├── .gitignore ├── LICENSE ├── README.md ├── bad generation.png ├── embeddings └── reddit_embeddings.json ├── final.py ├── good generation.png ├── main.py ├── reddit_posts.json └── scraped data.png /.gitignore: -------------------------------------------------------------------------------- 1 | .env -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Yash Kamble 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ### **README: Reddit Scraper + Llama3 Integration** 2 | 3 | --- 4 | 5 | ## **Overview** 6 | 7 | This project consists of two Python scripts designed to work together to create a pipeline for scraping Reddit posts, saving them as structured JSON data, and leveraging embeddings with a local **Llama3** model to provide intelligent answers based on the data. 8 | 9 | The system allows users to query the Llama3 model about the scraped Reddit data. By embedding the posts into vector representations, the project ensures that the model generates contextually relevant responses. This approach is particularly useful for summarizing large datasets or answering questions based on specific data sources. 10 | 11 | --- 12 | 13 | ## **Project Structure** 14 | 15 | ### **1. Reddit Scraper Script (`main.py`)** 16 | This script retrieves posts from Reddit based on a specific search term or subreddit, processes the data, and saves it as a structured JSON file. 17 | 18 | - **Key Features**: 19 | - Scrapes a specified number of Reddit posts related to a search term. 20 | - Collects post details like title, body, images, and links. 21 | - Outputs the data in a clean JSON format for further processing. 22 | 23 | - **Output**: A JSON file containing Reddit post data. 24 | 25 | --- 26 | 27 | ### **2. Embeddings + Llama3 Script (`final.py`)** 28 | This script takes the JSON data from the Reddit scraper, converts it into embeddings, and uses a local **Llama3** model to answer user queries based on the data. 29 | 30 | - **Key Features**: 31 | - Parses the JSON file into paragraphs or chunks. 32 | - Generates vector embeddings for both the data and the user query using a model like `nomic-embed-text`. 33 | - Matches the user query with the most relevant chunks of Reddit data based on cosine similarity. 34 | - Provides answers through the Llama3 model using the relevant context. 35 | 36 | - **Output**: Context-aware responses to user queries based on the Reddit data. 37 | 38 | --- 39 | 40 | ## **Workflow** 41 | 42 | ### **Step 1: Scrape Reddit Data** 43 | 1. Run `main.py` to fetch posts based on a given search term or subreddit. 44 | 2. Specify the number of posts to retrieve and the output file name. 45 | 3. The script generates a JSON file with the retrieved posts. 46 | 47 | ### **Step 2: Query Llama3** 48 | 1. Run `final.py` to load the generated JSON file. 49 | 2. The script embeds the Reddit data and allows the user to input a query. 50 | 3. It matches the query with the most relevant chunks from the embeddings and uses Llama3 to generate a response. 51 | 52 | --- 53 | 54 | ## **Example Use Case** 55 | 56 | 1. **Scraping Reddit**: 57 | - Search for posts about *"latest SaaS tools"* on Reddit. 58 | - Save the results in `reddit_posts.json`. 59 | 60 | 2. **Querying Llama3**: 61 | - Ask the system: *"What are the top tools mentioned in the posts?"* 62 | - The system provides an intelligent answer derived from the scraped Reddit data. 63 | 64 | --- 65 | 66 | ## **Key Dependencies** 67 | 1. **For Reddit Scraper**: 68 | - `praw` for accessing Reddit data. 69 | - `requests` for downloading images or additional links (if required). 70 | 71 | 2. **For Embeddings + Llama3**: 72 | - `ollama` for embedding generation and Llama3 interaction. 73 | - `numpy` for cosine similarity calculations. 74 | - `json` for parsing and handling the scraped data. 75 | 76 | --- 77 | 78 | ## **Features** 79 | 80 | ### **1. Reddit Scraper** 81 | - Supports fetching multiple posts from any subreddit or search query. 82 | - Captures the title, body, image links, and external URLs from Reddit posts. 83 | 84 | ### **2. Llama3 Query System** 85 | - Converts scraped data into high-dimensional vector embeddings for efficient similarity matching. 86 | - Leverages the Llama3 model to generate context-aware answers to user queries. 87 | 88 | ### **3. Modular and Extensible** 89 | - The embedding and Llama3 system is independent of Reddit data, allowing other sources to be added seamlessly. 90 | - API keys and configurations are managed securely using environment variables. 91 | 92 | --- 93 | 94 | ## **Future Improvements** 95 | 1. **Expand Data Sources**: 96 | - Extend the scraper to include other platforms like Twitter, YouTube, or news APIs. 97 | 2. **Enhanced Query Handling**: 98 | - Add multi-turn conversations and follow-up question support for Llama3. 99 | 3. **Improved Embeddings**: 100 | - Experiment with more advanced embedding models for better similarity matching. 101 | 102 | --- 103 | 104 | ## **Credits** 105 | - **PRAW**: For simplifying Reddit data retrieval. 106 | - **Ollama**: For enabling local Llama3 model interaction. 107 | - **Numpy**: For vector math and similarity computations. 108 | 109 | --- 110 | ## **Output** 111 |

112 | Reddit Scraper Output 113 |
114 | Example of data scraped from Reddit posts. 115 |

116 | 117 |

118 | Reddit Scraper Output 119 |
120 | Bad output generated from context. 121 |

122 | 123 |

124 | Reddit Scraper Output 125 |
126 | Good output generated from context + better prompt. 127 |

128 | 129 | Feel free to customize this pipeline for your specific use case or extend it to include more advanced features! 130 | -------------------------------------------------------------------------------- /bad generation.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hackice20/scraper-LLM/649ae2eda08a4f49785239f7358f6cc75bd20609/bad generation.png -------------------------------------------------------------------------------- /embeddings/reddit_embeddings.json: -------------------------------------------------------------------------------- 1 | [[-0.29183489084243774, 0.5945023894309998, -1.8957642316818237, 0.45615723729133606, 0.5593013763427734, -0.4971383512020111, 1.0463225841522217, -0.46985453367233276, 0.16539502143859863, -1.6461457014083862, -0.2600412666797638, -0.5511511564254761, 0.4908452033996582, 1.3574892282485962, 0.5590411424636841, 0.12548235058784485, 0.18307088315486908, 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import time 3 | import os 4 | import json 5 | import numpy as np # type: ignore 6 | from numpy.linalg import norm # type: ignore 7 | 8 | # Load the Reddit JSON file 9 | def load_reddit_json(filename="reddit_posts.json"): 10 | with open(filename, "r") as f: 11 | return json.load(f) 12 | 13 | # Save embeddings to a file 14 | def save_embeddings(filename, embeddings): 15 | if not os.path.exists("embeddings"): 16 | os.makedirs("embeddings") 17 | with open(f"embeddings/{filename}.json", "w") as f: 18 | json.dump(embeddings, f) 19 | 20 | # Load embeddings from a file 21 | def load_embeddings(filename): 22 | if not os.path.exists(f"embeddings/{filename}.json"): 23 | return None 24 | with open(f"embeddings/{filename}.json", "r") as f: 25 | return json.load(f) 26 | 27 | # Generate embeddings using Ollama's API 28 | def get_embeddings(posts, filename, modelname="nomic-embed-text"): 29 | embeddings = load_embeddings(filename) 30 | if embeddings: 31 | return embeddings 32 | 33 | # Generate embeddings for all posts 34 | embeddings = [] 35 | for post in posts: 36 | content = f"{post['title']} {post['selftext']}" 37 | embedding = ollama.embeddings(model=modelname, prompt=content)["embedding"] 38 | embeddings.append(embedding) 39 | time.sleep(0.5) # Avoid rate-limiting issues 40 | save_embeddings(filename, embeddings) 41 | return embeddings 42 | 43 | # Find the most similar chunks using cosine similarity 44 | def find_most_similar(needle, haystack): 45 | needle_norm = norm(needle) 46 | similarity_scores = [ 47 | np.dot(needle, item) / (needle_norm * norm(item)) for item in haystack 48 | ] 49 | return sorted(zip(similarity_scores, range(len(haystack))), reverse=True) 50 | 51 | # Main function 52 | def main(): 53 | SYSTEM_PROMPT = """You are a helpful assistant that would think less and genrate quick responses related to the prompts""" 54 | reddit_json_file = "reddit_posts.json" 55 | embeddings_file = "reddit_embeddings" 56 | 57 | # Load Reddit posts 58 | posts = load_reddit_json(reddit_json_file) 59 | print(f"Loaded {len(posts)} posts from {reddit_json_file}") 60 | 61 | # Generate or load embeddings 62 | embeddings = get_embeddings(posts, embeddings_file) 63 | 64 | while True: 65 | # User input 66 | prompt = input("\nWhat do you want to know? (Type 'exit' to quit) -> ") 67 | if prompt.lower() == "exit": 68 | break 69 | 70 | # Generate embedding for the query prompt 71 | prompt_embedding = ollama.embeddings( 72 | model="nomic-embed-text", prompt=prompt 73 | )["embedding"] 74 | 75 | # Find the most similar Reddit posts 76 | most_similar_chunks = find_most_similar(prompt_embedding, embeddings)[:5] 77 | 78 | # Prepare context from the most similar chunks 79 | context = "\n".join( 80 | f"Title: {posts[item[1]]['title']}\n{posts[item[1]]['selftext']}\n" 81 | for item in most_similar_chunks 82 | ) 83 | 84 | # Query Llama3 with the context 85 | response = ollama.chat( 86 | model="llama3", 87 | messages=[ 88 | {"role": "system", "content": SYSTEM_PROMPT + "\n\n" + context}, 89 | {"role": "user", "content": prompt}, 90 | ], 91 | ) 92 | 93 | print("\n--- Llama3 Response ---") 94 | print(response["message"]["content"]) 95 | print("\n") 96 | 97 | if __name__ == "__main__": 98 | main() 99 | -------------------------------------------------------------------------------- /good generation.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hackice20/scraper-LLM/649ae2eda08a4f49785239f7358f6cc75bd20609/good generation.png -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | import praw # type: ignore 2 | import os 3 | import json 4 | from dotenv import load_dotenv # type: ignore 5 | 6 | # Load environment variables from .env file 7 | load_dotenv() 8 | 9 | # Reddit API credentials 10 | reddit = praw.Reddit( 11 | client_id=os.getenv("CLIENT_ID"), 12 | client_secret=os.getenv("CLIENT_SECRET"), 13 | user_agent=os.getenv("USER_AGENT") 14 | ) 15 | 16 | # Function to fetch subreddit posts 17 | def fetch_subreddit_posts(subreddit_name, query, limit=30): 18 | """ 19 | Fetches posts from a subreddit related to a specific query. 20 | """ 21 | try: 22 | subreddit = reddit.subreddit(subreddit_name) 23 | search_results = subreddit.search(query, limit=limit) 24 | 25 | results = [] 26 | for post in search_results: 27 | image_url = post.url if post.url.endswith((".jpg", ".jpeg", ".png", ".gif")) else None 28 | external_link = post.url if not image_url else None 29 | 30 | results.append({ 31 | "title": post.title, 32 | "selftext": post.selftext, 33 | "image_url": image_url, 34 | "external_link": external_link, 35 | }) 36 | return results 37 | except Exception as e: 38 | print(f"Error: {e}") 39 | return [] 40 | 41 | # Save results to a JSON file 42 | def save_to_json(data, filename="reddit_posts.json"): 43 | """ 44 | Saves the data to a JSON file. 45 | """ 46 | try: 47 | with open(filename, 'w') as json_file: 48 | json.dump(data, json_file, indent=4) 49 | print(f"Data saved to {filename}") 50 | except Exception as e: 51 | print(f"Error saving data to JSON: {e}") 52 | 53 | # Example usage 54 | if __name__ == "__main__": 55 | subreddit_name = input("Enter subreddit name (e.g., 'learnpython', 'AskReddit'): ") 56 | query = input("Enter the search query: ") 57 | posts = fetch_subreddit_posts(subreddit_name=subreddit_name, query=query, limit=10) 58 | 59 | print(f"\nFetched {len(posts)} posts related to '{query}' from r/{subreddit_name}:\n") 60 | 61 | # Save posts to a JSON file without Llama3 analysis 62 | save_to_json(posts) 63 | -------------------------------------------------------------------------------- /reddit_posts.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "title": "This is how I find my startup ideas. How do you find yours?", 4 | "selftext": "Hey everyone,\n\nI often see questions around here about how to come up with startup ideas or SAAS concepts. I think it's one that a lot of people struggle with so just wanted to see if anyone is doing anything out of the ordinary to get their creative juices flowing. Usually my startup ideas come when I least expect it, as in when I'm not actually trying to think of an idea but I've found a few ways that help me at least get the balls in motion for coming up with ideas and they generally plant seeds for me to come up with ideas at a later stage. This worked for my [startup idea](https://www.findmicrosaasideas.com/micro-saas-ideas-collection) so hopefully it can help some people out.\n\n**Trustpilot**\n\n1. Go to Trustpilot\n2. Select the business category that your interested in\n3. Check out the top companies\n4. Filter by 2 & 3 star reviews (1 star reviews aren't the best for this because it's just people hating on the product)\n5. See if you could solve any of the frustrations of the customers and you might just stumble upon a new startup idea\n\n[**Acquire.com**](http://acquire.com)\n\n1. Go to (you'll need to sign up)\n2. Filter companies that are doing over 100K a year (you can change this number but filtering over this number can get you some really good ideas)\n3. Look for product ideas that resonates with you\n4. Are there any features that are missing from the product? If so you could be onto something.\n\n**Ahref keyword generator**\n\n1. Go to [https://ahrefs.com/keyword-generator](https://ahrefs.com/keyword-generator)\n2. Search for random stuff to find keywords with a high volume (number of people searching) with a low KD (Keyword difficulty - how difficult it would be to rank in the top 10 on google. 20 and below would be what we're looking for here)\n3. Based on what you find do a search on Google and see if there is any tool on top of Google.\n4. If not then you've found an idea that might be worth going with. If there already is a tool maybe you could build a better one?\n\nThese are my go tos on how to get the creative juices flowing and they tend to help me when I'm drawing blanks on coming up with ideas.\n\nThen when I have an idea for a product I ask myself the following questions to make sure it's something I want to pursue:\n\n>Would I use the product\n\n>Can I find the audience for this product?\n\n>Can I win their attention?\n\n>Can I build it in 1 month max?", 5 | "image_url": null, 6 | "external_link": "https://www.reddit.com/r/SaaS/comments/1asdjly/this_is_how_i_find_my_startup_ideas_how_do_you/" 7 | }, 8 | { 9 | "title": "Weekly Feedback Post - SaaS Products, Ideas, Companies", 10 | "selftext": "This is a weekly post where you're free to post your SaaS ideas, products, companies etc. that need feedback. Here, people who are willing to share feedback are going to join conversations. Posts asking for feedback outside this weekly one will be removed!\n\n[\ud83c\udf99\ufe0f P.S: Check out The Usual SaaSpects, this subreddit's podcast!](https://anchor.fm/theusualsaaspects)", 11 | "image_url": null, 12 | "external_link": "https://www.reddit.com/r/SaaS/comments/1gqbaeq/weekly_feedback_post_saas_products_ideas_companies/" 13 | }, 14 | { 15 | "title": "Weekly Feedback Post - SaaS Products, Ideas, Companies", 16 | "selftext": "This is a weekly post where you're free to post your SaaS ideas, products, companies etc. that need feedback. Here, people who are willing to share feedback are going to join conversations. Posts asking for feedback outside this weekly one will be removed!\n\n[\ud83c\udf99\ufe0f P.S: Check out The Usual SaaSpects, this subreddit's podcast!](https://anchor.fm/theusualsaaspects)", 17 | "image_url": null, 18 | "external_link": "https://www.reddit.com/r/SaaS/comments/1dovtgt/weekly_feedback_post_saas_products_ideas_companies/" 19 | }, 20 | { 21 | "title": "Weekly Feedback Post - SaaS Products, Ideas, Companies", 22 | "selftext": "This is a weekly post where you're free to post your SaaS ideas, products, companies etc. that need feedback. Here, people who are willing to share feedback are going to join conversations. Posts asking for feedback outside this weekly one will be removed!\n\n[\ud83c\udf99\ufe0f P.S: Check out The Usual SaaSpects, this subreddit's podcast!](https://anchor.fm/theusualsaaspects)", 23 | "image_url": null, 24 | "external_link": "https://www.reddit.com/r/SaaS/comments/1gfktmq/weekly_feedback_post_saas_products_ideas_companies/" 25 | }, 26 | { 27 | "title": "Weekly Feedback Post - SaaS Products, Ideas, Companies", 28 | "selftext": "This is a weekly post where you're free to post your SaaS ideas, products, companies etc. that need feedback. Here, people who are willing to share feedback are going to join conversations. Posts asking for feedback outside this weekly one will be removed!\n\n[\ud83c\udf99\ufe0f P.S: Check out The Usual SaaSpects, this subreddit's podcast!](https://anchor.fm/theusualsaaspects)", 29 | "image_url": null, 30 | "external_link": "https://www.reddit.com/r/SaaS/comments/1afg94w/weekly_feedback_post_saas_products_ideas_companies/" 31 | }, 32 | { 33 | "title": "Weekly Feedback Post - SaaS Products, Ideas, Companies", 34 | "selftext": "This is a weekly post where you're free to post your SaaS ideas, products, companies etc. that need feedback. Here, people who are willing to share feedback are going to join conversations. Posts asking for feedback outside this weekly one will be removed!\n\n[\ud83c\udf99\ufe0f P.S: Check out The Usual SaaSpects, this subreddit's podcast!](https://anchor.fm/theusualsaaspects)", 35 | "image_url": null, 36 | "external_link": "https://www.reddit.com/r/SaaS/comments/wuvxe9/weekly_feedback_post_saas_products_ideas_companies/" 37 | }, 38 | { 39 | "title": "Weekly Feedback Post - SaaS Products, Ideas, Companies", 40 | "selftext": "This is a weekly post where you're free to post your SaaS ideas, products, companies etc. that need feedback. Here, people who are willing to share feedback are going to join conversations. Posts asking for feedback outside this weekly one will be removed!\n\n[\ud83c\udf99\ufe0f P.S: Check out The Usual SaaSpects, this subreddit's podcast!](https://anchor.fm/theusualsaaspects)", 41 | "image_url": null, 42 | "external_link": "https://www.reddit.com/r/SaaS/comments/1eaylwm/weekly_feedback_post_saas_products_ideas_companies/" 43 | }, 44 | { 45 | "title": "Weekly Feedback Post - SaaS Products, Ideas, Companies", 46 | "selftext": "This is a weekly post where you're free to post your SaaS ideas, products, companies etc. that need feedback. Here, people who are willing to share feedback are going to join conversations. Posts asking for feedback outside this weekly one will be removed!\n\n[\ud83c\udf99\ufe0f P.S: Check out The Usual SaaSpects, this subreddit's podcast!](https://anchor.fm/theusualsaaspects)", 47 | "image_url": null, 48 | "external_link": "https://www.reddit.com/r/SaaS/comments/1fe7bta/weekly_feedback_post_saas_products_ideas_companies/" 49 | }, 50 | { 51 | "title": "Weekly Feedback Post - SaaS Products, Ideas, Companies", 52 | "selftext": "This is a weekly post where you're free to post your SaaS ideas, products, companies etc. that need feedback. Here, people who are willing to share feedback are going to join conversations. Posts asking for feedback outside this weekly one will be removed!\n\n[\ud83c\udf99\ufe0f P.S: Check out The Usual SaaSpects, this subreddit's podcast!](https://anchor.fm/theusualsaaspects)", 53 | "image_url": null, 54 | "external_link": "https://www.reddit.com/r/SaaS/comments/1gvnv55/weekly_feedback_post_saas_products_ideas_companies/" 55 | }, 56 | { 57 | "title": "Weekly Feedback Post - SaaS Products, Ideas, Companies", 58 | "selftext": "This is a weekly post where you're free to post your SaaS ideas, products, companies etc. that need feedback. Here, people who are willing to share feedback are going to join conversations. Posts asking for feedback outside this weekly one will be removed!\n\n[\ud83c\udf99\ufe0f P.S: Check out The Usual SaaSpects, this subreddit's podcast!](https://anchor.fm/theusualsaaspects)", 59 | "image_url": null, 60 | "external_link": "https://www.reddit.com/r/SaaS/comments/17g24fb/weekly_feedback_post_saas_products_ideas_companies/" 61 | } 62 | ] -------------------------------------------------------------------------------- /scraped data.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hackice20/scraper-LLM/649ae2eda08a4f49785239f7358f6cc75bd20609/scraped data.png --------------------------------------------------------------------------------