code stringlengths 141 97.3k | apis listlengths 1 24 | extract_api stringlengths 113 214k |
|---|---|---|
"""
This script demonstrates how to use the llama_index library to create and query a vector store index.
It loads documents from a directory, creates an index, and allows querying the index.
usage: python hello_persist.py "What is the author's name and job now?"
"""
import os
import sys
import argparse
import loggin... | [
"llama_index.core.StorageContext.from_defaults",
"llama_index.core.VectorStoreIndex.from_documents",
"llama_index.core.SimpleDirectoryReader",
"llama_index.core.load_index_from_storage",
"llama_index.embeddings.openai.OpenAIEmbedding"
] | [((1804, 1870), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Query a vector store index."""'}), "(description='Query a vector store index.')\n", (1827, 1870), False, 'import argparse\n'), ((628, 641), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (639, 641), False, 'from doten... |
import streamlit as st
from llama_hub.youtube_transcript import YoutubeTranscriptReader
from llama_hub.youtube_transcript import is_youtube_video
from llama_index import (
VectorStoreIndex,
StorageContext,
load_index_from_storage,
)
from llama_index.prompts import ChatMessage, MessageRole
from llama_index.... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.StorageContext.from_defaults",
"llama_index.load_index_from_storage",
"llama_index.tools.ToolMetadata"
] | [((1466, 1499), 'streamlit.session_state.get', 'st.session_state.get', (['"""video_url"""'], {}), "('video_url')\n", (1486, 1499), True, 'import streamlit as st\n'), ((1684, 1749), 'streamlit.header', 'st.header', (['f"""This page has run {st.session_state.counter} times."""'], {}), "(f'This page has run {st.session_st... |
import os
from typing import Any, Optional
from llama_index.core.readers.base import BasePydanticReader
from llama_index.core.schema import Document
DEFAULT_TOKEN_JSON_PATH = 'token.json'
DEFAULT_SERVICE_ACCOUNT_JSON_PATH = 'service_account.json'
DEFAULT_CREDENTIALS_JSON_PATH = 'credentials.json'
HEADING_STYLE_TEMPL... | [
"llama_index.core.schema.Document"
] | [((3044, 3098), 'googleapiclient.discovery.build', 'discovery.build', (['"""docs"""', '"""v1"""'], {'credentials': 'credentials'}), "('docs', 'v1', credentials=credentials)\n", (3059, 3098), True, 'import googleapiclient.discovery as discovery\n'), ((4002, 4038), 'os.path.exists', 'os.path.exists', (['self.token_json_p... |
import utils
import os
import openai
import sys
from dotenv import load_dotenv
load_dotenv()
api_key = os.getenv("API_KEY")
openai.api_key
os.environ['OPENAI_API_KEY'] = api_key
#
# examples
# https://github.com/kevintsai/Building-and-Evaluating-Advanced-RAG-Applications
#
# SimpleDirectoryReader is a class that rea... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.llms.OpenAI",
"llama_index.ServiceContext.from_defaults",
"llama_index.SimpleDirectoryReader"
] | [((82, 95), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (93, 95), False, 'from dotenv import load_dotenv\n'), ((106, 126), 'os.getenv', 'os.getenv', (['"""API_KEY"""'], {}), "('API_KEY')\n", (115, 126), False, 'import os\n'), ((1774, 1825), 'llama_index.llms.OpenAI', 'OpenAI', ([], {'model': '"""gpt-4-1106-p... |
# Import the necessary libraries
import random
import time
from llama_index.llms import OpenAI
import streamlit as st
from llama_index import VectorStoreIndex, ServiceContext, StorageContext, set_global_service_context
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from llama_index.embeddings import... | [
"llama_index.vector_stores.ChromaVectorStore",
"llama_index.llms.OpenAI",
"llama_index.indices.prompt_helper.PromptHelper",
"llama_index.indices.vector_store.retrievers.VectorIndexRetriever",
"llama_index.node_parser.SentenceSplitter",
"llama_index.set_global_service_context"
] | [((855, 895), 'streamlit.title', 'st.title', (['"""🦜🔗 Tourism Assistant Chatbot"""'], {}), "('🦜🔗 Tourism Assistant Chatbot')\n", (863, 895), True, 'import streamlit as st\n'), ((5721, 5781), 'llama_index.set_global_service_context', 'set_global_service_context', (['st.session_state.service_context'], {}), '(st.sess... |
import os
import json
import logging
import sys
import requests
from dotenv import load_dotenv
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
from llama_index.core import VectorStoreIndex, Document
from llama_index.tools.brave_search import BraveSearchToolSpec
from llama_index.readers.we... | [
"llama_index.core.Document",
"llama_index.core.VectorStoreIndex.from_documents",
"llama_index.tools.brave_search.BraveSearchToolSpec"
] | [((496, 569), 'urllib3.util.retry.Retry', 'Retry', ([], {'total': '(5)', 'backoff_factor': '(0.1)', 'status_forcelist': '[500, 502, 503, 504]'}), '(total=5, backoff_factor=0.1, status_forcelist=[500, 502, 503, 504])\n', (501, 569), False, 'from urllib3.util.retry import Retry\n'), ((675, 733), 'logging.basicConfig', 'l... |
import qdrant_client
from llama_index.llms import Ollama
from llama_index import (
VectorStoreIndex,
ServiceContext,
)
from llama_index.vector_stores.qdrant import QdrantVectorStore
# re-initialize the vector store
client = qdrant_client.QdrantClient(
path="./qdrant_data"
)
vector_store = QdrantVectorStore... | [
"llama_index.vector_stores.qdrant.QdrantVectorStore",
"llama_index.VectorStoreIndex.from_vector_store",
"llama_index.ServiceContext.from_defaults",
"llama_index.llms.Ollama"
] | [((233, 281), 'qdrant_client.QdrantClient', 'qdrant_client.QdrantClient', ([], {'path': '"""./qdrant_data"""'}), "(path='./qdrant_data')\n", (259, 281), False, 'import qdrant_client\n'), ((303, 361), 'llama_index.vector_stores.qdrant.QdrantVectorStore', 'QdrantVectorStore', ([], {'client': 'client', 'collection_name': ... |
import os
from llama_index.core import StorageContext, VectorStoreIndex, load_index_from_storage
from llama_index.readers.file import PDFReader
# def get_index(data, index_name):
# index = None
# if not os.path.exists(index_name):
# print('Building index', index_name)
# index = VectorStoreIn... | [
"llama_index.readers.file.PDFReader",
"llama_index.core.StorageContext.from_defaults",
"llama_index.core.VectorStoreIndex.from_documents"
] | [((1404, 1440), 'os.path.join', 'os.path.join', (['"""data"""', '"""Malaysia.pdf"""'], {}), "('data', 'Malaysia.pdf')\n", (1416, 1440), False, 'import os\n'), ((952, 963), 'llama_index.readers.file.PDFReader', 'PDFReader', ([], {}), '()\n', (961, 963), False, 'from llama_index.readers.file import PDFReader\n'), ((1030,... |
from llama_index.retrievers import BaseRetriever
from llama_index import QueryBundle
from llama_index.schema import NodeWithScore
from llama_index.vector_stores import VectorStoreQuery
from typing import List, Sequence, Any
from llama_index.tools import BaseTool, adapt_to_async_tool
from llama_index import Document, Ve... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.tools.adapt_to_async_tool",
"llama_index.retrievers.VectorIndexRetriever",
"llama_index.Document"
] | [((1143, 1228), 'llama_index.retrievers.VectorIndexRetriever', 'VectorIndexRetriever', ([], {'index': 'self._index', 'similarity_top_k': 'self._similarity_top_k'}), '(index=self._index, similarity_top_k=self._similarity_top_k\n )\n', (1163, 1228), False, 'from llama_index.retrievers import VectorIndexRetriever\n'), ... |
from typing import List
from fastapi.responses import StreamingResponse
from app.utils.json import json_to_model
from app.utils.index import get_index
from fastapi import APIRouter, Depends, HTTPException, Request, status
from llama_index import VectorStoreIndex
from llama_index.llms.base import MessageRole, ChatMess... | [
"llama_index.llms.base.ChatMessage"
] | [((374, 385), 'fastapi.APIRouter', 'APIRouter', ([], {}), '()\n', (383, 385), False, 'from fastapi import APIRouter, Depends, HTTPException, Request, status\n'), ((798, 816), 'fastapi.Depends', 'Depends', (['get_index'], {}), '(get_index)\n', (805, 816), False, 'from fastapi import APIRouter, Depends, HTTPException, Re... |
import os
from llama_index import (
VectorStoreIndex,
SimpleDirectoryReader,
StorageContext,
load_index_from_storage,
)
BOT_NAME = os.environ["BOT_NAME"]
def construct_index(directory_data, directory_index, force_reload=False):
# check if storage already exists
if not os.path.exists(directory... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.StorageContext.from_defaults",
"llama_index.load_index_from_storage",
"llama_index.SimpleDirectoryReader"
] | [((541, 583), 'llama_index.VectorStoreIndex.from_documents', 'VectorStoreIndex.from_documents', (['documents'], {}), '(documents)\n', (572, 583), False, 'from llama_index import VectorStoreIndex, SimpleDirectoryReader, StorageContext, load_index_from_storage\n'), ((871, 928), 'llama_index.StorageContext.from_defaults',... |
from llama_index import SimpleDirectoryReader, ServiceContext, VectorStoreIndex
from llama_index.llms import OpenAI, ChatMessage, MessageRole
from llama_index.chat_engine.condense_plus_context import CondensePlusContextChatEngine
from dotenv import load_dotenv
import os
load_dotenv()
vector_index = None
history = []
... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.ServiceContext.from_defaults",
"llama_index.chat_engine.condense_plus_context.CondensePlusContextChatEngine.from_defaults",
"llama_index.SimpleDirectoryReader",
"llama_index.llms.OpenAI",
"llama_index.llms.ChatMessage"
] | [((271, 284), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (282, 284), False, 'from dotenv import load_dotenv\n'), ((379, 425), 'llama_index.llms.OpenAI', 'OpenAI', ([], {'model': '"""gpt-3.5-turbo"""', 'temperature': '(0.5)'}), "(model='gpt-3.5-turbo', temperature=0.5)\n", (385, 425), False, 'from llama_inde... |
import streamlit as st
import openai
from llama_index.storage.docstore import SimpleDocumentStore
from llama_index.vector_stores import FaissVectorStore
from llama_index.storage.index_store import SimpleIndexStore
from llama_index import load_index_from_storage
from llama_index.storage.storage_context import StorageCo... | [
"llama_index.storage.docstore.SimpleDocumentStore.from_persist_dir",
"llama_index.vector_stores.FaissVectorStore.from_persist_dir",
"llama_index.query_engine.CitationQueryEngine.from_args",
"llama_index.storage.index_store.SimpleIndexStore.from_persist_dir",
"llama_index.load_index_from_storage"
] | [((983, 1050), 'streamlit.set_page_config', 'st.set_page_config', ([], {'layout': '"""wide"""', 'page_title': '"""Precedents Database"""'}), "(layout='wide', page_title='Precedents Database')\n", (1001, 1050), True, 'import streamlit as st\n'), ((1056, 1084), 'streamlit.title', 'st.title', (['"""Query Precedents"""'], ... |
import streamlit as st
from dotenv import load_dotenv
load_dotenv()
import os
import tempfile
from llama_index import SimpleDirectoryReader, StorageContext, LLMPredictor
from llama_index import VectorStoreIndex
from llama_index import ServiceContext
from llama_index.embeddings.langchain import LangchainEmbedding
from... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.ServiceContext.from_defaults",
"llama_index.StorageContext.from_defaults",
"llama_index.SimpleDirectoryReader"
] | [((55, 68), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (66, 68), False, 'from dotenv import load_dotenv\n'), ((860, 890), 'llama_index.StorageContext.from_defaults', 'StorageContext.from_defaults', ([], {}), '()\n', (888, 890), False, 'from llama_index import SimpleDirectoryReader, StorageContext, LLMPredic... |
from dotenv import load_dotenv
import os
import streamlit as st
import pandas as pd
from llama_index.core.query_engine import PandasQueryEngine
from llama_index.core.tools import QueryEngineTool, ToolMetadata
from llama_index.core.agent import ReActAgent
from llama_index.llms.openai import OpenAI
from prompts import ... | [
"llama_index.core.agent.ReActAgent.from_tools",
"llama_index.core.query_engine.PandasQueryEngine",
"llama_index.core.tools.ToolMetadata",
"llama_index.llms.openai.OpenAI"
] | [((468, 481), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (479, 481), False, 'from dotenv import load_dotenv\n'), ((501, 539), 'os.path.join', 'os.path.join', (['"""data"""', '"""population.csv"""'], {}), "('data', 'population.csv')\n", (513, 539), False, 'import os\n'), ((556, 584), 'pandas.read_csv', 'pd.r... |
# general imports
from constants import *
# streamlit imports
import streamlit as st
from utils import *
from streamlit_lottie import st_lottie
# llama index imports
import openai
from llama_index import (
VectorStoreIndex,
download_loader,
ServiceContext,
set_global_service_context,
)
from llama_inde... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.ServiceContext.from_defaults",
"llama_index.llms.OpenAI",
"llama_index.download_loader",
"llama_index.set_global_service_context"
] | [((1017, 1080), 'llama_index.llms.OpenAI', 'OpenAI', ([], {'model': '"""gpt-4-1106-preview"""', 'system_prompt': 'system_prompt'}), "(model='gpt-4-1106-preview', system_prompt=system_prompt)\n", (1023, 1080), False, 'from llama_index.llms import OpenAI\n'), ((1187, 1248), 'llama_index.ServiceContext.from_defaults', 'Se... |
import pathlib
import tempfile
from io import BytesIO
import openai
import streamlit as st
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.core.chat_engine import ContextChatEngine
from llama_index.llms.openai import OpenAI
from sidebar import sidebar_params
st.set_page_config(p... | [
"llama_index.core.VectorStoreIndex.from_documents",
"llama_index.core.SimpleDirectoryReader",
"llama_index.llms.openai.OpenAI"
] | [((300, 386), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""Chat with Documents"""', 'layout': '"""wide"""', 'page_icon': '"""🔥"""'}), "(page_title='Chat with Documents', layout='wide',\n page_icon='🔥')\n", (318, 386), True, 'import streamlit as st\n'), ((383, 414), 'streamlit.title', ... |
import logging
from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
from llama_index.vector_stores import ChromaVectorStore
from llama_index.storage.storage_context import StorageContext
# from IPython.display import Markdown, display
from llama_index.node_parser import SentenceSplitter
from ... | [
"llama_index.node_parser.SentenceSplitter",
"llama_index.SimpleDirectoryReader"
] | [((373, 412), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.INFO'}), '(level=logging.INFO)\n', (392, 412), False, 'import logging\n'), ((422, 449), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (439, 449), False, 'import logging\n'), ((740, 790), 'llama_index.Simp... |
from dotenv import load_dotenv
import os
from typing import List
from llama_index.core.node_parser import SimpleNodeParser
from llama_index.core.settings import Settings
from llama_index.llms.openai import OpenAI
from llama_index.core.embeddings import resolve_embed_model
from llama_index.core import VectorStoreIndex
... | [
"llama_index.core.embeddings.resolve_embed_model",
"llama_index.core.response.notebook_utils.display_source_node",
"llama_index.llms.openai.OpenAI"
] | [((1816, 1829), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (1827, 1829), False, 'from dotenv import load_dotenv\n'), ((1862, 1889), 'llama_index.llms.openai.OpenAI', 'OpenAI', ([], {'model': 'open_ai_model'}), '(model=open_ai_model)\n', (1868, 1889), False, 'from llama_index.llms.openai import OpenAI\n'), (... |
from fastapi import FastAPI, File, UploadFile, HTTPException
import openai
from dotenv import load_dotenv
import os
import json
from llama_index.core import SimpleDirectoryReader
from llama_index.core.node_parser import SimpleFileNodeParser
from llama_index.vector_stores.weaviate import WeaviateVectorStore
from llama_i... | [
"llama_index.core.StorageContext.from_defaults",
"llama_index.core.VectorStoreIndex.from_vector_store",
"llama_index.core.VectorStoreIndex.from_documents",
"llama_index.core.SimpleDirectoryReader",
"llama_index.vector_stores.weaviate.WeaviateVectorStore"
] | [((402, 415), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (413, 415), False, 'from dotenv import load_dotenv\n'), ((423, 432), 'fastapi.FastAPI', 'FastAPI', ([], {}), '()\n', (430, 432), False, 'from fastapi import FastAPI, File, UploadFile, HTTPException\n'), ((444, 476), 'os.environ.get', 'os.environ.get',... |
from init import *
from llama_index import SimpleDirectoryReader, LLMPredictor, ServiceContext
from llama_index.node_parser import SimpleNodeParser
from llama_index import VectorStoreIndex
from llama_index.llms import OpenAI
from llama_index import download_loader
class Index:
def __init__(self, dir="data"):
... | [
"llama_index.ServiceContext.from_defaults",
"llama_index.VectorStoreIndex",
"llama_index.llms.OpenAI",
"llama_index.download_loader",
"llama_index.node_parser.SimpleNodeParser.from_defaults"
] | [((653, 727), 'llama_index.ServiceContext.from_defaults', 'ServiceContext.from_defaults', ([], {'llm_predictor': 'llm_predictor', 'chunk_size': '(1000)'}), '(llm_predictor=llm_predictor, chunk_size=1000)\n', (681, 727), False, 'from llama_index import SimpleDirectoryReader, LLMPredictor, ServiceContext\n'), ((749, 810)... |
def get_agent(list_filters,openai_key,pinecone_key):
import logging
import sys
import os
import pandas as pd
import pinecone
import openai
from llama_index import VectorStoreIndex
from llama_index.vector_stores import PineconeVectorStore
from llama_index.query_engine import Retrieve... | [
"llama_index.tools.FunctionTool.from_defaults",
"llama_index.vector_stores.PineconeVectorStore",
"llama_index.tools.ToolMetadata",
"llama_index.query_engine.RetrieverQueryEngine",
"llama_index.VectorStoreIndex.from_vector_store",
"llama_index.llms.OpenAI",
"llama_index.llms.ChatMessage",
"llama_index.... | [((721, 779), 'logging.basicConfig', 'logging.basicConfig', ([], {'stream': 'sys.stdout', 'level': 'logging.INFO'}), '(stream=sys.stdout, level=logging.INFO)\n', (740, 779), False, 'import logging\n'), ((1006, 1063), 'pinecone.init', 'pinecone.init', ([], {'api_key': 'api_key', 'environment': '"""gcp-starter"""'}), "(a... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
This script allows you to ask questions to the Alice in Wonderland book.
It uses the GPT-3 model to create a vector index of the book, and then
allows you to ask questions to the index.
'''
import os
import yaml
import openai
from llama_index import (
GPTVectorSto... | [
"llama_index.storage.docstore.SimpleDocumentStore.from_persist_dir",
"llama_index.GPTVectorStoreIndex.from_documents",
"llama_index.storage.index_store.SimpleIndexStore.from_persist_dir",
"llama_index.download_loader",
"llama_index.load_index_from_storage",
"llama_index.vector_stores.SimpleVectorStore.fro... | [((862, 885), 'os.path.expanduser', 'os.path.expanduser', (['"""~"""'], {}), "('~')\n", (880, 885), False, 'import os\n'), ((1376, 1404), 'llama_index.download_loader', 'download_loader', (['"""PDFReader"""'], {}), "('PDFReader')\n", (1391, 1404), False, 'from llama_index import GPTVectorStoreIndex, StorageContext, Sim... |
import os
from dotenv import load_dotenv
from IPython.display import Markdown, display
from llama_index.legacy import VectorStoreIndex, ServiceContext
from llama_index.legacy.vector_stores import ChromaVectorStore
from llama_index.legacy.storage.storage_context import StorageContext
from llama_index.legacy.embeddings ... | [
"llama_index.legacy.VectorStoreIndex.from_documents",
"llama_index.legacy.readers.web.BeautifulSoupWebReader",
"llama_index.legacy.embeddings.HuggingFaceEmbedding",
"llama_index.legacy.node_parser.SimpleNodeParser",
"llama_index.legacy.ServiceContext.from_defaults",
"llama_index.legacy.storage.storage_con... | [((846, 905), 'logging.basicConfig', 'logging.basicConfig', ([], {'stream': 'sys.stdout', 'level': 'logging.DEBUG'}), '(stream=sys.stdout, level=logging.DEBUG)\n', (865, 905), False, 'import logging\n'), ((1029, 1042), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (1040, 1042), False, 'from dotenv import load_... |
# Just runs .complete to make sure the LLM is listening
from llama_index.llms import Ollama
from pathlib import Path
import qdrant_client
from llama_index import (
VectorStoreIndex,
ServiceContext,
download_loader,
)
from llama_index.llms import Ollama
from llama_index.storage.storage_context import Storage... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.vector_stores.qdrant.QdrantVectorStore",
"llama_index.ServiceContext.from_defaults",
"llama_index.storage.storage_context.StorageContext.from_defaults",
"llama_index.llms.Ollama",
"llama_index.download_loader"
] | [((406, 435), 'llama_index.download_loader', 'download_loader', (['"""JSONReader"""'], {}), "('JSONReader')\n", (421, 435), False, 'from llama_index import VectorStoreIndex, ServiceContext, download_loader\n'), ((539, 558), 'llama_index.llms.Ollama', 'Ollama', ([], {'model': 'model'}), '(model=model)\n', (545, 558), Fa... |
from pathlib import Path
from llama_hub.file.unstructured import UnstructuredReader
from pathlib import Path
from llama_index import download_loader
from llama_index import SimpleDirectoryReader, VectorStoreIndex
from dotenv import load_dotenv
import os
from llama_index.node_parser import SimpleNodeParser
import pineco... | [
"llama_index.GPTVectorStoreIndex.from_documents",
"llama_index.vector_stores.PineconeVectorStore",
"llama_index.ServiceContext.from_defaults",
"llama_index.StorageContext.from_defaults",
"llama_index.SimpleDirectoryReader",
"llama_index.download_loader",
"llama_index.embeddings.openai.OpenAIEmbedding"
] | [((796, 809), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (807, 809), False, 'from dotenv import load_dotenv\n'), ((827, 847), 'os.getenv', 'os.getenv', (['"""api_key"""'], {}), "('api_key')\n", (836, 847), False, 'import os\n'), ((926, 955), 'os.getenv', 'os.getenv', (['"""pinecone_api_key"""'], {}), "('pin... |
"""Read PDF files."""
import shutil
from pathlib import Path
from typing import Any, List
from llama_index.langchain_helpers.text_splitter import SentenceSplitter
from llama_index.readers.base import BaseReader
from llama_index.readers.schema.base import Document
# https://github.com/emptycrown/llama-hub/blob/main/l... | [
"llama_index.langchain_helpers.text_splitter.SentenceSplitter",
"llama_index.readers.schema.base.Document"
] | [((1102, 1122), 'pdfminer.pdfinterp.PDFResourceManager', 'PDFResourceManager', ([], {}), '()\n', (1120, 1122), False, 'from pdfminer.pdfinterp import PDFPageInterpreter, PDFResourceManager\n'), ((1185, 1195), 'io.StringIO', 'StringIO', ([], {}), '()\n', (1193, 1195), False, 'from io import StringIO\n'), ((1273, 1283), ... |
import chromadb
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import StorageContext
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core.indices.service_context import Service... | [
"llama_index.core.StorageContext.from_defaults",
"llama_index.embeddings.huggingface.HuggingFaceEmbedding",
"llama_index.core.VectorStoreIndex.from_documents",
"llama_index.vector_stores.chroma.ChromaVectorStore",
"llama_index.core.SimpleDirectoryReader",
"llama_index.core.indices.service_context.ServiceC... | [((403, 420), 'chromadb.Client', 'chromadb.Client', ([], {}), '()\n', (418, 420), False, 'import chromadb\n'), ((555, 611), 'llama_index.embeddings.huggingface.HuggingFaceEmbedding', 'HuggingFaceEmbedding', ([], {'model_name': '"""BAAI/bge-base-en-v1.5"""'}), "(model_name='BAAI/bge-base-en-v1.5')\n", (575, 611), False,... |
from llama_index.llms import OpenAI
from llama_index.embeddings import HuggingFaceEmbedding
from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
import os
documents = SimpleDirectoryReader("./competition").load_data()
os.environ['OPENAI_API_KEY'] = 'sk-QnjWfyoAPGLysSCIfjozT3BlbkFJ4A0TyC0ZzaV... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.ServiceContext.from_defaults",
"llama_index.SimpleDirectoryReader",
"llama_index.llms.OpenAI",
"llama_index.embeddings.HuggingFaceEmbedding"
] | [((346, 403), 'llama_index.embeddings.HuggingFaceEmbedding', 'HuggingFaceEmbedding', ([], {'model_name': '"""BAAI/bge-large-en-v1.5"""'}), "(model_name='BAAI/bge-large-en-v1.5')\n", (366, 403), False, 'from llama_index.embeddings import HuggingFaceEmbedding\n'), ((411, 457), 'llama_index.llms.OpenAI', 'OpenAI', ([], {'... |
import os
import openai
import tiktoken
import logging
import sys
from dotenv import load_dotenv
from threading import Lock
from llama_index import (SimpleDirectoryReader,
VectorStoreIndex,
ServiceContext,
StorageContext,
... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.tools.query_engine.QueryEngineTool.from_defaults",
"llama_index.ServiceContext.from_defaults",
"llama_index.StorageContext.from_defaults",
"llama_index.selectors.pydantic_selectors.PydanticSingleSelector.from_defaults",
"llama_index.SimpleDirecto... | [((991, 1049), 'logging.basicConfig', 'logging.basicConfig', ([], {'stream': 'sys.stdout', 'level': 'logging.INFO'}), '(stream=sys.stdout, level=logging.INFO)\n', (1010, 1049), False, 'import logging\n'), ((1162, 1175), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (1173, 1175), False, 'from dotenv import load... |
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
documents = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
print(response) | [
"llama_index.core.VectorStoreIndex.from_documents",
"llama_index.core.SimpleDirectoryReader"
] | [((134, 176), 'llama_index.core.VectorStoreIndex.from_documents', 'VectorStoreIndex.from_documents', (['documents'], {}), '(documents)\n', (165, 176), False, 'from llama_index.core import VectorStoreIndex, SimpleDirectoryReader\n'), ((82, 113), 'llama_index.core.SimpleDirectoryReader', 'SimpleDirectoryReader', (['"""./... |
from typing import List
from llama_index import Document, TwitterTweetReader
from social_gpt.ingestion.scraper.social_scraper import SocialScraper
class TwitterScraper(SocialScraper):
def scrape(self) -> List[Document]:
TwitterTweetReader() | [
"llama_index.TwitterTweetReader"
] | [((237, 257), 'llama_index.TwitterTweetReader', 'TwitterTweetReader', ([], {}), '()\n', (255, 257), False, 'from llama_index import Document, TwitterTweetReader\n')] |
import os
from typing import List
import googleapiclient
from dotenv import load_dotenv
from llama_index import Document
from progress.bar import IncrementalBar
from youtube_transcript_api import YouTubeTranscriptApi
from social_gpt.ingestion.scraper.social_scraper import SocialScraper
load_dotenv()
YOUTUBE_API_SER... | [
"llama_index.Document"
] | [((290, 303), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (301, 303), False, 'from dotenv import load_dotenv\n'), ((644, 689), 'youtube_transcript_api.YouTubeTranscriptApi.get_transcript', 'YouTubeTranscriptApi.get_transcript', (['video_id'], {}), '(video_id)\n', (679, 689), False, 'from youtube_transcript_a... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import logging
import sys
from llama_index import SimpleDirectoryReader, GPTSimpleVectorIndex
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
# read the document of data dir
do... | [
"llama_index.GPTSimpleVectorIndex",
"llama_index.SimpleDirectoryReader"
] | [((153, 211), 'logging.basicConfig', 'logging.basicConfig', ([], {'stream': 'sys.stdout', 'level': 'logging.INFO'}), '(stream=sys.stdout, level=logging.INFO)\n', (172, 211), False, 'import logging\n'), ((457, 488), 'llama_index.GPTSimpleVectorIndex', 'GPTSimpleVectorIndex', (['documents'], {}), '(documents)\n', (477, 4... |
import dotenv
import os
from llama_index.readers.github import GithubRepositoryReader, GithubClient
from llama_index.core import (VectorStoreIndex, StorageContext, PromptTemplate, load_index_from_storage, Settings)
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.ollama import ... | [
"llama_index.core.StorageContext.from_defaults",
"llama_index.llms.ollama.Ollama",
"llama_index.embeddings.huggingface.HuggingFaceEmbedding",
"llama_index.readers.github.GithubClient",
"llama_index.readers.github.GithubRepositoryReader",
"llama_index.core.PromptTemplate",
"llama_index.core.VectorStoreIn... | [((415, 435), 'dotenv.load_dotenv', 'dotenv.load_dotenv', ([], {}), '()\n', (433, 435), False, 'import dotenv\n'), ((886, 912), 'llama_index.readers.github.GithubClient', 'GithubClient', (['github_token'], {}), '(github_token)\n', (898, 912), False, 'from llama_index.readers.github import GithubRepositoryReader, Github... |
import os
from dotenv import load_dotenv
load_dotenv()
import s3fs
from llama_index import (
SimpleDirectoryReader,
VectorStoreIndex,
StorageContext,
load_index_from_storage
)
# load documents
documents = SimpleDirectoryReader('../../../examples/paul_graham_essay/data/').load_data()
print(len(documents))... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.StorageContext.from_defaults",
"llama_index.load_index_from_storage",
"llama_index.SimpleDirectoryReader"
] | [((41, 54), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (52, 54), False, 'from dotenv import load_dotenv\n'), ((329, 371), 'llama_index.VectorStoreIndex.from_documents', 'VectorStoreIndex.from_documents', (['documents'], {}), '(documents)\n', (360, 371), False, 'from llama_index import SimpleDirectoryReader,... |
import sys
import logging
import chromadb
import streamlit as st
from llama_index.llms import OpenAI
from llama_index import SimpleDirectoryReader, VectorStoreIndex
from llama_index.vector_stores import ChromaVectorStore
from llama_index.storage.storage_context import StorageContext
from llama_index import ServiceCon... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.ServiceContext.from_defaults",
"llama_index.vector_stores.ChromaVectorStore",
"llama_index.storage.storage_context.StorageContext.from_defaults",
"llama_index.VectorStoreIndex.from_vector_store",
"llama_index.node_parser.file.markdown.MarkdownNod... | [((553, 572), 'logging.getLogger', 'logging.getLogger', ([], {}), '()\n', (570, 572), False, 'import logging\n'), ((5510, 5544), 'llama_index.node_parser.file.markdown.MarkdownNodeParser.from_defaults', 'MarkdownNodeParser.from_defaults', ([], {}), '()\n', (5542, 5544), False, 'from llama_index.node_parser.file.markdow... |
from flask_restx import Resource
from flask import request, render_template, Response
import openai
import os
import json
from llama_index import GPTSimpleVectorIndex
from llama_index import Document
from furl import furl
from PyPDF2 import PdfReader
os.environ["OPENAI_API_KEY"] = "sk-MEVQvovmcLV7uodMC2aTT3BlbkFJRbhfQ... | [
"llama_index.GPTSimpleVectorIndex.from_documents",
"llama_index.GPTSimpleVectorIndex.load_from_disk",
"llama_index.Document"
] | [((407, 434), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (416, 434), False, 'import os\n'), ((491, 501), 'furl.furl', 'furl', (['link'], {}), '(link)\n', (495, 501), False, 'from furl import furl\n'), ((653, 664), 'furl.furl', 'furl', (['title'], {}), '(title)\n', (657, 664), Fals... |
from __future__ import annotations
import os
import dataclasses
from typing import TYPE_CHECKING, ClassVar
import time
import httpx
from rich import print
from xiaogpt.bot.base_bot import BaseBot, ChatHistoryMixin
from xiaogpt.utils import split_sentences
if TYPE_CHECKING:
import openai
from llama_index.embedd... | [
"llama_index.core.StorageContext.from_defaults",
"llama_index.llms.azure_openai.AzureOpenAI",
"llama_index.embeddings.azure_openai.AzureOpenAIEmbedding",
"llama_index.core.PromptTemplate",
"llama_index.core.VectorStoreIndex.from_documents",
"llama_index.core.SimpleDirectoryReader",
"llama_index.core.loa... | [((1030, 1081), 'dataclasses.field', 'dataclasses.field', ([], {'default_factory': 'list', 'init': '(False)'}), '(default_factory=list, init=False)\n', (1047, 1081), False, 'import dataclasses\n'), ((6247, 6286), 'dataclasses.field', 'dataclasses.field', ([], {'default_factory': 'dict'}), '(default_factory=dict)\n', (6... |
import logging
import sys
import requests
import os
from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
import torch
from llama_index.llms import LlamaCPP
from llama_index.llms.llama_utils import messages_to_prompt, completion_to_prompt
from langchain.embeddings.huggingface import HuggingFac... | [
"llama_index.llms.LlamaCPP"
] | [((511, 570), 'logging.basicConfig', 'logging.basicConfig', ([], {'stream': 'sys.stdout', 'level': 'logging.DEBUG'}), '(stream=sys.stdout, level=logging.DEBUG)\n', (530, 570), False, 'import logging\n'), ((854, 886), 'os.path.join', 'os.path.join', (['"""Data"""', '"""test.pdf"""'], {}), "('Data', 'test.pdf')\n", (866,... |
from llama_index import SimpleDirectoryReader, VectorStoreIndex, LLMPredictor, PromptHelper
from langchain.chat_models import ChatOpenAI
import gradio as gr
from pprint import pprint; import IPython
import sys
import os
from pathlib import Path
# Check if the environment variable exists
if "OPENAIKEY" in os.environ:
... | [
"llama_index.GPTVectorStoreIndex.from_documents",
"llama_index.SimpleDirectoryReader"
] | [((745, 790), 'llama_index.GPTVectorStoreIndex.from_documents', 'GPTVectorStoreIndex.from_documents', (['documents'], {}), '(documents)\n', (779, 790), False, 'from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader\n'), ((1396, 1411), 'IPython.embed', 'IPython.embed', ([], {}), '()\n', (1409, 1411), False, ... |
import argparse
import copy
import logging
import os
import sys
import warnings
from typing import Optional, List, Callable
from langchain.llms import OpenAI
import faiss
import gradio as gr
import torch
import torch.distributed as dist
import transformers
from accelerate import dispatch_model, infer_auto_device_map
fr... | [
"llama_index.GPTFaissIndex.load_from_disk",
"llama_index.SimpleDirectoryReader",
"llama_index.LLMPredictor",
"llama_index.PromptHelper"
] | [((13996, 14021), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (14019, 14021), False, 'import argparse\n'), ((14291, 14338), 'transformers.LlamaTokenizer.from_pretrained', 'LlamaTokenizer.from_pretrained', (['args.model_path'], {}), '(args.model_path)\n', (14321, 14338), False, 'from transfor... |
from typing import List, Set
from llama_index.core import Document, KnowledgeGraphIndex, StorageContext
from llama_index.core.query_engine import BaseQueryEngine
from llama_index.core import load_index_from_storage
import os
def load_kg_graph_index_storage_context(kg_graph_storage_dir: str) -> StorageContext:
retu... | [
"llama_index.core.StorageContext.from_defaults",
"llama_index.core.KnowledgeGraphIndex.from_documents"
] | [((323, 385), 'llama_index.core.StorageContext.from_defaults', 'StorageContext.from_defaults', ([], {'persist_dir': 'kg_graph_storage_dir'}), '(persist_dir=kg_graph_storage_dir)\n', (351, 385), False, 'from llama_index.core import Document, KnowledgeGraphIndex, StorageContext\n'), ((782, 818), 'os.path.exists', 'os.pat... |
from llama_index import SimpleDirectoryReader, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
from langchain import OpenAI
import sys
import os
def construct_index(src_path, out_path):
# set maximum input size
max_input_size = 4096
# set number of output tokens
num_outputs = 512
# set maximum chu... | [
"llama_index.PromptHelper",
"llama_index.GPTSimpleVectorIndex",
"llama_index.SimpleDirectoryReader"
] | [((565, 664), 'llama_index.PromptHelper', 'PromptHelper', (['max_input_size', 'num_outputs', 'max_chunk_overlap'], {'chunk_size_limit': 'chunk_size_limit'}), '(max_input_size, num_outputs, max_chunk_overlap,\n chunk_size_limit=chunk_size_limit)\n', (577, 664), False, 'from llama_index import SimpleDirectoryReader, G... |
import streamlit as st
from llama_index import VectorStoreIndex, ServiceContext, Document
from llama_index.llms import OpenAI
import openai
from llama_index import SimpleDirectoryReader
st.set_page_config(page_title="Chat with the docs, powered by LlamaIndex")
openai.api_key = st.secrets.openai_key
st.title("C... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.llms.OpenAI",
"llama_index.SimpleDirectoryReader"
] | [((193, 267), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""Chat with the docs, powered by LlamaIndex"""'}), "(page_title='Chat with the docs, powered by LlamaIndex')\n", (211, 267), True, 'import streamlit as st\n'), ((309, 365), 'streamlit.title', 'st.title', (['"""Chat with the custom do... |
from llama_index import StorageContext, load_index_from_storage, ServiceContext
import gradio as gr
import sys
import os
import logging
from utils import get_automerging_query_engine
from utils import get_sentence_window_query_engine
import configparser
from TTS.api import TTS
from gtts import gTTS
import simpleaudio a... | [
"llama_index.prompts.base.PromptTemplate",
"llama_index.ServiceContext.from_defaults",
"llama_index.StorageContext.from_defaults",
"llama_index.load_index_from_storage"
] | [((1610, 1637), 'configparser.ConfigParser', 'configparser.ConfigParser', ([], {}), '()\n', (1635, 1637), False, 'import configparser\n'), ((4094, 4149), 'logging.basicConfig', 'logging.basicConfig', ([], {'stream': 'sys.stdout', 'level': 'log_level'}), '(stream=sys.stdout, level=log_level)\n', (4113, 4149), False, 'im... |
from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
from langchain import OpenAI
import os
def construct_index(directory_path):
# set maximum input size
max_input_size = 4096
# set number of output tokens
num_outputs = 2000
# set ma... | [
"llama_index.PromptHelper",
"llama_index.GPTSimpleVectorIndex",
"llama_index.GPTSimpleVectorIndex.load_from_disk",
"llama_index.SimpleDirectoryReader"
] | [((577, 676), 'llama_index.PromptHelper', 'PromptHelper', (['max_input_size', 'num_outputs', 'max_chunk_overlap'], {'chunk_size_limit': 'chunk_size_limit'}), '(max_input_size, num_outputs, max_chunk_overlap,\n chunk_size_limit=chunk_size_limit)\n', (589, 676), False, 'from llama_index import SimpleDirectoryReader, G... |
import os
import streamlit as st
from dotenv import load_dotenv
from llama_index import GPTVectorStoreIndex, LLMPredictor, PromptHelper, ServiceContext
from langchain.llms.openai import OpenAI
from biorxiv_manager import BioRxivManager
load_dotenv()
openai_api_key = os.getenv("OPENAI_API_KEY")
st.title("Ask BioRxiv"... | [
"llama_index.PromptHelper",
"llama_index.GPTVectorStoreIndex.from_documents",
"llama_index.ServiceContext.from_defaults"
] | [((238, 251), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (249, 251), False, 'from dotenv import load_dotenv\n'), ((269, 296), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (278, 296), False, 'import os\n'), ((298, 321), 'streamlit.title', 'st.title', (['"""Ask BioRxiv"""'... |
import logging
from llama_index.langchain_helpers.agents.tools import LlamaIndexTool
from llama_index.vector_stores.types import ExactMatchFilter, MetadataFilters
from app.llama_index.index import setup_index
from app.llama_index.query_engine import setup_query_engine
from app.database.crud import get_vectorized_elect... | [
"llama_index.langchain_helpers.agents.tools.LlamaIndexTool",
"llama_index.vector_stores.types.ExactMatchFilter"
] | [((424, 433), 'app.database.database.Session', 'Session', ([], {}), '()\n', (431, 433), False, 'from app.database.database import Session\n'), ((469, 518), 'app.database.crud.get_vectorized_election_programs_from_db', 'get_vectorized_election_programs_from_db', (['session'], {}), '(session)\n', (509, 518), False, 'from... |
import os
from configparser import ConfigParser, SectionProxy
from typing import Any, Type
from llama_index import (
LLMPredictor,
ServiceContext,
VectorStoreIndex,
)
from llama_index.embeddings.base import BaseEmbedding
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.indices imp... | [
"llama_index.LLMPredictor",
"llama_index.ServiceContext.from_defaults",
"llama_index.storage.storage_context.StorageContext.from_defaults",
"llama_index.llm_predictor.StructuredLLMPredictor",
"llama_index.indices.loading.load_index_from_storage",
"llama_index.llms.openai.OpenAI",
"llama_index.embeddings... | [((1023, 1037), 'configparser.ConfigParser', 'ConfigParser', ([], {}), '()\n', (1035, 1037), False, 'from configparser import ConfigParser, SectionProxy\n'), ((2725, 2812), 'llama_index.ServiceContext.from_defaults', 'ServiceContext.from_defaults', ([], {'llm_predictor': 'llm_predictor', 'embed_model': 'embed_model'}),... |
from components.store import get_storage_context
from llama_index import VectorStoreIndex
from llama_index.retrievers import (
VectorIndexRetriever,
)
from models.gpts import get_gpts_by_uuids
def search_gpts(question):
storage_context = get_storage_context()
index = VectorStoreIndex.from_documents([], st... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.retrievers.VectorIndexRetriever"
] | [((248, 269), 'components.store.get_storage_context', 'get_storage_context', ([], {}), '()\n', (267, 269), False, 'from components.store import get_storage_context\n'), ((282, 350), 'llama_index.VectorStoreIndex.from_documents', 'VectorStoreIndex.from_documents', (['[]'], {'storage_context': 'storage_context'}), '([], ... |
"""LanceDB vector store with cloud storage support."""
import os
from typing import Any, Optional
from dotenv import load_dotenv
from llama_index.schema import NodeRelationship, RelatedNodeInfo, TextNode
from llama_index.vector_stores import LanceDBVectorStore as LanceDBVectorStoreBase
from llama_index.vector_stores.l... | [
"llama_index.schema.RelatedNodeInfo",
"llama_index.vector_stores.lancedb._to_lance_filter",
"llama_index.vector_stores.lancedb._to_llama_similarities"
] | [((490, 503), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (501, 503), False, 'from dotenv import load_dotenv\n'), ((1464, 1492), 'os.getenv', 'os.getenv', (['"""LANCEDB_API_KEY"""'], {}), "('LANCEDB_API_KEY')\n", (1473, 1492), False, 'import os\n'), ((1520, 1547), 'os.getenv', 'os.getenv', (['"""LANCEDB_REGI... |
from typing import List
from fastapi.responses import StreamingResponse
from llama_index.chat_engine.types import BaseChatEngine
from app.engine.index import get_chat_engine
from fastapi import APIRouter, Depends, HTTPException, Request, status
from llama_index.llms.base import ChatMessage
from llama_index.llms.types... | [
"llama_index.llms.base.ChatMessage"
] | [((390, 401), 'fastapi.APIRouter', 'APIRouter', ([], {}), '()\n', (399, 401), False, 'from fastapi import APIRouter, Depends, HTTPException, Request, status\n'), ((636, 660), 'fastapi.Depends', 'Depends', (['get_chat_engine'], {}), '(get_chat_engine)\n', (643, 660), False, 'from fastapi import APIRouter, Depends, HTTPE... |
from typing import List
from fastapi.responses import StreamingResponse
from llama_index.chat_engine.types import BaseChatEngine
from app.engine.index import get_chat_engine
from fastapi import APIRouter, Depends, HTTPException, Request, status
from llama_index.llms.base import ChatMessage
from llama_index.llms.types... | [
"llama_index.llms.base.ChatMessage"
] | [((390, 401), 'fastapi.APIRouter', 'APIRouter', ([], {}), '()\n', (399, 401), False, 'from fastapi import APIRouter, Depends, HTTPException, Request, status\n'), ((636, 660), 'fastapi.Depends', 'Depends', (['get_chat_engine'], {}), '(get_chat_engine)\n', (643, 660), False, 'from fastapi import APIRouter, Depends, HTTPE... |
from typing import List
from fastapi.responses import StreamingResponse
from llama_index.chat_engine.types import BaseChatEngine
from app.engine.index import get_chat_engine
from fastapi import APIRouter, Depends, HTTPException, Request, status
from llama_index.llms.base import ChatMessage
from llama_index.llms.types... | [
"llama_index.llms.base.ChatMessage"
] | [((390, 401), 'fastapi.APIRouter', 'APIRouter', ([], {}), '()\n', (399, 401), False, 'from fastapi import APIRouter, Depends, HTTPException, Request, status\n'), ((636, 660), 'fastapi.Depends', 'Depends', (['get_chat_engine'], {}), '(get_chat_engine)\n', (643, 660), False, 'from fastapi import APIRouter, Depends, HTTPE... |
# Copyright (c) Timescale, Inc. (2023)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | [
"llama_index.tools.query_engine.QueryEngineTool.from_defaults",
"llama_index.indices.vector_store.VectorStoreIndex.from_vector_store",
"llama_index.indices.vector_store.retrievers.VectorIndexAutoRetriever",
"llama_index.llms.OpenAI",
"llama_index.vector_stores.types.MetadataInfo",
"llama_index.agent.OpenA... | [((7098, 7170), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""Time machine demo"""', 'page_icon': '"""🧑\u200d💼"""'}), "(page_title='Time machine demo', page_icon='🧑\\u200d💼')\n", (7116, 7170), True, 'import streamlit as st\n'), ((7166, 7195), 'streamlit.markdown', 'st.markdown', (['"""#... |
# Copyright (c) Timescale, Inc. (2023)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | [
"llama_index.tools.query_engine.QueryEngineTool.from_defaults",
"llama_index.indices.vector_store.VectorStoreIndex.from_vector_store",
"llama_index.indices.vector_store.retrievers.VectorIndexAutoRetriever",
"llama_index.llms.OpenAI",
"llama_index.vector_stores.types.MetadataInfo",
"llama_index.agent.OpenA... | [((7098, 7170), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""Time machine demo"""', 'page_icon': '"""🧑\u200d💼"""'}), "(page_title='Time machine demo', page_icon='🧑\\u200d💼')\n", (7116, 7170), True, 'import streamlit as st\n'), ((7166, 7195), 'streamlit.markdown', 'st.markdown', (['"""#... |
import logging
from threading import Thread
from typing import Any, List, Optional, Type
from llama_index.core.base.base_query_engine import BaseQueryEngine
from llama_index.core.base.llms.types import ChatMessage, MessageRole
from llama_index.core.base.response.schema import RESPONSE_TYPE, StreamingResponse
from llam... | [
"llama_index.core.callbacks.CallbackManager",
"llama_index.core.base.llms.generic_utils.messages_to_history_str",
"llama_index.core.settings.callback_manager_from_settings_or_context",
"llama_index.core.base.llms.types.ChatMessage",
"llama_index.core.prompts.base.PromptTemplate",
"llama_index.core.embeddi... | [((1220, 1247), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1237, 1247), False, 'import logging\n'), ((1579, 1611), 'llama_index.core.prompts.base.PromptTemplate', 'PromptTemplate', (['DEFAULT_TEMPLATE'], {}), '(DEFAULT_TEMPLATE)\n', (1593, 1611), False, 'from llama_index.core.prompts... |
from typing import List
from llama_index.readers.base import BaseReader
from llama_index.readers.youtube_transcript import YoutubeTranscriptReader
from llama_index.schema import Document
class LyzrYoutubeReader(BaseReader):
def __init__(self) -> None:
try:
from youtube_transcript_api import Y... | [
"llama_index.readers.youtube_transcript.YoutubeTranscriptReader"
] | [((623, 648), 'llama_index.readers.youtube_transcript.YoutubeTranscriptReader', 'YoutubeTranscriptReader', ([], {}), '()\n', (646, 648), False, 'from llama_index.readers.youtube_transcript import YoutubeTranscriptReader\n')] |
from typing import List
from llama_index.readers.base import BaseReader
from llama_index.readers.youtube_transcript import YoutubeTranscriptReader
from llama_index.schema import Document
class LyzrYoutubeReader(BaseReader):
def __init__(self) -> None:
try:
from youtube_transcript_api import Y... | [
"llama_index.readers.youtube_transcript.YoutubeTranscriptReader"
] | [((623, 648), 'llama_index.readers.youtube_transcript.YoutubeTranscriptReader', 'YoutubeTranscriptReader', ([], {}), '()\n', (646, 648), False, 'from llama_index.readers.youtube_transcript import YoutubeTranscriptReader\n')] |
from typing import List
from llama_index.readers.base import BaseReader
from llama_index.readers.youtube_transcript import YoutubeTranscriptReader
from llama_index.schema import Document
class LyzrYoutubeReader(BaseReader):
def __init__(self) -> None:
try:
from youtube_transcript_api import Y... | [
"llama_index.readers.youtube_transcript.YoutubeTranscriptReader"
] | [((623, 648), 'llama_index.readers.youtube_transcript.YoutubeTranscriptReader', 'YoutubeTranscriptReader', ([], {}), '()\n', (646, 648), False, 'from llama_index.readers.youtube_transcript import YoutubeTranscriptReader\n')] |
import sys
import asyncio
import logging
import warnings
import nest_asyncio
from typing import List, Set
from bs4 import BeautifulSoup, Tag
from typing import List
from llama_index.schema import Document
IS_IPYKERNEL = "ipykernel_launcher" in sys.argv[0]
if IS_IPYKERNEL:
nest_asyncio.apply()
logger = logging.... | [
"llama_index.schema.Document"
] | [((312, 339), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (329, 339), False, 'import logging\n'), ((281, 301), 'nest_asyncio.apply', 'nest_asyncio.apply', ([], {}), '()\n', (299, 301), False, 'import nest_asyncio\n'), ((676, 710), 'bs4.BeautifulSoup', 'BeautifulSoup', (['html', '"""htm... |
import sys
import asyncio
import logging
import warnings
import nest_asyncio
from typing import List, Set
from bs4 import BeautifulSoup, Tag
from typing import List
from llama_index.schema import Document
IS_IPYKERNEL = "ipykernel_launcher" in sys.argv[0]
if IS_IPYKERNEL:
nest_asyncio.apply()
logger = logging.... | [
"llama_index.schema.Document"
] | [((312, 339), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (329, 339), False, 'import logging\n'), ((281, 301), 'nest_asyncio.apply', 'nest_asyncio.apply', ([], {}), '()\n', (299, 301), False, 'import nest_asyncio\n'), ((676, 710), 'bs4.BeautifulSoup', 'BeautifulSoup', (['html', '"""htm... |
import sys
import asyncio
import logging
import warnings
import nest_asyncio
from typing import List, Set
from bs4 import BeautifulSoup, Tag
from typing import List
from llama_index.schema import Document
IS_IPYKERNEL = "ipykernel_launcher" in sys.argv[0]
if IS_IPYKERNEL:
nest_asyncio.apply()
logger = logging.... | [
"llama_index.schema.Document"
] | [((312, 339), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (329, 339), False, 'import logging\n'), ((281, 301), 'nest_asyncio.apply', 'nest_asyncio.apply', ([], {}), '()\n', (299, 301), False, 'import nest_asyncio\n'), ((676, 710), 'bs4.BeautifulSoup', 'BeautifulSoup', (['html', '"""htm... |
import logging
from typing import Optional, Union
from llama_index import ServiceContext
from llama_index.callbacks import CallbackManager
from llama_index.embeddings.utils import EmbedType
from llama_index.llms.utils import LLMType
from llama_index.prompts import PromptTemplate
from llama_index.prompts.base import Ba... | [
"llama_index.ServiceContext.from_defaults",
"llama_index.prompts.PromptTemplate",
"llama_index.callbacks.CallbackManager",
"llama_index.node_parser.SimpleNodeParser.from_defaults"
] | [((409, 436), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (426, 436), False, 'import logging\n'), ((1016, 1120), 'llama_index.node_parser.SimpleNodeParser.from_defaults', 'SimpleNodeParser.from_defaults', ([], {'chunk_size': '(750)', 'chunk_overlap': '(100)', 'callback_manager': 'callb... |
from llama_index import SimpleDirectoryReader, LLMPredictor, ServiceContext, GPTVectorStoreIndex
from llama_index.response.pprint_utils import pprint_response
from langchain.chat_models import ChatOpenAI
from llama_index.tools import QueryEngineTool, ToolMetadata
from llama_index.query_engine import SubQuestionQueryEng... | [
"llama_index.GPTVectorStoreIndex.from_documents",
"llama_index.tools.ToolMetadata",
"llama_index.query_engine.SubQuestionQueryEngine.from_defaults",
"llama_index.ServiceContext.from_defaults",
"llama_index.SimpleDirectoryReader",
"llama_index.set_global_service_context"
] | [((460, 473), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (471, 473), False, 'from dotenv import load_dotenv\n'), ((503, 561), 'logging.basicConfig', 'logging.basicConfig', ([], {'stream': 'sys.stdout', 'level': 'logging.INFO'}), '(stream=sys.stdout, level=logging.INFO)\n', (522, 561), False, 'import logging... |
import tiktoken
from llama_index.core import MockEmbedding, VectorStoreIndex, SimpleDirectoryReader, Settings
from llama_index.core.callbacks import CallbackManager, TokenCountingHandler
from llama_index.core.llms.mock import MockLLM
embed_model = MockEmbedding(embed_dim=1536)
llm = MockLLM(max_tokens=256)
token_count... | [
"llama_index.core.callbacks.CallbackManager",
"llama_index.core.MockEmbedding",
"llama_index.core.llms.mock.MockLLM",
"llama_index.core.VectorStoreIndex.from_documents",
"llama_index.core.SimpleDirectoryReader"
] | [((249, 278), 'llama_index.core.MockEmbedding', 'MockEmbedding', ([], {'embed_dim': '(1536)'}), '(embed_dim=1536)\n', (262, 278), False, 'from llama_index.core import MockEmbedding, VectorStoreIndex, SimpleDirectoryReader, Settings\n'), ((285, 308), 'llama_index.core.llms.mock.MockLLM', 'MockLLM', ([], {'max_tokens': '... |
from llama_index.core import SimpleDirectoryReader
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.extractors import SummaryExtractor
reader = SimpleDirectoryReader('files')
documents = reader.load_data()
parser = SentenceSplitter(include_prev_next_rel=True)
nodes = parser.get_nodes_fro... | [
"llama_index.core.node_parser.SentenceSplitter",
"llama_index.core.extractors.SummaryExtractor",
"llama_index.core.SimpleDirectoryReader"
] | [((176, 206), 'llama_index.core.SimpleDirectoryReader', 'SimpleDirectoryReader', (['"""files"""'], {}), "('files')\n", (197, 206), False, 'from llama_index.core import SimpleDirectoryReader\n'), ((247, 291), 'llama_index.core.node_parser.SentenceSplitter', 'SentenceSplitter', ([], {'include_prev_next_rel': '(True)'}), ... |
from llama_index.readers.file import FlatReader
from pathlib import Path
reader = FlatReader()
document = reader.load_data(Path("files/sample_document1.txt"))
print(f"Metadata: {document[0].metadata}")
print(f"Text: {document[0].text}")
| [
"llama_index.readers.file.FlatReader"
] | [((83, 95), 'llama_index.readers.file.FlatReader', 'FlatReader', ([], {}), '()\n', (93, 95), False, 'from llama_index.readers.file import FlatReader\n'), ((124, 158), 'pathlib.Path', 'Path', (['"""files/sample_document1.txt"""'], {}), "('files/sample_document1.txt')\n", (128, 158), False, 'from pathlib import Path\n')] |
"""Read PDF files."""
import shutil
from pathlib import Path
from typing import Any, List
from llama_index.langchain_helpers.text_splitter import SentenceSplitter
from llama_index.readers.base import BaseReader
from llama_index.readers.schema.base import Document
# https://github.com/emptycrown/llama-hub/blob/main/l... | [
"llama_index.langchain_helpers.text_splitter.SentenceSplitter",
"llama_index.readers.schema.base.Document"
] | [((1102, 1122), 'pdfminer.pdfinterp.PDFResourceManager', 'PDFResourceManager', ([], {}), '()\n', (1120, 1122), False, 'from pdfminer.pdfinterp import PDFPageInterpreter, PDFResourceManager\n'), ((1185, 1195), 'io.StringIO', 'StringIO', ([], {}), '()\n', (1193, 1195), False, 'from io import StringIO\n'), ((1273, 1283), ... |
"""Read PDF files."""
import shutil
from pathlib import Path
from typing import Any, List
from llama_index.langchain_helpers.text_splitter import SentenceSplitter
from llama_index.readers.base import BaseReader
from llama_index.readers.schema.base import Document
# https://github.com/emptycrown/llama-hub/blob/main/l... | [
"llama_index.langchain_helpers.text_splitter.SentenceSplitter",
"llama_index.readers.schema.base.Document"
] | [((1102, 1122), 'pdfminer.pdfinterp.PDFResourceManager', 'PDFResourceManager', ([], {}), '()\n', (1120, 1122), False, 'from pdfminer.pdfinterp import PDFPageInterpreter, PDFResourceManager\n'), ((1185, 1195), 'io.StringIO', 'StringIO', ([], {}), '()\n', (1193, 1195), False, 'from io import StringIO\n'), ((1273, 1283), ... |
"""Read PDF files."""
import shutil
from pathlib import Path
from typing import Any, List
from llama_index.langchain_helpers.text_splitter import SentenceSplitter
from llama_index.readers.base import BaseReader
from llama_index.readers.schema.base import Document
# https://github.com/emptycrown/llama-hub/blob/main/l... | [
"llama_index.langchain_helpers.text_splitter.SentenceSplitter",
"llama_index.readers.schema.base.Document"
] | [((1102, 1122), 'pdfminer.pdfinterp.PDFResourceManager', 'PDFResourceManager', ([], {}), '()\n', (1120, 1122), False, 'from pdfminer.pdfinterp import PDFPageInterpreter, PDFResourceManager\n'), ((1185, 1195), 'io.StringIO', 'StringIO', ([], {}), '()\n', (1193, 1195), False, 'from io import StringIO\n'), ((1273, 1283), ... |
from typing import Any, List
import tiktoken
from bs4 import BeautifulSoup
from llama_index.readers.base import BaseReader
from llama_index.readers.schema.base import Document
staticPath = "static"
def encode_string(string: str, encoding_name: str = "p50k_base"):
encoding = tiktoken.get_encoding(encoding_name)
... | [
"llama_index.readers.schema.base.Document"
] | [((283, 319), 'tiktoken.get_encoding', 'tiktoken.get_encoding', (['encoding_name'], {}), '(encoding_name)\n', (304, 319), False, 'import tiktoken\n'), ((437, 473), 'tiktoken.get_encoding', 'tiktoken.get_encoding', (['encoding_name'], {}), '(encoding_name)\n', (458, 473), False, 'import tiktoken\n'), ((664, 700), 'tikto... |
from typing import Any, List
import tiktoken
from bs4 import BeautifulSoup
from llama_index.readers.base import BaseReader
from llama_index.readers.schema.base import Document
staticPath = "static"
def encode_string(string: str, encoding_name: str = "p50k_base"):
encoding = tiktoken.get_encoding(encoding_name)
... | [
"llama_index.readers.schema.base.Document"
] | [((283, 319), 'tiktoken.get_encoding', 'tiktoken.get_encoding', (['encoding_name'], {}), '(encoding_name)\n', (304, 319), False, 'import tiktoken\n'), ((437, 473), 'tiktoken.get_encoding', 'tiktoken.get_encoding', (['encoding_name'], {}), '(encoding_name)\n', (458, 473), False, 'import tiktoken\n'), ((664, 700), 'tikto... |
from typing import Any, List
import tiktoken
from bs4 import BeautifulSoup
from llama_index.readers.base import BaseReader
from llama_index.readers.schema.base import Document
staticPath = "static"
def encode_string(string: str, encoding_name: str = "p50k_base"):
encoding = tiktoken.get_encoding(encoding_name)
... | [
"llama_index.readers.schema.base.Document"
] | [((283, 319), 'tiktoken.get_encoding', 'tiktoken.get_encoding', (['encoding_name'], {}), '(encoding_name)\n', (304, 319), False, 'import tiktoken\n'), ((437, 473), 'tiktoken.get_encoding', 'tiktoken.get_encoding', (['encoding_name'], {}), '(encoding_name)\n', (458, 473), False, 'import tiktoken\n'), ((664, 700), 'tikto... |
from typing import Any, List
import tiktoken
from bs4 import BeautifulSoup
from llama_index.readers.base import BaseReader
from llama_index.readers.schema.base import Document
staticPath = "static"
def encode_string(string: str, encoding_name: str = "p50k_base"):
encoding = tiktoken.get_encoding(encoding_name)
... | [
"llama_index.readers.schema.base.Document"
] | [((283, 319), 'tiktoken.get_encoding', 'tiktoken.get_encoding', (['encoding_name'], {}), '(encoding_name)\n', (304, 319), False, 'import tiktoken\n'), ((437, 473), 'tiktoken.get_encoding', 'tiktoken.get_encoding', (['encoding_name'], {}), '(encoding_name)\n', (458, 473), False, 'import tiktoken\n'), ((664, 700), 'tikto... |
import os
# Uncomment to specify your OpenAI API key here (local testing only, not in production!), or add corresponding environment variable (recommended)
# os.environ['OPENAI_API_KEY']= ""
from llama_index import LLMPredictor, PromptHelper, ServiceContext
from langchain.llms.openai import OpenAI
from llama_index ... | [
"llama_index.PromptHelper",
"llama_index.ServiceContext.from_defaults",
"llama_index.StorageContext.from_defaults",
"llama_index.load_index_from_storage"
] | [((380, 441), 'os.environ.get', 'os.environ.get', (['"""OPENAI_API_BASE"""', '"""http://localhost:8080/v1"""'], {}), "('OPENAI_API_BASE', 'http://localhost:8080/v1')\n", (394, 441), False, 'import os\n'), ((766, 825), 'llama_index.PromptHelper', 'PromptHelper', (['max_input_size', 'num_output', 'max_chunk_overlap'], {}... |
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.huggingface import HuggingFaceLLM
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from llama_index.llms.huggingface import HuggingFaceInferenceAPI
from llama_index.llms.azure_openai import AzureOpe... | [
"llama_index.core.base.llms.types.CompletionResponse"
] | [((443, 456), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (454, 456), False, 'from dotenv import load_dotenv\n'), ((662, 687), 'os.getenv', 'os.getenv', (['"""HF_TOKEN"""', '""""""'], {}), "('HF_TOKEN', '')\n", (671, 687), False, 'import os\n'), ((698, 733), 'os.getenv', 'os.getenv', (['"""AZURE_OPENAI_TOKEN... |
from dotenv import load_dotenv
load_dotenv()
from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader, load_index_from_storage, StorageContext
from llama_index.storage.docstore import SimpleDocumentStore
from llama_index.vector_stores import SimpleVectorStore
from llama_index.storage.index_store import Simpl... | [
"llama_index.storage.docstore.SimpleDocumentStore.from_persist_dir",
"llama_index.GPTVectorStoreIndex.from_documents",
"llama_index.graph_stores.SimpleGraphStore.from_persist_dir",
"llama_index.SimpleDirectoryReader",
"llama_index.storage.index_store.SimpleIndexStore.from_persist_dir",
"llama_index.load_i... | [((31, 44), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (42, 44), False, 'from dotenv import load_dotenv\n'), ((450, 495), 'llama_index.GPTVectorStoreIndex.from_documents', 'GPTVectorStoreIndex.from_documents', (['documents'], {}), '(documents)\n', (484, 495), False, 'from llama_index import GPTVectorStoreIn... |
from rag.agents.interface import Pipeline
from rich.progress import Progress, SpinnerColumn, TextColumn
from typing import Any
from pydantic import create_model
from typing import List
import warnings
import box
import yaml
import timeit
from rich import print
from llama_index.core import SimpleDirectoryReader
from lla... | [
"llama_index.multi_modal_llms.ollama.OllamaMultiModal",
"llama_index.core.output_parsers.PydanticOutputParser",
"llama_index.core.SimpleDirectoryReader"
] | [((512, 574), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {'category': 'DeprecationWarning'}), "('ignore', category=DeprecationWarning)\n", (535, 574), False, 'import warnings\n'), ((575, 630), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {'category': 'UserWarnin... |
import functools
import os
import random
import tempfile
import traceback
import asyncio
from collections import defaultdict
import aiohttp
import discord
import aiofiles
import httpx
import openai
import tiktoken
from functools import partial
from typing import List, Optional, cast
from pathlib import Path
from datet... | [
"llama_index.GPTVectorStoreIndex.from_documents",
"llama_index.langchain_helpers.agents.IndexToolConfig",
"llama_index.GithubRepositoryReader",
"llama_index.OpenAIEmbedding",
"llama_index.query_engine.RetrieverQueryEngine",
"llama_index.SimpleDirectoryReader",
"llama_index.download_loader",
"llama_ind... | [((2731, 2770), 'services.environment_service.EnvService.get_max_deep_compose_price', 'EnvService.get_max_deep_compose_price', ([], {}), '()\n', (2768, 2770), False, 'from services.environment_service import EnvService\n'), ((2784, 2813), 'llama_index.download_loader', 'download_loader', (['"""EpubReader"""'], {}), "('... |
import os
from langchain import OpenAI
from llama_index import GPTSimpleVectorIndex, SimpleDirectoryReader, LLMPredictor, download_loader, SQLDatabase, GPTSQLStructStoreIndex
import sqlalchemy
import time
DatabaseReader = download_loader('DatabaseReader')
databasePath = f'sqlite:///{os.path.dirname(__file__)}/vulns.db... | [
"llama_index.GPTSQLStructStoreIndex",
"llama_index.download_loader",
"llama_index.SQLDatabase"
] | [((223, 256), 'llama_index.download_loader', 'download_loader', (['"""DatabaseReader"""'], {}), "('DatabaseReader')\n", (238, 256), False, 'from llama_index import GPTSimpleVectorIndex, SimpleDirectoryReader, LLMPredictor, download_loader, SQLDatabase, GPTSQLStructStoreIndex\n'), ((372, 410), 'sqlalchemy.create_engine'... |
# https://blog.streamlit.io/build-a-chatbot-with-custom-data-sources-powered-by-llamaindex/
import os
import streamlit as st
from llama_index.core import ServiceContext, Document, SimpleDirectoryReader, VectorStoreIndex, Settings
from llama_index.llms.ollama import Ollama
from llama_index.embeddings.huggingface import... | [
"llama_index.embeddings.huggingface.HuggingFaceEmbedding",
"llama_index.core.VectorStoreIndex.from_documents",
"llama_index.llms.ollama.Ollama",
"llama_index.core.SimpleDirectoryReader"
] | [((357, 394), 'os.getenv', 'os.getenv', (['"""OLLAMA_HOST"""', '"""localhost"""'], {}), "('OLLAMA_HOST', 'localhost')\n", (366, 394), False, 'import os\n'), ((506, 573), 'llama_index.llms.ollama.Ollama', 'Ollama', ([], {'model': '"""zephyr"""', 'base_url': "('http://' + OLLAMA_HOST + ':11434')"}), "(model='zephyr', bas... |
import argparse
import logging
import sys
import re
import os
import argparse
import requests
from pathlib import Path
from urllib.parse import urlparse
from llama_index import ServiceContext, StorageContext
from llama_index import set_global_service_context
from llama_index import VectorStoreIndex, SimpleDirectoryRe... | [
"llama_index.postprocessor.MetadataReplacementPostProcessor",
"llama_index.readers.file.flat_reader.FlatReader",
"llama_index.prompts.ChatMessage",
"llama_index.ServiceContext.from_defaults",
"llama_index.StorageContext.from_defaults",
"llama_index.VectorStoreIndex",
"llama_index.SimpleDirectoryReader",... | [((2448, 2465), 'requests.get', 'requests.get', (['url'], {}), '(url)\n', (2460, 2465), False, 'import requests\n'), ((2063, 2076), 'urllib.parse.urlparse', 'urlparse', (['url'], {}), '(url)\n', (2071, 2076), False, 'from urllib.parse import urlparse\n'), ((3032, 3049), 'requests.get', 'requests.get', (['url'], {}), '(... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# ================================================== #
# This file is a part of PYGPT package #
# Website: https://pygpt.net #
# GitHub: https://github.com/szczyglis-dev/py-gpt #
# MIT License ... | [
"llama_index.core.StorageContext.from_defaults",
"llama_index.core.indices.vector_store.base.VectorStoreIndex",
"llama_index.core.load_index_from_storage"
] | [((3613, 3659), 'llama_index.core.StorageContext.from_defaults', 'StorageContext.from_defaults', ([], {'persist_dir': 'path'}), '(persist_dir=path)\n', (3641, 3659), False, 'from llama_index.core import StorageContext, load_index_from_storage\n'), ((3792, 3865), 'llama_index.core.load_index_from_storage', 'load_index_f... |
from __future__ import annotations
from typing import Optional
import os
from llama_index.core import ServiceContext
from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.azure_openai import AzureOpenAI
from llama_index.core.... | [
"llama_index.llms.azure_openai.AzureOpenAI",
"llama_index.core.ServiceContext.from_defaults",
"llama_index.embeddings.azure_openai.AzureOpenAIEmbedding",
"llama_index.core.llms.openai_utils.CHAT_MODELS.update",
"llama_index.core.llms.openai_utils.ALL_AVAILABLE_MODELS.update",
"llama_index.core.llms.OpenAI... | [((948, 998), 'llama_index.core.llms.openai_utils.ALL_AVAILABLE_MODELS.update', 'ALL_AVAILABLE_MODELS.update', (['_EXTENDED_CHAT_MODELS'], {}), '(_EXTENDED_CHAT_MODELS)\n', (975, 998), False, 'from llama_index.core.llms.openai_utils import ALL_AVAILABLE_MODELS, CHAT_MODELS\n'), ((999, 1040), 'llama_index.core.llms.open... |
from llama_index import SimpleDirectoryReader, LLMPredictor, ServiceContext, GPTVectorStoreIndex
from langchain.chat_models import ChatOpenAI
from dotenv import load_dotenv
import os
import graphsignal
import logging
import time
import random
load_dotenv()
logging.basicConfig()
logger = logging.getLogger()
logger.set... | [
"llama_index.set_global_service_context",
"llama_index.GPTVectorStoreIndex.from_documents",
"llama_index.ServiceContext.from_defaults",
"llama_index.SimpleDirectoryReader"
] | [((244, 257), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (255, 257), False, 'from dotenv import load_dotenv\n'), ((259, 280), 'logging.basicConfig', 'logging.basicConfig', ([], {}), '()\n', (278, 280), False, 'import logging\n'), ((290, 309), 'logging.getLogger', 'logging.getLogger', ([], {}), '()\n', (307,... |
# Ref https://github.com/amrrs/QABot-LangChain/blob/main/Q%26A_Bot_with_Llama_Index_and_LangChain.ipynb
#from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
from langchain import OpenAI
from llama_index import SimpleDirectoryReader, LangchainEmbedding, GPTListInd... | [
"llama_index.GPTSimpleVectorIndex.from_documents",
"llama_index.GPTSimpleVectorIndex.load_from_disk",
"llama_index.ServiceContext.from_defaults",
"llama_index.SimpleDirectoryReader",
"llama_index.PromptHelper"
] | [((716, 815), 'llama_index.PromptHelper', 'PromptHelper', (['max_input_size', 'num_outputs', 'max_chunk_overlap'], {'chunk_size_limit': 'chunk_size_limit'}), '(max_input_size, num_outputs, max_chunk_overlap,\n chunk_size_limit=chunk_size_limit)\n', (728, 815), False, 'from llama_index import SimpleDirectoryReader, L... |
# chroma.py
import streamlit as st
import os
import re
from pathlib import Path
import chromadb
from chromadb.config import Settings
from llama_index import GPTVectorStoreIndex, load_index_from_storage
from llama_index.vector_stores import ChromaVectorStore
from utils.model_settings import sentenceTransformers, get_se... | [
"llama_index.vector_stores.ChromaVectorStore",
"llama_index.load_index_from_storage",
"llama_index.storage.storage_context.StorageContext.from_defaults"
] | [((665, 686), 'utils.model_settings.get_service_context', 'get_service_context', ([], {}), '()\n', (684, 686), False, 'from utils.model_settings import sentenceTransformers, get_service_context, get_embed_model\n'), ((1363, 1418), 'llama_index.vector_stores.ChromaVectorStore', 'ChromaVectorStore', ([], {'chroma_collect... |
# /app/src/tools/doc_search.py
import logging
# Primary Components
from llama_index import ServiceContext, VectorStoreIndex
from llama_index.vector_stores.qdrant import QdrantVectorStore
from qdrant_client import QdrantClient
from src.utils.config import load_config, setup_environment_variables
from src.utils.embeddi... | [
"llama_index.vector_stores.qdrant.QdrantVectorStore",
"llama_index.VectorStoreIndex.from_vector_store",
"llama_index.ServiceContext.from_defaults"
] | [((384, 411), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (401, 411), False, 'import logging\n'), ((1157, 1170), 'src.utils.config.load_config', 'load_config', ([], {}), '()\n', (1168, 1170), False, 'from src.utils.config import load_config, setup_environment_variables\n'), ((1179, 121... |
import logging
import traceback
from typing import Sequence, List, Optional, Dict
from llama_index import Document
from llama_index.callbacks import CBEventType, CallbackManager
from llama_index.callbacks.schema import EventPayload
from llama_index.node_parser import NodeParser, SimpleNodeParser
from llama_index.node_... | [
"llama_index.text_splitter.get_default_text_splitter",
"llama_index.utils.get_tqdm_iterable",
"llama_index.callbacks.CallbackManager"
] | [((893, 964), 'pydantic.Field', 'Field', ([], {'description': '"""The text splitter to use when splitting documents."""'}), "(description='The text splitter to use when splitting documents.')\n", (898, 964), False, 'from pydantic import Field\n'), ((1008, 1099), 'pydantic.Field', 'Field', ([], {'default': '(True)', 'de... |
# RAG/TAG Tiger - llm.py
# Copyright (c) 2024 Stuart Riffle
# github.com/stuartriffle/ragtag-tiger
import os
import torch
from .files import *
from .lograg import lograg, lograg_verbose, lograg_error
from .timer import TimerUntil
openai_model_default = "gpt-3.5-turbo-instruct"
google_model_default = "models/tex... | [
"llama_index.llms.MistralAI",
"llama_index.llms.Perplexity",
"llama_index.llms.Gemini",
"llama_index.llms.OpenAILike",
"llama_index.ServiceContext.from_defaults",
"llama_index.llms.Replicate",
"llama_index.llms.OpenAI",
"llama_index.llms.LlamaCPP",
"llama_index.llms.PaLM",
"llama_index.llms.Huggin... | [((8029, 8090), 'llama_index.ServiceContext.from_defaults', 'ServiceContext.from_defaults', ([], {'embed_model': '"""local"""', 'llm': 'result'}), "(embed_model='local', llm=result)\n", (8057, 8090), False, 'from llama_index import ServiceContext, set_global_service_context\n'), ((1986, 2158), 'llama_index.llms.OpenAI'... |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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
#
# ht... | [
"llama_index.utils.get_tokenizer",
"llama_index.callbacks.token_counting.get_llm_token_counts",
"llama_index.utilities.token_counting.TokenCounter"
] | [((1450, 1483), 'contextvars.ContextVar', 'ContextVar', (['"""trace"""'], {'default': 'None'}), "('trace', default=None)\n", (1460, 1483), False, 'from contextvars import ContextVar\n'), ((2085, 2105), 'opentelemetry.trace.get_tracer', 'get_tracer', (['__name__'], {}), '(__name__)\n', (2095, 2105), False, 'from opentel... |
from pathlib import Path
from llama_index import download_loader
ImageReader = download_loader("ImageReader")
# If the Image has key-value pairs text, use text_type = "key_value"
loader = ImageReader(text_type = "key_value")
documents = loader.load_data(file=Path('./receipt.webp'))
print(documents) | [
"llama_index.download_loader"
] | [((80, 110), 'llama_index.download_loader', 'download_loader', (['"""ImageReader"""'], {}), "('ImageReader')\n", (95, 110), False, 'from llama_index import download_loader\n'), ((261, 283), 'pathlib.Path', 'Path', (['"""./receipt.webp"""'], {}), "('./receipt.webp')\n", (265, 283), False, 'from pathlib import Path\n')] |
# https://github.com/jerryjliu/llama_index/blob/main/examples/langchain_demo/LangchainDemo.ipynb
# Using LlamaIndex as a Callable Tool
from langchain.agents import Tool
from langchain.chains.conversation.memory import ConversationBufferMemory
from langchain.chat_models import ChatOpenAI
from langchain.agents import i... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.tools.ToolMetadata",
"llama_index.query_engine.SubQuestionQueryEngine.from_defaults",
"llama_index.LLMPredictor",
"llama_index.ServiceContext.from_defaults",
"llama_index.SimpleDirectoryReader"
] | [((874, 992), 'langchain.HuggingFaceHub', 'HuggingFaceHub', ([], {'repo_id': 'repo_id', 'model_kwargs': "{'temperature': 0.1, 'truncation': 'only_first', 'max_length': 1024}"}), "(repo_id=repo_id, model_kwargs={'temperature': 0.1,\n 'truncation': 'only_first', 'max_length': 1024})\n", (888, 992), False, 'from langch... |
"""This module provides functionality for loading chat prompts.
The main function in this module is `load_chat_prompt`, which loads a chat prompt from a given JSON file.
The JSON file should contain two keys: "system_template" and "human_template", which correspond to the system and user messages respectively.
Typica... | [
"llama_index.llms.ChatMessage",
"llama_index.ChatPromptTemplate"
] | [((626, 653), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (643, 653), False, 'import logging\n'), ((2217, 2237), 'pathlib.Path', 'pathlib.Path', (['f_name'], {}), '(f_name)\n', (2229, 2237), False, 'import pathlib\n'), ((2706, 2734), 'llama_index.ChatPromptTemplate', 'ChatPromptTemplat... |
# Copyright 2023 osiworx
# Licensed under the Apache License, Version 2.0 (the "License"); you
# may not use this file except in compliance with the License. You
# may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
#... | [
"llama_index.embeddings.huggingface.HuggingFaceEmbedding",
"llama_index.vector_stores.milvus.MilvusVectorStore",
"llama_index.core.VectorStoreIndex.from_documents",
"llama_index.core.SimpleDirectoryReader",
"llama_index.core.storage.storage_context.StorageContext.from_defaults"
] | [((894, 1036), 'llama_index.vector_stores.milvus.MilvusVectorStore', 'MilvusVectorStore', ([], {'uri': '"""http://localhost:19530"""', 'port': '(19530)', 'collection_name': '"""llama_index_prompts_large"""', 'dim': '(384)', 'similarity_metric': '"""L2"""'}), "(uri='http://localhost:19530', port=19530, collection_name\n... |
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... | [
"llama_index.llms.litellm.LiteLLM",
"llama_index.core.StorageContext.from_defaults"
] | [((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
from dotenv import load_dotenv
from llama_index.chat_engine.condense_plus_context import CondensePlusContextChatEngine
from llama_index.llms.openai import OpenAI
from llama_index.llms.types import ChatMessage, MessageRole
from llama_index.query_engine import RetrieverQueryEngine
from llama_index.retrievers i... | [
"llama_index.llms.types.ChatMessage",
"llama_index.chat_engine.condense_plus_context.CondensePlusContextChatEngine.from_defaults",
"llama_index.retrievers.PathwayRetriever",
"llama_index.query_engine.RetrieverQueryEngine.from_args",
"llama_index.llms.openai.OpenAI"
] | [((443, 456), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (454, 456), False, 'from dotenv import load_dotenv\n'), ((622, 674), 'os.environ.get', 'os.environ.get', (['"""PATHWAY_HOST"""', 'DEFAULT_PATHWAY_HOST'], {}), "('PATHWAY_HOST', DEFAULT_PATHWAY_HOST)\n", (636, 674), False, 'import os\n'), ((932, 977), ... |
# My OpenAI Key
import logging
import os
import sys
from IPython.display import Markdown, display
from llama_index import GPTTreeIndex, SimpleDirectoryReader
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
os.environ["OPENAI_API_KEY"... | [
"llama_index.SimpleDirectoryReader",
"llama_index.GPTTreeIndex.load_from_disk",
"llama_index.GPTTreeIndex.from_documents"
] | [((160, 218), 'logging.basicConfig', 'logging.basicConfig', ([], {'stream': 'sys.stdout', 'level': 'logging.INFO'}), '(stream=sys.stdout, level=logging.INFO)\n', (179, 218), False, 'import logging\n'), ((324, 351), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (333, 351), False, 'imp... |
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