#4141 PostgreSQL Database for RAG and KG

#4141 Discuss PostgreSQL database for RAG

PostgreSQL has evolved from a traditional relational database into a powerhouse for Retrieval-Augmented Generation (RAG). While specialized vector databases (like Pinecone or Milvus) exist, many developers are sticking with Postgres because it allows them to keep their structured data and vector embeddings in the same place.

Here is how Postgres handles the RAG workflow and why it’s becoming a top choice.

1. The Secret Sauce: pgvector

Standard Postgres doesn't "speak" vectors out of the box. The pgvector extension is the industry standard that adds vector capabilities. It allows you to:

Store embeddings (arrays of numbers representing text/images) in a VECTOR column.
Perform similarity searches using distances like Cosine Distance, L2 Distance, or Inner Product.
Query your vectors using standard SQL.

Example Query

SELECT content, metadata 
FROM documents 
ORDER BY embedding <=> '[0.12, -0.05, ...]' 
LIMIT 5;

(The <=> operator finds the "nearest neighbors" using cosine distance.)

2. Speed and Indexing

As your dataset grows, scanning every single row becomes too slow. Postgres handles this with specialized indexing:

IVFFlat (Inverted File Flat): Groups vectors into clusters. It’s fast but requires a "training" step and can lose a bit of accuracy.
HNSW (Hierarchical Navigable Small Worlds): The gold standard for RAG. It builds a graph-based index that allows for lightning-fast lookups with very high accuracy, even with millions of rows.

3. Why Use Postgres Instead of a Specialized Vector DB?

Feature	Postgres (pgvector)	Specialized Vector DBs
Data Consistency	ACID compliant. No "syncing" issues.	Often eventually consistent.
Hybrid Search	Combine SQL filters (e.g., `WHERE user_id = 1`) with vector search easily.	Requires complex metadata filtering or dual-querying.
Tooling	Works with everything (Drizzle, Prisma, SQLAlchemy, LangChain).	Requires specific drivers and learning a new API.
Complexity	One database to manage.	"Frankenstein" architecture (managing two different DBs).

4. The "Hybrid Search" Advantage

True RAG performance often requires more than just vector similarity. Postgres excels at Hybrid Search, which combines:

Semantic Search: Finding meaning via embeddings.
Keyword Search (BM25): Using Postgres's built-in Full-Text Search for exact matches (names, acronyms, or specific product IDs).

By using a technique called Reciprocal Rank Fusion (RRF), you can merge results from both methods to give the LLM the most relevant context possible.

5. Potential Limitations

Scaling: While Postgres scales well, extremely massive datasets (hundreds of millions of high-dimensional vectors) might require more manual tuning of memory and vacuum settings compared to a cloud-native vector DB.
Resource Intensity: HNSW indexes are memory-hungry. You’ll need to ensure your RAM is sufficient to keep the index "warm" for the best performance.

My Take

If you are already using Postgres for your application data, don't move. Adding pgvector is almost always the right move for 90% of RAG use cases. It simplifies your stack and keeps your data logic in one place.

Would you like me to help you write a Python script using LangChain or LlamaIndex to connect to a Postgres instance?

What about PostgreSQL for knowledge graphs?

PostgreSQL is surprisingly capable as a Knowledge Graph (KG) store, particularly for GraphRAG (a hybrid approach that combines vector search with graph reasoning).

While a dedicated graph database like Neo4j is "native" (pointers are used to traverse nodes), Postgres can be transformed into a graph database through two main paths: Relational Modeling or the Apache AGE extension.

1. The Power of Apache AGE

If you want a true Knowledge Graph experience inside Postgres, Apache AGE is the go-to extension. It allows you to run Cypher (the standard graph query language) directly inside your SQL queries.

Nodes & Edges: You can create labels like :Person or :Concept and connect them with edges like :RELATE_TO.
Hybrid Queries: You can query relational data (orders, users) and the knowledge graph in a single statement.

OpenCypher Support:

SELECT * FROM cypher('my_graph', $$
  MATCH (p:Person {name: 'Einstein'})-[:WORKS_AT]->(u:University)
  RETURN u.name
$$) AS (university_name agtype);

2. Why Use Postgres for Knowledge Graphs?

For many "GraphRAG" use cases, Postgres is actually superior to a standalone graph DB:

Multi-Model Mastery: You can store the raw text in a standard table, the embeddings in pgvector, and the entities/relationships in Apache AGE. This creates a "unified brain" for your AI.
Relational Filters: KGs often need "boring" filters (e.g., "Only look at facts from 2023"). Postgres handles these B-Tree index filters much faster than most native graph databases.
ACID Compliance: Your knowledge graph updates are transactional. If a relationship is added, it’s immediately and safely committed alongside your other app data.

3. Postgres vs. Neo4j for Knowledge Graphs

Feature	PostgreSQL (AGE / SQL)	Neo4j (Native Graph)
Traversal Speed	Fast for 2–3 "hops."	Blazing fast for 5+ hops.
Storage	Relational tables under the hood.	Native graph storage (pointers).
Complexity	Easy to manage if you know Postgres.	Requires managing a new system.
AI/RAG Setup	Best for "all-in-one" RAG stacks.	Best for deep network analysis.

4. How to build it (The RAG Blueprint)

If you are building a "GraphRAG" system in 2026, here is the recommended architecture:

Extract: Use an LLM to extract "Triplets" (Subject → Predicate → Object) from your documents.
Store:
- Nodes: Entities (e.g., "PostgreSQL") stored as rows.
- Edges: Relationships (e.g., "is a", "supports") stored in a join table or Apache AGE.
- Vectors: Embeddings of the entity descriptions using pgvector.
Retrieve: * First, use pgvector to find the most relevant "starting nodes."
- Then, perform a "1-hop" or "2-hop" expansion to gather related facts (e.g., "Postgres supports pgvector, which is used for RAG").

My Recommendation

If your knowledge graph is a supplement to your AI (providing context and preventing hallucinations), stay with Postgres. The overhead of moving data to a separate graph DB usually outweighs the performance gains unless you are doing massive social-network-style traversals.

Would you like a sample schema for a simple "Triplets" table to get started without installing extensions?

Yes

To build a knowledge graph in PostgreSQL without specialized extensions, you use a Triple Store or a Nodes/Edges model. This is excellent for RAG because you can join these tables with your existing data and even use pgvector on the same rows.

Here is a production-ready schema and a query to traverse it.

1. The Schema (Nodes & Edges)

Instead of one massive table, it’s best to separate Entities from Relationships. This allows you to store metadata (like vector embeddings for RAG) on the entities themselves.

-- 1. Create the Entities (Nodes)
CREATE TABLE entities (
    id TEXT PRIMARY KEY,       -- e.g., 'postgres_db'
    label TEXT,                -- e.g., 'Database'
    metadata JSONB,            -- Extra properties
    embedding VECTOR(1536)     -- For RAG: semantic vector of the entity description
);

-- 2. Create the Relationships (Edges/Triplets)
CREATE TABLE relationships (
    id SERIAL PRIMARY KEY,
    subject_id TEXT REFERENCES entities(id),   -- Source node
    predicate TEXT,                            -- The "verb" (e.g., 'supports')
    object_id TEXT REFERENCES entities(id),    -- Target node
    weight FLOAT DEFAULT 1.0,                  -- Strength of relationship
    
    -- Crucial for performance
    UNIQUE (subject_id, predicate, object_id)
);

-- 3. Optimization Indexes
CREATE INDEX idx_rel_subject ON relationships(subject_id);
CREATE INDEX idx_rel_object ON relationships(object_id);

2. Graph Traversal with Recursive CTEs

One of the most powerful features in Postgres for graphs is the Recursive Common Table Expression (CTE). This allows you to follow "hops" through your graph without knowing the depth in advance.

Scenario: Find all technologies related to "PostgreSQL" up to 2 hops away.

WITH RECURSIVE graph_path AS (
    -- Initial Step: Find direct connections for 'postgres_db'
    SELECT 
        subject_id, 
        predicate, 
        object_id, 
        1 AS depth
    FROM relationships
    WHERE subject_id = 'postgres_db'

    UNION ALL

    -- Recursive Step: Follow those connections to the next level
    SELECT 
        r.subject_id, 
        r.predicate, 
        r.object_id, 
        gp.depth + 1
    FROM relationships r
    INNER JOIN graph_path gp ON r.subject_id = gp.object_id
    WHERE gp.depth < 2  -- Limit depth to avoid infinite loops/huge results
)
SELECT DISTINCT object_id FROM graph_path;

3. Why this works for RAG

This structure solves the "context" problem in AI:

Vector Search: You use pgvector on the entities table to find the most relevant "starting node" based on a user query.
Graph Expansion: You run the SQL query above to find everything connected to that node.
Final Prompt: You feed the LLM both the vector-matched text and the graph relationships (e.g., "Note: PostgreSQL supports pgvector, which is a tool for RAG").

Pro-Tips for Scaling

JSONB for Schema-less: Use the metadata column for properties that vary between nodes (e.g., "date_founded" for a company vs "atomic_weight" for a chemical).
Bi-directional search: If your graph is undirected, you might want to query both subject_id and object_id in your recursive CTE.
Consistency: If you delete a node, the REFERENCES constraint ensures you don't leave "ghost" relationships behind.

Would you like me to show you how to combine a vector search and a graph hop into a single SQL query?

Yes

Combining a vector search and a graph traversal in one query is the "holy grail" of GraphRAG. It allows you to find a relevant starting point semantically (via embeddings) and then immediately grab the surrounding context (via relationships).

Here is the SQL pattern to perform this in a single, efficient step.

The "Vector-to-Graph" Query

This query uses a CTE (Common Table Expression) to find the most relevant entity first, and then feeds that entity into a recursive search to find its neighbors.

WITH RECURSIVE 
-- Step 1: Find the most relevant node using vector similarity
starting_node AS (
    SELECT id, label
    FROM entities
    ORDER BY embedding <=> '[0.1, -0.2, 0.5, ...]' -- Your query embedding
    LIMIT 1
),
-- Step 2: Traverse the graph starting from that node
graph_context AS (
    -- Anchor: The node we found via vector search
    SELECT 
        s.id, 
        s.label, 
        CAST(NULL AS TEXT) as relationship, 
        0 AS depth
    FROM starting_node s

    UNION ALL

    -- Recursive Step: Find everything connected to the previous level
    SELECT 
        e.id, 
        e.label, 
        r.predicate, 
        gc.depth + 1
    FROM entities e
    JOIN relationships r ON e.id = r.object_id
    JOIN graph_context gc ON r.subject_id = gc.id
    WHERE gc.depth < 2 -- "2-hop" expansion
)
SELECT * FROM graph_context;

Why this is a "killer feature" for RAG

Contextual Awareness: Standard RAG only sees "chunks" of text. This query gives your LLM the broader neighborhood. If the user asks about "Postgres," the vector search finds the "Postgres" node, and the graph expansion pulls in "pgvector," "SQL," and "Relational DB."
Accuracy: By limiting the vector search to LIMIT 1 or LIMIT 3, you ensure you aren't pulling in a mess of unrelated data. You find the right entry point and let the graph handle the relevance from there.
Performance: Because you are joining on indexed foreign keys (subject_id, object_id), the recursive part of the query is extremely fast—usually taking only a few milliseconds even with large graphs.

Pro Tip: The "Weighted" Retrieval

If your relationships have weights (e.g., how "strong" a connection is), you can modify the ORDER BY in the final select to prioritize the most important facts for your LLM's limited context window.

-- Inside the recursive part
SELECT ..., (gc.total_weight * r.weight) as total_weight
-- Then finally
ORDER BY depth ASC, total_weight DESC
LIMIT 20;

Would you like me to show you how to structure the final prompt to the LLM so it actually understands this graph data?

Yes

To get the most out of your PostgreSQL GraphRAG setup, you shouldn't just dump the raw table data into the prompt. LLMs perform much better when the graph data is "verbalized"—turned into natural, readable sentences or a clean, structured list.

Here is how to structure the final prompt to the LLM.

1. The "Verbalized" Context Pattern

The key is to separate your semantic search results (the chunks of text) from your graph knowledge (the facts).

Example Prompt Structure:

You are an expert assistant. Answer the user's question using ONLY the provided context.

### 1. DIRECT KNOWLEDGE (from Vector Search)
[This is the text chunk found via pgvector similarity]
"PostgreSQL supports pgvector, an extension for storing and searching vector embeddings. 
It is widely used for building AI-powered search and recommendation engines."

### 2. RELATED CONCEPTS (from Graph Expansion)
Below are facts related to the entities identified in your query:
- pgvector [is a tool for] RAG.
- pgvector [supports] HNSW indexing.
- HNSW indexing [improves] search speed for large datasets.
- RAG [requires] an LLM for final generation.

### 3. USER QUESTION
"How does pgvector help with search speed in a RAG system?"

### 4. RESPONSE
[The LLM's answer will now be much more accurate because it understands the "why" and "how" through the graph links.]

2. Converting SQL Rows to "Prompt Strings"

In your application code (Python/Node.js), you should format the results of the query we wrote earlier.

Don't send this: [{subject_id: "pgvector", predicate: "supports", object_id: "HNSW"}]

Send this: "Fact: pgvector supports HNSW."

Quick Python helper (Pseudo-code):

# Assuming 'results' is the output from your Postgres graph query
graph_context = "\n".join([f"- {r['subject']} {r['predicate']} {r['object']}." for r in results])

full_prompt = f"""
Use the following graph context to help answer the question:
{graph_context}

Question: {user_query}
"""

3. Why this structure prevents Hallucinations

By providing the relationships, you are giving the LLM a "reasoning path."

Vector search tells the LLM what is relevant.
Graph context tells the LLM how those things are connected.

Without the graph, the LLM might guess the relationship between "pgvector" and "HNSW." With the graph, it knows for a fact that one supports the other.

4. Pro Tip: Use "Community Summaries"

If your graph is very dense (too many hops), don't send every relationship. Instead:

Store a summary of highly connected nodes (e.g., a "Summary" column in your entities table).
If a node is a "hub" (many connections), include its summary in the prompt rather than 50 individual triples.

Would you like me to help you design the Python logic that handles the LLM call and formats these results automatically?

Yes

To implement this in Python, you can use a standard database driver like psycopg3 (the modern successor to psycopg2) alongside a framework like LangChain.

The logic follows three steps:

Generate Embedding: Convert the user's question into a vector.
Execute Hybrid SQL: Use the recursive query we designed to get both the starting entity and its neighbors.
Verbalize & Prompt: Clean the raw database rows into a string the LLM can read.

The Python Implementation

import psycopg
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

# 1. Setup & DB Connection
conn = psycopg.connect("postgresql://user:pass@localhost:5432/dbname")
embeddings = OpenAIEmbeddings()
llm = ChatOpenAI(model="gpt-4o")

def get_graph_rag_context(user_query):
    # Convert query to vector
    query_vector = embeddings.embed_query(user_query)
    
    # The hybrid SQL query
    sql = """
    WITH RECURSIVE starting_node AS (
        SELECT id, label FROM entities
        ORDER BY embedding <=> %s::vector LIMIT 1
    ),
    graph_context AS (
        SELECT s.id, s.label, CAST(NULL AS TEXT) as rel, 0 AS depth
        FROM starting_node s
        UNION ALL
        SELECT e.id, e.label, r.predicate, gc.depth + 1
        FROM entities e
        JOIN relationships r ON e.id = r.object_id
        JOIN graph_context gc ON r.subject_id = gc.id
        WHERE gc.depth < 2
    )
    SELECT * FROM graph_context;
    """
    
    with conn.cursor() as cur:
        cur.execute(sql, (str(query_vector),))
        rows = cur.fetchall()
    
    # 2. Verbalize the Triplets for the LLM
    facts = []
    for row in rows:
        if row[2]: # row[2] is the predicate
            facts.append(f"Fact: {row[1]} is related via '{row[2]}' to the main topic.")
        else:
            facts.append(f"Primary Entity Found: {row[1]}")
            
    return "\n".join(facts)

# 3. Final Execution
user_input = "Tell me about PostgreSQL's relationship with AI."
context = get_graph_rag_context(user_input)

prompt = ChatPromptTemplate.from_template("""
Answer the question based on these graph facts:
{context}

Question: {question}
""")

chain = prompt | llm
response = chain.invoke({"context": context, "question": user_input})
print(response.content)

Why this Python logic is powerful

Minimal Latency: You are doing exactly one database trip to get both the semantic match and the graph relationship.
Dynamic Context: By changing depth < 2 to depth < 3, you can instantly make your RAG system "smarter" (but potentially more "noisy").
Verbalization: The loop that creates the facts list ensures the LLM sees clean sentences. LLMs are much better at following - Entity A supports Entity B than parsing raw JSON lists.

Advanced Step: Multi-hop weighting

If you find that 2-hop searches are bringing in too much irrelevant data, you can add a Similarity Score check within the Python loop to only include neighbors that are also somewhat semantically relevant to the original question.

Would you like me to show you how to add "Relationship Weights" to the SQL query so the LLM only gets the most important connections?

From <https://gemini.google.com/app/> Google Gemini 3.0 Pro (Fast)

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