Building a robust RAG (Retrieval-Augmented Generation) system that incorporates Large Language Model (LLM) and is performant is not easy, which is why ActiveLoop’s Deep Memory comes to our aid as by significantly improving the quality of retrieval of useful information in the dataset.
In this blog post, we will delve into the process of building this system and evaluate the improvements on three distinct datasets comparing the quality of responses with and without the Deep Memory feature.
To improve RAG applications with Deep Memory, we start by recognizing the limitations of traditional retrieval methods like RAG or query-based document retrieval. Often, the retrieved text chunks don’t fully meet expectations and for this reason we looked for an approach that would give us better results.
In our exploration, we’ll focus on enhancing index storage within the retrieval pipeline by leveraging fine-tuning techniques. Unlike conventional approaches, Deep Memory offers an automatic and efficient way to fine-tune retrieval steps based on provided data chunks. This approach outperforms classic, generic RAG applications by adapting to specific dataset characteristics and improving overall solution efficacy.
Deep Memory is a technique developed by Activeloop that enables optimizing vector stores for specific use cases to achieve higher accuracy in LLM applications. Some key points about Deep Memory:
- Deep Memory significantly enhances Deep Lake’s vector search accuracy by up to 22%, achieved through learning an index from labeled queries customized for your application, with no impact on search time. This significantly improves the user experience of LLM applications.
- Deep Memory can also reduce costs by decreasing the amount of context (k) that needs to be injected into the LLM prompt to achieve a given accuracy, thereby reducing token usage.
- In addition to the native
deeplakelibrary, Deep Memory can also be easily integrated with LlamaIndex and LangChain
In summary, Activeloop’s Deep Memory is a powerful tool that significantly enhances retrieval accuracy in LLM applications in a cost-effective manner by optimizing vector stores for specific use cases, if you want to find out more in detail how it works, read our guide here.
if you want to try the application we will talk about in this guide, you can find the code in our Github repository.
Let’s build a Deep Memory RAG application!
Preparing the Dataset
Initially, we selected a dataset focused on medical information, particularly concerning Covid-19. This dataset is highly intricate, which comprises technical and mathematical details. Subsequently, we expanded our testing to include two additional datasets—one containing legal information and another comprising finance-related data.
Here are the open-source datasets that we have chosen to incorporate into our application:
Finance: We selected the FinQA Dataset, which features text covering various economic topics such as acquisitions. This dataset is particularly advantageous for our application because it includes a question-answer (QA) format, eliminating the need to generate questions and answers.
Our project is centered around deep question answering over financial data, with the goal of automating the analysis of extensive collections of financial documents. Unlike tasks in general domains, the finance domain presents challenges that involve complex numerical reasoning and understanding of diverse representations. This necessitates specialized approaches tailored to handling financial datasets effectively.
The dataset is available here.Legal: The Legalbench Dataset comprises questions and answers related to legal topics, covering subjects such as company legal rights and policies. This dataset delves into a highly specialized and detailed area of law, making accurate information retrieval crucial for this task.
LegalBench tasks encompass various types of classification (binary and multi-class), extraction, generation, and entailment, applied across different types of legal texts like statutes, judicial opinions, and contracts. These tasks span multiple areas of law, including evidence, contracts, and civil procedure.
LegalBench serves as a benchmark for evaluating different legal reasoning tasks, offering a comprehensive framework for assessing performance in legal text understanding and analysis.
The dataset is available hereBiomedical: For our biomedical topic, we selected the CORD-19 Dataset, which focuses on COVID-19 research. Given the significance and widespread discussion of this topic, it’s crucial to extract comprehensive information, making this dataset ideal for testing.
CORD-19 is a collection of academic papers dedicated to COVID-19 and related coronavirus research. Curated and maintained by the Semantic Scholar team at the Allen Institute for AI, this dataset is designed to facilitate text mining and natural language processing (NLP) research in the context of pandemic-related studies.
The dataset is available here
These three datasets are hosted within the Activeloop organization space, requiring them to be loaded into a Tensor Database format to leverage the Deep Memory functionality effectively. This format optimization ensures efficient data handling and retrieval, enhancing the performance of the retrieval process when utilizing Deep Memory.
A Tensor Database format refers to a data storage structure specifically designed to handle tensors efficiently. In the context of machine learning and deep learning, tensors are multi-dimensional arrays that represent data used for training and inference. A Tensor Database organizes and manages these tensors in a structured way, optimizing data access and retrieval for machine learning tasks. It facilitates quick and scalable access to tensor data, which is essential for deep learning applications involving large datasets and complex models.
To understand how a VectorStore can be created in Tensor Database mode we can refer to the provided code snippet, the variable user_hub is the organization name, which in this case is "activeloop", and name_db represents the specific dataset name within the organization’s space. This structure allows for easy creation and loading of datasets hosted in the Activeloop platform, thanks to the {"tensor_db": True} you will be able to create the dataset in Tensor Database mode and then be able to exploit the Deep Memory functionality. Here’s how you might use these variables in practice:
1from langchain_community.vectorstores import DeepLake
2from langchain_openai import OpenAIEmbeddings
3embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
4
5def create_vector_store(user_hub, name_db):
6 vector_store_db = DeepLake(
7 f"hub://{user_hub}/{name_db}",
8 embedding_function=embeddings.embed_documents,
9 runtime={"tensor_db": True},
10 )
11 return vector_store_db.vectorstore
12
The datasets were prepared through a preprocessing pipeline consisting of three main steps:
- Data Collection: Gathering the necessary data.
- Data Chunking: Dividing the collected data into smaller chunks.
- Question Generation: Creating sample questions for each chunk, along with computing a relevance score that measures how pertinent each question is to its corresponding text chunk. This relevance score is crucial for effective Deep Memory finetuning.
Data Collection
Classical data collection in a project typically involves the systematic gathering of relevant information or datasets to support the project’s objectives. This process comprehends several key steps:
Define Requirements: Clearly outline the data needs and objectives of the project. Identify the specific types of data required to achieve project goals.
Identify Data Sources: Determine where to obtain the necessary data. This may involve accessing existing databases, public repositories, APIs, or conducting surveys and experiments to generate new data.
Data Gathering: Collect the identified data according to the project’s requirements. This could involve downloading datasets, extracting information from documents, or capturing data through surveys or other means.
Data Cleaning and Preprocessing: Prepare the collected data for analysis by cleaning and preprocessing it. This includes handling missing values, removing duplicates, standardizing formats, and transforming data into a suitable structure for analysis.
Data Quality Assurance: Perform quality checks to ensure the collected data is accurate, complete, and reliable. Address any issues or inconsistencies found during this process.
Data Chunking
Data Chunking refers to the process of dividing a larger piece of data into smaller segments or “chunks” based on certain criteria. The goal of chunk generation is to break down the data into more manageable and meaningful units for further analysis or processing.
In natural language processing (NLP), chunk generation often involves segmenting text into meaningful phrases or syntactic units. This can be achieved using techniques such as:
Text Segmentation: Breaking a large document or paragraph into smaller sentences or paragraphs based on punctuation marks (e.g., periods, commas).
Tokenization: Splitting text into individual words or tokens, which can then be grouped into phrases or chunks based on specific rules or patterns.
Chunk generation is essential for preprocessing text data in natural language processing (NLP). By breaking down large blocks of text into smaller, meaningful chunks, such as phrases or syntactic units, chunking facilitates more efficient and effective analysis and modeling of complex datasets. These smaller units allow NLP models to focus on specific segments of text, enabling tasks like information extraction, text summarization, named entity recognition, and syntactic parsing. Chunk generation helps streamline data processing workflows, making it easier to handle and analyze large volumes of text data in NLP.
Here are examples of generated text chunks categorized by domain:
Legal:
“Except (i) for such public disclosure as may be necessary, in the good faith judgment of the disclosing Party consistent with advice of counsel, for the disclosing Party not to be in violation of any applicable law, regulation or order, or (ii) with the prior written consent of the order Party, neither Part shall: (x) make any disclosure (and each Party shall direct its Representatives not to make any disclosure) to any person of (A) the fact that discussions, negotiations or investigations are taking or have taken place concerning a Transaction, (B) the existence or contents of this Agreement, or the fact that either Party has requested or received Evaluation Material from the other Party, or © any of the terms, conditions or other facts with respect to any proposed Transaction, including the status of the discussions or negotiations related thereto, or (y) make any public statement concerning a proposed Transaction.”Biomedical:
“In response, many cities introduced widespread interventions intended to reduce the spread. There is evidence [3] that those cities which implemented these interventions later had fewer deaths. This seemingly counter-intuitive observation suggests that those cities which were slow to respond were the most successful. The world is currently faced with a pandemic of novel coronavirus disease 2019 (COVID- 19) , which is caused by Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) and has no vaccine or cure. It is predicted the development of a safe and effective vaccine to prevent COVID-19 will take one year to 18 months, by which time it is likely that several hundreds of thousands to millions of people may have been infected. With a rapidly growing number of cases and deaths around the world, this emerging threat requires a nimble and targeted means of protection. Since coronaviruses causing COVID-19, Severe Acute Respiratory Syndrome (SARS), and Middle East Respiratory Syndrome (MERS) are able to suddenly transfer to humans from diverse animal hosts that act as viral reservoirs, there is a pressing need to develop methods to combat other potential coronaviruses that may emerge in the future [1] [2] [3] . A recent report further showed two strains (L and S) of SARS-CoV-2 with different genome sequences are circulating and likely evolving, further highlighting the need for a pan-coronavirus vaccination strategy 4 . Wuhan, the capital of Hubei province in China, reported an outbreak of atypical pneumonia caused by a new coronavirus detected on December 31, 2019, which was named 2019 coronavirus disease . [1] [2] [3] [4] Cases have surfaced in other Chinese cities, as well as other countries and regions. 5-7 The number of cases in Wuhan and other cities in China is on the rise. Since February 20, authorities have reported more than 70,000 confirmed cases and 2,122 deaths across all Chinese provinces. More than 26 countries or regions have reported confirmed cases. 8 With the number of confirmed cases increasing, the implementation of corresponding outbreak control policies began on January 23. By following these procedures, most cities have been controlled, except Wuhan; however, there have been outbreaks in countries outside China.”
- Finance:
"( a ) included in other assets on our consolidated balance sheet . ( b ) included in other liabilities on our consolidated balance sheet . all derivatives are carried on our consolidated balance sheet at fair value . derivative balances are presented on the consolidated balance sheet on a net basis taking into consideration the effects of legally enforceable master netting agreements and any related cash collateral exchanged with counterparties . further discussion regarding the rights of setoff associated with these legally enforceable master netting agreements is included in the offsetting , counterparty credit risk , and contingent features section below . our exposure related to risk participations where we sold protection is discussed in the credit derivatives section below . any nonperformance risk , including credit risk , is included in the determination of the estimated net fair value of the derivatives . further discussion on how derivatives are accounted for is included in note 1 accounting policies . derivatives designated as hedging instruments under gaap certain derivatives used to manage interest rate risk as part of our asset and liability risk management activities are designated as accounting hedges under gaap . derivatives hedging the risks associated with changes in the fair value of assets or liabilities are considered fair value hedges , derivatives hedging the variability of expected future cash flows are considered cash flow hedges , and derivatives hedging a net investment in a foreign subsidiary are considered net investment hedges . designating derivatives as accounting hedges allows for gains and losses on those derivatives , to the extent effective , to be recognized in the income statement in the same period the hedged items affect earnings . the pnc financial services group , inc . 2013 form 10-k 189 ."
These texts illustrate how text data has been segmented into meaningful units for legal, biomedical, and finance domains. Each chunk represents a specific segment of text relevant to its respective domain.
There are several efficient strategies for segmenting text into chunks. One approach is using a separator character such as “.” or "\n", or defining a standard chunk length. Alternatively, a combination of these methods can be employed.
Our recommendation is to create chunks that are sufficiently sized and potentially overlap to preserve relevant information.
One drawback of using non-overlapping chunks is the risk of losing context or information continuity. Depending on the nature of the data and the analysis or modeling requirements, experimenting with overlapping chunks can often yield more comprehensive results.
1from langchain_text_splitters import RecursiveCharacterTextSplitter
2
3# Divide text in chunks
4def create_chunks(context, chunk_size=300, chunk_overlap=50):
5 # Initialize the text splitter with custom parameters
6 custom_text_splitter = RecursiveCharacterTextSplitter(
7 # Set custom chunk size
8 chunk_size = chunk_size,
9 chunk_overlap = chunk_overlap,
10 # Use length of the text as the size measure
11 length_function = len,
12 )
13
14 chunks = custom_text_splitter.split_text(context)
15 return chunks
16
LangChain is an open source framework designed to simplify the creation of applications using large language models (LLMs). It provides tools and abstractions to improve the customization, accuracy, and relevancy of the information generated by LLMs.
The RecursiveCharacterTextSplitter in LangChain is a tool used for splitting large text documents into smaller, more manageable sections based on a specified chunk size and a set of characters. This tool employs recursion as its core mechanism to achieve text splitting. By recursively trying to split the text using different characters until finding a suitable split, the RecursiveCharacterTextSplitter ensures that each resulting chunk conforms to the chosen specifications.
Questions and Relevance Generation
This step involves leveraging language models like LLMs (Large Language Models) to generate questions and relevance scores without the need for a specifically trained model.
To achieve this task, we utilize the capabilities of modern LLMs. Through a technique known as prompt engineering, we can prompt a language model to generate a question for each text chunk and simultaneously produce a relevance score using a classification approach. This process involves calling the language model with appropriate prompts and parsing the model’s output to extract the required data.
To facilitate this, we construct a dataset that pairs questions with their corresponding relevance scores. The relevance score consists of pairs (corpus.id: str, significance: str), which indicate where the answer can be found within the corpus and how much relevance it has. An answer might have multiple locations or different levels of significance, captured by these pairs.
The relevance information is crucial for training Deep Memory, a feature that optimizes the embedding space to enhance retrieval accuracy. Deep Memory utilizes the provided questions, text chunks, and relevance scores to fine-tune the embedding space, enabling more effective retrieval of relevant information from the dataset.
In summary, taking advantage of prompt engineering with LLMs and constructing a dataset with questions and relevance annotations, Deep Memory can be trained to optimize embedding spaces for improved accuracy in information retrieval tasks. This approach leverages the power of language models to generate context-aware questions and relevance scores, facilitating more nuanced and effective retrieval of information.
1questions = ["question 1", ...]
2relevance = [[(corpus.dataset.id[0], 1), ...], ...]
3
4job_id = corpus.deep_memory.train(
5 queries = questions,
6 relevance = relevance,
7 embedding_function = embeddings.embed_documents,
8)
9
An example of how to generate questions and relevance scores is the following:
1def get_chunk_question(context):
2 system_message = """
3 Generate a question related to the context.
4 The input is provided in the following format:
5 Context: context to use
6 The output is in the json following format:
7 "question": "Text of the question"
8
9 The context is: {context}
10 """
11
12 client = OpenAI()
13
14 prompt = system_message.format(
15 context=context,
16 )
17
18 response = client.chat.completions.create(
19 model="gpt-4o",
20 messages=[{"role": "user", "content": prompt}],
21 response_format={"type": "json_object"},
22 )
23 response_message = response.choices[0].message.content
24 cleaned_response = json.loads(response_message)
25 cleaned_response = cleaned_response["question"]
26 return cleaned_response
27
28
Exploring Deep Memory
Deep Memory is a powerful tool included in the High Performance Features of Deep Lake. It significantly enhances the retrieval accuracy of LLM models by optimizing your vector store for your specific use case, thereby improving the overall performance of your LLM application.
This optimization is achieved through fine-tuning the embeddings of your embedding model using your own dataset enriched with additional information, including Questions and Relevance Scores (indicating how closely the question is related to the text). This approach enables the embedding model to better understand and retrieve relevant information, tailored to your application’s needs.
Creating the Deep Memory Vector Store
The Deep Lake instance is initialized with the following key parameters:
1from langchain_community.vectorstores import DeepLake
2from langchain_openai import OpenAIEmbeddings
3
4embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
5
6def load_vector_store(user_hub, name_db):
7 vector_store_db = DeepLake(
8 f"hub://{user_hub}/{name_db}",
9 embedding_function=embeddings.embed_documents,
10 runtime={"tensor_db": True},
11 read_only=True,
12 )
13 return vector_store_db.vectorstore
14
f"hub://{user_hub}/{name_db}": Specifies the location of the dataset within the user hub.
embedding_function=embeddings.embed_documents: Specifies the embedding function to be used for generating embeddings.
read_only=True: Sets the Deep Lake instance to read-only mode, allowing for retrieval of embeddings without modification.
runtime={"tensor_db": True}: Parameter in the Deep Lake initialization specifies that the Deep Lake instance should be configured to use a tensor database (tensor_db) for its runtime environment. This parameter indicates that the Deep Lake instance will leverage a tensor database backend for efficient storage and retrieval of embeddings.
Implementing Fine-Tuning
Below is the code snippet used to implement the fine-tuning process for this part:
1def training_job(vector_store_db, chunk_question_quantity: int):
2
3 questions = []
4 relevances = []
5
6 for idx, el in enumerate(vector_store_db.dataset):
7 if idx >= chunk_question_quantity:
8 break
9 print(f"Generating question: {idx}")
10 chunk_id = str(el.id.data()["value"])
11 text = str(el.text.data()["value"])
12 print(f"Processing chunk: {idx}")
13 single_question = get_chunk_question(text)
14 questions.append(single_question)
15 relevances.append([(chunk_id, 1)])
16
17 job_id = vector_store_db.deep_memory.train(
18 queries=questions,
19 relevance=relevances,
20 embedding_function=embeddings.embed_documents,
21 )
22 vector_store_db.deep_memory.status(job_id)
23 return vector_store_db
24
Iterating Over Dataset:
The function iterates through the dataset stored in vector_store_db. It processes each element el up to the specified chunk_question_quantity.
For each element, it retrieves the chunk ID chunk_id and text text.
It then generates a single question single_question for the text using the get_chunk_question function (see the previous chapter).
The generated question is appended to the questions list, and a relevance score (in the form of a list with a single tuple (chunk_id, 1) is appended to the relevances list. The relevance score indicates a high relevance 1 for the corresponding chunk.
Training Deep Memory:
Once all questions and relevance scores are collected, the function uses the vector_store_db.deep_memory.train method to initiate a training job.
It provides the collected questions, relevances, and the embedding_function as inputs to the training job.
The method returns a job_id, which can be used to monitor the status of the training job.
Monitoring Job Status:
The function then retrieves and prints the status of the training job using vector_store_db.deep_memory.status(job_id).
Return Value:
Finally, the function returns the updated vector_store_db instance after the training job is initiated.
This function essentially automates the process of generating questions for text chunks stored in a vector store database, assigning relevance scores, and training a Deep Memory model to optimize the embedding space based on the provided information. The resulting trained model can be used for enhanced retrieval and analysis tasks.
For a more detailed exploration you can see an example of how, during the training phase, questions are generated starting from chunks:
Legal Dataset:
- Chunk: "Confidential Information means all confidential information relating to the Purpose which the Disclosing Party or any of its Affiliates, discloses or makes available, to the Receiving Party or any of its Affiliates, before, on or after the Effective Date. This includes the fact that discussions and negotiations are taking place concerning the Purpose and the status of those discussions and negotiations."
- Question: What is the definition of Confidential Information?
Biomedical Dataset:
- Chunk: "The P2 64 and P3 regions encode the non-structural proteins 2B and 2C and 3A, 3B (1-3) (VPg), 3C pro and 4 structural protein-coding regions is replaced by reporter genes, allow the study of genome 68 replication without the requirement for high containment."
- Question: What are the non-structural proteins encoded by the P2 64 and P3 regions?
Finance Dataset:
- Chunk: "the deferred fuel cost revisions variance resulted from a revised unbilled sales pricing estimate made in december 2002 and a further revision made in the first quarter of 2003 to more closely align the fuel component of that pricing with expected recoverable fuel costs . the asset retirement obligation variance was due to the implementation of sfas 143 , “accounting for asset retirement obligations” adopted in january 2003 . see “critical accounting estimates” for more details on sfas 143 . the increase was offset by decommissioning expense and had no effect on net income . the volume variance was due to a decrease in electricity usage in the service territory . billed usage decreased 1868 gwh in the industrial sector including the loss of a large industrial customer to cogeneration."
- Question: What was the impact of the asset retirement obligation variance on net income?
Now that we’ve completed the setup, it’s time to test our enhanced RAG applications.
Deep Memory Search
After creating the Deep Memory Dataset, we can search for the right piece of text for our question using the following code:
1def get_answer(vector_store_db, user_question, deep_memory):
2 # deep memory inside the vectore store ==> deep_memory=True
3 answer = vector_store_db.search(
4 embedding_data=user_question,
5 embedding_function=embeddings.embed_query,
6 deep_memory=deep_memory,
7 return_view=False,
8 )
9 print(answer)
10 return answer
11
get_answer allows querying a vector store database with a user question, utilizing specified embedding techniques and optionally leveraging deep memory (setting deep_memory=True) for improved retrieval of relevant answers. The function provides a straightforward way to retrieve answers based on user queries using pre-trained embeddings and optimization techniques like deep memory.
Developing a Deep Memory Search with Gradio
We developed a Gradio application to facilitate easier testing of our system.
The interface enables us to select the dataset for testing, input a question, and immediately generate an answer. Additionally, we can compare the response from the Deep Memory model with the response from the model without Deep Memory integration. This comparison helps evaluate the effectiveness of the Deep Memory feature in improving answer quality and relevance.
Classic RAG vs Deep Memory
To test out the improvements of the Deep Memory step, we prepared and shared three datasets we mentioned earlier: Legal, Medical, Finance.
In the output windows, you can see the benefits of this amazing tool when compared to more classical approaches.
If you’d like to test these models, we recommend trying one of the following questions:
- Legal Dataset:
- What are the provisions of this Agreement regarding the disclosure of Confidential Information to third parties?
- Biomedical Dataset:
- What are the advantages of using the new package to visualize data?
- Finance Dataset:
- What were the primary factors that contributed to the improvement in net cash provided by operating activities during 2015?
The following example shows how the model with deep memory and dataset finance is more efficient in the response generated:
Deep Memory model:
“The provisions of this Agreement state that disclosure of Confidential Information to third party consultants and professional advisors is allowed, as long as those third parties agree to be bound by this Agreement. Additionally, both parties are required to keep any confidential information they may have access to confidential, unless required by law or necessary to perform their obligations under this Agreement. This includes not only the information itself, but also the terms of the Agreement and the fact that the parties are considering a business arrangement”
Non Deep Memory model:
“The provisions of this Agreement state that disclosure of Confidential Information to third party consultants and professional advisors is allowed, as long as those third parties agree to be bound by this Agreement. Additionally, the Confidential Information includes the terms of this agreement, the fact that the information is being made available, and the possibility of a business arrangement between the parties.”
Evaluation Metrics
After evaluating our datasets, it’s evident that Deep Memory significantly enhances the retrieval of relevant information based on user-provided questions.
The following metrics demonstrate how the Deep Memory feature enhances performance:
Legal Dataset:
1---- Evaluating without Deep Memory ----
2Recall@1: 25.5%
3Recall@3: 57.7%
4Recall@5: 66.5%
5Recall@10: 74.5%
6Recall@50: 90.0%
7Recall@100: 92.5%
8
9---- Evaluating with Deep Memory ----
10Recall@1: 28.0%
11Recall@3: 65.0%
12Recall@5: 75.0%
13Recall@10: 84.0%
14Recall@50: 93.5%
15Recall@100: 96.0%
16
Biomedical Dataset:
1---- Evaluating without Deep Memory ----
2Recall@1: 44.5%
3Recall@3: 67.0%
4Recall@5: 74.5%
5Recall@10: 80.0%
6Recall@50: 91.0%
7Recall@100: 95.5%
8
9---- Evaluating with Deep Memory ----
10Recall@1: 42.0%
11Recall@3: 69.0%
12Recall@5: 78.0%
13Recall@10: 81.0%
14Recall@50: 94.0%
15Recall@100: 97.0%
16
Financial Dataset:
1---- Evaluating without Deep Memory ----
2Recall@1: 23.0%
3Recall@3: 66.0%
4Recall@5: 81.0%
5Recall@10: 89.5%
6Recall@50: 98.0%
7Recall@100: 99.0%
8
9---- Evaluating with Deep Memory ----
10Recall@1: 28.0%
11Recall@3: 77.0%
12Recall@5: 90.5%
13Recall@10: 94.5%
14Recall@50: 98.0%
15Recall@100: 98.0%
16
In conclusion, it’s important to acknowledge that success in natural language processing relies not only on the quality and diversity of the data but also on the effectiveness of the retrieval strategy. While large and diverse datasets are invaluable, the way information is retrieved and presented significantly impacts model performance.
As demonstrated in this guide, tools like Deep Memory enhance accuracy and efficiency, leading to the generation of more relevant answers. Leveraging such tools is crucial for optimizing NLP applications and achieving superior performance in information retrieval tasks.
FAQs:
What is a RAG system?
Retrieval-Augmented Generation (RAG) is used to optimize the output of a large language model by incorporating references from an authoritative knowledge base beyond its training data, ensuring more informed responses.
What is chunking technique?
Chunking is a communication technique that breaks down extensive information into smaller, easier-to-digest segments, aiding audience comprehension and retention of key details.
What is the RecursiveCharacterTextSplitter in LangChain?
The RecursiveCharacterTextSplitter in LangChain is a tool used for splitting large text documents into smaller, more manageable sections based on a specified chunk size and a set of characters. This tool employs recursion as its core mechanism to achieve text splitting.
What is open source data?
Open data refers to data that is freely accessible to everyone, including companies, citizens, the media, and consumers. One common definition of open data is that it can be used, modified, and shared by anyone for any purpose.
What is the largest dataset for LLM?
Common Corpus is the largest publicly available dataset used for training Large Language Models (LLMs), comprising 500 billion words sourced from a diverse range of cultural heritage initiatives. This multilingual corpus is the largest of its kind to date, encompassing texts in English, French, Dutch, Spanish, German, and Italian.
What are LLM evaluation metrics?
The primary evaluation metrics used for Large Language Models (LLMs) today include relevance, hallucination detection, question-answering accuracy, toxicity, and retrieval-specific measures. Each evaluation for an LLM system will utilize different templates depending on the specific aspects being assessed.