LangChain - Advanced RAG Techniques for better Retrieval Performance

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In this Video I will show you multiple techniques to improve RAG Applications. We will have a look at ParentDocumentRetrievers, MultiQueryRetrievers, Ensemble Retrievers, Document Compressors, Self-Querying and Time Weighted VectorStore Retrivers

Timestamps
0:00 Introduction
0:55 Chunksize Experiment
5:45 ParentDocumentRetriever
7:15 MultiQueryRetriever
10:18 Contextual Compression
15:35 Emsemble Retriever
17:29 Self-Querying Retriever
21:10 Time-weighted VectorStore Retriever

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Great useful content, with clear explanation. 👍

wylhias

Thank you so much for this tutorial! It is exactly the stuff I was looking for!

StyrmirSaevarsson

Nice work! few new methods of Langchain I was not aware of :)

santasalo

Already Love your content ❤

Would love to see you making Production Ready Chatbot Pt 2 along with deployment part. Thankyou for producing quality content for free.

say.xy_

Excellent information!! Thank you. Liked and Subscribed.

newcooldiscoveries

Thanks for great video of this topic.
can you also post some videos related to LoRA with any LLMs of your choice.

sivajanumm

Thank you so much this is really good stuff

Chevignay

Thank you so much for making this video! You create valuable content. I just have one question. I'm currently utilizing the Azure Search Service, and I'm curious if it's feasible to integrate all the retrievers. I've attempted to use LangChain with it, but my options seem limited to searching with specific parameters and filters. Unfortunately, there's not a lot of information available on how to effectively use these retrievers in conjunction with the Azure Search Service.

syedhaideralizaidi

Thanks for the video, what is x & y dim in the scatter plot (5.19)?

theindianrover

Thank you for the amazing tutorial! I was wondering, instead of using ChatOpenAi, how can I utilize a llama 2 model locally? Specifically, I couldn't find any implementation, for example, for contextual compression, where you pass compressor = with the ChatOpenAi (llm). How can I achieve this locally with llama 2? My use case involves private documents, so I'm looking for solutions using open-source LLMS.

moonly

Thank you for the video:). In your opinion which method of retrieval will give me the most accurate output ( the cost is not as important in my case )? I work in pharma industry - tolerance to LMMs mistakes is very low.

micbab-vgmu

Fantastic video! :D
Quick question: Do you know how it's possible to create a local vector database that's queried via code, so the database doesn't get initialised each time the script is run?
Would really appreciate your help!

quengelbeard

Thank you, can you handle theproblem of retrieval when we ask question out of context of rag or greeting for exemple ?

ghazouaniahmed

hi, in retrievalQa from langchain, we have a retriever that retrieves docs from a vector db and provides a context to the llm, let's say i'm using gpt3.5 whose max tokens is 4096... how do i handle huge context to be sent to it ? any suggestions will be appreciated

akshaykumarmishra

Nice tutorial . May I know the theme used for visual studio code please

karthikb.s.k.

I'm a beginner here and I've been using langchain from your videos. Is the advanced RAG instead of doing something like my code below where instead of using the search type as similarity, I'm using the types that you showed in the video yet everything else stays the same like using ConversationalRetrievalChain, prompt, memory etc...?

= "similarity_score_threshold", search_kwargs = {"score_threshold":0.8})

Also, which would you recommend to retrieve for large documents? I need to do RAG over 80 PDF documents and have been struggling with accuracy.

Lastly, in your OpenAi embeddings, why are you using chunk_size= 1 when by default, its chunk_size = 1000? Can you explain this part also please and thank you in advance

yazanrisheh

Nice video. Can you please create a video on evaluation of RAG? I think a lot of people would be interested in this.

saurabhjain

PDFInfoNotInstalledError: Unable to get page count. Is poppler installed and in PATH?

whitedeviljr

Wait what, I thought FAISS didnt support metadata filters ?
Weird that TimeWaited works with it no ?

vicvicking

hum .. you forgot to remove your OpenAI API Key from the source code !

lefetznove

LangChain - Advanced RAG Techniques for better Retrieval Performance

LangChain - Advanced RAG Techniques for better Retrieval Performance

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