Master PDF Chat with LangChain - Your essential guide to queries on documents 

It’s very generous of you, giving us the source code, and explaining everything clearly. This is the kind of channels that deserve a subscription and follow.

This is amazing and very well done. This channel has become my go to every morning. Thank you.

Amazing tutorial video! The pace is just perfect for learning. 👍Thanks!

Thanks a lot, your efforts are much appreciated

Thank you Sam, for your amazing explaination on how and why of Q&A on PDFs using LangChain. Looking forward to more such developer oriented educational videos.

Outstanding video. Well done showing such a powerful technique.

This is amazing, thanks for taking the time to do this

This is brilliant content. Thanks Sam.

Brilliant stuff. Really fascinating explanation on how to customise your own AI.

Very nice explanation, great job!

Thank you, so much Sam.

thanks, i understood it . a really fantastic video

Very helpful, many thanks!

great overview, thanks!

Fantastic video! I appreciate the inclusion of a Colab project for us to experiment with. It would be amazing to see a similar tutorial on loading multiple PDFs from a Google Drive folder (e.g., "data") , recursively into a Colab project, enabling interaction for creating outlines, glossaries, taxonomies, and more from multiple pdf sources. I'm interested in an approach resembling ChatGPT, where you can input a long passage of text and generate new content from it, going beyond semantic searches and summaries.

thank you for showing us this

Great video. Thanks

This is the video i was searching for

Thanks so much!

Love your videos bro

Fantastic stuff. You've got such a knack for describing this stuff. I hope the AIs spare you when they take over.

Sam - Thank you for this great conceptual explainer on the basic building blocks of leveraging LLMs with Langchain for our own content corpus. One question on the specific use of the PromptTemplate around 12:00 minutes into the video - Prompt has 2 dynamic variables in there named {context} and {question}. However, in the chain.run command, the variables being used are "input_documents" and "question". Where does the variable {context} get defined for the template to use and elaborate in its response?

*New Subscriber* Great video! I am interested in learning more on how to load in multiple PDF’s. Thanks

🎉 thanks for the great explanation, can you explain the process with open assistant and a free vectores store, and fine tuning

Great vidos Sam a lot of people jumping on the bandwagon with LLM's, langChain etc but your is clear and well constructed FAB I would love to see how you could use pinecone as a replacement for the vector store you used as i was unable to make it work with the one in the video.

Hey Sam, love your lectures! Any resources about free alternatives of OpenAI embeddings? Would be really useful! Thanks!

Great

Great work, looking forward for the free Huggingface alternative istead of OpenAI. Appreciate your work. You got my sub br

Thank you for this very useful tutorial. I'm curious to know what you would do differently today when querying one or many PDFs. And, what's the best approach (using RAG?) in January, 2024 to simultaneously query several types such as Word, PDF and text?

Can you please do a video on llama index (previously gpt index). It offers such nice ways to query data over for example lists or tree of indices or knowledge graphs. Ultimately I'd use llama index to handle the indices and storing/retrieving data then langchain for the chat and chaining part. The cool thing about llama index is you can use different ways to retrieve relevant documents/data by for example using a support vector machine to get the top k highest matching docs instead of using cosine similarity and taking the top k of that. I think it would be nice to see because the examples provided by llama index can be a bit confusing but the whole project deserves way more exposure!

Brilliant video thanks Sam. Do you know if the LangChain Text Splitter would take titles into consideration when splitting the text? Titles often provide important contextual information, and preserving their relationship with the subsequent text is crucial for maintaining context and meaning.

Thanks Sam for this fantastic video. I am trying to read a complex pdf for example annual result pdf of a company containing all details with financial details in tabular format. Any suggestions how to preprocess and create embedding.

Awesome,I have a query, 15:47 can we make the LLM to be focused only on the document information not the external world information.

I've written a prompt for GPT-4 that I use with chatGPT to transform it into a legal assistant, and the results have been stellar. Is it possible to encode this prompt into the system you describe so that the bot operates with it in mind?

Great video I have a question... How can we find from which page the answer is generated and how can we get the generated answer page content alone in pqge document

Great! Any suggestions to use HugginFace LLM open-source models?

Thanks a lot for your videos, been following for quite a while and am continually impressed with clarity and quality. Regarding langchain, I've noticed that it uses GPT3 for mostly everything by default, even for chat chains. Is there any particular reason why it's not using (chat)GPT 3.5? Especially seeing as it's currently cheaper. Is it about temperature etc., that's hard to set for chat, or is there some other reason?

Op 12 min is nice

what is the difference between the vector storage you are using vs a solution like Pinecone?

How can we provide the page number of the pdf document (logical page number) in the 'source_documents' as well?

How to remove context confusion Suppose in following text ' I have cat. His name is tom. He plays with my dog , his name is Moti. He is very charming. I bought a neckband of red color for him. I feed it to the llm. My first question will be 1) What is name of cat. >> Tom Second question will be What is color of Neckband >>> red This should be answer because Tom don't have Neckband. How to fix this...

Thank you for the video. Have you tried to remove stop words from the text chunks before creating the embeddings, if this degrades the search results?

Thank you for the tutorial! How on earth did I only now stumble upon your channel? By the way, I have a couple of questions to ask you as I am creating a PDF RAG app also recently: 1. Is it possible to add memory into the RetrievalQA chain that you show in the video? 2. How might I create a custom prompt in RetrievalQA to designate a role, for instance, "You are a legal consultant to a multinational corporation, your task is to use the context given below to answer the question..."? 3. Can I achieve the same thing using LCEL (including custom prompt & memory)?

Suppose I have a pdf file consisting of medical information with only unstructured table in it. How to create LLM model that is pretrained on medical dataset to answer queries of the user based on given tabular data in the pdf

Awesome explanation ! really appreciate your effort . but does this work on a more complicated Pdf, such as Pdf that contains some sort of table/graphs ? i faced some issues before when trying to read tables from a pdf using something like tabula.

great video very thorough and well explained. I created my own version of the program with only minor changes. However, it seems to struggle with retrieval of certain information in pdf's of real estate offering memorandums. Do you have any recommendations on how to fix this?

Fantastic video, very well explained with excellent diagram. The Colab gift is very generous of you. BTW, when loading two PDFs, and the two have each a Table of Content, when asking a query about ToC the answer returns only one ToC. Any idea how to overcome this?

Hey Sam, could we use that recursively to add short term and long term memory to the system? storing the chat content permanently into a vector store for short term and using GPT to compress after a given size to then store the compressed version as long term memory recursively. That would would allow for a bot with a real lifetime memory.

Hello, my case uses that I need to distill 2million words down to 10k, My problem has been Max word input not being enough anywhere I look, how can I achieve this desired output please? Thank you

How can you integrate pinecone to this? Can you do also a followup video and integrate Pinecone in the same exmaple?

Hi, Brilliant video. Extremely helpful. Had one question though: How can you chunk a pdf file(with images) or an Excel file?

Great Video, thanks @samwitteveenai. Are you using Jupyterlab? if not how do you reformat the return of the model? for example when you return the prompt of the model, you create a new split cell, what is that?

Your videos are awesome. I was able to build a flutter app to work with a python backend running in replit, using fastapi to serve API endpoints. In my app, I can upload a PDF file and chat with it using an agent with memory. It works fine. However, I need to allow multiple users, each one to have its own agent with its own memory. I have no idea how to acomplish this. What path would you recommend?

@samwitteveenai can i get the image as a output based on the questions if yes how can I do it ?

What if i dont want to use OpenAI model and want to use someother custom model?

Cool, is possible to do it with Vicuna llama ?

Thanks for the great video. If I have a document with already questions and answers. What is the best way to load the documents in the vector store? Only the answers or both? How to give langchain prompt template a positive and negative examples so that the llm can do a classification? Thanks in advance!

This is great! I can't get the return_intermedia_steps ranking to work unfortunately, but everything else worked pretty well.

Nice video! Is it possible to see also the original text(s) where the chat is extracting the information from?

Hi, Sam. Excellent video! For "text_splitter=CharacterTextSplitter(separator=" ",chunk_size=1000,chunk_overlap=200) texts=text_splitter.split_text(raw_text)" if I did not add separator=" " in CharacterTexySplitter method, why the length of texts is equal to 1 ? Hope to get your answer:)

love the videos, thanks a lot

Can you make one not using openai please

if I do not use other llms , how can I know if it is supported by LangChain?

thanks again for another amazing video!🤩 I'm trying to follow the same method that you showed in this video, but sometimes my model answers out of the given PDF, do you have any idea on how can I solve it? I tried to play with the prompt or prompt template but didn't help too much... is there any way to guarantee never answer out of the given PDF?

Thanks for the video! For a highly technical pdf (Maths, Physics, etc..) would this be useful at all? Is there a way to make images and formulas also be "vectorized"?

do i need gpt 4 or any paid plan for this ?

Hi, do you have any idea how to create a chatbot that references our own document, but if it does not find any result, it will reference openAI’s database instead of giving “no context found”. Am a beginner so I appreciate the help!

I love this approach and i think it is key to the functionality of LLM's, but the longer i watch the weaker and more limited the project appears introducing more and more potential bottlenecks. How powerful is this tool in it's current state? How high is the price we pay for Pdf access?

Nice video :) Does the docsearch method also use LLM calls and therefore cost tokens or is this handled withouth LLM calls?

Sam, thanks for the great videos you created. This video helps me to resolve an issue which I have been struggled with for a while. Before, I used the similar flow (load document, split, embedding, vector store, then query) but without using chain. Somehow, the response time for the query is long (more than 10 seconds). I started to use chain after watching your video. The response time dropped to 3 seconds. Thank you so much. BTW, when we do load_qa_chain(OpenAI(), chain_type="stuff") , can we specify OpenAI model version (e.g. gpt-3.5-turbo or text-davinci-003). It will be great if we can use gpt-3.5-turbo, because it's 10 times cheaper compared to text-davinci-003 😅 Thanks again

Ok so this let's the model answer based on context from the vector store. What if the user wants to relate something from the custom knowledge base and from other data that GPT already was trained on? Is there a way that the language model can still piece together an answer that's outside of the given context? For example: how can elon musk use this document to help stop rockets from exploding?

What about adding the concept of memory?

Hi, Sam! Why we need to introduce retriever in the chain?

how to edit the prompt

Thank you sam! This is informative . I'm working on similar projects with more than 200 PDF documents, each one with 300 pages in Avg . So How to approach this ? Any Idea? .

Whenever OpenAI is involved, it should be mentioned in the title or thumbnail. Thanks...

This is a good explanation for how it works but is there a app or website where I could upload a PDF and ask it questions about a document? 🤔

Is there any way to build the same chatbot or question answering system which will utilize the information in a given website?

Hey How to check the prompt for conversational retrival chain

Does anyone of you know what document loader to use for uploaded PDFs? I'm using FastAPI to upload PDFs that I'd like to load them up to LangChain

How can you export embeddings to avoid repeated charges?

How Pincone DB is different compared to FAISS ?

very interesting. Does this scale to, say, 100'000 pages? Does it work well with something selfhosted like Vicuna?

Hi Sam! Nice video, but I have a question: what If we have multiple pdfs and want to query over those pdfs?

Why would you want some overlap of the chunks?

Can you build the same system with langchain and huggingface models

hello Sam, I have my daughter will do soon her final year training, and i advised her to do a searchable library of around 100 books (which is an encyclopedia of arabic language) using GPT chatbot. can you please advise what are the best tools to reach this goal , and can it be done in a timeframe of 6 weeks. Thank you Arezki

can you add an Agent without using OpenAI, instead using any other open sourced model? I cannot find any this type of example, that's gonna be very useful. A lot of people don't like using OpenAI, however there is really very less examples not using it.

can we use it with azure open api

Sam, pls make a video on JSON questionnaire using langchain... i have a big JSON document with 5000+ user records inside it.. how to query that data in langchain.. Ex: What is the email id of John, .. In which country does john live? how many users are from UK.. etc.

And that's exactly what I've always said will be coming soon 😎 the future is here!

Excellent video, but using OpenAI model(s) repeatedly is a disservice to all. OpenAI/Pinecone charge for using their models/services (Pinecone recently, suddenly changed their free tier availability for new accounts) but with solid FOSS (free open source) models available, FOSS LLMs should be preferred in example code. Thank you.

It can't even get the author right. :/ Similar to things like ChatPDF that just hallucinate things that have nothing to do with the document.

This does not work for technical documentation. I tried it with a proprietary programming language manual months ago, then asked it to write code using that language and it was useless. All it really is is a sophisticated searching tool, good only for natural languages like fiction or commentary etc.

ModuleNotFoundError: No module named 'langchain.chains.question_answering' not able to get rid of this error

I failed on this line of code "docsearch = FAISS.from_texts(texts, embeddings)" it returns ValueError: not enough values to unpack (expected 2, got 1). do you know what is the problem? i have duplicated the entire steps of yours

Hi Sam, For : chain = load_qa_chain(OpenAI(), chain_type="map_rerank", return_intermediate_steps=True ) query = "who are openai?" docs = docsearch.similarity_search(query,k=10) results = chain({"input_documents": docs, "question": query}, return_only_outputs=True) results I am getting the error: --------------------------------------------------------------------------- ValueError Traceback (most recent call last) in () 6 query = "who are openai?" 7 docs = docsearch.similarity_search(query,k=10) ----> 8 results = chain({"input_documents": docs, "question": query}, return_only_outputs=True) 9 results 10 7 frames /usr/local/lib/python3.10/dist-packages/langchain/output_parsers/regex.py in parse(self, text) 26 else: 27 if self.default_output_key is None: ---> 28 raise ValueError(f"Could not parse output: {text}") 29 else: 30 return { ValueError: Could not parse output: OpenAI is an artificial intelligence research laboratory consisting of the for-profit company OpenAI LP and its parent organization, the non-profit OpenAI Inc. Score: 100 I even saw a issue open on langchain : github.com/hwchase17/langchain/issues/3970. I would really appreciate any assistance to address this concern. Thanks, Ankush Singal

Hey Sam, I've said it before, but I just have to thank you again for your incredible videos! Your choice of words and facts, along with your soothing voice, make everything so easy to understand. I had an idea that I think would be a fantastic addition to your channel. You know those "@domainofscience" "Map of..." videos, like "The Map of Quantum Computing" (ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE--UlxHPIEVqA.html)? It would be amazing if you could make a "Map of AI" video in the same style. I believe you have a unique talent for breaking down complex topics and making them accessible to everyone. Keep being awesome, Sam! Can't wait to see more of your great content. Best, Fredrik

how would you code it to have it generate questions about the pdf in this example?