Retrieval Augmented Generation (RAG) seems to be quite popular these days. Along the wave of Large Language Models (LLM’s), it is one of the popular techniques to get LLM’s to perform better on specific tasks such as question answering on in-house documents. Some time ago, I played on a Kaggle competition that allowed me to try it out and learn a bit better than random experiments on my own. Here are a few learnings from that and the following experiments while writing this article.
All images, unless otherwise noted, are by the author. Generated with the help of ChatGPT+/DALL-E3 (where noted), or taken from my personal Jupyter notebooks.
RAG has two main parts, retrieval and generation. In the first part, retrieval is used to fetch (chunks of) documents related to the query of interest. Generation uses those fetched chunks as added input, called context, to the answer generation model in the second part. This added context is intended to give the generator more up-to-date, hopefully better, information to base its generated answer on than just its base training data.
LLM’s have a maximum context or sequence window length they can handle, and the generated input context for RAG needs to be short enough to fit into this sequence window. We want to fit as much relevant information into this context as possible, so getting the best “chunks” of text from the potential input documents is important. These chunks should optimally be the most relevant ones for generating the correct answer to the question posed to the RAG system.
As a first step, the input text is typically chunked into smaller pieces. A basic pre-processing step in RAG is converting these chunks into embeddings using a specific embedding model. A typical sequence window for an embedding model is 512 tokens, which also makes a practical target for chunk size. Once the documents are chunked and encoded into embeddings, a similarity search using the embeddings can be performed to build the context for generating the answer.
I have found ai/langchain” rel=”noopener ugc nofollow” target=”_blank”>Langchain to provide useful tools for input loading and chunking. For example, chunking a document with Langchain (in this case, using tokenizer for Flan-T5-Large model) is as simple as:
from transformers import AutoTokenizer
from langchain.text_splitter import RecursiveCharacterTextSplitter #This is the Flan-T5-Large model I used for the Kaggle competition
llm = "/mystuff/llm/flan-t5-large/flan-t5-large"
tokenizer = AutoTokenizer.from_pretrained(llm, local_files_only=True)
text_splitter = RecursiveCharacterTextSplitter
.from_huggingface_tokenizer(tokenizer, chunk_size=12,
chunk_overlap=2,
separators=("\n\n", "\n", ". "))
section_text="Hello. This is some text to split. With a few "\
"uncharacteristic words to chunk, expecting 2 chunks."
texts = text_splitter.split_text(section_text)
print(texts)
This produces the following two chunks:
('Hello. This is some text to split',
'. With a few uncharacteristic words to chunk, expecting 2 chunks.')
In the above code, chunk_size 12 tells LangChain to aim for a maximum of 12 tokens per chunk. Depending on the text structure, this may not always be 100% exact. However, in my experience it works generally well. Something to keep in mind is the difference between tokens vs words. Here is an example of tokenizing the above section_text:
section_text="Hello. This is some text to split. With a few "\
"uncharacteristic words to chunk, expecting 2 chunks."
encoded_text = tokenizer(section_text)
tokens = tokenizer.convert_ids_to_tokens(encoded_text('input_ids'))
print(tokens)
Resulting output tokens:
('▁Hello', '.', '▁This', '▁is', '▁some', '▁text', '▁to', '▁split', '.',
'▁With', '▁', 'a', '▁few', '▁un', 'character', 'istic', '▁words',
'▁to', '▁chunk', ',', '▁expecting', '▁2', '▁chunk', 's', '.', '</s>')
Most words in the section_text form a token on their own, as they are common words in texts. However, for special forms of words, or domain words this can be a bit more complicated. For example, here the word “uncharacteristic” becomes three tokens (“ un”, “ character”, “ istic”). This is because the model tokenizer knows those 3 partial sub-words but not the entire word (“ uncharacteristic “). Each model comes with its own tokenizer to match these rules in input and model training.
In chunking, the RecursiveCharacterTextSplitter from Langchain used in above code counts these tokens, and looks for given separators to split the text into chunks as requested. Trials with different chunk sizes may be useful. In my Kaggle experiment I started with the maximum size for the embedding model, which was 512 tokens. Then proceeded to try chunk sizes of 256, 128, and 64 tokens.
The Kaggle competition I mentioned was about multiple-choice question answering based on Wikipedia data. The task was to select the correct answer option from the multiple options for each question. The obvious approach was to use RAG to find required information from a Wikipedia dump, and use it to generate the correct. Here is the first question from competition data, and its answer options to illustrate:
The multiple-choice questions were an interesting topic to try out RAG. But the most common RAG use case is, I believe, answering questions based on source documents. Kind of like a chatbot, but typically question answering over domain specific or (company) internal documents. I use this basic question answering use case to demonstrate RAG in this article.
As an example RAG question for this article, I needed something the LLM would not know the answer to directly based on its training data alone. I used Wikipedia data, and since it is likely used as part of training data for LLM’s, I needed a question related to something after the model was trained. The model I used for this article was Zephyr 7B beta, trained in early 2023. Finally, I settled on asking about the Google Bard ai chatbot. It has had many developments over the past year, after the Zephyr training date. I also have a decent knowledge of Bard to evaluate the LLM’s answers. Thus I used “what is google bard? “ as an example question for this article.
The first phase of retrieval in RAG is based on the embedding vectors, which are really just points in a multidimensional space. They look something like this (only the first 10 values here):
q_embeddings(:10)
array((-0.45518905, -0.6450379, 0.3097812, -0.4861114 , -0.08480848,
-0.1664767 , 0.1875889, 0.3513346, -0.04495572, 0.12551129),
These embedding vectors can be used to compare the words/sentences, and their relations, against each other. These vectors can be built using embedding models. A nice set of those models with various stats per model can be found on the MTEB leaderboard. Using one of those models is as simple as this:
from sentence_transformers import SentenceTransformer, utilembedding_model_path = "/mystuff/llm/bge-small-en"
embedding_model = SentenceTransformer(embedding_model_path, device='cuda')
The model page on HuggingFace typically shows the example code. The above loads the model “ bge-small-en “ from local disk. To create the embeddings using this model is just:
question = "what is google bard?"
q_embeddings = embedding_model.encode(question)
In this case, the embedding model is used to encode the given question into an embedding vector. The vector is the same as the example above:
q_embeddings.shape
(, 384)q_embeddings(:10)
array((-0.45518905, -0.6450379, 0.3097812, -0.4861114 , -0.08480848,
-0.1664767 , 0.1875889, 0.3513346, -0.04495572, 0.12551129),
dtype=float32)
The shape (, 384) tells me q_embeddings is a single vector (as opposed to embedding a list of multiple texts at once) of length 384 floats. The slice above shows the first 10 values out of those 384. Some models use longer vectors for more accurate relations, others, like this one, shorter (here 384). Again, MTEB leaderboard has good examples. The small ones require less space and computation, larger ones give some improvements in representing the relations between chunks, and sometimes sequence length.
For my RAG similarity search, I first needed embeddings for the question. This is the q_embeddings above. This needed to be compared against embedding vectors of all the searched articles (or their chunks). In this case all the chunked Wikipedia articles. To build embedding for all of those:
article_embeddings = embedding_model.encode(article_chunks)
Here article_chunks is a list of all chunks for all articles from the English Wikipedia dump. This way they can be batch-encoded.
Implementing similarity search over a large set of documents / document chunks is not too complicated at a basic level. A common way is to calculate cosine similarity between the query and document vectors, and sort accordingly. However, at large scale, this sometimes gets a bit complicated to manage. Vector databases are tools that make this management and search easier / more efficient at scale.
For example, Weaviate is a vector database that was used in ai-journey-webinar” rel=”noopener ugc nofollow” target=”_blank”>StackOverflow’s ai-based search. In its latest versions, it can also be used in an embedded mode, which should have made it usable even in a Kaggle notebook. It is also used in some ai/vector-databases-embeddings-applications/lesson/1/introduction” rel=”noopener ugc nofollow” target=”_blank”>Deeplearning.ai LLM short courses, so at least seems somewhat popular. Of course, there are many others and it is good to make comparisons, this field also evolves fast.
In my trials, I used FAISS from Facebook/Meta research as the vector database. FAISS is more of a library than a client-server database, and was thus simple to use in a Kaggle notebook. And it worked quite nicely.
Once the chunking and embedding of all the articles was all done, I built a Pandas DataFrame with all the relevant information. Here is an example with the first 5 chunks of the Wikipedia dump I used, for a document titled Anarchism:
Each row in this table (a Pandas DataFrame) contains data for a single chunk after the chunking process. It has 5 columns:
- chunk_id: allows me to map chunk embeddings to the chunk text later.
- doc_id: allows mapping the chunks back to their document.
- doc_title: for trialing approaches such as adding the doc title to each chunk.
- chunk_title: article subsection title for the chunk, same purpose as doc_title
- chunk: the actual chunk text
Here are the embeddings for the first five Anarchism chunks, same order as the DataFrame above:
(( 0.042624 -0.131264 -0.266858 ... -0.329627 0.178211 0.248001)
(-0.120318 -0.110153 -0.059611 ... -0.297150 -0.043165 0.558150)
( 0.116761 -0.066759 -0.498548 ... -0.330301 0.019448 0.326484)
(-0.517585 0.183634 0.186501 ... 0.134235 -0.033262 0.498731)
(-0.245819 -0.189427 0.159848 ... -0.077107 -0.111901 0.483461))
Each row is partially only shown here, but illustrates the idea.
Earlier I encoded the query vector for query “ what is google bard? “‘, followed by encoding all the article chunks. With these two sets of embeddings, the first part of RAG search is simple: finding the documents “semantically” closest to the query. In practice just calculating a measure such as cosine similarity between the query embedding vector and all the chunk vectors, and sorting by the similarity score.
Here are the top 10 “semantically” closest chunks to the q_embeddings:
Each row in this table (DataFrame) represents a chunk. The sim_score here is the calculated cosine similarity score, and the rows are sorted from highest cosine similarity to lowest. The table shows the top 10 highest sim_score rows.
A pure embeddings based similarity search is very fast and low-cost in terms of computation. However, it is not quite as accurate as some other approaches. Re-ranking is a term used to describe the process of using another model to more accurately sort this initial list of top documents, with a more computationally expensive model. This model is usually too expensive to run against all documents and chunks, but running it on the set of top chunks after the initial similarity search is much more feasible. Re-ranking helps to get a better list of final chunks to build the input context for the generation part of RAG.
The same MTEB leaderboard that hosts metrics for the embedding models also has re-ranking scores for many models. In this case I used the bge-reranker-base model for re-ranking:
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer rerank_model_path = "/mystuff/llm/bge-reranker-base"
rerank_tokenizer = AutoTokenizer.from_pretrained(rerank_model_path)
rerank_model = AutoModelForSequenceClassification
.from_pretrained(rerank_model_path)
rerank_model.eval()
def calculate_rerank_scores(pairs):
with torch.no_grad(): inputs = rerank_tokenizer(pairs, padding=True,
truncation=True, return_tensors='pt',
max_length=512)
scores = rerank_model(**inputs, return_dict=True)
.logits.view(-1, ).float()
return scores
question = questions(idx)
pairs = ((question, chunk) for chunk in doc_chunks_all(idx))
rerank_scores = calculate_rerank_scores(pairs)
df("rerank_score") = rerank_scores
After adding rerank_score to the chunk DataFrame, and sorting with it:
Comparing the two tables above (first sorted by sim_score vs now by rerank_score), there are some clear differences. Sorting by the plain similarity score ( sim_score) from embeddings, the Tenor page is the 5th most similar chunk. Since Tenor appears to be a GIF search engine hosted by Google, I guess it makes some sense to see its embeddings close to the question “ what is google bard? “. But it has nothing really to do with Bard itself, except that Tenor is a Google product in a similar domain.
However, after sorting by the rerank_score, the results make much more sense. Tenor is gone from the top 10, and only the last two chunks from the top 10 list appear to be unrelated. These are about the names “Bard” and “Bård”. Possibly because the best source of information on Google Bard appears to be the page on Google Bard, which in the above tables is document with id 6026776. After that I guess RAG runs out of good article matches and goes a bit off-road (Bård). Which is also seen in the negative re-rank scores for those two last rows/chunks of the table.
Typically there would likely be many relevant documents and chunks across those documents, not just the 1 document and 8 chunks as above. But in this case this limitation helps illustrate the difference in basic embeddings-based similarity search and re-ranking, and how re-ranking can positively affect the end result.
What do we do once we have collected the top chunks for RAG input? We need to build the context for the generator model from these chunks. At its simplest, this is just a concatenation of the selected top chunks into a long text sequence. The maximum length of this sequence in constrained by the used model. As I used the Zephyr 7B model, I used 4096 tokens as the maximum length. The Zephyr page gives this as a flexible sequence limit (with sliding attention window). Longer context seems better, but it appears this is not always the case. Better try it.
Here is the base code I used to generate the answer with this context:
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch llm_answer_path = "/mystuff/llm/zephyr-7b-beta"
torch_device = "cuda:0"
tokenizer = AutoTokenizer.from_pretrained(llm_answer_path,
local_files_only=True)
llm_answer = AutoModelForCausalLM.from_pretrained(llm_answer_path,
device_map=torch_device, local_files_only=True,
torch_dtype=torch.float16)
# assuming here that "context" contains the pre-built context
query = "answer the following question, "\
"based on your knowledge and the provided context. "\n
"Keep the answer concise.\n\nquestion:" + question +
"\n\ncontext:"+context
input_ids = tokenizer.encode(query+"\n\nANSWER:", return_tensors='pt',
return_attention_mask=False).to(torch_device)
greedy_output = llm_answer.generate(input_ids, max_new_tokens=1024,
do_sample=True)
answer = tokenizer.decode(greedy_output(0), skip_special_tokens=True)
print(answer(len(query):))
As noted, in this case the context was just a concatenation of the top ranked chunks.
For comparison, first lets try what the model answers without any added context, i.e. based on its training data alone:
query = "what is google bard?"
input_ids = tokenizer.encode(query+"\n\nANSWER:", return_tensors='pt',
return_attention_mask=False).to(torch_device)
greedy_output = llm_answer.generate(input_ids, max_new_tokens=1024,
do_sample=True)
answer = tokenizer.decode(greedy_output(0), skip_special_tokens=True)
print(answer(len(query):))
This gives (one of many runs, slight variations but generally similar):
ANSWER:
Google Bard is an experimental, ai-based language model developed by
Google's sister company, DeepMind. Its primary use is to generate
human-like text responses to prompts, which can help in tasks such as
content creation, idea generation, and text summarization. Bard is
trained on a vast amount of textual data and can provide highly
relevant and contextually accurate responses, making it a useful tool
in various applications where text generation is required. However, as
an experimental feature, Bard's accuracy and effectiveness may still be
improving, and it is not yet publicly available for use.
Generally accurate, but missing much of the latest developments. In comparison, lets try with providing the generated context to the question:
query = "answer the following question, "\
"based on your knowledge and the provided context. "\
"Keep the answer concise.\n\n"\
"question:" + question + "\n\ncontext:"+context
input_ids = tokenizer.encode(query+"\n\nANSWER:", return_tensors='pt',
return_attention_mask=False).to(torch_device)
greedy_output = llm_answer.generate(input_ids, max_new_tokens=1024,
do_sample=True)
answer = tokenizer.decode(greedy_output(0), skip_special_tokens=True)
print(answer(len(query):))
The following is an example answer with the top sim_score sorted chunks as context (includes the Tenor and Bård page chunks):
ANSWER:
Bard is a conversational generative artificial intelligence (ai)
chatbot developed by Google based initially on the LaMDA family of
large language models (LLMs) and later PaLM. It was launched in a
limited capacity in March 2023 to mixed reviews, and expanded to other
countries in May, following the launch of Microsoft's Bing Chat. Google
researcher Jacob Devlin resigned from the company after alleging Bard
had surreptitiously leveraged data from ChatGPT. Tenor is an online GIF
search engine and database owned by Google, available on Android, iOS,
and macOS. Its main product is the GIF Keyboard, which is integrated
into numerous apps and messaging services. Bård is a Norwegian
masculine given name of Old Norse origin. It is sometimes used as a
surname. It may refer to several people. Tenor and Bård are not related.
This is not a very good answer since it starts talking about completely non-related topics here, Tenor and Bård. Partly because in this case the Tenor chunk is included in the context, and chunk order also generally less optimal as it is not re-ranked.
In comparison, with rerank_score sorted context chunks (better chunk ordering and Tenor gone):
ANSWER:
Google Bard is a conversational generative artificial intelligence (ai)
chatbot, based initially on the LaMDA family of large language models
(LLMs) and later on PaLM, developed by Google to compete with OpenAI's
ChatGPT. It was rolled out in a limited capacity in March 2023 and
expanded to more countries in May, prompting a mixed reception from
critics, who raised safety and ethical concerns about its accuracy and
usefulness. Google has promised that Bard will be tightly integrated
with other Google ai products and services, leading to claims that a
new ai-powered version of the Google Assistant, dubbed "Assistant with
Bard", is being prepared for launch. Google has also stressed that Bard
is still in its early stages and being continuously refined, with plans
to upgrade it with new personalization and productivity features, while
stressing that it remains distinct from Google Search.
Now the unrelated topics are gone and the answer in general is better and more to the point.
This highlights that it is not only important to find proper context to give to the model, but also to trim out the unrelated context. At least in this case, the Zephyr model was not able to directly identify which part of the context was relevant, but rather seems to have summarized the it all. Cannot really fault the model, as I gave it that context and asked to use it.
Looking at the re-rank scores for the chunks, a general filtering approach based on metrics such as negative re-rank scores would have solved this issue also in the above case, as the “bad” chunks in this case have a negative re-rank score.
Something to note is that Google released a new and much improved Gemini family of models for Bard, around the time I was writing this article. It is not mentioned in the generated answers here since the Wikipedia dumps are generated with a slight delay. So as one might imagine, it is important to try to have up-to-date information in the context, and to keep it relevant and focused.
Embeddings are a great tool, but sometimes it is a bit difficult to really grasp how they are working, and what is happening with the similarity search. A basic approach is to plot the embeddings against each other to get some insight into their relations.
Building such a visualization is quite simple with PCA and visualization libraries. It involves mapping the embedding vectors to 2 or 3 dimensions, and plotting the results. Here I map from those 384 dimensions to 2, and plot the result:
import seaborn as sns
import numpy as np fp_embeddings = embedding_model.encode(first_chunks)
q_embeddings_reshaped = q_embeddings.reshape(1, -1)
combined_embeddings = np.concatenate((fp_embeddings, q_embeddings_reshaped))
df_embedded_pca = pd.DataFrame(X_pca, columns=("x", "y"))
# text is short version of chunk text (plot title)
df_embedded_pca("text") = titles
# row_type = article or question per each embedding
df_embedded_pca("row_type") = row_types
X = combined_embeddings pca = PCA(n_components=2).fit(X)
X_pca = pca.transform(X)
plt.figure(figsize=(16,10))
sns.scatterplot(x="x", y="y", hue="row_type",
palette={"article": "blue", "question": "red"},
data=df_embedded_pca, #legend="full",
alpha=0.8, s=100 )
for i in range(df_embedded_pca.shape(0)):
plt.annotate(df_embedded_pca("text").iloc(i),
(df_embedded_pca("x").iloc(i), df_embedded_pca("y").iloc(i)),
fontsize=20 )
plt.legend(fontsize='20')
# Change the font size for x and y axis ticks plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
# Change the font size for x and y axis labels
plt.xlabel('X', fontsize=16)
plt.ylabel('Y', fontsize=16)
For the top 10 articles in the “ what is google bard? “ question, this gives the following visualization:
In this plot, the red dot is the embedding for the question “ what is google bard?”. The blue dots are the closest Wikipedia article matches, according to sim_score.
The Bard article is obviously the closest one to the question, while the rest are a bit further off. The Tenor article seems to be about second closest, while the Bård one is a bit further away, possibly due to the loss of information in mapping from 384 dimensions to 2. Due to this, the visualization is not perfectly accurate but helpful for quick human overview.
The following figure illustrates an actual error finding from my Kaggle code using a similar PCA plot. Looking for a bit of insights, I tried a simple question about the first article in the Wikipedia dump (“ Anarchism”). With the question “ what is the definition of anarchism? “ . The following is what the PCA visualization looked like for the closest articles, the marked outliers are perhaps the most interesting part:
The red dot in the bottom left corner is again the question. The cluster of blue dots next to it are all related articles about anarchism. And then there are the two outlier dots on the top right. I removed the titles from the plot to keep it readable. The two outlier articles seemed to have nothing to do with the question when looking.
Why is this? As I indexed the articles with various chunk sizes of 512, 256, 128, and 64, I had some issues in processing all the articles for 256 chunk size, and restarted the chunking in the middle. This resulted in some differences in indices of some of those embeddings vs the chunk texts I had stored. After noticing these strange looking results, I re-calculated the embeddings with the 256 token chunk size, and compared the results vs size 512, noted this difference. Too bad the competition was done at that time
In the above I discussed chunking the documents and using similarity search + re-ranking as a method to find relevant chunks and build a context for the question answering. I found sometimes it is also useful to consider how the initial documents to chunk are selected vs just the chunks themselves.
As example methods, the ai/short-courses/building-evaluating-advanced-rag/” rel=”noopener ugc nofollow” target=”_blank”>advanced RAG course on ai/” rel=”noopener ugc nofollow” target=”_blank”>DeepLearning.ai , presents two approaches: sentence windowing, and hierarchical chunk merging. In summary this looks at nearby-chunks and if multiple are ranked high by their scores, takes them as a single large chunk. The “hierarchy” coming from considering larger and larger chunk combinations for joint relevance. Aiming for more cohesive context vs random ordered small chunks, giving the generator LLM better input to work with.
As a simple example of this, here is the re-ranked set of top chunks for my above Bard example:
The leftmost column here is the index of the chunk. In my generation, I just took the top chunks in this sorted order as in the table. If we wanted to make the context a bit more coherent, we could sort the final selected chunks by their order within a document. If there is a small piece missing between highly ranked chunks, adding the missing one (e.g., here chunk id 7) could help in missing gaps, similar to the hierarchical merging. This could be something to try as a final step for final gains.
In my Kaggle experiments, I performed initial document selection based on the first chunk only. In part due to Kaggle’s resource limits, but it appeared to have some other advantages as well. Typically, an article’s beginning acts as a summary (introduction or abstract). Initial chunk selection from such ranked articles may help select chunks with more relevant overall context.
This is visible in my Bard example above, where both the rerank_score and sim_score are highest for the first chunk of the best article. To try to improve this, I also tried using a larger chunk size for this initial document selection, to include more of the introduction for better relevance. Then chunked the top selected documents with smaller chunk sizes for experimenting on how good the context is with each size.
While I could not run the initial search on all chunks of all documents on Kaggle due to resource limitations, I tried it outside of Kaggle. In these trials, I noticed that sometimes single chunks of unrelated articles get ranked high, while in reality misleading for the answer generation. For example, actor biography in a related movie. Initial document relevance selection may help avoid this. Unfortunately, I did not have time to study this further with different configurations, and good re-ranking may already help.
Finally, repeating the same information in multiple chunks in the context is not very useful. Top ranking of the chunks does not guarantee that they best complement each other, or best chunk diversity. For example, LangChain has a special chunk selector for Maximum Marginal Relevance. It does this by penalizing new chunks by how close they are to the already added chunks.
I used a very simple question / query for my RAG example here (“ what is google bard?”), and simple is good to illustrate the basic RAG concept. This is a pretty short query input considering that the embedding model I used had a 512 token maximum sequence length. If I encode this question into tokens using the tokenizer for the embedding model ( bge-small-en), I get the following tokens:
('(CLS)', 'what', 'is', 'google', 'bard', '?', '(SEP)')
Which amounts to a total of 7 tokens. With a maximum sequence length of 512, this leaves plenty of room if I want to use a longer query sentence. Sometimes this can be useful, especially if the information we want to retrieve is not such a simple query, or if the domain is more complex. For a very small query, the semantic search may not work best, as noted also in the Stack Overflows ai Journey posting.
For example, the Kaggle competition had a set of questions, each with 5 answer options to pick from. I initially tried RAG with just the question as the input for the embedding model. The search results were not too great, so I tried again with the question + all the answer options as the query. This produced much better results.
As an example, the first question in the training dataset of the competition:
Which of the following statements accurately describes the impact of
Modified Newtonian Dynamics (MOND) on the observed "missing baryonic mass"
discrepancy in galaxy clusters?
This is 32 tokens for the bge-small-en model. So about 480 still left to fit into the maximum 512 token sequence length.
Here is the first question along with the 5 answer options given for it:
Concatenating the question and the given options into one RAG query gives this a length 235 tokens, with still more than 50% of embedding model sequence length left. In my case, this approach produced much better results. Both from manual inspection, and for the competition score. Thus, experimenting with different ways to make the RAG query itself more expressive is worth a try.
Finally, there is the topic of hallucinations, where the model produces text that is incorrect or fabricated. The Tenor example from my sim_score sorting is one kind of an example, even if the generator did base it on the actual given context. So better keep the context good I guess :).
To address hallucinations, the chatbots from the big ai companies ( Google Bard, ChatGPT, Bing Chat) all provide means to link parts of their generated answers to verifiable sources. Bard has a specific “G” button that performs a Google search and highlights parts of the generated answer that match the search results. Too bad we do not always have a world-class search-engine for our data to help.
Bing Chat has a similar approach, highlighting parts of the answer and adding a reference to the source websites. ChatGPT has a slightly different approach; I had to explicitly ask it to verify its answer and update with latest developments, telling it to use its browser tool. After this, it did an internet search and linked to specific websites as sources. The source quality seemed to vary quite a bit as in any internet search. Of course, for internal documents this type of web search is not possible. However, linking to the source should always be possible even internally.
I also asked Bard, ChatGPT+, and Bing for ideas on detecting hallucinations. The results included an LLM hallucination ranking index, including RAG hallucination. When tuning LLM’s, it might also help to set the ai/” rel=”noopener ugc nofollow” target=”_blank”>temperature parameter to zero for the LLM to generate deterministic, most probable output tokens.
Finally, as this is a very common problem, there seem to be various approaches being built to address this challenge a bit better. For example, specific LLM’s to help detect halluciations seem to be a promising area. I did not have time to try them, but certainly relevant in bigger projects.
Besides implementing a working RAG solution, it is also nice to be able to tell something about how well it works. In the Kaggle competition this was quite simple. I just ran the solution to try to answer the given questions in the training dataset, comparing to the correct answers given in the training data. Or submitted the model for scoring on the Kaggle competition test set. The better the answer score, the better one could call the RAG solution, even if there was more to the score.
In many cases, a suitable evaluation dataset for domain specific RAG may not be available. For this scenario, one might want to start with some generic NLP evaluation datasets, such as this list. Tools such as LangChain also come with support for auto-generating questions and answers, and evaluating them. In this case, an LLM is used to create example questions and answers for a given set of documents, and another LLM is used to evaluate whether the RAG can provide the correct answer to these questions. This is perhaps better explained in this ai/langchain/lesson/6/evaluation” rel=”noopener ugc nofollow” target=”_blank”>tutorial on RAG evaluation with LangChain.
While the generic solutions are likely good to start with, in a real project I would try to collect a real dataset of questions and answers from the domain experts and the intended users of the RAG solution. As the LLM is typically expected to generate a natural language response, this can vary a lot while still being correct. For this reason, evaluating if the answer was correct or not is not as straightforward as a regular expression or similar pattern matching. Here, I find the idea of using another LLM to evaluate whether the given response matches a reference response a very useful tool. These models can deal with the text variation much better.
RAG is a very nice tool, and is quite a popular topic these days with the high interest in LLM’s in general. While RAG and embeddings have been around for a good while, the latest powerful LLM’s and their fast evolution have perhaps made them more interesting for many advanced use cases. I expect the field to keep evolving at a good pace, and it is sometimes a bit difficult to keep up to date on everything. For this, summaries such as reviews on RAG developments can give points to at least keep the main developments in sight.
The RAG approach in general is quite simple: find a set of chunks of text similar to the given query, concatenate them into a context, and ask the LLM for an answer. However, as I tried to show here, there can be various issues to consider in how to make this work well and efficiently for different needs. From good context retrieval, to ranking and selecting the best results, and finally being able to link the results back to actual source documents. And evaluating the resulting query contexts and answers. And as Stack Overflow people noted, sometimes the more traditional lexical or hybrid search is very useful as well, even if semantic search is cool.
That’s all for today. RAG on…
