🐍 Embeddings

SurrealDB offers comprehensive support for vector embeddings, enabling powerful semantic search and machine learning capabilities across your data. Through integrations with leading embedding providers, you can easily store, index and query high-dimensional vectors alongside your regular data.

LangChain
Vanilla

More details and providers in LangChain Embedding models documentation.

Ollama

from langchain_ollama import OllamaEmbeddings

vector_store = SurrealDBVectorStore(
    OllamaEmbeddings(model="all-minilm:22m"),
    conn
)

More Ollama embedding models in their documentation.

OpenAI

Requires OPENAI_API_KEY environment variable.

from langchain_openai import OpenAIEmbeddings

vector_store = SurrealDBVectorStore(
    OpenAIEmbeddings(model="text-embedding-3-large"),
    conn
)

Mistral

Requires MISTRALAI_API_KEY environment variable.

from langchain_mistralai import MistralAIEmbeddings

vector_store = SurrealDBVectorStore(
    MistralAIEmbeddings(model="mistral-embed"),
    conn
)

SentenceTransformer

from langchain_huggingface import HuggingFaceEmbeddings

vector_store = SurrealDBVectorStore(
    HuggingFaceEmbeddings(
        model_name="sentence-transformers/all-MiniLM-L6-v2"
    ),
    conn
)

More SentenceTransformer models in their documentation.

AWS Bedrock

from langchain_aws import BedrockEmbeddings

vector_store = SurrealDBVectorStore(
    BedrockEmbeddings(model_id="amazon.titan-embed-text-v2:0"),
    conn
)

Gemini

Requires GOOGLE_API_KEY environment variable.

from langchain_google_genai import GoogleGenerativeAIEmbeddings

vector_store = SurrealDBVectorStore(
    GoogleGenerativeAIEmbeddings(model="models/embedding-001"),
    conn
)

Then, to query the vector store using similarity search:

doc1 = Document(
    page_content="SurrealDB is the ultimate multi-model database for AI applications",
    metadata={"key": "sdb"},
)
doc2 = Document(
    page_content="Surrealism is an artistic and cultural movement that emerged in the early 20th century",
    metadata={"key": "surrealism"},
)
vector_store.add_documents(documents=[doc1, doc2], ids=["1", "2"])

results = vector_store.similarity_search_with_score(query=q, k=2)
for doc, score in results:
    print(f"• [{score:.0%}]: {doc.page_content}")
top_match = results[0][0]

Find an example in Minimal LangChain chatbot example with vector and graph.

Ollama

import ollama

embedding = ollama.embed(model="all-minilm:22m", input=text)
conn.create("documents", { "content": text, "embedding": embedding })

More Ollama embedding models in their documentation.

OpenAI

from openai import OpenAI
client = OpenAI()

text = "Your text string"
response = client.embeddings.create(
    input=text,
    model="text-embedding-3-small"
)

conn.create("documents", { "content": text, "embedding": response.data[0].embedding })

More info in OpenAI embeddings documentation.

Sentence Transformers

from sentence_transformers import SentenceTransformer

st = SentenceTransformer("all-MiniLM-L6-v2")
embedding = st.encode(text).tolist()
conn.create("documents", { "content": text, "embedding": embedding })

More SentenceTransformer models in their documentation.

To query the vector store using similarity search:

k = 5
query_embedding = st.encode("this is your query text")
res = conn.query(
    f"""
    SELECT
        *,
        vector::similarity::knn() AS dist
    FROM documents
    WHERE embedding <|{k}|> $vector;
    """,
    {
        "vector": query_embedding.tolist(),
    },
)

This requires an index the be created beforehand. Refer to the vector search cheat sheet.

Examples above assume you have a DB connection like this:

conn = Surreal("localhost")
conn.signin({"username": "root", "password": "secret"})
conn.use("test_ns", "test_db")

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Embeddings Quick start with Rust

🐍 Embeddings

Ollama

OpenAI

Mistral

SentenceTransformer

AWS Bedrock

Gemini

Ollama

OpenAI

Sentence Transformers

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