Embedding-Free Retrieval-Augmented Generation: RAG Without Vector Databases

Retrieval-Augmented Generation (RAG) has become the standard technique for supplying large language models with grounded, external context. The classic RAG workflow depends on embeddings (numerical vector representations of text) together with a vector database that enables semantic retrieval.

In a typical setup, documents are broken into smaller chunks, transformed into high-dimensional vectors through embedding models, stored inside a vector database, and searched through nearest-neighbor retrieval to locate the most relevant context for an LLM. This allows models to retrieve information based on semantic meaning rather than exact phrasing.

Even so, the combination of “vector DB + embeddings” often comes with meaningful trade-offs in cost, operational complexity, and overall performance. Because of these issues, more attention has shifted toward alternatives that avoid embedding-based retrieval entirely. Researchers are increasingly developing systems that perform RAG without embedding models and without vector search. In this article, we explain what embedding-free RAG is, why it is gaining traction, and how it differs from traditional vector database approaches.

Key Takeaways

• Traditional RAG systems depend on embeddings and vector databases. Content is chunked, converted into high-dimensional vectors, and indexed in a vector database using nearest-neighbor search to deliver semantic context to LLMs.
• Vector search introduces drawbacks such as semantic gaps, weaker retrieval precision, and limited interpretability. These weaknesses become more problematic in precision-heavy domains where embeddings may surface passages that sound related but do not actually contain the answer.
• Embedding-based RAG brings infrastructure overhead and higher costs. Creating embeddings, operating a vector database, and re-indexing changing data can demand extensive compute and storage.
• Embedding-free RAG can replace embeddings and vector search with other techniques. These include keyword-based retrieval (BM25), LLM-guided iterative retrieval (ELITE), knowledge-graph-driven methods (GraphRAG), and prompt-based retrieval (Prompt-RAG) to reduce semantic and operational limitations.
• RAG without embeddings can improve interpretability, reduce latency, lower storage needs, and adapt more easily to specific domains. This can be especially relevant for fields like healthcare, law, and finance, as well as scenarios where transparency or cross-document reasoning is essential.

Traditional RAG and Vector Databases

In the conventional RAG design, embeddings and vector search form the foundation of the retrieval step.

During offline indexing, source documents are divided into chunks, and each chunk is embedded using an embedding model to create a vector-based representation. These vectors are stored inside a vector database designed for fast nearest-neighbor lookups.

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During online querying, an incoming user query is embedded into the same vector space. The system then searches the vector store to return the top-k closest chunk vectors. The text chunks associated with those most similar embeddings are then passed into the LLM alongside the query so the model can generate an answer with supporting context.

The central benefit of this pipeline is that embeddings capture semantic similarity. This enables matching a question to passages that use different wording but express the same meaning. Vector databases are also built to serve similarity search efficiently at scale, keeping latency manageable even when the corpus grows to millions of chunks.

Limitations of Embeddings & Vector Search

Although widely adopted, vector-based RAG has several clear limitations. Let’s review some of the key ones:

Semantic Gaps

Semantic gaps are a frequent issue in embedding/vector retrieval. Dense similarity can reflect broad topical closeness rather than direct answer relevance. As a result, systems may return passages that feel semantically aligned but do not contain the correct information—especially when accuracy depends on exact numbers, specific dates, or negation. Embeddings may also perform poorly with specialized terminology, uncommon entities, or multi-step questions that require linking information across multiple documents.

Retrieval Accuracy

These semantic issues can translate into weak retrieval accuracy in practical RAG deployments. If the embedding model fails to represent the relationship between a question and its true answer, the top vector matches may not include the supporting passage. Some reports suggest that many RAG pipelines struggle to retrieve the correct evidence text. One practitioner shared that even after improving the “Chunking + Embedding + Vector Store” pipeline, correct-chunk retrieval accuracy is “usually below 60%.” In such cases, RAG systems may generate incorrect or incomplete answers because the context provided is not truly relevant.

Lack of Interpretability and Control

Embedding-based retrieval also makes it difficult to understand why an answer was missed or why a wrong passage was selected, because vector reasoning is not transparent. Retrieval becomes a black-box process. Fine-tuning retrieval behavior—such as prioritizing specific keywords or particular data fields—can be difficult when the system relies entirely on learned embedding representations.

Infrastructure Complexity and Cost

Embedding workflows introduce both offline and online costs. Offline, generating embeddings for thousands of documents requires time and compute resources, often involving GPUs. Online, operating a vector database service can be expensive, particularly because it may require large amounts of memory. For teams without dedicated infrastructure, this can be a major burden. There is also the continuous maintenance cost of the index itself, since updated data can require embedding regeneration and re-indexing.

Traditional vector database RAG has delivered significant value by enabling semantic search for LLMs. However, these limitations have encouraged researchers to explore retrieval augmentation beyond vector databases.

What Is RAG Without Embeddings?

RAG without embeddings describes any RAG approach that does not rely on vector embeddings as the primary mechanism for retrieving relevant context for generation. It removes the typical process of “embedding the query and documents, then performing nearest-neighbor vector search.”

So how can relevant information be retrieved without embeddings? Several approaches are emerging:

Lexical or Keyword-Based Retrieval

One of the most straightforward embedding-free RAG strategies is to use lexical keyword search, also called sparse retrieval.

Instead of comparing continuous vectors, the system searches for shared keywords or tokens between the query and documents using techniques such as BM25. Despite being considered “old school,” this sparse retrieval method can still be highly effective and, in many scenarios, performs competitively—sometimes matching or closely trailing modern vector-based approaches.

Keyword search remains competitive in practice. For instance, one XetHub benchmark found BM25 to be “not much worse” than top-tier OpenAI embedding models. According to that researcher, reaching an 85% recall of relevant documents might require returning 7 results through embeddings and vector search, compared to 8 results using the classical keyword method. The difference in accuracy was described as “insignificant, considering the cost of maintaining a vector database as well as an embedding service.”

Put differently, a well-optimized keyword retrieval system can capture a large portion of the value of retrieval augmentation without requiring the overhead of a vector database.

This can be implemented by generating a strong search query from the user prompt—potentially using an LLM to extract the most meaningful terms—and then running that query against a full-text retrieval engine such as Elasticsearch or an SQL full-text index.

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The LLM can then use those retrieved texts as context. This approach benefits from the precision signal of lexical matches, since the retrieved documents are very likely to contain the query terms or close equivalents. In some cases, that can produce more relevant context than dense embeddings.

LLM-based Iterative Search (Reasoning as Retrieval)

Another embedding-free RAG strategy is to use the LLM itself as the retrieval mechanism through reasoning and inference. Instead of scoring vector similarity, the system effectively “asks the LLM” to determine where an answer is most likely to be found. For example, an LLM agent could be given a list of document titles or summaries and instructed to reason about which document is the best candidate for containing the answer, then retrieve and inspect it.

This idea forms the basis of Agent-based RAG, where an LLM agent uses tools to search a document catalog by title or metadata before diving into deeper analysis.

In the same direction, a research framework called ELITE (Embedding-Less Retrieval with Iterative Text Exploration) enables an LLM to progressively narrow in on relevant text through iterative exploration. ELITE uses a custom importance measure that helps guide the search process.

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The diagram above illustrates an embedding-free RAG loop. A user query is sent into an LLM, which produces cues used to retrieve a snippet from the corpus. That snippet is evaluated using an importance measure to decide which window of text should be targeted next. The refined focus is then returned to the LLM, repeating in a loop until a stopping condition is reached, after which the system returns the final answer.

This method relies on the model’s language understanding and logical reasoning to carry out retrieval. Rather than handing retrieval off to an embedding index, the LLM itself is used to identify and refine the search path.

Structured Knowledge and Graph-Based Retrieval

Rather than storing a knowledge base as unstructured text chunks inside a vector index, this method organizes information inside a knowledge graph or another symbolic data structure.

In graph-oriented RAG, entities (for example, people, locations, or concepts) become nodes, while relationships become edges. These structures are derived from the original text or an underlying database. When a user submits a query, the system retrieves relevant nodes and traverses edges to assemble a set of facts or connected information. That structured result is then provided to the LLM.

Microsoft recently introduced GraphRAG, which “keeps the good bits of RAG but slips a knowledge graph between the indexer and the retriever”.

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With GraphRAG, the output is not simply “chunks that look similar” to the query. Instead, the retriever returns a subgraph containing relevant entities and relationships. This gives the LLM a structured “memory palace” that reflects how facts connect to one another.

This becomes especially useful for complex questions that require multi-hop reasoning or combining facts across sources (for example, realizing that Person A who did X is connected to Person B mentioned elsewhere).

Some GraphRAG implementations still use embeddings at specific stages (such as embedding the text inside each node’s context to perform similarity search within a neighborhood). However, the key idea remains: the graph introduces symbolic relational structure that pure vector retrieval does not provide.

Prompt-Based Retrieval (Embedding-Free Prompt RAG)

Another more recent research direction explores whether LLM prompting can be used to perform retrieval without explicit vectors. One example is Prompt-RAG, proposed in a 2024 paper focused on the domain of Korean medicine.

Instead of building a vector index, Prompt-RAG constructs a structured Table-of-Contents (ToC) from the documents. The system then prompts the LLM to select the sections (headings) that match the query.

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The content under those chosen headings is then merged into the context. The LLM is directly responsible for interpreting the query, understanding the document structure, and deciding what to retrieve. No embedding vectors are required. In that specific domain, the approach was shown to outperform traditional embedding-based RAG. This indicates that prompt-guided retrieval can be a strong alternative when embeddings fail to represent a domain’s semantics. RAG without embeddings replaces vector search with either classical information retrieval methods or LLM-driven reasoning. In a way, it reverses the trend of recent years. We are moving “back” to symbols and text for retrieval, but powered by the stronger reasoning abilities of modern LLMs.

Benefits of Embedding-Free RAG

Why consider these alternatives at all? There are several potential advantages to RAG without embeddings. If we recall the limitations described earlier, many of those issues can be addressed with these other techniques:

It’s important to note that these advantages are not automatic. The alternative methods often introduce different challenges (such as the compute cost of repeated LLM calls, or the engineering effort required to build and maintain a knowledge graph). Still, removing dependence on vector databases can eliminate many of the pain points found in current RAG systems.

Use Cases and Comparisons

When should an embedding-free RAG architecture be chosen over the classic vector database approach? The answer depends on your task, your data, your constraints, and many other factors. Below are some scenarios and how each approach compares:

Tip: Many teams are going hybrid—perform quick lexical filters, then vector search, then LLM re-rank; for complex/multi-hop or regulated queries, fall back to graph/agent retrieval

Future of RAG Architectures

Will embedding-free methods overtake or replace vector databases in RAG? A more realistic outcome is coexistence and complementary use. Below are several trends and predictions about where RAG architectures may be heading:

Vector DBs remain strong for massive-scale semantic similarity. Embedding-free methods perform better for reasoning, structure, and interpretability. The future points toward hybrid, adaptive systems that apply each approach where it fits best.

FAQ SECTION

Why does traditional RAG rely on embeddings and vector databases?

Traditional RAG operates by embedding text chunks into a vector space and storing those embeddings in a vector database. When answering a query, the system embeds the user query into the same vector space and performs nearest-neighbor search. The retrieved passages are then used as context for answering the query. This enables RAG to retrieve passages whose meaning aligns with the query, even when the exact wording is different.

What are the main limitations of embeddings and vector search in RAG?

Even though embeddings and vector search are extremely useful, they still come with limitations. They suffer from semantic gaps. Among the retrieved passages, only a portion may actually contain the answer, while many are only topically similar. This reduces retrieval accuracy, particularly in precision-sensitive domains such as law or healthcare. Vector search is also a black box, making it difficult to understand why a passage was retrieved. In addition, storing and maintaining embeddings and vector databases introduces major infrastructure cost and complexity, including the need to re-index whenever any document changes.

How does RAG without embeddings work, and what benefits does it offer?

RAG without embeddings uses retrieval mechanisms other than vector search. This includes keyword-based retrieval, iterative LLM-guided search, and graph-based or prompt-based retrieval. These methods can increase retrieval precision and lower indexing overhead. They can also reduce compute costs and improve interpretability. Embedding-free RAG is especially promising in specialized or low-data domains (healthcare, finance, legal) where embeddings often fail to capture domain-specific semantics.

Conclusion

Embedding-free RAG is emerging as an important alternative to traditional vector database approaches. In many situations, embeddings and vector search are still the best choice for large-scale semantic retrieval. However, they also introduce complexity, cost, and accuracy issues.

In contrast, keyword search, knowledge graphs, and LLM-based reasoning and interpretation are widely used embedding-free RAG methods. They can be simpler, faster, and more interpretable.

Vector databases deliver fast semantic similarity search, but they struggle with reasoning-heavy and domain-specific problems. In those areas, embedding-free RAG often performs better. Hybrid systems can combine strengths from both approaches to create adaptive pipelines that improve accuracy, reduce latency, and build more trust.

Many of the concepts described here (such as ELITE’s iterative retrieval or GraphRAG) have open-source implementations or are offered as services. It is possible to run these systems on your own data to evaluate how they compare against a vector search baseline.