Integrating Vector Databases with LLM: Techniques & Challenges

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August 29, 2024

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25 min read

Large Language Models (LLM) are AI algorithms trained on vast datasets to process, understand, and generate responses using human language. These models function based on self-supervised learning techniques, enabling them to perform sentiment analysis or text summarization tasks.

You have to use streamlined prompt techniques to generate meaningful responses from LLMs. However, maintaining a consistent and accurate flow of information from LLMs is the biggest challenge. To overcome this limitation, you can integrate LLMs with vector databases. These databases help LLMs generate accurate results by imparting them a contextual understanding of a given query or prompt.

This article explains vector database LLM integration to enable you to enhance the search and data retrieval capabilities of any LLM. It provides information on methods, challenges, and breakthrough developments to make LLMs more efficient.

What are Vector Databases?

A vector database enables you to store data in the form of vectors, which are mathematical representations of data. A vector is a multidimensional array of any data type, including unstructured data such as images, text, or audio. The number of dimensions in each vector can vary from a few to thousands, depending on the data type, such as video or image.

Vector differs from the original data only in terms of representation but retains the data’s actual meaning. The numerical representation of data points into an array of vectors is known as vector embedding.

To understand how vector databases work, you must understand the concept of embedding. Unstructured data, such as images or text, has no standardized format. It is transformed into a numerical format through embedding. In this process, a neural network assigns a unique code to each word in a text or contents in an image.

This code captures the semantic meaning of that data and helps AI algorithms or ML models to understand its relationship with similar data points. As a result, vector databases enable faster and more accurate data search and retrieval.

The vector database uses specialized search techniques, such as Approximate Nearest Neighbour (ANN) search, which helps you to search and retrieve data records similar to the input query. For this reason, vector databases are used in audio software such as Jamahook to search songs based on similar tunes. You can also utilize it to design applications for searching articles based on the same or related topics.

Besides this, you can use a vector database for generative AI applications to store information in a format AI understands. You can leverage it to generate real-time and personalized content as per your requirements.

The image above illustrates a vector database. Here, you can see the word data points Wolf and Dog are close to each other because they belong to the same family. The word Cat is near the Dog because both are animals. On the other hand, the words Banana and Apple are close to each other and away from animal words because they belong to the fruit category.

Methods to Integrate Vector Databases with LLM

You can use vector databases for content-based data retrieval. They can search for contextually similar information even if some specific keywords are missing. This feature makes them a suitable solution for data retrieval in large language models (LLMs). You can use the following two methods for LLM vector database integration:

Retrieval Integration

Vector indexing is a technique for organizing similar vectors to streamline the data retrieval process. You can integrate LLMs with vector databases to leverage vector indexing to retrieve context-based information. Vector indexing relates vector embeddings linked to the input entity with contexts such as text snippets, images, graphs, and other related information in the database.

When you give LLM a prompt with a specific entity, it processes the prompt and uses the entity’s vector to search the database and receive relevant information. The retrieval integration approach is used in Retrieval Augmented Generation (RAG) to extract information from authentic sources and improve the operational efficiency of LLMs.

Consider a real-life example in which you have to write a research paper on ethics in AI. You prompt LLM by asking what are the ethical concerns surrounding the use of AI in surveillance.

The LLM processes this query and generates vector embeddings that represent the semantic meaning of terms in the query. The vector indexing then aligns these vector embeddings with similar embeddings containing information on academic papers and case studies related to AI surveillance or privacy concerns. The vector database returns this data, and LLM generates this as a response to your query.

The retrieval integration is a simple approach as the vector database is plugged as an external entity without making any changes to LLM. The key examples of this approach are the FiLM and the CalmAbiding systems, both based on GPT models. They use ConceptNet, a knowledge graph for information retrieval.

You can also use Airbyte, an ELT data integration tool for retrieval integration between vector databases like Pinecone and LLM frameworks like OpenAI. With Airbyte connectors, you can extract unstructured data and load it directly to Pinecone, which can then be integrated with LLM for context-based data retrieval.

Injection Integration

In this integration method, you can add information from vector databases directly into LLMs through parameter updates or supplementary training.

In the parameter updation approach, you can use external vectors to update LLM parameters before the main training. This injects important vocabulary and entities directly into the LLM weights, which are a subset of parameters.

Another way could be to introduce additional objectives while fine-tuning the model to optimize it for predicting the properties of external vectors. For example, models can be trained in masked language modeling to reconstruct missing dimensions of vector database embedding.

Injection integration tightly couples LLM with vector databases by infusing information directly into the model. However, this approach requires introducing modifications to the model architecture.

Enriching Context for LLMs with Vector Databases

Enriching LLMs to get highly optimized outcomes can be a complex task. For this, targeted training can be a helpful approach. Here, models are retrained or fine-tuned on datasets based on specific domain knowledge. However, this is not efficient as it requires high computational resources and expertise. To eliminate these limitations, you can integrate LLMs with vector databases.

The process of enriching LLMs with contexts from vector databases includes the following steps:

Embedding Generation

The first step is embedding generation, in which unstructured data, such as text or images, is transformed into numerical format. These embeddings are stored in vector databases and contain the data's semantic essence.

Query Embedding and Retrieval

When any query is received, LLM first converts it into a vector representation. Then, the vector database integrated with LLM is searched for similar vector embeddings that contain information relevant to the query.

Contextual Enrichment

The results retrieved from the vector database enrich the context for LLM’s response generation. You can assess the generated response based on accuracy, relevance, and user satisfaction. You can also fine-tune vector databases, embeddings, and LLM from this information to give more accurate results.

Challenges & Limitations of Integrating Vector Databases with LLM

Here are some challenges & limitations of integrating vector databases with LLM:

Data Quality

If the data source you use to create vector embeddings is not authentic or outdated, it will lead to data quality issues and impact LLM's overall performance.

Biases

The inaccurate training data may also contain biases, which are reflected in vector embeddings generated by LLMs. The biases can be transmitted throughout the integrated system, resulting in incorrect outcomes.

Integration Complexity

Integrating LLMs with vector databases involves complex engineering, which requires expertise. You need to have proper knowledge of how vector databases and LLMs function. Ensuring interoperability between vector embedding models, databases, and LLMs is also difficult.

Scalability

As the volume of data and vector embeddings increase, you may find it challenging to scale vector databases to accommodate this increased amount of data. Even though vector databases can be scaled horizontally and vertically, adding more servers for horizontal scaling and upgrading them for vertical scaling can be complex.

In many applications, the data source used for training changes continuously. It is complex to update vector databases frequently in response to these changes.

Costs

The cost of infrastructure and computational resources required to integrate vector databases with LLMs is high. Using licensed vector databases, LLM APIs, and machine learning frameworks also requires high expenditures. You need to hire a team of experts to integrate LLM with vector databases, which further adds to expenses.

Emerging Techniques & Recent Advances

Some of the emerging techniques to streamline LLM functionalities are as follows:

Hybrid Search and Retrieval

Hybrid search and retrieval techniques combine semantic and keyword searches. Semantic searches enable you to understand the underlying meaning behind a search query. Keyword searches are done using the BM25 algorithm, which retrieves information by ranking and scoring relevant documents related to a particular query. Using both these search techniques provides you with refined LLM outcomes.

Contextual Compression

Context-based search is an essential aspect of vector database LLM integration. However, context overflow is the biggest challenge of an integrated system. You do not know what queries your document format data will face when you ingest it into the integrated system. As a result, relevant information may be buried in the document with a lot of irrelevant text, hampering the functioning of the vector database and LLM integrated system.

Here, contextual compression can help you as it enables you to extract only the most important information related to the query from all the retrieved documents. This involves the usage of summarization, keyword extraction, and chunking techniques. With these processes, you can filter out non-essential information and improve the quality of input to LLM.

Multi-Modal LLMs

Multi-modal LLMs are advanced LLMs with input and output in different modalities. They are capable of processing and generating text-to-image and vice versa information. An excellent example of a multi-modal LLM is ChatGPT-4, which can convert document images into digitized text or screenshots into usable code.

Unlike traditional vector search, multi-modal LLMs use a combination of visual, text, and metadata to query and search results. This makes them high-performing LLMs that give accurate and refined outputs.

Role of Airbyte in Vector Database LLM Integration

‍Data integration is a vital aspect of vector database LLM integration. It involves consolidating raw data from multiple sources and converting it into vector embeddings before storing it in a vector database.

For effective data integration, you can use Airbyte, a data integration tool. It offers a vast library of 350+ connectors to extract structured and unstructured data from various sources. Using Airbyte connectors, you can extract unstructured data and directly load it into vector databases like Pinecone, Weaviate, or Milvus. This data can then be converted into vector embeddings and integrated with LLMs through retrieval or injection integration methods for optimized LLM outputs.

If the connector you want to use is not in the existing set, you can build one yourself using Airbyte’s Connector Development Kit (CDK).

Some of the key features of Airbyte are as follows:

Support to Vector Databases: Airbyte enables you to extract unstructured data and load it to any of the eight vector databases it supports, including Pinecone and Chroma. You can then generate vector embeddings from this data using LLM providers such as OpenAI or Cohere. Airbyte also allows you to leverage native vector support offered by Snowflake Cortex and BigQuery’s Vertex AI by providing these services as destinations.
‍RAG Transformations: You can integrate Airbyte with LLM frameworks like LangChain or LlamaIndex to perform RAG transformations like chunking to streamline outcomes of LLM outcomes.
‍PyAirbyte: PyAirbyte is a Python library provided by Airbyte that allows you to extract data from multiple sources using Airbyte-supported connectors into your Python ecosystem. You can also extract raw data using PyAirbyte to perform downstream LLM operations with LangChain and OpenAI.
‍Change Data Capture: The change data capture (CDC) feature of Airbyte enables you to instantly reflect changes made at the source data in the destination data system. In vector database LLM integration, this helps you to update vector embeddings according to changes made in the source dataset to get more accurate LLM outcomes.

Conclusion

Integrating vector databases with LLM facilitates building a scalable and accurate data retrieval system. This blog gives a detailed overview of vector databases LLM integration. It also explains integration methods, challenges, and emerging advancements in data retrieval solutions. You can leverage this guide to create AI-based applications with highly efficient semantic search and context-focused data retrieval capabilities.

FAQs

How to integrate vector db with LLM?

You can perform vector database LLM integration using two methods: retrieval integration and injection integration. In retrieval integration, the vector database is externally integrated with LLM to retrieve accurate data using vector indexing. In injection integration, the information is ingested into LLM through parameter updates before training or by supplementary training.

Do you need a vector database for LLM?

It is not always essential to use vector databases for LLM operations. For instance, you can use a traditional database to prepare a simple chatbot with basic conversational capabilities. However, using a vector database is imperative for advanced operations such as retrieval augmented generation or semantic search.

How do LLM large language models benefit from utilizing vector databases like Chroma DB?

Chroma DB is an open-source vector database that enables you to store vector embeddings and related metadata, which can then be used in LLMs. You first have to create a Chroma DB collection similar to a table in relational databases. It uses the all-MiniLM-L6-V2 model to generate vector embeddings of this data. On receiving a query, the vector databases search for related vector embeddings and provide them to LLM to produce the required response.

Which vector database does Chatgpt use?

ChatGPT performs all its operations using Cosmos DB as a vector database. The LLM uses semi-structured data stored and indexed in Cosmos DB to extract data and generate responses. Cosmos DB's latency is in the range of 10 milliseconds, making it suitable for real-time applications.

Is Redis a vector database?

Redis is a data caching solution that offers vector database services in its stack, enterprise, and cloud versions. The vector database capability is not available in the open-source version. Redis vector database allows you to store, index, and search vectors for LLM operations. It also supports hybrid querying of vectors.