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Embeddings
Mistral

Mistral

Modern open-source RAG pipelines need two things:

  1. High-quality embeddings – Mistral-Embed (mistral-embed) returns 1 024-dimensional float vectors that rival OpenAI + Cohere.

  2. Blazing-fast vector storage – SurrealDB (≥ v1.5) ships an in-memory HNSW index, queried with the <|K,EF|> operator in SurrealQL.

Below you’ll wire them together, from install → ingestion → search → production-ready script.

Prerequisites

pip install mistralai surrealdb

Set two environment variables (or hard-code them if you must):

export SDB_URL="http://localhost:8000/rpc"   # ← SurrealDB RPC endpoint
export MISTRAL_API_KEY="sk-…"                # ← your Mistral key

Connect and create the schema

from mistralai.client import MistralClient
from surrealdb import Surreal
import os, asyncio

# ----- 1.1 · Config -----------------------------------------------------------------
SDB_URL  = os.getenv("SDB_URL", "http://localhost:8000/rpc")
SDB_USER = os.getenv("SDB_USER", "root")
SDB_PASS = os.getenv("SDB_PASS", "secret")
NS, DB   = "demo", "demo"
TABLE    = "mistral_docs"
MODEL    = "mistral-embed"

# ----- 1.2 · Clients ----------------------------------------------------------------
sdb   = Surreal(SDB_URL)
mistr = MistralClient(api_key=os.environ["MISTRAL_API_KEY"])

async def init_db():
    await sdb.signin({"user": SDB_USER, "pass": SDB_PASS})
    await sdb.use(NS, DB)

    # one quick embedding → get true vector dimension
    dim = len(mistr.embeddings(model=MODEL, input=["ping"]).data[0].embedding)

    schema = """
    DEFINE TABLE $tb SCHEMALESS PERMISSIONS NONE;
    DEFINE FIELD text      ON $tb TYPE string;
    DEFINE FIELD embedding ON $tb TYPE array;

    DEFINE INDEX hnsw_idx ON $tb
      FIELDS embedding
      HNSW DIMENSION {dim}
      DIST   COSINE;
    """
    await sdb.query(schema, {"tb": TABLE})

asyncio.run(init_db())

Why detect the dimension dynamically?

Future-proofing: if Mistral introduces a small or large Embed model with a different dimension, the code auto-adapts.

Embed and bulk-insert documents

DOCS = [
    "SurrealDB offers an in-memory HNSW vector index for low-latency search.",
    "Mistral-Embed produces 1 024-dimensional embeddings.",
    "You can build a completely open-source RAG stack with these two tools.",
]

async def insert_docs(docs, batch=64):
    rows = []
    for i in range(0, len(docs), batch):
        chunk = docs[i : i + batch]
        vecs  = mistr.embeddings(model=MODEL, input=chunk).data
        rows += [
            {
                "id":        f"{TABLE}:{i+j}",
                "text":      chunk[j],
                "embedding": vec.embedding,
            }
            for j, vec in enumerate(vecs)
        ]
    await sdb.query(f"INSERT INTO {TABLE} $data", {"data": rows})

asyncio.run(insert_docs(DOCS))

Why bulk-insert? One SurrealQL call → one network round-trip — much faster than inserting row-by-row.

Search with a natural-language query

async def search(query: str, k: int = 3, ef: int = 64):
    q_vec = mistr.embeddings(model=MODEL, input=[query]).data[0].embedding
    surql = """
    LET $q := $vec;
    SELECT id, text, vector::distance::knn() AS score
    FROM $tb
    WHERE embedding <|{k},{ef}|> $q
    ORDER BY score;
    """
    res = await sdb.query(surql, {"vec": q_vec, "tb": TABLE})
    return res[0].result

hits = asyncio.run(search("Which database supports native vector search?"))
for h in hits:
    print(f"⭐ {h['text']}  (score={h['score']:.4f})")

<|K,EF|> activates the HNSW K-nearest-neighbour operator (K=3, efSearch=64). vector::distance::knn() exposes the cosine distance already computed inside the index—no post-processing needed.

Full script (ready to run)

# mistral_surreal_demo.py
from __future__ import annotations
import os, asyncio
from mistralai.client import MistralClient
from surrealdb import Surreal

SDB_URL  = os.getenv("SDB_URL", "http://localhost:8000/rpc")
SDB_USER = os.getenv("SDB_USER", "root")
SDB_PASS = os.getenv("SDB_PASS", "secret")
NS, DB, TABLE = "demo", "demo", "mistral_docs"
MODEL   = "mistral-embed"
KEY     = os.environ["MISTRAL_API_KEY"]  # export first!

sdb, mistr = Surreal(SDB_URL), MistralClient(api_key=KEY)

DOCS = [
    "SurrealDB's vector index is built on HNSW.",
    "Mistral-Embed vectors offer strong semantic quality.",
    "Together they form a fast, open-source search stack.",
]

async def main():
    await sdb.signin({"user": SDB_USER, "pass": SDB_PASS})
    await sdb.use(NS, DB)

    dim = len(mistr.embeddings(model=MODEL, input=["x"]).data[0].embedding)
    await sdb.query("""
        DEFINE TABLE $tb SCHEMALESS PERMISSIONS NONE;
        DEFINE FIELD text ON $tb TYPE string;
        DEFINE FIELD embedding ON $tb TYPE array;
        DEFINE INDEX hnsw_idx ON $tb FIELDS embedding
               HNSW DIMENSION {dim} DIST COSINE;
    """, {"tb": TABLE})

    # ingest if empty
    if (await sdb.query(f"SELECT count() FROM {TABLE};"))[0].result[0]["count"] == 0:
        rows = []
        vecs = mistr.embeddings(model=MODEL, input=DOCS).data
        rows = [
            {"id": f"{TABLE}:{i}", "text": DOCS[i], "embedding": v.embedding}
            for i, v in enumerate(vecs)
        ]
        await sdb.query(f"INSERT INTO {TABLE} $data", {"data": rows})

    # search
    q_vec = mistr.embeddings(model=MODEL,
                             input=["open-source vector database"] ).data[0].embedding
    res = await sdb.query("""
        LET $q := $vec;
        SELECT text, vector::distance::knn() AS score
        FROM {TABLE}
        WHERE embedding <|3,64|> $q
        ORDER BY score;
    """, {"vec": q_vec})
    print(res[0].result)

if __name__ == "__main__":
    asyncio.run(main())

About quantisation

SurrealDB currently stores vectors as float32 / float64 arrays and does not ship built-in binary or int8 quantisation. If memory is critical you can:

  1. Quantise offline to int8 (e.g. with faiss or sentence-transformers).

  2. Store the int8 arrays in another field (SurrealQL’s array type is agnostic).

  3. Execute a two-stage search: coarse K-NN on the int8 field, then rescore on the full-precision field.

You’re done 🚀

You now have a clean, fully-async SurrealDB setup that stores Mistral-Embed vectors, supports fast HNSW search, and can be dropped into any RAG or semantic-search workflow.

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