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Automate Your Accounting: PDF Invoice to JSON with LangChain #python #coding #programming #ai Stop wasting time and money on expensive OCR software by building your own private invoice extraction tool with Python, LangChain, and Gemini 2.0 Flash. This Short shows you how to use PyPDF for text extraction and...

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Coding Together

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38,600 görüntüleme • 7 ay önce •via TikTok

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LangChain RAG App in Python – Chat with any PDF | Tutorial #python #coding #programming #ai #langchain Never upload sensitive documents to an AI chat tool. Instead, build your own PDF analyzer with Python, LangChain, and the Gemini API. In this short video we cover text extraction, vector embeddings, FAISS vectorization, and a full RAG pipeline. All in under 65 lines of code. Your data stays local, and the total API cost? About one cent per Document. Drop your questions below.
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LangChain RAG App in Python – Chat with any PDF | Tutorial #python #coding #programming #ai #langchain Never upload sensitive documents to an AI chat tool. Instead, build your own PDF analyzer with Python, LangChain, and the Gemini API. In this short video we cover text extraction, vector embeddings, FAISS vectorization, and a full RAG pipeline. All in under 65 lines of code. Your data stays local, and the total API cost? About one cent per Document. Drop your questions below.

Coding Together

14,300 görüntüleme • 7 ay önce

How to Auto-Extract PDF Invoices Into Excel With Python and Gemini AI #python #coding #programming Here is how to build a PDF Invoice to Excel automation in Python with around 80 lines of code using the Gemini API for free. In this video you will learn how to extract structured data from PDF invoices using a JSON schema, loop through a whole folder of files with glob, and append every result into one growing Excel spreadsheet using pandas. Let me know if you want the source code. I could share it via GitHub.
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How to Auto-Extract PDF Invoices Into Excel With Python and Gemini AI #python #coding #programming Here is how to build a PDF Invoice to Excel automation in Python with around 80 lines of code using the Gemini API for free. In this video you will learn how to extract structured data from PDF invoices using a JSON schema, loop through a whole folder of files with glob, and append every result into one growing Excel spreadsheet using pandas. Let me know if you want the source code. I could share it via GitHub.

Coding Together

25,200 görüntüleme • 4 ay önce

Transforming Invoice Data into JSON: Local LLM with LlamaIndex & Pydantic 🚀 Complete video: Code: I explain how to get structured JSON output with LlamaIndex and dynamic Pydantic class. This helps to implement the use case of data extraction from invoice documents. The solution runs on the local machine, thanks to Ollama. I'm using a MacBook Air M1 with 8GB RAM. LlamaIndex 🦙 Pydantic ollama #Python #LLM #RAG
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Transforming Invoice Data into JSON: Local LLM with LlamaIndex & Pydantic 🚀 Complete video: Code: I explain how to get structured JSON output with LlamaIndex and dynamic Pydantic class. This helps to implement the use case of data extraction from invoice documents. The solution runs on the local machine, thanks to Ollama. I'm using a MacBook Air M1 with 8GB RAM. LlamaIndex 🦙 Pydantic ollama #Python #LLM #RAG

Andrej Baranovskij

147,999 görüntüleme • 2 yıl önce

Agentic Document Extraction now supports field extraction! Many doc extraction use cases extract specific fields from forms and other structured documents. You can now input a picture or PDF of an invoice, request the vendor name, item list, and prices, and get back the extracted fields. Or input a medical form and specify a schema to extract patient name, patient ID, insurance number, etc. One cool feature: If you don't feel like writing a schema (json specification of what fields to extract) yourself, upload one sample document and write a natural language prompt saying what you want, and we automatically generate a schema for you. See the video for details!
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Agentic Document Extraction now supports field extraction! Many doc extraction use cases extract specific fields from forms and other structured documents. You can now input a picture or PDF of an invoice, request the vendor name, item list, and prices, and get back the extracted fields. Or input a medical form and specify a schema to extract patient name, patient ID, insurance number, etc. One cool feature: If you don't feel like writing a schema (json specification of what fields to extract) yourself, upload one sample document and write a natural language prompt saying what you want, and we automatically generate a schema for you. See the video for details!

Andrew Ng

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The incredible thing about AI is that it lets you transform unstructured data into anything you want. Here's Box AI using Claude 3.7 to turn an invoice into JSON. When you can transform anything, you can automate everything.
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The incredible thing about AI is that it lets you transform unstructured data into anything you want. Here's Box AI using Claude 3.7 to turn an invoice into JSON. When you can transform anything, you can automate everything.

Aaron Levie

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New Short Course: Getting Structured LLM Output! Learn how to get structured outputs from your LLM applications in this course, built in partnership with .txt, and taught by Will Kurt, a Founding Engineer, and , Developer Relations Engineer. It's challenging for software to automatically parse through an LLM's freeform text outputs. Structured outputs—like JSON—solve this by converting natural language into consistent, clear, data that a machine can read and process. This course teaches you how to generate structured outputs while building several use cases, including a social media analysis agent. You’ll learn about structured outputs and efficient ways to generate outputs in your defined schema or format. You’ll begin by using structured output APIs, then use re-prompting libraries like “instructor” to generate structured output. Finally, you’ll learn how constrained decoding works; this is a very clever technique in which constraints are applied on each subsequent token generated, blocking any tokens that don’t fit your defined schema. In detail, you’ll: - Learn why structured outputs are important, how they allow for scalable software development, and the different approaches to generate them, including vendor-provided APIs, re-prompting libraries, and structured generation. - Build a simple social media agent using OpenAI’s structured output API, learn how to define a model's desired structured output using Pydantic, and perform basic programming with your outputs, such as importing structured data into a data frame using pandas. - Learn how to use the open-source library "instructor," which checks the structured output of the model and re-prompts the model until it validates the desired output, and explore the limitations of this approach. - Understand how structured generation by the “outlines” library works by modifying LLM logits, on a per-generated-token basis based on the desired format, to give a particular output structure. - Learn how regular expressions, which outlines works with, are represented as finite-state machines, and how they can be used to develop a range of structured outputs beyond JSON. By the end of this course, you’ll have broadened your knowledge of the approaches you can use to get structured outputs from your LLM applications. Please sign up here:
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New Short Course: Getting Structured LLM Output! Learn how to get structured outputs from your LLM applications in this course, built in partnership with .txt, and taught by Will Kurt, a Founding Engineer, and , Developer Relations Engineer. It's challenging for software to automatically parse through an LLM's freeform text outputs. Structured outputs—like JSON—solve this by converting natural language into consistent, clear, data that a machine can read and process. This course teaches you how to generate structured outputs while building several use cases, including a social media analysis agent. You’ll learn about structured outputs and efficient ways to generate outputs in your defined schema or format. You’ll begin by using structured output APIs, then use re-prompting libraries like “instructor” to generate structured output. Finally, you’ll learn how constrained decoding works; this is a very clever technique in which constraints are applied on each subsequent token generated, blocking any tokens that don’t fit your defined schema. In detail, you’ll: - Learn why structured outputs are important, how they allow for scalable software development, and the different approaches to generate them, including vendor-provided APIs, re-prompting libraries, and structured generation. - Build a simple social media agent using OpenAI’s structured output API, learn how to define a model's desired structured output using Pydantic, and perform basic programming with your outputs, such as importing structured data into a data frame using pandas. - Learn how to use the open-source library "instructor," which checks the structured output of the model and re-prompts the model until it validates the desired output, and explore the limitations of this approach. - Understand how structured generation by the “outlines” library works by modifying LLM logits, on a per-generated-token basis based on the desired format, to give a particular output structure. - Learn how regular expressions, which outlines works with, are represented as finite-state machines, and how they can be used to develop a range of structured outputs beyond JSON. By the end of this course, you’ll have broadened your knowledge of the approaches you can use to get structured outputs from your LLM applications. Please sign up here:

Andrew Ng

89,792 görüntüleme • 1 yıl önce

everyone is sleeping on this new JSON extraction model! - open-weights - matches Gemini Flash 3.5 - while being 3x faster pulling structured data out of documents usually takes a chain of OCR, an LLM, and extra code to repair broken JSON. lift, from Datalab, does all of this in a single step. you give lift a PDF or image along with a JSON schema, and get back a JSON object that matches that schema. the model reads every page of a document in one pass, so values that span multiple pages still come out right. any document you can describe in a schema works, from invoices to research papers. here's what you get with lift: - 90.2% field accuracy on 11,000 fields - 9B model, nearly matches Gemini Flash 3.5 at 3x the speed - built not to hallucinate; returns null if missing - self-hostable with vLLM or Hugging Face GitHub repo: (don't forget to star 🌟)
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everyone is sleeping on this new JSON extraction model! - open-weights - matches Gemini Flash 3.5 - while being 3x faster pulling structured data out of documents usually takes a chain of OCR, an LLM, and extra code to repair broken JSON. lift, from Datalab, does all of this in a single step. you give lift a PDF or image along with a JSON schema, and get back a JSON object that matches that schema. the model reads every page of a document in one pass, so values that span multiple pages still come out right. any document you can describe in a schema works, from invoices to research papers. here's what you get with lift: - 90.2% field accuracy on 11,000 fields - 9B model, nearly matches Gemini Flash 3.5 at 3x the speed - built not to hallucinate; returns null if missing - self-hostable with vLLM or Hugging Face GitHub repo: (don't forget to star 🌟)

Akshay 🚀

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You can now clone any website just by writing a prompt. Simply add the Firecrawl MCP server to your favorite AI coding tool for improved web data extraction, and let Claude code it for you. Here’s how:
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You can now clone any website just by writing a prompt. Simply add the Firecrawl MCP server to your favorite AI coding tool for improved web data extraction, and let Claude code it for you. Here’s how:

Alvaro Cintas

83,031 görüntüleme • 1 yıl önce

This Python script helps you better understand how embeddings work for SEO. Input a sentence + a query and BERT will calculate a content "Similarity Score": Search engines use embedding models to translate your content into numeric values. This is how they're able to mathematically determine whether a page on your site is actually relevant to a query someone is searching for. Once both the content and query are run through embeddings - they can calculate the "cosine similarity" or how similar the two entities are to each one. With this Python script, you can actually visualize how cosine similarity works for yourself. To you use you'll simply: 1. Save the Python script to a text file 2. Use your Terminal to run the script 3. You'll be prompted for both a "Sentence" and "Query" 4. Once entered, the script will calculate a "Similarity Score" between the text and query. This is how relevant your sentence is for the target keyword. By running this, you'll see that search engines are able to come with an calculation of content relevance. You'll need to think about the content on your site the same way - which sections have high scores and which ones have low ones? I've linked the Python script in the comments below. No coding knowledge is required and it walks you step by step on how to implement it.
2:37

This Python script helps you better understand how embeddings work for SEO. Input a sentence + a query and BERT will calculate a content "Similarity Score": Search engines use embedding models to translate your content into numeric values. This is how they're able to mathematically determine whether a page on your site is actually relevant to a query someone is searching for. Once both the content and query are run through embeddings - they can calculate the "cosine similarity" or how similar the two entities are to each one. With this Python script, you can actually visualize how cosine similarity works for yourself. To you use you'll simply: 1. Save the Python script to a text file 2. Use your Terminal to run the script 3. You'll be prompted for both a "Sentence" and "Query" 4. Once entered, the script will calculate a "Similarity Score" between the text and query. This is how relevant your sentence is for the target keyword. By running this, you'll see that search engines are able to come with an calculation of content relevance. You'll need to think about the content on your site the same way - which sections have high scores and which ones have low ones? I've linked the Python script in the comments below. No coding knowledge is required and it walks you step by step on how to implement it.

Chris Long

13,210 görüntüleme • 2 yıl önce

You can now clone any website just by writing a prompt. Simply add the new Firecrawl MCP server to your favorite AI coding tool for improved web data extraction, and let Claude code it for you. Here’s how:
0:24

You can now clone any website just by writing a prompt. Simply add the new Firecrawl MCP server to your favorite AI coding tool for improved web data extraction, and let Claude code it for you. Here’s how:

Alvaro Cintas

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🔗 New LangChain Academy Course: Introduction to LangChain (Python) 🔗 Learn how to build with LangChain – our open source framework that makes it easy to start building agents with any model provider. In this course, you’ll create agents that can reason, use tools, and take action, and learn how to debug their behavior with LangSmith. Along the way, you’ll: - Build an agent with the `create_agent` abstraction - Use LangChain’s core building blocks: Models, Messages, Memory, and Tools - Customize your agent with middleware - Debug your agent with LangSmith Observability & Studio By the end of the course, you’ll have assembled a full team of personal assistants. Enroll for free ➡️
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🔗 New LangChain Academy Course: Introduction to LangChain (Python) 🔗 Learn how to build with LangChain – our open source framework that makes it easy to start building agents with any model provider. In this course, you’ll create agents that can reason, use tools, and take action, and learn how to debug their behavior with LangSmith. Along the way, you’ll: - Build an agent with the `create_agent` abstraction - Use LangChain’s core building blocks: Models, Messages, Memory, and Tools - Customize your agent with middleware - Debug your agent with LangSmith Observability & Studio By the end of the course, you’ll have assembled a full team of personal assistants. Enroll for free ➡️

LangChain

41,241 görüntüleme • 10 ay önce

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!
3:46

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Andrew Ng

244,628 görüntüleme • 2 yıl önce

JSON is just "Jason" — and he runs every automation you build* JSON is pronounced "Jason" — like the actor — and it's the single most useful thing to understand before you get into automation. Here's the plain-English version. Every time one app sends data to another — your signup form to your email tool, your store to your spreadsheet, your AI to your other tools — it doesn't send sentences. It sends JSON: a little labeled packet of information. It's the one language every app uses to talk to every other app. And it's simpler than it looks: 1. JSON is just labeled data inside curly braces { }. 2. Each piece is a label and a value — like "first_name": "Kim", "last_name": "Kardashian", "order": 3. 3. One app fills in the labels and ships it over; the other app reads the exact same labels and grabs the values — so two programs built by total strangers understand each other perfectly. 4. The moment you touch automation (Zapier, Make, n8n) and pull data from one app into another, it arrives as JSON — your contacts, orders, and leads, all as labeled packets. 5. Find the label you want, grab its value, and you can wire almost any app into any other app. The best part: you don't have to write or decode it by hand anymore. Paste any JSON into AI and it tells you exactly what's inside in plain English. Describe what you want and it writes the exact structure your tool needs. The scary syntax is the machine's job now — knowing what JSON is and what it's for is yours. You don't need to become a programmer. You need to know it's called JSON, know your automations run on it, and let AI handle the syntax. #aiautomation #automation #aiagents #aiskills
2:31

JSON is just "Jason" — and he runs every automation you build* JSON is pronounced "Jason" — like the actor — and it's the single most useful thing to understand before you get into automation. Here's the plain-English version. Every time one app sends data to another — your signup form to your email tool, your store to your spreadsheet, your AI to your other tools — it doesn't send sentences. It sends JSON: a little labeled packet of information. It's the one language every app uses to talk to every other app. And it's simpler than it looks: 1. JSON is just labeled data inside curly braces { }. 2. Each piece is a label and a value — like "first_name": "Kim", "last_name": "Kardashian", "order": 3. 3. One app fills in the labels and ships it over; the other app reads the exact same labels and grabs the values — so two programs built by total strangers understand each other perfectly. 4. The moment you touch automation (Zapier, Make, n8n) and pull data from one app into another, it arrives as JSON — your contacts, orders, and leads, all as labeled packets. 5. Find the label you want, grab its value, and you can wire almost any app into any other app. The best part: you don't have to write or decode it by hand anymore. Paste any JSON into AI and it tells you exactly what's inside in plain English. Describe what you want and it writes the exact structure your tool needs. The scary syntax is the machine's job now — knowing what JSON is and what it's for is yours. You don't need to become a programmer. You need to know it's called JSON, know your automations run on it, and let AI handle the syntax. #aiautomation #automation #aiagents #aiskills

Professor Glitch

314,700 görüntüleme • 3 ay önce

See Google’s Gemini 2.5 Pro in action with the Box AI API! We put it to the test on complex invoice calculations and analyzing 10-K financial documents. Leverage Gemini 2.5 Pro via the Box AI API for advanced document processing, data extraction, and Q&A in your applications.
4:01

See Google’s Gemini 2.5 Pro in action with the Box AI API! We put it to the test on complex invoice calculations and analyzing 10-K financial documents. Leverage Gemini 2.5 Pro via the Box AI API for advanced document processing, data extraction, and Q&A in your applications.

Box

489,097 görüntüleme • 1 yıl önce

🔥 New LangChain Academy Course: LangChain Essentials (Python & TypeScript) 🔥 Learn the basics of LangChain – our open source framework that makes it easy to start building agents with any model provider. Last week, we released LangChain 1.0. We’ve completely rewritten LangChain to be opinionated, focused, and powered by LangGraph’s runtime. It includes a new `create_agent` abstraction to build agents quickly, middleware for flexibility, and standard content blocks that work across any model provider. In this quickstart course, you'll learn how to: - Build an agent with the `create_agent` abstraction - Use LangChain’s core building blocks: Models, Messages, Memory, and Tools - Customize your agent with middleware - Debug and test your agent with LangSmith Observability & Evaluation Enroll for free ➡️
1:37

🔥 New LangChain Academy Course: LangChain Essentials (Python & TypeScript) 🔥 Learn the basics of LangChain – our open source framework that makes it easy to start building agents with any model provider. Last week, we released LangChain 1.0. We’ve completely rewritten LangChain to be opinionated, focused, and powered by LangGraph’s runtime. It includes a new `create_agent` abstraction to build agents quickly, middleware for flexibility, and standard content blocks that work across any model provider. In this quickstart course, you'll learn how to: - Build an agent with the `create_agent` abstraction - Use LangChain’s core building blocks: Models, Messages, Memory, and Tools - Customize your agent with middleware - Debug and test your agent with LangSmith Observability & Evaluation Enroll for free ➡️

LangChain

50,518 görüntüleme • 1 yıl önce

Looking for a way to automate note-taking for your Zoom calls? I built a Zoom Meeting Summarizer using open source models and software. 📁 File Handling: Reads transcript files in WebVTT format 📋 Meeting Notes Extraction: Utilizes Phi-3 with ollama and to extract detailed meeting notes from transcripts. 🧹 Data Cleaning: Cleans and validates the extracted JSON using a second LLM. 🗂️ JSON and Markdown Output: Saves the extracted information in both JSON and Markdown formats. Code in comments.
4:22

Looking for a way to automate note-taking for your Zoom calls? I built a Zoom Meeting Summarizer using open source models and software. 📁 File Handling: Reads transcript files in WebVTT format 📋 Meeting Notes Extraction: Utilizes Phi-3 with ollama and to extract detailed meeting notes from transcripts. 🧹 Data Cleaning: Cleans and validates the extracted JSON using a second LLM. 🗂️ JSON and Markdown Output: Saves the extracted information in both JSON and Markdown formats. Code in comments.

metamike

16,180 görüntüleme • 2 yıl önce

INTRODUCING Notte Building the agentic internet with the strongest web browser for LLM agents. We transform ANY webpage into structured text, enabling better web understanding and navigation. Plug any LLM to to build your own AI agent
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INTRODUCING Notte Building the agentic internet with the strongest web browser for LLM agents. We transform ANY webpage into structured text, enabling better web understanding and navigation. Plug any LLM to to build your own AI agent

Notte

225,232 görüntüleme • 1 yıl önce

New short course on Building Applications with Vector Databases, taught by Pinecone’s Tim Tully! At the heart of a vector database is the ability to store a collection of vectors and then query against that, meaning input a new vector and find similar ones. This is useful for many AI applications. In this course, you'll learn how to use vector databases to build: (i) Semantic Search: Create a text search tool that goes beyond keyword matching, and instead focuses on the meaning of content. (ii) RAG (retrieval augmented generation): Enhance your LLM output by incorporating context from sources the model wasn't trained on. (iii) Recommender System: Combine semantic search and RAG to recommend topics, and demonstrate it with a news article recommender. (iv) Hybrid Search: Build an application that finds items using both images and descriptive text -- by combining both sparse and dense vector representations of the data -- using an eCommerce dataset as an example. (v) Image Similarity: Use image vector embeddings to create an app to compare facial features, using a database of public figures to determine the likeness between them. (vi) Anomaly Detection: Build an anomaly detection app that identifies unusual patterns in network communication logs. I hope you’ll enjoy learning how to build all these types of applications! Please sign up here:
2:19

New short course on Building Applications with Vector Databases, taught by Pinecone’s Tim Tully! At the heart of a vector database is the ability to store a collection of vectors and then query against that, meaning input a new vector and find similar ones. This is useful for many AI applications. In this course, you'll learn how to use vector databases to build: (i) Semantic Search: Create a text search tool that goes beyond keyword matching, and instead focuses on the meaning of content. (ii) RAG (retrieval augmented generation): Enhance your LLM output by incorporating context from sources the model wasn't trained on. (iii) Recommender System: Combine semantic search and RAG to recommend topics, and demonstrate it with a news article recommender. (iv) Hybrid Search: Build an application that finds items using both images and descriptive text -- by combining both sparse and dense vector representations of the data -- using an eCommerce dataset as an example. (v) Image Similarity: Use image vector embeddings to create an app to compare facial features, using a database of public figures to determine the likeness between them. (vi) Anomaly Detection: Build an anomaly detection app that identifies unusual patterns in network communication logs. I hope you’ll enjoy learning how to build all these types of applications! Please sign up here:

Andrew Ng

137,169 görüntüleme • 2 yıl önce

Turn any GitHub repository into LLM-ready text! Simply replace "hub" with "ingest" in a GitHub URL and receive a prompt-friendly text ingest for LLMs. Gitingest is 100% open-source and provides: - Directory structure - A brief summary of the project - The entire content as LLM-ready text Plus, it comes with a nice python package and you can run the UI locally! Stay tuned, I'm working on something really cool with this!✨ Link to the GitHub repo in the next tweet! _____ Find me → Akshay 🚀 ✔️ For more insights and tutorials on ML and AI Engineering!
0:29

Turn any GitHub repository into LLM-ready text! Simply replace "hub" with "ingest" in a GitHub URL and receive a prompt-friendly text ingest for LLMs. Gitingest is 100% open-source and provides: - Directory structure - A brief summary of the project - The entire content as LLM-ready text Plus, it comes with a nice python package and you can run the UI locally! Stay tuned, I'm working on something really cool with this!✨ Link to the GitHub repo in the next tweet! _____ Find me → Akshay 🚀 ✔️ For more insights and tutorials on ML and AI Engineering!

Akshay 🚀

191,584 görüntüleme • 1 yıl önce