/v1')\n", (362, 409), False, 'import os\n'), ((423, 468), 'os.environ.get', 'os.environ.get', (['"""MODEL_NAME"""', '"""gpt-3.5-turbo"""'], {}), "('MODEL_NAME',... |
## This is a fork/based from https://gist.github.com/wiseman/4a706428eaabf4af1002a07a114f61d6
from io import StringIO
import sys
import os
from typing import Dict, Optional
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents.tools import Tool
from langchain.llms... | [
"langchain.agents.initialize_agent",
"langchain.llms.OpenAI"
] | [((348, 409), 'os.environ.get', 'os.environ.get', (['"""OPENAI_API_BASE"""', '"""http://localhost:8080/v1"""'], {}), "('OPENAI_API_BASE', 'http://localhost:8080/v1')\n", (362, 409), False, 'import os\n'), ((423, 468), 'os.environ.get', 'os.environ.get', (['"""MODEL_NAME"""', '"""gpt-3.5-turbo"""'], {}), "('MODEL_NAME',... |
"""Utility functions for mlflow.langchain."""
import contextlib
import json
import logging
import os
import shutil
import types
import warnings
from functools import lru_cache
from importlib.util import find_spec
from typing import NamedTuple
import cloudpickle
import yaml
from packaging import version
from packaging... | [
"langchain.schema.output_parser.StrOutputParser",
"langchain.agents.initialize_agent",
"langchain.chains.loading.load_chain"
] | [((2074, 2101), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (2091, 2101), False, 'import logging\n'), ((10682, 10728), 'os.path.join', 'os.path.join', (['path', '_MODEL_DATA_YAML_FILE_NAME'], {}), '(path, _MODEL_DATA_YAML_FILE_NAME)\n', (10694, 10728), False, 'import os\n'), ((17545, 1... |
import json
import os
import sys
from simple_agent_app import SimpleAgentApp
from azure.identity import DefaultAzureCredential
from parse import *
# add parent directory to path
sys.path.insert(0, str(os.path.abspath(os.path.join(os.path.dirname(__file__), "../"))))
import langchain_utils
import utils
credential = De... | [
"langchain_utils.create_plugins_static"
] | [((318, 376), 'azure.identity.DefaultAzureCredential', 'DefaultAzureCredential', ([], {'additionally_allowed_tenants': "['*']"}), "(additionally_allowed_tenants=['*'])\n", (340, 376), False, 'from azure.identity import DefaultAzureCredential\n'), ((579, 609), 'utils.load_secrets', 'utils.load_secrets', (['credential'],... |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
import langchain
from langchain.llms import Replicate
from flask import Flask
from flask import request
import os
import requests
import json
class Whats... | [
"langchain.llms.Replicate"
] | [((1502, 1609), 'langchain.llms.Replicate', 'Replicate', ([], {'model': 'llama2_13b_chat', 'model_kwargs': "{'temperature': 0.01, 'top_p': 1, 'max_new_tokens': 500}"}), "(model=llama2_13b_chat, model_kwargs={'temperature': 0.01, 'top_p':\n 1, 'max_new_tokens': 500})\n", (1511, 1609), False, 'from langchain.llms impo... |
import langchain
from langchain.cache import InMemoryCache
from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
langchain.llm_cache = InMemoryCache()
llm = OpenAI(temperature=0.9)
prompt = PromptTemplate(
input_variables=["product"],
template="W... | [
"langchain.chains.LLMChain",
"langchain.prompts.PromptTemplate",
"langchain.cache.InMemoryCache",
"langchain.llms.OpenAI"
] | [((199, 214), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (212, 214), False, 'from langchain.cache import InMemoryCache\n'), ((223, 246), 'langchain.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0.9)'}), '(temperature=0.9)\n', (229, 246), False, 'from langchain.llms import OpenAI\n'), ((256, 37... |
import time
from dotenv import load_dotenv
import langchain
from langchain.llms import OpenAI
from langchain.callbacks import get_openai_callback
from langchain.cache import InMemoryCache
load_dotenv()
# to make caching obvious, we use a slow model
llm = OpenAI(model_name="text-davinci-002")
langchain.llm_cache = In... | [
"langchain.cache.InMemoryCache",
"langchain.llms.OpenAI",
"langchain.callbacks.get_openai_callback"
] | [((189, 202), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (200, 202), False, 'from dotenv import load_dotenv\n'), ((257, 294), 'langchain.llms.OpenAI', 'OpenAI', ([], {'model_name': '"""text-davinci-002"""'}), "(model_name='text-davinci-002')\n", (263, 294), False, 'from langchain.llms import OpenAI\n'), ((3... |
import langchain
from langchain.chains.summarize import load_summarize_chain
from langchain.docstore.document import Document
from langchain.text_splitter import CharacterTextSplitter
from steamship import File, Task
from steamship.invocable import PackageService, post
from steamship_langchain.cache import SteamshipCa... | [
"langchain.text_splitter.CharacterTextSplitter",
"langchain.chains.summarize.load_summarize_chain",
"langchain.docstore.document.Document"
] | [((613, 635), 'steamship.invocable.post', 'post', (['"""summarize_file"""'], {}), "('summarize_file')\n", (617, 635), False, 'from steamship.invocable import PackageService, post\n'), ((1078, 1106), 'steamship.invocable.post', 'post', (['"""summarize_audio_file"""'], {}), "('summarize_audio_file')\n", (1082, 1106), Fal... |
import langchain
import os
import streamlit as st
import requests
import sounddevice as sd
import wavio
os.environ["OPENAI_API_KEY"]="ADD KEY"
import openai
from openai import OpenAI
client=OpenAI()
from langchain.prompts import ChatPromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain.prompts imp... | [
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.schema.messages.SystemMessage",
"langchain.chat_models.ChatOpenAI"
] | [((191, 199), 'openai.OpenAI', 'OpenAI', ([], {}), '()\n', (197, 199), False, 'from openai import OpenAI\n'), ((1151, 1163), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {}), '()\n', (1161, 1163), False, 'from langchain.chat_models import ChatOpenAI\n'), ((1462, 1520), 'streamlit.set_page_config', 'st.set_pag... |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law o... | [
"langchain.cache.InMemoryCache",
"langchain.llms.OpenAI",
"langchain.PromptTemplate"
] | [((847, 862), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (860, 862), False, 'from langchain.cache import InMemoryCache\n'), ((951, 986), 'configparser.ConfigParser', 'ConfigParser', ([], {'comment_prefixes': 'None'}), '(comment_prefixes=None)\n', (963, 986), False, 'from configparser import Con... |
"""QA using native LangChain features"""
from dotenv import load_dotenv
from genai import Client, Credentials
from genai.extensions.langchain import LangChainInterface
from genai.schema import DecodingMethod, TextGenerationParameters
try:
from langchain_core.output_parsers import StrOutputParser
from langcha... | [
"langchain_core.prompts.PromptTemplate",
"langchain_core.output_parsers.StrOutputParser"
] | [((659, 672), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (670, 672), False, 'from dotenv import load_dotenv\n'), ((857, 998), 'genai.schema.TextGenerationParameters', 'TextGenerationParameters', ([], {'decoding_method': 'DecodingMethod.SAMPLE', 'max_new_tokens': '(100)', 'min_new_tokens': '(1)', 'temperatur... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Project : AI. @by PyCharm
# @File : chatbase
# @Time : 2023/7/5 15:29
# @Author : betterme
# @WeChat : meutils
# @Software : PyCharm
# @Description :
from meutils.pipe import *
from langchain.schema import Document
from langchain.... | [
"langchain.chains.question_answering.load_qa_chain",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.memory.ConversationBufferWindowMemory",
"langchain.chat_models.ChatOpenAI"
] | [((1213, 1245), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {'chunk_size': '(100)'}), '(chunk_size=100)\n', (1229, 1245), False, 'from langchain.embeddings import OpenAIEmbeddings\n'), ((1307, 1396), 'langchain.memory.ConversationBufferWindowMemory', 'ConversationBufferWindowMemory', ([], {'memory... |
import langchain
from langchain.chat_models.base import BaseChatModel, SimpleChatModel
from langchain.schema import (
AIMessage,
BaseMessage,
ChatGeneration,
ChatResult,
HumanMessage,
SystemMessage,
)
from typing import Any, Dict, List, Mapping, Optional, Sequence, TypedDict
import websocket
imp... | [
"langchain.schema.AIMessage",
"langchain.schema.ChatResult",
"langchain.schema.ChatGeneration"
] | [((1236, 1265), 'langchain.schema.AIMessage', 'AIMessage', ([], {'content': 'output_str'}), '(content=output_str)\n', (1245, 1265), False, 'from langchain.schema import AIMessage, BaseMessage, ChatGeneration, ChatResult, HumanMessage, SystemMessage\n'), ((1287, 1318), 'langchain.schema.ChatGeneration', 'ChatGeneration'... |
import logging
import requests
from typing import Optional, List, Dict, Mapping, Any
import langchain
from langchain.llms.base import LLM
from langchain.cache import InMemoryCache
logging.basicConfig(level=logging.INFO)
# 启动llm的缓存
langchain.llm_cache = InMemoryCache()
class AgentZhipuAI(LLM):
import zhipuai as... | [
"langchain.chains.LLMChain",
"langchain.cache.InMemoryCache",
"langchain.prompts.PromptTemplate",
"langchain.chains.ConversationChain"
] | [((183, 222), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.INFO'}), '(level=logging.INFO)\n', (202, 222), False, 'import logging\n'), ((256, 271), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (269, 271), False, 'from langchain.cache import InMemoryCache\n'), ((1830, 1884)... |
'''
Example script to automatically write a screenplay from a newsgroup post using agents with Crew.ai (https://github.com/joaomdmoura/crewAI)
You can also try it out with a personal email with many replies back and forth and see it turn into a movie script.
Demonstrates:
- multiple API endpoints (offical Mistral, ... | [
"langchain.chat_models.openai.ChatOpenAI",
"langchain_community.chat_models.ChatAnyscale",
"langchain_mistralai.chat_models.ChatMistralAI"
] | [((2292, 2556), 'crewai.Agent', 'Agent', ([], {'role': '"""spamfilter"""', 'goal': '"""Decide whether a text is spam or not."""', 'backstory': '"""You are an expert spam filter with years of experience. You DETEST advertisements, newsletters and vulgar language."""', 'llm': 'mixtral', 'verbose': '(True)', 'allow_delega... |
"""Chat agent with question answering
"""
import os
from utils.giphy import GiphyAPIWrapper
from dataclasses import dataclass
from langchain.chains import LLMChain, LLMRequestsChain
from langchain import Wikipedia, OpenAI
from langchain.agents.react.base import DocstoreExplorer
from langchain.agents import (
Zero... | [
"langchain.agents.initialize_agent",
"langchain.agents.AgentExecutor.from_agent_and_tools",
"langchain.utilities.GoogleSearchAPIWrapper",
"langchain.Wikipedia",
"langchain.agents.conversational.base.ConversationalAgent",
"langchain.SerpAPIWrapper",
"langchain.agents.conversational.base.ConversationalAge... | [((726, 741), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (739, 741), False, 'from langchain.cache import InMemoryCache\n'), ((1293, 1342), 'langchain.OpenAI', 'OpenAI', ([], {'temperature': '(0)', 'model_name': '"""gpt-3.5-turbo"""'}), "(temperature=0, model_name='gpt-3.5-turbo')\n", (1299, 134... |
import os
import pinecone
from rich.console import Console
from rich.markdown import Markdown
import langchain
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA
from langchain.embeddings import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.vectorstores import Pi... | [
"langchain.llms.OpenAI",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.prompts.PromptTemplate",
"langchain.vectorstores.Pinecone"
] | [((371, 398), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (380, 398), False, 'import os\n'), ((418, 443), 'os.getenv', 'os.getenv', (['"""PINECONE_KEY"""'], {}), "('PINECONE_KEY')\n", (427, 443), False, 'import os\n'), ((467, 500), 'os.getenv', 'os.getenv', (['"""PINECONE_ENVIRONME... |
from typing import Union, Callable, List, Dict, Any, TypeVar
from lionagi.libs.sys_util import SysUtil
T = TypeVar("T")
def to_langchain_document(datanode: T, **kwargs: Any) -> Any:
"""
Converts a generic data node into a Langchain Document.
This function transforms a node, typically from another data... | [
"langchain.schema.Document"
] | [((110, 122), 'typing.TypeVar', 'TypeVar', (['"""T"""'], {}), "('T')\n", (117, 122), False, 'from typing import Union, Callable, List, Dict, Any, TypeVar\n'), ((877, 910), 'lionagi.libs.sys_util.SysUtil.check_import', 'SysUtil.check_import', (['"""langchain"""'], {}), "('langchain')\n", (897, 910), False, 'from lionagi... |
import re
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
import langchain
from langchain import LLMChain
from langchain.agents.agent import AgentOutputParser
from langchain.schema import AgentAction, AgentFinish, OutputParserException
from .prompts import (FINAL_ANSWER_ACTION, FORMAT_INSTRUCTION... | [
"langchain.schema.OutputParserException"
] | [((1023, 1056), 're.search', 're.search', (['regex', 'text', 're.DOTALL'], {}), '(regex, text, re.DOTALL)\n', (1032, 1056), False, 'import re\n'), ((1097, 1159), 'langchain.schema.OutputParserException', 'OutputParserException', (['f"""Could not parse LLM output: `{text}`"""'], {}), "(f'Could not parse LLM output: `{te... |
from typing import List
from uuid import uuid4
from langchain.prompts import ChatPromptTemplate
from langchain.schema import AIMessage, HumanMessage, SystemMessage
from langchain_community.chat_models.fake import FakeListChatModel
from honcho import Honcho
from honcho.ext.langchain import langchain_message_converter... | [
"langchain.prompts.ChatPromptTemplate.from_messages",
"langchain_community.chat_models.fake.FakeListChatModel",
"langchain.schema.SystemMessage",
"langchain.schema.HumanMessage"
] | [((356, 415), 'honcho.Honcho', 'Honcho', ([], {'app_name': 'app_name', 'base_url': '"""http://localhost:8000"""'}), "(app_name=app_name, base_url='http://localhost:8000')\n", (362, 415), False, 'from honcho import Honcho\n'), ((596, 634), 'langchain_community.chat_models.fake.FakeListChatModel', 'FakeListChatModel', ([... |
""" A simple cloud consultant bot that can answer questions
about kubernetes, aws and cloud native."""
import langchain
from langchain.agents import Tool, AgentType, initialize_agent
from langchain.tools import HumanInputRun
from langchain.callbacks import HumanApprovalCallbackHandler
from langchain.vectorstores impor... | [
"langchain.agents.initialize_agent",
"langchain.tools.HumanInputRun",
"langchain.memory.ConversationBufferMemory",
"langchain.embeddings.openai.OpenAIEmbeddings",
"langchain.chat_models.ChatOpenAI",
"langchain.agents.Tool",
"langchain.vectorstores.Chroma"
] | [((893, 908), 'langchain.tools.HumanInputRun', 'HumanInputRun', ([], {}), '()\n', (906, 908), False, 'from langchain.tools import HumanInputRun\n'), ((917, 955), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '(0)', 'model': 'MODEL'}), '(temperature=0, model=MODEL)\n', (927, 955), False, 'from l... |
import langchain.text_splitter as splitter
# local imports
import settings_template as settings
class SplitterCreator():
"""
Splitter class to import into other modules
"""
def __init__(self, text_splitter_method=None, chunk_size=None, chunk_overlap=None) -> None:
self.text_splitter_method = s... | [
"langchain.text_splitter.NLTKTextSplitter",
"langchain.text_splitter.RecursiveCharacterTextSplitter"
] | [((779, 909), 'langchain.text_splitter.NLTKTextSplitter', 'splitter.NLTKTextSplitter', ([], {'separator': '"""\n\n"""', 'language': '"""english"""', 'chunk_size': 'self.chunk_size', 'chunk_overlap': 'self.chunk_overlap'}), "(separator='\\n\\n', language='english', chunk_size=\n self.chunk_size, chunk_overlap=self.ch... |
from langchain.cache import SQLiteCache
import langchain
from pydantic import BaseModel
from creator.code_interpreter import CodeInterpreter
from creator.config.load_config import load_yaml_config
import os
# Load configuration from YAML
yaml_config = load_yaml_config()
# Helper function to prepend '~/' to paths if... | [
"langchain.cache.SQLiteCache"
] | [((254, 272), 'creator.config.load_config.load_yaml_config', 'load_yaml_config', ([], {}), '()\n', (270, 272), False, 'from creator.config.load_config import load_yaml_config\n'), ((2004, 2058), 'os.path.join', 'os.path.join', (['project_dir', '_build_in_skill_library_dir'], {}), '(project_dir, _build_in_skill_library_... |
from __future__ import annotations
import asyncio
import functools
import logging
import os
import warnings
from contextlib import contextmanager
from contextvars import ContextVar
from typing import Any, Dict, Generator, List, Optional, Type, TypeVar, Union, cast
from uuid import UUID, uuid4
import langchain
from la... | [
"langchain.schema.get_buffer_string",
"langchain.callbacks.stdout.StdOutCallbackHandler",
"langchain.callbacks.tracers.stdout.ConsoleCallbackHandler",
"langchain.callbacks.openai_info.OpenAICallbackHandler",
"langchain.callbacks.tracers.langchain.LangChainTracer",
"langchain.callbacks.tracers.langchain_v1... | [((1036, 1063), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1053, 1063), False, 'import logging\n'), ((1208, 1251), 'contextvars.ContextVar', 'ContextVar', (['"""openai_callback"""'], {'default': 'None'}), "('openai_callback', default=None)\n", (1218, 1251), False, 'from contextvars i... |
"""Base interface that all chains should implement."""
import inspect
import json
import warnings
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import yaml
from pydantic import BaseModel, Field, root_validator, validator
import langchain
from langchai... | [
"langchain.schema.RunInfo",
"langchain.callbacks.manager.AsyncCallbackManager.configure",
"langchain.callbacks.manager.CallbackManager.configure"
] | [((816, 849), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, exclude=True)\n', (821, 849), False, 'from pydantic import BaseModel, Field, root_validator, validator\n'), ((904, 937), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, exc... |
"""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.RunInfo",
"langchain.schema.AIMessage",
"langchain.llm_cache.lookup",
"langchain.llm_cache.updat... | [((2315, 2352), 'pydantic.Field', 'Field', ([], {'default_factory': '_get_verbosity'}), '(default_factory=_get_verbosity)\n', (2320, 2352), False, 'from pydantic import Extra, Field, root_validator, validator\n'), ((2426, 2459), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, ... |
"""Base interface for large language models to expose."""
import json
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Mapping, Optional, Tuple, Union
import yaml
from pydantic import BaseModel, Extra, Field, validator
import langchain
from langchain.callbacks import ge... | [
"langchain.schema.Generation",
"langchain.llm_cache.update",
"langchain.llm_cache.lookup",
"langchain.schema.LLMResult",
"langchain.callbacks.get_callback_manager"
] | [((1991, 2028), 'pydantic.Field', 'Field', ([], {'default_factory': '_get_verbosity'}), '(default_factory=_get_verbosity)\n', (1996, 2028), False, 'from pydantic import BaseModel, Extra, Field, validator\n'), ((2119, 2162), 'pydantic.Field', 'Field', ([], {'default_factory': 'get_callback_manager'}), '(default_factory=... |
from typing import Optional
import langchain
from dotenv import load_dotenv
from langchain import PromptTemplate, chains
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from pydantic import ValidationError
from rmrkl import ChatZeroShotAgent, RetryAgentExecutor
from .prompts import FOR... | [
"langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler",
"langchain.chains.LLMChain",
"langchain.PromptTemplate"
] | [((1543, 1556), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (1554, 1556), False, 'from dotenv import load_dotenv\n'), ((2451, 2541), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['question', 'agent_ans']", 'template': 'REPHRASE_TEMPLATE'}), "(input_variables=['question', 'agent_ans... |
import langchain_hb
from colorama import Fore, Back, Style
import sys
import json
import requests
import os
import agent
class hb():
stats = True
exe = {
"command" : "",
"type": ""
}
index = langchain_hb.initialize_index()
def get_openAIKey():
return os.environ["OPENAI_KEY"... | [
"langchain_hb.initialize_index",
"langchain_hb.ask_ai"
] | [((224, 255), 'langchain_hb.initialize_index', 'langchain_hb.initialize_index', ([], {}), '()\n', (253, 255), False, 'import langchain_hb\n'), ((820, 936), 'requests.get', 'requests.get', (['url'], {'params': "{'human_request': human_request, 'ai_response': ai_response, 'ai_sources':\n ai_sources}"}), "(url, params=... |
import streamlit as st
import langchain
from langchain.utilities import SQLDatabase
from langchain_experimental.sql import SQLDatabaseChain
from langchain.chat_models import ChatOpenAI
from langsmith import Client
from langchain.smith import RunEvalConfig, run_on_dataset
from pydantic import BaseModel, Field
db = SQLD... | [
"langchain_experimental.sql.SQLDatabaseChain.from_llm",
"langchain.utilities.SQLDatabase.from_uri",
"langchain.chat_models.ChatOpenAI"
] | [((316, 360), 'langchain.utilities.SQLDatabase.from_uri', 'SQLDatabase.from_uri', (['"""sqlite:///Chinook.db"""'], {}), "('sqlite:///Chinook.db')\n", (336, 360), False, 'from langchain.utilities import SQLDatabase\n'), ((367, 392), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '(0)'}), '(temper... |
import langchain
from langchain.agents import initialize_agent
from llama_index import GPTListIndex, GPTIndexMemory
from langchain.callbacks import get_openai_callback
from langchain.agents import AgentType
class Eunomia:
def __init__(
self,
tools,
model="text-davinci-003",
temp=0.... | [
"langchain.OpenAI",
"langchain.agents.initialize_agent",
"langchain.callbacks.get_openai_callback",
"langchain.chat_models.ChatOpenAI"
] | [((980, 996), 'llama_index.GPTListIndex', 'GPTListIndex', (['[]'], {}), '([])\n', (992, 996), False, 'from llama_index import GPTListIndex, GPTIndexMemory\n'), ((1014, 1116), 'llama_index.GPTIndexMemory', 'GPTIndexMemory', ([], {'index': 'index', 'memory_key': '"""chat_history"""', 'query_kwargs': "{'response_mode': 'c... |
"""
Utilities for ingesting different types of documents.
This includes cutting text into chunks and cleaning text.
"""
import re
from typing import Callable, Dict, List, Tuple
import langchain.docstore.document as docstore
import langchain.text_splitter as splitter
from loguru import logger
class IngestUtils:
""... | [
"langchain.text_splitter.NLTKTextSplitter",
"langchain.docstore.document.Document",
"langchain.text_splitter.RecursiveCharacterTextSplitter"
] | [((881, 921), 're.sub', 're.sub', (['"""(\\\\w)-\\\\n(\\\\w)"""', '"""\\\\1\\\\2"""', 'text'], {}), "('(\\\\w)-\\\\n(\\\\w)', '\\\\1\\\\2', text)\n", (887, 921), False, 'import re\n'), ((1072, 1111), 're.sub', 're.sub', (['"""(?<!\\\\n)\\\\n(?!\\\\n)"""', '""" """', 'text'], {}), "('(?<!\\\\n)\\\\n(?!\\\\n)', ' ', text... |
import numpy as np
from llama_index.core import StorageContext, load_index_from_storage
from llama_index.llms.litellm import LiteLLM
from langchain_google_genai import ChatGoogleGenerativeAI
from trulens_eval.feedback.provider.langchain import Langchain
from trulens_eval import Tru, Feedback, TruLlama
from trulens_eva... | [
"langchain_google_genai.ChatGoogleGenerativeAI"
] | [((519, 570), 'llama_index.llms.litellm.LiteLLM', 'LiteLLM', ([], {'model': '"""gemini/gemini-pro"""', 'temperature': '(0.1)'}), "(model='gemini/gemini-pro', temperature=0.1)\n", (526, 570), False, 'from llama_index.llms.litellm import LiteLLM\n'), ((687, 744), 'langchain_google_genai.ChatGoogleGenerativeAI', 'ChatGoog... |
import os
import json
import re
import string
import time
from tqdm import tqdm
import langchain
from langchain.document_loaders import UnstructuredFileLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
import pickle
from langchain.prompts import PromptTemplate
from langchain.vectorstores import... | [
"langchain.document_loaders.UnstructuredFileLoader",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.chains.RetrievalQA",
"langchain.vectorstores.FAISS.from_embeddings",
"langchain.chains.LLMChain",
"langchain.prompts.PromptTemplate",
"langchain.chains.combine_documents.stuff.StuffD... | [((570, 595), 'sys.path.append', 'sys.path.append', (['ROOT_DIR'], {}), '(ROOT_DIR)\n', (585, 595), False, 'import sys\n'), ((543, 568), 'os.path.abspath', 'os.path.abspath', (['__file__'], {}), '(__file__)\n', (558, 568), False, 'import os\n'), ((9331, 9354), 'os.walk', 'os.walk', (['self.data_path'], {}), '(self.data... |
"""Base interface that all chains should implement."""
from __future__ import annotations
import asyncio
import inspect
import json
import logging
import warnings
from abc import ABC, abstractmethod
from functools import partial
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import langc... | [
"langchain.pydantic_v1.Field",
"langchain.callbacks.manager.AsyncCallbackManager.configure",
"langchain.load.dump.dumpd",
"langchain.callbacks.manager.CallbackManager.configure",
"langchain.schema.RunInfo",
"langchain.pydantic_v1.validator",
"langchain.pydantic_v1.root_validator"
] | [((858, 885), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (875, 885), False, 'import logging\n'), ((3854, 3887), 'langchain.pydantic_v1.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, exclude=True)\n', (3859, 3887), False, 'from langchain.pydantic_v1 im... |
import os
from fastapi import FastAPI, WebSocket
from fastapi.responses import HTMLResponse
from google.api_core.client_options import ClientOptions
from google.cloud.speech_v1 import SpeechAsyncClient
from google.cloud.texttospeech_v1 import TextToSpeechAsyncClient
from langchain_community.chat_models import ChatVert... | [
"langchain_core.prompts.ChatPromptTemplate.from_messages",
"langchain_core.output_parsers.StrOutputParser",
"langchain_community.chat_models.ChatVertexAI"
] | [((1420, 1429), 'fastapi.FastAPI', 'FastAPI', ([], {}), '()\n', (1427, 1429), False, 'from fastapi import FastAPI, WebSocket\n'), ((1746, 1771), 'google.cloud.texttospeech_v1.TextToSpeechAsyncClient', 'TextToSpeechAsyncClient', ([], {}), '()\n', (1769, 1771), False, 'from google.cloud.texttospeech_v1 import TextToSpeec... |
import os
import langchain
from langchain.chains import LLMChain, LLMRequestsChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.llms import VertexAI
from langchain import PromptTemplate, LLMChain
#langchain.debug = True
#template = """Question: {question}
#
#Answer: L... | [
"langchain.LLMChain",
"langchain.PromptTemplate",
"langchain.llms.VertexAI"
] | [((2164, 2196), 'langchain.llms.VertexAI', 'VertexAI', ([], {'max_output_tokens': '(1024)'}), '(max_output_tokens=1024)\n', (2172, 2196), False, 'from langchain.llms import VertexAI\n'), ((2210, 2289), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['query', 'requests_result']", 'template': 'te... |
import langchain
from dotenv import load_dotenv
from langchain.chains import HypotheticalDocumentEmbedder, RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
langchain.debug = True
load_dotenv()
# HyDE (LLMが生成した仮説的な回答のベク... | [
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.vectorstores.FAISS.load_local",
"langchain.chat_models.ChatOpenAI",
"langchain.chains.HypotheticalDocumentEmbedder.from_llm",
"langchain.embeddings.OpenAIEmbeddings"
] | [((280, 293), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (291, 293), False, 'from dotenv import load_dotenv\n'), ((347, 365), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (363, 365), False, 'from langchain.embeddings import OpenAIEmbeddings\n'), ((373, 426), 'langchain.chat... |
import os
from langchain.embeddings import OpenAIEmbeddings
import langchain
from annoy import AnnoyIndex
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from sentence_transformers import SentenceTransforme... | [
"langchain.embeddings.OpenAIEmbeddings"
] | [((353, 388), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {'openai_api_key': '""""""'}), "(openai_api_key='')\n", (369, 388), False, 'from langchain.embeddings import OpenAIEmbeddings\n'), ((397, 471), 'sentence_transformers.SentenceTransformer', 'SentenceTransformer', (['"""sentence-transformers/... |
import discord
from discord import app_commands
from discord.ext import commands
import langchain
from langchain.document_loaders import YoutubeLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains.summarize import load_summarize_chain
import torch
class YoutubeSummaryCog(c... | [
"langchain.chains.summarize.load_summarize_chain",
"langchain.document_loaders.YoutubeLoader.from_youtube_url",
"langchain.text_splitter.RecursiveCharacterTextSplitter"
] | [((425, 528), 'discord.app_commands.command', 'app_commands.command', ([], {'name': '"""youtubesummary"""', 'description': '"""Summarize a YouTube video given its URL"""'}), "(name='youtubesummary', description=\n 'Summarize a YouTube video given its URL')\n", (445, 528), False, 'from discord import app_commands\n')... |
import os
import re
import langchain
import molbloom
import paperqa
import paperscraper
from langchain import SerpAPIWrapper
from langchain.base_language import BaseLanguageModel
from langchain.tools import BaseTool
from langchain.embeddings.openai import OpenAIEmbeddings
from pypdf.errors import PdfReadError
from ch... | [
"langchain.embeddings.openai.OpenAIEmbeddings",
"langchain.prompts.PromptTemplate",
"langchain.chains.llm.LLMChain"
] | [((757, 1091), 'langchain.prompts.PromptTemplate', 'langchain.prompts.PromptTemplate', ([], {'input_variables': "['question']", 'template': '"""\n I would like to find scholarly papers to answer\n this question: {question}. Your response must be at\n most 10 words long.\n \'A search query th... |
from pathlib import Path
from phi.assistant import Assistant
from phi.knowledge.langchain import LangChainKnowledgeBase
from langchain.embeddings import OpenAIEmbeddings
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma... | [
"langchain.text_splitter.CharacterTextSplitter",
"langchain.embeddings.OpenAIEmbeddings"
] | [((1254, 1297), 'phi.knowledge.langchain.LangChainKnowledgeBase', 'LangChainKnowledgeBase', ([], {'retriever': 'retriever'}), '(retriever=retriever)\n', (1276, 1297), False, 'from phi.knowledge.langchain import LangChainKnowledgeBase\n'), ((1306, 1398), 'phi.assistant.Assistant', 'Assistant', ([], {'knowledge_base': 'k... |
"""Console script for blackboard_pagi."""
# Blackboard-PAGI - LLM Proto-AGI using the Blackboard Pattern
# Copyright (c) 2023. Andreas Kirsch
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software F... | [
"langchain.llms.openai.OpenAI",
"langchain.cache.SQLiteCache"
] | [((983, 996), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {}), '()\n', (994, 996), False, 'from langchain.cache import SQLiteCache\n'), ((1000, 1015), 'click.command', 'click.command', ([], {}), '()\n', (1013, 1015), False, 'import click\n'), ((1059, 1088), 'click.echo', 'click.echo', (['"""blackboard-pagi"""'],... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Project : AI. @by PyCharm
# @File : OpenAIEmbeddings
# @Time : 2023/7/11 18:40
# @Author : betterme
# @WeChat : meutils
# @Software : PyCharm
# @Description :
import langchain
from langchain.embeddings import OpenAIEmbeddings as _O... | [
"langchain.embeddings.OpenAIEmbeddings"
] | [((1391, 1418), 'langchain.embeddings.OpenAIEmbeddings', '_OpenAIEmbeddings', ([], {}), '(**kwargs)\n', (1408, 1418), True, 'from langchain.embeddings import OpenAIEmbeddings as _OpenAIEmbeddings\n')] |
####################################################################################
# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.or... | [
"langchain.llms.VertexAI"
] | [((1273, 1385), 'langchain.llms.VertexAI', 'VertexAI', ([], {'model_name': '"""text-bison@001"""', 'max_output_tokens': '(1024)', 'temperature': '(0)', 'top_p': '(0)', 'top_k': '(1)', 'verbose': '(True)'}), "(model_name='text-bison@001', max_output_tokens=1024, temperature=0,\n top_p=0, top_k=1, verbose=True)\n", (1... |
# Blackboard-PAGI - LLM Proto-AGI using the Blackboard Pattern
# Copyright (c) 2023. Andreas Kirsch
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License,... | [
"langchain.schema.AIMessage",
"langchain.llm_cache.update",
"langchain.schema.Generation",
"langchain.llm_cache.lookup"
] | [((1220, 1280), 'langchain.llm_cache.lookup', 'langchain.llm_cache.lookup', (['messages_prompt', 'self.model_name'], {}), '(messages_prompt, self.model_name)\n', (1246, 1280), False, 'import langchain\n'), ((1796, 1900), 'langchain.schema.Generation', 'Generation', ([], {'text': 'chat_result.generations[0].message.cont... |
import os
import pathlib
import langchain
import langchain.cache
import langchain.globals
CACHE_BASE = pathlib.Path(f'{os.environ["HOME"]}/.cache/mitaskem/')
CACHE_BASE.mkdir(parents=True, exist_ok=True)
_LLM_CACHE_PATH = CACHE_BASE/'langchain_llm_cache.sqlite'
langchain.globals.set_llm_cache(langchain.cache.SQLiteCac... | [
"langchain.cache.SQLiteCache"
] | [((104, 158), 'pathlib.Path', 'pathlib.Path', (['f"""{os.environ[\'HOME\']}/.cache/mitaskem/"""'], {}), '(f"{os.environ[\'HOME\']}/.cache/mitaskem/")\n', (116, 158), False, 'import pathlib\n'), ((295, 353), 'langchain.cache.SQLiteCache', 'langchain.cache.SQLiteCache', ([], {'database_path': '_LLM_CACHE_PATH'}), '(datab... |
"""Base interface that all chains should implement."""
import json
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import yaml
from pydantic import BaseModel, Extra, Field, validator
import langchain
from langchain.callbacks import get_callback_manager
... | [
"langchain.callbacks.get_callback_manager"
] | [((1401, 1458), 'pydantic.Field', 'Field', ([], {'default_factory': 'get_callback_manager', 'exclude': '(True)'}), '(default_factory=get_callback_manager, exclude=True)\n', (1406, 1458), False, 'from pydantic import BaseModel, Extra, Field, validator\n'), ((1493, 1530), 'pydantic.Field', 'Field', ([], {'default_factory... |
import time #← 実行時間を計測するためにtimeモジュールをインポート
import langchain
from langchain.cache import InMemoryCache #← InMemoryCacheをインポート
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage
langchain.llm_cache = InMemoryCache() #← llm_cacheにInMemoryCacheを設定
chat = ChatOpenAI()
start = time.tim... | [
"langchain.cache.InMemoryCache",
"langchain.schema.HumanMessage",
"langchain.chat_models.ChatOpenAI"
] | [((237, 252), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (250, 252), False, 'from langchain.cache import InMemoryCache\n'), ((291, 303), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {}), '()\n', (301, 303), False, 'from langchain.chat_models import ChatOpenAI\n'), ((312, 323), 'time.t... |
import re
import urllib
from time import sleep
import langchain
import molbloom
import pandas as pd
import pkg_resources
import requests
import tiktoken
from langchain import LLMChain, PromptTemplate
from langchain.llms import BaseLLM
from langchain.tools import BaseTool
from chemcrow.utils import is_smiles, pubchem_... | [
"langchain.LLMChain",
"langchain.PromptTemplate"
] | [((1729, 1744), 'chemcrow.utils.is_smiles', 'is_smiles', (['text'], {}), '(text)\n', (1738, 1744), False, 'from chemcrow.utils import is_smiles, pubchem_query2smiles, tanimoto\n'), ((4644, 4686), 'tiktoken.encoding_for_model', 'tiktoken.encoding_for_model', (['encoding_name'], {}), '(encoding_name)\n', (4671, 4686), Fa... |
# from __future__ import annotations
import os
import re
import itertools
import openai
import tiktoken
import json
from dotenv import load_dotenv
from typing import Any, Dict, List, Optional
from pydantic import Extra
from langchain.schema.language_model import BaseLanguageModel
from langchain.callbacks.manager im... | [
"langchain.prompts.PromptTemplate.from_template",
"langchain.tools.DuckDuckGoSearchRun"
] | [((1312, 1333), 'langchain.tools.DuckDuckGoSearchRun', 'DuckDuckGoSearchRun', ([], {}), '()\n', (1331, 1333), False, 'from langchain.tools import DuckDuckGoSearchRun\n'), ((2942, 3011), 'langchain.prompts.PromptTemplate.from_template', 'PromptTemplate.from_template', (['prompts.EXECUTE_PLAN_PROMPT_SEARCH_TOOL'], {}), '... |
from __future__ import annotations
import time
from abc import abstractmethod
from typing import Any, List, Tuple, Union
import gradio_client as grc
import huggingface_hub
from gradio_client.client import Job
from gradio_client.utils import QueueError
try:
import langchain as lc
LANGCHAIN_INSTALLED = True
e... | [
"langchain.agents.Tool"
] | [((3706, 3781), 'langchain.agents.Tool', 'lc.agents.Tool', ([], {'name': 'self.name', 'func': 'self.run', 'description': 'self.description'}), '(name=self.name, func=self.run, description=self.description)\n', (3720, 3781), True, 'import langchain as lc\n'), ((742, 794), 'gradio_client.Client.duplicate', 'grc.Client.du... |
#!/Users/mark/dev/ml/langchain/read_github/langchain_github/env/bin/python
# change above to the location of your local Python venv installation
import sys, os, shutil
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.append(parent_dir)
import pathlib
from langchain.docstore.docume... | [
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.docstore.document.Document",
"langchain.text_splitter.MarkdownTextSplitter",
"langchain.chat_models.ChatOpenAI",
"langchain.document_loaders.unstructured.UnstructuredFileLoader",
"langchain.text_splitter.PythonCodeTextSplitter",
"langc... | [((245, 272), 'sys.path.append', 'sys.path.append', (['parent_dir'], {}), '(parent_dir)\n', (260, 272), False, 'import sys, os, shutil\n'), ((667, 692), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '(0)'}), '(temperature=0)\n', (677, 692), False, 'from langchain.chat_models import ChatOpenAI\n... |
import inspect
from storage.logger_config import logger
from langchain.tools import BaseTool
import langchain.tools as ltools
from langchain.agents.agent_toolkits import FileManagementToolkit
from langchain.agents import load_tools
import streamlit as st
import os
Local_dir = os.path.dirname(os.path.realpath(__file_... | [
"langchain.agents.agent_toolkits.FileManagementToolkit",
"langchain.agents.load_tools"
] | [((296, 322), 'os.path.realpath', 'os.path.realpath', (['__file__'], {}), '(__file__)\n', (312, 322), False, 'import os\n'), ((1869, 1930), 'langchain.agents.agent_toolkits.FileManagementToolkit', 'FileManagementToolkit', ([], {'root_dir': "(Local_dir + '\\\\..\\\\workspace')"}), "(root_dir=Local_dir + '\\\\..\\\\works... |
import inspect
import os
import langchain
from langchain.cache import SQLiteCache
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.schema.output_parser import StrOutputParser
# os.environ['OPENAI_API_BASE'] = "https://shale.live/v1"
os.environ['OPENAI_API_BA... | [
"langchain.schema.output_parser.StrOutputParser",
"langchain.prompts.ChatPromptTemplate.from_messages",
"langchain.chat_models.ChatOpenAI",
"langchain.cache.SQLiteCache"
] | [((947, 981), 'os.path.join', 'os.path.join', (['dir', '""".langchain.db"""'], {}), "(dir, '.langchain.db')\n", (959, 981), False, 'import os\n'), ((1048, 1088), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {'database_path': 'database_path'}), '(database_path=database_path)\n', (1059, 1088), False, 'from langchai... |
from __future__ import annotations
import logging
from typing import Any, Dict, Iterable, List, Optional, Tuple
from zep_python import API_URL, NotFoundError, ZepClient
from zep_python.document import Document as ZepDocument
from zep_python.document import DocumentCollection
try:
from langchain_core.documents im... | [
"langchain_core.documents.Document"
] | [((565, 584), 'logging.getLogger', 'logging.getLogger', ([], {}), '()\n', (582, 584), False, 'import logging\n'), ((1990, 2033), 'zep_python.ZepClient', 'ZepClient', ([], {'api_url': 'api_url', 'api_key': 'api_key'}), '(api_url=api_url, api_key=api_key)\n', (1999, 2033), False, 'from zep_python import API_URL, NotFound... |
import os
import json
from typing import List
from dotenv import load_dotenv
from pydantic import BaseModel, Field
from supabase.client import Client, create_client
from langchain.chat_models import ChatOpenAI
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.tools import StructuredTool
from langc... | [
"langchain.chains.openai_functions.create_structured_output_chain",
"langchain.tools.StructuredTool",
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.chat_models.ChatOpenAI",
"langchain.prompts.ChatPromptTemplate.from_messages",
"langchain.prompts.SystemMessagePromptTemplate.from_... | [((528, 541), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (539, 541), False, 'from dotenv import load_dotenv\n'), ((799, 824), 'os.getenv', 'os.getenv', (['"""SUPABASE_URL"""'], {}), "('SUPABASE_URL')\n", (808, 824), False, 'import os\n'), ((840, 865), 'os.getenv', 'os.getenv', (['"""SUPABASE_KEY"""'], {}), ... |
####################################################################################
# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.or... | [
"langchain.agents.initialize_agent",
"langchain.agents.load_tools",
"langchain.llms.VertexAI"
] | [((1859, 1872), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (1870, 1872), False, 'from dotenv import load_dotenv\n'), ((1881, 1997), 'langchain.llms.VertexAI', 'VertexAI', ([], {'model_name': '"""text-bison@001"""', 'max_output_tokens': '(1024)', 'temperature': '(0.25)', 'top_p': '(0)', 'top_k': '(1)', 'verb... |
import django
django.setup()
from sefaria.model.text import Ref, library
import re
import langchain
from langchain.cache import SQLiteCache
from langchain.chat_models import ChatOpenAI
from langchain.chat_models import ChatAnthropic
from langchain.prompts import PromptTemplate
from langchain.schema import HumanMessage... | [
"langchain.chat_models.ChatAnthropic",
"langchain.prompts.PromptTemplate.from_template",
"langchain.schema.SystemMessage",
"langchain.cache.SQLiteCache"
] | [((14, 28), 'django.setup', 'django.setup', ([], {}), '()\n', (26, 28), False, 'import django\n'), ((358, 400), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {'database_path': '""".langchain.db"""'}), "(database_path='.langchain.db')\n", (369, 400), False, 'from langchain.cache import SQLiteCache\n'), ((591, 615),... |
import streamlit as st
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.prompts.prompt import PromptTemplate
from langchain.callbacks import get_openai_callback
#fix Error: module 'langchain' has no attribute 'verbose'
import langchain
langchain.verb... | [
"langchain.chains.ConversationalRetrievalChain.from_llm",
"langchain.prompts.prompt.PromptTemplate",
"langchain.callbacks.get_openai_callback",
"langchain.chat_models.ChatOpenAI"
] | [((1142, 1219), 'langchain.prompts.prompt.PromptTemplate', 'PromptTemplate', ([], {'template': 'qa_template', 'input_variables': "['context', 'question']"}), "(template=qa_template, input_variables=['context', 'question'])\n", (1156, 1219), False, 'from langchain.prompts.prompt import PromptTemplate\n'), ((1364, 1432),... |
"""
A simple CUI application to visualize and query a customer database using the `textual` package.
"""
from dataclasses import dataclass
import langchain
from langchain.cache import SQLiteCache
from langchain.llms import OpenAI
from textual.app import App, ComposeResult
from textual.containers import Horizontal
from... | [
"langchain.llms.OpenAI",
"langchain.cache.SQLiteCache"
] | [((447, 460), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {}), '()\n', (458, 460), False, 'from langchain.cache import SQLiteCache\n'), ((472, 495), 'langchain.llms.OpenAI', 'OpenAI', ([], {'max_tokens': '(1024)'}), '(max_tokens=1024)\n', (478, 495), False, 'from langchain.llms import OpenAI\n'), ((499, 521), 'l... |
import langchain
from dotenv import load_dotenv
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.retrievers import BM25Retriever, EnsembleRetriever
from langchain.vectorstores import FAISS
langchain.verbose = T... | [
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.chat_models.ChatOpenAI",
"langchain.retrievers.BM25Retriever.from_texts",
"langchain.vectorstores.FAISS.from_texts",
"langchain.retrievers.EnsembleRetriever",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((325, 338), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (336, 338), False, 'from dotenv import load_dotenv\n'), ((1707, 1725), 'langchain.embeddings.openai.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (1723, 1725), False, 'from langchain.embeddings.openai import OpenAIEmbeddings\n'), ((1731, 17... |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
import langchain
from langchain.llms import Replicate
from flask import Flask
from flask import request
import os
import requests
import json
class Whats... | [
"langchain.llms.Replicate"
] | [((1502, 1609), 'langchain.llms.Replicate', 'Replicate', ([], {'model': 'llama2_13b_chat', 'model_kwargs': "{'temperature': 0.01, 'top_p': 1, 'max_new_tokens': 500}"}), "(model=llama2_13b_chat, model_kwargs={'temperature': 0.01, 'top_p':\n 1, 'max_new_tokens': 500})\n", (1511, 1609), False, 'from langchain.llms impo... |
import langchain
from langchain.cache import InMemoryCache
from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
langchain.llm_cache = InMemoryCache()
llm = OpenAI(temperature=0.9)
prompt = PromptTemplate(
input_variables=["product"],
template="W... | [
"langchain.chains.LLMChain",
"langchain.prompts.PromptTemplate",
"langchain.cache.InMemoryCache",
"langchain.llms.OpenAI"
] | [((199, 214), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (212, 214), False, 'from langchain.cache import InMemoryCache\n'), ((223, 246), 'langchain.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0.9)'}), '(temperature=0.9)\n', (229, 246), False, 'from langchain.llms import OpenAI\n'), ((256, 37... |
import langchain
from langchain.chains.summarize import load_summarize_chain
from langchain.docstore.document import Document
from langchain.text_splitter import CharacterTextSplitter
from steamship import File, Task
from steamship.invocable import PackageService, post
from steamship_langchain.cache import SteamshipCa... | [
"langchain.text_splitter.CharacterTextSplitter",
"langchain.chains.summarize.load_summarize_chain",
"langchain.docstore.document.Document"
] | [((613, 635), 'steamship.invocable.post', 'post', (['"""summarize_file"""'], {}), "('summarize_file')\n", (617, 635), False, 'from steamship.invocable import PackageService, post\n'), ((1078, 1106), 'steamship.invocable.post', 'post', (['"""summarize_audio_file"""'], {}), "('summarize_audio_file')\n", (1082, 1106), Fal... |
import langchain
import os
import streamlit as st
import requests
import sounddevice as sd
import wavio
os.environ["OPENAI_API_KEY"]="ADD KEY"
import openai
from openai import OpenAI
client=OpenAI()
from langchain.prompts import ChatPromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain.prompts imp... | [
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.schema.messages.SystemMessage",
"langchain.chat_models.ChatOpenAI"
] | [((191, 199), 'openai.OpenAI', 'OpenAI', ([], {}), '()\n', (197, 199), False, 'from openai import OpenAI\n'), ((1151, 1163), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {}), '()\n', (1161, 1163), False, 'from langchain.chat_models import ChatOpenAI\n'), ((1462, 1520), 'streamlit.set_page_config', 'st.set_pag... |
import langchain
import os
import streamlit as st
import requests
import sounddevice as sd
import wavio
os.environ["OPENAI_API_KEY"]="ADD KEY"
import openai
from openai import OpenAI
client=OpenAI()
from langchain.prompts import ChatPromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain.prompts imp... | [
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.schema.messages.SystemMessage",
"langchain.chat_models.ChatOpenAI"
] | [((191, 199), 'openai.OpenAI', 'OpenAI', ([], {}), '()\n', (197, 199), False, 'from openai import OpenAI\n'), ((1151, 1163), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {}), '()\n', (1161, 1163), False, 'from langchain.chat_models import ChatOpenAI\n'), ((1462, 1520), 'streamlit.set_page_config', 'st.set_pag... |
import langchain
import os
import streamlit as st
import requests
import sounddevice as sd
import wavio
os.environ["OPENAI_API_KEY"]="ADD KEY"
import openai
from openai import OpenAI
client=OpenAI()
from langchain.prompts import ChatPromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain.prompts imp... | [
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.schema.messages.SystemMessage",
"langchain.chat_models.ChatOpenAI"
] | [((191, 199), 'openai.OpenAI', 'OpenAI', ([], {}), '()\n', (197, 199), False, 'from openai import OpenAI\n'), ((1151, 1163), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {}), '()\n', (1161, 1163), False, 'from langchain.chat_models import ChatOpenAI\n'), ((1462, 1520), 'streamlit.set_page_config', 'st.set_pag... |
import langchain
import os
import streamlit as st
import requests
import sounddevice as sd
import wavio
os.environ["OPENAI_API_KEY"]="ADD KEY"
import openai
from openai import OpenAI
client=OpenAI()
from langchain.prompts import ChatPromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain.prompts imp... | [
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.schema.messages.SystemMessage",
"langchain.chat_models.ChatOpenAI"
] | [((191, 199), 'openai.OpenAI', 'OpenAI', ([], {}), '()\n', (197, 199), False, 'from openai import OpenAI\n'), ((1151, 1163), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {}), '()\n', (1161, 1163), False, 'from langchain.chat_models import ChatOpenAI\n'), ((1462, 1520), 'streamlit.set_page_config', 'st.set_pag... |
import os
import threading
from chainlit.config import config
from chainlit.logger import logger
def init_lc_cache():
use_cache = config.project.cache is True and config.run.no_cache is False
if use_cache:
try:
import langchain
except ImportError:
return
from ... | [
"langchain.cache.SQLiteCache"
] | [((767, 783), 'threading.Lock', 'threading.Lock', ([], {}), '()\n', (781, 783), False, 'import threading\n'), ((487, 542), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {'database_path': 'config.project.lc_cache_path'}), '(database_path=config.project.lc_cache_path)\n', (498, 542), False, 'from langchain.cache imp... |
import os
import threading
from chainlit.config import config
from chainlit.logger import logger
def init_lc_cache():
use_cache = config.project.cache is True and config.run.no_cache is False
if use_cache:
try:
import langchain
except ImportError:
return
from ... | [
"langchain.cache.SQLiteCache"
] | [((767, 783), 'threading.Lock', 'threading.Lock', ([], {}), '()\n', (781, 783), False, 'import threading\n'), ((487, 542), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {'database_path': 'config.project.lc_cache_path'}), '(database_path=config.project.lc_cache_path)\n', (498, 542), False, 'from langchain.cache imp... |
import os
import threading
from chainlit.config import config
from chainlit.logger import logger
def init_lc_cache():
use_cache = config.project.cache is True and config.run.no_cache is False
if use_cache:
try:
import langchain
except ImportError:
return
from ... | [
"langchain.cache.SQLiteCache"
] | [((767, 783), 'threading.Lock', 'threading.Lock', ([], {}), '()\n', (781, 783), False, 'import threading\n'), ((487, 542), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {'database_path': 'config.project.lc_cache_path'}), '(database_path=config.project.lc_cache_path)\n', (498, 542), False, 'from langchain.cache imp... |
import os
import threading
from chainlit.config import config
from chainlit.logger import logger
def init_lc_cache():
use_cache = config.project.cache is True and config.run.no_cache is False
if use_cache:
try:
import langchain
except ImportError:
return
from ... | [
"langchain.cache.SQLiteCache"
] | [((767, 783), 'threading.Lock', 'threading.Lock', ([], {}), '()\n', (781, 783), False, 'import threading\n'), ((487, 542), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {'database_path': 'config.project.lc_cache_path'}), '(database_path=config.project.lc_cache_path)\n', (498, 542), False, 'from langchain.cache imp... |
import logging
import requests
from typing import Optional, List, Dict, Mapping, Any
import langchain
from langchain.llms.base import LLM
from langchain.cache import InMemoryCache
logging.basicConfig(level=logging.INFO)
# 启动llm的缓存
langchain.llm_cache = InMemoryCache()
class AgentZhipuAI(LLM):
import zhipuai as... | [
"langchain.chains.LLMChain",
"langchain.cache.InMemoryCache",
"langchain.prompts.PromptTemplate",
"langchain.chains.ConversationChain"
] | [((183, 222), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.INFO'}), '(level=logging.INFO)\n', (202, 222), False, 'import logging\n'), ((256, 271), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (269, 271), False, 'from langchain.cache import InMemoryCache\n'), ((1830, 1884)... |
'''
Example script to automatically write a screenplay from a newsgroup post using agents with Crew.ai (https://github.com/joaomdmoura/crewAI)
You can also try it out with a personal email with many replies back and forth and see it turn into a movie script.
Demonstrates:
- multiple API endpoints (offical Mistral, ... | [
"langchain.chat_models.openai.ChatOpenAI",
"langchain_community.chat_models.ChatAnyscale",
"langchain_mistralai.chat_models.ChatMistralAI"
] | [((2292, 2556), 'crewai.Agent', 'Agent', ([], {'role': '"""spamfilter"""', 'goal': '"""Decide whether a text is spam or not."""', 'backstory': '"""You are an expert spam filter with years of experience. You DETEST advertisements, newsletters and vulgar language."""', 'llm': 'mixtral', 'verbose': '(True)', 'allow_delega... |
'''
Example script to automatically write a screenplay from a newsgroup post using agents with Crew.ai (https://github.com/joaomdmoura/crewAI)
You can also try it out with a personal email with many replies back and forth and see it turn into a movie script.
Demonstrates:
- multiple API endpoints (offical Mistral, ... | [
"langchain.chat_models.openai.ChatOpenAI",
"langchain_community.chat_models.ChatAnyscale",
"langchain_mistralai.chat_models.ChatMistralAI"
] | [((2292, 2556), 'crewai.Agent', 'Agent', ([], {'role': '"""spamfilter"""', 'goal': '"""Decide whether a text is spam or not."""', 'backstory': '"""You are an expert spam filter with years of experience. You DETEST advertisements, newsletters and vulgar language."""', 'llm': 'mixtral', 'verbose': '(True)', 'allow_delega... |
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