code stringlengths 161 233k | apis listlengths 1 24 | extract_api stringlengths 162 68.5k |
|---|---|---|
from io import BytesIO
from flask import Flask, jsonify
import os
# import tweepy
from dotenv import load_dotenv
from flask import request,jsonify
import snscrape.modules.twitter as snstwitter
import requests
from goose3 import Goose
from wordcloud import WordCloud, STOPWORDS
import plotly.graph_objs as go
import json
... | [
"llama_index.GPTVectorStoreIndex.from_documents"
] | [((1520, 1535), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (1525, 1535), False, 'from flask import Flask, jsonify\n'), ((1536, 1545), 'flask_cors.CORS', 'CORS', (['app'], {}), '(app)\n', (1540, 1545), False, 'from flask_cors import CORS\n'), ((1613, 1626), 'dotenv.load_dotenv', 'load_dotenv', ([], {}),... |
import sqlite3
import pandas as pd
import llama_index
import os
import openai
from IPython.display import Markdown, display
from sqlalchemy import (
create_engine,
MetaData,
Table,
Column,
String,
Integer,
select,
text
)
from llama_index import SQLDatabase, ServiceContext
from llama_inde... | [
"llama_index.ServiceContext.from_defaults",
"llama_index.llms.OpenAI",
"llama_index.SQLDatabase",
"llama_index.indices.struct_store.sql_query.NLSQLTableQueryEngine"
] | [((1122, 1158), 'llama_index.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0)', 'model': '"""gpt-4"""'}), "(temperature=0, model='gpt-4')\n", (1128, 1158), False, 'from llama_index.llms import OpenAI\n'), ((1168, 1210), 'sqlalchemy.create_engine', 'create_engine', (['"""sqlite:///company_info.db"""'], {}), "('sqlite:/... |
import os
from typing import Optional, Dict
import openai
import pandas as pd
from langchain.llms import OpenAI
import llama_index
from llama_index.readers.schema.base import Document
from llama_index import SimpleWebPageReader, QuestionAnswerPrompt
from llama_index import ServiceContext, StorageContext, load_index_fr... | [
"llama_index.SimpleWebPageReader",
"llama_index.LLMPredictor",
"llama_index.OpenAIEmbedding",
"llama_index.StorageContext.from_defaults",
"llama_index.QuestionAnswerPrompt",
"llama_index.load_index_from_storage",
"llama_index.readers.schema.base.Document"
] | [((6636, 6688), 'llama_index.StorageContext.from_defaults', 'StorageContext.from_defaults', ([], {'persist_dir': 'index_path'}), '(persist_dir=index_path)\n', (6664, 6688), False, 'from llama_index import ServiceContext, StorageContext, load_index_from_storage\n'), ((6759, 6832), 'llama_index.load_index_from_storage', ... |
"""
This script is used to summarize conversations from Zendesk support tickets.
It reads text files containing comments from the ticket and generates a summary
that includes information about the participants, problems raised, key events,
current status of the ticket, and log lines from the messages.
The script uses ... | [
"llama_index.core.SimpleDirectoryReader",
"llama_index.llms.gemini.Gemini",
"llama_index.core.response_synthesizers.TreeSummarize",
"llama_index.core.ServiceContext.from_defaults"
] | [((1113, 1152), 'os.makedirs', 'os.makedirs', (['SUMMARY_DIR'], {'exist_ok': '(True)'}), '(SUMMARY_DIR, exist_ok=True)\n', (1124, 1152), False, 'import os\n'), ((2083, 2091), 'llama_index.llms.gemini.Gemini', 'Gemini', ([], {}), '()\n', (2089, 2091), False, 'from llama_index.llms.gemini import Gemini\n'), ((2110, 2168)... |
"""Google GenerativeAI Attributed Question and Answering (AQA) service.
The GenAI Semantic AQA API is a managed end to end service that allows
developers to create responses grounded on specified passages based on
a user query. For more information visit:
https://developers.generativeai.google/guide
"""
import loggin... | [
"llama_index.legacy.core.response.schema.Response",
"llama_index.legacy.schema.TextNode",
"llama_index.legacy.indices.query.schema.QueryBundle",
"llama_index.legacy.vector_stores.google.generativeai.genai_extension.build_generative_service"
] | [((1114, 1141), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1131, 1141), False, 'import logging\n'), ((2809, 2842), 'llama_index.legacy.vector_stores.google.generativeai.genai_extension.build_generative_service', 'genaix.build_generative_service', ([], {}), '()\n', (2840, 2842), True,... |
"""Elasticsearch vector store."""
import asyncio
import uuid
from logging import getLogger
from typing import Any, Callable, Dict, List, Literal, Optional, Union, cast
import nest_asyncio
import numpy as np
from llama_index.legacy.bridge.pydantic import PrivateAttr
from llama_index.legacy.schema import BaseNode, Met... | [
"llama_index.legacy.bridge.pydantic.PrivateAttr",
"llama_index.legacy.schema.TextNode",
"llama_index.legacy.vector_stores.utils.node_to_metadata_dict",
"llama_index.legacy.vector_stores.utils.metadata_dict_to_node"
] | [((640, 659), 'logging.getLogger', 'getLogger', (['__name__'], {}), '(__name__)\n', (649, 659), False, 'from logging import getLogger\n'), ((2343, 2396), 'elasticsearch.AsyncElasticsearch', 'elasticsearch.AsyncElasticsearch', ([], {}), '(**connection_params)\n', (2375, 2396), False, 'import elasticsearch\n'), ((3719, 3... |
# You can find this code for Chainlit python streaming here (https://docs.chainlit.io/concepts/streaming/python)
import os
import requests
import chainlit as cl
from dotenv import load_dotenv
import llama_index
from llama_index.core import set_global_handler
from llama_index.llms.openai import OpenAI
from llama_inde... | [
"llama_index.core.VectorStoreIndex.from_documents",
"llama_index.llms.openai.OpenAI",
"llama_index.vector_stores.faiss.FaissVectorStore",
"llama_index.core.StorageContext.from_defaults",
"llama_index.core.load_index_from_storage",
"llama_index.core.set_global_handler",
"llama_index.core.SimpleDirectoryR... | [((1242, 1255), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (1253, 1255), False, 'from dotenv import load_dotenv\n'), ((1261, 1346), 'llama_index.core.set_global_handler', 'set_global_handler', (['"""wandb"""'], {'run_args': "{'project': 'aie1-llama-index-middleterm'}"}), "('wandb', run_args={'project': 'aie... |
"""
This script is used to summarize conversations from Zendesk support tickets.
It reads text files containing comments from the ticket and generates a summary
that includes information about the participants, problems raised, key events,
current status of the ticket, and log lines from the messages.
The script uses ... | [
"llama_index.core.SimpleDirectoryReader",
"llama_index.llms.ollama.Ollama",
"llama_index.core.response_synthesizers.TreeSummarize",
"llama_index.core.ServiceContext.from_defaults"
] | [((1201, 1240), 'os.makedirs', 'os.makedirs', (['SUMMARY_DIR'], {'exist_ok': '(True)'}), '(SUMMARY_DIR, exist_ok=True)\n', (1212, 1240), False, 'import os\n'), ((2215, 2263), 'llama_index.llms.ollama.Ollama', 'Ollama', ([], {'model': 'MODEL', 'request_timeout': 'TIMEOUT_SEC'}), '(model=MODEL, request_timeout=TIMEOUT_SE... |
"""Response builder class.
This class provides general functions for taking in a set of text
and generating a response.
Will support different modes, from 1) stuffing chunks into prompt,
2) create and refine separately over each chunk, 3) tree summarization.
"""
import logging
from abc import abstractmethod
from ty... | [
"llama_index.core.indices.prompt_helper.PromptHelper.from_llm_metadata",
"llama_index.core.base.response.schema.StreamingResponse",
"llama_index.core.base.query_pipeline.query.InputKeys.from_keys",
"llama_index.core.base.query_pipeline.query.OutputKeys.from_keys",
"llama_index.core.base.response.schema.Resp... | [((1679, 1714), 'llama_index.core.instrumentation.get_dispatcher', 'instrument.get_dispatcher', (['__name__'], {}), '(__name__)\n', (1704, 1714), True, 'import llama_index.core.instrumentation as instrument\n'), ((1725, 1752), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1742, 1752), F... |
"""
This script is used to summarize conversations from Zendesk support tickets.
It reads text files containing comments from the ticket and generates a summary
that includes information about the participants, problems raised, key events,
current status of the ticket, and log lines from the messages.
The script uses ... | [
"llama_index.core.SimpleDirectoryReader",
"llama_index.core.ServiceContext.from_defaults",
"llama_index.llms.anthropic.Anthropic",
"llama_index.core.response_synthesizers.TreeSummarize"
] | [((1211, 1250), 'os.makedirs', 'os.makedirs', (['SUMMARY_DIR'], {'exist_ok': '(True)'}), '(SUMMARY_DIR, exist_ok=True)\n', (1222, 1250), False, 'import os\n'), ((2181, 2220), 'llama_index.llms.anthropic.Anthropic', 'Anthropic', ([], {'model': 'MODEL', 'max_tokens': '(1024)'}), '(model=MODEL, max_tokens=1024)\n', (2190,... |
"""Google Generative AI Vector Store.
The GenAI Semantic Retriever API is a managed end-to-end service that allows
developers to create a corpus of documents to perform semantic search on
related passages given a user query. For more information visit:
https://developers.generativeai.google/guide
"""
import logging
i... | [
"llama_index.vector_stores.google.genai_extension.build_semantic_retriever",
"llama_index.vector_stores.google.genai_extension.get_corpus",
"llama_index.vector_stores.google.genai_extension.Config",
"llama_index.vector_stores.google.genai_extension.EntityName.from_str",
"llama_index.core.bridge.pydantic.Fie... | [((812, 839), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (829, 839), False, 'import logging\n'), ((2859, 2881), 'llama_index.vector_stores.google.genai_extension.Config', 'genaix.Config', ([], {}), '(**attrs)\n', (2872, 2881), True, 'import llama_index.vector_stores.google.genai_exten... |
from typing import Any
from llama_index.core.callbacks.base_handler import BaseCallbackHandler
from llama_index.core.callbacks.simple_llm_handler import SimpleLLMHandler
def set_global_handler(eval_mode: str, **eval_params: Any) -> None:
"""Set global eval handlers."""
import llama_index.core
llama_inde... | [
"llama_index.callbacks.wandb.WandbCallbackHandler",
"llama_index.callbacks.deepeval.deepeval_callback_handler",
"llama_index.callbacks.argilla.argilla_callback_handler",
"llama_index.callbacks.honeyhive.honeyhive_callback_handler",
"llama_index.callbacks.openinference.OpenInferenceCallbackHandler",
"llama... | [((941, 976), 'llama_index.callbacks.wandb.WandbCallbackHandler', 'WandbCallbackHandler', ([], {}), '(**eval_params)\n', (961, 976), False, 'from llama_index.callbacks.wandb import WandbCallbackHandler\n'), ((1424, 1467), 'llama_index.callbacks.openinference.OpenInferenceCallbackHandler', 'OpenInferenceCallbackHandler'... |
"""Google GenerativeAI Attributed Question and Answering (AQA) service.
The GenAI Semantic AQA API is a managed end to end service that allows
developers to create responses grounded on specified passages based on
a user query. For more information visit:
https://developers.generativeai.google/guide
"""
import loggin... | [
"llama_index.indices.query.schema.QueryBundle",
"llama_index.schema.TextNode",
"llama_index.vector_stores.google.generativeai.genai_extension.build_generative_service",
"llama_index.core.response.schema.Response"
] | [((1051, 1078), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1068, 1078), False, 'import logging\n'), ((2739, 2772), 'llama_index.vector_stores.google.generativeai.genai_extension.build_generative_service', 'genaix.build_generative_service', ([], {}), '()\n', (2770, 2772), True, 'impor... |
"""Elasticsearch vector store."""
import asyncio
import uuid
from logging import getLogger
from typing import Any, Callable, Dict, List, Literal, Optional, Union, cast
import nest_asyncio
import numpy as np
from llama_index.bridge.pydantic import PrivateAttr
from llama_index.schema import BaseNode, MetadataMode, Tex... | [
"llama_index.vector_stores.utils.metadata_dict_to_node",
"llama_index.bridge.pydantic.PrivateAttr",
"llama_index.schema.TextNode",
"llama_index.vector_stores.utils.node_to_metadata_dict"
] | [((599, 618), 'logging.getLogger', 'getLogger', (['__name__'], {}), '(__name__)\n', (608, 618), False, 'from logging import getLogger\n'), ((2444, 2497), 'elasticsearch.AsyncElasticsearch', 'elasticsearch.AsyncElasticsearch', ([], {}), '(**connection_params)\n', (2476, 2497), False, 'import elasticsearch\n'), ((3820, 3... |
"""Base query engine."""
import logging
from abc import abstractmethod
from typing import Any, Dict, List, Optional, Sequence
from llama_index.core.base.query_pipeline.query import (
ChainableMixin,
InputKeys,
OutputKeys,
QueryComponent,
validate_and_convert_stringable,
)
from llama_index.core.bas... | [
"llama_index.core.instrumentation.events.query.QueryStartEvent",
"llama_index.core.instrumentation.events.query.QueryEndEvent",
"llama_index.core.base.query_pipeline.query.InputKeys.from_keys",
"llama_index.core.base.query_pipeline.query.OutputKeys.from_keys",
"llama_index.core.instrumentation.get_dispatche... | [((785, 820), 'llama_index.core.instrumentation.get_dispatcher', 'instrument.get_dispatcher', (['__name__'], {}), '(__name__)\n', (810, 820), True, 'import llama_index.core.instrumentation as instrument\n'), ((830, 857), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (847, 857), False, 'i... |
import logging
import sys
import torch
import bentoml
import llama_index
from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
from llama_index.llms import HuggingFaceLLM
import typing as t
from typing import Any, List
from InstructorEmbedding import INSTRUCTOR
from llama_index.bridge.pydan... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.SimpleDirectoryReader",
"llama_index.prompts.PromptTemplate",
"llama_index.llms.HuggingFaceLLM",
"llama_index.bridge.pydantic.PrivateAttr"
] | [((446, 504), 'logging.basicConfig', 'logging.basicConfig', ([], {'stream': 'sys.stdout', 'level': 'logging.INFO'}), '(stream=sys.stdout, level=logging.INFO)\n', (465, 504), False, 'import logging\n'), ((3971, 4037), 'bentoml.Service', 'bentoml.Service', (['"""llamaindex_service"""'], {'runners': '[llamaindex_runner]'}... |
import llama_index
import chromadb
from importlib.metadata import version
print(f"LlamaIndex version: {version('llama_index')}")
print(f"Chroma version: {version('chromadb')}")
# Load API key from .env file
import os
from dotenv import load_dotenv, find_dotenv
load_dotenv(find_dotenv())
# Define embe... | [
"llama_index.embeddings.huggingface.HuggingFaceEmbedding",
"llama_index.llms.openai.OpenAI",
"llama_index.core.VectorStoreIndex",
"llama_index.core.postprocessor.MetadataReplacementPostProcessor",
"llama_index.core.StorageContext.from_defaults",
"llama_index.core.node_parser.SentenceWindowNodeParser.from_... | [((511, 557), 'llama_index.llms.openai.OpenAI', 'OpenAI', ([], {'model': '"""gpt-3.5-turbo"""', 'temperature': '(0.1)'}), "(model='gpt-3.5-turbo', temperature=0.1)\n", (517, 557), False, 'from llama_index.llms.openai import OpenAI\n'), ((582, 599), 'llama_index.embeddings.openai.OpenAIEmbedding', 'OpenAIEmbedding', ([]... |
import os, openai
from dotenv import load_dotenv
load_dotenv()
OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
openai.api_key = OPENAI_API_KEY
# pip install google-search-results
from dotenv import load_dotenv
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseMo... | [
"llama_index.ServiceContext.from_defaults",
"llama_index.indices.loading.load_index_from_storage",
"llama_index.set_global_service_context",
"llama_index.StorageContext.from_defaults"
] | [((49, 62), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (60, 62), False, 'from dotenv import load_dotenv\n'), ((80, 112), 'os.environ.get', 'os.environ.get', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (94, 112), False, 'import llama_index, os\n'), ((380, 389), 'fastapi.FastAPI', 'FastAPI', ([], ... |
"""FastAPI app creation, logger configuration and main API routes."""
import llama_index
from private_gpt.di import global_injector
from private_gpt.launcher import create_app
# Add LlamaIndex simple observability
llama_index.set_global_handler("simple")
app = create_app(global_injector)
| [
"llama_index.set_global_handler"
] | [((217, 257), 'llama_index.set_global_handler', 'llama_index.set_global_handler', (['"""simple"""'], {}), "('simple')\n", (247, 257), False, 'import llama_index\n'), ((265, 292), 'private_gpt.launcher.create_app', 'create_app', (['global_injector'], {}), '(global_injector)\n', (275, 292), False, 'from private_gpt.launc... |
"""
Astra DB Vector store index.
An index based on a DB table with vector search capabilities,
powered by the astrapy library
"""
import json
import logging
from typing import Any, Dict, List, Optional, cast
from warnings import warn
import llama_index.core
from llama_index.core.bridge.pydantic import PrivateAttr
f... | [
"llama_index.core.vector_stores.utils.metadata_dict_to_node",
"llama_index.core.vector_stores.utils.node_to_metadata_dict",
"llama_index.core.indices.query.embedding_utils.get_top_k_mmr_embeddings",
"llama_index.core.bridge.pydantic.PrivateAttr",
"llama_index.core.vector_stores.types.VectorStoreQueryResult"... | [((852, 879), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (869, 879), False, 'import logging\n'), ((2070, 2083), 'llama_index.core.bridge.pydantic.PrivateAttr', 'PrivateAttr', ([], {}), '()\n', (2081, 2083), False, 'from llama_index.core.bridge.pydantic import PrivateAttr\n'), ((2118, ... |
"""Google GenerativeAI Attributed Question and Answering (AQA) service.
The GenAI Semantic AQA API is a managed end to end service that allows
developers to create responses grounded on specified passages based on
a user query. For more information visit:
https://developers.generativeai.google/guide
"""
import loggin... | [
"llama_index.vector_stores.google.genai_extension.build_generative_service",
"llama_index.core.schema.TextNode",
"llama_index.core.llms.mock.MockLLM",
"llama_index.core.base.response.schema.Response",
"llama_index.core.indices.query.schema.QueryBundle"
] | [((1057, 1084), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1074, 1084), False, 'import logging\n'), ((2707, 2740), 'llama_index.vector_stores.google.genai_extension.build_generative_service', 'genaix.build_generative_service', ([], {}), '()\n', (2738, 2740), True, 'import llama_index... |
from langfuse import Langfuse
from llama_index.llms.openai import OpenAI
import llama_index.core
llama_index.core.set_global_handler("langfuse")
from llama_index.core.llms import ChatMessage
langfuse = Langfuse()
dataset = langfuse.get_dataset("term-extraction")
prompt = langfuse.get_prompt("extraction-prompt-1")
mod... | [
"llama_index.llms.openai.OpenAI",
"llama_index.core.llms.ChatMessage"
] | [((203, 213), 'langfuse.Langfuse', 'Langfuse', ([], {}), '()\n', (211, 213), False, 'from langfuse import Langfuse\n'), ((325, 360), 'llama_index.llms.openai.OpenAI', 'OpenAI', ([], {'model': '"""gpt-4-turbo-preview"""'}), "(model='gpt-4-turbo-preview')\n", (331, 360), False, 'from llama_index.llms.openai import OpenAI... |
from unittest.mock import MagicMock, patch
import pytest
from llama_index.legacy.core.response.schema import Response
from llama_index.legacy.schema import Document
try:
import google.ai.generativelanguage as genai
has_google = True
except ImportError:
has_google = False
from llama_index.legacy.indices.... | [
"llama_index.legacy.vector_stores.google.generativeai.genai_extension.get_config",
"llama_index.legacy.indices.managed.google.generativeai.GoogleIndex.create_corpus",
"llama_index.legacy.indices.managed.google.generativeai.GoogleIndex.from_corpus",
"llama_index.legacy.schema.Document",
"llama_index.legacy.i... | [((693, 752), 'pytest.mark.skipif', 'pytest.mark.skipif', (['(not has_google)'], {'reason': 'SKIP_TEST_REASON'}), '(not has_google, reason=SKIP_TEST_REASON)\n', (711, 752), False, 'import pytest\n'), ((754, 798), 'unittest.mock.patch', 'patch', (['"""google.auth.credentials.Credentials"""'], {}), "('google.auth.credent... |
from unittest.mock import MagicMock, patch
import pytest
try:
import google.ai.generativelanguage as genai
has_google = True
except ImportError:
has_google = False
from llama_index.legacy.response_synthesizers.google.generativeai import (
GoogleTextSynthesizer,
set_google_config,
)
from llama_in... | [
"llama_index.legacy.response_synthesizers.google.generativeai.GoogleTextSynthesizer.from_defaults",
"llama_index.legacy.schema.TextNode",
"llama_index.legacy.response_synthesizers.google.generativeai.set_google_config",
"llama_index.legacy.vector_stores.google.generativeai.genai_extension.get_config"
] | [((663, 722), 'pytest.mark.skipif', 'pytest.mark.skipif', (['(not has_google)'], {'reason': 'SKIP_TEST_REASON'}), '(not has_google, reason=SKIP_TEST_REASON)\n', (681, 722), False, 'import pytest\n'), ((724, 768), 'unittest.mock.patch', 'patch', (['"""google.auth.credentials.Credentials"""'], {}), "('google.auth.credent... |
"""Global eval handlers."""
from typing import Any
from llama_index.callbacks.arize_phoenix_callback import arize_phoenix_callback_handler
from llama_index.callbacks.base_handler import BaseCallbackHandler
from llama_index.callbacks.deepeval_callback import deepeval_callback_handler
from llama_index.callbacks.honeyhi... | [
"llama_index.callbacks.wandb_callback.WandbCallbackHandler",
"llama_index.callbacks.honeyhive_callback.honeyhive_callback_handler",
"llama_index.callbacks.simple_llm_handler.SimpleLLMHandler",
"llama_index.callbacks.arize_phoenix_callback.arize_phoenix_callback_handler",
"llama_index.callbacks.promptlayer_h... | [((1068, 1103), 'llama_index.callbacks.wandb_callback.WandbCallbackHandler', 'WandbCallbackHandler', ([], {}), '(**eval_params)\n', (1088, 1103), False, 'from llama_index.callbacks.wandb_callback import WandbCallbackHandler\n'), ((1161, 1204), 'llama_index.callbacks.open_inference_callback.OpenInferenceCallbackHandler'... |
"""Global eval handlers."""
from typing import Any
from llama_index.callbacks.argilla_callback import argilla_callback_handler
from llama_index.callbacks.arize_phoenix_callback import arize_phoenix_callback_handler
from llama_index.callbacks.base_handler import BaseCallbackHandler
from llama_index.callbacks.deepeval_... | [
"llama_index.callbacks.wandb_callback.WandbCallbackHandler",
"llama_index.callbacks.honeyhive_callback.honeyhive_callback_handler",
"llama_index.callbacks.simple_llm_handler.SimpleLLMHandler",
"llama_index.callbacks.arize_phoenix_callback.arize_phoenix_callback_handler",
"llama_index.callbacks.promptlayer_h... | [((1144, 1179), 'llama_index.callbacks.wandb_callback.WandbCallbackHandler', 'WandbCallbackHandler', ([], {}), '(**eval_params)\n', (1164, 1179), False, 'from llama_index.callbacks.wandb_callback import WandbCallbackHandler\n'), ((1237, 1280), 'llama_index.callbacks.open_inference_callback.OpenInferenceCallbackHandler'... |
"""Elasticsearch vector store."""
import asyncio
import uuid
from logging import getLogger
from typing import Any, Callable, Dict, List, Literal, Optional, Union, cast
import nest_asyncio
import numpy as np
from llama_index.schema import BaseNode, MetadataMode, TextNode
from llama_index.vector_stores.types import (
... | [
"llama_index.vector_stores.utils.metadata_dict_to_node",
"llama_index.schema.TextNode",
"llama_index.vector_stores.utils.node_to_metadata_dict"
] | [((534, 553), 'logging.getLogger', 'getLogger', (['__name__'], {}), '(__name__)\n', (543, 553), False, 'from logging import getLogger\n'), ((2379, 2432), 'elasticsearch.AsyncElasticsearch', 'elasticsearch.AsyncElasticsearch', ([], {}), '(**connection_params)\n', (2411, 2432), False, 'import elasticsearch\n'), ((3755, 3... |
"""Elasticsearch vector store."""
import asyncio
import uuid
from logging import getLogger
from typing import Any, Callable, Dict, List, Literal, Optional, Union, cast
import nest_asyncio
import numpy as np
from llama_index.schema import BaseNode, MetadataMode, TextNode
from llama_index.vector_stores.types import (
... | [
"llama_index.vector_stores.utils.metadata_dict_to_node",
"llama_index.schema.TextNode",
"llama_index.vector_stores.utils.node_to_metadata_dict"
] | [((534, 553), 'logging.getLogger', 'getLogger', (['__name__'], {}), '(__name__)\n', (543, 553), False, 'from logging import getLogger\n'), ((2379, 2432), 'elasticsearch.AsyncElasticsearch', 'elasticsearch.AsyncElasticsearch', ([], {}), '(**connection_params)\n', (2411, 2432), False, 'import elasticsearch\n'), ((3755, 3... |
"""Elasticsearch vector store."""
import asyncio
import uuid
from logging import getLogger
from typing import Any, Callable, Dict, List, Literal, Optional, Union, cast
import nest_asyncio
import numpy as np
from llama_index.schema import BaseNode, MetadataMode, TextNode
from llama_index.vector_stores.types import (
... | [
"llama_index.vector_stores.utils.metadata_dict_to_node",
"llama_index.schema.TextNode",
"llama_index.vector_stores.utils.node_to_metadata_dict"
] | [((534, 553), 'logging.getLogger', 'getLogger', (['__name__'], {}), '(__name__)\n', (543, 553), False, 'from logging import getLogger\n'), ((2379, 2432), 'elasticsearch.AsyncElasticsearch', 'elasticsearch.AsyncElasticsearch', ([], {}), '(**connection_params)\n', (2411, 2432), False, 'import elasticsearch\n'), ((3755, 3... |
"""Global eval handlers."""
from typing import Any
from llama_index.callbacks.arize_phoenix_callback import arize_phoenix_callback_handler
from llama_index.callbacks.base_handler import BaseCallbackHandler
from llama_index.callbacks.honeyhive_callback import honeyhive_callback_handler
from llama_index.callbacks.open_... | [
"llama_index.callbacks.wandb_callback.WandbCallbackHandler",
"llama_index.callbacks.honeyhive_callback.honeyhive_callback_handler",
"llama_index.callbacks.simple_llm_handler.SimpleLLMHandler",
"llama_index.callbacks.arize_phoenix_callback.arize_phoenix_callback_handler",
"llama_index.callbacks.promptlayer_h... | [((990, 1025), 'llama_index.callbacks.wandb_callback.WandbCallbackHandler', 'WandbCallbackHandler', ([], {}), '(**eval_params)\n', (1010, 1025), False, 'from llama_index.callbacks.wandb_callback import WandbCallbackHandler\n'), ((1083, 1126), 'llama_index.callbacks.open_inference_callback.OpenInferenceCallbackHandler',... |
"""Global eval handlers."""
from typing import Any
from llama_index.callbacks.arize_phoenix_callback import arize_phoenix_callback_handler
from llama_index.callbacks.base_handler import BaseCallbackHandler
from llama_index.callbacks.honeyhive_callback import honeyhive_callback_handler
from llama_index.callbacks.open_... | [
"llama_index.callbacks.wandb_callback.WandbCallbackHandler",
"llama_index.callbacks.honeyhive_callback.honeyhive_callback_handler",
"llama_index.callbacks.simple_llm_handler.SimpleLLMHandler",
"llama_index.callbacks.arize_phoenix_callback.arize_phoenix_callback_handler",
"llama_index.callbacks.promptlayer_h... | [((990, 1025), 'llama_index.callbacks.wandb_callback.WandbCallbackHandler', 'WandbCallbackHandler', ([], {}), '(**eval_params)\n', (1010, 1025), False, 'from llama_index.callbacks.wandb_callback import WandbCallbackHandler\n'), ((1083, 1126), 'llama_index.callbacks.open_inference_callback.OpenInferenceCallbackHandler',... |
"""Global eval handlers."""
from typing import Any
from llama_index.callbacks.arize_phoenix_callback import arize_phoenix_callback_handler
from llama_index.callbacks.base_handler import BaseCallbackHandler
from llama_index.callbacks.honeyhive_callback import honeyhive_callback_handler
from llama_index.callbacks.open_... | [
"llama_index.callbacks.wandb_callback.WandbCallbackHandler",
"llama_index.callbacks.honeyhive_callback.honeyhive_callback_handler",
"llama_index.callbacks.simple_llm_handler.SimpleLLMHandler",
"llama_index.callbacks.arize_phoenix_callback.arize_phoenix_callback_handler",
"llama_index.callbacks.promptlayer_h... | [((990, 1025), 'llama_index.callbacks.wandb_callback.WandbCallbackHandler', 'WandbCallbackHandler', ([], {}), '(**eval_params)\n', (1010, 1025), False, 'from llama_index.callbacks.wandb_callback import WandbCallbackHandler\n'), ((1083, 1126), 'llama_index.callbacks.open_inference_callback.OpenInferenceCallbackHandler',... |
"""Google GenerativeAI Attributed Question and Answering (AQA) service.
The GenAI Semantic AQA API is a managed end to end service that allows
developers to create responses grounded on specified passages based on
a user query. For more information visit:
https://developers.generativeai.google/guide
"""
import loggin... | [
"llama_index.legacy.core.response.schema.Response",
"llama_index.legacy.schema.TextNode",
"llama_index.legacy.indices.query.schema.QueryBundle",
"llama_index.legacy.vector_stores.google.generativeai.genai_extension.build_generative_service"
] | [((1114, 1141), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1131, 1141), False, 'import logging\n'), ((2809, 2842), 'llama_index.legacy.vector_stores.google.generativeai.genai_extension.build_generative_service', 'genaix.build_generative_service', ([], {}), '()\n', (2840, 2842), True,... |
"""Google GenerativeAI Attributed Question and Answering (AQA) service.
The GenAI Semantic AQA API is a managed end to end service that allows
developers to create responses grounded on specified passages based on
a user query. For more information visit:
https://developers.generativeai.google/guide
"""
import loggin... | [
"llama_index.legacy.core.response.schema.Response",
"llama_index.legacy.schema.TextNode",
"llama_index.legacy.indices.query.schema.QueryBundle",
"llama_index.legacy.vector_stores.google.generativeai.genai_extension.build_generative_service"
] | [((1114, 1141), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1131, 1141), False, 'import logging\n'), ((2809, 2842), 'llama_index.legacy.vector_stores.google.generativeai.genai_extension.build_generative_service', 'genaix.build_generative_service', ([], {}), '()\n', (2840, 2842), True,... |
"""Elasticsearch vector store."""
import asyncio
import uuid
from logging import getLogger
from typing import Any, Callable, Dict, List, Literal, Optional, Union, cast
import nest_asyncio
import numpy as np
from llama_index.legacy.bridge.pydantic import PrivateAttr
from llama_index.legacy.schema import BaseNode, Met... | [
"llama_index.legacy.bridge.pydantic.PrivateAttr",
"llama_index.legacy.schema.TextNode",
"llama_index.legacy.vector_stores.utils.node_to_metadata_dict",
"llama_index.legacy.vector_stores.utils.metadata_dict_to_node"
] | [((640, 659), 'logging.getLogger', 'getLogger', (['__name__'], {}), '(__name__)\n', (649, 659), False, 'from logging import getLogger\n'), ((2343, 2396), 'elasticsearch.AsyncElasticsearch', 'elasticsearch.AsyncElasticsearch', ([], {}), '(**connection_params)\n', (2375, 2396), False, 'import elasticsearch\n'), ((3719, 3... |
"""Elasticsearch vector store."""
import asyncio
import uuid
from logging import getLogger
from typing import Any, Callable, Dict, List, Literal, Optional, Union, cast
import nest_asyncio
import numpy as np
from llama_index.legacy.bridge.pydantic import PrivateAttr
from llama_index.legacy.schema import BaseNode, Met... | [
"llama_index.legacy.bridge.pydantic.PrivateAttr",
"llama_index.legacy.schema.TextNode",
"llama_index.legacy.vector_stores.utils.node_to_metadata_dict",
"llama_index.legacy.vector_stores.utils.metadata_dict_to_node"
] | [((640, 659), 'logging.getLogger', 'getLogger', (['__name__'], {}), '(__name__)\n', (649, 659), False, 'from logging import getLogger\n'), ((2343, 2396), 'elasticsearch.AsyncElasticsearch', 'elasticsearch.AsyncElasticsearch', ([], {}), '(**connection_params)\n', (2375, 2396), False, 'import elasticsearch\n'), ((3719, 3... |
"""Google Generative AI Vector Store.
The GenAI Semantic Retriever API is a managed end-to-end service that allows
developers to create a corpus of documents to perform semantic search on
related passages given a user query. For more information visit:
https://developers.generativeai.google/guide
"""
import logging
i... | [
"llama_index.vector_stores.google.genai_extension.build_semantic_retriever",
"llama_index.vector_stores.google.genai_extension.get_corpus",
"llama_index.vector_stores.google.genai_extension.Config",
"llama_index.vector_stores.google.genai_extension.EntityName.from_str",
"llama_index.core.bridge.pydantic.Fie... | [((812, 839), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (829, 839), False, 'import logging\n'), ((2859, 2881), 'llama_index.vector_stores.google.genai_extension.Config', 'genaix.Config', ([], {}), '(**attrs)\n', (2872, 2881), True, 'import llama_index.vector_stores.google.genai_exten... |
"""Google GenerativeAI Attributed Question and Answering (AQA) service.
The GenAI Semantic AQA API is a managed end to end service that allows
developers to create responses grounded on specified passages based on
a user query. For more information visit:
https://developers.generativeai.google/guide
"""
import loggin... | [
"llama_index.indices.query.schema.QueryBundle",
"llama_index.schema.TextNode",
"llama_index.vector_stores.google.generativeai.genai_extension.build_generative_service",
"llama_index.core.response.schema.Response"
] | [((1051, 1078), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1068, 1078), False, 'import logging\n'), ((2739, 2772), 'llama_index.vector_stores.google.generativeai.genai_extension.build_generative_service', 'genaix.build_generative_service', ([], {}), '()\n', (2770, 2772), True, 'impor... |
"""Google GenerativeAI Attributed Question and Answering (AQA) service.
The GenAI Semantic AQA API is a managed end to end service that allows
developers to create responses grounded on specified passages based on
a user query. For more information visit:
https://developers.generativeai.google/guide
"""
import loggin... | [
"llama_index.indices.query.schema.QueryBundle",
"llama_index.schema.TextNode",
"llama_index.vector_stores.google.generativeai.genai_extension.build_generative_service",
"llama_index.core.response.schema.Response"
] | [((1051, 1078), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1068, 1078), False, 'import logging\n'), ((2739, 2772), 'llama_index.vector_stores.google.generativeai.genai_extension.build_generative_service', 'genaix.build_generative_service', ([], {}), '()\n', (2770, 2772), True, 'impor... |
"""FastAPI app creation, logger configuration and main API routes."""
import llama_index
from private_gpt.di import global_injector
from private_gpt.launcher import create_app
# Add LlamaIndex simple observability
llama_index.set_global_handler("simple")
app = create_app(global_injector)
| [
"llama_index.set_global_handler"
] | [((217, 257), 'llama_index.set_global_handler', 'llama_index.set_global_handler', (['"""simple"""'], {}), "('simple')\n", (247, 257), False, 'import llama_index\n'), ((265, 292), 'private_gpt.launcher.create_app', 'create_app', (['global_injector'], {}), '(global_injector)\n', (275, 292), False, 'from private_gpt.launc... |
"""FastAPI app creation, logger configuration and main API routes."""
import llama_index
from private_gpt.di import global_injector
from private_gpt.launcher import create_app
# Add LlamaIndex simple observability
llama_index.set_global_handler("simple")
app = create_app(global_injector)
| [
"llama_index.set_global_handler"
] | [((217, 257), 'llama_index.set_global_handler', 'llama_index.set_global_handler', (['"""simple"""'], {}), "('simple')\n", (247, 257), False, 'import llama_index\n'), ((265, 292), 'private_gpt.launcher.create_app', 'create_app', (['global_injector'], {}), '(global_injector)\n', (275, 292), False, 'from private_gpt.launc... |
"""FastAPI app creation, logger configuration and main API routes."""
import llama_index
from private_gpt.di import global_injector
from private_gpt.launcher import create_app
# Add LlamaIndex simple observability
llama_index.set_global_handler("simple")
app = create_app(global_injector)
| [
"llama_index.set_global_handler"
] | [((217, 257), 'llama_index.set_global_handler', 'llama_index.set_global_handler', (['"""simple"""'], {}), "('simple')\n", (247, 257), False, 'import llama_index\n'), ((265, 292), 'private_gpt.launcher.create_app', 'create_app', (['global_injector'], {}), '(global_injector)\n', (275, 292), False, 'from private_gpt.launc... |
"""FastAPI app creation, logger configuration and main API routes."""
import llama_index
from private_gpt.di import global_injector
from private_gpt.launcher import create_app
# Add LlamaIndex simple observability
llama_index.set_global_handler("simple")
app = create_app(global_injector)
| [
"llama_index.set_global_handler"
] | [((217, 257), 'llama_index.set_global_handler', 'llama_index.set_global_handler', (['"""simple"""'], {}), "('simple')\n", (247, 257), False, 'import llama_index\n'), ((265, 292), 'private_gpt.launcher.create_app', 'create_app', (['global_injector'], {}), '(global_injector)\n', (275, 292), False, 'from private_gpt.launc... |
"""
Astra DB Vector store index.
An index based on a DB table with vector search capabilities,
powered by the astrapy library
"""
import json
import logging
from typing import Any, Dict, List, Optional, cast
from warnings import warn
import llama_index.core
from llama_index.core.bridge.pydantic import PrivateAttr
f... | [
"llama_index.core.vector_stores.utils.metadata_dict_to_node",
"llama_index.core.vector_stores.utils.node_to_metadata_dict",
"llama_index.core.indices.query.embedding_utils.get_top_k_mmr_embeddings",
"llama_index.core.bridge.pydantic.PrivateAttr",
"llama_index.core.vector_stores.types.VectorStoreQueryResult"... | [((852, 879), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (869, 879), False, 'import logging\n'), ((2070, 2083), 'llama_index.core.bridge.pydantic.PrivateAttr', 'PrivateAttr', ([], {}), '()\n', (2081, 2083), False, 'from llama_index.core.bridge.pydantic import PrivateAttr\n'), ((2118, ... |
"""Google GenerativeAI Attributed Question and Answering (AQA) service.
The GenAI Semantic AQA API is a managed end to end service that allows
developers to create responses grounded on specified passages based on
a user query. For more information visit:
https://developers.generativeai.google/guide
"""
import loggin... | [
"llama_index.vector_stores.google.genai_extension.build_generative_service",
"llama_index.core.schema.TextNode",
"llama_index.core.llms.mock.MockLLM",
"llama_index.core.base.response.schema.Response",
"llama_index.core.indices.query.schema.QueryBundle"
] | [((1057, 1084), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1074, 1084), False, 'import logging\n'), ((2707, 2740), 'llama_index.vector_stores.google.genai_extension.build_generative_service', 'genaix.build_generative_service', ([], {}), '()\n', (2738, 2740), True, 'import llama_index... |
from unittest.mock import MagicMock, patch
import pytest
from llama_index.legacy.core.response.schema import Response
from llama_index.legacy.schema import Document
try:
import google.ai.generativelanguage as genai
has_google = True
except ImportError:
has_google = False
from llama_index.legacy.indices.... | [
"llama_index.legacy.vector_stores.google.generativeai.genai_extension.get_config",
"llama_index.legacy.indices.managed.google.generativeai.GoogleIndex.create_corpus",
"llama_index.legacy.indices.managed.google.generativeai.GoogleIndex.from_corpus",
"llama_index.legacy.schema.Document",
"llama_index.legacy.i... | [((693, 752), 'pytest.mark.skipif', 'pytest.mark.skipif', (['(not has_google)'], {'reason': 'SKIP_TEST_REASON'}), '(not has_google, reason=SKIP_TEST_REASON)\n', (711, 752), False, 'import pytest\n'), ((754, 798), 'unittest.mock.patch', 'patch', (['"""google.auth.credentials.Credentials"""'], {}), "('google.auth.credent... |
from unittest.mock import MagicMock, patch
import pytest
try:
import google.ai.generativelanguage as genai
has_google = True
except ImportError:
has_google = False
from llama_index.legacy.response_synthesizers.google.generativeai import (
GoogleTextSynthesizer,
set_google_config,
)
from llama_in... | [
"llama_index.legacy.response_synthesizers.google.generativeai.GoogleTextSynthesizer.from_defaults",
"llama_index.legacy.schema.TextNode",
"llama_index.legacy.response_synthesizers.google.generativeai.set_google_config",
"llama_index.legacy.vector_stores.google.generativeai.genai_extension.get_config"
] | [((663, 722), 'pytest.mark.skipif', 'pytest.mark.skipif', (['(not has_google)'], {'reason': 'SKIP_TEST_REASON'}), '(not has_google, reason=SKIP_TEST_REASON)\n', (681, 722), False, 'import pytest\n'), ((724, 768), 'unittest.mock.patch', 'patch', (['"""google.auth.credentials.Credentials"""'], {}), "('google.auth.credent... |
from typing import Any, Dict, List, Optional, Tuple
from llama_index.core.base.base_query_engine import BaseQueryEngine
from llama_index.core.base.response.schema import RESPONSE_TYPE
from llama_index.core.callbacks.schema import CBEventType, EventPayload
from llama_index.core.indices.composability.graph import Compos... | [
"llama_index.core.instrumentation.get_dispatcher",
"llama_index.core.settings.callback_manager_from_settings_or_context",
"llama_index.core.schema.NodeWithScore"
] | [((585, 620), 'llama_index.core.instrumentation.get_dispatcher', 'instrument.get_dispatcher', (['__name__'], {}), '(__name__)\n', (610, 620), True, 'import llama_index.core.instrumentation as instrument\n'), ((1649, 1734), 'llama_index.core.settings.callback_manager_from_settings_or_context', 'callback_manager_from_set... |
from llama_index import (
SimpleDirectoryReader,
VectorStoreIndex,
ServiceContext,
)
from llama_index.llms import LlamaCPP
from llama_index.llms.llama_utils import messages_to_prompt, completion_to_prompt
import llama_index.llms.llama_cpp
from langchain.embeddings import HuggingFaceEmbeddings
import co... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.ServiceContext.from_defaults",
"llama_index.SimpleDirectoryReader"
] | [((431, 491), 'langchain.embeddings.HuggingFaceEmbeddings', 'HuggingFaceEmbeddings', ([], {'model_name': 'config.EMBEDDING_MODEL_URL'}), '(model_name=config.EMBEDDING_MODEL_URL)\n', (452, 491), False, 'from langchain.embeddings import HuggingFaceEmbeddings\n'), ((538, 600), 'llama_index.ServiceContext.from_defaults', '... |
import time
import llama_index
from atlassian import Bitbucket
import os
import sys
sys.path.append('../')
import local_secrets as secrets
start_time = time.time()
stash = Bitbucket('https://git.techstyle.net', token=secrets.stash_token)
os.environ['OPENAI_API_KEY'] = secrets.techstyle_openai_key
project ='DATASICENCE... | [
"llama_index.GPTSimpleVectorIndex",
"llama_index.Document"
] | [((84, 106), 'sys.path.append', 'sys.path.append', (['"""../"""'], {}), "('../')\n", (99, 106), False, 'import sys\n'), ((153, 164), 'time.time', 'time.time', ([], {}), '()\n', (162, 164), False, 'import time\n'), ((173, 238), 'atlassian.Bitbucket', 'Bitbucket', (['"""https://git.techstyle.net"""'], {'token': 'secrets.... |
import json
from typing import Dict, List
import llama_index.query_engine
from llama_index import ServiceContext, QueryBundle
from llama_index.callbacks import CBEventType, LlamaDebugHandler, CallbackManager
from llama_index.indices.base import BaseIndex
from llama_index.indices.query.base import BaseQueryEngine
from ... | [
"llama_index.ServiceContext.from_defaults",
"llama_index.callbacks.LlamaDebugHandler",
"llama_index.selectors.LLMSingleSelector.from_defaults",
"llama_index.tools.QueryEngineTool.from_defaults",
"llama_index.response.schema.Response",
"llama_index.callbacks.CallbackManager"
] | [((1288, 1314), 'llama_index.response.schema.Response', 'Response', (['f"""我是{self.name}"""'], {}), "(f'我是{self.name}')\n", (1296, 1314), False, 'from llama_index.response.schema import RESPONSE_TYPE, Response\n'), ((2113, 2203), 'llama_index.tools.QueryEngineTool.from_defaults', 'QueryEngineTool.from_defaults', ([], {... |
import qdrant_client
from llama_index import (
VectorStoreIndex,
ServiceContext,
)
from llama_index.llms import Ollama
from llama_index.vector_stores.qdrant import QdrantVectorStore
import llama_index
llama_index.set_global_handler("simple")
# re-initialize the vector store
client = qdrant_client.QdrantClient... | [
"llama_index.ServiceContext.from_defaults",
"llama_index.set_global_handler",
"llama_index.vector_stores.qdrant.QdrantVectorStore",
"llama_index.VectorStoreIndex.from_vector_store",
"llama_index.llms.Ollama"
] | [((210, 250), 'llama_index.set_global_handler', 'llama_index.set_global_handler', (['"""simple"""'], {}), "('simple')\n", (240, 250), False, 'import llama_index\n'), ((294, 342), 'qdrant_client.QdrantClient', 'qdrant_client.QdrantClient', ([], {'path': '"""./qdrant_data"""'}), "(path='./qdrant_data')\n", (320, 342), Fa... |
import os
import glob
import llama_index
from llama_index.core import ServiceContext
from llama_index.llms.ollama import Ollama
from llama_index.core import SimpleDirectoryReader
from llama_index.core.response_synthesizers import TreeSummarize
# MODEL = "mistral"
MODEL = "llama2"
# MODEL = "llama2:text" # Doesn't f... | [
"llama_index.core.SimpleDirectoryReader",
"llama_index.llms.ollama.Ollama",
"llama_index.core.response_synthesizers.TreeSummarize",
"llama_index.core.ServiceContext.from_defaults"
] | [((525, 564), 'os.makedirs', 'os.makedirs', (['SUMMARY_DIR'], {'exist_ok': '(True)'}), '(SUMMARY_DIR, exist_ok=True)\n', (536, 564), False, 'import os\n'), ((1540, 1588), 'llama_index.llms.ollama.Ollama', 'Ollama', ([], {'model': 'MODEL', 'request_timeout': 'TIMEOUT_SEC'}), '(model=MODEL, request_timeout=TIMEOUT_SEC)\n... |
from io import BytesIO
from flask import Flask, jsonify
import os
# import tweepy
from dotenv import load_dotenv
from flask import request,jsonify
import snscrape.modules.twitter as snstwitter
from snscrape.modules.twitter import TwitterSearchScraper, TwitterSearchScraperMode
import requests
from goose3 import Goose
fr... | [
"llama_index.GPTVectorStoreIndex.from_documents",
"llama_index.TwitterTweetReader"
] | [((973, 988), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (978, 988), False, 'from flask import Flask, jsonify\n'), ((1057, 1070), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (1068, 1070), False, 'from dotenv import load_dotenv\n'), ((1078, 1106), 'os.getenv', 'os.getenv', (['"""HUGGINGFACE_A... |
## main function of AWS Lambda function
import llama_index
from llama_index import download_loader
import boto3
import json
import urllib.parse
from llama_index import SimpleDirectoryReader
def main(event, context):
# extracting s3 bucket and key information from SQS message
print(event)
s3_info = json.lo... | [
"llama_index.download_loader"
] | [((313, 352), 'json.loads', 'json.loads', (["event['Records'][0]['body']"], {}), "(event['Records'][0]['body'])\n", (323, 352), False, 'import json\n'), ((628, 692), 'llama_index.download_loader', 'download_loader', (['"""S3Reader"""'], {'custom_path': '"""/tmp/llamahub_modules"""'}), "('S3Reader', custom_path='/tmp/ll... |
"""Download."""
import json
import logging
import os
import subprocess
import sys
from enum import Enum
from importlib import util
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import pkg_resources
import requests
from pkg_resources import DistributionNotFound
from llama_index.download.... | [
"llama_index.download.utils.get_exports",
"llama_index.download.utils.initialize_directory"
] | [((637, 664), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (654, 664), False, 'import logging\n'), ((5550, 5583), 'os.path.exists', 'os.path.exists', (['requirements_path'], {}), '(requirements_path)\n', (5564, 5583), False, 'import os\n'), ((7403, 7471), 'llama_index.download.utils.ini... |
import json
from typing import Dict, List
import llama_index.query_engine
from llama_index import ServiceContext, QueryBundle
from llama_index.callbacks import CBEventType, LlamaDebugHandler, CallbackManager
from llama_index.indices.base import BaseIndex
from llama_index.indices.query.base import BaseQueryEngine
from ... | [
"llama_index.ServiceContext.from_defaults",
"llama_index.callbacks.CallbackManager",
"llama_index.callbacks.LlamaDebugHandler",
"llama_index.response.schema.Response"
] | [((1298, 1324), 'llama_index.response.schema.Response', 'Response', (['f"""我是{self.name}"""'], {}), "(f'我是{self.name}')\n", (1306, 1324), False, 'from llama_index.response.schema import RESPONSE_TYPE, Response\n'), ((2530, 2571), 'common.llm.create_llm', 'create_llm', (['cb_manager', 'LLM_CACHE_ENABLED'], {}), '(cb_man... |
import asyncio
import math
import numpy as np
import random
import tqdm
from functools import reduce
from typing import Any, List, Dict, Sequence, Union, Coroutine, Iterable
from llama_index.core.async_utils import asyncio_module
from llama_index.core.bridge.pydantic import BaseModel, Field
from llama_index.core.llms ... | [
"llama_index.packs.diff_private_simple_dataset.events.SyntheticExampleEndEvent",
"llama_index.core.instrumentation.get_dispatcher",
"llama_index.core.bridge.pydantic.Field",
"llama_index.packs.diff_private_simple_dataset.templates.zero_shot_completion_template.format",
"llama_index.packs.diff_private_simple... | [((1280, 1315), 'llama_index.core.instrumentation.get_dispatcher', 'instrument.get_dispatcher', (['__name__'], {}), '(__name__)\n', (1305, 1315), True, 'import llama_index.core.instrumentation as instrument\n'), ((1589, 1654), 'llama_index.core.bridge.pydantic.Field', 'Field', ([], {'description': '"""Instruction assoc... |
"""Download."""
import json
import logging
import os
import subprocess
import sys
from enum import Enum
from importlib import util
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import pkg_resources
import requests
from pkg_resources import DistributionNotFound
from llama_index.download.... | [
"llama_index.download.utils.get_exports",
"llama_index.download.utils.initialize_directory"
] | [((637, 664), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (654, 664), False, 'import logging\n'), ((5550, 5583), 'os.path.exists', 'os.path.exists', (['requirements_path'], {}), '(requirements_path)\n', (5564, 5583), False, 'import os\n'), ((7403, 7471), 'llama_index.download.utils.ini... |
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