Langchain rag pdf reddit.

Langchain rag pdf reddit But, everyone runs into the same set of problems imo which includes: access to ground truth for measuring factual correctness - if a RAG's ultimate goal is to correctly fetch the context that has the factual answer, this can only be measured by comparing against the actual ground truth that needs manual intervention. I'm working on building a RAG project with a lot of user manuals, technical stuff and so on. RAG is very dependent on your data, and what kind of optimization strategies to do or not to do is what makes RAG decent, and since no OpenSource solutions knows what kind of data you have they fail. Hmm, BERTopic with LLM based topic labeling. Share Add a Comment We would like to show you a description here but the site won’t allow us. I needed the text to be highlighted as well and the pg numbers. load() 2. Note: Here we focus on Q&A for unstructured data. Lastly, best learning / troubleshooting is in source code documentation , first. It was closed source until 3 weeks ago, and the tech stack is the basis for a SaaS site used by attorneys for searches so has been validated for scaling and I love LangChain. Numbers don’t really work the same way. PDFs are ubiquitous & easy to obtain – your Word, Excel & Text files can be easily saved as PDFs & uploaded to the app! f. Please be respectful of each other when posting, and note that users new to the subreddit might experience posting limitations until they become more active and longer members of the community. You can use AutoML tools like AutoRAG to optimize RAG using your dataset. Supports automatic PDF text chunking, embedding, and similarity-based retrieval. PDF has a lot of tables & forms. So the problem I'm working on is the prompts are fixed(not one liner QnA but half a page types) and the input pdf can change. Also, for now, the idea is to use the data from pdf docs, word docs or data downloaded in json format. I'm wondering for those of you who found the answers from you QA systems to be good, did you guys just drop the PDF / Word / etc into the program and let the RecursiveCharacterSplitter in langchain do the work, or did you guys do some preprocessing before you writing your own query is the best way because you can tweak it, but in many use cases we are just taking the user input in sentence form and trying to get matches, so that's where the separate llm call or keyword module does the job. Hi folks! Currently working on a Micro SaaS and ended up needing to convert a PDF to JSON. is there a strategy to create this vector store efficiently? currently it takes very long time to create it (can take up to 5 days) Step 6: Load and parse the PDF documents. Here’s the analogy that I’ve come up with to help my fried GenX brain to understand the concept: RAG is like taking a collection of documents and shredding them into little pieces (with an embedding model) and then shoving them into a toilet (vector database) and then having a toddler (the LLM) glue random pieces of the We are developing an RAG (Retrieval-Augmented Generation) system based on Elasticsearch and Langchain (Python users) for processing PDF files containing drug information. It’s taken me a while to understand how RAG generally works. All those apps that have a functionality of talk with your pdf, when dealing with a long pdf they use RAG or a map-reduce ? I don't see how one could use RAG to answer questions like "summarise me this doc" and running map-reduce all the time sounds expensive. LangChain has a number of components designed to help build Q&A applications, and RAG applications more generally. I built a custom parser using pdfplumber because I know converting pdf2image and using a model will work but I think is overwhelming, checking for tables (and converting to JSON), extracting paragraphs between chapters and only evaluating the extracted images (and not the entire page) gave me best results overall vs the current langchain pdf loaders. However I am facing the problem, that often a important topic starts at the end of a page and continues in the next page. RecursiveCharacterTextSplitter has worked better in my experience as well, but it depends on PDF structures you're dealing with. This will get the basic components in place for you, and then you’ll have to add other components or enhancements to consistently return high quality results. I'm more or less completely new to LangChain, but I envision it as the best tool to solve the following task. Currently, I am overwhelmed with the choices that we have right from parsing pdf files (like llama_index), embed and store in vector database (qdrant) and run different models (groq for example) and then create an API (probably using lang serve). If you’re looking to implement cached datastores for user convo’s or biz specific knowledge or implementing multiple agents in a chain or mid-stream re-context actions etc, use Langchain. If you are interested for RAG over structured data, check out our tutorial on doing question/answering over SQL data. Excluding the facts that isn't open source and limited for commercial use. 01% to do with langchain and the chatting part and 99. I’ve been playing around with large text summary models on hugging face but the hallucinations are insane, like 50% of the summary is made up… Basically the RAG pipeline(or any other method) should be able to quickly switch between different LLM models, or databases or any other components when it comes to deploying on a production setup. Reply reply More replies More replies Temperature being the same doesn't mean a lot, if it's above 0. Thank you so much, you're king, the fact is afaik in Azure you still need to choose a premade type of document so it doesn't suit my case, my company wants to parse unstructured PDFs with tables, screenshots and stuff, and the topic of the PDF can be varied, we were thinking about Nougat and Unstructured but they still need to improve. After making great RAG evaluation dataset, the 90% of your work is done. OK I'll bite. I ve being playing with langgraph last week and so far i like it very much. For example, you can source a model's (Llama-3) API from watsonx ai and integrate it with LangChain to create a RAG application. I am trying to build a chatbot using RAG and LangChain that will update the PDFs based on the user prompt and the pdfs will be stored in a db (chromedb) that will be connected to the chatbot. Rag. RAG is the general approach. They are speaking out their inexperience in this new field. LangChain offers various methods for chunking, vector database storing, embedding, and retrieval. Hey! I am trying to create a vector store using langchain and faiss for RAG(Retrieval-augmented generation) with about 6 millions abstracts. Having perfect chunks in legal would be a great deal. Upload any PDF – Simply click the upload button & navigate to any PDF on your device. Multimodal RAG with GPT-4o and Pathway: Accurate Table Data Analysis from Financial Documents Resources Hey r/langchain I'm sharing a showcase on how we used GPT-4o to improve retrieval accuracy on documents containing visual elements such as tables and charts, applying GPT-4o in both the parsing and answering stages. All these ChatGpt wrappers really confuses me. More specifically, you'll use a Document Loader to load text in a format usable by an LLM, then build a retrieval-augmented generation (RAG) pipeline to answer questions, including citations from the source material. Watched lots and lots of youtube videos, researched langchain documentation, so I’ve written the code like that (don't worry, it works :)): Loaded pdfs loader = PyPDFDirectoryLoader("pdfs") docs = loader. Nous parlons en anglais et en français. We would like to show you a description here but the site won’t allow us. The option that I am testing is multi modal vector, based on unstructured library for pdf extraction. PDFs contain lot of tabular data too, which I cant see the tabular format from the extracted data (I used an pdf parser to extract the text data from the pdf). Thanks for the response! What Python module are you using for converting PDF to image? Currently using the PyPDFLoader in LangChain to load the PDF, I am aware i don't need to use this and there are other, but if i can reduce to one package for this functionality that would be even better, to clarify, for this approach allows the text_splitter. You would populate your RAG database with "chunks" from those PDF documents. Normal OCR technique doesn't maintain the proper table/form formatting. Save those embeddings in a vector store, then use a RAG retrieval method from LlamaIndex or LangChain to parse user queries and return top PDF matches from the vector store. All the links that been shared are great! Something some may not seen is also github repo elasticsearch-labs. Embeddings: if ada, sbert don't work, learn customized embeddings Pipeline providers: you shop for those after 10 iterations and multiple revisions of your metadata and chunking. Context. The primary components of LangChain include: Prompt Templates: Used for managing and customizing prompts by changing input variables dynamically. I would also like to know which embedding model you used and how you dealt with the sequence length. We attempt to help people make data-driven decisions by comparing the various models on their private documents. It is designed to integrate LLMs with other computational elements to create complex, useful systems. pdf') I am creating a RAG application but I am having this problem: I have multiple files containing the companies projects lists along with their descriptions, used frameworks etc. Apr 7, 2024 · In this video, I have a super quick tutorial showing you how to create a multi-agent chatbot using LangChain, MCP, RAG, and Ollama to build… This project is a straightforward implementation of a Retrieval-Augmented Generation (RAG) system in Python. There are multiple LangChain RAG tutorials online. For the mathematical question answering part, there are 2 things to consider : For quicker understanding, check out their Cookbook tab in langchain docs website. Additionally, it utilizes the Pinecone vector database to efficiently store and retrieve vectors associated with PDF documents. Secondly, do not listen anyone who says Langchain/ Llama-index is crap. Feb 24, 2025 · LangChain提供了丰富的PDF解析工具，适用于不同场景的文档处理需求。如果你在AI文档处理、RAG（检索增强生成）应用中需要高效PDF解析，LangChain的PDFLoader体系是最佳选择！ Save those embeddings in a vector store, then use a RAG retrieval method from LlamaIndex or LangChain to parse user queries and return top PDF matches from the vector store. So I want to automate the conversion of a legal document (5-20 pages) into a different type of document with plain/lay English + adhere to a specific style and format guidelines (20-100 pages) that are in 3 separate reference pdf documents. Yes, I have analyzed data and explored various chunking and loading techniques, including character splitter, recursive text splitter, spaCy text splitter, and sentence splitter, to analyze data. I can look for a good example if you need. Hi, not sure if this is the right subreddit, but i see there are plenty of questions about RAG here. py module and a test script . Splited the text The #1 driver of bad RAG is bad segmentation. Get the Reddit app Scan this QR code to download the app now Checkout my new tutorial on how to build a recommendation system using RAG and LangChain https Classify PDF based on separate RAG database I’m trying to set something up where a user can upload a pdf and have it classified based on a resource I converted into a vector database. I recently discovered llamaparse proprietary solution. I am creating a RAG program where I used 20 pdfs which contains lease agreement of different tenants. Open file. These are applications that can answer questions about specific source information. This is an extensive tutorial where I go in detail about: Developing a RAG pipeline to process and retrieve the most relevant PDF documents from the arXiv API. What I'm trying to create is a script that takes two PDF documents, where one is the application criteria and the other is the application itself, and compares the content to determine what is omitted in one document and addressed in the other. Given that I've been playing around with LangChain for a while now and writing about it, I ended up using the Output Parsers to achieve this. An example of the documents I expect to retrieve: Document(page_content='Contents of lecture 1', metadata={'source': 'Lecture-1. It allows you to load PDF documents from a local directory, process them, and ask questions about their content using locally running language models via Ollama and the LangChain framework. You can get high performance RAG in a few hours. They've lead to a significant improvement in our RAG search and I wanted to share what we've learned. The SaaS nature allows us (Vectara) to optimize the underlying latency and minimize it significantly. document_loaders import UnstructuredFileLoader loader = UnstructuredFileLoader("my. We've spent a lot of time building new techniques for parsing and searching PDFs. I like the idea to have control over each step and being able to select which information is passed to each node and how the response of the nodes are added for the state, it gives a lot of control of the token usage and to guide the nodes responses. Been struggling with parsing pdf with complex layout, table, imagines. Meet your fellow game developers as well as engine contributors, stay up to date on Godot news, and share your projects and resources with each other. Plus, many people don't know this, but mathematically speaking temperature can't be set to 0, as it's in the denominator of the softmax formula. You may want to try Vectara, which provides RAG-in-a-box (and integrated into LangChain), and a simple API for chatbots. split_documents()? We would like to show you a description here but the site won’t allow us. Eg. Now, I am looking to scale up to around 30-40k files and I am unsure if this will work seamlessly. These applications use a technique known as Retrieval Augmented Generation, or RAG. LangChain is a framework for building applications powered by large language models (LLMs). Llmware also has RAG instruct trained models in Hugging Face that can run on CPUs for free experimentation/POCs and also industry specific embedding models. experts on a specific topic, so they knlw which questions they would ask and which answers they would expect). Hi, I want to manually create a Evaluation dataset for RAG with complex Pdfs. there's this GraphCypherQAChain in langchain you can use to translate natural language to KG's query language and get the result in natural language but your prompts need to match with entities and relationships in your kg else it will tell you it doesn't know the answer, similar chat with db using sql. Some answ Hello everyone, I am just starting with RAG. This code defines a method load_documents to load and parse PDF documents from given file paths. Hey r/LangChain , I published a new article where I built an observable semantic research paper application. Documentation was easy to understand and development was straightforward. Jul 17, 2024 · Chunking is crucial for building effective Retrieval-Augmented Generation (RAG) pipelines, especially with long documents like PDFs, because it breaks text into manageable sections, allowing the A Python-based tool for extracting text from PDFs and answering user questions using LangChain and OpenAI's GPT models with a Retrieval-Augmented Generation (RAG) approach. I am building a RAG for "chat with Internal PDF" use case. It is an integrated and easy to use RAG platform that has native PDF and Office document parsing, text chunking, vector embedding, hybrid searching and fact checking with source citations. Most of the libraries to parse pdf transforms the tables in text and not necessarily ordered. 1. RAG is to link document sources and can be updated almost instantly depending on your connectors. Previously, I used LangChain v0. I'm making the tool for deciding which RAG strategy is best, called AutoRAG. Sorry to revive an old thread and I ran into the same issue and found a bit of solution. HELP: How can I make a rag Q/A app that allows the user to upload a pdf to the conversation and so that the model can understand the context of the pdf; I tried to perform an ensemble retrievel but at some point the chunks lose the context of the entire pdf Any thoughts on how I can handle large text summary? The context is reading through hundreds of email chains and summarizing them. I remember something about the files saying. I used TheBloke/Llama-2-7B-Chat-GGML to run on CPU but you can try higher parameter Llama2-Chat models if you have good GPU power. Our solution includes the following components: I'm trying to make an LLM powered RAG application without LangChain that can answer questions about a document (pdf) and I want to know some of the strategies and libraries that you guys have used to transform your text for text embedding. I am using text-embedding-ada-002 model because I think langchain currently does not support v3 Langchain is a good place to start and learn the ropes, and for agentic behaviour etc, it nicely abstracts some tedious steps. Langchain provides everything needed, and has lots of tutorials on how to do it. I have simply started to run documents through all the libraries and see which one retains the information I want and use that in a given pipelines. Very much case by case. I am using langchain framework to work with FAISS and openAI Embeddings. The program is designed to process text from a PDF file, generate embeddings for the text chunks using OpenAI's embedding service, and then produce responses to prompts based on the embeddings. Could you pls let me know a step by step process what's the best way to build a high accuracy RAG Chatbot with PDF data? I have been refering to multiple resources and experimented multiple stuff, but the accuracy of the RAG is not upto the expectations, and FYI the PDF that im using is 27 pages with many formats (not just tables). I'm trying to make an LLM powered RAG application without LangChain that can answer questions about a document (pdf) and I want to know some of the strategies and libraries that you guys have used to transform your text for text embedding. Hello, For the pdf part, you should first embed your pdf document(s) and store it inside a vector database. I'm actively developing this and hope to help lots of people to deicde RAG strategy to their own data. I already tried synthetic dataset creation but think you get more reliable evaluation results with human labeled data (e. Yes, consider the privacy of the document, you can do it locally. It consists of two main parts: the core functionality implemented in the rag. The Smart PDF Reader is a comprehensive project that harnesses the power of the Retrieval-Augmented Generation (RAG) model over a Large Language Model (LLM) powered by Langchain. First pass, I used this tutorial… LLMWare has end to end RAG implementation system from document ingestion (native PDF parsers), text chunking, fact checking, embedding, and also links to most models, including HF models. can someone please guide me with the stack? im thinking langchain and memgraph for DB but more tools and options and stack? thanks! from langchain_community. Hello, I have a pdf where I am expecting some answers to the questions asked and I am seeing that phi3 mode is generating better output than llama… The best part is that an actual useful RAG have about 0. The document does not mention how to you can view the steps on page 60 in the document. I noticed that web version of GPT-4 (after the update following dev day) is now able to extract tabular data in attached pdf files pretty accurately (e. pdf", mode="elements") docs = loader. Using Regex I preprocessed the extracted text data (remove the whitespaces and replace the special characters) Was looking to see whether it might replace my planned RAG implementation for the company I work for, saw the 20 doc limit and went "NARP", now back to doing it in Langchain after all. In my experience developing RAG-based applications with LangChain, I was surprised to find that there aren't any simple, reliable ways to chunk files. here’s every detail Function call. Wanted to build a bot to chat with pdf. I am currently working on implementing RAG for a specific use case, and I have made good progress with a working example. So to get a better output I changed these parameters : 1. But I recently built a RAG application with langchain, and removed langchain everywhere other than the document retrieval API to improve performance in speed and accuracy. 9 % to do with data structure and effective embeddings :P Splitting a pdf into 1 or 2 pages and then embed that or something similar does simply not work effectively. LLMs are not really meant to be search engines (and in fact studies have shown that they are not great at this) so even fine-tuning will have a lot of limitations for finding information. Welcome to Canada’s official subreddit! This is the place to engage on all things Canada. elasticsearch-labs has a number of notebook examples on search and genai, one in particular that shows naive RAG without LangChain using OpenAI. Developing a Chainlit driven web app with a Copilot for online paper retrieval. I tried langchain too, but a lot of time got wasted in just navigating the documentation combined with the fact that I use LLMs for coding who have outdated documentation of their own, I ended up ditching langchain and doubled down on llamaindex. So far, I have created embeddings for about 10-15 PDF/HTML files and I am using Qdrant locally (via Docker) to manage them. The type of question I want an answer for is: "Give me all the projects built using FastAPI" (as an example) I know there is a ton of interest in document QA systems, which makes sense since it has good business values to most organizations. I did some rag with tables and it is tricky, depending on the information and structure of the tables. the pdfs contains both text and data. I want to know what is the best open source tool out there for parsing my PDFs before sending it to the other parts of my RAG. The rag should never share the source of its response My pipeline Pdf Document Langchain I swear most people just upload pdfs and spend one minute on a prompt and call it a RAG system. e. One of the most powerful applications enabled by LLMs is sophisticated question-answering (Q&A) chatbots. With my current ingestion pipeline, the results are very mixed. I need to extract this table into JSON or xml format to feed as context to the LLM to get correct answers. If you learnt to think in one language you use that language to think. Hi, in my RAG app I am loading PDF-files with PyPDFLoader and I am chunking the PDFs with the RecursiveCharacterTextSplitter. I wrote about this on my blog and it works like magic In fact, it's not just PDF you could convert. Thank you for your comment. I can't ignore tables/forms as they contain a lot of meaningful information needed in RAG. Documentation in Langchain portal comes second. Splitting using recursive character split, embedding using open ai and storing it in chroma db. I finally used a python library base in Java that extract the tables and formates as data frame. Read to context Rag is good with keywords that matter but it changes the words to numbers to vectorise. If you want good RAG you have to do it yourself. I'm planning to use OpenAI for chunking and indexing information that will be analyzed by the bot. We benchmarked several PDF models - Marker, EasyOCR, Unstructured and OCRMyPDF. load() docs[:5] Now I figured out that this loads every line of the PDF into a list entry (PDF with 22 pages ended up with 580 entries). RecursiveSplitter, CharacterSplitter and the like REALLY shouldn't be used to segment content for an LLM. Could you please suggest me some techniques which i can use to improve the RAG with large data. Recently, I tried building a more complex app, an alternative to Perplexity AI using open-source LLMs, which proved challenging. x for a simple invoice extraction app. The official subreddit for the Godot Engine. I'm thinking there are three challenges facing RAG systems with table-heavy documents: Chunking such that it doesn't break up the tables, or at least when the tables are broken up they retain their headers or context. In this tutorial, you'll create a system that can answer questions about PDF files. In general I'd say just base it on your evaluation metrics, RAG can be unpredictable about what will work best. I'm working on a basic RAG which is really good with a snaller pdf like 15-20 pdf but as soon as i go about 50 or 100 the reterival doesn't seem to be working good enough. g. Just wonder how to summarize several different aspects of a topic. It iterates through each PDF file path, attempts to load the document using PyPDFLoader, and appends the loaded pages to the self. I am currently using PypdfLoader from langchain to load the pdfs. What do you think is going on? Courses/books to get into Generative AI (GenAI)? Looking to get familiar with tools like Langchain, vector databases, LLM APIs etc. Just started using RAG with LangChain the last couple of weeks for a project at work. For the mathematical question answering part, there are 2 things to consider : What's the best way to RAG your pdf or word document now around 10 pages long to analyse its tokens? Before I reckon it's langchain but it's buggy; if you used chatgpt pro can only work for 2 pages of text. I wanted to set RAG strategy setups easily with YAML file, and automatically benchmark each RAG strategy and select the best combination. Right now I’m using LlamaParse and it works really well. I'm calling it "reverse" because most of the examples or discussions I see talk about thr usecase where prompts are variable but docs might be fixed. The good news the langchain library includes preprocessing components that can help with this, albeit you might need a deeper understanding of how it works. Some examples: Table - SEC Docs are notoriously hard for PDF -> tables. The loader alone will not be enough to abstract meaningful text from complex tables and charts. Any type Yeah, you can do it with 3 lines of code for a proof-of-concept in langchain, but then it's shit. So when we use temperature 0 in the API call, OpenAI most likely replaces that 0 with a very small number, but maybe that small number is still We would like to show you a description here but the site won’t allow us. Not opposed to building with OpenAI's new Assistants API, but will need to function call out to a proper vector DB to cover my usecase. Optical Character Recognition (OCR) is used to extract text accurately & enables the use of scanned documents too! I built a custom parser using pdfplumber because I know converting pdf2image and using a model will work but I think is overwhelming, checking for tables (and converting to JSON), extracting paragraphs between chapters and only evaluating the extracted images (and not the entire page) gave me best results overall vs the current langchain pdf loaders. Concepts A typical RAG application has two main components: Plus one for llamaindex. documents list. Any prompt suggestions to overcome this particular scenario. Then LangChain map reduce on all texts of a cluster/topic using the prompt: "Tell me the top three improvement suggestions"? One demand that rag isn't able to fullfill is to never mention document reference in the response. Hi, I am creating an Agent RAG chatbot application which uses Tools. vs Bard with Gemini Pro). I need a rag to help me get the info from the PDFs in a neat manner but also pull up the images and the PDF associated with the query. Here is my code for RAG implementation using Llama2-7B-Chat, LangChain, Streamlit and FAISS vector store. HI Community, I have a PDF with text and some data in tabular format. I want to retrieve building regulations ( max height, area, etc) information from pdfs using a llm. Most of the pdf extraction libraries start with some specific use cases anyways, so they end up specializing for the use case. If all you’re doing is RAG over pdf’s, use the GPT’s feature or Assistants API. I am using RAG to do QA over it. For do that, you have to make great RAG evaluation dataset with much more time. The default Text Splitters that LangChain offers employ a naive form of chunking that doesn't consider positioning data like sections, subsections, paragraphs or tables. With RAG, the inferring system basically looks up the answer in a database and initializes inference context with it, then infers on the question. For artists, writers, gamemasters, musicians, programmers, philosophers and scientists alike! The creation of new worlds and new universes has long been a key element of speculative fiction, from the fantasy works of Tolkien and Le Guin, to the science-fiction universes of Delany and Asimov, to the tabletop realm of Gygax and Barker, and beyond. Hi folks, I often see questions about which open source pdf model or APIs are best for extraction from PDF. ~10 PDFs, each with ~300 pages. I think many products are trying to solve for evals. heqhkhc crdgptk ohcuewsx dncsn npczw hrepqdqw uxbnj ajnl ymxuorr jyzvnk