code stringlengths 161 233k | apis listlengths 1 24 | extract_api stringlengths 162 68.5k |
|---|---|---|
"""Answer inserter."""
from abc import abstractmethod
from typing import Any, Dict, List, Optional
from llama_index.core.llms.llm import LLM
from llama_index.core.prompts.base import BasePromptTemplate, PromptTemplate
from llama_index.core.prompts.mixin import (
PromptDictType,
PromptMixin,
PromptMixinTyp... | [
"llama_index.core.prompts.base.PromptTemplate",
"llama_index.core.settings.llm_from_settings_or_context"
] | [((4287, 4336), 'llama_index.core.prompts.base.PromptTemplate', 'PromptTemplate', (['DEFAULT_ANSWER_INSERT_PROMPT_TMPL'], {}), '(DEFAULT_ANSWER_INSERT_PROMPT_TMPL)\n', (4301, 4336), False, 'from llama_index.core.prompts.base import BasePromptTemplate, PromptTemplate\n'), ((4860, 4915), 'llama_index.core.settings.llm_fr... |
"""Retrieval evaluators."""
from typing import Any, List, Optional, Sequence, Tuple
from llama_index.legacy.bridge.pydantic import Field
from llama_index.legacy.core.base_retriever import BaseRetriever
from llama_index.legacy.evaluation.retrieval.base import (
BaseRetrievalEvaluator,
RetrievalEvalMode,
)
from... | [
"llama_index.legacy.bridge.pydantic.Field"
] | [((1038, 1085), 'llama_index.legacy.bridge.pydantic.Field', 'Field', (['...'], {'description': '"""Retriever to evaluate"""'}), "(..., description='Retriever to evaluate')\n", (1043, 1085), False, 'from llama_index.legacy.bridge.pydantic import Field\n'), ((1151, 1209), 'llama_index.legacy.bridge.pydantic.Field', 'Fiel... |
"""Retrieval evaluators."""
from typing import Any, List, Optional, Sequence, Tuple
from llama_index.legacy.bridge.pydantic import Field
from llama_index.legacy.core.base_retriever import BaseRetriever
from llama_index.legacy.evaluation.retrieval.base import (
BaseRetrievalEvaluator,
RetrievalEvalMode,
)
from... | [
"llama_index.legacy.bridge.pydantic.Field"
] | [((1038, 1085), 'llama_index.legacy.bridge.pydantic.Field', 'Field', (['...'], {'description': '"""Retriever to evaluate"""'}), "(..., description='Retriever to evaluate')\n", (1043, 1085), False, 'from llama_index.legacy.bridge.pydantic import Field\n'), ((1151, 1209), 'llama_index.legacy.bridge.pydantic.Field', 'Fiel... |
from typing import Any, List, Optional
from llama_index.legacy.bridge.pydantic import Field, PrivateAttr
from llama_index.legacy.callbacks import CallbackManager
from llama_index.legacy.core.embeddings.base import (
DEFAULT_EMBED_BATCH_SIZE,
BaseEmbedding,
)
from llama_index.legacy.embeddings.huggingface_utils... | [
"llama_index.legacy.embeddings.huggingface_utils.format_query",
"llama_index.legacy.embeddings.huggingface_utils.get_pooling_mode",
"llama_index.legacy.embeddings.pooling.Pooling",
"llama_index.legacy.bridge.pydantic.PrivateAttr",
"llama_index.legacy.bridge.pydantic.Field",
"llama_index.legacy.embeddings.... | [((567, 613), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""Folder name to load from."""'}), "(description='Folder name to load from.')\n", (572, 613), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((636, 681), 'llama_index.legacy.bridge.pydantic.Field', ... |
from typing import Any, List, Optional
from llama_index.legacy.bridge.pydantic import Field, PrivateAttr
from llama_index.legacy.callbacks import CallbackManager
from llama_index.legacy.core.embeddings.base import (
DEFAULT_EMBED_BATCH_SIZE,
BaseEmbedding,
)
from llama_index.legacy.embeddings.huggingface_utils... | [
"llama_index.legacy.embeddings.huggingface_utils.format_query",
"llama_index.legacy.embeddings.huggingface_utils.get_pooling_mode",
"llama_index.legacy.embeddings.pooling.Pooling",
"llama_index.legacy.bridge.pydantic.PrivateAttr",
"llama_index.legacy.bridge.pydantic.Field",
"llama_index.legacy.embeddings.... | [((567, 613), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""Folder name to load from."""'}), "(description='Folder name to load from.')\n", (572, 613), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((636, 681), 'llama_index.legacy.bridge.pydantic.Field', ... |
"""Autoretriever prompts."""
from llama_index.legacy.prompts.base import PromptTemplate
from llama_index.legacy.prompts.prompt_type import PromptType
from llama_index.legacy.vector_stores.types import (
FilterOperator,
MetadataFilter,
MetadataInfo,
VectorStoreInfo,
VectorStoreQuerySpec,
)
# NOTE: ... | [
"llama_index.legacy.vector_stores.types.MetadataInfo",
"llama_index.legacy.vector_stores.types.MetadataFilter",
"llama_index.legacy.prompts.base.PromptTemplate"
] | [((3927, 4033), 'llama_index.legacy.prompts.base.PromptTemplate', 'PromptTemplate', ([], {'template': 'DEFAULT_VECTARA_QUERY_PROMPT_TMPL', 'prompt_type': 'PromptType.VECTOR_STORE_QUERY'}), '(template=DEFAULT_VECTARA_QUERY_PROMPT_TMPL, prompt_type=\n PromptType.VECTOR_STORE_QUERY)\n', (3941, 4033), False, 'from llama... |
"""Autoretriever prompts."""
from llama_index.legacy.prompts.base import PromptTemplate
from llama_index.legacy.prompts.prompt_type import PromptType
from llama_index.legacy.vector_stores.types import (
FilterOperator,
MetadataFilter,
MetadataInfo,
VectorStoreInfo,
VectorStoreQuerySpec,
)
# NOTE: ... | [
"llama_index.legacy.vector_stores.types.MetadataInfo",
"llama_index.legacy.vector_stores.types.MetadataFilter",
"llama_index.legacy.prompts.base.PromptTemplate"
] | [((3927, 4033), 'llama_index.legacy.prompts.base.PromptTemplate', 'PromptTemplate', ([], {'template': 'DEFAULT_VECTARA_QUERY_PROMPT_TMPL', 'prompt_type': 'PromptType.VECTOR_STORE_QUERY'}), '(template=DEFAULT_VECTARA_QUERY_PROMPT_TMPL, prompt_type=\n PromptType.VECTOR_STORE_QUERY)\n', (3941, 4033), False, 'from llama... |
"""Faithfulness evaluation."""
from __future__ import annotations
from typing import Any, List, Optional, Sequence, Union
from llama_index.core.evaluation.base import BaseEvaluator, EvaluationResult
from llama_index.core.multi_modal_llms.base import MultiModalLLM
from llama_index.core.prompts import BasePromptTempla... | [
"llama_index.core.prompts.PromptTemplate",
"llama_index.core.evaluation.base.EvaluationResult",
"llama_index.core.schema.ImageNode",
"llama_index.multi_modal_llms.openai.OpenAIMultiModal"
] | [((468, 1757), 'llama_index.core.prompts.PromptTemplate', 'PromptTemplate', (['"""Please tell if a given piece of information is supported by the visual as well as textual context information.\nYou need to answer with either YES or NO.\nAnswer YES if any of the image(s) and textual context supports the information, eve... |
"""Faithfulness evaluation."""
from __future__ import annotations
from typing import Any, List, Optional, Sequence, Union
from llama_index.core.evaluation.base import BaseEvaluator, EvaluationResult
from llama_index.core.multi_modal_llms.base import MultiModalLLM
from llama_index.core.prompts import BasePromptTempla... | [
"llama_index.core.prompts.PromptTemplate",
"llama_index.core.evaluation.base.EvaluationResult",
"llama_index.core.schema.ImageNode",
"llama_index.multi_modal_llms.openai.OpenAIMultiModal"
] | [((468, 1757), 'llama_index.core.prompts.PromptTemplate', 'PromptTemplate', (['"""Please tell if a given piece of information is supported by the visual as well as textual context information.\nYou need to answer with either YES or NO.\nAnswer YES if any of the image(s) and textual context supports the information, eve... |
"""Faithfulness evaluation."""
from __future__ import annotations
from typing import Any, List, Optional, Sequence, Union
from llama_index.core.evaluation.base import BaseEvaluator, EvaluationResult
from llama_index.core.multi_modal_llms.base import MultiModalLLM
from llama_index.core.prompts import BasePromptTempla... | [
"llama_index.core.prompts.PromptTemplate",
"llama_index.core.evaluation.base.EvaluationResult",
"llama_index.core.schema.ImageNode",
"llama_index.multi_modal_llms.openai.OpenAIMultiModal"
] | [((468, 1757), 'llama_index.core.prompts.PromptTemplate', 'PromptTemplate', (['"""Please tell if a given piece of information is supported by the visual as well as textual context information.\nYou need to answer with either YES or NO.\nAnswer YES if any of the image(s) and textual context supports the information, eve... |
"""RAG LLM."""
from typing import Any
from llama_index.core.constants import DEFAULT_CONTEXT_WINDOW
from llama_index.core.llms import (
CompletionResponse,
CompletionResponseGen,
CustomLLM,
LLMMetadata,
)
from llama_index.core.llms.callbacks import llm_completion_callback
from pydantic import Field
f... | [
"llama_index.core.llms.CompletionResponse",
"llama_index.core.llms.callbacks.llm_completion_callback",
"llama_index.core.llms.LLMMetadata"
] | [((751, 795), 'pydantic.Field', 'Field', (['...'], {'description': '"""The MetaGPT\'s LLM."""'}), '(..., description="The MetaGPT\'s LLM.")\n', (756, 795), False, 'from pydantic import Field\n'), ((822, 877), 'metagpt.utils.token_counter.TOKEN_MAX.get', 'TOKEN_MAX.get', (['config.llm.model', 'DEFAULT_CONTEXT_WINDOW'], ... |
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.storage.storage_context.StorageContext.from_defaults",
"llama_index.llm_predictor.StructuredLLMPredictor",
"llama_index.llms.openai.OpenAI",
"llama_index.LLMPredictor",
"llama_index.ServiceContext.from_defaults",
"llama_index.indices.loading.load_index_from_storage",
"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'}),... |
import logging
import tempfile
from pathlib import Path
from typing import AnyStr, BinaryIO
from injector import inject, singleton
from llama_index.core.node_parser import SentenceWindowNodeParser
from llama_index.core.storage import StorageContext
from private_gpt.components.embedding.embedding_component import Embe... | [
"llama_index.core.storage.StorageContext.from_defaults",
"llama_index.core.node_parser.SentenceWindowNodeParser.from_defaults"
] | [((788, 815), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (805, 815), False, 'import logging\n'), ((1171, 1346), 'llama_index.core.storage.StorageContext.from_defaults', 'StorageContext.from_defaults', ([], {'vector_store': 'vector_store_component.vector_store', 'docstore': 'node_store... |
import logging
from llama_index.prompts.prompts import QuestionAnswerPrompt
QUESTION_ANSWER_PROMPT_TMPL_CN = (
"上下文信息如下所示： \n"
"---------------------\n"
"{context_str}"
"\n---------------------\n"
"我的问题是：{query_str}\n"
)
QUESTION_ANSWER_PROMPT_TMPL_EN = (
"Context information is below. \n"
... | [
"llama_index.prompts.prompts.QuestionAnswerPrompt"
] | [((510, 561), 'logging.info', 'logging.info', (['"""=====> Use English prompt template!"""'], {}), "('=====> Use English prompt template!')\n", (522, 561), False, 'import logging\n'), ((577, 629), 'llama_index.prompts.prompts.QuestionAnswerPrompt', 'QuestionAnswerPrompt', (['QUESTION_ANSWER_PROMPT_TMPL_EN'], {}), '(QUE... |
from llama_index.core import Document
from llama_index.core.node_parser import CodeSplitter
from llama_index.retrievers.bm25 import BM25Retriever
from llama_index.core import VectorStoreIndex
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core import get_response_synthesizer
from llama_... | [
"llama_index.core.node_parser.CodeSplitter",
"llama_index.core.query_engine.RetrieverQueryEngine",
"llama_index.retrievers.bm25.BM25Retriever.from_defaults",
"llama_index.core.VectorStoreIndex",
"llama_index.core.PromptTemplate",
"llama_index.core.Document",
"llama_index.core.get_response_synthesizer"
] | [((869, 964), 'llama_index.core.node_parser.CodeSplitter', 'CodeSplitter', ([], {'language': '"""html"""', 'chunk_lines': '(40)', 'chunk_lines_overlap': '(200)', 'max_chars': 'MAX_CHARS'}), "(language='html', chunk_lines=40, chunk_lines_overlap=200,\n max_chars=MAX_CHARS)\n", (881, 964), False, 'from llama_index.cor... |
import io
from pathlib import Path
from typing import List
import requests
## TODO: Make this token-aware.
def convert_messages_to_str(messages: List[dict]) -> str:
"""convert messages in OpenAI API format into a single string.
Args:
messages List[dict]: A list of dictionary in the following format:
... | [
"llama_index.core.node_parser.text.SentenceSplitter",
"llama_index.core.readers.download.download_loader",
"llama_index.core.Document"
] | [((3916, 3994), 'llama_index.core.node_parser.text.SentenceSplitter', 'SentenceSplitter', ([], {'chunk_size': 'chunk_size', 'chunk_overlap': 'chunk_overlap'}), '(chunk_size=chunk_size, chunk_overlap=chunk_overlap, **kwargs)\n', (3932, 3994), False, 'from llama_index.core.node_parser.text import SentenceSplitter\n'), ((... |
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'}), '([], ... |
import datetime
import io
import json
import os
import sys
import tempfile
import traceback
from typing import Optional, Dict, Any
import aiohttp
import re
import discord
import openai
from bs4 import BeautifulSoup
from discord.ext import pages
from langchain.utilities import (
GoogleSearchAPIWrapper,
)
from langc... | [
"llama_index.SimpleDirectoryReader",
"llama_index.response_synthesizers.get_response_synthesizer",
"llama_index.ServiceContext.from_defaults",
"llama_index.OpenAIEmbedding",
"llama_index.retrievers.VectorIndexRetriever",
"llama_index.GPTVectorStoreIndex.from_documents",
"llama_index.query_engine.Retriev... | [((3894, 3925), 'services.environment_service.EnvService.get_allowed_guilds', 'EnvService.get_allowed_guilds', ([], {}), '()\n', (3923, 3925), False, 'from services.environment_service import EnvService\n'), ((3948, 3984), 'services.environment_service.EnvService.get_user_input_api_keys', 'EnvService.get_user_input_api... |
"""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.vector_stores.lancedb._to_llama_similarities",
"llama_index.schema.RelatedNodeInfo",
"llama_index.vector_stores.lancedb._to_lance_filter"
] | [((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 llama_index import (
VectorStoreIndex,
SimpleDirectoryReader,
load_index_from_storage,
StorageContext,
Prompt,
)
from agenta import post, FloatParam, TextParam
import os
def ingest():
if not os.path.exists("./storage"):
documents = SimpleDirectoryReader("data").load_data()
... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.SimpleDirectoryReader",
"llama_index.StorageContext.from_defaults",
"llama_index.Prompt",
"llama_index.load_index_from_storage"
] | [((957, 971), 'llama_index.Prompt', 'Prompt', (['prompt'], {}), '(prompt)\n', (963, 971), False, 'from llama_index import VectorStoreIndex, SimpleDirectoryReader, load_index_from_storage, StorageContext, Prompt\n'), ((222, 249), 'os.path.exists', 'os.path.exists', (['"""./storage"""'], {}), "('./storage')\n", (236, 249... |
from typing import Dict, List, Optional
import logging
from pathlib import Path
from datetime import datetime
import s3fs
from fsspec.asyn import AsyncFileSystem
from llama_index import (
ServiceContext,
VectorStoreIndex,
StorageContext,
load_indices_from_storage,
)
from llama_index.vector_stores.types ... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.readers.file.docs_reader.PDFReader",
"llama_index.ServiceContext.from_defaults",
"llama_index.llms.OpenAI",
"llama_index.StorageContext.from_defaults",
"llama_index.llms.ChatMessage",
"llama_index.load_indices_from_storage",
"llama_index.node... | [((1919, 1946), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1936, 1946), False, 'import logging\n'), ((1994, 2014), 'nest_asyncio.apply', 'nest_asyncio.apply', ([], {}), '()\n', (2012, 2014), False, 'import nest_asyncio\n'), ((2151, 2261), 's3fs.S3FileSystem', 's3fs.S3FileSystem', ([]... |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: MIT
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without res... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.SimpleDirectoryReader",
"llama_index.vector_stores.FaissVectorStore",
"llama_index.vector_stores.FaissVectorStore.from_persist_dir",
"llama_index.StorageContext.from_defaults",
"llama_index.load_index_from_storage"
] | [((1848, 1881), 'os.path.exists', 'os.path.exists', (['"""storage-default"""'], {}), "('storage-default')\n", (1862, 1881), False, 'import faiss, os\n'), ((1886, 1915), 'os.listdir', 'os.listdir', (['"""storage-default"""'], {}), "('storage-default')\n", (1896, 1915), False, 'import faiss, os\n'), ((1991, 2043), 'llama... |
"""Memory for agents."""
import os
from llama_index.core import GPTVectorStoreIndex, GPTListIndex
from llama_index.core import Document, ServiceContext
from llama_index.legacy import LLMPredictor
from llama_index.core import StorageContext, load_index_from_storage
from langchain_community.chat_models import ChatOpenAI
... | [
"llama_index.core.StorageContext.from_defaults",
"llama_index.core.load_index_from_storage",
"llama_index.core.ServiceContext.from_defaults",
"llama_index.core.Document"
] | [((337, 376), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.INFO'}), '(level=logging.INFO)\n', (356, 376), False, 'import logging\n'), ((386, 413), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (403, 413), False, 'import logging\n'), ((1330, 1377), 'llama_index.co... |
import logging
from typing import List, Optional
import requests
from llama_index.callbacks.base import CallbackManager
from llama_index.constants import DEFAULT_SIMILARITY_TOP_K
from llama_index.core.base_retriever import BaseRetriever
from llama_index.indices.managed.zilliz.base import ZillizCloudPipelineIndex
from... | [
"llama_index.schema.TextNode"
] | [((511, 538), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (528, 538), False, 'import logging\n'), ((1978, 2052), 'requests.post', 'requests.post', (['self.search_pipeline_url'], {'headers': 'self.headers', 'json': 'params'}), '(self.search_pipeline_url, headers=self.headers, json=param... |
import sys
import os
# this is needed to import classes from the API. it will be removed when the worker is refactored
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../')))
import logging
import json
import pika
import time
import fitz
from pathlib import Path
from shared.job_status impo... | [
"llama_index.download_loader"
] | [((847, 916), 'logging.basicConfig', 'logging.basicConfig', ([], {'filename': '"""./extract-log.txt"""', 'level': 'logging.INFO'}), "(filename='./extract-log.txt', level=logging.INFO)\n", (866, 916), False, 'import logging\n'), ((917, 993), 'logging.basicConfig', 'logging.basicConfig', ([], {'filename': '"""./extract-e... |
import os
import json
import time
from typing import List
import faiss
import pypdf
import random
import itertools
import text_utils
import pandas as pd
import altair as alt
import streamlit as st
from io import StringIO
from llama_index import Document
from langchain.llms import Anthropic
from langchain.chains import ... | [
"llama_index.LangchainEmbedding"
] | [((13312, 13350), 'streamlit.sidebar.image', 'st.sidebar.image', (['"""img/diagnostic.jpg"""'], {}), "('img/diagnostic.jpg')\n", (13328, 13350), True, 'import streamlit as st\n'), ((15130, 15159), 'streamlit.header', 'st.header', (['"""`Auto-evaluator`"""'], {}), "('`Auto-evaluator`')\n", (15139, 15159), True, 'import ... |
import numpy as np
import json
from fastapi import HTTPException, FastAPI, websockets
import json # for JSON parsing and packing
# for easy async code
# for absolute project paths
import os
# astro chat engines
from chat_objects import *
from chat_engine import ChatEngine
from vedastro import * # install via pip... | [
"llama_index.core.VectorStoreIndex.from_documents",
"llama_index.core.SimpleDirectoryReader",
"llama_index.core.SummaryIndex.from_documents"
] | [((816, 841), 'fastapi.FastAPI', 'FastAPI', ([], {'title': '"""Chat API"""'}), "(title='Chat API')\n", (823, 841), False, 'from fastapi import HTTPException, FastAPI, websockets\n'), ((1553, 1570), 'chat_engine.ChatEngine', 'ChatEngine', (['"""MK7"""'], {}), "('MK7')\n", (1563, 1570), False, 'from chat_engine import Ch... |
import os
import pygments
from prompt_toolkit import print_formatted_text
from prompt_toolkit.formatted_text import PygmentsTokens
from pygments_markdown_lexer import MarkdownLexer
from typing import Any
from langchain import ConversationChain, OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.chains.c... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.SimpleDirectoryReader",
"llama_index.ServiceContext.from_defaults",
"llama_index.StorageContext.from_defaults",
"llama_index.load_index_from_storage"
] | [((2499, 2523), 'gpttrace.config.cfg.get', 'cfg.get', (['"""DEFAULT_MODEL"""'], {}), "('DEFAULT_MODEL')\n", (2506, 2523), False, 'from gpttrace.config import cfg\n'), ((2534, 2582), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'model_name': 'model_name', 'temperature': '(0)'}), '(model_name=model_name, tempe... |
from typing import List
import streamlit as st
import os, shutil
from llama_index import VectorStoreIndex, ServiceContext, Document
from llama_index.llms import OpenAI
from llama_index import SimpleDirectoryReader
def load_data():
reader = SimpleDirectoryReader(input_dir="src/data", recursive=True)
docs = rea... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.SimpleDirectoryReader",
"llama_index.llms.OpenAI"
] | [((246, 305), 'llama_index.SimpleDirectoryReader', 'SimpleDirectoryReader', ([], {'input_dir': '"""src/data"""', 'recursive': '(True)'}), "(input_dir='src/data', recursive=True)\n", (267, 305), False, 'from llama_index import SimpleDirectoryReader\n'), ((695, 766), 'llama_index.VectorStoreIndex.from_documents', 'Vector... |
import os
import logging
from typing import Iterator
from llama_index import (
LLMPredictor,
StorageContext,
VectorStoreIndex,
load_index_from_storage,
get_response_synthesizer,
ServiceContext,
)
from langchain.chat_models import ChatOpenAI
from llama_index.indices.postprocessor import Similarit... | [
"llama_index.get_response_synthesizer",
"llama_index.ServiceContext.from_defaults",
"llama_index.retrievers.VectorIndexRetriever",
"llama_index.StorageContext.from_defaults",
"llama_index.text_splitter.TokenTextSplitter",
"llama_index.node_parser.SimpleNodeParser.from_defaults",
"llama_index.callbacks.b... | [((780, 819), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.INFO'}), '(level=logging.INFO)\n', (799, 819), False, 'import logging\n'), ((829, 856), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (846, 856), False, 'import logging\n'), ((2237, 2264), 'os.walk', 'os.... |
import json
from typing import Any, Hashable
import nest_asyncio
import pandas as pd
import requests
from llama_index import ServiceContext
from llama_index.llms import OpenAI
from ragas import metrics
from tenacity import retry, stop_after_attempt, wait_random_exponential
from tqdm import tqdm
from wandbot.evaluatio... | [
"llama_index.llms.OpenAI"
] | [((689, 709), 'wandbot.utils.get_logger', 'get_logger', (['__name__'], {}), '(__name__)\n', (699, 709), False, 'from wandbot.utils import cachew, get_logger\n'), ((711, 731), 'nest_asyncio.apply', 'nest_asyncio.apply', ([], {}), '()\n', (729, 731), False, 'import nest_asyncio\n'), ((885, 989), 'wandbot.evaluation.eval.... |
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.llms.OpenAI",
"llama_index.vector_stores.types.MetadataInfo",
"llama_index.set_global_service_context",
"llama_index.indices.vector_store.retrievers.VectorIndexAutoRetriever",
"llama_index.agent.OpenAIAgent.from_tools",
"llama_... | [((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', (['"""#... |
from langchain.prompts import PromptTemplate
from langchain.output_parsers import PydanticOutputParser
from llama_index import VectorStoreIndex, ServiceContext, StorageContext
from llama_index.vector_stores import FaissVectorStore
from llama_index.tools import QueryEngineTool, ToolMetadata
from llama_index.query_engin... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.vector_stores.FaissVectorStore",
"llama_index.node_parser.UnstructuredElementNodeParser",
"llama_index.ServiceContext.from_defaults",
"llama_index.tools.ToolMetadata",
"llama_index.StorageContext.from_defaults",
"llama_index.VectorStoreIndex",
... | [((1272, 1416), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""Annual Report Analyzer"""', 'page_icon': '""":card_index_dividers:"""', 'initial_sidebar_state': '"""expanded"""', 'layout': '"""wide"""'}), "(page_title='Annual Report Analyzer', page_icon=\n ':card_index_dividers:', initial_... |
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.tools.ToolOutput",
"llama_index.core.chat_engine.utils.response_gen_from_query_engine",
"llama_index.core.prompts.base.PromptTemplate",
"llama_index.core.callbacks.CallbackManager",
"llama_index.core.settings.callback_manager_from_settings_or_context",
"llama_index.core.base.llms.types.C... | [((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 Settings import settings
from llama_index.core import VectorStoreIndex, StorageContext, load_index_from_storage, SimpleDirectoryReader, Settings, get_response_synthesizer
from llama_index.embeddings.ollama import OllamaEmbedding
from llama_index.llms.ollama import Ollama
from llama_index.readers.web import MainCon... | [
"llama_index.core.VectorStoreIndex.from_documents",
"llama_index.readers.web.MainContentExtractorReader",
"llama_index.core.StorageContext.from_defaults",
"llama_index.core.load_index_from_storage",
"llama_index.core.postprocessor.SimilarityPostprocessor",
"llama_index.core.SimpleDirectoryReader",
"llam... | [((608, 678), 'llama_index.embeddings.ollama.OllamaEmbedding', 'OllamaEmbedding', ([], {'base_url': 'settings.host', 'model_name': '"""nomic-embed-text"""'}), "(base_url=settings.host, model_name='nomic-embed-text')\n", (623, 678), False, 'from llama_index.embeddings.ollama import OllamaEmbedding\n'), ((861, 909), 'lla... |
import logging
import os
import sys
from pathlib import Path
from dotenv import load_dotenv
from langchain.llms.octoai_endpoint import OctoAIEndpoint as OctoAiCloudLLM
from langchain.embeddings.octoai_embeddings import OctoAIEmbeddings
from llama_index import (
LLMPredictor,
ServiceContext,
download_loade... | [
"llama_index.ServiceContext.from_defaults",
"llama_index.GPTVectorStoreIndex.from_documents",
"llama_index.download_loader",
"llama_index.LLMPredictor"
] | [((557, 578), 'os.chdir', 'os.chdir', (['current_dir'], {}), '(current_dir)\n', (565, 578), False, 'import os\n'), ((611, 654), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.CRITICAL'}), '(level=logging.CRITICAL)\n', (630, 654), False, 'import logging\n'), ((685, 698), 'dotenv.load_dotenv', 'loa... |
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')] |
# Databricks notebook source
# MAGIC %md
# MAGIC # Understanding Embeddings
# MAGIC Embeddings are just vectors and we can visualise and analyse them as such \
# MAGIC A common way to look at and explore embeddings is to use TSNE visualisations. \
# MAGIC This can be applied to our VectorDB Data too.
# MAGIC
# MAGIC Se... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.evaluation.DatasetGenerator.from_documents",
"llama_index.download_loader",
"llama_index.LLMPredictor",
"llama_index.ServiceContext.from_defaults",
"llama_index.callbacks.OpenInferenceCallbackHandler",
"llama_index.set_global_service_context",
... | [((3171, 3216), 'numpy.array', 'np.array', (['encoded_sentences'], {'dtype': 'np.float32'}), '(encoded_sentences, dtype=np.float32)\n', (3179, 3216), True, 'import numpy as np\n'), ((3349, 3397), 'faiss.IndexFlatIP', 'faiss.IndexFlatIP', (['vector_format_encode.shape[1]'], {}), '(vector_format_encode.shape[1])\n', (336... |
import asyncio
import io
import json
import logging
import os
import tempfile
import time
from logging import getLogger
from fastapi import APIRouter, Request, status
from fastapi.encoders import jsonable_encoder
from typing import List, Tuple
# This is here to satisfy runtime import needs
# that pyinstaller appears... | [
"llama_index.schema.TextNode",
"llama_index.schema.RelatedNodeInfo",
"llama_index.Document"
] | [((975, 986), 'logging.getLogger', 'getLogger', ([], {}), '()\n', (984, 986), False, 'from logging import getLogger\n'), ((1001, 1023), 'snowflake.SnowflakeGenerator', 'SnowflakeGenerator', (['(42)'], {}), '(42)\n', (1019, 1023), False, 'from snowflake import SnowflakeGenerator\n'), ((1034, 1045), 'fastapi.APIRouter', ... |
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... |
# coding=utf-8
import glob, pprint, traceback, pydoc, threading, asyncio
import os, re, webbrowser, platform, zipfile, subprocess, config
from prompt_toolkit.input import create_input
from prompt_toolkit.keys import Keys
from datetime import date
from db.StatisticsWordsSqlite import StatisticsWordsSqlite
from util.Vlc... | [
"llama_index.SimpleDirectoryReader",
"llama_index.ServiceContext.from_defaults",
"llama_index.llms.OpenAI",
"llama_index.StorageContext.from_defaults",
"llama_index.load_index_from_storage",
"llama_index.GPTVectorStoreIndex.from_documents"
] | [((63135, 63172), 're.split', 're.split', (['"""[ ]*?:::[ ]*?"""', 'command', '(1)'], {}), "('[ ]*?:::[ ]*?', command, 1)\n", (63143, 63172), False, 'import os, re, webbrowser, platform, zipfile, subprocess, config\n'), ((65168, 65182), 'db.BiblesSqlite.BiblesSqlite', 'BiblesSqlite', ([], {}), '()\n', (65180, 65182), F... |
import os
import pickle
import requests
from flask import Flask, jsonify, request
from flask_cors import CORS
from google.auth.transport.requests import Request
from google_auth_oauthlib.flow import InstalledAppFlow
from llama_index import GPTSimpleVectorIndex, download_loader
os.environ['OPENAI_API_KEY'] = 'xxxx'
d... | [
"llama_index.GPTSimpleVectorIndex",
"llama_index.download_loader"
] | [((969, 984), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (974, 984), False, 'from flask import Flask, jsonify, request\n'), ((985, 994), 'flask_cors.CORS', 'CORS', (['app'], {}), '(app)\n', (989, 994), False, 'from flask_cors import CORS\n'), ((461, 491), 'os.path.exists', 'os.path.exists', (['"""token... |
import os
import zipfile
from pathlib import Path
from dotenv import load_dotenv
from llama_index.core import (KnowledgeGraphIndex, SimpleDirectoryReader,
StorageContext, load_index_from_storage)
from llama_index.graph_stores.neo4j import Neo4jGraphStore
from llama_index.llms.openai impor... | [
"llama_index.core.KnowledgeGraphIndex.from_documents",
"llama_index.core.StorageContext.from_defaults",
"llama_index.core.SimpleDirectoryReader",
"llama_index.graph_stores.neo4j.Neo4jGraphStore"
] | [((464, 497), 'dotenv.load_dotenv', 'load_dotenv', ([], {'dotenv_path': 'env_path'}), '(dotenv_path=env_path)\n', (475, 497), False, 'from dotenv import load_dotenv\n'), ((508, 530), 'os.getenv', 'os.getenv', (['"""NEO4J_URI"""'], {}), "('NEO4J_URI')\n", (517, 530), False, 'import os\n'), ((541, 568), 'os.getenv', 'os.... |
import tempfile, os
from routers.utils.engine import Engine
from llama_index import download_loader
from fastapi import APIRouter, UploadFile, HTTPException
engine = Engine()
router = APIRouter()
@router.post("/pdf")
async def file(upload_file: UploadFile, namespace: str):
"""
Loader: https://llamahub.ai/l/fi... | [
"llama_index.download_loader"
] | [((167, 175), 'routers.utils.engine.Engine', 'Engine', ([], {}), '()\n', (173, 175), False, 'from routers.utils.engine import Engine\n'), ((185, 196), 'fastapi.APIRouter', 'APIRouter', ([], {}), '()\n', (194, 196), False, 'from fastapi import APIRouter, UploadFile, HTTPException\n'), ((380, 418), 'os.path.splitext', 'o... |
import os
from llama_index import GPTSimpleVectorIndex, SimpleDirectoryReader, download_loader
from llama_index import Document, LLMPredictor, PromptHelper, QuestionAnswerPrompt, JSONReader
from langchain.llms import OpenAIChat, OpenAI
from zipfile import ZipFile
from googlesearch import search as google_search
from ba... | [
"llama_index.GPTSimpleVectorIndex",
"llama_index.download_loader",
"llama_index.QuestionAnswerPrompt",
"llama_index.PromptHelper",
"llama_index.GPTSimpleVectorIndex.load_from_disk",
"llama_index.JSONReader",
"llama_index.Document"
] | [((2322, 2382), 'llama_index.PromptHelper', 'PromptHelper', (['max_input_size', 'num_outputs', 'max_chunk_overlap'], {}), '(max_input_size, num_outputs, max_chunk_overlap)\n', (2334, 2382), False, 'from llama_index import Document, LLMPredictor, PromptHelper, QuestionAnswerPrompt, JSONReader\n'), ((2396, 2490), 'llama_... |
import json
from langchain.prompts import PromptTemplate
import tiktoken
from llama_index.callbacks import CallbackManager, TokenCountingHandler
from llama_index.node_parser import SimpleNodeParser
from llama_index.vector_stores import WeaviateVectorStore
from llama_index import (
VectorStoreIndex, SimpleDirector... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.SimpleDirectoryReader",
"llama_index.VectorStoreIndex.from_vector_store",
"llama_index.VectorStoreIndex",
"llama_index.ServiceContext.from_defaults",
"llama_index.StorageContext.from_defaults",
"llama_index.node_parser.SimpleNodeParser.from_def... | [((499, 515), 'app_state.init_app_state', 'init_app_state', ([], {}), '()\n', (513, 515), False, 'from app_state import state, init_app_state, _set_state_cb\n'), ((1129, 1175), 'streamlit.cache_data', 'st.cache_data', ([], {'ttl': '(60 * 60)', 'show_spinner': '(False)'}), '(ttl=60 * 60, show_spinner=False)\n', (1142, 1... |
import logging
from typing import List, Optional
import tiktoken
from llama_index.indices.base import BaseIndex
from transformers import AutoTokenizer
from tiktoken import Encoding
from llama_index import (
VectorStoreIndex,
SimpleDirectoryReader,
ServiceContext,
StorageContext,
)
from llama_index.lan... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.SimpleDirectoryReader",
"llama_index.langchain_helpers.text_splitter.TokenTextSplitter",
"llama_index.ServiceContext.from_defaults",
"llama_index.StorageContext.from_defaults",
"llama_index.node_parser.SimpleNodeParser",
"llama_index.load_index... | [((697, 738), 'transformers.AutoTokenizer.from_pretrained', 'AutoTokenizer.from_pretrained', (['model_name'], {}), '(model_name)\n', (726, 738), False, 'from transformers import AutoTokenizer\n'), ((1440, 1628), 'llama_index.langchain_helpers.text_splitter.TokenTextSplitter', 'TokenTextSplitter', ([], {'chunk_size': 'c... |
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Dict, List, Tuple
from uuid import uuid4
from llama_index.legacy.llm_predictor.vellum.types import (
VellumCompiledPrompt,
VellumRegisteredPrompt,
)
from llama_index.legacy.llm_predictor.vellum.utils import convert_to_kebab_case
from ll... | [
"llama_index.legacy.llm_predictor.vellum.types.VellumCompiledPrompt",
"llama_index.legacy.llm_predictor.vellum.utils.convert_to_kebab_case",
"llama_index.legacy.llm_predictor.vellum.types.VellumRegisteredPrompt"
] | [((1114, 1144), 'vellum.client.Vellum', 'Vellum', ([], {'api_key': 'vellum_api_key'}), '(api_key=vellum_api_key)\n', (1120, 1144), False, 'from vellum.client import Vellum\n'), ((3100, 3187), 'llama_index.legacy.llm_predictor.vellum.types.VellumCompiledPrompt', 'VellumCompiledPrompt', ([], {'text': 'result.prompt.text'... |
# 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.VectorStoreIndex.from_documents",
"llama_index.SimpleDirectoryReader",
"llama_index.ServiceContext.from_defaults",
"llama_index.vector_stores.MilvusVectorStore",
"llama_index.StorageContext.from_defaults",
"llama_index.embeddings.HuggingFaceEmbedding"
] | [((946, 1086), 'llama_index.vector_stores.MilvusVectorStore', 'MilvusVectorStore', ([], {'uri': '"""http://localhost:19530"""', 'port': '(19530)', 'collection_name': '"""llama_index_prompts_all"""', 'dim': '(384)', 'similarity_metric': '"""L2"""'}), "(uri='http://localhost:19530', port=19530, collection_name\n ='lla... |
import streamlit as st
import logging, sys, os
import openai
from dotenv import load_dotenv
from llama_index.agent import OpenAIAgent
from llama_index.llms import OpenAI
from llama_hub.tools.zapier.base import ZapierToolSpec
#loads dotenv lib to retrieve API keys from .env file
load_dotenv()
openai.api_key = os.getenv... | [
"llama_index.llms.OpenAI"
] | [((280, 293), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (291, 293), False, 'from dotenv import load_dotenv\n'), ((311, 338), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (320, 338), False, 'import logging, sys, os\n'), ((368, 426), 'logging.basicConfig', 'logging.basicC... |
import os
from flask import Flask, render_template, request, redirect
from llama_index import download_loader
from llama_index import LLMPredictor, GPTSimpleVectorIndex, PromptHelper, ServiceContext
from langchain import OpenAI
os.environ["OPENAI_API_KEY"] = "insert_your_key_here"
current_script_path = os.path.dirnam... | [
"llama_index.download_loader",
"llama_index.ServiceContext.from_defaults",
"llama_index.GPTSimpleVectorIndex.from_documents",
"llama_index.PromptHelper",
"llama_index.GPTSimpleVectorIndex.load_from_disk"
] | [((442, 457), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (447, 457), False, 'from flask import Flask, render_template, request, redirect\n'), ((322, 347), 'os.path.abspath', 'os.path.abspath', (['__file__'], {}), '(__file__)\n', (337, 347), False, 'import os\n'), ((360, 401), 'os.path.join', 'os.path.j... |
from llama_index import ServiceContext
from app.context import create_base_context
from app.engine.constants import CHUNK_OVERLAP, CHUNK_SIZE
def create_service_context():
base = create_base_context()
return ServiceContext.from_defaults(
llm=base.llm,
embed_model=base.embed_model,
chu... | [
"llama_index.ServiceContext.from_defaults"
] | [((186, 207), 'app.context.create_base_context', 'create_base_context', ([], {}), '()\n', (205, 207), False, 'from app.context import create_base_context\n'), ((219, 347), 'llama_index.ServiceContext.from_defaults', 'ServiceContext.from_defaults', ([], {'llm': 'base.llm', 'embed_model': 'base.embed_model', 'chunk_size'... |
from portkey_ai import Message, Portkey
from typing import Optional, Union, List, Any, Mapping, cast, Sequence
from portkey_ai.api_resources.utils import PortkeyResponse
from portkey_ai.llms.llama_index.utils import (
IMPORT_ERROR_MESSAGE,
is_chat_model,
modelname_to_contextsize,
)
try:
from llama_ind... | [
"llama_index.llms.base.llm_chat_callback",
"llama_index.llms.base.CompletionResponse",
"llama_index.llms.base.ChatResponse",
"llama_index.bridge.pydantic.PrivateAttr",
"llama_index.llms.base.llm_completion_callback",
"llama_index.llms.base.ChatMessage"
] | [((866, 879), 'llama_index.bridge.pydantic.PrivateAttr', 'PrivateAttr', ([], {}), '()\n', (877, 879), False, 'from llama_index.bridge.pydantic import PrivateAttr\n'), ((2779, 2804), 'llama_index.llms.base.llm_completion_callback', 'llm_completion_callback', ([], {}), '()\n', (2802, 2804), False, 'from llama_index.llms.... |
# Import the required modules from the llama_index library
from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader, GPTListIndex
# Function to perform a search query on the documents
def search(query):
# Load the documents from the 'data' directory
documents = SimpleDirectoryReader('data').loa... | [
"llama_index.SimpleDirectoryReader",
"llama_index.GPTVectorStoreIndex.from_documents"
] | [((427, 472), 'llama_index.GPTVectorStoreIndex.from_documents', 'GPTVectorStoreIndex.from_documents', (['documents'], {}), '(documents)\n', (461, 472), False, 'from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader, GPTListIndex\n'), ((287, 316), 'llama_index.SimpleDirectoryReader', 'SimpleDirectoryReader',... |
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.callbacks.CallbackManager",
"llama_index.node_parser.SimpleNodeParser.from_defaults",
"llama_index.prompts.PromptTemplate"
] | [((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.SimpleDirectoryReader",
"llama_index.ServiceContext.from_defaults",
"llama_index.tools.ToolMetadata",
"llama_index.set_global_service_context",
"llama_index.query_engine.SubQuestionQueryEngine.from_defaults",
"llama_index.GPTVectorStoreIndex.from_documents"
] | [((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... |
from llama_index.schema import Document
from llmsherpa.readers import LayoutPDFReader
import pandas as pd
llmsherpa_api_url = "https://readers.llmsherpa.com/api/document/developer/parseDocument?renderFormat=all"
def parse_source_item(item):
documents = []
if item['type'] == 'csv':
data = pd.read_cs... | [
"llama_index.schema.Document"
] | [((310, 335), 'pandas.read_csv', 'pd.read_csv', (["item['path']"], {}), "(item['path'])\n", (321, 335), True, 'import pandas as pd\n'), ((675, 709), 'llmsherpa.readers.LayoutPDFReader', 'LayoutPDFReader', (['llmsherpa_api_url'], {}), '(llmsherpa_api_url)\n', (690, 709), False, 'from llmsherpa.readers import LayoutPDFRe... |
"""Adapted from https://github.com/jerryjliu/llama_index/blob/main/docs/examples/finetuning/embeddings/eval_utils.py"""
from llama_index.schema import TextNode
from llama_index import ServiceContext, VectorStoreIndex
import pandas as pd
from tqdm import tqdm
def evaluate(
dataset,
embed_model,
top_k=10,
... | [
"llama_index.ServiceContext.from_defaults",
"llama_index.schema.TextNode",
"llama_index.VectorStoreIndex"
] | [((446, 499), 'llama_index.ServiceContext.from_defaults', 'ServiceContext.from_defaults', ([], {'embed_model': 'embed_model'}), '(embed_model=embed_model)\n', (474, 499), False, 'from llama_index import ServiceContext, VectorStoreIndex\n'), ((587, 663), 'llama_index.VectorStoreIndex', 'VectorStoreIndex', (['nodes'], {'... |
from llama_index.core.llms import ChatMessage, MessageRole
from llama_index.core.prompts import ChatPromptTemplate
chat_text_qa_msgs = [
ChatMessage(
role=MessageRole.SYSTEM,
content=("""You are an expert Q&A system that is trusted around the world. You are answering texts on Hindu scriptures. Alwa... | [
"llama_index.core.prompts.ChatPromptTemplate",
"llama_index.core.llms.ChatMessage"
] | [((1612, 1649), 'llama_index.core.prompts.ChatPromptTemplate', 'ChatPromptTemplate', (['chat_text_qa_msgs'], {}), '(chat_text_qa_msgs)\n', (1630, 1649), False, 'from llama_index.core.prompts import ChatPromptTemplate\n'), ((2678, 2724), 'llama_index.core.prompts.ChatPromptTemplate', 'ChatPromptTemplate', (['training_ch... |
import sys
sys.stdout.reconfigure(encoding="utf-8")
sys.stdin.reconfigure(encoding="utf-8")
import streamlit as st
import streamlit.components.v1 as components
import re
import random
CODE_BUILD_KG = """
# 准备 GraphStore
os.environ['NEBULA_USER'] = "root"
os.environ['NEBULA_PASSWORD'] = "nebula" # default passwor... | [
"llama_index.storage.storage_context.StorageContext.from_defaults",
"llama_index.LLMPredictor",
"llama_index.graph_stores.NebulaGraphStore",
"llama_index.ServiceContext.from_defaults",
"llama_index.query_engine.KnowledgeGraphQueryEngine",
"llama_index.llms.AzureOpenAI"
] | [((12, 52), 'sys.stdout.reconfigure', 'sys.stdout.reconfigure', ([], {'encoding': '"""utf-8"""'}), "(encoding='utf-8')\n", (34, 52), False, 'import sys\n'), ((53, 92), 'sys.stdin.reconfigure', 'sys.stdin.reconfigure', ([], {'encoding': '"""utf-8"""'}), "(encoding='utf-8')\n", (74, 92), False, 'import sys\n'), ((2988, 3... |
# wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10q/uber_10q_march_2022.pdf' -O './data/uber_10q_march_2022.pdf'
# wget "https://meetings.wmo.int/Cg-19/PublishingImages/SitePages/FINAC-43/7%20-%20EC-77-Doc%205%20Financial%20Statements%20for%202022%20(FINAC).pptx" -O './data/prese... | [
"llama_index.core.VectorStoreIndex.from_documents",
"llama_index.core.StorageContext.from_defaults",
"llama_index.embeddings.fastembed.FastEmbedEmbedding",
"llama_index.vector_stores.qdrant.QdrantVectorStore",
"llama_index.llms.groq.Groq"
] | [((378, 398), 'nest_asyncio.apply', 'nest_asyncio.apply', ([], {}), '()\n', (396, 398), False, 'import nest_asyncio\n'), ((466, 479), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (477, 479), False, 'from dotenv import load_dotenv\n'), ((561, 593), 'os.getenv', 'os.getenv', (['"""LLAMA_CLOUD_API_KEY"""'], {}),... |
from llama_index.core import (
SimpleDirectoryReader,
VectorStoreIndex,
set_global_handler
)
import phoenix as px
px.launch_app()
set_global_handler("arize_phoenix")
documents = SimpleDirectoryReader('files').load_data()
index = VectorStoreIndex.from_documents(documents)
qe = index.as_query_engine()
res... | [
"llama_index.core.VectorStoreIndex.from_documents",
"llama_index.core.SimpleDirectoryReader",
"llama_index.core.set_global_handler"
] | [((129, 144), 'phoenix.launch_app', 'px.launch_app', ([], {}), '()\n', (142, 144), True, 'import phoenix as px\n'), ((145, 180), 'llama_index.core.set_global_handler', 'set_global_handler', (['"""arize_phoenix"""'], {}), "('arize_phoenix')\n", (163, 180), False, 'from llama_index.core import SimpleDirectoryReader, Vect... |
import os
import time
import asyncio
from dotenv import load_dotenv
from backend.eval import(
generate_eval_questions
)
from backend.llm import get_llm
from backend.ingestion import (
load_markdown_docs,
create_vector_index_locally
)
from backend.eval import (
evaluate_query_engine
)
from llama_index... | [
"llama_index.ServiceContext.from_defaults",
"llama_index.query_engine.SubQuestionQueryEngine.from_defaults",
"llama_index.evaluation.ResponseEvaluator"
] | [((786, 816), 'backend.llm.get_llm', 'get_llm', (['(1)', '"""gpt-3.5-turbo"""', '(0)'], {}), "(1, 'gpt-3.5-turbo', 0)\n", (793, 816), False, 'from backend.llm import get_llm\n'), ((837, 874), 'llama_index.ServiceContext.from_defaults', 'ServiceContext.from_defaults', ([], {'llm': 'llm'}), '(llm=llm)\n', (865, 874), Fal... |
import streamlit as st
import openai
import os.path as op
from llama_index import ServiceContext
from llama_index import VectorStoreIndex,download_loader
from pathlib import Path
from llama_index.retrievers import VectorIndexRetriever
from llama_index.llms import OpenAI
# specify path to CSV file, OPENAI api_key, and... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.ServiceContext.from_defaults",
"llama_index.llms.OpenAI",
"llama_index.download_loader"
] | [((389, 409), 'os.path.exists', 'op.exists', (['FILE_PATH'], {}), '(FILE_PATH)\n', (398, 409), True, 'import os.path as op\n'), ((520, 669), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""Chatbot for doctor appointment"""', 'page_icon': '"""🦙"""', 'layout': '"""centered"""', 'initial_sideba... |
from llama_index.core import (ServiceContext, SQLDatabase, VectorStoreIndex,
set_global_service_context)
from llama_index.core.indices.struct_store.sql_query import \
SQLTableRetrieverQueryEngine
from llama_index.core.objects import (ObjectIndex, SQLTableNodeMapping,
... | [
"llama_index.core.set_global_service_context",
"llama_index.core.objects.SQLTableNodeMapping",
"llama_index.core.objects.SQLTableSchema",
"llama_index.core.ServiceContext.from_defaults",
"llama_index.core.objects.ObjectIndex.from_objects",
"llama_index.core.prompts.base.Prompt"
] | [((1002, 1012), 'sqlalchemy.MetaData', 'MetaData', ([], {}), '()\n', (1010, 1012), False, 'from sqlalchemy import MetaData, create_engine\n'), ((1801, 1839), 'llama_index.core.objects.SQLTableNodeMapping', 'SQLTableNodeMapping', (['self.sql_database'], {}), '(self.sql_database)\n', (1820, 1839), False, 'from llama_inde... |
import os
import time
from dotenv import load_dotenv
from llama_index.agent import ReActAgent
from llama_index.tools import FunctionTool
from llama_index.callbacks import LlamaDebugHandler, CallbackManager
import subprocess
from tools.llm import MYLLM
from tools.tools import MYTOOLBOX
load_dotenv()
debug_mode = Tru... | [
"llama_index.callbacks.CallbackManager",
"llama_index.tools.FunctionTool.from_defaults",
"llama_index.callbacks.LlamaDebugHandler",
"llama_index.agent.ReActAgent.from_tools"
] | [((288, 301), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (299, 301), False, 'from dotenv import load_dotenv\n'), ((336, 384), 'llama_index.callbacks.LlamaDebugHandler', 'LlamaDebugHandler', ([], {'print_trace_on_end': 'debug_mode'}), '(print_trace_on_end=debug_mode)\n', (353, 384), False, 'from llama_index.... |
from llama_index.core import Settings, Document, VectorStoreIndex
from llama_index.core.node_parser import SentenceWindowNodeParser
doc = Document(
text="Sentence 1. Sentence 2. Sentence 3."
)
text_splitter = SentenceWindowNodeParser.from_defaults(
window_size=2 ,
window_metadata_key="ContextWindow",
o... | [
"llama_index.core.VectorStoreIndex.from_documents",
"llama_index.core.node_parser.SentenceWindowNodeParser.from_defaults",
"llama_index.core.Document"
] | [((138, 190), 'llama_index.core.Document', 'Document', ([], {'text': '"""Sentence 1. Sentence 2. Sentence 3."""'}), "(text='Sentence 1. Sentence 2. Sentence 3.')\n", (146, 190), False, 'from llama_index.core import Settings, Document, VectorStoreIndex\n'), ((213, 348), 'llama_index.core.node_parser.SentenceWindowNodePa... |
from key1 import KEY
from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader
from llama_index import download_loader, StorageContext
import os
import openai
os.environ['OPENAI_API_KEY'] = KEY
def create_vector(dir):
documents = SimpleDirectoryReader("data").load_data()
index = GPTVectorStoreInde... | [
"llama_index.SimpleDirectoryReader",
"llama_index.GPTVectorStoreIndex.from_documents",
"llama_index.StorageContext.from_defaults"
] | [((302, 347), 'llama_index.GPTVectorStoreIndex.from_documents', 'GPTVectorStoreIndex.from_documents', (['documents'], {}), '(documents)\n', (336, 347), False, 'from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader\n'), ((371, 401), 'llama_index.StorageContext.from_defaults', 'StorageContext.from_defaults',... |
import asyncio
from llama_index.core import KeywordTableIndex
from llama_index.core import SimpleDirectoryReader
async def retrieve(retriever, query, label):
response = await retriever.aretrieve(query)
print(f"{label} retrieved {str(len(response))} nodes")
async def main():
reader = SimpleDirectoryReader(... | [
"llama_index.core.SimpleDirectoryReader",
"llama_index.core.KeywordTableIndex.from_documents"
] | [((298, 328), 'llama_index.core.SimpleDirectoryReader', 'SimpleDirectoryReader', (['"""files"""'], {}), "('files')\n", (319, 328), False, 'from llama_index.core import SimpleDirectoryReader\n'), ((376, 419), 'llama_index.core.KeywordTableIndex.from_documents', 'KeywordTableIndex.from_documents', (['documents'], {}), '(... |
import json
import logging
import streamlit as st
import sys
import os
from dotenv import load_dotenv
from llama_index.chat_engine.types import ChatMode
from llama_index.llms import ChatMessage
from libs.llama_utils import get_llama_memary_index, get_llama_store_index, create_document_index_by_texts, \
create_docum... | [
"llama_index.llms.ChatMessage"
] | [((742, 755), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (753, 755), False, 'from dotenv import load_dotenv\n'), ((719, 740), 'os.path.abspath', 'os.path.abspath', (['""".."""'], {}), "('..')\n", (734, 740), False, 'import os\n'), ((806, 831), 'libs.session.PageSessionState', 'PageSessionState', (['botname'... |
"""Common settings for RAG model"""
from llama_index.core import Settings
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from app.logger.logger import custom_logger
def settings():
"""Set the settings for RAG."""
Settings.llm = OpenAI(model="gpt-3.5-turb... | [
"llama_index.llms.openai.OpenAI",
"llama_index.embeddings.openai.OpenAIEmbedding"
] | [((294, 345), 'llama_index.llms.openai.OpenAI', 'OpenAI', ([], {'model': '"""gpt-3.5-turbo-1106"""', 'temperature': '(0.0)'}), "(model='gpt-3.5-turbo-1106', temperature=0.0)\n", (300, 345), False, 'from llama_index.llms.openai import OpenAI\n'), ((373, 442), 'llama_index.embeddings.openai.OpenAIEmbedding', 'OpenAIEmbed... |
from src._classinits import *
from src._errorsinit import *
from src._readapitokens import *
from src._wikidatasearch import *
from llama_index import LLMPredictor,GPTSimpleVectorIndex,PromptHelper,download_loader
from llama_index import GPTSimpleKeywordTableIndex,GPTListIndex
from llama_index.indices.composabili... | [
"llama_index.download_loader",
"llama_index.ServiceContext.from_defaults",
"llama_index.GPTSimpleVectorIndex.from_documents",
"llama_index.node_parser.SimpleNodeParser",
"llama_index.PromptHelper",
"llama_index.GPTSimpleVectorIndex.load_from_disk"
] | [((601, 654), 'warnings.filterwarnings', 'filterwarnings', (['"""ignore"""'], {'category': 'DeprecationWarning'}), "('ignore', category=DeprecationWarning)\n", (615, 654), False, 'from warnings import filterwarnings\n'), ((659, 705), 'warnings.filterwarnings', 'filterwarnings', (['"""ignore"""'], {'category': 'UserWarn... |
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.VectorStoreIndex.from_documents",
"llama_index.core.llms.mock.MockLLM",
"llama_index.core.callbacks.CallbackManager",
"llama_index.core.SimpleDirectoryReader",
"llama_index.core.MockEmbedding"
] | [((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': '... |
#!/usr/bin/env python3
##
# Chat with PDF document using OpenAI, LlamaIndex, and Gradio
# github.com/deadbits
##
import os
import sys
import logging
import argparse
import openai
import gradio as gr
import urllib.request
from pathlib import Path
from langchain import OpenAI
from llama_index import GPTSimpleVectorInd... | [
"llama_index.ServiceContext.from_defaults",
"llama_index.GPTSimpleVectorIndex.from_documents",
"llama_index.download_loader"
] | [((496, 539), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.CRITICAL'}), '(level=logging.CRITICAL)\n', (515, 539), False, 'import logging\n'), ((797, 825), 'llama_index.download_loader', 'download_loader', (['"""PDFReader"""'], {}), "('PDFReader')\n", (812, 825), False, 'from llama_index import ... |
# Import standard libraries
import os
import re
import json
import getpass
import logging
# Import third-party libraries for web scraping, API interactions, and data processing
import requests
import pandas as pd
from bs4 import BeautifulSoup
# Import libraries for interacting with OpenAI and other language models
im... | [
"llama_index.llms.OpenAI",
"llama_index.embeddings.OpenAIEmbedding"
] | [((854, 893), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.INFO'}), '(level=logging.INFO)\n', (873, 893), False, 'import logging\n'), ((990, 1044), 'llama_index.llms.OpenAI', 'OpenAI', ([], {'model': '"""gpt-4"""', 'temperature': '(0.1)', 'max_tokens': '(512)'}), "(model='gpt-4', temperature=0.... |
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... | [
"llama_index.GPTSimpleVectorIndex.load_from_disk",
"llama_index.GPTSimpleVectorIndex",
"llama_index.SimpleDirectoryReader",
"llama_index.PromptHelper"
] | [((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 os
import sys
from random import randrange
from typing import List, Union
from datetime import date
from dotenv import load_dotenv
from fastapi import FastAPI, File, Request, UploadFile, Path
from fastapi.logger import logger
from fastapi.responses import JSONResponse
from pyngrok import ngrok
import os
import ... | [
"llama_index.vector_stores.weaviate.WeaviateVectorStore",
"llama_index.core.VectorStoreIndex.from_documents",
"llama_index.core.StorageContext.from_defaults",
"llama_index.core.Document",
"llama_index.llms.clarifai.Clarifai"
] | [((1032, 1052), 'nest_asyncio.apply', 'nest_asyncio.apply', ([], {}), '()\n', (1050, 1052), False, 'import nest_asyncio\n'), ((1109, 1123), 'datetime.datetime.now', 'datetime.now', ([], {}), '()\n', (1121, 1123), False, 'from datetime import datetime, timedelta, timezone\n'), ((1214, 1227), 'dotenv.load_dotenv', 'load_... |
from ..launcher.processor_event import ProcessorEvent
from ..launcher.event_type import EventType
from ...llms.utils.max_token_for_model import max_token_for_model, nb_token_for_input
from ...llms.prompt_engine.simple_prompt_engine import SimplePromptEngine
from ...llms.prompt_engine.vector_index_prompt_engine import V... | [
"llama_index.llms.base.ChatMessage"
] | [((3084, 3130), 'llama_index.llms.base.ChatMessage', 'ChatMessage', ([], {'role': '"""system"""', 'content': 'system_msg'}), "(role='system', content=system_msg)\n", (3095, 3130), False, 'from llama_index.llms.base import ChatMessage\n'), ((3144, 3194), 'llama_index.llms.base.ChatMessage', 'ChatMessage', ([], {'role': ... |
import json
from llama_index.core.service_context_elements.llm_predictor import LLMPredictor
from llama_index.core.utilities.sql_wrapper import SQLDatabase
from llama_index.core.response_synthesizers import get_response_synthesizer
from llama_index.embeddings.langchain import LangchainEmbedding
from llama_index.core.re... | [
"llama_index.core.prompts.PromptTemplate",
"llama_index.core.indices.struct_store.sql_query.NLSQLTableQueryEngine",
"llama_index.postprocessor.colbert_rerank.ColbertRerank",
"llama_index.core.tools.ToolMetadata",
"llama_index.core.query_engine.RetrieverQueryEngine",
"llama_index.core.schema.ImageDocument"... | [((3043, 3075), 'app.database.dbc.get_llm_by_name', 'dbc.get_llm_by_name', (['db', 'llmName'], {}), '(db, llmName)\n', (3062, 3075), False, 'from app.database import dbc\n'), ((4079, 4112), 'app.database.dbc.get_project_by_name', 'dbc.get_project_by_name', (['db', 'name'], {}), '(db, name)\n', (4102, 4112), False, 'fro... |
from llama_index.embeddings import OpenAIEmbedding
from llama_index.llms import OpenAI
from llama_index.ingestion import IngestionPipeline
from llama_index.extractors import TitleExtractor, SummaryExtractor
from llama_index.text_splitter import SentenceSplitter
from llama_index.schema import MetadataMode
# from langcha... | [
"llama_index.vector_stores.AstraDBVectorStore",
"llama_index.llms.Ollama",
"llama_index.ServiceContext.from_defaults",
"llama_index.llms.OpenAI",
"llama_index.llms.LangChainLLM",
"llama_index.ingestion.IngestionPipeline",
"llama_index.set_global_service_context",
"llama_index.extractors.TitleExtractor... | [((1967, 2029), 'llama_index.ServiceContext.from_defaults', 'ServiceContext.from_defaults', ([], {'embed_model': 'embed_model', 'llm': 'llm'}), '(embed_model=embed_model, llm=llm)\n', (1995, 2029), False, 'from llama_index import ServiceContext\n'), ((2034, 2077), 'llama_index.set_global_service_context', 'set_global_s... |
#
# Graph Database Ingester For 5e SRD Content
#
import dotenv
dotenv.load_dotenv()
import json
from llama_index.core import Document, KnowledgeGraphIndex, StorageContext, VectorStoreIndex, load_index_from_storage, load_graph_from_storage
from llama_index.core.extractors import TitleExtractor, QuestionsAnsweredExtract... | [
"llama_index.core.KnowledgeGraphIndex.build_index_from_nodes",
"llama_index.core.StorageContext.from_defaults",
"llama_index.core.extractors.QuestionsAnsweredExtractor",
"llama_index.core.ingestion.IngestionPipeline",
"llama_index.core.load_graph_from_storage",
"llama_index.core.Document",
"llama_index.... | [((63, 83), 'dotenv.load_dotenv', 'dotenv.load_dotenv', ([], {}), '()\n', (81, 83), False, 'import dotenv\n'), ((1601, 1652), 'src.file_utils.createOutputFile', 'createOutputFile', (['"""./kg-output"""', '"""srd-graph-result"""'], {}), "('./kg-output', 'srd-graph-result')\n", (1617, 1652), False, 'from src.file_utils i... |
import streamlit as st
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.core.agent import ReActAgent, AgentRunner, ReActChatFormatter
from llama_index.core.retrievers import AutoMergingRetriever
from llama_index.llms.openai import OpenAI
from llama_index.core.tools import FunctionTool
fro... | [
"llama_index.llms.openai.OpenAI",
"llama_index.core.VectorStoreIndex.from_vector_store",
"llama_index.core.StorageContext.from_defaults",
"llama_index.core.agent.ReActChatFormatter.from_defaults",
"llama_index.core.retrievers.AutoMergingRetriever",
"llama_index.core.tools.FunctionTool.from_defaults",
"l... | [((1003, 1065), 'llama_index.llms.openai.OpenAI', 'OpenAI', ([], {'model': 'HYDE_LLM_MODEL', 'temperature': 'HYDE_LLM_TEMPERATURE'}), '(model=HYDE_LLM_MODEL, temperature=HYDE_LLM_TEMPERATURE)\n', (1009, 1065), False, 'from llama_index.llms.openai import OpenAI\n'), ((1127, 1163), 'rag.weaviate_utils.get_weaviate_client... |
"""
Module for setting up a Streamlit application that searches and summarizes content
from the Huberman Lab Podcast. Uses llama_index for data indexing and retrieval, and
OpenAI for text embedding and generation.
"""
import streamlit as st
import openai
from llama_index.core import (
StorageContext,
load_inde... | [
"llama_index.llms.openai.OpenAI",
"llama_index.core.retrievers.VectorIndexRetriever",
"llama_index.core.StorageContext.from_defaults",
"llama_index.core.load_index_from_storage",
"llama_index.core.get_response_synthesizer",
"llama_index.core.postprocessor.SimilarityPostprocessor",
"llama_index.embedding... | [((712, 838), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""TLDHubeR"""', 'page_icon': '"""🧠"""', 'layout': '"""centered"""', 'initial_sidebar_state': '"""auto"""', 'menu_items': 'None'}), "(page_title='TLDHubeR', page_icon='🧠', layout='centered',\n initial_sidebar_state='auto', menu_i... |
#! coding: utf-8
import os
from typing import List
from llama_index import ServiceContext, StorageContext, VectorStoreIndex, SimpleDirectoryReader, TreeIndex
from llama_index.node_parser import SimpleNodeParser
from llama_index.schema import BaseNode
from llama_index.text_splitter import SentenceSplitter
from build... | [
"llama_index.text_splitter.SentenceSplitter",
"llama_index.StorageContext.from_defaults"
] | [((482, 504), 'common.llm.create_llm', 'create_llm', ([], {'timeout': '(60)'}), '(timeout=60)\n', (492, 504), False, 'from common.llm import create_llm\n'), ((1072, 1098), 'os.path.exists', 'os.path.exists', (['index_file'], {}), '(index_file)\n', (1086, 1098), False, 'import os\n'), ((1172, 1202), 'llama_index.Storage... |
from llama_index.chat_engine.types import StreamingAgentChatResponse
import streamlit as st
from llama_index import SimpleDirectoryReader, VectorStoreIndex
# TODO: this is caching the resource globally, not per-session
# Each user session should have their own index
@st.cache_resource(show_spinner="Indexing documents... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.SimpleDirectoryReader"
] | [((270, 325), 'streamlit.cache_resource', 'st.cache_resource', ([], {'show_spinner': '"""Indexing documents..."""'}), "(show_spinner='Indexing documents...')\n", (287, 325), True, 'import streamlit as st\n'), ((430, 472), 'llama_index.VectorStoreIndex.from_documents', 'VectorStoreIndex.from_documents', (['documents'], ... |
from pathlib import Path
from typing import Dict, Iterator, List, cast
from llama_index import ServiceContext, StorageContext
from llama_index.embeddings.cohereai import CohereEmbedding
from llama_index.indices.loading import load_index_from_storage
from llama_index.indices.vector_store import VectorStoreIndex
from ll... | [
"llama_index.embeddings.cohereai.CohereEmbedding",
"llama_index.response_synthesizers.get_response_synthesizer",
"llama_index.ServiceContext.from_defaults",
"llama_index.llms.OpenAI",
"llama_index.llms.ChatMessage",
"llama_index.node_parser.SimpleNodeParser.from_defaults"
] | [((801, 869), 'pydantic.Field', 'Field', ([], {'description': '"""The question to ask the parental leave statute."""'}), "(description='The question to ask the parental leave statute.')\n", (806, 869), False, 'from pydantic import BaseModel, Field\n'), ((971, 998), 'pydantic.Field', 'Field', ([], {'default_factory': 'd... |
import chromadb
from chromadb.config import Settings
from llama_index.vector_stores import ChromaVectorStore
from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from llama_index.embeddings import LangchainEmbedding
from... | [
"llama_index.ServiceContext.from_defaults",
"llama_index.VectorStoreIndex.from_vector_store",
"llama_index.vector_stores.ChromaVectorStore"
] | [((384, 421), 'chromadb.PersistentClient', 'chromadb.PersistentClient', (['"""src/data"""'], {}), "('src/data')\n", (409, 421), False, 'import chromadb\n'), ((608, 661), 'llama_index.ServiceContext.from_defaults', 'ServiceContext.from_defaults', ([], {'embed_model': 'embed_model'}), '(embed_model=embed_model)\n', (636,... |
import os
import requests
from dotenv import load_dotenv
from llama_index import SimpleDirectoryReader, StorageContext, VectorStoreIndex, load_index_from_storage
from pathlib import Path
import helper.data as dhelp
load_dotenv()
LOCAL_FILE_NAME = 'data/TR-61850.pdf'
INDEX_STORAGE_DIR = './storage'
def build_inde... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.load_index_from_storage",
"llama_index.SimpleDirectoryReader",
"llama_index.StorageContext.from_defaults"
] | [((219, 232), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (230, 232), False, 'from dotenv import load_dotenv\n'), ((531, 590), 'helper.data.get_service_context', 'dhelp.get_service_context', ([], {'chunk_size': '(512)', 'chunk_overlap': '(75)'}), '(chunk_size=512, chunk_overlap=75)\n', (556, 590), True, 'imp... |
from approaches.index.store.cosmos_index_store import CosmosIndexStore
from llama_index import StorageContext
from approaches.index.store.cosmos_doc_store import CosmosDocumentStore
from llama_index import load_index_from_storage
import os
import openai
from langchain.chat_models import AzureChatOpenAI
from langchain.... | [
"llama_index.indices.knowledge_graph.base.GPTKnowledgeGraphIndex",
"llama_index.langchain_helpers.text_splitter.TokenTextSplitter",
"llama_index.LLMPredictor",
"llama_index.ServiceContext.from_defaults",
"llama_index.StorageContext.from_defaults",
"llama_index.load_index_from_storage",
"llama_index.Docu... | [((832, 845), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (843, 845), False, 'from dotenv import load_dotenv\n'), ((1039, 1074), 'os.environ.get', 'os.environ.get', (['"""AZURE_OPENAI_BASE"""'], {}), "('AZURE_OPENAI_BASE')\n", (1053, 1074), False, 'import os\n'), ((1098, 1153), 'os.environ.get', 'os.environ.... |
"""
Chat with transcripts stored by Llamaindex in Weaviate Cloud
"""
import openai
import streamlit as st
import weaviate
from llama_index import ServiceContext
from llama_index import VectorStoreIndex
from llama_index.llms import OpenAI
from llama_index.vector_stores import WeaviateVectorStore
st.set_page_config(
... | [
"llama_index.VectorStoreIndex.from_vector_store",
"llama_index.llms.OpenAI",
"llama_index.vector_stores.WeaviateVectorStore"
] | [((297, 443), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""Chat with my Youtube Channel"""', 'page_icon': '"""🦙"""', 'layout': '"""centered"""', 'initial_sidebar_state': '"""auto"""', 'menu_items': 'None'}), "(page_title='Chat with my Youtube Channel', page_icon='🦙',\n layout='centere... |
# Debug stuff
import os
import readline
print("Current Working Directory:", os.getcwd())
#env_var = os.getenv('OPENAI_API_KEY')
#print(env_var)
# Sets llama-index
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdou... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.SimpleDirectoryReader"
] | [((191, 250), 'logging.basicConfig', 'logging.basicConfig', ([], {'stream': 'sys.stdout', 'level': 'logging.DEBUG'}), '(stream=sys.stdout, level=logging.DEBUG)\n', (210, 250), False, 'import logging\n'), ((459, 501), 'llama_index.VectorStoreIndex.from_documents', 'VectorStoreIndex.from_documents', (['documents'], {}), ... |
import base64
import nest_asyncio
import streamlit as st
from llama_index import (LLMPredictor, ServiceContext, StorageContext,
load_index_from_storage, set_global_service_context)
from llama_index.llms import LlamaCPP
from llama_index.memory import ChatMemoryBuffer
nest_asyncio.apply()
# ht... | [
"llama_index.memory.ChatMemoryBuffer.from_defaults",
"llama_index.LLMPredictor",
"llama_index.ServiceContext.from_defaults",
"llama_index.llms.LlamaCPP",
"llama_index.StorageContext.from_defaults",
"llama_index.set_global_service_context",
"llama_index.load_index_from_storage"
] | [((293, 313), 'nest_asyncio.apply', 'nest_asyncio.apply', ([], {}), '()\n', (311, 313), False, 'import nest_asyncio\n'), ((679, 751), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""UTPL Chat"""', 'page_icon': '"""🤖"""', 'layout': '"""wide"""'}), "(page_title='UTPL Chat', page_icon='🤖', lay... |
import streamlit as st
import os
import sys
import logging
from llama_index import TreeIndex, SimpleDirectoryReader, StorageContext, get_response_synthesizer, load_index_from_storage, ServiceContext
from llama_index.llms import OpenAI, ChatMessage
from llama_index.query_engine import RetrieverQueryEngine
from llama_ind... | [
"llama_index.SimpleDirectoryReader",
"llama_index.text_splitter.CodeSplitter",
"llama_index.ServiceContext.from_defaults",
"llama_index.TreeIndex.from_documents",
"llama_index.llms.OpenAI",
"llama_index.StorageContext.from_defaults",
"llama_index.llms.ChatMessage",
"llama_index.node_parser.SimpleNodeP... | [((457, 477), 'nest_asyncio.apply', 'nest_asyncio.apply', ([], {}), '()\n', (475, 477), False, 'import nest_asyncio\n'), ((478, 536), 'logging.basicConfig', 'logging.basicConfig', ([], {'stream': 'sys.stdout', 'level': 'logging.INFO'}), '(stream=sys.stdout, level=logging.INFO)\n', (497, 536), False, 'import logging\n')... |
import sounddevice as sd
import wavio
import whisper
from llama_index.llms import LlamaCPP
from llama_index.llms.base import ChatMessage
import streamlit as st
# Function to record audio
def record_audio(output_filename, duration, sample_rate):
st.write("Recording...")
audio_data = sd.rec(int(duratio... | [
"llama_index.llms.LlamaCPP",
"llama_index.llms.base.ChatMessage"
] | [((259, 283), 'streamlit.write', 'st.write', (['"""Recording..."""'], {}), "('Recording...')\n", (267, 283), True, 'import streamlit as st\n'), ((404, 413), 'sounddevice.wait', 'sd.wait', ([], {}), '()\n', (411, 413), True, 'import sounddevice as sd\n'), ((455, 486), 'streamlit.write', 'st.write', (['"""Recording finis... |
import os
import sys
import shutil
import openai
from dotenv import load_dotenv
load_dotenv()
openai.api_key = os.getenv("OPENAI_API_KEY")
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
import langchain
from llama_index import StorageContext, load_index_from_storage
from llama_index import (
VectorStore... | [
"llama_index.SimpleDirectoryReader",
"llama_index.get_response_synthesizer",
"llama_index.retrievers.VectorIndexRetriever",
"llama_index.StorageContext.from_defaults",
"llama_index.indices.postprocessor.SimilarityPostprocessor",
"llama_index.load_index_from_storage"
] | [((80, 93), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (91, 93), False, 'from dotenv import load_dotenv\n'), ((111, 138), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (120, 138), False, 'import os\n'), ((170, 197), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {})... |
import logging
import os
from typing import List
from decouple import config
from langchain import OpenAI
from llama_index import GPTSimpleVectorIndex, LLMPredictor, PromptHelper
from llama_index.response.schema import Response, SourceNode
from pydantic import BaseModel
from data_provider import LlamaDataProvider, Lo... | [
"llama_index.GPTSimpleVectorIndex",
"llama_index.PromptHelper"
] | [((384, 408), 'decouple.config', 'config', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (390, 408), False, 'from decouple import config\n'), ((699, 797), 'llama_index.PromptHelper', 'PromptHelper', (['max_input_size', 'num_output', 'max_chunk_overlap'], {'chunk_size_limit': 'chunk_size_limit'}), '(max_input_... |
import os
import json
from urllib.parse import urlparse
import streamlit as st
from scrape_utils import scrape
from llama_index import download_loader, GPTSimpleVectorIndex, SimpleDirectoryReader, ServiceContext, LLMPredictor, PromptHelper
from llama_index.llm_predictor.chatgpt import ChatGPTLLMPredictor
from llama_ind... | [
"llama_index.download_loader",
"llama_index.GPTSimpleVectorIndex.load_from_disk",
"llama_index.ServiceContext.from_defaults",
"llama_index.node_parser.SimpleNodeParser",
"llama_index.PromptHelper",
"llama_index.GPTSimpleVectorIndex",
"llama_index.langchain_helpers.memory_wrapper.GPTIndexChatMemory"
] | [((4729, 4759), 'streamlit.header', 'st.header', (['"""LearnWithGPT Demo"""'], {}), "('LearnWithGPT Demo')\n", (4738, 4759), True, 'import streamlit as st\n'), ((4809, 4856), 'streamlit.text_area', 'st.text_area', (['"""Enter a URL to scrape and index"""'], {}), "('Enter a URL to scrape and index')\n", (4821, 4856), Tr... |
from typing import Any, List, Optional, Sequence
from llama_index.core.base.base_query_engine import BaseQueryEngine
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.base.response.schema import RESPONSE_TYPE
from llama_index.core.bridge.pydantic import BaseModel
from llama_index.cor... | [
"llama_index.core.settings.llm_from_settings_or_context",
"llama_index.core.settings.callback_manager_from_settings_or_context",
"llama_index.core.response_synthesizers.get_response_synthesizer"
] | [((4987, 5055), 'llama_index.core.settings.callback_manager_from_settings_or_context', 'callback_manager_from_settings_or_context', (['Settings', 'service_context'], {}), '(Settings, service_context)\n', (5028, 5055), False, 'from llama_index.core.settings import Settings, callback_manager_from_settings_or_context, llm... |
import os
from starlette.concurrency import run_in_threadpool
import google.generativeai as genai
from llama_index.llms.gemini import Gemini
from llama_index.embeddings import GooglePaLMEmbedding
from gateway_llms.app.utils.logs import LogApplication, log_function
GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
genai.... | [
"llama_index.embeddings.GooglePaLMEmbedding",
"llama_index.llms.gemini.Gemini"
] | [((285, 312), 'os.getenv', 'os.getenv', (['"""GOOGLE_API_KEY"""'], {}), "('GOOGLE_API_KEY')\n", (294, 312), False, 'import os\n'), ((314, 353), 'google.generativeai.configure', 'genai.configure', ([], {'api_key': 'GOOGLE_API_KEY'}), '(api_key=GOOGLE_API_KEY)\n', (329, 353), True, 'import google.generativeai as genai\n'... |
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