🐍 Embeddings

from langchain_ollama import OllamaEmbeddings

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

from langchain_openai import OpenAIEmbeddings

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

from langchain_mistralai import MistralAIEmbeddings

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

from langchain_huggingface import HuggingFaceEmbeddings

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

from langchain_aws import BedrockEmbeddings

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

from langchain_google_genai import GoogleGenerativeAIEmbeddings

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

import ollama

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

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 })

from sentence_transformers import SentenceTransformer

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

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(),
    },
)