Hi r/Rag,

My brother and I have been working on Morphik - an open source, end-to-end, research-driven RAG system. We recently migrated our LLM provider to support LiteLLM, and we now support all models that LiteLLM does!

This includes: embedding models, completion models, our GraphRAG systems, and even our metadata extraction layer.

Use gemini for knowledge graphs, Openai for embeddings, Claude for completions, and Ollama for extractions. Or any other permutation. All with single-line changes in our configuration file.

Lmk what you think!

Hello guys, still trying to wrap my head around what an MCP is actually useful for. Can it be interesting to implement it in a RAG use case where my MCP Server would basically be a database (I'm specifically thinking about Neo4j graph database where I not only have a vector index but also other linked data that could be extracted using generated cypher queries (two different tools in this scenario)). On the other side, I have a hard time understanding what an MCP Client is ? In my case, I'm working with Gemini, are there existing MCP clients supporting gemini that I can just connect to an MCP server if I have one ?

I've been reading about MCP now and AFAIU it's just a transformation later on top of the data APIs of your actual data sources you want to build the RAG on. Couldn't it just have been a library instead of a full blown service? For example I'm seeing MCP servers to interact with your local filesystem as well. Isn't that an extreme overhead to spawn up a service to call os APIs where it would have been much easier to just call the os APIs directly from your application?

So I have Qdrant set up for my RAG project.

I'm looking to refine the vector search so that it returns the most relevant entries from my embedded documents in Qdrant. I have implemented keyword filtering to help with this.

The problem I am facing now is that my Qdrant instance contains a document with a very large table of contents. Said TOC contains every keyword I am using using in the project. Naturally, every query that filters by keyword (and quite a few that don't) regularly return sections from the table of contents and nothing else. This is useless to me. I need to access the meat of my documents.

I don't want to re-embed the document sans TOC because I would really like to incorporate something in my code that is able to recognize and work around situations such as this.

Any thoughts on the best way to approach this?

Once I can get relevant entries from Qdrant as it stands now, I'll re-embed the document with the TOC removed.

I'm trying out LightRAG with gemma2:2b and nomic-embed-text, both through the Ollama API. I'm feeding it the text from the 1st Harry Potter book. It correctly finds nodes like Hagrid, Hermione, Dumbledore etc. but there is this weird noise where it for some reason adds the World Athletics, Tokyo, Carbon fiber spikes and other random things from seemingly unknown sources, here's the sample of the graphxml file :
Has anyone else encountered this issue?

<node id="100m Sprint Record">

<data key="d0">100m Sprint Record</data>

<data key="d1">record</data>

<data key="d2">A 100-meter sprint record was achieved by Noah Carter, an athlete who broke the previous record&lt;SEP&gt;A 100-meter sprint record was achieved by Noah Carter, an athlete who broke the previous record.&lt;SEP&gt;A milestone achievement in athletics that is broken by Noah Carter during the Championship.&lt;SEP&gt;A milestone in athletics achieved by Noah Carter during the championship.&lt;SEP&gt;The **100m sprint record** is a benchmark in athletics that holds significant importance. It represents the fastest time ever achieved in sprinting and was recently broken by athlete Noah Carter at the World Athletics Championship. This new record marks a notable achievement for both athletic competition and Harry Potter's journey within the story. The 100m sprint record serves as a symbolic benchmark for Harry's progress throughout the book series, signifying his advancement in skill and potential. The record holds special significance within the Harry Potter universe, acting as a turning point in Harry's life. Notably, the record is frequently discussed in the context of athletics and its impact on Harry's character development.

&lt;SEP&gt;The 100m sprint record is a benchmark in athletics, recently broken by Noah Carter.&lt;SEP&gt;The record of the 100m sprint was broken and Harry, Ron, and Hermione will have to deal with the consequences. &lt;SEP&gt;The 100m sprint record has been broken by Noah Carter.&lt;SEP&gt;The record for the 100m sprint has been broken by Noah Carter.&lt;SEP&gt;The 100m Sprint record set by Harry Potter in the World Athletics Championship broke a long-standing record.&lt;SEP&gt;A new record for the fastest 100-meter sprint has been set by Noah Carter.&lt;SEP&gt;A new record for the fastest 100-meter sprint has been set by Noah Carter. &lt;SEP&gt;A new 100m sprint record was set by Noah Carter.&lt;SEP&gt;The achievement of a 100m sprint represents Harry's athletic ambition, highlighting his dedication to it&lt;SEP&gt;This refers to a significant achievement and record that Harry aims to achieve, showcasing his athletic spirit.&lt;SEP&gt;The 100-meter sprint record is a benchmark in athletics, recently set by Harry Potter.</data>

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<data key="d4">unknown_source</data>

</node>

<node id="Carbon-Fiber Spikes">

<data key="d0">Carbon-Fiber Spikes</data>

<data key="d1">equipment</data>

<data key="d2">Advanced running shoes that enhance speed and traction&lt;SEP&gt;Advanced spiking shoes used for enhanced speed and traction.&lt;SEP&gt;Advanced sprinting shoes designed for enhanced speed and traction.&lt;SEP&gt;Carbon-fiber spikes are advanced sprinting shoes that provide enhanced speed and traction, used by athletes like Noah Carter for a speed advantage.&lt;SEP&gt;The **Carbon-Fiber Spikes** are advanced sprinting shoes designed to enhance both speed and traction. They are widely used by athletes, particularly sprinters, to improve performance during races. These high-tech spikes are made with carbon fibers and designed to deliver a competitive advantage on the track.

Let me know if you have any other entities or descriptions that I need to include!

&lt;SEP&gt;Carbon-fiber spikes are advanced sprinting shoes that provide enhanced speed and traction.&lt;SEP&gt;Carbon-fiber spikes are advanced sprinting shoes that provide enhanced speed and traction.&lt;SEP&gt;Carbon-fiber spikes are advanced sprinting shoes that provide enhanced speed and traction.&lt;SEP&gt;Advanced sprinting shoes that provide enhanced speed and traction.&lt;SEP&gt;Carbon-fiber spikes are advanced sprinting shoes that provide enhanced speed and traction.&lt;SEP&gt;Carbon-fiber spikes are advanced sprinting shoes that provide enhanced speed and traction.&lt;SEP&gt;Advanced sprinting shoes that improve performance and speed.&lt;SEP&gt;Advanced sprinting shoes used to enhance performance and speed.&lt;SEP&gt;Carbon-fiber spikes were used to enhance speed and traction during the race.&lt;SEP&gt;advanced running shoes that enhance speed and traction&lt;SEP&gt;Carbon-fiber spikes are advanced sprinting shoes that provide enhanced speed and traction.&lt;SEP&gt;Carbon-fiber spikes provide enhanced speed and traction.&lt;SEP&gt;Advanced sprinting shoes designed to improve performance and speed&lt;SEP&gt;Advanced sprinting shoes designed to improve performance and speed.&lt;SEP&gt;Carbon-fiber spikes are specialized athletic footwear used to enhance speed and traction in sprinting&lt;SEP&gt;Carbon-fiber spikes are specialized athletic footwear used to enhance speed and traction in sprinting.&lt;SEP&gt;Advanced sprinting shoes that provide enhanced speed and traction&lt;SEP&gt;Carbon-fiber spikes are advanced sprinting shoes that provide enhanced speed and traction.&lt;SEP&gt;Carbon-fiber spikes are advanced sprinting shoes that provide enhanced speed and traction.</data>

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<data key="d4">unknown_source</data>

</node>

<node id="World Athletics Federation">

<data key="d0">World Athletics Federation</data>

<data key="d1">organization</data>

<data key="d2">The **World Athletics Federation** (also known as IAAF) is a globally recognized governing body that oversees athletic competitions and records, playing a crucial role in sports governance. It is responsible for validating and recognizing new sprint records, ensuring their legitimacy within international athletics. The federation sets standards and regulates international athletics, including the World Athletics Championship.

It acts as the regulatory authority for track and field disciplines, overseeing events like the 100m sprint record. This organization ensures the integrity of athletic competitions by verifying records and maintaining a standard across diverse athletic fields. The **World Athletics Federation** is the official governing body responsible for managing and upholding the standards of track and field, ensuring the legitimacy and fairness of competitions worldwide.

&lt;SEP&gt;The World Athletics Federation is the governing body overseeing the World Athletics Championship and record validations.&lt;SEP&gt;The governing body for athletics, responsible for record validations.&lt;SEP&gt;The World Athletics Federation is the governing body overseeing the World Athletics Championship and record validations.&lt;SEP&gt;The World Athletics Federation is the governing body overseeing the World Athletics Championship and record validations.&lt;SEP&gt;The World Athletics Federation oversees record validations and manages competitions&lt;SEP&gt;The World Athletics Federation oversees the record validations and manages competitions&lt;SEP&gt;The World Athletics Federation is the governing body overseeing the World Athletics Championship and record validations.&lt;SEP&gt;The governing body of track and field events, responsible for upholding records and regulations.&lt;SEP&gt;The World Athletics Federation oversees and validates athletic records, including world championship results.&lt;SEP&gt;The World Athletics Federation oversees record validations and manages championships&lt;SEP&gt;The World Athletics Federation oversees record validations and manages championships.&lt;SEP&gt;The World Athletics Federation is the governing body overseeing the World Athletics Championship and record validations.&lt;SEP&gt;The World Athletics Federation is responsible for validating and recognizing new sprint records.&lt;SEP&gt;The World Athletics Federation governs the sport of athletics, including record validation.&lt;SEP&gt;The World Athletics Federation is the governing body overseeing the World Athletics Championship and record validations.</data>

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Hi everyone, I'm Jeff, the cofounder of Chroma. We're working on creating best practices for building powerful and reliable AI applications with retrieval.

In this technical report, we introduce representative generative benchmarking—custom evaluation sets built from your own data and reflective of the queries users actually make in production. These benchmarks are designed to test retrieval systems under similar conditions they face in production, rather than relying on artificial or generic datasets.

Benchmarking is essential for evaluating AI systems, especially in tasks like document retrieval where outputs are probabilistic and highly context-dependent. However, widely used benchmarks like MTEB are often overly clean, generic, and in many cases, have been memorized by the embedding models during training. We show that strong results on public benchmarks can fail to generalize to production settings, and we present a generation method that produces realistic queries representative of actual user queries.

Check out our technical report here: https://research.trychroma.com/generative-benchmarking

I shipped a feature on CrawlChat.app that - Takes a Github URL - Fetches repository issues - Turn them into RAG - Let people get help from it on chat widget, Discord bot, or as MCP

Hey guys, I’m receiving CSV files from BI reports that list the tables and columns used for each report. I need to understand these tables and columns since they’re from SAP. There are over 100 reports like this, and I need to map the source table and columns to build a star schema data model.

PS: The task is to perform a data migration from SAP to another system.

I was thinking if GPT could help me build this data model. It could map the relations from the previous reports and identify dimensions and fact tables. When new files are received, GPT could analyse them, map them, and expand the data model.

I’ve populated the tables and columns to graph and analyse the relationships, but I haven’t been able to build the structure yet. Since new tables are created and mapped, the data model has to be expanded.

Can the GPT hold the previous data model context, it need to tell the PK, FK and dim and facts.

Is there any way I could get this done properly.

Anyone measuring?

We're sitting around 300-500ms depending on the size of the query.

I know 200ms of this is simply the routing, but curious to know what others are seeing in their implementations.

Hi everyone, im building a RAG app. I am using chroma db as the vectorstore. I have a problem that when i pass my embedding to chroma it does not persiste them or save them i memory while running. Sometimes it just crashes (with exit code -1073741819) , other times the script runs completely but the vectors are not stored. I have tried using the implementation from the chromadb library and the LangChain integration. When i run the same exact script with the same exact dependencies and versions ( from the same requirements file) on a Linux machine it works perfectly ( im on Windows). Does anyone know what the problem might be and how to fix it?

Hey, very new to RAG! I'm trying to search for emails using RAG and I've built a very barebones solution. It literally just embeds each subject+body combination (some of these emails are pretty long so definitely not ideal). The outputs are pretty bad atm, which chunking methods + other changes should I start with?

Edit: The user asks natural language questions about their email, forgot to add earlier

This playlist comprises of numerous tutorials on MCP servers including

1. What is MCP?
2. How to use MCPs with any LLM (paid APIs, local LLMs, Ollama)?
3. How to develop custom MCP server?
4. GSuite MCP server tutorial for Gmail, Calendar integration
5. WhatsApp MCP server tutorial
6. Discord and Slack MCP server tutorial
7. Powerpoint and Excel MCP server
8. Blender MCP for graphic designers
9. Figma MCP server tutorial
10. Docker MCP server tutorial
11. Filesystem MCP server for managing files in PC
12. Browser control using Playwright and puppeteer
13. Why MCP servers can be risky
14. SQL database MCP server tutorial
15. Integrated Cursor with MCP servers
16. GitHub MCP tutorial
17. Notion MCP tutorial
18. Jupyter MCP tutorial

Hope this is useful !!

Playlist : https://youtube.com/playlist?list=PLnH2pfPCPZsJ5aJaHdTW7to2tZkYtzIwp&si=XHHPdC6UCCsoCSBZ

I’m building a multilingual system that needs to handle responses in international languages (e.g., French, Spanish ). The flow involves:

User speaks in their language → Speech-to-text

Convert to English → Search knowledge base

Translate English response → Text-to-speech in the user’s language

Questions:

Should I expand my knowledge base to multiple languages or use the Google Translate API for dynamic translation?

Which approach would be better for scalability and accuracy?

Any tips on integrating Speech-to-Text, Vector DB, Translation API, and Text-to-Speech smoothly?

What do you think about the quality of data retrieval between Graphrag & Lightrag? My task involves extracting patterns & insights from a wide range of documents & topics. From what I have seen the graph generated by Lightrag is good but seems to lack a coherent structure. On the Lightrag paper they seem to have metrics showing almost similar or better performance to Graphrag, but I am skeptical.

I'm building an agentic rag system for a client, but have had some problems with vector search and decided to create a custom retrieval method that filters and does not use any embedding or database. I'm still "retrieving" from an knowledge-base. But I wonder if this still is considered a rag system?

Hey everyone,

I recently came across Cole Medin’s YouTube channel and found his RAG tutorials pretty impressive at first glance. Before diving deeper, though, I’d really appreciate some input from those with more experience.

Would you consider Cole Medin’s content a solid and reliable resource for learning RAG? Or do you think his material is too basic for practical, production-level use? If there’s another YouTuber, blogger, or resource you’d recommend as a better starting point, I’d love to hear about it.

Thanks!

Lets say I want to use Langchain. This one tool is compulsory. Can you suggest me some best case scenario and tools to make a RAG pipeline that is related to news summary related data.
Users query would be " Give me latest news on NVIDIA." or something like that.

Meta just released LLaMA 4 with a massive 10 million token context window. With this kind of capacity, how much does RAG still matter? Could bigger context models make RAG mostly obsolete in the near future?

Watch this video

https://youtube.com/shorts/jP08A8bhTVE?si=-fc_rBxP_Afn4T8J

Hi

I have a question for experts here now in 2025 what's the best RAG solution that has the fastest & most accurate results, we need the speed since we're connecting it to video so speed and currently we're using Vectara as RAG solution + OpenAI

I am helping my client scale this and want to know what's the best solution now, with all the fuss around RAG is dead ( I don't htink so) what's the best solution?! where should I look into?

We're dealing mostly with PDFs with visuals and alot of them so semantic search is important

I made a previous post on Step by Step RAG and mentioned that RAG wasn't necessarily about vector databases and embedding models, but about retrieval, from any source.

I thought about this some more and after playing with Haystack and Hayhooks, I realized that Hayhooks had all the tools I needed to make search-based RAG tools available to some Letta agents I was using.

I've packaged up the pipelines into a turnkey solution using Docker Compose, and I've been using Hayhooks as a tools server quite effectively. I feel like I've barely scratched the surface of what Haystack can do -- I'm really impressed with it.

https://github.com/wsargent/groundedllm