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Building and Evaluating Advanced RAG
Beginner
1h55m
Topics
AI Frameworks
Evaluation and Monitoring
GenAI Applications
LLMOps
RAG
Search and Retrieval
an advanced RAG technique, our sentence window retrieval method. In this method, we retrieve based on smaller sentences to better match the relevant context, and then synthesize based on an expanded context window around the sentence. Let's check out how to set it up. First, some context. The standard RAG pipeline uses the same text chunk for both embedding and synthesis. The issue is that embedding and synthesis. The issue is that embedding-based retrieval typically works well with smaller chunks, whereas the LLM needs more context and bigger chunks to synthesize a good answer. What sentence window retrieval does is decouple the two a bit. We first embed smaller chunks or sentences and store them in a vector database. We also add context of the sentences that occur before and after to each chunk. During retrieval, we retrieve the sentences that are most relevant to the question with a similarity search and then replace the sentence with a full surrounding context. This allows us to expand the context that's actually fed to the LLM in order to answer the question. This notebook will introduce the various components needed to construct a sentence window retriever with Llama Index. The various components will be covered in detail. At the end, Anupam will show you how to experiment with parameters and evaluation with TruEra. This is the same setup that you've used in the previous lessons, so make sure to install the relevant packages, such as Llama Index and Truelines eval. For this quick start, you'll need an open AI key similar to previous lessons. This open AI key is used for embeddings, LLMs, and also the evaluation piece. Now we set up and inspect our documents to use for iteration and experimentation. Similar to the first lesson, we encourage you to upload your own PDF file as well. As with before, we'll load in the How to Build a Career in AI eBook. It's the same document as before. So we see that it's a list of documents, there are 41 pages, the object schemas are document object, and here is some sample text from the first page. The next piece is, we'll merge these into a single document because it helps with overall text blending accuracy when using more advanced retrievers. Now let's set up the sentence window retrieval method, and we'll go through how to set this up more in depth. We'll start with a window size of 3 and a top K value of 6. First, we'll import what we call a sentence window node parser. The sentence window node parser is an object that will split a document into individual sentences and then augment each sentence chunk with the surrounding context around that sentence. Here we demonstrate how the node parser works with a small example. We see that our text, which has three sentences, gets split into three nodes. Each node contains a single sentence with the metadata containing a larger window around the sentence. We'll show what that metadata looks like for the second node right here. You see that this metadata contains the original sentence, but also the sentence that occurred before and after it. We encourage you to try out your own text too. For instance, let's try something like this. For this sample text, let's take a look at the surrounding metadata for the first node. Since the window size is 3, we have two additional adjacent nodes that occur in front, and of course none behind it because it's the first node. So we see that we have the original sentence, or hello, but also foobar and cat dog. The next step is actually build the index, and the first thing we'll do is a setup in LLM. In this case, we'll use OpenAI, specifically GPT-355 Turbo, with a temperature of 0.1. The next step is to set up a service context object, which, as a reminder, is a wrapper object that contains all the context needed for indexing, including the AL1, embedding model, and the node parser. Note that the embedding model that we specify is the "bge small model," and we actually download and run it locally from HuggingFace. This is a compact, fast, and accurate for its size embedding model. We can also use other embedding model. For instance, a related model is "bge large," which we have in the commented-out code below. The next step is the setup of VectorStoreIndex with the source document. Because we've defined the node parser as part of the service context, what this will do is it will take the source document, transform it into a series of sentences augmented with surrounding contexts, and embed it, and load it into the VectorStore. We can save the index to disk so that you can load it later without rebuilding it. If you've already built the index, saved it, and you don't want to rebuild it, here is a handy block of code that allows you to load the index from the existing file if it exists, otherwise, it will build it. The index is now built. The next step is to set up and run the query engine. First, what we'll do is we'll define what we call a metadata replacement post-processor. This takes a value stored in the metadata and replaces a node text with that value. And so this is done after retrieving the nodes and before sending the nodes to the outline. We'll first walk through how this works. Using the nodes we created with the sentence window node parser, we can test this post-processor. Note that we made a backup of the original nodes. Let's take a look at the second node again. Great. Now let's apply the post-processor on top of these nodes. If we now take a look at the text of the second node, we see that it's been replaced with a full context, including the sentences that occurred before and after the current node. The next step is to add the sentence transformer re-rank model. This takes the query and retrieve nodes and re-order the nodes in order relevance using a specialized model for the task. Generally, you will make the initial similarity top K larger, and then the re-ranker will rescore the nodes and return a smaller top N, so filter out a smaller set. An example of a re-ranker is bge-re-ranker based. This is a re-ranker based on the bge embeddings. This string represents the model's name from HuggingFace, and you can find more details on the model from HuggingFace. Let's take a look at how this re-ranker works. We'll input some toy data and then see how the re-ranker can actually re-rank the initial set of nodes to a new set of nodes. Let's assume the original query is I want a dog, and the initial set of score nodes is this is a cat with a score of 0.6, and then this is a dog with a score of 0.4. Intuitively, you would expect that the second node actually has a higher score, so it matches the query more. And so that's where the re-ranker can come in. Here, we see the re-ranker properly surfaces the known about dogs and gave it a high score of irrelevance. Now let's apply this to our actual query engine. As mentioned earlier, we want a larger similarity top K than the top N value we chose for the re-ranker. In order to give the re-ranker a fair chance at surfacing the proper information. We set the top K equal to 6 and top N equals to 2, which means that we first fetch the six most similar chunks using the sentence window retrieval, and then we filter for the top two most relevant chunks using the sentence re-ranker. Now that we have the full query engine set up, let's run through a basic example. Let's ask a question over this dataset. What are the keys to building a career in AI? And we get back to response. We see that the final response is that the keys to building a career in AI are learning foundational technical skills, working on projects, and finding a job. Now that we have the sentence window query engine in place, let's put everything together. We'll put a lot of code into this notebook cell, but note that this is essentially the same as the function in the utils.BAAI file. We have functions for building the sentence window index that we showed earlier in this notebook. It consists of being able to use the sentence window node parser to extract out sentences from documents and augment it with surrounding contexts. It contains setting up the sentence context or using the service context object. It also consists of setting up a vector sort index, using the source documents and the service context containing the LLM embedding model and node parser. The second part of this is actually getting the sentence window query entered, which we showed consists of getting the sentence window retriever, using the metadata replacement post processor to actually replace a node with the surrounding context, and then finally using a re-ranking module to filter for the top N results. We combine all of this using the as query intro module. Let's first call build sentence window index with the source document, them, as well as the save directory. And then let's call the second function to get the sentence window query engine. Great. Now you're ready to experiment with sentence window retrieval. In the next section, Audit Prompt will show you how to actually run evaluations using the sentence window retriever, so that you can evaluate the results and actually play around the parameters and see how that affects the performance of your engine. After running through these examples, we encourage you to add your own questions and then even define your own evaluation benchmarks just to play around with this and get a sense of how everything works. Thanks, Jerry. Now that you have set up the sentence window retriever, let's see how we can evaluate it with the RAG triad and compare its performance to the basic rag with experiment tracking. Let us now see how we can evaluate and iterate on the sentence window size parameter to make the right trade-offs between the evaluation metrics, or the quality of the app, and the cost of running the application and evaluation. We will gradually increase the sentence window size starting with 1, evaluate the successive app versions with TrueLens and the RAG triad, track experiments to pick the best sentence window size, and as we go through this exercise, we will want to note the trade-offs between token usage or cost. As we increase the window size, the token usage and cost will go up, as in many cases will context relevance. At the same time, increasing the window size in the beginning, we expect will improve context relevance and therefore will also indirectly improve groundedness. One of the reasons for that is when the retrieval step does not produce sufficiently relevant context, the LLM in the completion step will tend to fill in those gaps by leveraging its pre-existing knowledge from the pre-training stage rather than explicitly relying on the retrieved pieces of context. And this choice can result in lower groundedness scores because recall groundedness means components of the final response should be traceable back to the retrieved pieces of context. Consequently, what we expect is that as you keep increasing your sentence window size, context relevance will increase up to a certain point, as will groundedness, and then beyond that point, we will see context relevance either flatten out or decrease, and groundedness is likely going to follow a similar pattern as well. In addition, there is also a very interesting relationship between context relevance and groundedness that you can see in practice. When context relevance is low, groundedness tends to be low as well. This is because the LLM will usually try to fill in the gaps in the retrieved pieces of context by leveraging its knowledge from the pre-training stage. This results in a reduction in groundedness, even if the answers actually happen to be quite relevant. As context relevance increases, groundedness also tends to increase up to a certain point. But if the context size becomes too big, even if the context relevance is high, there could be a drop in the groundedness because the LLM can get overwhelmed with contexts that are too large and fall back on its pre-existing knowledge base from the training phase. Let us now experiment with the sentence window size. I will walk you through a notebook to load a few questions for evaluation and then gradually increase the sentence window size and observe the impact of that on the RAG triad evaluation metrics. First, we load a set of pre-generated evaluation questions. And you can see here some of these questions from this list. Next, we run the evaluations for each question in that preloaded set of evaluation questions. And then, with the true recorder object, we record the prompts, the responses, the intermediate results of the application, as well as the evaluation results, in the true database. Let's now adjust the sentence window size parameter and look at the impact of that on the different RAG triad evaluation metrics. We will first reset the true database. With this code snippet, we set the sentence window size to 1. You'll notice that in this instruction. Everything else is the same as before. Then we set the sentence window engine with the get sentence window query engine associated with this index. And next up, we are ready to set up the true recorder with the sentence window size set to 1. And this sets up the definition of all the feedback functions for the RAG triad, including answer relevance, context relevance, and groundedness. And now we have everything set up to run the evaluations for the setup with the sentence window size set to 1. Okay, that ran beautifully. Now let's look at it in the dashboard. You'll see that this instruction brings up a locally hosted StreamLit app, and you can click on the link to get to the StreamLit app. So the app leader board shows us the aggregate metrics for all the 21 records that we ran through and evaluated with TrueLens. The average latency here is 4.57 seconds. The total cost is about two cents. Total number of tokens processed is about 9,000. And you can see the evaluation metrics. The application does reasonably well in answer relevance and groundedness, but on context relevance, it's quite poor. Let's now drill down and look at the individual records that were processed by the application and evaluated. If I scroll to the right, I can see some examples where the application is not doing so well on these metrics. So let me pick this row, and then we can go deeper and examine how it's doing. So the question here is, in the context of project selection and execution, explain the difference between ready-fire and ready-fire-aim approaches. Provide examples where each approach might be more beneficial. You can see the overall response here in detail from the RAG. And then, if you scroll down, we can see the overall scores for groundedness, context relevance, and answer relevance. Two pieces of context were retrieved in this example. And for 1 of the pieces of retrieved context, context relevance is quite low. Let's drill down into that example and take a closer look. What you'll see here with this example is that the piece of context is quite small. Remember that we are using a sentence window of size 1, which means we have only added 1 sentence extra in the beginning and 1 sentence extra at the end around the retrieve piece of context. And that produces a fairly small piece of context that is missing out on important information that would make it relevant to the question that was asked. Similarly, if you look at groundedness, we will see that both of these pieces have retrieved the sentences. In the final summary, the groundedness scores are quite low. Let's pick the one with the higher groundedness score, which has a bit more justification. And if we look at this example, what we will see is there are a few sentences here in the beginning for which there is good supporting evidence in the retrieved piece of context. And so the score here is high. It's a score of 10 on a scale of 0 to 10. But then for these sentences down here, there wasn't supporting evidence, and therefore the groundedness score is 0. Let's take a concrete example. Maybe this one. It's saying it's often used in situations where the cost of execution is relatively low and where the ability to iterate and adapt quickly is more important than upfront planning. This does feel like a plausible piece of text that could be useful as part of the response to the question. However, it wasn't there in the retrieved piece of context, so it's not backed up by any supporting evidence in the retrieved context. This could possibly have been part of what the model had learned during its training phase, where either from the same document, Andrew's document here on career advice for AI, or some other source talking about the same topic, the model may have learned similar information. But it's not grounded in that it is not, the sentence is not supported by the retrieved piece of context in this particular instance. So this is a general issue when the sentence window is too small, that context relevance tends to be low. And as a consequence, groundedness also becomes low because the LLM starts making use of its pre-existing knowledge from its training phase to start answering questions instead of just relying on the supplied context. Now that I've shown you a failure mode with sentence windows set to one, I want to walk through a few more steps to see how the metrics improve as we change the sentence window size. For the purpose of going through the notebook quickly, I'm going to reload the evaluation questions, but in this instance, just set it to the one question where the model had problem, this particular question, which we just walked through with the sentence window size set at 1. And then I want to run this through with the sentence window size set to 3. This code snippet is going to set up the rag with sentence window size set up three, and also set up the true recorder for it. We now have the definition of the feedback function set up in addition to the RAG, with the sentence window set at size 3. Next up, we are going to run the evaluations with that eval for that particular evaluation question that we have looked through in some detail with the sentence window set through one, where we observe the failure mode that has run successfully. Let's now look at the results with sentence window engine set to three in the TruLens dashboard. You can see the results here, I ran it on the one record. That was the problematic record when we looked at sentence window size one. And you can see a huge increase in the context relevance. It went up from 0.57 to 0.9. Now if I select the app and look at this example in some more detail, let's now look at the same question that we looked at with sentence window set at one. Now we are at 3. Here's the full final response. Now if you look at the retrieved pieces of context, you'll notice that this particular piece of retrieved context is similar to the one that we had retrieved earlier with sentence window set at size 1. But now it has the expansion because of the bigger sentence window size. For this section, we'll see that this context got a context-relevant score of 0.9, which is higher than the score of 0.8 that the smaller context had gotten earlier. And this example shows that with an expansion in the sentence window size, even reasonably good pieces of retrieved context can get even better. Once the completion step is done with these significantly better pieces of context, the groundedness score goes up quite a bit. We'd see that by finding supporting evidence across these two pieces of highly relevant context, the groundedness score actually goes up all the way to one. So increasing the sentence window size from one to three led to a substantial improvement in the evaluation metrics of the RAG triad. Both groundedness and context relevance went up significantly, as did answer relevance. And now we can look at sentence window set to five. If you look at the metrics here, a couple of things to observe. One is the total tokens has gone up, and this could have an impact on the cost if you were to increase the number of records. So that's one of the trade-offs that I mentioned earlier. As you increase the sentence window size, it gets more expensive because more tokens are being processed by the LLMs during evaluation. The other thing to observe is that while context relevance, and answer relevance have remained flat, groundedness has actually dropped with the increase in the sentence window size. And this can happen after a certain point because, as the context size increases, the LLM can get overwhelmed in the completion step with too much information. And in the process of summarization, it can start introducing its own pre-existing knowledge instead of just the information in the retrieved pieces of context. So to wrap things up here, it turns out that as we gradually increase the sentence window size from 1 to 3 to 5, 3, the size of 3 is the best choice for us for this particular evaluation. And we see the increase in context relevance and answer relevance and groundedness as we go from one to 3, and then a reduction or degradation in the groundedness step with a further increase to a size of five. As you are playing with the notebook, we encourage you to rerun it with more records in these two steps. Examine the individual records which are causing problems for specific metrics like context relevance or groundedness, and get some intuition and build some intuition around why the failure modes are happening and what to do to address them, and in the next section, we will look at another advanced rack technique auto merging to address some of those failure modes. Irrelevant context can creep into the final response, resulting in not such great scores in groundedness or answer relevance.
