Embedders

Medha is embedder-agnostic. You plug in any implementation of BaseEmbedder at construction time. Four adapters are provided out of the box.

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Comparison

Embedder	Dimensions	API Key	Cost	Best For
FastEmbedAdapter	384–768	No	Free	Dev, on-prem, air-gapped
OpenAIAdapter	1536–3072	Yes	$	General purpose
CohereAdapter	1024	Yes	$$	Multilingual, enterprise
GeminiAdapter	768	Yes	$	Google ecosystem

Tip

You can switch embedders at any time, but existing cache entries will no longer match because their stored embeddings were produced by the old model. See the FAQ for a migration strategy.

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FastEmbedAdapter — Local ONNX

Install

pip install "medha-archai[fastembed]"

FastEmbed runs ONNX models in-process with no API key, no network calls (after first model download), and no per-token cost. The default model is BAAI/bge-small-en-v1.5 (384 dimensions).

from medha.embeddings.fastembed_adapter import FastEmbedAdapter

# Default model
embedder = FastEmbedAdapter()

# Larger model for higher accuracy
embedder = FastEmbedAdapter(model_name="BAAI/bge-large-en-v1.5")

Async usage:

import asyncio
from medha.embeddings.fastembed_adapter import FastEmbedAdapter

async def main():
    embedder = FastEmbedAdapter()
    vector = await embedder.embed("How many active users do we have?")
    print(len(vector))  # 384

asyncio.run(main())

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OpenAIAdapter — OpenAI

Install

pip install "medha-archai[openai]"

Uses the OpenAI Embeddings API. The default model is text-embedding-3-small (1536 dimensions); text-embedding-3-large gives 3072 dimensions.

import os
from medha.embeddings.openai_adapter import OpenAIAdapter

embedder = OpenAIAdapter(
    api_key=os.environ["OPENAI_API_KEY"],
    model="text-embedding-3-small",
)

Async usage:

import asyncio, os
from medha.embeddings.openai_adapter import OpenAIAdapter

async def main():
    embedder = OpenAIAdapter(api_key=os.environ["OPENAI_API_KEY"])
    vector = await embedder.embed("How many active users do we have?")
    print(len(vector))  # 1536

asyncio.run(main())

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CohereAdapter — Cohere

Install

pip install "medha-archai[cohere]"

Uses Cohere's Embed v3 API. Produces 1024-dimensional embeddings with strong multilingual and domain-specific performance.

import os
from medha.embeddings.cohere_adapter import CohereAdapter

embedder = CohereAdapter(
    api_key=os.environ["COHERE_API_KEY"],
    model="embed-english-v3.0",      # or embed-multilingual-v3.0
    input_type="search_query",       # or "search_document" for stored entries
)

Async usage:

import asyncio, os
from medha.embeddings.cohere_adapter import CohereAdapter

async def main():
    embedder = CohereAdapter(api_key=os.environ["COHERE_API_KEY"])
    vector = await embedder.embed("How many active users do we have?")
    print(len(vector))  # 1024

asyncio.run(main())

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GeminiAdapter — Google Gemini

Install

pip install "medha-archai[gemini]"

Uses the Google Generative AI Embeddings API. Produces 768-dimensional embeddings and integrates naturally with Google Cloud infrastructure.

import os
from medha.embeddings.gemini_adapter import GeminiAdapter

embedder = GeminiAdapter(
    api_key=os.environ["GOOGLE_API_KEY"],
    model="models/embedding-001",
)

Async usage:

import asyncio, os
from medha.embeddings.gemini_adapter import GeminiAdapter

async def main():
    embedder = GeminiAdapter(api_key=os.environ["GOOGLE_API_KEY"])
    vector = await embedder.embed("How many active users do we have?")
    print(len(vector))  # 768

asyncio.run(main())

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Custom Embedder

Implement BaseEmbedder to plug in any embedding model:

from medha.interfaces.embedder import BaseEmbedder


class MyCustomEmbedder(BaseEmbedder):
    """Wraps any embedding model."""

    def __init__(self, model):
        self._model = model

    async def embed(self, text: str) -> list[float]:
        # Must return a list of floats (the embedding vector)
        return await self._model.encode_async(text)

    async def embed_batch(self, texts: list[str]) -> list[list[float]]:
        # Optional: override for batched efficiency
        return await self._model.encode_batch_async(texts)

Then pass it to Medha exactly like any built-in adapter:

from medha import Medha, Settings

embedder = MyCustomEmbedder(my_model)
settings = Settings(backend_type="memory")

async with Medha("demo", embedder=embedder, settings=settings) as cache:
    ...

See Interfaces (ABCs) for the full BaseEmbedder contract.