code stringlengths 141 78.9k | apis listlengths 1 23 | extract_api stringlengths 142 73.2k |
|---|---|---|
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... |
"""Utilities for running language models or Chains over datasets."""
from __future__ import annotations
import asyncio
import functools
import inspect
import itertools
import logging
import uuid
import warnings
from enum import Enum
from typing import (
TYPE_CHECKING,
Any,
Callable,
Coroutine,
Dic... | [
"langchain.schema.messages.messages_from_dict",
"langchain.smith.evaluation.string_run_evaluator.StringRunEvaluatorChain.from_run_and_data_type",
"langchain.evaluation.schema.EvaluatorType",
"langchain.callbacks.tracers.langchain.LangChainTracer",
"langchain.callbacks.tracers.evaluation.EvaluatorCallbackHan... | [((1500, 1527), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1517, 1527), False, 'import logging\n'), ((2799, 2816), 'urllib.parse.urlparse', 'urlparse', (['api_url'], {}), '(api_url)\n', (2807, 2816), False, 'from urllib.parse import urlparse, urlunparse\n'), ((27519, 27539), 'asyncio... |
from langchain.llms import HuggingFacePipeline, CTransformers
import langchain
from ingest import load_db
from langchain.cache import InMemoryCache
from langchain.schema import prompt
from langchain.chains import RetrievalQA
from langchain.callbacks import StdOutCallbackHandler
from langchain import PromptTemplate
impo... | [
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.llms.CTransformers",
"langchain.callbacks.StdOutCallbackHandler",
"langchain.cache.InMemoryCache",
"langchain.PromptTemplate"
] | [((403, 418), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (416, 418), False, 'from langchain.cache import InMemoryCache\n'), ((709, 732), 'langchain.callbacks.StdOutCallbackHandler', 'StdOutCallbackHandler', ([], {}), '()\n', (730, 732), False, 'from langchain.callbacks import StdOutCallbackHand... |
# Import Langchain dependencies
from langchain.document_loaders import PyPDFLoader
from langchain.indexes import VectorstoreIndexCreator
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
# Bring in streamlit... | [
"langchain.document_loaders.PyPDFLoader",
"langchain.embeddings.HuggingFaceEmbeddings",
"langchain.text_splitter.RecursiveCharacterTextSplitter"
] | [((539, 717), 'watsonxlangchain.LangChainInterface', 'LangChainInterface', ([], {'credentials': 'creds', 'model': '"""meta-llama/llama-2-70b-chat"""', 'params': "{'decoding_method': 'sample', 'max_new_tokens': 200, 'temperature': 0.5}", 'project_id': '""""""'}), "(credentials=creds, model='meta-llama/llama-2-70b-chat',... |
import streamlit as st
import langchain
from langchain_community.document_loaders import RecursiveUrlLoader, TextLoader, JSONLoader
from langchain_community.document_transformers import Html2TextTransformer
from langchain.docstore.document import Document
from langchain_community.embeddings.openai import OpenAIEmbeddi... | [
"langchain.agents.openai_functions_agent.agent_token_buffer_memory.AgentTokenBufferMemory",
"langchain.text_splitter.CharacterTextSplitter",
"langchain.agents.AgentExecutor",
"langchain_community.embeddings.openai.OpenAIEmbeddings",
"langchain_community.vectorstores.Chroma.from_documents",
"langchain.docs... | [((1732, 1759), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (1741, 1759), False, 'import os, openai, requests, json, zeep, datetime, pandas as pd\n'), ((1857, 1875), 'langchain_community.embeddings.openai.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (1873, 1875), False,... |
# Using flask to make an api
# import necessary libraries and functions
from flask import Flask, jsonify, request, render_template
from pydantic import BaseModel
from ast import literal_eval
import os
import openai
openai.api_key = os.getenv("OPENAI_API_KEY")
import langchain
from langchain.vectorstores import FAI... | [
"langchain.vectorstores.FAISS.load_local",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((235, 262), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (244, 262), False, 'import os\n'), ((412, 427), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (417, 427), False, 'from flask import Flask, jsonify, request, render_template\n'), ((1396, 1414), 'langchain.embedd... |
# -*- coding: utf-8 -*-
import random
import streamlit as st
from langchain.llms import OpenAI
from langchain.text_splitter import RecursiveCharacterTextSplitter
#from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FA... | [
"langchain.LLMChain",
"langchain.embeddings.HuggingFaceEmbeddings",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.llms.OpenAI",
"langchain.vectorstores.FAISS.from_documents"
] | [((1047, 1159), 'langchain.llms.OpenAI', 'OpenAI', ([], {'model_name': '"""text-davinci-003"""', 'temperature': '(0.2)', 'max_tokens': '(512)', 'openai_api_key': "st.secrets['api_key']"}), "(model_name='text-davinci-003', temperature=0.2, max_tokens=512,\n openai_api_key=st.secrets['api_key'])\n", (1053, 1159), Fals... |
import langchain
from langchain.agents import load_tools, initialize_agent, AgentType
from langchain.chat_models import ChatOpenAI
langchain.verbose = True
langchain.debug = True
def get_chat():
return ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
if __name__ == "__main__":
chat = get_chat()
to... | [
"langchain.agents.initialize_agent",
"langchain.agents.load_tools",
"langchain.chat_models.ChatOpenAI"
] | [((209, 262), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'model_name': '"""gpt-3.5-turbo"""', 'temperature': '(0)'}), "(model_name='gpt-3.5-turbo', temperature=0)\n", (219, 262), False, 'from langchain.chat_models import ChatOpenAI\n'), ((326, 350), 'langchain.agents.load_tools', 'load_tools', (["['termina... |
from llama_index import (
GPTVectorStoreIndex,
ServiceContext,
)
from llama_index.postprocessor import SentenceTransformerRerank
from llama_index.embeddings import LangchainEmbedding
from langchain.embeddings.huggingface import (
HuggingFaceBgeEmbeddings,
)
from llama_index.vector_stores import WeaviateVect... | [
"langchain_community.llms.HuggingFaceTextGenInference",
"langchain.embeddings.huggingface.HuggingFaceBgeEmbeddings"
] | [((1058, 1076), 'huggingface_hub.commands.user.login', 'login', ([], {'token': 'token'}), '(token=token)\n', (1063, 1076), False, 'from huggingface_hub.commands.user import login\n'), ((1116, 1160), 'weaviate.Client', 'weaviate.Client', (['"""http://192.168.88.10:8080"""'], {}), "('http://192.168.88.10:8080')\n", (1131... |
import os
import time
import pickle as pkl
# import re
# import yaml
import toml
import logging
from datetime import date
# import aiohttp
import pandas as pd
from pytrends.request import TrendReq
import serpapi
from serpapi import GoogleSearch
import asyncio
import streamlit as st
import streamlit.components.v1 as co... | [
"langchain.docstore.document.Document",
"langchain.text_splitter.RecursiveCharacterTextSplitter"
] | [((872, 885), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (883, 885), False, 'from dotenv import load_dotenv\n'), ((1028, 1055), 'os.path.exists', 'os.path.exists', (['config_path'], {}), '(config_path)\n', (1042, 1055), False, 'import os\n'), ((1341, 1393), 'logging.info', 'logging.info', (['f"""session sta... |
from langchain import OpenAI, SQLDatabase
from langchain_experimental.sql import SQLDatabaseChain
from langchain.memory import ConversationBufferMemory
from langchain.agents import (AgentType,
AgentExecutor,
create_react_agent,
c... | [
"langchain.text_splitter.CharacterTextSplitter",
"langchain_experimental.sql.SQLDatabaseChain",
"langchain.agents.initialize.initialize_agent",
"langchain.tools.Tool",
"langchain.document_loaders.pdf.PyPDFLoader",
"langchain.memory.ConversationBufferMemory",
"langchain_community.document_loaders.text.Te... | [((1372, 1405), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {}), "('ignore')\n", (1395, 1405), False, 'import warnings\n'), ((1458, 1572), 'langchain.SQLDatabase.from_uri', 'SQLDatabase.from_uri', (['f"""postgresql+psycopg2://postgres:{constants.DBPASS}@localhost:5433/{constants.DB}"""'], {... |
"""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, Field, validator
import langchain
from langchain.callbacks import get_callback_manager
from la... | [
"langchain.callbacks.get_callback_manager"
] | [((646, 703), 'pydantic.Field', 'Field', ([], {'default_factory': 'get_callback_manager', 'exclude': '(True)'}), '(default_factory=get_callback_manager, exclude=True)\n', (651, 703), False, 'from pydantic import BaseModel, Field, validator\n'), ((738, 775), 'pydantic.Field', 'Field', ([], {'default_factory': '_get_verb... |
from typing import Dict, List, Optional
from langchain.agents.load_tools import (
_EXTRA_LLM_TOOLS,
_EXTRA_OPTIONAL_TOOLS,
_LLM_TOOLS,
)
from langflow.custom import customs
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.tools.constants import (
ALL_TOOLS_NAMES,
CU... | [
"langchain.agents.load_tools._LLM_TOOLS.keys",
"langchain.agents.load_tools._EXTRA_LLM_TOOLS.keys",
"langchain.agents.load_tools._EXTRA_OPTIONAL_TOOLS.keys"
] | [((709, 811), 'langflow.template.field.base.TemplateField', 'TemplateField', ([], {'field_type': '"""str"""', 'required': '(True)', 'is_list': '(False)', 'show': '(True)', 'placeholder': '""""""', 'value': '""""""'}), "(field_type='str', required=True, is_list=False, show=True,\n placeholder='', value='')\n", (722, ... |
#!/usr/bin/env python3
from restapi_helper import LangChainHelper
from langchain.schema import HumanMessage
print('==Simple message predict==')
with LangChainHelper() as lch:
text = 'Hey there!'
messages = [HumanMessage(content=text)]
print(lch.predict_messages(messages))
print('==As English t... | [
"langchain.schema.HumanMessage"
] | [((157, 174), 'restapi_helper.LangChainHelper', 'LangChainHelper', ([], {}), '()\n', (172, 174), False, 'from restapi_helper import LangChainHelper\n'), ((355, 372), 'restapi_helper.LangChainHelper', 'LangChainHelper', ([], {}), '()\n', (370, 372), False, 'from restapi_helper import LangChainHelper\n'), ((572, 589), 'r... |
import streamlit as st
# Import transformer classes for generaiton
from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer, GPT2Tokenizer, GPT2LMHeadModel, GPT2Model
# Import torch for datatype attributes
import torch
# Import the prompt wrapper...but for llama index
from llama_index.prompts.prompts... | [
"langchain.embeddings.huggingface.HuggingFaceEmbeddings"
] | [((1422, 1473), 'streamlit.title', 'st.title', (['"""LLM Deployment Prototype for Production"""'], {}), "('LLM Deployment Prototype for Production')\n", (1430, 1473), True, 'import streamlit as st\n'), ((1474, 1718), 'streamlit.caption', 'st.caption', (['"""Special thanks to my mentor, Medkham Chanthavong, for all the ... |
from langchain_openai import ChatOpenAI
from langchain.chains import LLMChain
from langchain.prompts import MessagesPlaceholder, HumanMessagePromptTemplate, ChatPromptTemplate
from langchain.memory import ConversationBufferMemory, FileChatMessageHistory
from dotenv import load_dotenv
import sqlite3
import sqlparse
impo... | [
"langchain_openai.ChatOpenAI",
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.prompts.MessagesPlaceholder",
"langchain.memory.FileChatMessageHistory",
"langchain.chains.LLMChain"
] | [((476, 547), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {'category': 'LangChainDeprecationWarning'}), "('ignore', category=LangChainDeprecationWarning)\n", (499, 547), False, 'import warnings\n'), ((550, 563), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (561, 563), False, 'from... |
import langchain_visualizer # isort:skip # noqa: F401
import asyncio
import vcr_langchain as vcr
from fvalues import FValue
from langchain import PromptTemplate
from langchain.llms import OpenAI
# ========================== Start of langchain example code ==========================
# https://langchain.readthedocs.i... | [
"langchain_visualizer.visualize",
"langchain.llms.OpenAI",
"langchain.PromptTemplate"
] | [((434, 524), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['adjective']", 'template': '"""Tell me a {adjective} joke."""'}), "(input_variables=['adjective'], template=\n 'Tell me a {adjective} joke.')\n", (448, 524), False, 'from langchain import PromptTemplate\n'), ((837, 855), 'vcr_lang... |
from typing import Optional, List
from langchain.chains.openai_functions import create_structured_output_runnable
from langchain_community.chat_models import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field
import logging
import langchain
from dr... | [
"langchain_core.prompts.ChatPromptTemplate.from_messages",
"langchain.chains.openai_functions.create_structured_output_runnable",
"langchain_community.chat_models.ChatOpenAI"
] | [((453, 480), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (470, 480), False, 'import logging\n'), ((512, 552), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.DEBUG'}), '(level=logging.DEBUG)\n', (531, 552), False, 'import logging\n'), ((571, 594), 'logging.Stream... |
import logging
from langchain.chat_models import ChatOpenAI
from dreamsboard.dreams.builder_cosplay_code.base import StructuredDreamsStoryboard
from dreamsboard.dreams.dreams_personality_chain.base import StoryBoardDreamsGenerationChain
import langchain
from dreamsboard.engine.generate.code_generate import QueryProg... | [
"langchain.chat_models.ChatOpenAI"
] | [((545, 572), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (562, 572), False, 'import logging\n'), ((623, 646), 'logging.StreamHandler', 'logging.StreamHandler', ([], {}), '()\n', (644, 646), False, 'import logging\n'), ((806, 859), 'dreamsboard.engine.storage.storage_context.StorageCon... |
from model.chain_spec import ChainSpec, LLMChainSpec, SequentialChainSpec, CaseChainSpec, APIChainSpec, ReformatChainSpec, TransformChainSpec, VectorSearchChainSpec
from model.chain_revision import ChainRevision
from model.lang_chain_context import LangChainContext
from langchain.llms.fake import FakeListLLM
from model... | [
"langchain.llms.fake.FakeListLLM"
] | [((471, 620), 'model.chain_spec.LLMChainSpec', 'LLMChainSpec', ([], {'chain_id': '(1)', 'input_keys': "['input1', 'input2']", 'output_key': '"""output1"""', 'prompt': '"""prompt"""', 'llm_key': '"""llm_key"""', 'chain_type': '"""llm_chain_spec"""'}), "(chain_id=1, input_keys=['input1', 'input2'], output_key=\n 'outp... |
import openai
import os
import dotenv
from llama_index.agent.openai import OpenAIAgent
from llama_index.llms.azure_openai import AzureOpenAI
from llama_index.core.tools.tool_spec.load_and_search.base import LoadAndSearchToolSpec
from llama_index.tools.google import GoogleSearchToolSpec
from llama_index.tools.weather im... | [
"langchain.embeddings.OpenAIEmbeddings"
] | [((577, 597), 'dotenv.load_dotenv', 'dotenv.load_dotenv', ([], {}), '()\n', (595, 597), False, 'import dotenv\n'), ((615, 647), 'os.environ.get', 'os.environ.get', (['"""GOOGLE_API_KEY"""'], {}), "('GOOGLE_API_KEY')\n", (629, 647), False, 'import os\n'), ((664, 695), 'os.environ.get', 'os.environ.get', (['"""GOOGLE_CSE... |
"""
.. 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'), (... |
"""
.. 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'), (... |
"""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 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 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... |
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... |
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... |
"""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, ... |
"""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, ... |
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... |
"""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... |
"""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... |
"""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... |
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'], {}), "('... |
"""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, ... |
"""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, ... |
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 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 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'... |
"""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... |
"""
.. 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.utils.get_from_env",
"langchain.schema.Generation",
"langchain.load.dump.dumps",
"langchain.vectorstores.redis.Redis.from_existing_index",
"langchain.vectorstores.redis.Redis",
"langchain.load.load.loads"
] | [((1483, 1510), 'logging.getLogger', 'logging.getLogger', (['__file__'], {}), '(__file__)\n', (1500, 1510), False, 'import logging\n'), ((3955, 3973), 'sqlalchemy.ext.declarative.declarative_base', 'declarative_base', ([], {}), '()\n', (3971, 3973), False, 'from sqlalchemy.ext.declarative import declarative_base\n'), (... |
"""
.. 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.utils.get_from_env",
"langchain.schema.Generation",
"langchain.load.dump.dumps",
"langchain.vectorstores.redis.Redis.from_existing_index",
"langchain.vectorstores.redis.Redis",
"langchain.load.load.loads"
] | [((1483, 1510), 'logging.getLogger', 'logging.getLogger', (['__file__'], {}), '(__file__)\n', (1500, 1510), False, 'import logging\n'), ((3955, 3973), 'sqlalchemy.ext.declarative.declarative_base', 'declarative_base', ([], {}), '()\n', (3971, 3973), False, 'from sqlalchemy.ext.declarative import declarative_base\n'), (... |
"""
.. 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.utils.get_from_env",
"langchain.schema.Generation",
"langchain.load.dump.dumps",
"langchain.vectorstores.redis.Redis.from_existing_index",
"langchain.vectorstores.redis.Redis",
"langchain.load.load.loads"
] | [((1483, 1510), 'logging.getLogger', 'logging.getLogger', (['__file__'], {}), '(__file__)\n', (1500, 1510), False, 'import logging\n'), ((3955, 3973), 'sqlalchemy.ext.declarative.declarative_base', 'declarative_base', ([], {}), '()\n', (3971, 3973), False, 'from sqlalchemy.ext.declarative import declarative_base\n'), (... |
"""Utilities for running language models or Chains over datasets."""
from __future__ import annotations
import asyncio
import functools
import inspect
import itertools
import logging
import uuid
import warnings
from enum import Enum
from typing import (
TYPE_CHECKING,
Any,
Callable,
Coroutine,
Dic... | [
"langchain.schema.messages.messages_from_dict",
"langchain.smith.evaluation.string_run_evaluator.StringRunEvaluatorChain.from_run_and_data_type",
"langchain.evaluation.schema.EvaluatorType",
"langchain.callbacks.tracers.langchain.LangChainTracer",
"langchain.callbacks.tracers.evaluation.EvaluatorCallbackHan... | [((1500, 1527), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1517, 1527), False, 'import logging\n'), ((2799, 2816), 'urllib.parse.urlparse', 'urlparse', (['api_url'], {}), '(api_url)\n', (2807, 2816), False, 'from urllib.parse import urlparse, urlunparse\n'), ((27519, 27539), 'asyncio... |
"""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, Field, validator
import langchain
from langchain.callbacks import get_callback_manager
from la... | [
"langchain.callbacks.get_callback_manager"
] | [((646, 703), 'pydantic.Field', 'Field', ([], {'default_factory': 'get_callback_manager', 'exclude': '(True)'}), '(default_factory=get_callback_manager, exclude=True)\n', (651, 703), False, 'from pydantic import BaseModel, Field, validator\n'), ((738, 775), 'pydantic.Field', 'Field', ([], {'default_factory': '_get_verb... |
"""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, Field, validator
import langchain
from langchain.callbacks import get_callback_manager
from la... | [
"langchain.callbacks.get_callback_manager"
] | [((646, 703), 'pydantic.Field', 'Field', ([], {'default_factory': 'get_callback_manager', 'exclude': '(True)'}), '(default_factory=get_callback_manager, exclude=True)\n', (651, 703), False, 'from pydantic import BaseModel, Field, validator\n'), ((738, 775), 'pydantic.Field', 'Field', ([], {'default_factory': '_get_verb... |
"""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, Field, validator
import langchain
from langchain.callbacks import get_callback_manager
from la... | [
"langchain.callbacks.get_callback_manager"
] | [((646, 703), 'pydantic.Field', 'Field', ([], {'default_factory': 'get_callback_manager', 'exclude': '(True)'}), '(default_factory=get_callback_manager, exclude=True)\n', (651, 703), False, 'from pydantic import BaseModel, Field, validator\n'), ((738, 775), 'pydantic.Field', 'Field', ([], {'default_factory': '_get_verb... |
"""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, Field, validator
import langchain
from langchain.callbacks import get_callback_manager
from la... | [
"langchain.callbacks.get_callback_manager"
] | [((646, 703), 'pydantic.Field', 'Field', ([], {'default_factory': 'get_callback_manager', 'exclude': '(True)'}), '(default_factory=get_callback_manager, exclude=True)\n', (651, 703), False, 'from pydantic import BaseModel, Field, validator\n'), ((738, 775), 'pydantic.Field', 'Field', ([], {'default_factory': '_get_verb... |
from typing import Dict, List, Optional
from langchain.agents.load_tools import (
_EXTRA_LLM_TOOLS,
_EXTRA_OPTIONAL_TOOLS,
_LLM_TOOLS,
)
from langflow.custom import customs
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.tools.constants import (
ALL_TOOLS_NAMES,
CU... | [
"langchain.agents.load_tools._LLM_TOOLS.keys",
"langchain.agents.load_tools._EXTRA_LLM_TOOLS.keys",
"langchain.agents.load_tools._EXTRA_OPTIONAL_TOOLS.keys"
] | [((709, 811), 'langflow.template.field.base.TemplateField', 'TemplateField', ([], {'field_type': '"""str"""', 'required': '(True)', 'is_list': '(False)', 'show': '(True)', 'placeholder': '""""""', 'value': '""""""'}), "(field_type='str', required=True, is_list=False, show=True,\n placeholder='', value='')\n", (722, ... |
from typing import Dict, List, Optional
from langchain.agents.load_tools import (
_EXTRA_LLM_TOOLS,
_EXTRA_OPTIONAL_TOOLS,
_LLM_TOOLS,
)
from langflow.custom import customs
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.tools.constants import (
ALL_TOOLS_NAMES,
CU... | [
"langchain.agents.load_tools._LLM_TOOLS.keys",
"langchain.agents.load_tools._EXTRA_LLM_TOOLS.keys",
"langchain.agents.load_tools._EXTRA_OPTIONAL_TOOLS.keys"
] | [((709, 811), 'langflow.template.field.base.TemplateField', 'TemplateField', ([], {'field_type': '"""str"""', 'required': '(True)', 'is_list': '(False)', 'show': '(True)', 'placeholder': '""""""', 'value': '""""""'}), "(field_type='str', required=True, is_list=False, show=True,\n placeholder='', value='')\n", (722, ... |
from typing import Dict, List, Optional
from langchain.agents.load_tools import (
_EXTRA_LLM_TOOLS,
_EXTRA_OPTIONAL_TOOLS,
_LLM_TOOLS,
)
from langflow.custom import customs
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.tools.constants import (
ALL_TOOLS_NAMES,
CU... | [
"langchain.agents.load_tools._LLM_TOOLS.keys",
"langchain.agents.load_tools._EXTRA_LLM_TOOLS.keys",
"langchain.agents.load_tools._EXTRA_OPTIONAL_TOOLS.keys"
] | [((709, 811), 'langflow.template.field.base.TemplateField', 'TemplateField', ([], {'field_type': '"""str"""', 'required': '(True)', 'is_list': '(False)', 'show': '(True)', 'placeholder': '""""""', 'value': '""""""'}), "(field_type='str', required=True, is_list=False, show=True,\n placeholder='', value='')\n", (722, ... |
import langchain_visualizer # isort:skip # noqa: F401
import asyncio
import vcr_langchain as vcr
from fvalues import FValue
from langchain import PromptTemplate
from langchain.llms import OpenAI
# ========================== Start of langchain example code ==========================
# https://langchain.readthedocs.i... | [
"langchain_visualizer.visualize",
"langchain.llms.OpenAI",
"langchain.PromptTemplate"
] | [((434, 524), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['adjective']", 'template': '"""Tell me a {adjective} joke."""'}), "(input_variables=['adjective'], template=\n 'Tell me a {adjective} joke.')\n", (448, 524), False, 'from langchain import PromptTemplate\n'), ((837, 855), 'vcr_lang... |
# coding=utf-8
import json
import hashlib
from datetime import datetime
import os
import time
import openai
import flet as ft
import re
import shutil
from flet import (
ElevatedButton,
FilePicker,
FilePickerResultEvent,
Page,
Row,
Text,
icons,
)
from prompt_engineering imp... | [
"langchain.chains.summarize.load_summarize_chain",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.llms.OpenAI",
"langchain.docstore.document.Document",
"langchain.prompts.PromptTemplate"
] | [((6345, 6470), 'openai.Completion.create', 'openai.Completion.create', ([], {'model': '"""text-ada-001"""', 'prompt': 'f"""你要总结这一文本的关键词，并以python列表的形式返回数个关键词字符串:{content}。"""', 'temperature': '(0)'}), "(model='text-ada-001', prompt=\n f'你要总结这一文本的关键词，并以python列表的形式返回数个关键词字符串:{content}。', temperature=0)\n", (6369, 6470... |
import os
import langchain.text_splitter
from langchain import PromptTemplate, LLMChain
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.llms import LlamaCpp
try:
from extensions.telegram_bot.source.generators.ab... | [
"langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler",
"langchain.llms.LlamaCpp",
"langchain.LLMChain",
"langchain.PromptTemplate"
] | [((905, 1003), 'langchain.llms.LlamaCpp', 'LlamaCpp', ([], {'model_path': 'model_path', 'n_ctx': 'n_ctx', 'callback_manager': 'callback_manager', 'verbose': '(True)'}), '(model_path=model_path, n_ctx=n_ctx, callback_manager=\n callback_manager, verbose=True)\n', (913, 1003), False, 'from langchain.llms import LlamaC... |
import os
import openai
from dotenv import load_dotenv
from langchain.chat_models import AzureChatOpenAI
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.callbacks.base import BaseCallbackHandler
from langchain.vectorstores import FAISS
from langchain.chain... | [
"langchain.document_loaders.UnstructuredWordDocumentLoader",
"langchain.document_loaders.UnstructuredFileLoader",
"langchain.vectorstores.FAISS.load_local",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.document_loaders.UnstructuredPowerPointLoader",
"langchain.vectorstores.FAISS.sa... | [((4507, 4520), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (4518, 4520), False, 'from dotenv import load_dotenv\n'), ((5613, 5676), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {'deployment': 'embedding_deployment', 'chunk_size': '(1)'}), '(deployment=embedding_deployment, chunk_size=1)... |
import os
import re
import langchain
import paperqa
import paperscraper
from langchain.base_language import BaseLanguageModel
from langchain.tools import BaseTool
from pypdf.errors import PdfReadError
def paper_scraper(search: str, pdir: str = "query") -> dict:
try:
return paperscraper.search_papers(sear... | [
"langchain.prompts.PromptTemplate",
"langchain.chains.llm.LLMChain"
] | [((419, 753), '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 tha... |
"""
Class for Langchain chain, this chain makes a request to OpenAI to provide information
in a given location and time period.
"""
import os
import logging
from pathlib import Path
import langchain
PROMPT_STRING = """
You just gave historical information for {location} around the time period of {time_period} and \n... | [
"langchain.OpenAI",
"langchain.PromptTemplate"
] | [((1201, 1272), 'langchain.PromptTemplate', 'langchain.PromptTemplate', ([], {'input_variables': 'input', 'template': 'PROMPT_STRING'}), '(input_variables=input, template=PROMPT_STRING)\n', (1225, 1272), False, 'import langchain\n'), ((1512, 1587), 'langchain.OpenAI', 'langchain.OpenAI', ([], {'openai_api_key': 'self.o... |
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from langchain.chains import LLMChain
from langchain.prompts.few_shot import FewShotPromptTemplate
def get_prompt(is_zh: bool = False, sydney: bool = False) -> 'FewShotPromptTemplate':
from langchain.prompts.few_shot import FewShotPromptTemplate
fro... | [
"langchain.chains.LLMChain",
"langchain.prompts.prompt.PromptTemplate",
"langchain.llms.OpenAIChat",
"langchain.prompts.few_shot.FewShotPromptTemplate"
] | [((400, 498), 'langchain.prompts.prompt.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['question', 'answer']", 'template': '"""Q: {question}\n{answer}"""'}), '(input_variables=[\'question\', \'answer\'], template=\n """Q: {question}\n{answer}""")\n', (414, 498), False, 'from langchain.prompts.prompt i... |
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores.faiss import FAISS
from langchain.embeddings import OpenAIEmbeddings
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from PyP... | [
"langchain.text_splitter.CharacterTextSplitter",
"langchain.memory.ConversationBufferMemory",
"langchain.chat_models.ChatOpenAI",
"langchain.vectorstores.faiss.FAISS.from_texts",
"langchain.embeddings.OpenAIEmbeddings"
] | [((1580, 1607), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (1589, 1607), False, 'import os\n'), ((1282, 1379), 'langchain.text_splitter.CharacterTextSplitter', 'CharacterTextSplitter', ([], {'separator': '"""\n"""', 'chunk_size': '(800)', 'chunk_overlap': '(200)', 'length_function... |
import zipfile
from langchain.chat_models import ChatOpenAI
from langchain.schema import (
HumanMessage,
SystemMessage
)
import langchain
from langchain.cache import SQLiteCache
langchain.llm_cache = SQLiteCache(
database_path=".langchain.db"
) # caches queries that are the same.
def generate_code(ques... | [
"langchain.schema.HumanMessage",
"langchain.schema.SystemMessage",
"langchain.chat_models.ChatOpenAI",
"langchain.cache.SQLiteCache"
] | [((211, 253), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {'database_path': '""".langchain.db"""'}), "(database_path='.langchain.db')\n", (222, 253), False, 'from langchain.cache import SQLiteCache\n'), ((621, 688), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '(0)', 'model': 'model_typ... |
import langchain
from langchain.chat_models import ChatOpenAI
from langchain_core.tools import Tool
langchain.verbose = True
langchain.debug = True
def get_chat():
return ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
def my_super_func(params):
return 42
if __name__ == "__main__":
tools = [
... | [
"langchain_core.tools.Tool.from_function",
"langchain.chat_models.ChatOpenAI"
] | [((178, 231), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'model_name': '"""gpt-3.5-turbo"""', 'temperature': '(0)'}), "(model_name='gpt-3.5-turbo', temperature=0)\n", (188, 231), False, 'from langchain.chat_models import ChatOpenAI\n'), ((326, 427), 'langchain_core.tools.Tool.from_function', 'Tool.from_fun... |
import os
import tkinter as tk
from tkinter import Label, Entry, Button, Text, Scrollbar
import langchain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chat_models import ChatOpenAI
class ProjectEvaluatorApp:
def __init__(self, root):
self.root = root
self.root.... | [
"langchain.chat_models.ChatOpenAI"
] | [((3005, 3012), 'tkinter.Tk', 'tk.Tk', ([], {}), '()\n', (3010, 3012), True, 'import tkinter as tk\n'), ((380, 446), 'tkinter.Label', 'Label', (['root'], {'text': '"""Rate your coding ability on a scale of 1 to 5:"""'}), "(root, text='Rate your coding ability on a scale of 1 to 5:')\n", (385, 446), False, 'from tkinter... |
from __future__ import annotations
from collections import OrderedDict
from typing import Any, Dict, List, Optional, Tuple
import langchain
import numpy as np
import orjson
import pandas as pd
from langchain.cache import InMemoryCache
from peewee import ModelSelect, fn
from .constants import *
from .orm import Knowl... | [
"langchain.cache.InMemoryCache"
] | [((506, 521), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (519, 521), False, 'from langchain.cache import InMemoryCache\n'), ((11527, 11540), 'collections.OrderedDict', 'OrderedDict', ([], {}), '()\n', (11538, 11540), False, 'from collections import OrderedDict\n'), ((4157, 4207), 'orjson.dumps'... |
import os
try:
from genai.credentials import Credentials
from genai.schemas import GenerateParams
from genai.extensions.langchain import LangChainInterface
from langchain import PromptTemplate
from langchain.chains import LLMChain, SimpleSequentialChain
except ImportError:
raise ImportError("Could not... | [
"langchain.chains.LLMChain",
"langchain.chains.SimpleSequentialChain",
"langchain.PromptTemplate"
] | [((520, 548), 'os.getenv', 'os.getenv', (['"""GENAI_KEY"""', 'None'], {}), "('GENAI_KEY', None)\n", (529, 548), False, 'import os\n'), ((559, 587), 'os.getenv', 'os.getenv', (['"""GENAI_API"""', 'None'], {}), "('GENAI_API', None)\n", (568, 587), False, 'import os\n'), ((634, 676), 'genai.credentials.Credentials', 'Cred... |
#%% Import Flask and create an app object
import config
from dotenv import load_dotenv
load_dotenv()
import os
import json
import asyncio
import openai
import pprint as pp
import markdown
# openai.api_key = os.getenv("OPENAI_API_KEY")
# os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
# Import Flask and cre... | [
"langchain.agents.initialize_agent",
"langchain.agents.agent_toolkits.PlayWrightBrowserToolkit.from_browser",
"langchain.agents.load_tools",
"langchain.chat_models.ChatOpenAI"
] | [((87, 100), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (98, 100), False, 'from dotenv import load_dotenv\n'), ((403, 418), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (408, 418), False, 'from flask import Flask, render_template, request, jsonify\n'), ((1220, 1265), 'langchain.chat_models.Ch... |
import torch
from langchain.llms.base import LLM
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from llama_index import LangchainEmbedding
from llama_index import SimpleDirectoryReader, LLMPredictor, PromptHelper, GPTSimpleVectorIndex
from peft import PeftModel
from transformers import LlamaTo... | [
"langchain.embeddings.huggingface.HuggingFaceEmbeddings"
] | [((460, 505), 'transformers.LlamaTokenizer.from_pretrained', 'LlamaTokenizer.from_pretrained', (['hf_model_path'], {}), '(hf_model_path)\n', (490, 505), False, 'from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig\n'), ((517, 606), 'transformers.LlamaForCausalLM.from_pretrained', 'LlamaForCausalL... |
import langchain.graphs.neo4j_graph as neo4j_graph
import os
import sys
import ast
sys.path.append('backendPython')
from llms import *
from dotenv import load_dotenv, find_dotenv
load_dotenv(find_dotenv()) # read local .env file
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate, FewShot... | [
"langchain.chains.LLMChain",
"langchain.graphs.neo4j_graph.Neo4jGraph",
"langchain.prompts.FewShotPromptTemplate",
"langchain.prompts.PromptTemplate"
] | [((83, 115), 'sys.path.append', 'sys.path.append', (['"""backendPython"""'], {}), "('backendPython')\n", (98, 115), False, 'import sys\n'), ((392, 526), 'langchain.graphs.neo4j_graph.Neo4jGraph', 'neo4j_graph.Neo4jGraph', ([], {'url': "os.environ['NEO4J_URI']", 'username': "os.environ['NEO4J_USERNAME']", 'password': "o... |
# Import langchain modules
from langchain.memory import Memory, ConversationBufferMemory
from langchain.agents import BaseMultiActionAgent, AgentExecutor
# Import other modules and classes
from research_agent import ResearchAgent
class ConversationMemory(Memory):
def __init__(self):
# Initialize ... | [
"langchain.memory.ConversationBufferMemory"
] | [((505, 531), 'langchain.memory.ConversationBufferMemory', 'ConversationBufferMemory', ([], {}), '()\n', (529, 531), False, 'from langchain.memory import Memory, ConversationBufferMemory\n'), ((1122, 1198), 'research_agent.ResearchAgent', 'ResearchAgent', (['prompt_template', 'language_model', 'stop_sequence', 'output_... |
from langchain.llms import HuggingFacePipeline
import langchain
from ingest import create_vector_db
from langchain.cache import InMemoryCache
from langchain.schema import prompt
from langchain.chains import RetrievalQA
from langchain.callbacks import StdOutCallbackHandler
from langchain import PromptTemplate
from trans... | [
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.llms.HuggingFacePipeline",
"langchain.callbacks.StdOutCallbackHandler",
"langchain.cache.InMemoryCache",
"langchain.PromptTemplate"
] | [((448, 463), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (461, 463), False, 'from langchain.cache import InMemoryCache\n'), ((754, 777), 'langchain.callbacks.StdOutCallbackHandler', 'StdOutCallbackHandler', ([], {}), '()\n', (775, 777), False, 'from langchain.callbacks import StdOutCallbackHand... |
"""
implement the actions as tools so we can validate inputs
"""
import langchain
from langchain.schema import AgentAction, AgentFinish
from langchain.schema.output import LLMResult
from langchain.agents import AgentType, initialize_agent
from langchain.tools import Tool, StructuredTool
from langchain.tools.b... | [
"langchain.agents.initialize_agent",
"langchain.tools.base.ToolException",
"langchain.tools.StructuredTool.from_function",
"langchain.chat_models.ChatOpenAI"
] | [((11966, 12189), 'langchain.tools.StructuredTool.from_function', 'StructuredTool.from_function', ([], {'name': '"""click"""', 'func': 'click', 'description': '"""This action clicks on an element specified by the element_id in the input."""', 'return_direct': 'SHOULD_RETURN_DIRECT', 'handle_tool_error': '_handle_error'... |
import os
import streamlit as st
import langchain.memory
import langchain.llms
import langchain.chains
from apikey import apikey
from langchain.memory import ConversationBufferMemory
from langchain.memory import ChatMessageHistory
from langchain.llms import OpenAI
from langchain.chains import ConversationChain
from lan... | [
"langchain.chains.ConversationChain",
"langchain.memory.ConversationBufferMemory",
"langchain.llms.OpenAI",
"langchain.memory.ChatMessageHistory"
] | [((447, 467), 'langchain.memory.ChatMessageHistory', 'ChatMessageHistory', ([], {}), '()\n', (465, 467), False, 'from langchain.memory import ChatMessageHistory\n'), ((558, 603), 'langchain.memory.ConversationBufferMemory', 'ConversationBufferMemory', ([], {'chat_memory': 'history'}), '(chat_memory=history)\n', (582, 6... |
import sys
import getpass
from dotenv import load_dotenv, dotenv_values
import pandas as pd
from IPython.display import display, Markdown, Latex, HTML, JSON
import langchain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from cmd import PROMPT
imp... | [
"langchain.chains.LLMChain",
"langchain.llms.OpenAI"
] | [((394, 457), 'sys.path.append', 'sys.path.append', (['"""/Users/dovcohen/Documents/Projects/AI/NL2SQL"""'], {}), "('/Users/dovcohen/Documents/Projects/AI/NL2SQL')\n", (409, 457), False, 'import sys\n'), ((6238, 6252), 'pandas.DataFrame', 'pd.DataFrame', ([], {}), '()\n', (6250, 6252), True, 'import pandas as pd\n'), (... |
"""Web base loader class."""
import langchain_community.document_loaders as dl
from langchain.docstore.document import Document
import asyncio
import datetime
from io import StringIO
import logging
import re
import warnings
from typing import Any, AsyncGenerator, Dict, Iterator, List, Optional, Tuple, Union
import insp... | [
"langchain.docstore.document.Document"
] | [((914, 951), 're.sub', 're.sub', (['pattern', '"""\\\\1"""', 'markdown_text'], {}), "(pattern, '\\\\1', markdown_text)\n", (920, 951), False, 'import re\n'), ((1742, 1788), 're.sub', 're.sub', (['"""(\\\\n){4,}"""', '"""\n\n\n"""', 'simplified_text'], {}), "('(\\\\n){4,}', '\\n\\n\\n', simplified_text)\n", (1748, 1788... |
import streamlit as st
import os
# Utils
import time
from typing import List
# Langchain
import langchain
from pydantic import BaseModel
from vertexai.language_models import TextGenerationModel
# Vertex AI
from langchain.llms import VertexAI
from llm_experiments.utils import here
os.environ["GOOGLE_APPLICATION_CRED... | [
"langchain.llms.VertexAI"
] | [((400, 518), 'langchain.llms.VertexAI', 'VertexAI', ([], {'model_name': '"""text-bison@001"""', 'max_output_tokens': '(1024)', 'temperature': '(0.3)', 'top_p': '(0.8)', 'top_k': '(40)', 'verbose': '(True)'}), "(model_name='text-bison@001', max_output_tokens=1024, temperature=\n 0.3, top_p=0.8, top_k=40, verbose=Tru... |
#Multi-agent decentralized speaker selection:
'''
This notebook showcases how to implement a multi-agent simulation without a fixed schedule for who speaks when.
Instead the agents decide for themselves who speaks. We can implement this by having each agent bid to speak.
Whichever agent’s bid is the highest gets to ... | [
"langchain.PromptTemplate",
"langchain.schema.SystemMessage",
"langchain.chat_models.ChatOpenAI",
"langchain.schema.HumanMessage"
] | [((4907, 5006), 'langchain.schema.SystemMessage', 'SystemMessage', ([], {'content': '"""You can add detail to the description of each presidential candidate."""'}), "(content=\n 'You can add detail to the description of each presidential candidate.')\n", (4920, 5006), False, 'from langchain.schema import AIMessage, ... |
import torch
from transformers import BitsAndBytesConfig
from langchain import HuggingFacePipeline
from langchain import PromptTemplate, LLMChain
from pathlib import Path
import langchain
import json
import chromadb
from chromadb.config import Settings
from langchain.llms import HuggingFacePipeline
from langchain.docum... | [
"langchain.document_loaders.DirectoryLoader",
"langchain.embeddings.HuggingFaceEmbeddings",
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.llms.HuggingFacePipeline",
"langchain.vectorstores.Chroma.from_documents"
] | [((1266, 1371), 'langchain.document_loaders.DirectoryLoader', 'DirectoryLoader', (['rootdir'], {'glob': '"""**/*.txt"""', 'loader_cls': 'TextLoader', 'loader_kwargs': "{'encoding': 'utf-8'}"}), "(rootdir, glob='**/*.txt', loader_cls=TextLoader,\n loader_kwargs={'encoding': 'utf-8'})\n", (1281, 1371), False, 'from la... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# 配置环境变量
import os
from LangChain_study.common import ChatParam
os.environ["OPENAI_API_KEY"] = ChatParam.OPENAI_API_KEY
os.environ["OPENAI_API_BASE"] = ChatParam.OPENAI_API_BASE
# 初始化LLM模型
import langchain
from langchain.llms import OpenAI
llm = OpenAI(model_name="text-d... | [
"langchain.cache.InMemoryCache",
"langchain.llms.OpenAI"
] | [((295, 348), 'langchain.llms.OpenAI', 'OpenAI', ([], {'model_name': '"""text-davinci-002"""', 'n': '(2)', 'best_of': '(2)'}), "(model_name='text-davinci-002', n=2, best_of=2)\n", (301, 348), False, 'from langchain.llms import OpenAI\n'), ((457, 472), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', ... |
import pandas as pd
from langchain.document_loaders.word_document import Docx2txtLoader
# this does not work, some how, I can not install some of its requirement libs.
from langchain.document_loaders.word_document import UnstructuredWordDocumentLoader
# from langchain.text_splitter import CharacterTextSplitter
import l... | [
"langchain.text_splitter.CharacterTextSplitter",
"langchain.document_loaders.word_document.Docx2txtLoader"
] | [((477, 594), 'langchain.document_loaders.word_document.Docx2txtLoader', 'Docx2txtLoader', (['"""../../data/raw/6. HR.03.V3.2023. Nội quy Lao động_Review by Labor Department - Final.DOCX"""'], {}), "(\n '../../data/raw/6. HR.03.V3.2023. Nội quy Lao động_Review by Labor Department - Final.DOCX'\n )\n", (491, 594),... |
"""Create a ConversationalRetrievalChain for question/answering."""
import imp
import logging
import sys
from typing import Union
from langchain.callbacks.base import BaseCallbackManager, BaseCallbackHandler
from langchain.callbacks.tracers import LangChainTracer
from langchain.chains import ConversationalRetrievalCha... | [
"langchain.callbacks.base.BaseCallbackManager",
"langchain.chains.question_answering.load_qa_chain",
"langchain.callbacks.tracers.LangChainTracer",
"langchain.chains.llm.LLMChain"
] | [((1450, 1473), 'langchain.callbacks.base.BaseCallbackManager', 'BaseCallbackManager', (['[]'], {}), '([])\n', (1469, 1473), False, 'from langchain.callbacks.base import BaseCallbackManager, BaseCallbackHandler\n'), ((1497, 1536), 'langchain.callbacks.base.BaseCallbackManager', 'BaseCallbackManager', (['[rephrase_handl... |
# Drive Imports
import yaml
import asyncio
from deferred_imports import langchain, imports_done
import webbrowser
# Global Variables
dictionaries_folder_path=""
structure_dictionary_path=""
information_dictionary_path=""
folder_dictionary_path=""
# Information Mapping
async def a_update_mapping(your_dictionary,over... | [
"langchain.prompts.chat.SystemMessagePromptTemplate.from_template",
"langchain.chat_models.ChatOpenAI",
"langchain.vectorstores.Chroma.from_documents",
"langchain.prompts.chat.HumanMessagePromptTemplate.from_template",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.prompts.chat.ChatPromptTemplate.fro... | [((1100, 1119), 'deferred_imports.imports_done.wait', 'imports_done.wait', ([], {}), '()\n', (1117, 1119), False, 'from deferred_imports import langchain, imports_done\n'), ((2780, 2838), 'langchain.prompts.chat.SystemMessagePromptTemplate.from_template', 'SystemMessagePromptTemplate.from_template', (['system_template'... |
import tensorflow
import dotenv
import transformers
from tensorflow import keras
from dotenv import find_dotenv, load_dotenv
from transformers import pipeline
import langchain
from langchain import PromptTemplate, LLMChain, OpenAI
import requests
import os
import openai
import streamlit as st
HUGGINGFACEHUB_API_TOKE... | [
"langchain.OpenAI",
"langchain.PromptTemplate"
] | [((324, 361), 'os.getenv', 'os.getenv', (['"""HUGGINGFACEHUB_API_TOKEN"""'], {}), "('HUGGINGFACEHUB_API_TOKEN')\n", (333, 361), False, 'import os\n'), ((379, 406), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (388, 406), False, 'import os\n'), ((420, 433), 'dotenv.find_dotenv', 'fin... |
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from redundant_filter_retriever import RedundantFilterRetriever
from dotenv import load_dotenv
import langchain
langchain.debug = True
load_... | [
"langchain.vectorstores.Chroma",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.chat_models.ChatOpenAI"
] | [((315, 328), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (326, 328), False, 'from dotenv import load_dotenv\n'), ((337, 349), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {}), '()\n', (347, 349), False, 'from langchain.chat_models import ChatOpenAI\n'), ((363, 381), 'langchain.embeddings.OpenAIEmb... |
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