How and Why to Use LLMs for Chunk-Based Information Retrieval
Retrieve pipeline — Image by the author
In this article, I aim to explain how and why it’s beneficial to use a Large Language Model (LLM) for chunk-based information retrieval.
I use OpenAI’s GPT-4 model as an example, but this approach can be applied with any other LLM, such as those from Hugging Face, Claude, and others.
Everyone can access this article for free.
Considerations on standard information retrieval
The primary concept involves having a list of documents (chunks of text) stored in a database, which could be retrieve based on some filter and conditions.
Typically, a tool is used to enable hybrid search (such as Azure AI Search, LlamaIndex, etc.), which allows:
Figure 1- Default hybrid search pipeline — Image by the author
Figure 1 shows the classic retrieval pipeline:
This solution achieves good results but has some limitations:
An example of a typical retrieval issue
Let’s consider the “documents” array, which represents an example of a knowledge base that could lead to incorrect chunk selection.
documents = [
"Chunk 1: This document contains information about topic A.",
"Chunk 2: Insights related to topic B can be found here.",
"Chunk 3: This chunk discusses topic C in detail.",
"Chunk 4: Further insights on topic D are covered here.",
"Chunk 5: Another chunk with more data on topic E.",
"Chunk 6: Extensive research on topic F is presented.",
"Chunk 7: Information on topic G is explained here.",
"Chunk 8: This document expands on topic H. It also talk about topic B",
"Chunk 9: Nothing about topic B are given.",
"Chunk 10: Finally, a discussion of topic J. This document doesn't contain information about topic B"
]
Let’s assume we have a RAG system, consisting of a vector database with hybrid search capabilities and an LLM-based prompt, to which the user poses the following question: “I need to know something about topic B.”
As shown in Figure 2, the search also returns an incorrect chunk that, while semantically relevant, is not suitable for answering the question and, in some cases, could even confuse the LLM tasked with providing a response.
Figure 2 — Example of information retrieval that can lead to errors — Image by the author
In this example, the user requests information about “topic B,” and the search returns chunks that include “This document expands on topic H. It also talks about topic B” and “Insights related to topic B can be found here.” as well as the chunk stating, “Nothing about topic B are given”.
While this is the expected behavior of hybrid search (as chunks reference “topic B”), it is not the desired outcome, as the third chunk is returned without recognizing that it isn’t helpful for answering the question.
The retrieval didn’t produce the intended result, not only because the BM25 search found the term “topic B” in the third Chunk but also because the vector search yielded a high cosine similarity.
To understand this, refer to Figure 3, which shows the cosine similarity values of the chunks relative to the question, using OpenAI’s text-embedding-ada-002 model for embeddings.
Figure 3 — Cosine similarity with text-embedding-ada-002- Image by the author
It is evident that the cosine similarity value for “Chunk 9” is among the highest, and that between this chunk and chunk 10, which references “topic B,” there is also chunk 1, which does not mention “topic B”.
This situation remains unchanged even when measuring distance using a different method, as seen in the case of Minkowski distance.
Utilizing LLMs for Information Retrieval: An Example
The solution I will describe is inspired by what has been published in my GitHub repository https://github.com/peronc/LLMRetriever/.
The idea is to have the LLM analyze which chunks are useful for answering the user’s question, not by ranking the returned chunks (as in the case of RankGPT) but by directly evaluating all the available chunks.
Figure 4- LLM Retrieve pipeline — Image by the author
In summary, as shown in Figure 4, the system receives a list of documents to analyze, which can come from any data source, such as file storage, relational databases, or vector databases.
The chunks are divided into groups and processed in parallel by a number of threads proportional to the total amount of chunks.
The logic for each thread includes a loop that iterates through the input chunks, calling an OpenAI prompt for each one to check its relevance to the user’s question.
The prompt returns the chunk along with a boolean value: true if it is relevant and false if it is not.
Lets’go coding 😊
To explain the code, I will simplify by using the chunks present in the documents array (I will reference a real case in the conclusions).
First of all, I import the necessary standard libraries, including os, langchain, and dotenv.
import os
from langchain_openai.chat_models.azure import AzureChatOpenAI
from dotenv import load_dotenv
Next, I import my LLMRetrieverLib/llm_retrieve.py class, which provides several static methods essential for performing the analysis.
from LLMRetrieverLib.retriever import llm_retriever
Following that, I need to import the necessary variables required for utilizing Azure OpenAI GPT-4o model.
load_dotenv()
azure_deployment = os.getenv("AZURE_DEPLOYMENT")
temperature = float(os.getenv("TEMPERATURE"))
api_key = os.getenv("AZURE_OPENAI_API_KEY")
endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
api_version = os.getenv("API_VERSION")
Next, I proceed with the initialization of the LLM.
# Initialize the LLM
llm = AzureChatOpenAI(api_key=api_key, azure_endpoint=endpoint, azure_deployment=azure_deployment, api_version=api_version,temperature=temperature)
We are ready to begin: the user asks a question to gather additional information about Topic B.
question = "I need to know something about topic B"
At this point, the search for relevant chunks begins, and to do this, I use the function llm_retrieve.process_chunks_in_parallel from the LLMRetrieverLib/retriever.py library, which is also found in the same repository.
relevant_chunks = LLMRetrieverLib.retriever.llm_retriever.process_chunks_in_parallel(llm, question, documents, 3)
To optimize performance, the function llm_retrieve.process_chunks_in_parallel employs multi-threading to distribute chunk analysis across multiple threads.
The main idea is to assign each thread a subset of chunks extracted from the database and have each thread analyze the relevance of those chunks based on the user’s question.
At the end of the processing, the returned chunks are exactly as expected:
['Chunk 2: Insights related to topic B can be found here.',
'Chunk 8: This document expands on topic H. It also talk about topic B']
Finally, I ask the LLM to provide an answer to the user’s question:
final_answer = LLMRetrieverLib.retriever.llm_retriever.generate_final_answer_with_llm(llm, relevant_chunks, question)
print("Final answer:")
print(final_answer)
Below is the LLM’s response, which is trivial since the content of the chunks, while relevant, is not exhaustive on the topic of Topic B:
Topic B is covered in both Chunk 2 and Chunk 8.
Chunk 2 provides insights specifically related to topic B, offering detailed information and analysis.
Chunk 8 expands on topic H but also includes discussions on topic B, potentially providing additional context or perspectives.
Scoring Scenario
Now let’s try asking the same question but using an approach based on scoring.
I ask the LLM to assign a score from 1 to 10 to evaluate the relevance between each chunk and the question, considering only those with a relevance higher than 5.
To do this, I call the function llm_retriever.process_chunks_in_parallel, passing three additional parameters that indicate, respectively, that scoring will be applied, that the threshold for being considered valid must be greater than or equal to 5, and that I want a printout of the chunks with their respective scores.
relevant_chunks = llm_retriever.process_chunks_in_parallel(llm, question, documents, 3, True, 5, True)
The retrieval phase with scoring produces the following result:
score: 1 - Chunk 1: This document contains information about topic A.
score: 1 - Chunk 7: Information on topic G is explained here.
score: 1 - Chunk 4: Further insights on topic D are covered here.
score: 9 - Chunk 2: Insights related to topic B can be found here.
score: 7 - Chunk 8: This document expands on topic H. It also talk about topic B
score: 1 - Chunk 5: Another chunk with more data on topic E.
score: 1 - Chunk 9: Nothing about topic B are given.
score: 1 - Chunk 3: This chunk discusses topic C in detail.
score: 1 - Chunk 6: Extensive research on topic F is presented.
score: 1 - Chunk 10: Finally, a discussion of topic J. This document doesn't contain information about topic B
It’s the same as before, but with an interesting score 😊.
Finally, I once again ask the LLM to provide an answer to the user’s question, and the result is similar to the previous one:
Chunk 2 provides insights related to topic B, offering foundational information and key points.
Chunk 8 expands on topic B further, possibly providing additional context or details, as it also discusses topic H.
Together, these chunks should give you a well-rounded understanding of topic B. If you need more specific details, let me know!
Considerations
This retrieval approach has emerged as a necessity following some previous experiences.
I have noticed that pure vector-based searches produce useful results but are often insufficient when the embedding is performed in a language other than English.
Using OpenAI with sentences in Italian makes it clear that the tokenization of terms is often incorrect; for example, the term “canzone,” which means “song” in Italian, gets tokenized into two distinct words: “can” and “zone”.
This leads to the construction of an embedding array that is far from what was intended.
In cases like this, hybrid search, which also incorporates term frequency counting, leads to improved results, but they are not always as expected.
So, this retrieval methodology can be utilized in the following ways:
Let’s discuss costs and performance
Of course, all that glitters is not gold, as one must consider response times and costs.
In a real use case, I retrieved the chunks from a relational database consisting of 95 text segments semantically split using my LLMChunkizerLib/chunkizer.py library from two Microsoft Word documents, totaling 33 pages.
The analysis of the relevance of the 95 chunks to the question was conducted by calling OpenAI's APIs from a local PC with non-guaranteed bandwidth, averaging around 10Mb, resulting in response times that varied from 7 to 20 seconds.
Naturally, on a cloud system or by using local LLMs on GPUs, these times can be significantly reduced.
I believe that considerations regarding response times are highly subjective: in some cases, it is acceptable to take longer to provide a correct answer, while in others, it is essential not to keep users waiting too long.
Similarly, considerations about costs are also quite subjective, as one must take a broader perspective to evaluate whether it is more important to provide as accurate answers as possible or if some errors are acceptable.
In certain fields, the damage to one’s reputation caused by incorrect or missing answers can outweigh the expense of tokens.
Furthermore, even though the costs of OpenAI and other providers have been steadily decreasing in recent years, those who already have a GPU-based infrastructure, perhaps due to the need to handle sensitive or confidential data, will likely prefer to use a local LLM.
Conclusions
In conclusion, I hope to have provided my perspective on how retrieval can be approached.
If nothing else, I aim to be helpful and perhaps inspire others to explore new methods in their own work.
Remember, the world of information retrieval is vast, and with a little creativity and the right tools, we can uncover knowledge in ways we never imagined!
If you’d like to discuss this further, feel free to connect with me on LinkedIn
GitHub repositories can be found here:
• https://github.com/peronc/LLMRetriever/
• https://github.com/peronc/LLMChunkizer/
How and Why to use LLMs for Chunk-Based Information Retrieval was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
Retrieve pipeline — Image by the author
In this article, I aim to explain how and why it’s beneficial to use a Large Language Model (LLM) for chunk-based information retrieval.
I use OpenAI’s GPT-4 model as an example, but this approach can be applied with any other LLM, such as those from Hugging Face, Claude, and others.
Everyone can access this article for free.
Considerations on standard information retrieval
The primary concept involves having a list of documents (chunks of text) stored in a database, which could be retrieve based on some filter and conditions.
Typically, a tool is used to enable hybrid search (such as Azure AI Search, LlamaIndex, etc.), which allows:
- performing a text-based search using term frequency algorithms like TF-IDF (e.g., BM25);
- conducting a vector-based search, which identifies similar concepts even when different terms are used, by calculating vector distances (typically cosine similarity);
- combining elements from steps 1 and 2, weighting them to highlight the most relevant results.
Figure 1- Default hybrid search pipeline — Image by the author
Figure 1 shows the classic retrieval pipeline:
- the user asks the system a question: “I would like to talk about Paris”;
- the system receives the question, converts it into an embedding vector (using the same model applied in the ingestion phase), and finds the chunks with the smallest distances;
- the system also performs a text-based search based on frequency;
- the chunks returned from both processes undergo further evaluation and are reordered based on a ranking formula.
This solution achieves good results but has some limitations:
- not all relevant chunks are always retrieved;
- sometime some chunks contain anomalies that affect the final response.
An example of a typical retrieval issue
Let’s consider the “documents” array, which represents an example of a knowledge base that could lead to incorrect chunk selection.
documents = [
"Chunk 1: This document contains information about topic A.",
"Chunk 2: Insights related to topic B can be found here.",
"Chunk 3: This chunk discusses topic C in detail.",
"Chunk 4: Further insights on topic D are covered here.",
"Chunk 5: Another chunk with more data on topic E.",
"Chunk 6: Extensive research on topic F is presented.",
"Chunk 7: Information on topic G is explained here.",
"Chunk 8: This document expands on topic H. It also talk about topic B",
"Chunk 9: Nothing about topic B are given.",
"Chunk 10: Finally, a discussion of topic J. This document doesn't contain information about topic B"
]
Let’s assume we have a RAG system, consisting of a vector database with hybrid search capabilities and an LLM-based prompt, to which the user poses the following question: “I need to know something about topic B.”
As shown in Figure 2, the search also returns an incorrect chunk that, while semantically relevant, is not suitable for answering the question and, in some cases, could even confuse the LLM tasked with providing a response.
Figure 2 — Example of information retrieval that can lead to errors — Image by the author
In this example, the user requests information about “topic B,” and the search returns chunks that include “This document expands on topic H. It also talks about topic B” and “Insights related to topic B can be found here.” as well as the chunk stating, “Nothing about topic B are given”.
While this is the expected behavior of hybrid search (as chunks reference “topic B”), it is not the desired outcome, as the third chunk is returned without recognizing that it isn’t helpful for answering the question.
The retrieval didn’t produce the intended result, not only because the BM25 search found the term “topic B” in the third Chunk but also because the vector search yielded a high cosine similarity.
To understand this, refer to Figure 3, which shows the cosine similarity values of the chunks relative to the question, using OpenAI’s text-embedding-ada-002 model for embeddings.
Figure 3 — Cosine similarity with text-embedding-ada-002- Image by the author
It is evident that the cosine similarity value for “Chunk 9” is among the highest, and that between this chunk and chunk 10, which references “topic B,” there is also chunk 1, which does not mention “topic B”.
This situation remains unchanged even when measuring distance using a different method, as seen in the case of Minkowski distance.
Utilizing LLMs for Information Retrieval: An Example
The solution I will describe is inspired by what has been published in my GitHub repository https://github.com/peronc/LLMRetriever/.
The idea is to have the LLM analyze which chunks are useful for answering the user’s question, not by ranking the returned chunks (as in the case of RankGPT) but by directly evaluating all the available chunks.
Figure 4- LLM Retrieve pipeline — Image by the author
In summary, as shown in Figure 4, the system receives a list of documents to analyze, which can come from any data source, such as file storage, relational databases, or vector databases.
The chunks are divided into groups and processed in parallel by a number of threads proportional to the total amount of chunks.
The logic for each thread includes a loop that iterates through the input chunks, calling an OpenAI prompt for each one to check its relevance to the user’s question.
The prompt returns the chunk along with a boolean value: true if it is relevant and false if it is not.
Lets’go coding 😊
To explain the code, I will simplify by using the chunks present in the documents array (I will reference a real case in the conclusions).
First of all, I import the necessary standard libraries, including os, langchain, and dotenv.
import os
from langchain_openai.chat_models.azure import AzureChatOpenAI
from dotenv import load_dotenv
Next, I import my LLMRetrieverLib/llm_retrieve.py class, which provides several static methods essential for performing the analysis.
from LLMRetrieverLib.retriever import llm_retriever
Following that, I need to import the necessary variables required for utilizing Azure OpenAI GPT-4o model.
load_dotenv()
azure_deployment = os.getenv("AZURE_DEPLOYMENT")
temperature = float(os.getenv("TEMPERATURE"))
api_key = os.getenv("AZURE_OPENAI_API_KEY")
endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
api_version = os.getenv("API_VERSION")
Next, I proceed with the initialization of the LLM.
# Initialize the LLM
llm = AzureChatOpenAI(api_key=api_key, azure_endpoint=endpoint, azure_deployment=azure_deployment, api_version=api_version,temperature=temperature)
We are ready to begin: the user asks a question to gather additional information about Topic B.
question = "I need to know something about topic B"
At this point, the search for relevant chunks begins, and to do this, I use the function llm_retrieve.process_chunks_in_parallel from the LLMRetrieverLib/retriever.py library, which is also found in the same repository.
relevant_chunks = LLMRetrieverLib.retriever.llm_retriever.process_chunks_in_parallel(llm, question, documents, 3)
To optimize performance, the function llm_retrieve.process_chunks_in_parallel employs multi-threading to distribute chunk analysis across multiple threads.
The main idea is to assign each thread a subset of chunks extracted from the database and have each thread analyze the relevance of those chunks based on the user’s question.
At the end of the processing, the returned chunks are exactly as expected:
['Chunk 2: Insights related to topic B can be found here.',
'Chunk 8: This document expands on topic H. It also talk about topic B']
Finally, I ask the LLM to provide an answer to the user’s question:
final_answer = LLMRetrieverLib.retriever.llm_retriever.generate_final_answer_with_llm(llm, relevant_chunks, question)
print("Final answer:")
print(final_answer)
Below is the LLM’s response, which is trivial since the content of the chunks, while relevant, is not exhaustive on the topic of Topic B:
Topic B is covered in both Chunk 2 and Chunk 8.
Chunk 2 provides insights specifically related to topic B, offering detailed information and analysis.
Chunk 8 expands on topic H but also includes discussions on topic B, potentially providing additional context or perspectives.
Scoring Scenario
Now let’s try asking the same question but using an approach based on scoring.
I ask the LLM to assign a score from 1 to 10 to evaluate the relevance between each chunk and the question, considering only those with a relevance higher than 5.
To do this, I call the function llm_retriever.process_chunks_in_parallel, passing three additional parameters that indicate, respectively, that scoring will be applied, that the threshold for being considered valid must be greater than or equal to 5, and that I want a printout of the chunks with their respective scores.
relevant_chunks = llm_retriever.process_chunks_in_parallel(llm, question, documents, 3, True, 5, True)
The retrieval phase with scoring produces the following result:
score: 1 - Chunk 1: This document contains information about topic A.
score: 1 - Chunk 7: Information on topic G is explained here.
score: 1 - Chunk 4: Further insights on topic D are covered here.
score: 9 - Chunk 2: Insights related to topic B can be found here.
score: 7 - Chunk 8: This document expands on topic H. It also talk about topic B
score: 1 - Chunk 5: Another chunk with more data on topic E.
score: 1 - Chunk 9: Nothing about topic B are given.
score: 1 - Chunk 3: This chunk discusses topic C in detail.
score: 1 - Chunk 6: Extensive research on topic F is presented.
score: 1 - Chunk 10: Finally, a discussion of topic J. This document doesn't contain information about topic B
It’s the same as before, but with an interesting score 😊.
Finally, I once again ask the LLM to provide an answer to the user’s question, and the result is similar to the previous one:
Chunk 2 provides insights related to topic B, offering foundational information and key points.
Chunk 8 expands on topic B further, possibly providing additional context or details, as it also discusses topic H.
Together, these chunks should give you a well-rounded understanding of topic B. If you need more specific details, let me know!
Considerations
This retrieval approach has emerged as a necessity following some previous experiences.
I have noticed that pure vector-based searches produce useful results but are often insufficient when the embedding is performed in a language other than English.
Using OpenAI with sentences in Italian makes it clear that the tokenization of terms is often incorrect; for example, the term “canzone,” which means “song” in Italian, gets tokenized into two distinct words: “can” and “zone”.
This leads to the construction of an embedding array that is far from what was intended.
In cases like this, hybrid search, which also incorporates term frequency counting, leads to improved results, but they are not always as expected.
So, this retrieval methodology can be utilized in the following ways:
- as the primary search method: where the database is queried for all chunks or a subset based on a filter (e.g., a metadata filter);
- as a refinement in the case of hybrid search: (this is the same approach used by RankGPT) in this way, the hybrid search can extract a large number of chunks, and the system can filter them so that only the relevant ones reach the LLM while also adhering to the input token limit;
- as a fallback: in situations where a hybrid search does not yield the desired results, all chunks can be analyzed.
Let’s discuss costs and performance
Of course, all that glitters is not gold, as one must consider response times and costs.
In a real use case, I retrieved the chunks from a relational database consisting of 95 text segments semantically split using my LLMChunkizerLib/chunkizer.py library from two Microsoft Word documents, totaling 33 pages.
The analysis of the relevance of the 95 chunks to the question was conducted by calling OpenAI's APIs from a local PC with non-guaranteed bandwidth, averaging around 10Mb, resulting in response times that varied from 7 to 20 seconds.
Naturally, on a cloud system or by using local LLMs on GPUs, these times can be significantly reduced.
I believe that considerations regarding response times are highly subjective: in some cases, it is acceptable to take longer to provide a correct answer, while in others, it is essential not to keep users waiting too long.
Similarly, considerations about costs are also quite subjective, as one must take a broader perspective to evaluate whether it is more important to provide as accurate answers as possible or if some errors are acceptable.
In certain fields, the damage to one’s reputation caused by incorrect or missing answers can outweigh the expense of tokens.
Furthermore, even though the costs of OpenAI and other providers have been steadily decreasing in recent years, those who already have a GPU-based infrastructure, perhaps due to the need to handle sensitive or confidential data, will likely prefer to use a local LLM.
Conclusions
In conclusion, I hope to have provided my perspective on how retrieval can be approached.
If nothing else, I aim to be helpful and perhaps inspire others to explore new methods in their own work.
Remember, the world of information retrieval is vast, and with a little creativity and the right tools, we can uncover knowledge in ways we never imagined!
If you’d like to discuss this further, feel free to connect with me on LinkedIn
GitHub repositories can be found here:
• https://github.com/peronc/LLMRetriever/
• https://github.com/peronc/LLMChunkizer/
How and Why to use LLMs for Chunk-Based Information Retrieval was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
