From d89d9ad3d24193e475706d5c77896e0863ebc325 Mon Sep 17 00:00:00 2001 From: AjaySi Date: Sun, 18 Feb 2024 10:00:24 +0530 Subject: [PATCH] WIP-AI writer, Try Web research working. --- blogen.py | 28 +- lib/ai_web_researcher/google_serp_search.py | 1 - .../google_trends_researcher.py | 92 ++-- .../gpt_online_researcher.py | 71 ++- .../metaphor_basic_neural_web_search.py | 7 +- lib/ai_web_researcher/tavily_ai_search.py | 18 +- lib/get_text_response.py | 421 ------------------ .../how_to_llamaindex_2024-02-04_15-45-16 | 26 -- .../how_to_llamaindex_2024-02-04_16-19-57 | 0 .../how_to_llamaindex_2024-02-04_16-36-53 | 9 - .../how_to_llamaindex_2024-02-04_16-46-47 | 83 ---- .../how_to_llamaindex_2024-02-04_16-58-31 | 108 ----- .../how_to_llamaindex_2024-02-04_17-04-27 | 133 ------ .../how_to_llamaindex_2024-02-04_17-35-51 | 0 .../how_to_llamaindex_2024-02-04_17-40-52 | 100 ----- .../how_to_llamaindex_2024-02-04_17-44-02 | 82 ---- .../how_to_llamaindex_2024-02-04_18-05-08 | 76 ---- .../how_to_llamaindex_2024-02-04_18-23-42 | 116 ----- .../how_to_llamaindex_2024-02-04_18-31-53 | 78 ---- .../how_to_llamaindex_2024-02-04_19-06-07 | 98 ---- .../how_to_llamaindex_2024-02-04_19-31-58 | 82 ---- 21 files changed, 117 insertions(+), 1512 deletions(-) delete mode 100644 workspace/web_research_reports/how_to_llamaindex_2024-02-04_15-45-16 delete mode 100644 workspace/web_research_reports/how_to_llamaindex_2024-02-04_16-19-57 delete mode 100644 workspace/web_research_reports/how_to_llamaindex_2024-02-04_16-36-53 delete mode 100644 workspace/web_research_reports/how_to_llamaindex_2024-02-04_16-46-47 delete mode 100644 workspace/web_research_reports/how_to_llamaindex_2024-02-04_16-58-31 delete mode 100644 workspace/web_research_reports/how_to_llamaindex_2024-02-04_17-04-27 delete mode 100644 workspace/web_research_reports/how_to_llamaindex_2024-02-04_17-35-51 delete mode 100644 workspace/web_research_reports/how_to_llamaindex_2024-02-04_17-40-52 delete mode 100644 workspace/web_research_reports/how_to_llamaindex_2024-02-04_17-44-02 delete mode 100644 workspace/web_research_reports/how_to_llamaindex_2024-02-04_18-05-08 delete mode 100644 workspace/web_research_reports/how_to_llamaindex_2024-02-04_18-23-42 delete mode 100644 workspace/web_research_reports/how_to_llamaindex_2024-02-04_18-31-53 delete mode 100644 workspace/web_research_reports/how_to_llamaindex_2024-02-04_19-06-07 delete mode 100644 workspace/web_research_reports/how_to_llamaindex_2024-02-04_19-31-58 diff --git a/blogen.py b/blogen.py index 1690d87d..418ee606 100644 --- a/blogen.py +++ b/blogen.py @@ -1,6 +1,7 @@ import os from pathlib import Path +import requests import typer from PyInquirer import prompt from rich import print @@ -23,7 +24,7 @@ def prompt_for_time_range(): 'type': 'list', 'name': 'time_range', 'message': '๐Ÿ‘‹ Select Search result time range:', - 'choices': ["past day", "past week", "past month", "past year", "anytime"], + 'choices': ["anytime", "past year", "past month", "past week", "past day"], } ] answers = prompt(questions) @@ -86,7 +87,7 @@ def start_interactive_mode(): 3. Get from NewsApi 4. Get YOU.com News.""") elif mode == 'Quit': - typer.echo("Exiting, Fuck Off!") + typer.echo("Exiting, F*** Off!") raise typer.Exit() @@ -176,7 +177,8 @@ def do_web_research(): while True: print("________________________________________________________________") search_keywords = typer.prompt("๐Ÿ‘‹ Enter keywords for web research:") - if search_keywords and len(search_keywords.split()) >= 3: + # Giving a single keywords, yields bad results. + if search_keywords and len(search_keywords.split()) >= 2: break else: print("๐Ÿšซ Search keywords should be at least three words long. Please try again.") @@ -225,5 +227,25 @@ def do_web_research(): print(f"\n๐Ÿ’ฅ๐Ÿคฏ [bold red]ERROR ๐Ÿคฏ : Failed to do web research: {err}\n") +def check_internet(): + try: + # Attempt to send a GET request to a well-known website + response = requests.get("http://www.google.com", timeout=20) + if not response.status_code == 200: + print("๐Ÿ’ฅ๐Ÿคฏ WTFish, Internet is NOT available. Enjoy the wilderness..") + exit(1) + else: + return + except requests.ConnectionError: + print("๐Ÿ’ฅ๐Ÿคฏ WTFish: Internet is NOT available. Enjoy the wilderness..") + exit(1) + except requests.Timeout: + print("Request timed out. Internet might be slow.") + exit(1) + except Exception as e: + print("Internet: An error occurred:", e) + exit(1) + if __name__ == "__main__": + check_internet() app() diff --git a/lib/ai_web_researcher/google_serp_search.py b/lib/ai_web_researcher/google_serp_search.py index df8e65b4..eb214e2b 100644 --- a/lib/ai_web_researcher/google_serp_search.py +++ b/lib/ai_web_researcher/google_serp_search.py @@ -213,7 +213,6 @@ def process_search_results(search_results): data = [] logger.info(f"Google Search Parameters: {search_results.get('searchParameters', {})}") organic_results = search_results.get("organic", []) - print(search_results) # Displaying Organic Results organic_data = [] diff --git a/lib/ai_web_researcher/google_trends_researcher.py b/lib/ai_web_researcher/google_trends_researcher.py index a63d84ee..83da5d33 100644 --- a/lib/ai_web_researcher/google_trends_researcher.py +++ b/lib/ai_web_researcher/google_trends_researcher.py @@ -7,7 +7,7 @@ Features: - Visualizes Google Trends data, including interest over time and interest by region. - Retrieves related queries and topics for a set of search keywords. - Utilizes visualization libraries such as Matplotlib, Plotly, and Rich for displaying results. -- Incorporates logging for error handling and informative messages. +- Incorporates logger.for error handling and informative messages. Usage: - Provide a search term or a list of search terms for analysis. @@ -22,6 +22,7 @@ Modifications: Note: Ensure that the required libraries are installed using 'pip install pytrends requests_html tqdm tabulate plotly rich'. """ +import os import requests import numpy as np import sys @@ -37,14 +38,12 @@ import pandas as pd import matplotlib.pyplot as plt import plotly.express as px import plotly.io as pio -import logging from requests_html import HTML, HTMLSession from urllib.parse import quote_plus from tqdm import tqdm from tabulate import tabulate from pytrends.request import TrendReq -import wordcloud -logging.basicConfig(level=logging.INFO) +from wordcloud import WordCloud from loguru import logger # Configure logger @@ -75,7 +74,7 @@ def fetch_google_trends_interest_overtime(keyword): return data except Exception as e: - logging.error(f"Error in fetch_google_trends_data: {e}") + logger.error(f"Error in fetch_google_trends_data: {e}") return pd.DataFrame() @@ -151,10 +150,11 @@ def get_related_queries_and_save_csv(keywords, hl='en-US', tz=360, cat=0, timefr print("\n\033[1m๐Ÿ” Top\033[0m: The most popular search queries. Scoring is on a relative scale where a value of 100 is the most commonly searched query, 50 is a query searched half as often, and a value of 0 is a query searched for less than 1% as often as the most popular query.\n") print("\n\033[1m๐Ÿš€ Rising\033[0m: Queries with the biggest increase in search frequency since the last time period. Results marked 'Breakout' had a tremendous increase, probably because these queries are new and had few (if any) prior searches.\n") # Display the DataFrame using tabulate - print(tabulate(all_queries_df, headers='keys', tablefmt='fancy_grid')) + table = tabulate(all_queries_df, headers='keys', tablefmt='fancy_grid') + print(table) # Save the combined table to a file try: - save_in_file(all_queries_df) + save_in_file(table) except Exception as save_results_err: logger.error(f"Failed to save search results: {save_results_err}") return top_rising_queries @@ -178,6 +178,7 @@ def get_related_topics_and_save_csv(search_keywords): pytrends = TrendReq(hl='en-US', tz=360) # Build payload + # FIXME: Remove hardcoding. pytrends.build_payload(search_keywords, cat=0, timeframe='today 12-m') # Get related topics @@ -198,10 +199,7 @@ def get_related_topics_and_save_csv(search_keywords): # Rename columns to avoid duplicates and provide meaningful names df_top_topics.columns = ['Top- ' + col if col != 'topic_title' else col for col in df_top_topics.columns] df_rising_topics.columns = ['Rising- ' + col if col != 'topic_title' else col for col in df_rising_topics.columns] - - # Save to CSV all_topics_df = pd.concat([df_top_topics, df_rising_topics], axis=1) - #all_topics_df.to_csv('related_topics.csv', index=False) print(f"\n\n ๐Ÿ“ขโ—๐Ÿšจ Rising and Trending Keywords for {search_keywords}\n") print("\033[1m๐Ÿ” Top\033[0m: The most popular search topics.") @@ -209,11 +207,15 @@ def get_related_topics_and_save_csv(search_keywords): # Display the DataFrame using tabulate pd.set_option('display.max_rows', all_topics_df.shape[0]+1) print(all_topics_df.head(10)) - #print(tabulate(all_topics_df, headers='keys', tablefmt='fancy_grid')) + table = tabulate(all_topics_df, headers='keys', tablefmt='fancy_grid') + try: + save_in_file(table) + except Exception as save_results_err: + logger.error(f"Failed to save search results: {save_results_err}") return all_topics_df except Exception as e: - print(f"ERROR: An error occurred: {e}") + print(f"ERROR: An error occurred in related topics: {e}") return pd.DataFrame() @@ -224,7 +226,7 @@ def get_source(url): response.raise_for_status() # Raise an HTTPError for bad responses return response except requests.exceptions.RequestException as e: - logging.error(f"Error during HTTP request: {e}") + logger.error(f"Error during HTTP request: {e}") return None @@ -240,10 +242,10 @@ def get_results(query): else: return None except json.JSONDecodeError as e: - logging.error(f"Error decoding JSON response: {e}") + logger.error(f"Error decoding JSON response: {e}") return None except requests.exceptions.RequestException as e: - logging.error(f"Error during HTTP request: {e}") + logger.error(f"Error during HTTP request: {e}") return None @@ -256,7 +258,7 @@ def format_results(results): suggestions.append(suggestion) return suggestions except (KeyError, IndexError) as e: - logging.error(f"Error parsing search results: {e}") + logger.error(f"Error parsing search results: {e}") return [] @@ -288,7 +290,7 @@ def get_expanded_terms(query): return terms except Exception as e: - logging.error(f"Error in get_expanded_terms: {e}") + logger.error(f"Error in get_expanded_terms: {e}") return [] @@ -307,7 +309,7 @@ def get_expanded_suggestions(query): return all_results except Exception as e: - logging.error(f"Error in get_expanded_suggestions: {e}") + logger.error(f"Error in get_expanded_suggestions: {e}") return [] @@ -321,10 +323,14 @@ def get_suggestions_for_keyword(search_term): #expanded_results_df.to_csv('results.csv', index=False) pd.set_option('display.max_rows', expanded_results_df.shape[0]+1) expanded_results_df.drop_duplicates('Keywords', inplace=True) - + table = tabulate(expanded_results_df, headers=['Keywords', 'Relevance'], tablefmt='fancy_grid') + try: + save_in_file(table) + except Exception as save_results_err: + logger.error(f"Failed to save search results: {save_results_err}") return expanded_results_df except Exception as e: - logging.error(f"get_suggestions_for_keyword: Error in main: {e}") + logger.error(f"get_suggestions_for_keyword: Error in main: {e}") @@ -355,7 +361,7 @@ def perform_keyword_clustering(expanded_results_df, num_clusters=5): return expanded_results_df except Exception as e: - logging.error(f"Error in perform_keyword_clustering: {e}") + logger.error(f"Error in perform_keyword_clustering: {e}") return pd.DataFrame() @@ -404,7 +410,7 @@ def visualize_silhouette(X, labels): plt.show() except Exception as e: - logging.error(f"Error in visualize_silhouette: {e}") + logger.error(f"Error in visualize_silhouette: {e}") @@ -435,9 +441,9 @@ def print_and_return_top_keywords(expanded_results_df, num_clusters=5): table = tabulate(top_keywords_df, headers='keys', tablefmt='fancy_grid') # Save the combined table to a file try: - save_in_file(top_keywords_df) + save_in_file(table) except Exception as save_results_err: - logger.error(f"Failed to save search results: {save_results_err}") + logger.error(f"๐Ÿšจ Failed to save search results: {save_results_err}") print(table) return top_keywords_df @@ -484,11 +490,15 @@ def do_google_trends_analysis(search_term): for asearch_term in search_term: #FIXME: Lets work with a single root keyword. suggestions_df = get_suggestions_for_keyword(asearch_term) + if len(suggestions_df['Keywords']) > 10: + result_df = perform_keyword_clustering(suggestions_df) + # Display top keywords in each cluster + top_keywords = print_and_return_top_keywords(result_df) + all_the_keywords.append(top_keywords['Keywords'].tolist()) + else: + all_the_keywords.append(suggestions_df['Keywords'].tolist()) + all_the_keywords = ','.join([', '.join(filter(None, map(str, sublist))) for sublist in all_the_keywords]) - result_df = perform_keyword_clustering(suggestions_df) - # Display top keywords in each cluster - top_keywords = print_and_return_top_keywords(result_df) - all_the_keywords.append(top_keywords['Keywords'].tolist()) # # # FIXME: Get result from vision GPT. Fetch and visualize Google Trends data # #trends_data = fetch_google_trends_interest_overtime("llamaindex") @@ -496,23 +506,17 @@ def do_google_trends_analysis(search_term): # # FIXME: Plot Interest Over time. # result_df = plot_interest_by_region(search_term) # -# # Display additional information + # Display additional information result_df = get_related_topics_and_save_csv(search_term) # Extract 'Top' topic_title top_topic_title = result_df['topic_title'].values.tolist() - # Join each sublist into one string separated by comma #top_topic_title = [','.join(filter(None, map(str, sublist))) for sublist in top_topic_title] top_topic_title = ','.join([', '.join(filter(None, map(str, sublist))) for sublist in top_topic_title]) - print(f"\nRising and Top keywords: {top_topic_title}") - # Print or use the extracted topic titles - all_the_keywords = ','.join([', '.join(filter(None, map(str, sublist))) for sublist in all_the_keywords]) - print(f"\n\n๐Ÿ“ขโ—๐Ÿšจ Important keywords to target: {all_the_keywords}\n\n") - all_the_keywords += top_topic_title - print(all_the_keywords) + # TBD: Not getting great results OR unable to understand them. + #all_the_keywords += top_topic_title all_the_keywords = all_the_keywords.split(',') - # Split the list into chunks of 5 keywords chunk_size = 4 chunks = [all_the_keywords[i:i + chunk_size] for i in range(0, len(all_the_keywords), chunk_size)] @@ -520,11 +524,15 @@ def do_google_trends_analysis(search_term): combined_df = pd.DataFrame(chunks, columns=[f'K๐Ÿ“ขeyword Col{i + 1}' for i in range(chunk_size)]) # Print the table - print(tabulate(combined_df, headers='keys', tablefmt='fancy_grid')) - #combined_df = pd.DataFrame({'๐Ÿ“ขโ—๐Ÿšจ Important keywords to target': chunks}) + table = tabulate(combined_df, headers='keys', tablefmt='fancy_grid') + # Save the combined table to a file + try: + save_in_file(table) + except Exception as save_results_err: + logger.error(f"Failed to save search results: {save_results_err}") + print(table) - print(all_the_keywords) - generate_wordcloud(all_the_keywords.split(',')) + #generate_wordcloud(all_the_keywords) return(all_the_keywords) except Exception as e: - logging.error(f"Error in main: {e}") + logger.error(f"Error in Google Trends Analysis: {e}") diff --git a/lib/ai_web_researcher/gpt_online_researcher.py b/lib/ai_web_researcher/gpt_online_researcher.py index 142e5cd5..c13e0c91 100644 --- a/lib/ai_web_researcher/gpt_online_researcher.py +++ b/lib/ai_web_researcher/gpt_online_researcher.py @@ -47,33 +47,33 @@ def gpt_web_researcher(search_keywords, time_range=None, include_domains=list(), google_result = None tavily_result = None report = None -# try: -# logger.info(f"Doing Google search for: {search_keywords}\n") -# google_result = google_search(search_keywords) -# blog_titles.append(extract_info(google_result, "titles")) -# except Exception as err: -# logger.error(f"Failed to do Google Serpapi research: {err}") -# # Not failing, as tavily would do same and then GPT-V to search. -# -# try: -# # FIXME: Include the follow-up questions as blog FAQs. -# logger.info(f"Doing Tavily AI search for: {search_keywords}") -# tavily_result = get_tavilyai_results(search_keywords, include_domains) -# blog_titles.append(tavily_extract_information(tavily_result, "titles")) -# except Exception as err: -# logger.error(f"Failed to do Tavily AI Search: {err}") + try: + logger.info(f"Doing Google search for: {search_keywords}\n") + google_result = google_search(search_keywords) + blog_titles.append(extract_info(google_result, "titles")) + except Exception as err: + logger.error(f"Failed to do Google Serpapi research: {err}") + # Not failing, as tavily would do same and then GPT-V to search. -# try: -# logger.info(f"Start Semantic/Neural web search with Metahpor: {search_keywords}") -# response_articles = metaphor_search_articles( -# search_keywords, -# include_domains=include_domains, -# time_range=time_range, -# similar_url=similar_url) -# blog_titles.append(metaphor_extract_titles_or_text(response_articles, return_titles=True)) -# except Exception as err: -# logger.error(f"Failed to do Metaphor search: {err}") -# print(blog_titles) + try: + # FIXME: Include the follow-up questions as blog FAQs. + logger.info(f"Doing Tavily AI search for: {search_keywords}") + tavily_result = get_tavilyai_results(search_keywords, include_domains) + blog_titles.append(tavily_extract_information(tavily_result, "titles")) + except Exception as err: + logger.error(f"Failed to do Tavily AI Search: {err}") + + try: + logger.info(f"Start Semantic/Neural web search with Metahpor: {search_keywords}") + response_articles = metaphor_search_articles( + search_keywords, + include_domains=include_domains, + time_range=time_range, + similar_url=similar_url) + blog_titles.append(metaphor_extract_titles_or_text(response_articles, return_titles=True)) + except Exception as err: + logger.error(f"Failed to do Metaphor search: {err}") + print(blog_titles) try: logger.info(f"Do Google Trends analysis for given keywords: {search_keywords}") @@ -85,18 +85,7 @@ def gpt_web_researcher(search_keywords, time_range=None, include_domains=list(), # 1. Return a list of related keywords along with search volumes. # 2. New blog titles to write on(niche, top) and blog sections. # 3. Competitors list, similar urls if given. - - -class Result(NamedTuple): - url: str - id: str - title: str - score: float - published_date: str - author: str - text: str - highlights: List[str] - highlight_scores: List[float] + print(f"\n\nReview the analysis in this file at: {os.environ.get('SEARCH_SAVE_FILE')}\n") def metaphor_extract_titles_or_text(json_data, return_titles=True): @@ -110,12 +99,10 @@ def metaphor_extract_titles_or_text(json_data, return_titles=True): Returns: list: List of titles or text. """ - result_list = [Result(**result) for result in json_data] - if return_titles: - return [result.title for result in result_list] + return [(result.title) for result in json_data] else: - return [result.text for result in result_list] + return [result.text for result in json_data] def extract_info(json_data, info_type): diff --git a/lib/ai_web_researcher/metaphor_basic_neural_web_search.py b/lib/ai_web_researcher/metaphor_basic_neural_web_search.py index d0f330d4..d8cfa772 100644 --- a/lib/ai_web_researcher/metaphor_basic_neural_web_search.py +++ b/lib/ai_web_researcher/metaphor_basic_neural_web_search.py @@ -142,10 +142,11 @@ def metaphor_search_articles(query, logger.error(f"Failed in metaphor.search_and_contents: {err}") # From each webpage, get a summary of the web page. + print(search_response) contents_response = search_response.results - for content in tqdm(contents_response, desc="Reading Web URL content:", unit="content"): - summarized_content = summarize_web_content(content.text, "gemini") - content.text = summarized_content +# for content in tqdm(contents_response, desc="Reading Web URL content:", unit="content"): +# summarized_content = summarize_web_content(content.text, "gemini") +# content.text = summarized_content print_search_result(contents_response) diff --git a/lib/ai_web_researcher/tavily_ai_search.py b/lib/ai_web_researcher/tavily_ai_search.py index 18167465..42a8155c 100644 --- a/lib/ai_web_researcher/tavily_ai_search.py +++ b/lib/ai_web_researcher/tavily_ai_search.py @@ -129,18 +129,18 @@ def print_result_table(output_data): logger.error(f"Failed to save search results: {save_results_err}") # Display the 'follow_up_questions' in a table - table_headers = [f"Search Engine follow up questions for query: {output_data.get('query')}"] - table_data = [[output_data.get("follow_up_questions")]] - table = tabulate(table_data, + if output_data.get("follow_up_questions"): + table_headers = [f"Search Engine follow up questions for query: {output_data.get('query')}"] + table_data = [[output_data.get("follow_up_questions")]] + table = tabulate(table_data, headers=table_headers, tablefmt="fancy_grid", maxcolwidths=[80]) - print(table) - # Save the combined table to a file - try: - save_in_file(table) - except Exception as save_results_err: - logger.error(f"Failed to save search results: {save_results_err}") + print(table) + try: + save_in_file(table) + except Exception as save_results_err: + logger.error(f"Failed to save search results: {save_results_err}") def save_in_file(table_content): diff --git a/lib/get_text_response.py b/lib/get_text_response.py index 4004d360..248d9a60 100644 --- a/lib/get_text_response.py +++ b/lib/get_text_response.py @@ -11,30 +11,8 @@ import json import os import datetime #I wish import sys - -import openai -from tqdm import tqdm, trange import time -import re -from textwrap import dedent -from .gpt_providers.openai_gpt_provider import gen_new_from_given_img -from .gpt_providers.openai_chat_completion import openai_chatgpt -from .gpt_providers.gpt_vision_img_details import analyze_and_extract_details_from_image -from .generate_image_from_prompt import generate_image -from .write_blogs_from_youtube_videos import youtube_to_blog -from .wordpress_blog_uploader import compress_image, upload_blog_post, upload_media -from .gpt_online_researcher import do_online_research -from .save_blog_to_file import save_blog_to_file -from .optimize_images_for_upload import optimize_image -from .combine_research_and_blog import blog_with_research -from .get_blog_meta_desc import generate_blog_description -from .get_blog_title import generate_blog_title -from .get_tags import get_blog_tags -from .get_blog_category import get_blog_categories -from .convert_content_to_markdown import convert_tomarkdown_format -from .convert_markdown_to_html import convert_markdown_to_html -from .utils.youtube_keyword_research import research_yt from loguru import logger logger.remove() logger.add(sys.stdout, @@ -57,402 +35,3 @@ wordpress_url = '' wordpress_username = '' wordpress_password = '' -def generate_youtube_blog(yt_url_list, output_format="markdown"): - """Takes a list of youtube videos and generates blog for each one of them. - """ - # Use to store the blog in a string, to save in a *.md file. - blog_markdown_str = "" - if isinstance(yt_url_list, str): - yt_url_list = [yt_url_list] - for a_yt_url in yt_url_list: - try: - logger.info(f"Starting to write blog on URL: {a_yt_url}") - blog_markdown_str, yt_title = youtube_to_blog(a_yt_url) - logger.warning("\n\n--------------- First Draft of the Blog: --------\n\n") - logger.info(f"{blog_markdown_str}\n") - logger.warning("--------------------END of First draft----------\n\n") - if not yt_title or not blog_markdown_str: - logger.error("No content or title for audio to proceed.") - sys.exit(1) - except Exception as e: - logger.error(f"Error in youtube_to_blog: {e}") - sys.exit(1) - - try: - logger.info(f"Starting with online research for URL title: {yt_title}") - research_report = do_online_research(yt_title) - if not research_report: - logger.error(f"Error in do_online_research returned no report: {e}") - sys.exit(1) - logger.warning(f"\n\n---------------Online Research Report: {yt_title} --------\n\n") - logger.info(f"{research_report}\n") - logger.warning("--------------------END of Research Report----------\n\n") - except Exception as e: - logger.error(f"Error in do_online_research: {e}") - sys.exit(1) - - try: - logger.info("Preparing a blog content from audio script and online research content...") - blog_markdown_str = blog_with_research(research_report, blog_markdown_str) - logger.warning("\n\n--------------- Second Blog Draft after online research: --------\n\n") - logger.info(f"{blog_markdown_str}\n") - logger.warning("--------------------END of Second draft----------\n\n") - except Exception as e: - logger.error(f"Error in blog_with_research: {e}") - sys.exit(1) - - try: - # Get the title and meta description of the blog. - logger.info("Generating Blog Description.") - blog_meta_desc = generate_blog_description(blog_markdown_str, "gemini") - logger.info("Generating Blog Title.") - title = generate_blog_title(blog_meta_desc, "gemini") - logger.info(f"Title is {title} and description is {blog_meta_desc}") - # Regex pattern to match 'Title:', 'title:', 'TITLE:', etc., followed by optional whitespace - title = re.sub(re.compile(r'(?i)title:\s*'), '', title) - #blog_markdown_str = "# " + title.replace('"', '') + "\n\n" - - # Get blog tags and categories. - blog_tags = get_blog_tags(blog_meta_desc, "gemini") - logger.info(f"Blog tags are: {blog_tags}") - blog_categories = get_blog_categories(blog_meta_desc, "gemini") - logger.info(f"Blog categories are: {blog_categories}") - - # Generate an introduction for the blog - #blog_intro = get_blog_intro(title, blog_markdown_str) - #logger.info(f"The Blog intro is:\n {blog_intro}") - #blog_markdown_str = blog_markdown_str + "\n\n" + f"{blog_intro}" + "\n\n" - - # Generate an image based on meta description - logger.info(f"Calling Image generation with prompt: {blog_meta_desc}") - main_img_path = generate_image(blog_meta_desc, image_dir, "dalle3") - main_img_path = optimize_image(main_img_path) - - # Get a variation of the yt url screenshot to use in the blog. - #varied_img_path = gen_new_from_given_img(yt_img_path, image_dir) - #logger.info(f"Image path: {main_img_path} and varied path: {varied_img_path}") - #blog_markdown_str = blog_markdown_str + f'![img-description]({os.path.basename(varied_img_path)})' + f'_{yt_title}_' - - #stbdiff_img_path = generate_image(yt_img_path, image_dir, "stable_diffusion") - #logger.info(f"Image path: {main_img_path} from stable diffusion: {stbdiff_img_path}") - #blog_markdown_str = blog_markdown_str + f'![img-description]({os.path.basename(stbdiff_img_path)})' + f'_{yt_title}_' - - # Add the body of the blog content. - #blog_markdown_str = blog_markdown_str + "\n\n" + f'{yt_blog}' + "\n\n" - # Get the Conclusion of the blog, by passing the generated blog. - #blog_conclusion = get_blog_conclusion(blog_markdown_str) - # TBD: Add another image. - #blog_markdown_str = blog_markdown_str + "### Conclusion" + "\n\n" + f"{blog_conclusion}" + "\n" - - # Proofread the blog, edit and remove dubplicates and refine it further. - # Presently, fixing the blog keywords to be tags and categories. - #blog_keywords = f"{blog_tags} + {blog_categories}" - #blog_markdown_str = blog_proof_editor(blog_markdown_str, blog_keywords) - #logger.warning("\n\n--------------- 3rd draft after proofreading: --------\n\n") - #logger.info(f"{blog_markdown_str}\n") - #logger.warning("--------------------END of 3rd draft----------\n\n") - - # Check the type of blog format needed by the user. - if 'html' in output_format: - logger.info("Converting final blog to HTML format.") - blog_markdown_str = convert_markdown_to_html(blog_markdown_str) - elif 'markdown' in output_format: - logger.info("Converting final blog to Markdown style.") - blog_markdown_str = convert_tomarkdown_format(blog_markdown_str) - - logger.warning("\n\n--------------- Final Blog Content: --------\n\n") - logger.info(f"{blog_markdown_str}\n") - logger.warning("--------------------END of Blog Content----------\n\n") - - - # Try to save the blog content in a file, in whichever format. Just dump it. - try: - save_blog_to_file(blog_markdown_str, title, blog_meta_desc, - blog_tags, blog_categories, main_img_path, output_path) - except Exception as err: - logger.error(f"Failed to Save blog content: {err}") - - except Exception as e: - # raise assertionerror - logger.error(f"Error: Failed to generate_youtube_blog: {e}") - exit(1) - - -def generate_detailed_blog(num_blogs, blog_keywords, niche, num_subtopics, - wordpress=False, research_online=False, output_format="HTML"): - """ - This function will take a blog Topic to first generate sections for it - and then generate content for each section. - """ - # Use to store the blog in a string, to save in a *.md file. - blog_markdown_str = "" - - # TBD: Check if the generated topics are equal to what user asked. - blog_topic_arr = generate_blog_topics(blog_keywords, num_blogs, niche) - logger.info(f"Generated Blog Topics:---- \n{blog_topic_arr}\n") - # Split the string at newlines - blog_topic_arr = blog_topic_arr.split('\n') - - # For each of blog topic, generate content. - for a_blog_topic in blog_topic_arr: - # if md/html - a_blog_topic = a_blog_topic.replace('"', '') - a_blog_topic = re.sub(r'^[\d.\s]+', '', a_blog_topic) - blog_markdown_str = "# " + a_blog_topic + "\n\n" - - # Get the introduction specific to blog title and sub topics. - tpc_outlines = generate_topic_outline(a_blog_topic, num_subtopics) - tpc_outlines = tpc_outlines.split("\n") - - blog_intro = get_blog_intro(a_blog_topic, tpc_outlines) - logger.info(f"The intro is:\n{blog_intro}") - blog_markdown_str = blog_markdown_str + "### Introduction" + "\n\n" + f"{blog_intro}" + "\n\n" - - # Now, for each blog we have sub topic. Generate content for each of the sub topic. - for a_outline in tpc_outlines: - a_outline = a_outline.replace('"', '') - logger.info(f"Generating content for sub-topic: {a_outline}") - sub_topic_content = generate_topic_content(blog_keywords, a_outline) - # a_outline is sub topic heading, hence part ToC also. - #blog_markdown_str = blog_markdown_str + "\n\n" + f"### {a_outline}" + "\n\n" - blog_markdown_str = blog_markdown_str + "\n" + f"\n {sub_topic_content}" + "\n\n" - - # Get the Conclusion of the blog, by passing the generated blog. - blog_conclusion = get_blog_conclusion(blog_markdown_str) - blog_markdown_str = blog_markdown_str + "### Conclusion" + "\n" + f"{blog_conclusion}" + "\n" - - # logger.info/check the final blog content. - logger.info(f"Final blog content: {blog_markdown_str}") - - #if research_online: - # # Call on the got-researcher, tavily apis for this. So many apis floating around. - # report = do_online_research_on(blog_keywords) - # blog_markdown_str = blog_with_research(report, blog_markdown_str) - - blog_meta_desc = generate_blog_description(blog_markdown_str) - logger.info(f"\nThe blog meta description is:{blog_meta_desc}\n") - - # Generate an image based on meta description - logger.info(f"Calling Image generation with prompt: {blog_meta_desc}") - main_img_path = generate_image(blog_meta_desc, image_dir, "dalle3") - - blog_tags = get_blog_tags(blog_markdown_str) - logger.info(f"\nBlog tags for generated content: {blog_tags}\n") - - blog_categories = get_blog_categories(blog_markdown_str) - logger.info(f"Generated blog categories: {blog_categories}\n") - - # Use chatgpt to convert the text into HTML or markdown. - if 'html' in output_format: - blog_markdown_str = convert_markdown_to_html(blog_markdown_str) - - # Check if blog needs to be posted on wordpress. - if wordpress: - # Fixme: Fetch all tags and categories to check, if present ones are present and - # use them else create new ones. Its better to use chatgpt than string comparison. - # Similar tags and categories will be missed. - # blog_categories = - # blog_tags = - logger.info("Uploading the blog to wordpress.\n") - main_img_path = compress_image(main_img_path, quality=85) - try: - img_details = analyze_and_extract_details_from_image(main_img_path) - alt_text = img_details.get('alt_text') - img_description = img_details.get('description') - img_title = img_details.get('title') - caption = img_details.get('caption') - try: - media = upload_media(wordpress_url, wordpress_username, wordpress_password, - main_img_path, alt_text, img_description, img_title, caption) - except Exception as err: - sys.exit(f"Error occurred in upload_media: {err}") - except Exception as e: - sys.exit(f"Error occurred in analyze_and_extract_details_from_image: {e}") - - # Then create the post with the uploaded media as the featured image - media_id = media['id'] - blog_markdown_str = convert_markdown_to_html(blog_markdown_str) - try: - upload_blog_post(wordpress_url, wordpress_username, wordpress_password, a_blog_topic, - blog_markdown_str, media_id, blog_meta_desc, blog_categories, blog_tags, status='publish') - except Exception as err: - sys.exit(f"Failed to upload blog to wordpress.Error: {err}") - - # TBD: Save the blog content as a .md file. Markdown or HTML ? - save_blog_to_file(blog_markdown_str, - a_blog_topic, - blog_meta_desc, blog_tags, - blog_categories, main_img_path) - - # Now, we need perform some *basic checks on the blog content, such as: - # is_content_ai_generated.py, plagiarism_checker_from_known_sources.py - # seo_analyzer.py . These are present in the lib folder. - # prompt: Rewrite, improve and paraphrase [text] and use headings and subheadings - # to break up the content and make it easier to read using the keyword [keyword]. - - - -def generate_blog_topics(blog_keywords, num_blogs, niche): - """ - For a given prompt, generate blog topics. - Using the davinci-instruct-beta-v3 model. Itโ€™s proven to be an ideal - one for generating unique blog content. - Ex: Generate SEO optimized blog topics on given keywords - """ - prompt = f"""As an SEO specialist and blog writer, write {num_blogs} catchy - and SEO-friendly blog topics on {blog_keywords}. The blog title must be less than 80 characters. - The blog titles must follow best SEO practises, be engaging and invite/tempt users to read full blog. - Do not include descriptions, explanations. Do not number the result.""" - - # Beware of keywords stuffing, clustering, semantic should help avoid. - if num_blogs > 5: - # Get more keywords, based on user given keywords. - more_keywords = get_related_keywords(num_blogs, blog_keywords, niche) - prompt = prompt + """Use the following keywords wisely, without keyword stuffing: {more_keywords}""" - - logger.info(f"Prompt used for generating blog topics: \n{prompt}\n") - try: - response = openai_chatgpt(prompt) - return response - except Exception as err: - SystemError(f"Error in generating blog topics: {err}") - - - """ - Given a blog title generate an outline for it - """ - # TBD: Remove hardcoding, make dynamic - prompt = f"""As a SEO expert, suggest only {num_subtopics} beginner-friendly and - insightful sub topics for the blog title: {blog_title}. - Respond with only answer and no description, explanations.""" - - # The suggested {num_subtopics} outline should include few long-tailed keywords and most popular questions. - # TBD: Include --niche - logger.info(f"Prompt used for blog title Outline :\n{prompt}\n") - # TBD: Add logic for which_provider and which_model - try: - response = openai_chatgpt(prompt) - except Exception as err: - SystemError(f"Error in generating Blog Title: {err}") - return response - - -def generate_topic_content(blog_keywords, sub_topic): - """ - For each of given topic generate content for it. - """ - # The outline should contain various subheadings and include the starting sentence for each section. - # TBD: Depending on the usecase 'Voice and style' will change to professional etc. - prompt = f"""As a professional blogger and topic authority on {blog_keywords}, - craft factual (no more than 200 characters) subtopic content on {sub_topic}. - Your response should reflect Experience, Expertise, Authoritativeness and Trustworthiness from content. - Voice and style guide: Write in a professional manner, giving enlightening details and reasons. - Use natural language and phrases that a real person would use: in normal conversations. - Format your response using markdown. REMEMBER Not to include introduction or conclusion in your response. - Use headings(h3 to h6 only), subheadings, bullet points, and bold to organize the information.""" - logger.info(f"Generate topic content using prompt:\n{prompt}\n") - try: - response = openai_chatgpt(prompt) - return response - except Exception as err: - SystemError(f"Error in generating topic content: {err}") - - -def get_blog_intro(blog_title, blog_topics): - """ - Generate blog introduction as per title and sub topics - """ - prompt = f"""As a skilled wordsmith, I'll equip you with a blog title and relevant topics, tasking you with crafting an engaging introduction. Your challenge: Create a brief, compelling entry that entices readers to explore the entire post. This introduction must be concise (under 250 characters) yet powerful, clearly stating the blog's purpose and what readers stand to gain. Reply with only the introduction. - -Intrigue your audience from the start with vibrant language, employing strong verbs and vivid descriptions. Address a common challenge your readers face, demonstrating empathy and positioning yourself as their go-to expert. Pose thought-provoking questions that prompt reader engagement and contemplation. - -Remember, your words matter. This introduction serves as the cornerstone of the blog post. It should not only captivate attention but also encourage deeper exploration. Additionally, strategically integrate relevant keywords to enhance visibility on search engine results pages (SERPs). Your mission: Craft a blog introduction that resonates, leaving readers eager to delve further into the titled piece: '{blog_title}', covering these sub-topics: {blog_topics}.""" - - try: - # TBD: Add logic for which_provider and which_model - response = openai_chatgpt(prompt) - except Exception as err: - SystemError(f"Error in generating Blog Introduction: {err}") - return response - - -def get_blog_conclusion(blog_content): - """ - Accepts a blog content and concludes it. - """ - prompt = f"""As an expert SEO and blog writer, please conclude the given blog providing vital take aways, - summarise key points (no more than 300 characters) in bullet points. The blog content: {blog_content} - """ - logger.info(f"Generating blog conclusion iwth prompt: {prompt}") - try: - # TBD: Add logic for which_provider and which_model - response = openai_chatgpt(prompt) - except Exception as err: - SystemError(f"Error in generating blog conclusion: {err}") - else: - return response - - -def get_related_keywords(num_blogs, keywords, niche): - """ - Helper function to get more keywords from GPTs. - """ - # Check if niche: use long tailed, else use popular keywords. - if niche: - prompt = (f"Generate a list without description of the top {num_blogs} most popular and semantically" - f"related long-tailed keywords and entities for the topic of {keywords} that are used in" - "high-quality content and relevant to my competitors." - ) - else: - prompt = (f"Generate a list without description of the top {num_blogs} most popular and" - f" semantically related keywords and entities for the topic of {keywords} that are used" - " in high-quality content and relevant to my competitors." - ) - try: - # TBD: Add logic for which_provider and which_model - response = openai_chatgpt(prompt) - return response - except Exception as err: - SystemError(f"Error in getting related keywords.") - - -def blog_proof_editor(blog_content, blog_keywords): - """ - Helper for blog proof reading. - """ - if not blog_content and not blog_keywords: - logger.error("Blog proof reader has no content to proofread.") - exit(1) - - prompt = f"""I am looking for detailed editing and enhancement of the given blog post, - with a particular focus on originality. I will provide you with a blog content and its keywords. - The keywords for the blog are [{blog_keywords}]. Please go through the blog and make direct edits to improve it, - ensuring the final output is both high-quality and original. - Note: There are duplicates headings and corresponding paragraphs, rewrite into one subheading. - - Here are the specific guidelines to focus on: - - 1). Ensure Originality: Edit any sections that lack originality, replacing them with unique and creative content. - 2). Eliminate Repetitive Language: Rewrite repetitive phrases with varied and engaging language. - 3). Vocabulary and Grammar Enhancement: Directly correct any grammatical errors and upgrade the - vocabulary for better readability. - 4). Improve Sentence Structure: Enhance sentence construction for better clarity and flow. - 5). Tone and Brand Alignment: Adjust the tone, voice, personality of given content to make it unique. - 6). Optimize Content Structure: Reorganize the content for a more impactful presentation, - including better paragraphing and transitions. - 7). Remove Redundancies: Important, Cut out any redundant information or overly complex jargon. - 8). Refine Overall Structure: Make structural changes to improve the overall impact of the content. - 9). Remember, rewrite all content that repeated, while maintaining the formatting of the given blog text. - 10). Remember Not to include SEO meta description and Title in your final response. - 11). REMEMBER to maintain the formatting style of the provided blog. - 12). Judge if the given blog is about technology then provide code snippets and examples for it. - - Please make direct changes as per above guideline to the provided blog text below: - [{blog_content}]. """ - - try: - # TBD: Add logic for which_provider and which_model - response = openai_chatgpt(prompt) - return response - except Exception as err: - SystemError(f"Error Blog Proof Reading: {err}") diff --git a/workspace/web_research_reports/how_to_llamaindex_2024-02-04_15-45-16 b/workspace/web_research_reports/how_to_llamaindex_2024-02-04_15-45-16 deleted file mode 100644 index 288d9bf3..00000000 --- a/workspace/web_research_reports/how_to_llamaindex_2024-02-04_15-45-16 +++ /dev/null @@ -1,26 +0,0 @@ -โ•’โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•• -โ”‚ Rank โ”‚ Title โ”‚ Link โ”‚ Snippet โ”‚ -โ•žโ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ก -โ”‚ 1 โ”‚ What is LlamaIndex?: How โ”‚ https://nanonets.com/blog/llamainde โ”‚ The core essence of LlamaIndex lies in its ability โ”‚ -โ”‚ โ”‚ It Works, and Optimizing โ”‚ x/ โ”‚ to build structured indices over ingested data, โ”‚ -โ”‚ โ”‚ Data Query โ”‚ โ”‚ represented as either Documents or Nodes. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ 2 โ”‚ Starter Tutorial - โ”‚ https://docs.llamaindex.ai/en/lates โ”‚ The easiest way to get it is to download it via โ”‚ -โ”‚ โ”‚ LlamaIndex 0.9.43 โ”‚ t/getting_started/starter_example.h โ”‚ this link and save it in a folder called data . โ”‚ -โ”‚ โ”‚ โ”‚ tml โ”‚ Set ... โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ 3 โ”‚ LlamaIndex: Adding โ”‚ https://www.datacamp.com/tutorial/l โ”‚ You can download your resume by going on to the โ”‚ -โ”‚ โ”‚ Personal Data to LLMs - โ”‚ lama-index-adding-personal-data-to- โ”‚ Linkedin profile page, clicking on More, and then โ”‚ -โ”‚ โ”‚ DataCamp โ”‚ llms โ”‚ Save to PDF. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ 4 โ”‚ LlamaIndex 0.9.43 โ”‚ https://docs.llamaindex.ai/ โ”‚ LlamaIndex is a data framework for LLM-based โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ applications to ingest, structure, and access โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ private or domain-specific data. It's available in โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Python (these docs) ... โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ 5 โ”‚ Generative AI: An โ”‚ https://www.singlestore.com/blog/ge โ”‚ Delve into the world of LlamaIndex with this โ”‚ -โ”‚ โ”‚ Absolute Beginner's Guide โ”‚ nerative-ai-a-guide-to-llamaindex/ โ”‚ comprehensive beginner's guide, including an โ”‚ -โ”‚ โ”‚ to LlamaIndex โ”‚ โ”‚ insightful tutorial. โ”‚ -โ•˜โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•› - - diff --git a/workspace/web_research_reports/how_to_llamaindex_2024-02-04_16-19-57 b/workspace/web_research_reports/how_to_llamaindex_2024-02-04_16-19-57 deleted file mode 100644 index e69de29b..00000000 diff --git a/workspace/web_research_reports/how_to_llamaindex_2024-02-04_16-36-53 b/workspace/web_research_reports/how_to_llamaindex_2024-02-04_16-36-53 deleted file mode 100644 index 26b89069..00000000 --- a/workspace/web_research_reports/how_to_llamaindex_2024-02-04_16-36-53 +++ /dev/null @@ -1,9 +0,0 @@ -โ•’โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•• -โ”‚ Search Engine follow up questions for query: how to llamaindex โ”‚ -โ•žโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ก -โ”‚ ['What are the benefits of llamaindex?', 'Are there any tutorials or guides on โ”‚ -โ”‚ how to implement llamaindex?', 'What are some alternative methods to โ”‚ -โ”‚ llamaindex?'] โ”‚ -โ•˜โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•› - - diff --git a/workspace/web_research_reports/how_to_llamaindex_2024-02-04_16-46-47 b/workspace/web_research_reports/how_to_llamaindex_2024-02-04_16-46-47 deleted file mode 100644 index dbc2f2cc..00000000 --- a/workspace/web_research_reports/how_to_llamaindex_2024-02-04_16-46-47 +++ /dev/null @@ -1,83 +0,0 @@ -โ•’โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•• -โ”‚ Title โ”‚ Snippet โ”‚ Link โ”‚ -โ•žโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ก -โ”‚ LlamaIndex: A Data Framework โ”‚ Setting up LlamaIndex LlamaIndex Use Cases How LlamaIndex โ”‚ https://www.datacamp.com/tutor โ”‚ -โ”‚ for the Large Language Models โ”‚ Works? to the LlamaIndex documentation.LlamaIndex is a data โ”‚ ial/llama-index-adding- โ”‚ -โ”‚ ... - DataCamp โ”‚ framework for Large Language Models (LLMs) based โ”‚ personal-data-to-llms โ”‚ -โ”‚ โ”‚ applications. LLMs like GPT-4 come pre-trained on massive โ”‚ โ”‚ -โ”‚ โ”‚ public datasets, allowing for incredible natural language โ”‚ โ”‚ -โ”‚ โ”‚ processing capabilities out of the box. However, their โ”‚ โ”‚ -โ”‚ โ”‚ utility is limited without access to your own private data. โ”‚ โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ A Beginner's Guide to โ”‚ What is LlamaIndex? How LlamaIndex works LlamaIndex core โ”‚ https://dev.to/pavanbelagatti/ โ”‚ -โ”‚ LlamaIndex! - DEV Community โ”‚ functionalities + applications you'd like to index.How โ”‚ a-beginners-guide-to- โ”‚ -โ”‚ โ”‚ LlamaIndex works LlamaIndex serves as a bridge, connecting โ”‚ llamaindex-3mip โ”‚ -โ”‚ โ”‚ the powerful capabilities of LLMs with diverse data sources, โ”‚ โ”‚ -โ”‚ โ”‚ thereby unlocking a new realm of applications that can โ”‚ โ”‚ -โ”‚ โ”‚ leverage the synergy between custom data and advanced โ”‚ โ”‚ -โ”‚ โ”‚ language models. โ”‚ โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ What is LlamaIndex?: How It โ”‚ Understanding LlamaIndex Then follow either of the two โ”‚ https://nanonets.com/blog/llam โ”‚ -โ”‚ Works, and Optimizing Data โ”‚ approaches below - Creating Llamaindex Documents LlamaIndex โ”‚ aindex/ โ”‚ -โ”‚ Query - Nanonets โ”‚ provides a high-level API that facilitates straightforward โ”‚ โ”‚ -โ”‚ โ”‚ querying, ideal for common use cases. LlamaIndex equips you โ”‚ โ”‚ -โ”‚ โ”‚ with a suite of tools to shape your knowledge โ”‚ โ”‚ -โ”‚ โ”‚ base:LlamaIndex is your go-to platform for creating robust โ”‚ โ”‚ -โ”‚ โ”‚ applications powered by Large Language Models (LLMs) over โ”‚ โ”‚ -โ”‚ โ”‚ your customized data. Be it a sophisticated Q&A system, an โ”‚ โ”‚ -โ”‚ โ”‚ interactive chatbot, or intelligent agents, LlamaIndex lays โ”‚ โ”‚ -โ”‚ โ”‚ down the foundation for your ventures into the realm of โ”‚ โ”‚ -โ”‚ โ”‚ Retrieval Augmented Generation (RAG). โ”‚ โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ LlamaIndex 0.9.42.post1 - Read โ”‚ LlamaIndex is a data framework for LLM-based applications to โ”‚ https://docs.llamaindex.ai/en/ โ”‚ -โ”‚ the Docs โ”‚ ingest, structure, and access private or domain-specific ๐Ÿฆ™ โ”‚ stable/ โ”‚ -โ”‚ โ”‚ How can LlamaIndex help?# LlamaIndex provides the following โ”‚ โ”‚ -โ”‚ โ”‚ tools: Getting Started# To install the library: pip install โ”‚ โ”‚ -โ”‚ โ”‚ llama-index LLM to generate an answer immediately, โ”‚ โ”‚ -โ”‚ โ”‚ LlamaIndex:LlamaIndex provides tools for beginners, advanced โ”‚ โ”‚ -โ”‚ โ”‚ users, and everyone in between. Our high-level API allows โ”‚ โ”‚ -โ”‚ โ”‚ beginner users to use LlamaIndex to ingest and query their โ”‚ โ”‚ -โ”‚ โ”‚ data in 5 lines of code. For more complex applications, our โ”‚ โ”‚ -โ”‚ โ”‚ lower-level APIs allow advanced users to customize and โ”‚ โ”‚ -โ”‚ โ”‚ extend any moduleโ€”data connectors, indices, retrievers, โ”‚ โ”‚ -โ”‚ โ”‚ query ... โ”‚ โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ Getting Started With โ”‚ LlamaIndex does number 3. Hereโ€™s how it works: Creating a โ”‚ https://betterprogramming.pub/ โ”‚ -โ”‚ LlamaIndex - Better โ”‚ new LlamaIndex Project 4. Store the Index 3. Index โ”‚ getting-started-with- โ”‚ -โ”‚ Programming โ”‚ Construction Actually, things do get simpler. Take a look at โ”‚ llamaindex-169bbf475a94 โ”‚ -โ”‚ โ”‚ the code below:The basic workflow in LlamaIndex Starting โ”‚ โ”‚ -โ”‚ โ”‚ with your documents, you first load them into LlamaIndex. It โ”‚ โ”‚ -โ”‚ โ”‚ comes with many ready-made readers for sources such as โ”‚ โ”‚ -โ”‚ โ”‚ databases, Discord, Slack, Google Docs, Notion, and (the one โ”‚ โ”‚ -โ”‚ โ”‚ we will use today) GitHub repos. Next, you use LlamaIndex to โ”‚ โ”‚ -โ”‚ โ”‚ parse the documents into nodes โ€” basically chunks of text. โ”‚ โ”‚ -โ•˜โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•› - - -โ•’โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•• -โ”‚ The answer to search query: how to llamaindex โ”‚ -โ•žโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ก -โ”‚ LlamaIndex is a data framework for LLM-based applications that allows users to โ”‚ -โ”‚ ingest, structure, and access private or domain-specific data. It provides tools โ”‚ -โ”‚ for beginners as well as advanced users. To get started with LlamaIndex, you can โ”‚ -โ”‚ install the library using the command "pip install llama-index". LlamaIndex โ”‚ -โ”‚ offers a high-level API that enables beginners to ingest and query their data โ”‚ -โ”‚ with just a few lines of code. For more complex applications, there are lower- โ”‚ -โ”‚ level APIs available for customization and extension. LlamaIndex supports โ”‚ -โ”‚ various data sources such as databases, Discord, Slack, Google Docs, Notion, and โ”‚ -โ”‚ GitHub repos. You can parse the documents into nodes using LlamaIndex. By โ”‚ -โ”‚ connecting LLMs with diverse data sources, LlamaIndex unlocks new possibilities โ”‚ -โ”‚ for applications that leverage the synergy between custom data and advanced โ”‚ -โ”‚ language models. โ”‚ -โ•˜โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•› - - -โ•’โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•• -โ”‚ Search Engine follow up questions for query: how to llamaindex โ”‚ -โ•žโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ก -โ”‚ ['What is the purpose of llamaindex?', 'What are the benefits of using โ”‚ -โ”‚ llamaindex?', 'Are there any alternative methods to achieve the same result as โ”‚ -โ”‚ llamaindex?'] โ”‚ -โ•˜โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•› - - diff --git a/workspace/web_research_reports/how_to_llamaindex_2024-02-04_16-58-31 b/workspace/web_research_reports/how_to_llamaindex_2024-02-04_16-58-31 deleted file mode 100644 index b5aec81f..00000000 --- a/workspace/web_research_reports/how_to_llamaindex_2024-02-04_16-58-31 +++ /dev/null @@ -1,108 +0,0 @@ -โ•’โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•• -โ”‚ Rank โ”‚ Title โ”‚ Link โ”‚ Snippet โ”‚ -โ•žโ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ก -โ”‚ 1 โ”‚ What is LlamaIndex?: How โ”‚ https://nanonets.com/blog/llamainde โ”‚ The core essence of LlamaIndex lies in its ability โ”‚ -โ”‚ โ”‚ It Works, and Optimizing โ”‚ x/ โ”‚ to build structured indices over ingested data, โ”‚ -โ”‚ โ”‚ Data Query โ”‚ โ”‚ represented as either Documents or Nodes. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ 2 โ”‚ Starter Tutorial - โ”‚ https://docs.llamaindex.ai/en/lates โ”‚ The easiest way to get it is to download it via โ”‚ -โ”‚ โ”‚ LlamaIndex 0.9.43 โ”‚ t/getting_started/starter_example.h โ”‚ this link and save it in a folder called data . โ”‚ -โ”‚ โ”‚ โ”‚ tml โ”‚ Set ... โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ 3 โ”‚ LlamaIndex: Adding โ”‚ https://www.datacamp.com/tutorial/l โ”‚ You can download your resume by going on to the โ”‚ -โ”‚ โ”‚ Personal Data to LLMs - โ”‚ lama-index-adding-personal-data-to- โ”‚ Linkedin profile page, clicking on More, and then โ”‚ -โ”‚ โ”‚ DataCamp โ”‚ llms โ”‚ Save to PDF. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ 4 โ”‚ LlamaIndex 0.9.43 โ”‚ https://docs.llamaindex.ai/ โ”‚ LlamaIndex is a data framework for LLM-based โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ applications to ingest, structure, and access โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ private or domain-specific data. It's available in โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Python (these docs) ... โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ 5 โ”‚ Generative AI: An โ”‚ https://www.singlestore.com/blog/ge โ”‚ Delve into the world of LlamaIndex with this โ”‚ -โ”‚ โ”‚ Absolute Beginner's Guide โ”‚ nerative-ai-a-guide-to-llamaindex/ โ”‚ comprehensive beginner's guide, including an โ”‚ -โ”‚ โ”‚ to LlamaIndex โ”‚ โ”‚ insightful tutorial. โ”‚ -โ•˜โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•› - - -โ•’โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•• -โ”‚ Title โ”‚ Snippet โ”‚ Link โ”‚ -โ•žโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ก -โ”‚ LlamaIndex Newsletter โ”‚ 4 min read 4 min read Published in ยทJan 2 LlamaIndex โ”‚ https://medium.com/@llama_inde โ”‚ -โ”‚ 2024-01-23 โ”‚ Newsletter 2024โ€“01โ€“02 3 min read 3 min read Published in โ”‚ x โ”‚ -โ”‚ โ”‚ ยทJan 9 LlamaIndex Newsletter 2024โ€“01โ€“09 ยทDec 19, 2023 โ”‚ โ”‚ -โ”‚ โ”‚ LlamaIndex Newsletter 2023โ€“12โ€“19 4 min read 4 min read โ”‚ โ”‚ -โ”‚ โ”‚ Published in ยทDec 12, 2023 LlamaIndex Newsletter โ”‚ โ”‚ -โ”‚ โ”‚ 2023โ€“12โ€“12LlamaIndex Newsletter 2024-01-16 Hello LlamaIndex โ”‚ โ”‚ -โ”‚ โ”‚ Enthusiasts ๐Ÿฆ™, Get ready for an exciting week at โ”‚ โ”‚ -โ”‚ โ”‚ LlamaIndex, teeming with dynamic community contributions and โ”‚ โ”‚ -โ”‚ โ”‚ insightful learning... โ”‚ โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ Welcome to my guide of โ”‚ Sign up Sign in Sign up Sign in Guide to LlamaIndex in 2024 โ”‚ https://medium.com/@Debaprasan โ”‚ -โ”‚ LlamaIndex! - Medium โ”‚ Debaprasann Bhoi Follow GoPenAI -- Listen Share LlamaIndex, โ”‚ nBhoi/guide-to-llamaindex- โ”‚ -โ”‚ โ”‚ previously known as the GPT Index, is a remarkable data โ”‚ in-2024-64caa8ef2e72 โ”‚ -โ”‚ โ”‚ framework aimed at helping you build applications with the โ”‚ โ”‚ -โ”‚ โ”‚ Llama Index at their core. Building the โ”‚ โ”‚ -โ”‚ โ”‚ LlamaIndex:LlamaIndex, previously known as the GPT Index, is โ”‚ โ”‚ -โ”‚ โ”‚ a remarkable data framework aimed at helping you build โ”‚ โ”‚ -โ”‚ โ”‚ applications with LLMs by providing essential tools that โ”‚ โ”‚ -โ”‚ โ”‚ facilitate data ingestion,... โ”‚ โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ Guide to LlamaIndex in 2024 โ”‚ Sign up Sign in Sign up Sign in Guide to LlamaIndex in 2024 โ”‚ https://blog.gopenai.com/guide โ”‚ -โ”‚ โ”‚ Debaprasann Bhoi Follow GoPenAI -- Listen Share LlamaIndex, โ”‚ -to-llamaindex- โ”‚ -โ”‚ โ”‚ previously known as the GPT Index, is a remarkable data โ”‚ in-2024-64caa8ef2e72 โ”‚ -โ”‚ โ”‚ framework aimed at helping you build applications with the โ”‚ โ”‚ -โ”‚ โ”‚ Llama Index at their core. Building the โ”‚ โ”‚ -โ”‚ โ”‚ LlamaIndex:LlamaIndex, previously known as the GPT Index, is โ”‚ โ”‚ -โ”‚ โ”‚ a remarkable data framework aimed at helping you build โ”‚ โ”‚ -โ”‚ โ”‚ applications with LLMs by providing essential tools that โ”‚ โ”‚ -โ”‚ โ”‚ facilitate data ingestion, structuring, retrieval, and โ”‚ โ”‚ -โ”‚ โ”‚ integration with various application frameworks. The โ”‚ โ”‚ -โ”‚ โ”‚ capabilities offered by LlamaIndex are numerous and highly โ”‚ โ”‚ -โ”‚ โ”‚ valuable: โ”‚ โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ A Beginner's Guide to โ”‚ you'd like to index. What is LlamaIndex? LlamaIndex core โ”‚ https://dev.to/pavanbelagatti/ โ”‚ -โ”‚ LlamaIndex! โ”‚ functionalities + applications Data indexingLlamaIndex is โ”‚ a-beginners-guide-to- โ”‚ -โ”‚ โ”‚ an advanced orchestration framework designed to amplify the โ”‚ llamaindex-3mip โ”‚ -โ”‚ โ”‚ capabilities of LLMs like GPT-4. While LLMs are inherently โ”‚ โ”‚ -โ”‚ โ”‚ powerful, having been trained on vast public datasets, they โ”‚ โ”‚ -โ”‚ โ”‚ often lack the means to interact with private or domain- โ”‚ โ”‚ -โ”‚ โ”‚ specific data. โ”‚ โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ LlamaIndex Newsletter โ”‚ Sign up Sign in Sign up Sign in LlamaIndex Newsletter โ”‚ https://blog.llamaindex.ai/lla โ”‚ -โ”‚ 2024-01-30 โ”‚ 2024โ€“01โ€“30 LlamaIndex Follow LlamaIndex Blog -- Listen Share โ”‚ maindex-newsletter-2024-01-30- โ”‚ -โ”‚ โ”‚ to supercharge your journey with LlamaIndex. from โ”‚ 0d01eb0d8cef โ”‚ -โ”‚ โ”‚ LlamaIndex, delivered directly to your inbox. -- -- Written โ”‚ โ”‚ -โ”‚ โ”‚ by LlamaIndex LlamaIndex Blog Help Status About Careers Blog โ”‚ โ”‚ -โ”‚ โ”‚ Privacy Terms Text to speech TeamsWe have launched RAG CLI: โ”‚ โ”‚ -โ”‚ โ”‚ A straightforward command-line tool for indexing and โ”‚ โ”‚ -โ”‚ โ”‚ searching any local file, featuring integration with โ”‚ โ”‚ -โ”‚ โ”‚ IngestionPipeline, QueryPipeline, and ChromaDB, with support โ”‚ โ”‚ -โ”‚ โ”‚ for local models and customizable logic. Docs, Tweet. We โ”‚ โ”‚ -โ”‚ โ”‚ have introduced JSONalyze, a query engine that swiftly โ”‚ โ”‚ -โ”‚ โ”‚ summarizes large JSON datasets. โ”‚ โ”‚ -โ•˜โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•› - - -โ•’โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•• -โ”‚ The answer to search query: how to llamaindex โ”‚ -โ•žโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ก -โ”‚ LlamaIndex is a data framework, previously known as the GPT Index, aimed at โ”‚ -โ”‚ helping users build applications with LLMs (Language Model Models) at their โ”‚ -โ”‚ core. It provides essential tools that facilitate data ingestion, structuring, โ”‚ -โ”‚ retrieval, and integration with various application frameworks. Some of the โ”‚ -โ”‚ capabilities offered by LlamaIndex include indexing and searching local files, โ”‚ -โ”‚ integration with IngestionPipeline, QueryPipeline, and ChromaDB, support for โ”‚ -โ”‚ local models, and customizable logic. To install LlamaIndex, you can use the โ”‚ -โ”‚ command "pip install llama-index" if you have Python installed. โ”‚ -โ•˜โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•› - - -โ•’โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•• -โ”‚ Search Engine follow up questions for query: how to llamaindex โ”‚ -โ•žโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ก -โ”‚ ['What are the benefits of llamaindex?', 'Are there any specific tools or โ”‚ -โ”‚ techniques for llamaindexing?', 'Can you provide examples of successful โ”‚ -โ”‚ companies that have implemented llamaindex?'] โ”‚ -โ•˜โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•› - - diff --git a/workspace/web_research_reports/how_to_llamaindex_2024-02-04_17-04-27 b/workspace/web_research_reports/how_to_llamaindex_2024-02-04_17-04-27 deleted file mode 100644 index 5f2524b3..00000000 --- a/workspace/web_research_reports/how_to_llamaindex_2024-02-04_17-04-27 +++ /dev/null @@ -1,133 +0,0 @@ -โ•’โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•• -โ”‚ Rank โ”‚ Title โ”‚ Link โ”‚ Snippet โ”‚ -โ•žโ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ก -โ”‚ 1 โ”‚ What is LlamaIndex?: How โ”‚ https://nanonets.com/blog/llamainde โ”‚ The core essence of LlamaIndex lies in its ability โ”‚ -โ”‚ โ”‚ It Works, and Optimizing โ”‚ x/ โ”‚ to build structured indices over ingested data, โ”‚ -โ”‚ โ”‚ Data Query โ”‚ โ”‚ represented as either Documents or Nodes. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ 2 โ”‚ Starter Tutorial - โ”‚ https://docs.llamaindex.ai/en/lates โ”‚ The easiest way to get it is to download it via โ”‚ -โ”‚ โ”‚ LlamaIndex 0.9.43 โ”‚ t/getting_started/starter_example.h โ”‚ this link and save it in a folder called data . โ”‚ -โ”‚ โ”‚ โ”‚ tml โ”‚ Set ... โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ 3 โ”‚ LlamaIndex: Adding โ”‚ https://www.datacamp.com/tutorial/l โ”‚ You can download your resume by going on to the โ”‚ -โ”‚ โ”‚ Personal Data to LLMs - โ”‚ lama-index-adding-personal-data-to- โ”‚ Linkedin profile page, clicking on More, and then โ”‚ -โ”‚ โ”‚ DataCamp โ”‚ llms โ”‚ Save to PDF. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ 4 โ”‚ LlamaIndex 0.9.43 โ”‚ https://docs.llamaindex.ai/ โ”‚ LlamaIndex is a data framework for LLM-based โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ applications to ingest, structure, and access โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ private or domain-specific data. It's available in โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Python (these docs) ... โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ 5 โ”‚ Generative AI: An โ”‚ https://www.singlestore.com/blog/ge โ”‚ Delve into the world of LlamaIndex with this โ”‚ -โ”‚ โ”‚ Absolute Beginner's Guide โ”‚ nerative-ai-a-guide-to-llamaindex/ โ”‚ comprehensive beginner's guide, including an โ”‚ -โ”‚ โ”‚ to LlamaIndex โ”‚ โ”‚ insightful tutorial. โ”‚ -โ•˜โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•› - - -โ•’โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•• -โ”‚ Related Search โ”‚ -โ•žโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ก -โ”‚ LlamaIndex vs LangChain โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ Is LlamaIndex free โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ Llama_index github โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ LlamaIndex documentation โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ LlamaIndex PDF โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ LlamaIndex course โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ Is LlamaIndex open source โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ LlamaIndex RAG โ”‚ -โ•˜โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•› - - -โ•’โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•• -โ”‚ Title โ”‚ Snippet โ”‚ Link โ”‚ -โ•žโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ก -โ”‚ LlamaIndex - Medium โ”‚ 4 min read 4 min read Published in ยทJan 2 LlamaIndex โ”‚ https://medium.com/@llama_inde โ”‚ -โ”‚ โ”‚ Newsletter 2024โ€“01โ€“02 3 min read 3 min read Published in โ”‚ x โ”‚ -โ”‚ โ”‚ ยทJan 9 LlamaIndex Newsletter 2024โ€“01โ€“09 ยทDec 19, 2023 โ”‚ โ”‚ -โ”‚ โ”‚ LlamaIndex Newsletter 2023โ€“12โ€“19 4 min read 4 min read โ”‚ โ”‚ -โ”‚ โ”‚ Published in ยทDec 12, 2023 LlamaIndex Newsletter โ”‚ โ”‚ -โ”‚ โ”‚ 2023โ€“12โ€“12LlamaIndex Newsletter 2024-01-02 Hello, Llama โ”‚ โ”‚ -โ”‚ โ”‚ Lovers ๐Ÿฆ™, Happy New Year! As we step into 2024, we're โ”‚ โ”‚ -โ”‚ โ”‚ thrilled to bring you a special edition of our newsletter, โ”‚ โ”‚ -โ”‚ โ”‚ packed with updates from the ... โ”‚ โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ Guide to LlamaIndex in 2024 - โ”‚ Sign up Sign in Sign up Sign in Guide to LlamaIndex in 2024 โ”‚ https://medium.com/@Debaprasan โ”‚ -โ”‚ Medium โ”‚ Debaprasann Bhoi Follow GoPenAI -- Listen Share LlamaIndex, โ”‚ nBhoi/guide-to-llamaindex- โ”‚ -โ”‚ โ”‚ previously known as the GPT Index, is a remarkable data โ”‚ in-2024-64caa8ef2e72 โ”‚ -โ”‚ โ”‚ framework aimed at helping you build applications with the โ”‚ โ”‚ -โ”‚ โ”‚ Llama Index at their core. Building the โ”‚ โ”‚ -โ”‚ โ”‚ LlamaIndex:LlamaIndex, previously known as the GPT Index, is โ”‚ โ”‚ -โ”‚ โ”‚ a remarkable data framework aimed at helping you build โ”‚ โ”‚ -โ”‚ โ”‚ applications with LLMs by providing essential tools that โ”‚ โ”‚ -โ”‚ โ”‚ facilitate data ingestion,... โ”‚ โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ Guide to LlamaIndex in 2024. โ”‚ Sign up Sign in Sign up Sign in Guide to LlamaIndex in 2024 โ”‚ https://blog.gopenai.com/guide โ”‚ -โ”‚ Welcome to my guide of โ”‚ Debaprasann Bhoi Follow GoPenAI -- Listen Share LlamaIndex, โ”‚ -to-llamaindex- โ”‚ -โ”‚ LlamaIndex! | by ... โ”‚ previously known as the GPT Index, is a remarkable data โ”‚ in-2024-64caa8ef2e72 โ”‚ -โ”‚ โ”‚ framework aimed at helping you build applications with the โ”‚ โ”‚ -โ”‚ โ”‚ Llama Index at their core. Building the โ”‚ โ”‚ -โ”‚ โ”‚ LlamaIndex:LlamaIndex, previously known as the GPT Index, is โ”‚ โ”‚ -โ”‚ โ”‚ a remarkable data framework aimed at helping you build โ”‚ โ”‚ -โ”‚ โ”‚ applications with LLMs by providing essential tools that โ”‚ โ”‚ -โ”‚ โ”‚ facilitate data ingestion, structuring, retrieval, and โ”‚ โ”‚ -โ”‚ โ”‚ integration with various application frameworks. The โ”‚ โ”‚ -โ”‚ โ”‚ capabilities offered by LlamaIndex are numerous and highly โ”‚ โ”‚ -โ”‚ โ”‚ valuable: โ”‚ โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ A Beginner's Guide to โ”‚ you'd like to index. LlamaIndex core functionalities + โ”‚ https://dev.to/pavanbelagatti/ โ”‚ -โ”‚ LlamaIndex! - DEV Community โ”‚ applications What is LlamaIndex? Data indexingLlamaIndex โ”‚ a-beginners-guide-to- โ”‚ -โ”‚ โ”‚ is an advanced orchestration framework designed to amplify โ”‚ llamaindex-3mip โ”‚ -โ”‚ โ”‚ the capabilities of LLMs like GPT-4. While LLMs are โ”‚ โ”‚ -โ”‚ โ”‚ inherently powerful, having been trained on vast public โ”‚ โ”‚ -โ”‚ โ”‚ datasets, they often lack the means to interact with private โ”‚ โ”‚ -โ”‚ โ”‚ or domain-specific data. โ”‚ โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ LlamaIndex Newsletter โ”‚ Sign up Sign in Sign up Sign in LlamaIndex Newsletter โ”‚ https://blog.llamaindex.ai/lla โ”‚ -โ”‚ 2024-01-30 โ”‚ 2024โ€“01โ€“30 LlamaIndex Follow LlamaIndex Blog -- Listen Share โ”‚ maindex-newsletter-2024-01-30- โ”‚ -โ”‚ โ”‚ to supercharge your journey with LlamaIndex. from โ”‚ 0d01eb0d8cef โ”‚ -โ”‚ โ”‚ LlamaIndex, delivered directly to your inbox. -- -- Written โ”‚ โ”‚ -โ”‚ โ”‚ by LlamaIndex LlamaIndex Blog Help Status About Careers Blog โ”‚ โ”‚ -โ”‚ โ”‚ Privacy Terms Text to speech TeamsWe have launched RAG CLI: โ”‚ โ”‚ -โ”‚ โ”‚ A straightforward command-line tool for indexing and โ”‚ โ”‚ -โ”‚ โ”‚ searching any local file, featuring integration with โ”‚ โ”‚ -โ”‚ โ”‚ IngestionPipeline, QueryPipeline, and ChromaDB, with support โ”‚ โ”‚ -โ”‚ โ”‚ for local models and customizable logic. Docs, Tweet. We โ”‚ โ”‚ -โ”‚ โ”‚ have introduced JSONalyze, a query engine that swiftly โ”‚ โ”‚ -โ”‚ โ”‚ summarizes large JSON datasets. โ”‚ โ”‚ -โ•˜โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•› - - -โ•’โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•• -โ”‚ The answer to search query: how to llamaindex โ”‚ -โ•žโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ก -โ”‚ Based on the given data, there are a few sources that provide information about โ”‚ -โ”‚ LlamaIndex. LlamaIndex is a data framework aimed at helping developers build โ”‚ -โ”‚ applications with large language models (LLMs) at their core. It offers tools โ”‚ -โ”‚ for data ingestion, structuring, retrieval, and integration with various โ”‚ -โ”‚ application frameworks. LlamaIndex is particularly useful for connecting custom โ”‚ -โ”‚ data sources to LLMs and can be used for web scraping, data indexing, and โ”‚ -โ”‚ natural language processing. There is also a command-line tool called RAG CLI โ”‚ -โ”‚ that allows indexing and searching of local files with integration to โ”‚ -โ”‚ IngestionPipeline, QueryPipeline, and ChromaDB. Additionally, there is a query โ”‚ -โ”‚ engine called JSONalyze that swiftly summarizes large JSON datasets. Please โ”‚ -โ”‚ note that the information provided may not be comprehensive, and it is โ”‚ -โ”‚ recommended to refer to the provided sources for more detailed information. โ”‚ -โ•˜โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•› - - -โ•’โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•• -โ”‚ Search Engine follow up questions for query: how to llamaindex โ”‚ -โ•žโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ก -โ”‚ ['What is the significance of llamaindex?', 'Are there any specific techniques โ”‚ -โ”‚ or tools for llamaindexing?', 'Can you provide examples of successful โ”‚ -โ”‚ llamaindexing?'] โ”‚ -โ•˜โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•› - - diff --git a/workspace/web_research_reports/how_to_llamaindex_2024-02-04_17-35-51 b/workspace/web_research_reports/how_to_llamaindex_2024-02-04_17-35-51 deleted file mode 100644 index e69de29b..00000000 diff --git a/workspace/web_research_reports/how_to_llamaindex_2024-02-04_17-40-52 b/workspace/web_research_reports/how_to_llamaindex_2024-02-04_17-40-52 deleted file mode 100644 index 986755f9..00000000 --- a/workspace/web_research_reports/how_to_llamaindex_2024-02-04_17-40-52 +++ /dev/null @@ -1,100 +0,0 @@ -โ•’โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•• -โ”‚ URL โ”‚ Title โ”‚ Published Date โ”‚ Summary โ”‚ -โ•žโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ก -โ”‚ https://tech.dentsus โ”‚ LlamaIndexใ‚’ไฝฟใฃใฆใƒญ โ”‚ 2024-01-22 โ”‚ - Retrieval-Augmented Generation (RAG) is a technique to โ”‚ -โ”‚ oken.com/entry/2024/ โ”‚ ใƒผใ‚ซใƒซ็’ฐๅขƒใงRAGใ‚’ๅฎŸ โ”‚ โ”‚ improve the accuracy and reduce hallucination of Large โ”‚ -โ”‚ 01/22/LlamaIndex%E3% โ”‚ ่กŒใ™ใ‚‹ๆ–นๆณ• โ”‚ โ”‚ Language Models (LLMs) by providing relevant information โ”‚ -โ”‚ 82%92%E4%BD%BF%E3%81 โ”‚ โ”‚ โ”‚ from a knowledge base. - LlamaIndex is a Python and โ”‚ -โ”‚ %A3%E3%81%A6%E3%83%A โ”‚ โ”‚ โ”‚ Typescript framework specifically designed for implementing โ”‚ -โ”‚ D%E3%83%BC%E3%82%AB% โ”‚ โ”‚ โ”‚ RAG-based applications. - To implement RAG locally, you can โ”‚ -โ”‚ E3%83%AB%E7%92%B0%E5 โ”‚ โ”‚ โ”‚ use LlamaIndex and a GPU-enabled environment such as Windows โ”‚ -โ”‚ %A2%83%E3%81%A7RAG%E โ”‚ โ”‚ โ”‚ with WSL and devcontainer. - A step-by-step guide is โ”‚ -โ”‚ 3%82%92%E5%AE%9F%E8% โ”‚ โ”‚ โ”‚ provided to build the local RAG implementation environment โ”‚ -โ”‚ A1%8C%E3%81%99%E3%82 โ”‚ โ”‚ โ”‚ and execute the RAG system using LlamaIndex. - The โ”‚ -โ”‚ %8B%E6%96%B9%E6%B3%9 โ”‚ โ”‚ โ”‚ implemented RAG system can answer questions based on the โ”‚ -โ”‚ 5 โ”‚ โ”‚ โ”‚ context derived from text files using a โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Multilingual-E5-large embedding model and ELYZA-japanese- โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Llama LLM model. - Suggestions for improving the performance โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ and accuracy of the RAG system are discussed, including โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ reducing query latency and optimizing context selection. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://levelup.gitc โ”‚ Live Indexing for โ”‚ 2024-01-08 โ”‚ - The analysis of PDFs can be a challenging task for AI โ”‚ -โ”‚ onnected.com/live- โ”‚ RAG: A Guide For โ”‚ โ”‚ systems due to their complex information, such as nested โ”‚ -โ”‚ indexing-for-rag-a- โ”‚ Real-Time Indexing โ”‚ โ”‚ tables, figures, equations, and photos. - Large Language โ”‚ -โ”‚ guide-for-real-time- โ”‚ Using LlamaIndex and โ”‚ โ”‚ Models (LLMs) often make mistakes and produce hallucinations โ”‚ -โ”‚ indexing-using- โ”‚ AWS โ”‚ โ”‚ when analyzing PDFs. - RAG frameworks like LlamaIndex and โ”‚ -โ”‚ llamaindex-and-aws-5 โ”‚ โ”‚ โ”‚ Langchain, along with the rise of LLMs, have transformed the โ”‚ -โ”‚ 1353083ace4?gi=472c9 โ”‚ โ”‚ โ”‚ ecosystem for creating full-stack applications. - LlamaIndex โ”‚ -โ”‚ 89ddb71&source=rss โ”‚ โ”‚ โ”‚ is a prominent RAG framework that allows users to create โ”‚ -โ”‚ ----5517fd7b58a6---4 โ”‚ โ”‚ โ”‚ chat-with-PDFs applications with minimal code. - To turn a โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ RAG application into an enterprise-grade application, AI โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ engineers need to address challenges like re-indexing and โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ live updating data. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://blog.llamain โ”‚ LlamaIndex Blog โ”‚ 2024-01-23 โ”‚ - The LlamaIndex Blog is the official blog of LlamaIndex. - โ”‚ -โ”‚ dex.ai/?gi=a117797fb โ”‚ โ”‚ โ”‚ Posts include release updates, guides, community showcases, โ”‚ -โ”‚ bc8 โ”‚ โ”‚ โ”‚ and more. - Recent posts discussed building a secure Multi- โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Tenancy RAG System, enhancing accessibility in AI, โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ introducing Query Pipelines within LlamaIndex, scaling โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ LlamaIndex with AWS and Hugging Face, and more. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://blog.llamain โ”‚ A Cheat Sheet and โ”‚ 2024-01-05 โ”‚ This web page provides a comprehensive overview of โ”‚ -โ”‚ dex.ai/a-cheat- โ”‚ Some Recipes For โ”‚ โ”‚ Retrieval-Augmented Generation (RAG) systems, covering the โ”‚ -โ”‚ sheet-and-some- โ”‚ Building Advanced โ”‚ โ”‚ basics, advanced techniques, and success requirements. RAG โ”‚ -โ”‚ recipes-for- โ”‚ RAG โ”‚ โ”‚ involves retrieving relevant documents from an external โ”‚ -โ”‚ building-advanced- โ”‚ โ”‚ โ”‚ knowledge base and feeding them along with the user's query โ”‚ -โ”‚ rag-803a9d94c41b โ”‚ โ”‚ โ”‚ to a large language model (LLM) for response generation. To โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ ensure the success of a RAG system, both retrieval and โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ generation components must perform well. Advanced RAG โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ techniques focus on enhancing these components independently โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ or simultaneously. The page presents sophisticated โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ techniques like Chunk-Size Optimization and Structured โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ External Knowledge to improve retrieval performance. โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Additionally, it emphasizes the significance of prompt โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ engineering, explorative data analysis, and dataset โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ selection in developing effective RAG systems. The goal of โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ advanced RAG is to refine the system to generate high- โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ quality, informative, and relevant responses to user โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ queries. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://dev.to/lgram โ”‚ Create Your Own โ”‚ 2024-01-13 โ”‚ The webpage contains instructions and code to create a local โ”‚ -โ”‚ mel/create-your-own- โ”‚ Local Chatbot with โ”‚ โ”‚ chatbot using Next.js, Llama.cpp, and ModelFusion. Llama.cpp โ”‚ -โ”‚ local-chatbot-with- โ”‚ Next.js, Llama.cpp, โ”‚ โ”‚ is used to serve the OpenHermes 2.5 Mistral LLM locally, the โ”‚ -โ”‚ nextjs-llamacpp-and- โ”‚ and ModelFusion โ”‚ โ”‚ Vercel AI SDK is used to handle stream forwarding and โ”‚ -โ”‚ modelfusion-461j โ”‚ โ”‚ โ”‚ rendering, and ModelFusion is used to integrate Llama.cpp โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ with the Vercel AI SDK. The chatbot is able to generate โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ responses to user messages in real time. Here is a summary โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ of the instructions: 1. **Set up Llama.cpp** - Clone the โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Llama.cpp repository and build it on your machine. - โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Download the OpenHermes 2.5 Mistral GGUF model from โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ HuggingFace and move it to the models/ directory of your โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ local Llama.cpp repository. - Start the Llama.cpp server. โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ 2. **Create the Next.js Project** - Create a new Next.js โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ project using the create-next-app command. - Configure the โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ project settings using the prompts. - Navigate to the โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ project directory. 3. **Install the Required Libraries** โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ - Install the Vercel AI SDK, ModelFusion, and the โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ ModelFusion Vercel AI SDK Integration using the npm install โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ command. 4. **Create an API Route for the Chatbot** - โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Create a new file named route.ts in the src/app/api/chat/ โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ directory. - Import the necessary libraries and classes. โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ - Create a POST request that takes a list of messages as โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ input. - Initialize a ModelFusion text generation model โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ and create a ModelFusion chat prompt from the AI SDK โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ messages. - Use ModelFusion to call Llama.cpp and generate โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ a streaming response. - Return the streaming text response โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ using the Vercel AI SDK. 5. **Add the Chat Interface** - โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Create a dedicated chat page at src/app/page.tsx. - Use โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ the useChat hook from the Vercel AI SDK to call the โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ /api/chat route and process the streaming response. - โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Render the messages as they arrive. - Clean up the global โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ styles for a more visually appealing chat interface. 6. โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ **Run the Chatbot Application** - Launch the development โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ server using the npm run dev command. - Navigate to โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ http://localhost:3000 in a browser to see the chat page. - โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Interact with the chatbot by typing messages into the input โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ field. The chatbot will be able to generate responses to โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ your messages in real-time. โ”‚ -โ•˜โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•› - - diff --git a/workspace/web_research_reports/how_to_llamaindex_2024-02-04_17-44-02 b/workspace/web_research_reports/how_to_llamaindex_2024-02-04_17-44-02 deleted file mode 100644 index 3b2cd4fa..00000000 --- a/workspace/web_research_reports/how_to_llamaindex_2024-02-04_17-44-02 +++ /dev/null @@ -1,82 +0,0 @@ -โ•’โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•• -โ”‚ URL โ”‚ Title โ”‚ Published Date โ”‚ Summary โ”‚ -โ•žโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ก -โ”‚ https://tech.dentsus โ”‚ LlamaIndexใ‚’ไฝฟใฃใฆใƒญ โ”‚ 2024-01-22 โ”‚ The webpage provides a step-by-step guide on how to โ”‚ -โ”‚ oken.com/entry/2024/ โ”‚ ใƒผใ‚ซใƒซ็’ฐๅขƒใงRAGใ‚’ๅฎŸ โ”‚ โ”‚ implement Retrieval-Augmented Generation (RAG) using the โ”‚ -โ”‚ 01/22/LlamaIndex%E3% โ”‚ ่กŒใ™ใ‚‹ๆ–นๆณ• โ”‚ โ”‚ LlamaIndex library, aiming for local deployment of LLM. It โ”‚ -โ”‚ 82%92%E4%BD%BF%E3%81 โ”‚ โ”‚ โ”‚ explains why utilizing LLM in a local environment can be โ”‚ -โ”‚ %A3%E3%81%A6%E3%83%A โ”‚ โ”‚ โ”‚ beneficial, such as dealing with confidential data or โ”‚ -โ”‚ D%E3%83%BC%E3%82%AB% โ”‚ โ”‚ โ”‚ restricted internet access. The instruction includes โ”‚ -โ”‚ E3%83%AB%E7%92%B0%E5 โ”‚ โ”‚ โ”‚ setting up the necessary environment using WSL, Dev โ”‚ -โ”‚ %A2%83%E3%81%A7RAG%E โ”‚ โ”‚ โ”‚ Container, and installing required libraries. Additionally, โ”‚ -โ”‚ 3%82%92%E5%AE%9F%E8% โ”‚ โ”‚ โ”‚ it describes the process of building a RAG system using โ”‚ -โ”‚ A1%8C%E3%81%99%E3%82 โ”‚ โ”‚ โ”‚ LlamaIndex, including loading data, initializing models, and โ”‚ -โ”‚ %8B%E6%96%B9%E6%B3%9 โ”‚ โ”‚ โ”‚ handling querying and responding tasks. The page also โ”‚ -โ”‚ 5 โ”‚ โ”‚ โ”‚ explores areas for improvement, discussing optimizations โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ like minimizing query response time, selecting relevant โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ contexts, and tweaking hardware and software configurations. โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Finally, it encourages readers to try out the RAG โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ implementation and appreciate the convenience of LlamaIndex โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ while acknowledging the complexity involved in constructing โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ effective RAG systems. The page is authored by Yamashita โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Tsuyoshi and reviewed by Wakamoto Ryosuke, using Shodo for โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ documentation. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://blog.llamain โ”‚ LlamaIndex Blog โ”‚ 2024-01-23 โ”‚ The LlamaIndex Blog is a hub for news, updates, and guides โ”‚ -โ”‚ dex.ai/?gi=a117797fb โ”‚ โ”‚ โ”‚ related to LlamaIndex, a search engine and platform for โ”‚ -โ”‚ bc8 โ”‚ โ”‚ โ”‚ building and deploying AI-powered applications. This blog โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ features release updates, community showcases, and guides on โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ using LlamaIndex. Articles range from introducing new โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ features to exploring building various systems using โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ LlamaIndex. Some notable topics covered in the blog include โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ building a secure Multi-Tenancy RAG System, enhancing โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ accessibility in AI with LlamaIndex and GPT3.5, and โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ introducing Query Pipelines. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://levelup.gitc โ”‚ Live Indexing for โ”‚ 2024-01-08 โ”‚ * The task of processing and answering questions from PDFs โ”‚ -โ”‚ onnected.com/live- โ”‚ RAG: A Guide For โ”‚ โ”‚ is difficult for AI systems due to complex information, such โ”‚ -โ”‚ indexing-for-rag-a- โ”‚ Real-Time Indexing โ”‚ โ”‚ as nested tables, figures, and equations. * RAG frameworks โ”‚ -โ”‚ guide-for-real-time- โ”‚ Using LlamaIndex and โ”‚ โ”‚ and large language models (LLMs) have evolved to create โ”‚ -โ”‚ indexing-using- โ”‚ AWS โ”‚ โ”‚ fully-stack applications, enabling a chat-with-PDFs โ”‚ -โ”‚ llamaindex-and-aws-5 โ”‚ โ”‚ โ”‚ application with minimal code. * Creating an enterprise RAG โ”‚ -โ”‚ 1353083ace4?gi=472c9 โ”‚ โ”‚ โ”‚ application requires addressing challenges such as re- โ”‚ -โ”‚ 89ddb71&source=rss โ”‚ โ”‚ โ”‚ indexing and live updates of data sources. โ”‚ -โ”‚ ----5517fd7b58a6---4 โ”‚ โ”‚ โ”‚ โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://dev.to/lgram โ”‚ Create Your Own โ”‚ 2024-01-13 โ”‚ This article explains how to create a chatbot using Next.js, โ”‚ -โ”‚ mel/create-your-own- โ”‚ Local Chatbot with โ”‚ โ”‚ Llama.cpp, and ModelFusion. Here's a concise summary: 1. โ”‚ -โ”‚ local-chatbot-with- โ”‚ Next.js, Llama.cpp, โ”‚ โ”‚ **Setup:** - Clone and build Llama.cpp, an LLM inference โ”‚ -โ”‚ nextjs-llamacpp-and- โ”‚ and ModelFusion โ”‚ โ”‚ engine. - Download the OpenHermes 2.5 Mistral model from โ”‚ -โ”‚ modelfusion-461j โ”‚ โ”‚ โ”‚ HuggingFace. - Start the Llama.cpp server. 2. **Next.js โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Project:** - Create a Next.js project. - Install โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ required libraries: Vercel AI SDK, ModelFusion, and โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ ModelFusion Vercel AI SDK Integration. 3. **API Route:** โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ - Create a POST API route in Next.js to handle chat โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ interactions. - Initialize a ModelFusion text generation โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ model with the OpenHermes model. - Create a ModelFusion โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ chat prompt from Vercel AI SDK messages and call the model. โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ - Return the streaming response using the โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ ModelFusionTextStream adapter. 4. **Chat Interface:** - โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Create a dedicated Chat page using the useChat hook from โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Vercel AI SDK to render chat messages. - Update global โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ styles for improved readability. 5. **Run the โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Application:** - Run the development server and navigate โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ to http://localhost:3000 to interact with the chatbot. This โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ chatbot is functional, leveraging these technologies to โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ provide real-time responses to user messages. The code is a โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ starting point for further exploration and customization. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://blog.llamain โ”‚ A Cheat Sheet and โ”‚ 2024-01-05 โ”‚ This article provides a comprehensive overview of Retrieval- โ”‚ -โ”‚ dex.ai/a-cheat- โ”‚ Some Recipes For โ”‚ โ”‚ Augmented Generation (RAG) systems, focusing on the advanced โ”‚ -โ”‚ sheet-and-some- โ”‚ Building Advanced โ”‚ โ”‚ techniques and strategies used to build effective RAG โ”‚ -โ”‚ recipes-for- โ”‚ RAG โ”‚ โ”‚ systems that can handle complex queries using external โ”‚ -โ”‚ building-advanced- โ”‚ โ”‚ โ”‚ knowledge bases. It covers success requirements, various โ”‚ -โ”‚ rag-803a9d94c41b โ”‚ โ”‚ โ”‚ techniques for Retrieval and Generation components, and โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ includes a RAG Cheat Sheet for reference. The techniques โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ include Chunk-Size Optimization, Structured External โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ knowledge, Sparse-Attention Mechanism, Referring and Fine- โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ tuning on Predictions. The article also addresses the โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ challenges encountered in implementing these techniques. โ”‚ -โ•˜โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•› - - diff --git a/workspace/web_research_reports/how_to_llamaindex_2024-02-04_18-05-08 b/workspace/web_research_reports/how_to_llamaindex_2024-02-04_18-05-08 deleted file mode 100644 index 55c44a86..00000000 --- a/workspace/web_research_reports/how_to_llamaindex_2024-02-04_18-05-08 +++ /dev/null @@ -1,76 +0,0 @@ -โ•’โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•• -โ”‚ URL โ”‚ Title โ”‚ Published Date โ”‚ Summary โ”‚ -โ•žโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ก -โ”‚ https://tech.dentsus โ”‚ LlamaIndexใ‚’ไฝฟใฃใฆใƒญ โ”‚ 2024-01-22 โ”‚ The article explains how to implement RAG (Retrieval- โ”‚ -โ”‚ oken.com/entry/2024/ โ”‚ ใƒผใ‚ซใƒซ็’ฐๅขƒใงRAGใ‚’ๅฎŸ โ”‚ โ”‚ Augmented Generation) using LlamaIndex, a library that lets โ”‚ -โ”‚ 01/22/LlamaIndex%E3% โ”‚ ่กŒใ™ใ‚‹ๆ–นๆณ• โ”‚ โ”‚ you use Large Language Models (LLMs) like ChatGPT locally. โ”‚ -โ”‚ 82%92%E4%BD%BF%E3%81 โ”‚ โ”‚ โ”‚ RAG helps LLM answer questions or generate text by providing โ”‚ -โ”‚ %A3%E3%81%A6%E3%83%A โ”‚ โ”‚ โ”‚ relevant context from external data sources. By integrating โ”‚ -โ”‚ D%E3%83%BC%E3%82%AB% โ”‚ โ”‚ โ”‚ an embedding model and an LLM, LlamaIndex allows you to load โ”‚ -โ”‚ E3%83%AB%E7%92%B0%E5 โ”‚ โ”‚ โ”‚ text data, create an index, and retrieve context-aware โ”‚ -โ”‚ %A2%83%E3%81%A7RAG%E โ”‚ โ”‚ โ”‚ responses to user queries. The article discusses setup, โ”‚ -โ”‚ 3%82%92%E5%AE%9F%E8% โ”‚ โ”‚ โ”‚ model selection, and code implementation using Python. It โ”‚ -โ”‚ A1%8C%E3%81%99%E3%82 โ”‚ โ”‚ โ”‚ also highlights potential improvements in terms of โ”‚ -โ”‚ %8B%E6%96%B9%E6%B3%9 โ”‚ โ”‚ โ”‚ performance and accuracy. โ”‚ -โ”‚ 5 โ”‚ โ”‚ โ”‚ โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://levelup.gitc โ”‚ Live Indexing for โ”‚ 2024-01-08 โ”‚ - PDFs contain valuable information, but analyzing them with โ”‚ -โ”‚ onnected.com/live- โ”‚ RAG: A Guide For โ”‚ โ”‚ Large Language Models (LLMs) is challenging due to their โ”‚ -โ”‚ indexing-for-rag-a- โ”‚ Real-Time Indexing โ”‚ โ”‚ complex structure. - The rise of Retrieval-Augmented โ”‚ -โ”‚ guide-for-real-time- โ”‚ Using LlamaIndex and โ”‚ โ”‚ Generation (RAG) frameworks and LLMs has simplified the โ”‚ -โ”‚ indexing-using- โ”‚ AWS โ”‚ โ”‚ creation of full-stack applications. - LlamaIndex, a โ”‚ -โ”‚ llamaindex-and-aws-5 โ”‚ โ”‚ โ”‚ prominent RAG framework, allows users to create chat-with- โ”‚ -โ”‚ 1353083ace4?gi=472c9 โ”‚ โ”‚ โ”‚ PDFs applications with just a few lines of code. - Creating โ”‚ -โ”‚ 89ddb71&source=rss โ”‚ โ”‚ โ”‚ an enterprise RAG application requires additional โ”‚ -โ”‚ ----5517fd7b58a6---4 โ”‚ โ”‚ โ”‚ considerations, such as re-indexing and live updates. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://www.youtube. โ”‚ Transforming Invoice โ”‚ 2024-01-08 โ”‚ This web page introduces Sparrow, an open-source solution โ”‚ -โ”‚ com/watch?v=VKeYaIEk โ”‚ Data into JSON: โ”‚ โ”‚ for document processing with local LLMs. The video โ”‚ -โ”‚ 82s&v=watch&feature= โ”‚ Local LLM with โ”‚ โ”‚ demonstrates how to use Sparrow with LlamaIndex and a โ”‚ -โ”‚ youtu.be โ”‚ LlamaIndex \u0026 โ”‚ โ”‚ dynamic Pydantic class to extract structured JSON output โ”‚ -โ”‚ โ”‚ Pydantic โ”‚ โ”‚ from invoice documents, running locally on a MacBook Air M1 โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ with 8GB RAM. The process involves configuring Sparrow, โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ creating a RAG pipeline, implementing a dynamic Pydantic โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ class, and setting up LlamaIndex with the Pydantic class to โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ produce JSON output. A step-by-step explanation of the setup โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ and implementation is provided. The end result is a โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ structured JSON output that can be easily used for further โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ processing or analysis. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://dev.to/lgram โ”‚ Create Your Own โ”‚ 2024-01-13 โ”‚ This article aims to guide readers in creating a local โ”‚ -โ”‚ mel/create-your-own- โ”‚ Local Chatbot with โ”‚ โ”‚ chatbot using Next.js, Llama.cpp, and ModelFusion. It begins โ”‚ -โ”‚ local-chatbot-with- โ”‚ Next.js, Llama.cpp, โ”‚ โ”‚ by explaining how to set up Llama.cpp along with the โ”‚ -โ”‚ nextjs-llamacpp-and- โ”‚ and ModelFusion โ”‚ โ”‚ necessary steps for building and downloading the OpenHermes โ”‚ -โ”‚ modelfusion-461j โ”‚ โ”‚ โ”‚ 2.5 Mistral GGUF model. Once Llama.cpp is ready, users can โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ start the server. The next step involves creating a Next.js โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ project, installing the required libraries, and setting up โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ an API route for handling chatbot interactions. The guide โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ provides detailed explanations of each of these steps, โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ including code snippets and explanations. Once the chatbot โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ interface has been added, users can run the chatbot โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ application using a command in their terminal. A screenshot โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ demonstrating the expected look of the running chatbot is โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ also included. In conclusion, this article serves as a โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ comprehensive guide for developers interested in creating a โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ local chatbot. It covers the setup process, API route โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ creation, frontend development, and application execution. โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ The guide encourages readers to explore the codebase and โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ modify it to suit their specific project needs. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://blog.llamain โ”‚ A Cheat Sheet and โ”‚ 2024-01-05 โ”‚ This blog post gives a detailed RAG cheat sheet. RAG, or โ”‚ -โ”‚ dex.ai/a-cheat- โ”‚ Some Recipes For โ”‚ โ”‚ Retrieval Augmented Generation system, involves retrieving โ”‚ -โ”‚ sheet-and-some- โ”‚ Building Advanced โ”‚ โ”‚ documents from an external knowledge base and passing it โ”‚ -โ”‚ recipes-for- โ”‚ RAG โ”‚ โ”‚ along with the user's query to an LLM for response โ”‚ -โ”‚ building-advanced- โ”‚ โ”‚ โ”‚ generation. It consists of a Retrieval component, an โ”‚ -โ”‚ rag-803a9d94c41b โ”‚ โ”‚ โ”‚ External Knowledge database, and a Generation component. For โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ a RAG system to be successful, it must be able to find the โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ most relevant documents to a user's query and make good use โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ of the retrieved documents to answer the query sufficiently. โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Advanced RAG involves applying more sophisticated techniques โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ and strategies to the Retrieval and Generation components to โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ achieve these requirements. It mentions two advanced โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ techniques for Retrieval, Chunk-Size Optimization and โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Structured External Knowledge, with code samples. โ”‚ -โ•˜โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•› - - diff --git a/workspace/web_research_reports/how_to_llamaindex_2024-02-04_18-23-42 b/workspace/web_research_reports/how_to_llamaindex_2024-02-04_18-23-42 deleted file mode 100644 index eae45fe8..00000000 --- a/workspace/web_research_reports/how_to_llamaindex_2024-02-04_18-23-42 +++ /dev/null @@ -1,116 +0,0 @@ -โ•’โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•• -โ”‚ URL โ”‚ Title โ”‚ Published Date โ”‚ Summary โ”‚ -โ•žโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ก -โ”‚ https://www.analytic โ”‚ Using Llamafiles to โ”‚ 2024-01-18 โ”‚ The article discusses Llamafiles, which simplify the process โ”‚ -โ”‚ svidhya.com/blog/202 โ”‚ Simplify LLM โ”‚ โ”‚ of running Large Language Models (LLMs) on consumer โ”‚ -โ”‚ 4/01/using- โ”‚ Execution โ”‚ โ”‚ hardware. Traditionally, running LLMs involved downloading โ”‚ -โ”‚ llamafiles-to- โ”‚ โ”‚ โ”‚ third-party software, creating Python environments, and โ”‚ -โ”‚ simplify-llm- โ”‚ โ”‚ โ”‚ writing code. Llamafiles address these challenges by โ”‚ -โ”‚ execution/ โ”‚ โ”‚ โ”‚ enabling users to download and run LLMs as single-file โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ executables. Additionally, the article explains the concept โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ of Llamafiles, including its benefits and limitations, as โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ well as how to create Llamafiles from quantized LLMs. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://levelup.gitc โ”‚ Live Indexing for โ”‚ 2024-01-08 โ”‚ - Most AI systems, including LLMs, struggle to process and โ”‚ -โ”‚ onnected.com/live- โ”‚ RAG: A Guide For โ”‚ โ”‚ answer questions from PDFs due to their complex information. โ”‚ -โ”‚ indexing-for-rag-a- โ”‚ Real-Time Indexing โ”‚ โ”‚ - RAG frameworks and Large Language Models (LLMs) have โ”‚ -โ”‚ guide-for-real-time- โ”‚ Using LlamaIndex and โ”‚ โ”‚ enabled the creation of full-stack applications for โ”‚ -โ”‚ indexing-using- โ”‚ AWS โ”‚ โ”‚ interacting with PDFs. - LlamaIndex is provided as an โ”‚ -โ”‚ llamaindex-and-aws-5 โ”‚ โ”‚ โ”‚ example of a RAG framework that allows users to create chat โ”‚ -โ”‚ 1353083ace4?gi=472c9 โ”‚ โ”‚ โ”‚ applications for interacting with PDFs with just a few lines โ”‚ -โ”‚ 89ddb71&source=rss โ”‚ โ”‚ โ”‚ of code. - The article also discusses additional challenges โ”‚ -โ”‚ ----5517fd7b58a6---4 โ”‚ โ”‚ โ”‚ for AI engineers in creating enterprise-grade RAG โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ applications such as re-indexing and live updates. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://tech.dentsus โ”‚ LlamaIndexใ‚’ไฝฟใฃใฆใƒญ โ”‚ 2024-01-22 โ”‚ This webpage discusses how to implement Retrieval-Augmented โ”‚ -โ”‚ oken.com/entry/2024/ โ”‚ ใƒผใ‚ซใƒซ็’ฐๅขƒใงRAGใ‚’ๅฎŸ โ”‚ โ”‚ Generation (RAG) using the LlamaIndex library in a local โ”‚ -โ”‚ 01/22/LlamaIndex%E3% โ”‚ ่กŒใ™ใ‚‹ๆ–นๆณ• โ”‚ โ”‚ environment. The goal is to leverage Large Language Models โ”‚ -โ”‚ 82%92%E4%BD%BF%E3%81 โ”‚ โ”‚ โ”‚ (LLMs) like ChatGPT while addressing limitations such as โ”‚ -โ”‚ %A3%E3%81%A6%E3%83%A โ”‚ โ”‚ โ”‚ data confidentiality and restricted internet access. The โ”‚ -โ”‚ D%E3%83%BC%E3%82%AB% โ”‚ โ”‚ โ”‚ article highlights the benefits of using a local setup for โ”‚ -โ”‚ E3%83%AB%E7%92%B0%E5 โ”‚ โ”‚ โ”‚ LLM applications and explains why the LlamaIndex framework โ”‚ -โ”‚ %A2%83%E3%81%A7RAG%E โ”‚ โ”‚ โ”‚ is suitable for this purpose. The author provides detailed โ”‚ -โ”‚ 3%82%92%E5%AE%9F%E8% โ”‚ โ”‚ โ”‚ instructions on setting up the environment, including โ”‚ -โ”‚ A1%8C%E3%81%99%E3%82 โ”‚ โ”‚ โ”‚ installing necessary software and configuring a development โ”‚ -โ”‚ %8B%E6%96%B9%E6%B3%9 โ”‚ โ”‚ โ”‚ container using Docker. Furthermore, the article guides โ”‚ -โ”‚ 5 โ”‚ โ”‚ โ”‚ readers through the process of loading data, initializing โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ LLM and embedding models, and implementing RAG using Python โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ code. It also includes a sample implementation of a chat โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ system that leverages RAG to answer questions based on a โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ provided text document. The author discusses the challenges โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ faced during implementation and suggests potential โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ improvements, such as optimizing performance by reducing โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ context information and leveraging more powerful hardware. โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ The article concludes by encouraging readers to experiment โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ with RAG and emphasizing the potential of this technology to โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ create useful applications. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://dev.to/lgram โ”‚ Create Your Own โ”‚ 2024-01-13 โ”‚ This blog post provides a detailed guide on how to build a โ”‚ -โ”‚ mel/create-your-own- โ”‚ Local Chatbot with โ”‚ โ”‚ local chatbot using several technologies. Here's a summary: โ”‚ -โ”‚ local-chatbot-with- โ”‚ Next.js, Llama.cpp, โ”‚ โ”‚ Objective of the blog post: - Build a chatbot that runs on โ”‚ -โ”‚ nextjs-llamacpp-and- โ”‚ and ModelFusion โ”‚ โ”‚ your computer using Next.js, Llama.cpp, and ModelFusion. - โ”‚ -โ”‚ modelfusion-461j โ”‚ โ”‚ โ”‚ Use the OpenHermes 2.5 Mistral LLM (large language model) โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ for natural language interaction. - Employ the Vercel AI SDK โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ for stream forwarding and rendering. - Integrate the โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Llama.cpp language model with the Vercel AI SDK through โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ ModelFusion. Necessary Steps: 1. Setup Llama.cpp: a) โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Clone the repository. b) Build Llama.cpp: Linux/Mac users โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ can run "make", Windows users can follow the instructions โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ provided. c) Download the OpenHermes 2.5 Mistral GGUF โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ model from HuggingFace and move it into the Llama.cpp โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ repository's "models/" directory. d) Start the Llama.cpp โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ server to enable the integration of the model into the โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ chatbot. 2. Create a Next.js Project: a) Create a new โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Next.js project using "npx create-next-app@latest llamacpp- โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ nextjs-chatbot". b) Configure the project with preferred โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ settings, including TypeScript, ESLint, Tailwind CSS, and โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ App Router. 3. Install Required Libraries: a) Install โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ libraries such as Vercel AI SDK, ModelFusion, and โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ ModelFusion Vercel AI SDK Integration. 4. Creating an API โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Route for the Chatbot: a) In the 'api/chat/' directory, โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ create 'route.ts' for handling chat interactions. b) โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Import relevant modules and initialize a ModelFusion text โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ generation model. c) Send the API request, process the โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ response, and generate a streaming response using โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ ModelFusion to access the Llama.cpp chat API. 5. Adding the โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Chat Interface: a) Establish a chat page, 'page.tsx' to โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ display the chatbot and use the 'useChat' hook from the โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Vercel AI SDK. b) Clean up the global styles for better โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ UI presentation. 6. Running the Chatbot Application: a) โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Launch the development server with "npm run dev". b) In a โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ browser, navigate to "http://localhost:3000" to interact โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ with the chatbot. Conclusion: The tutorial provides a step- โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ by-step guide to set up a local chatbot, enabling users to โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ explore AI and natural language processing. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://akash-mathur โ”‚ Advanced RAG: Query โ”‚ 2024-01-18 โ”‚ Welcome to the Advanced RAG Learning Series. This article โ”‚ -โ”‚ .medium.com/advanced โ”‚ Augmentation for โ”‚ โ”‚ series explores advanced techniques to heighten โ”‚ -โ”‚ -rag-query- โ”‚ Next-Level Search โ”‚ โ”‚ understanding and expertise in Retriever-Augmented โ”‚ -โ”‚ augmentation-for- โ”‚ using LlamaIndex๐Ÿฆ™ โ”‚ โ”‚ Generation (RAG) applications. Key concepts covered include โ”‚ -โ”‚ next-level-search- โ”‚ โ”‚ โ”‚ optimizing retrieval with extra context and metadata, โ”‚ -โ”‚ using-llamaindex-d36 โ”‚ โ”‚ โ”‚ improving retrieval efficiency via rerankers, and enhancing โ”‚ -โ”‚ 2fed7ecc3 โ”‚ โ”‚ โ”‚ query augmentation. The focus is on query transformations โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ which bridge user prompts and relevant information in vast โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ databases, particularly to address the challenge of โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ retrieval misalignment. Five powerful query transformation โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ techniques are explored, addressing the need to adapt to โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ LLMs' comprehension and generation capabilities. The โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ techniques explored are: - Hypothetical Document Embeddings โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ (HyDE), which creates a hypothetical answer document and โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ encodes it to retrieve relevant documents. - Sub-Question โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Query Engine, which decomposes complex queries into sub- โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ questions and retrieves results from dedicated data sources. โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ - Router Query Engine, which selects the most appropriate โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ query engine based on user queries and metadata. - Single- โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Step Query Decomposition, which breaks down complex โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ questions into simpler sub-queries for focused information โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ extraction. - Multi-Step Query Decomposition, which employs โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ a self-ask method to iteratively explore knowledge and โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ uncover hidden connections among facts. The article โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ provides code examples and GitHub links to assist in โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ practical implementation. It also highlights the ongoing โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ developments and potential future directions in query โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ augmentation research. โ”‚ -โ•˜โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•› - - diff --git a/workspace/web_research_reports/how_to_llamaindex_2024-02-04_18-31-53 b/workspace/web_research_reports/how_to_llamaindex_2024-02-04_18-31-53 deleted file mode 100644 index b75c499d..00000000 --- a/workspace/web_research_reports/how_to_llamaindex_2024-02-04_18-31-53 +++ /dev/null @@ -1,78 +0,0 @@ -โ•’โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•• -โ”‚ URL โ”‚ Title โ”‚ Published Date โ”‚ Summary โ”‚ -โ•žโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ก -โ”‚ https://tech.dentsus โ”‚ LlamaIndexใ‚’ไฝฟใฃใฆใƒญ โ”‚ 2024-01-22 โ”‚ LlamaIndex Library using Retrieval Augmented Generation โ”‚ -โ”‚ oken.com/entry/2024/ โ”‚ ใƒผใ‚ซใƒซ็’ฐๅขƒใงRAGใ‚’ๅฎŸ โ”‚ โ”‚ (RAG) to Implement Chatbot Systems Locally - LlamaIndex is a โ”‚ -โ”‚ 01/22/LlamaIndex%E3% โ”‚ ่กŒใ™ใ‚‹ๆ–นๆณ• โ”‚ โ”‚ library for ingesting, structuring, and accessing private or โ”‚ -โ”‚ 82%92%E4%BD%BF%E3%81 โ”‚ โ”‚ โ”‚ domain-specific data to build LLM-based applications. - It โ”‚ -โ”‚ %A3%E3%81%A6%E3%83%A โ”‚ โ”‚ โ”‚ facilitates local implementation of RAG, a technique that โ”‚ -โ”‚ D%E3%83%BC%E3%82%AB% โ”‚ โ”‚ โ”‚ combines document search and LLM to generate responses with โ”‚ -โ”‚ E3%83%AB%E7%92%B0%E5 โ”‚ โ”‚ โ”‚ reduced hallucination and improved accuracy. - The article โ”‚ -โ”‚ %A2%83%E3%81%A7RAG%E โ”‚ โ”‚ โ”‚ provides a step-by-step guide for setting up a development โ”‚ -โ”‚ 3%82%92%E5%AE%9F%E8% โ”‚ โ”‚ โ”‚ environment using WSL, devcontainer, and the LlamaIndex โ”‚ -โ”‚ A1%8C%E3%81%99%E3%82 โ”‚ โ”‚ โ”‚ library. - It demonstrates RAG implementation using Python โ”‚ -โ”‚ %8B%E6%96%B9%E6%B3%9 โ”‚ โ”‚ โ”‚ and explains how to configure the prompt, query engine, and โ”‚ -โ”‚ 5 โ”‚ โ”‚ โ”‚ other components. - The resulting chatbot can perform Q&A โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ tasks based on the provided context, as demonstrated with โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ examples using a text file derived from the้’็ฉบๆ–‡ๅบซ novel โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ ่ตฐใ‚Œใƒกใƒญใ‚น. - The author discusses potential improvements, โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ such as optimizing speed and accuracy. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://blog.llamain โ”‚ LlamaIndex Blog โ”‚ 2024-01-23 โ”‚ The LlamaIndex blog is the official blog of LlamaIndex, โ”‚ -โ”‚ dex.ai/?gi=a117797fb โ”‚ โ”‚ โ”‚ featuring release updates, guides, community showcases, and โ”‚ -โ”‚ bc8 โ”‚ โ”‚ โ”‚ more. The blog contains articles from January 2023 and โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ earlier, with titles such as "LlamaIndex Newsletter," โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ "Building Multi-Tenancy RAG System with LlamaIndex," "AI โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Voice Assistant with LlamaIndex and GPT3.5," "Join Thousands โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ in our Free Advanced RAG Certification," "Query Pipelines in โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ LlamaIndex," and more. The blog also provides a cheat sheet โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ and recipes for building advanced RAG, as well as โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ information on scaling LlamaIndex with AWS and Hugging Face. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://levelup.gitc โ”‚ Live Indexing for โ”‚ 2024-01-08 โ”‚ - PDFs contain valuable information, but AI systems struggle โ”‚ -โ”‚ onnected.com/live- โ”‚ RAG: A Guide For โ”‚ โ”‚ to process and understand them. - RAG frameworks and LLMs โ”‚ -โ”‚ indexing-for-rag-a- โ”‚ Real-Time Indexing โ”‚ โ”‚ have evolved to provide a readily deployable platform for โ”‚ -โ”‚ guide-for-real-time- โ”‚ Using LlamaIndex and โ”‚ โ”‚ creating full-stack applications. - With just a few lines โ”‚ -โ”‚ indexing-using- โ”‚ AWS โ”‚ โ”‚ of code, LlamaIndex can be used to create a chat-with-PDFs โ”‚ -โ”‚ llamaindex-and-aws-5 โ”‚ โ”‚ โ”‚ application. - Additional work is still required by AI โ”‚ -โ”‚ 1353083ace4?gi=472c9 โ”‚ โ”‚ โ”‚ engineers to create enterprise RAG applications, such as โ”‚ -โ”‚ 89ddb71&source=rss โ”‚ โ”‚ โ”‚ addressing the need to re-index and live update data โ”‚ -โ”‚ ----5517fd7b58a6---4 โ”‚ โ”‚ โ”‚ sources. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://dev.to/lgram โ”‚ Create Your Own โ”‚ 2024-01-13 โ”‚ Sure, here is a summary of the content of the webpage you โ”‚ -โ”‚ mel/create-your-own- โ”‚ Local Chatbot with โ”‚ โ”‚ provided: The article explains how to create a local โ”‚ -โ”‚ local-chatbot-with- โ”‚ Next.js, Llama.cpp, โ”‚ โ”‚ chatbot using Next.js, Llama.cpp, and ModelFusion. The โ”‚ -โ”‚ nextjs-llamacpp-and- โ”‚ and ModelFusion โ”‚ โ”‚ chatbot will run on the user's computer and will be able to โ”‚ -โ”‚ modelfusion-461j โ”‚ โ”‚ โ”‚ generate responses to user messages in real-time using the โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ OpenHermes 2.5 Mistral Large Language Model (LLM). To build โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ the chatbot, the user will need to set up Llama.cpp, create โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ a Next.js project, install the required libraries, configure โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ an API route for the chatbot, add a chat interface, and โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ finally run the chatbot application. The full code for a โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ starter project with more examples can be found on GitHub. โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ The article includes step-by-step instructions, code โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ snippets, and a screenshot of what the chatbot interface โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ looks like when running. The author also provides a brief โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ introduction to each technology used and explains the โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ architecture of the chatbot. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://blog.llamain โ”‚ A Cheat Sheet and โ”‚ 2024-01-05 โ”‚ This webpage shares a comprehensive RAG Cheat Sheet that โ”‚ -โ”‚ dex.ai/a-cheat- โ”‚ Some Recipes For โ”‚ โ”‚ provides motivations for RAG, techniques, and strategies for โ”‚ -โ”‚ sheet-and-some- โ”‚ Building Advanced โ”‚ โ”‚ creating advanced RAG systems. It begins with Basic RAG, โ”‚ -โ”‚ recipes-for- โ”‚ RAG โ”‚ โ”‚ where documents are retrieved from an external database and โ”‚ -โ”‚ building-advanced- โ”‚ โ”‚ โ”‚ passed along with the user query to an LLM for response โ”‚ -โ”‚ rag-803a9d94c41b โ”‚ โ”‚ โ”‚ generation. Two high-level success requirements for RAG are โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ defined: retrieval must find relevant documents, and โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ generation must use retrieved documents to answer user โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ queries. To achieve these requirements, advanced techniques โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ can address each requirement independently or โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ simultaneously. The webpage briefly describes chunk-size โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ optimization, structured external knowledge, and interleaved โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ retrieval as advanced Retrieval techniques. For the โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Generation component, advanced techniques include in-context โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ learning, prompt engineering, and policy learning. The โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ provided RAG cheat sheet offers a visual representation of โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ these concepts. โ”‚ -โ•˜โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•› - - diff --git a/workspace/web_research_reports/how_to_llamaindex_2024-02-04_19-06-07 b/workspace/web_research_reports/how_to_llamaindex_2024-02-04_19-06-07 deleted file mode 100644 index 3d74a2cf..00000000 --- a/workspace/web_research_reports/how_to_llamaindex_2024-02-04_19-06-07 +++ /dev/null @@ -1,98 +0,0 @@ -โ•’โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•• -โ”‚ URL โ”‚ Title โ”‚ Published Date โ”‚ Summary โ”‚ -โ•žโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ก -โ”‚ https://www.analytic โ”‚ Using Llamafiles to โ”‚ 2024-01-18 โ”‚ Sure, here's a summary of the web page content provided. โ”‚ -โ”‚ svidhya.com/blog/202 โ”‚ Simplify LLM โ”‚ โ”‚ **Summary** - Traditional LLM execution is tedious, โ”‚ -โ”‚ 4/01/using- โ”‚ Execution โ”‚ โ”‚ involving downloading 3rd party software, Python, Pytorch, โ”‚ -โ”‚ llamafiles-to- โ”‚ โ”‚ โ”‚ and HuggingFace libraries, and potentially writing code to โ”‚ -โ”‚ simplify-llm- โ”‚ โ”‚ โ”‚ run the model. - Llamafiles are single-file executables โ”‚ -โ”‚ execution/ โ”‚ โ”‚ โ”‚ that simplify running LLMs, eliminating the need for initial โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ library installation. - They leverage the llama.cpp C โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ library for quantized LLM execution on CPUs and the โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ cosmopolitan libc for cross-platform compatibility. - โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Available models are in the GGUF quantized format, designed โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ for efficient storage, sharing, and loading of LLMs on CPUs โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ and GPUs. - There are limitations to using Llamafiles, โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ including the need for quantized models and the lack of โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ support for LLMs requiring GPUs. - Llamafiles offer โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ advantages over traditional methods, such as faster โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ inference, offline usage, and potential cost reduction. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://tech.dentsus โ”‚ LlamaIndexใ‚’ไฝฟใฃใฆใƒญ โ”‚ 2024-01-22 โ”‚ - Retrieval-Augmented Generation (RAG) is a technique that โ”‚ -โ”‚ oken.com/entry/2024/ โ”‚ ใƒผใ‚ซใƒซ็’ฐๅขƒใงRAGใ‚’ๅฎŸ โ”‚ โ”‚ utilizes Large Language Models (LLMs) to improve the โ”‚ -โ”‚ 01/22/LlamaIndex%E3% โ”‚ ่กŒใ™ใ‚‹ๆ–นๆณ• โ”‚ โ”‚ accuracy and reduce hallucination in generated responses. โ”‚ -โ”‚ 82%92%E4%BD%BF%E3%81 โ”‚ โ”‚ โ”‚ - LlamaIndex is a data framework used for ingesting, โ”‚ -โ”‚ %A3%E3%81%A6%E3%83%A โ”‚ โ”‚ โ”‚ structuring, and accessing private or domain-specific data โ”‚ -โ”‚ D%E3%83%BC%E3%82%AB% โ”‚ โ”‚ โ”‚ for LLM-based applications. - This article demonstrates โ”‚ -โ”‚ E3%83%AB%E7%92%B0%E5 โ”‚ โ”‚ โ”‚ how to set up a local environment with WSL and Devcontainer โ”‚ -โ”‚ %A2%83%E3%81%A7RAG%E โ”‚ โ”‚ โ”‚ to utilize LLMs. - An example implementation of a RAG โ”‚ -โ”‚ 3%82%92%E5%AE%9F%E8% โ”‚ โ”‚ โ”‚ application using LlamaIndex is provided for answering โ”‚ -โ”‚ A1%8C%E3%81%99%E3%82 โ”‚ โ”‚ โ”‚ questions based on the context of a document. - Optimizing โ”‚ -โ”‚ %8B%E6%96%B9%E6%B3%9 โ”‚ โ”‚ โ”‚ the system's performance can be achieved by adjusting the โ”‚ -โ”‚ 5 โ”‚ โ”‚ โ”‚ context information and utilizing more powerful hardware. โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Creating a more effective RAG involves finding optimal โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ contexts and refining the search techniques. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://dev.to/lgram โ”‚ Create Your Own โ”‚ 2024-01-13 โ”‚ The blog post covers building a local chatbot using the โ”‚ -โ”‚ mel/create-your-own- โ”‚ Local Chatbot with โ”‚ โ”‚ Next.js framework. An AI chatbot uses the Vercel AI SDK to โ”‚ -โ”‚ local-chatbot-with- โ”‚ Next.js, Llama.cpp, โ”‚ โ”‚ handle stream forwarding and rendering, the ModelFusion โ”‚ -โ”‚ nextjs-llamacpp-and- โ”‚ and ModelFusion โ”‚ โ”‚ library to integrate Llama.cpp with the Vercel AI SDK, and โ”‚ -โ”‚ modelfusion-461j โ”‚ โ”‚ โ”‚ OpenHermes 2.5 Mistral as a powerful language model. The โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ architecture involves a user interface that sends messages โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ to the AI server, processed by Llama.cpp, and returned as โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ responses to the user. The initial steps include setting up โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Llama.cpp, downloading OpenHermes 2.5 Mistral GGUF, and โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ starting the Llama.cpp server. Creating the Next.js project โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ involves installing the required libraries and setting up โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ the API route using the useChat hook from the Vercel AI SDK. โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Adding the chat interface involves creating a separate page, โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ handling global styles, and more. Finally, running the โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ chatbot application lets users interact with the chatbot, โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ and the conclusion highlights the blog's intent as a โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ starting point for exploration. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://levelup.gitc โ”‚ Live Indexing for โ”‚ 2024-01-08 โ”‚ - LLMs (Large Language Models) are not effective at โ”‚ -โ”‚ onnected.com/live- โ”‚ RAG: A Guide For โ”‚ โ”‚ analyzing PDFs due to their complex information, leading to โ”‚ -โ”‚ indexing-for-rag-a- โ”‚ Real-Time Indexing โ”‚ โ”‚ errors and hallucinations. - RAG (Retrieval-Augmented โ”‚ -โ”‚ guide-for-real-time- โ”‚ Using LlamaIndex and โ”‚ โ”‚ Generation) frameworks like LlamaIndex and Langchain have โ”‚ -โ”‚ indexing-using- โ”‚ AWS โ”‚ โ”‚ made it easier to develop full-stack applications. - โ”‚ -โ”‚ llamaindex-and-aws-5 โ”‚ โ”‚ โ”‚ LlamaIndex requires minimal code to create a chat-with-PDFs โ”‚ -โ”‚ 1353083ace4?gi=472c9 โ”‚ โ”‚ โ”‚ application, making it user-friendly with a few prompts and โ”‚ -โ”‚ 89ddb71&source=rss โ”‚ โ”‚ โ”‚ configurations. - The article mentions the need for further โ”‚ -โ”‚ ----5517fd7b58a6---4 โ”‚ โ”‚ โ”‚ actions by AI engineers to create enterprise RAG โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ applications but doesn't provide specifics. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://www.youtube. โ”‚ Transforming Invoice โ”‚ 2024-01-08 โ”‚ This webpage showcases Sparrow, an open-source solution for โ”‚ -โ”‚ com/watch?v=VKeYaIEk โ”‚ Data into JSON: โ”‚ โ”‚ processing documents with local LLMs. The author uses โ”‚ -โ”‚ 82s&v=watch&feature= โ”‚ Local LLM with โ”‚ โ”‚ Starling LLM with Ollama and demonstrates the extraction of โ”‚ -โ”‚ youtu.be โ”‚ LlamaIndex \u0026 โ”‚ โ”‚ structured data from invoice documents. Here's a concise โ”‚ -โ”‚ โ”‚ Pydantic โ”‚ โ”‚ summary of the content: 1. Sparrow GitHub Repo: A link to โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ the project's GitHub repository is provided. 2. โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Introduction: The author introduces Sparrow as a solution โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ for document processing using LLMs and mentions that it runs โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ locally with Ollama. 3. Example: A simple example โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ demonstrates how to process a document and extract invoice- โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ related information in JSON format. 4. Configuration: The โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ author guides viewers on setting up the configuration for โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ the project. 5. RAG with Sparrow and LlamaIndex: The video โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ demonstrates how to use RAG (Retrieve Answers from Generated โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Text) along with Sparrow and LlamaIndex for document โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ processing. 6. RAG Pipeline Implementation: The author โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ provides a detailed walkthrough of implementing RAG pipeline โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ for document processing. 7. Pydantic Dynamic Class: A โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Pydantic dynamic class is created to generate structured โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ JSON output from the processed documents. 8. LlamaIndex โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Setup with Pydantic Class to Produce JSON Output: The video โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ demonstrates how to set up LlamaIndex with a Pydantic class โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ to obtain structured JSON output from the document โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ processing. 9. Query: Viewers are shown how to query โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ processed documents for specific information. 10. Summary: โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ The author summarizes the key points of the video, โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ highlighting the use of Sparrow for document processing with โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ LLMs. The video includes additional information about โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ connecting with the author via various platforms, such as โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ YouTube, Twitter, LinkedIn, and Medium. Hashtags related to โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ the video's topic are also mentioned. โ”‚ -โ•˜โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•› - - diff --git a/workspace/web_research_reports/how_to_llamaindex_2024-02-04_19-31-58 b/workspace/web_research_reports/how_to_llamaindex_2024-02-04_19-31-58 deleted file mode 100644 index add4410f..00000000 --- a/workspace/web_research_reports/how_to_llamaindex_2024-02-04_19-31-58 +++ /dev/null @@ -1,82 +0,0 @@ -โ•’โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•• -โ”‚ URL โ”‚ Title โ”‚ Published Date โ”‚ Summary โ”‚ -โ•žโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ก -โ”‚ https://tech.dentsus โ”‚ LlamaIndexใ‚’ไฝฟใฃใฆใƒญ โ”‚ 2024-01-22 โ”‚ The article talks about how to implement Retrieval-Augmented โ”‚ -โ”‚ oken.com/entry/2024/ โ”‚ ใƒผใ‚ซใƒซ็’ฐๅขƒใงRAGใ‚’ๅฎŸ โ”‚ โ”‚ Generation (RAG) using the LlamaIndex library in a local โ”‚ -โ”‚ 01/22/LlamaIndex%E3% โ”‚ ่กŒใ™ใ‚‹ๆ–นๆณ• โ”‚ โ”‚ environment. Reasons for choosing local environment for LLM โ”‚ -โ”‚ 82%92%E4%BD%BF%E3%81 โ”‚ โ”‚ โ”‚ utilization is discussed. LlamaIndex benefits and features โ”‚ -โ”‚ %A3%E3%81%A6%E3%83%A โ”‚ โ”‚ โ”‚ along with the required environment setup are also โ”‚ -โ”‚ D%E3%83%BC%E3%82%AB% โ”‚ โ”‚ โ”‚ mentioned. A detailed step-by-step guide to implement RAG โ”‚ -โ”‚ E3%83%AB%E7%92%B0%E5 โ”‚ โ”‚ โ”‚ using LlamaIndex is provided with sample questions and โ”‚ -โ”‚ %A2%83%E3%81%A7RAG%E โ”‚ โ”‚ โ”‚ answers. The article highlights aspects of this โ”‚ -โ”‚ 3%82%92%E5%AE%9F%E8% โ”‚ โ”‚ โ”‚ implementation that can be further improved in terms of โ”‚ -โ”‚ A1%8C%E3%81%99%E3%82 โ”‚ โ”‚ โ”‚ reducing time and increasing accuracy. Additionally, using โ”‚ -โ”‚ %8B%E6%96%B9%E6%B3%9 โ”‚ โ”‚ โ”‚ more RAM and processing power is suggested. Overall, the โ”‚ -โ”‚ 5 โ”‚ โ”‚ โ”‚ article explores the convenience of using LlamaIndex for RAG โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ implementation while highlighting areas for improvement to โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ build a more robust RAG system. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://blog.llamain โ”‚ LlamaIndex Blog โ”‚ 2024-01-23 โ”‚ The LlamaIndex blog offers updates on releases, guides, and โ”‚ -โ”‚ dex.ai/?gi=a117797fb โ”‚ โ”‚ โ”‚ community showcases. The recent posts include a newsletter โ”‚ -โ”‚ bc8 โ”‚ โ”‚ โ”‚ from January 23rd, news about building a secure multi- โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ tenancy RAG system, using LlamaIndex and GPT3.5 to build an โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ AI voice assistant, and launching a free course on advanced โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ RAG certification. Additionally, there are introductions to โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ new features like query pipelines and discussions on scaling โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ LlamaIndex with AWS and Hugging Face. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://levelup.gitc โ”‚ Live Indexing for โ”‚ 2024-01-08 โ”‚ The page discusses the challenges and solutions in โ”‚ -โ”‚ onnected.com/live- โ”‚ RAG: A Guide For โ”‚ โ”‚ processing and analyzing PDF documents using AI systems. The โ”‚ -โ”‚ indexing-for-rag-a- โ”‚ Real-Time Indexing โ”‚ โ”‚ author highlights the difficulty in extracting meaningful โ”‚ -โ”‚ guide-for-real-time- โ”‚ Using LlamaIndex and โ”‚ โ”‚ information from PDFs due to their complex structure and the โ”‚ -โ”‚ indexing-using- โ”‚ AWS โ”‚ โ”‚ presence of various elements like tables, figures, โ”‚ -โ”‚ llamaindex-and-aws-5 โ”‚ โ”‚ โ”‚ equations, and photos. The author also mentions the rise of โ”‚ -โ”‚ 1353083ace4?gi=472c9 โ”‚ โ”‚ โ”‚ Retrieval-Augmented Generation (RAG) frameworks and Large โ”‚ -โ”‚ 89ddb71&source=rss โ”‚ โ”‚ โ”‚ Language Models (LLMs) in 2022 and the evolution of the โ”‚ -โ”‚ ----5517fd7b58a6---4 โ”‚ โ”‚ โ”‚ ecosystem for creating full-stack applications. They โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ specifically highlight LlamaIndex as a prominent RAG โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ framework that simplifies the creation of chat applications โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ for interacting with PDFs. The page further mentions that โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ although creating a basic RAG application is relatively โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ simple, developing an enterprise-grade RAG application โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ requires addressing challenges related to live data โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ indexing, updates, real-time inference, and security. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://dev.to/lgram โ”‚ Create Your Own โ”‚ 2024-01-13 โ”‚ Sure, here's a brief summary of the webpage's content: โ”‚ -โ”‚ mel/create-your-own- โ”‚ Local Chatbot with โ”‚ โ”‚ **Title: Create Your Own Local Chatbot with Next.js, โ”‚ -โ”‚ local-chatbot-with- โ”‚ Next.js, Llama.cpp, โ”‚ โ”‚ Llama.cpp, and ModelFusion** - The blog post provides a โ”‚ -โ”‚ nextjs-llamacpp-and- โ”‚ and ModelFusion โ”‚ โ”‚ step-by-step guide to building a local chatbot using โ”‚ -โ”‚ modelfusion-461j โ”‚ โ”‚ โ”‚ Next.js, Llama.cpp, and ModelFusion. - Llama.cpp is an LLM โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ (large language model) inference engine that allows running โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ LLMs like OpenHermes 2.5 Mistral locally. - The Vercel AI โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ SDK is leveraged to manage stream forwarding and rendering, โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ while ModelFusion is utilized for integrating Llama.cpp with โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ the SDK. - Instructions are provided for setting up โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Llama.cpp, downloading the OpenHermes 2.5 Mistral model, and โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ starting the Llama.cpp server. - The creation of the โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Next.js project and installation of required libraries are โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ outlined. - Detailed steps for creating an API route for โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ the chatbot are explained. - The process of adding the chat โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ interface to the frontend and cleaning up global styles is โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ described. - The user can run the chatbot application โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ locally and interact with it via a user-friendly chat page. โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ - The code serves as a starting point for developing AI โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ projects using these tools. โ”‚ -โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค -โ”‚ https://blog.llamain โ”‚ A Cheat Sheet and โ”‚ 2024-01-05 โ”‚ This web page provides information on Retrieval-Augmented โ”‚ -โ”‚ dex.ai/a-cheat- โ”‚ Some Recipes For โ”‚ โ”‚ Generation (RAG) systems. RAG involves retrieving data from โ”‚ -โ”‚ sheet-and-some- โ”‚ Building Advanced โ”‚ โ”‚ an external knowledge database and sending it with a user โ”‚ -โ”‚ recipes-for- โ”‚ RAG โ”‚ โ”‚ query to an LLM for response generation. A basic RAG โ”‚ -โ”‚ building-advanced- โ”‚ โ”‚ โ”‚ involves retrieval, an external knowledge database, and a โ”‚ -โ”‚ rag-803a9d94c41b โ”‚ โ”‚ โ”‚ generation component. The success of a RAG system depends on โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ the retrieval and generation components meeting requirements โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ such as relevance and usefulness of answers. To achieve โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ these requirements, advanced techniques can be used in โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ Retrieval and Generation. Techniques for Retrieval include โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ chunk-size optimization and using structured external โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ knowledge, while techniques for Generation include LM โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ adapters, knowledge-aware training objectives, and answer โ”‚ -โ”‚ โ”‚ โ”‚ โ”‚ merging/reranking. โ”‚ -โ•˜โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•› - -