"Full Context CAG" thing with graph retrieval

AI Development
1

Okay, so been messing around with some seriously cool stuff lately! :exploding_head: Been diving deep into this “Full Context CAG” thing with graph retrieval, and honestly, it feels like it’s gonna totally change how AI writes code.

Forget the old way of chopping up docs into tiny bits (RIP traditional RAG :wave:). This new approach grabs the whole picture, giving the AI a proper brain :brain: to understand frameworks. The code it spits out is way more coherent, actually fits together, and just makes more sense. Plus, it seems way smarter at figuring things out.

Oh, and get this – I’ve been using llm.txt data for the LLM data! Surprisingly, it keeps things small but super informative, which is awesome for those big-brain AI models like Gemini.

Seriously think this is the future of coding platforms. So hyped to see where this goes! :sparkles::rocket: #AI #Coding #LangChain #GraphRetrieval #FutureOfCode

2

Can you elaborate which method exactly you are talking about?
You mean using google gemini cloud server and their cache instruction? or something else entirely?
your AI written post with annoying GPT 4.5 emotes is not so clear, plese dont…
do you mean some other method like graph RAG that is applied here? i am confused with the terminology.
If you see my prior post where i made local CAG app, when i was actually putting things together i could get more insight into limitations of this implementation because of how LLM work.

TLDR:
to get reliable results you need proper data prep for exact query pattern, and set the model parameters that fit best that exact dataset.
this is not just dumping whatever you have on the LLM and things magically work out. there is consequence to how that data is processed and accepted by the llm, structured, and requires understanding how that saved state works.

there is too much to say on the subject, but unless you tailor and prep the document to a specific querry during document prep, and properly configure the model setup, then you will be getting reliable result for that specific querry. there are tradeoffs.
under the hood all this LLM stuff is a bit more nuanced.
when you do edge case testing, instructions can break the retrieval accuracy. albeit i"ve been testing with smeller models, there is nuance in how the model uses the data.
in my little app i simulated various types of those behavior.
too long to explain. but especially for local things are vastly more nuanced.
also people very much misunderstand that there are several ways this can be set up, atleast when run local. either simply using KV cache, this loads it in each querry; or Warmed up llm with KV cach eloaded in at querry start or Warmed up Cache and LMM loaded in. this affects speed of first token greatly.
also if yo udo not instruct it to clear Memory and RESET KV, this is a problem because LLM store conversation in Cache, so the cache buildup will destroy result if you are using up to CAche limit already. so i set it up to have configurable behavior, 1 where it clears memory and resets after or beforenew chat. there are tradeoffs .

long story there is much nuance to using this method especially in production and if seeking repeatable results. you have to know those edge cases.
even though my testing was local, observations still valid.

3

:wave:
Here you go mate hope this is super nice and clear for you, I found this post and it explains it better than my Apprant GPT 4.5 post that I wrote myself lol.

Which Approach Reigns Supreme?

Introduction

Large Language Models (LLMs) have rapidly evolved, bringing forth powerful techniques to ground text generation in external knowledge. Historically, Retrieval-Augmented Generation (RAG) has been the go-to strategy for injecting updated or domain-specific information into LLM outputs. However, a new wave of research — most notably in the paper “Don’t Do RAG: When Cache-Augmented Generation is All You Need for Knowledge Tasks” (Chan et al., 2024) — introduces Cache-Augmented Generation (CAG) as an alternative or complementary method.

Below, we’ll explore how both RAG and CAG work, highlight key takeaways from Chan et al. (2024), and help you decide which approach is best for your needs.

1. Refresher: What Is Retrieval-Augmented Generation (RAG)?

Retrieval-Augmented Generation (RAG) enhances a language model’s responses by fetching external knowledge in real time.

  1. Pipeline
  • Retrieval: The system first pulls top-ranked documents or text snippets (e.g., from a vector database or a BM25-based index).
  • Augmentation: Those retrieved chunks are appended to the user’s query.
  • Generation: The LLM processes the augmented prompt to produce a final output.

2. Advantages of RAG

  • Fresh Knowledge: RAG can stay relevant by referencing continuously updated data.
  • Potentially Smaller LLM: Offloads “memory” of domain knowledge to an external source, so the model can be lighter.
  • Fact-Checking: By referencing actual documents, RAG reduces hallucinations (assuming top-quality retrieval).

3. Challenges of RAG

  • Latency: Each query triggers document retrieval, which can slow down responses.
  • Retrieval Errors: If the system selects irrelevant or outdated documents, the output suffers.
  • System Complexity: Maintaining an external index or database is non-trivial, especially if updates are frequent.

2. Enter Cache-Augmented Generation (CAG)

In contrast to on-demand retrieval, Cache-Augmented Generation (CAG) loads all relevant context into a large model’s extended context window and caches its runtime parameters. During inference, the model references this cache — no additional retrieval required.

  1. How CAG Works
  • Preloading Knowledge: A curated set of documents or domain knowledge is fed into the model before any live queries.
  • KV-Cache: Modern LLMs store intermediate states (the “KV cache”). CAG precomputes these states for the knowledge corpus, so they can be reused rapidly.
  • Streamlined Inference: When the user asks a question, the LLM already has “everything” loaded. No separate retrieval step is necessary.

2. Core Insights from Chan et al. (2024)

  • No Real-Time Retrieval: This eliminates retrieval latency and reduces the chance of retrieving irrelevant data.
  • Better Consistency: The model holds a holistic view of the knowledge base, which improves reasoning.
  • Extended Context Windows: With modern LLMs capable of tens or hundreds of thousands of tokens, you can include entire documents in a single context.
  • Efficiency Gains: On benchmarks like HotPotQA and SQuAD, CAG reduces generation time while matching or exceeding RAG accuracy.

3. CAG’s Benefits

  • Zero Retrieval Overhead: No waiting for a separate search to complete.
  • Simplicity: Fewer moving parts to maintain than a full RAG pipeline.
  • Unified Context: The model processes all relevant info from the start, enhancing multi-hop reasoning.

4. Potential Pitfalls

  • Context Size Limit: If your knowledge base is huge, you can’t load it all at once.
  • Upfront Compute: Precomputing and storing KV caches requires more initial setup.
  • Stale Data: If your corpus changes frequently (e.g., news), you’ll need to re-cache.

3. Head-to-Head: RAG vs. CAG in Practice

In the Don’t Do RAG paper, researchers at National Chengchi University compared both methods on question-answering tasks using HotPotQA (multi-hop reasoning) and SQuAD (single-passage understanding).

RAG Setup

  • Dense retrieval (using OpenAI Indexes) and sparse retrieval (e.g., BM25) to fetch top passages.
  • The LLM then concatenates these passages with the query.

CAG Setup

  • Precompute a KV-cache for all relevant documents.
  • During inference, simply load this cache plus the user’s question — no retrieval needed.

Key Performance Takeaways

  • Accuracy: CAG matched or surpassed RAG, especially when the entire corpus fit into the context window.
  • Latency & Complexity: RAG can be slower (because of the retrieval step), while CAG omits that step altogether.
  • When RAG Shines: If you need large or continuously updated data, on-demand retrieval is indispensable.
  • When CAG Reigns: Constrained, stable knowledge bases are perfect for CAG — fewer steps, fewer errors, faster replies.

“Our findings challenge the default reliance on RAG for knowledge integration tasks, offering a simplified, robust solution to harness the growing capabilities of long-context LLMs.”
— Chan et al. (2024)

4. When to Choose RAG

  • Rapidly Evolving Knowledge
  • Example: Tracking real-time stock prices or breaking news.
  • You need to fetch the latest information, and preloading would go stale quickly.
  • Massive Corpora
  • If your domain is huge, it’s impossible to load it all in the LLM’s context window.
  • RAG is essential for narrowing down relevant documents.
  • Citation Requirements
  • You want the system to show exactly which documents an answer came from, for transparency or compliance.
  • On-demand retrieval makes it easier to link sources.

5. When to Choose CAG

  • Small, Stable Knowledge Base
  • Your entire domain knowledge fits in the LLM’s context, and updates are infrequent.
  • No need for real-time retrieval if the data rarely changes.
  • Low Latency, High Consistency
  • Eliminates retrieval overhead — faster responses, consistent across queries.
  • Great for multi-turn conversations where you don’t want to re-fetch the same data repeatedly.
  • Reduced System Overhead
  • No special retrieval pipeline or indexing logic to maintain.
  • Precompute once, then quickly serve thousands of queries.

6. Can You Blend RAG and CAG?

Definitely. Although the Don’t Do RAG paper highlights a purely retrieval-free approach, a hybrid approach might still be useful if you:

  • Preload the most commonly referenced documents via a KV-cache.
  • Retrieve only rarely accessed or newly emerged content on demand.

This hybrid approach offers flexibility, though it brings some additional complexity — just not as much as a full-blown RAG pipeline.

7. So, Which Is Better?

No single answer fits every scenario:

  • Pick RAG if your knowledge environment is massive, fast-moving, and you frequently need the latest information.
  • Pick CAG if your domain is well-defined, stable, and you prioritize speed and simplicity (no retrieval step!).

Key Insight

As context windows continue to expand and long-context LLMs get more powerful, CAG will likely become even more appealing — often delivering faster, simpler pipelines with at least equivalent accuracy.

Conclusion

Cache-Augmented Generation (CAG) is redefining how we integrate knowledge into LLMs. By precomputing a knowledge cache, you cut out retrieval overhead and reduce system complexity — particularly for stable or moderately sized knowledge sets. Meanwhile, Retrieval-Augmented Generation (RAG) remains vital for large, dynamic contexts that demand real-time referencing.

Whichever path you choose, the latest research suggests an exciting future: extended context LLMs, paired with caching or retrieval solutions, will continue to boost efficiency and accuracy in AI-driven applications. If you’re tired of managing retrieval pipelines, CAG might just be your next favorite method — especially as context windows keep growing!

The results speak for themselves really.

	HotPotQA 	SQuAD

Size System Top-k BERT-Score BERT-Score
Small Sparse RAG 1 0.0673 0.7469
3 0.0673 0.7999
5 0.7549 0.8022
10 0.7461 0.8191
Dense RAG 1 0.7079 0.6445
3 0.7509 0.7304
5 0.7414 0.7583
10 0.7516 0.8035
CAG (Ours) 0.7759 0.8265
Medium Sparse RAG 1 0.6652 0.7036
3 0.7619 0.7471
5 0.7616 0.7467
10 0.7238 0.7420
Dense RAG 1 0.7135 0.6188
3 0.7464 0.6869
5 0.7278 0.7047
10 0.7451 0.7350
CAG (Ours) 0.7696 0.7512
Large Sparse RAG 1 0.6567 0.7135
3 0.7424 0.7510
5 0.7495 0.7543
10 0.7358 0.7548
Dense RAG 1 0.6969 0.6057
3 0.7426 0.6908
5 0.7300 0.7169
10 0.7398 0.7499
CAG (Ours) 0.7527 0.7640
Table 3. Comparison of Generation Time
Dataset Size System Generation Time (s)
HotpotQA Small CAG 0.85292
w/o CAG 9.24734
Medium CAG 1.66132
w/o CAG 28.81642
Large CAG 2.32667
w/o CAG 94.34917
SQuAD Small CAG 1.06509
w/o CAG 10.29533
Medium CAG 1.73114
w/o CAG 13.35784
Large CAG 2.40577
w/o CAG 31.08368

4

I must be spending to much time with AI :-/ Think I sound like it a little lol.:brain:

Link explains it even better than me.

has the table from above that not formated well.
https://arxiv.org/html/2412.15605v1

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pff…well, did you even read my post? this is an ai bot generated reply.

if you read my post you would have understood that not only i read that paper but build on top of it, and explored much more the subject myself.

you did not answer the question which usecase were you describing, what tools You are leveraging and which exact approach.

you simply did ai analysis here without context of my question.

i understand all this and wrote almost like a research paper if you look into it. i was asking which tool you used, how, to leverage the technique exactly.

6

I did read your post I can assure you. I am not AI

Sorry I am still testing a few methods and will give everyone the Github link when it is finished

Chunking (if necessary): While i aim for “Full Context,” there are large documents that could benefit from strategic chunking while still preserving context (e.g., breaking down a large class documentation into methods and properties).

LangChain provides excellent support for integrating with various vector stores and graph databases. I leverage LangChain’s graph-specific chains and retrievers to interact with the CAG.

Linking Embeddings to the Graph Database:

  • Node IDs as References: When we create nodes in the graph database to represent documentation sections or code components, i can store a reference (e.g., the unique ID) to the corresponding embedding in the vector store.
  • Relationships: The graph database will then focus on capturing the relationships between these entities (e.g., a code function uses another function, a documentation section refers to a specific class).
    Also using Weaviate for vector database that provides advanced features and a GraphQL API.
7

no way in any universe are your first 2 posts are not just 100 ai written, using ai is fine, but to a degree…

your reply is still fairly vague and does not show how you handle CAG limitations which is key to implementing it efficiently that i asked about.

Okay, so you’re using LangChain for the graph database integration - that’s what I was asking about. I’ve been working more with direct implementations rather than using LangChain’s abstractions, which gives me more control over the KV cache management that I mentioned.

When you’re using LangChain’s graph-specific chains and retrievers, how are you handling the memory/cache clearing between sessions? That was one of the key challenges I found simulating this locally- without proper reset protocols, the cache buildup can distort results as you approach the context limits. ther are other pitfalls too when using this approach.

more context in git Additiona readme files

Also, what’s your approach for the node ID references in the graph database? I found that granularity matters a lot here - too fine-grained and you lose coherence, too coarse and you lose specificity.

And sure, I’m curious about your IDE setup too if it has specific tooling for this kind of work. I’ve been building custom visualizations to help debug the retrieval patterns and i ran into alot of issues.

just trying to understand your workflow. i am actively thinking how i myself can leverage this approach , and hence my interest.

edit: details matter, and your implementation as far as i understand it you are not using strigtly CAG but a mimicking strategy by leveraging vector db, so terminology matters. as i explained before there is much nuance in how this is set up, what it does.
Or am i misunderstanding? where is your KV cache here ? otherwise it is not CAG. what i see is you are combination of embeddings and graph relationships with a purpose similar to the KV cache dynamically reconstructing context from the stored embeddings and graph relationships, which has nothing to do with CAG strictly spaking. unless i misunderstand. you are describing something else.

you are not using unified memory state, use chunking and vector stoere, and content reconstruction. so what you are doing is RAG with enhancements as far as i can see.

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Regarding your comment about the previous responses, I assure you that while I utilize AI to assist in generating comprehensive and informative content based on the research material, the aim is to provide well-structured and insightful information. I understand your concern about the level of detail and will focus on addressing your specific questions directly.

My phone rings a lot so I have to do things this way sorry, I have fed your questions to the AI model for the project and here are the answers, the paper is constructed by a senior dev that works for me and is 130 pages long so there is a lot to it with methods that are being used.

The main focus for us here will be a hybrid approach that combines graph-based retrieval for structured information and semantic search over text embeddings for broader context, potentially using Neo4j’s integrated vector search capabilities.

You’re right to point out the distinction between Cache-Aware Generation (CAG) and the approach described, which leans more towards Retrieval-Augmented Generation (RAG) with knowledge graph and vector database enhancements. The key difference lies in the memory state management. The described method uses a combination of embeddings and graph relationships to dynamically reconstruct context, which, while serving a similar purpose to the KV cache in CAG by providing relevant information, doesn’t strictly adhere to the unified memory state characteristic of CAG.

Regarding your specific questions:

  • CAG Limitations: You’re correct that efficiently handling CAG limitations, particularly around KV cache management and context window constraints, is crucial. The described RAG-enhanced approach addresses the context window limitation by focusing on retrieving only the most relevant information from the knowledge graph and vector database. Techniques like metadata filtering and semantic search aim to narrow down the context provided to the LLM, thus mitigating the risk of overwhelming the context window. However, the initial report didn’t delve into the intricacies of KV cache management as it wasn’t the primary focus of the described architecture.
  • LangChain Memory/Cache Clearing: When using LangChain for graph database integration, memory management, including cache clearing between sessions, would typically be handled at the application level or through LangChain’s memory management features if explicitly implemented. The initial report focused on the data retrieval aspects using knowledge graphs and vector databases, and the specifics of session management and cache clearing within LangChain would depend on the particular implementation choices made when building the application.
  • Node ID References in the Graph Database: The granularity of node ID references in the graph database is indeed a critical factor. A balanced approach is essential. Too fine-grained nodes might lead to a loss of overall coherence and increased complexity in the graph, while too coarse-grained nodes could result in a loss of specificity and make targeted information retrieval challenging. The optimal granularity often depends on the specific structure and content of the data being represented. For code snippets, for example, nodes could represent individual functions, classes, or logical blocks, with relationships defining the connections between them (e.g., function calls, class inheritance). Metadata, as discussed in the report, plays a crucial role in providing additional context and enabling filtering at different levels of granularity.
  • IDE Setup and Workflow: For developing and debugging such systems, a standard IDE like VS Code or IntelliJ with Python support is generally used. For visualizing graph databases, tools like Neo4j Browser are invaluable for inspecting the structure and data within the knowledge graph. Custom visualizations, as you mentioned, can be very helpful for debugging retrieval patterns. Libraries like NetworkX in Python can be used to create and manipulate graphs programmatically, allowing for custom visualization logic. Additionally, leveraging the debugging tools within the IDE to step through the code and inspect the data flow between the LLM, vector database, and knowledge graph is essential for understanding the system’s behavior.

I hope this clarifies the approach and addresses your questions more directly. The intention is to leverage the strengths of both knowledge graphs and vector databases within a RAG framework to achieve efficient and context-aware information retrieval for LLMs.

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this indeed clears things up. it is not CAG in any way because it does not lean into the 1 defining feature of CAG.

also just because the paper is 130p long does not mean it has substance. and if the paper you shared is by your “senior dev”… well. your senior dev is and accomplished vibe coder i see. because a human would be able to tell the differencebetween 2 drastically different concepts.

also the added comment where you drop another fully ai generated paper is not helping the case. Ai clearly took you on a ride here.

you completely missed the mark by just trusting AI to do the thinking. misunderstood the paper and then had AI generate something else entirely. i am still not sure i am talking to a human here…

the issue with your prior answer was precisely you trusted the AI to analyse and discuss a topic that is novel. meaning this is an area AI has no knowledge of. so it mistakenly did not make the distinction in areas that matter and confused terminology.

my point was you referenced a paper, misunderstood it and made something different, that is enhanced RAG. which is valid approach, but because people lean on AI to think nowdays, it missed the point of the paper. This is something that happens when you let the AI take the driver seat.

What you are doing is simply tech,iques to enhance how RAG, which is already well understood and there are many approaches and tools for. You are essentially working on a protocol to tailor it for code specific tasks.

your whole reply if you read into the substance is simply “this is not CAG but RAG”.

again, the whole reply is AI generated. and AI is not confrontational.

again, you do you, but i did not think this was a place to talk to ai but about ai.

and since you are all about that AI life, here is the Claude analysis of your AI generated paper:
After reviewing the document, I agree with your assessment. The paper appears to misuse or misunderstand the term “Context-Aware Generation” (CAG) as it relates to large language models.

The document characterizes CAG primarily as a technique that focuses on understanding the local coding environment and project-specific context, which is certainly valuable, but misses the technical essence of what CAG typically refers to in LLM literature.

True CAG, as you correctly point out, is more fundamentally about:

  1. KV cache management - Leveraging and manipulating the key-value cache that stores attention states from previous tokens
  2. State preloading - Priming the model with contextual information before generation begins
  3. Efficient context retention - Maintaining relevant information in the working memory of the model

The paper instead describes what is essentially just context-aware code completion or generation based on local code analysis, which is more akin to traditional IDE features with LLM enhancements, rather than the specific technical approach of managing and preloading KV caches.

Their proposed “hybrid” approach combining what they call “CAG” with Semantic RAG appears to be conflating different concepts. While the document discusses several valuable techniques for improving code generation by combining local context with external knowledge retrieval, it’s not using the term CAG in its technical sense related to model state management and cache optimization.

A true CAG approach would involve techniques for efficiently managing how context is loaded into the model’s state, precomputing attention patterns, and potentially persisting this state between interactions - elements that don’t appear to be addressed in the document.