aecp

Embedding providers deprecate models constantly — ada-002 is gone, text-embedding-3 is next. When that happens, you either re-embed your entire corpus (expensive, slow, risky) or get stuck on a dead model. AECP lets you switch without re-embedding: fit a lightweight linear transform from ~2K calibration texts, apply it to stored vectors, and gate the migration on measured retrieval retention. 87-91% retention on BEIR benchmarks.

Install

pip install aecp

Python >= 3.10. Core deps: numpy, scikit-learn, typer, rich.

Optional extras:

• pip install aecp[chroma] — ChromaDB adapter
• pip install aecp[langchain] — LangChain embeddings shim
• pip install aecp[llamaindex] — LlamaIndex query wrapper
• pip install aecp[sentence-transformers] — local model support
• pip install aecp[qdrant] — Qdrant store adapter
• pip install aecp[openai] — OpenAI client shim
• pip install aecp[all] — everything above

5-minute trial: query-time wrapper

Zero writes to your vector store. Map new-model queries into legacy space on-the-fly. Fully reversible.

LlamaIndex

from aecp.wrappers.llamaindex import AECPEmbedding
from aecp.mapping.registry import load_mapping

mapping = load_mapping("mapping.aecp")
wrapper = AECPEmbedding(
    new_model_embedder=your_llamaindex_embedder,
    transform_artifact_path="mapping.aecp",
)
# Use wrapper anywhere LlamaIndex expects a BaseEmbedding
# Queries are mapped; document embeddings raise AECPWrapperUsageError

OpenAI client

import openai
from aecp.wrappers.openai_shim import AECPOpenAI

client = openai.OpenAI()
shim = AECPOpenAI(client, "mapping.aecp")
response = shim.embeddings.create(input=["query text"], model="text-embedding-3-small")
# response.data[0].embedding is now in legacy-model space

LangChain

from aecp.adapters.langchain import AECPEmbeddings
from langchain_openai import OpenAIEmbeddings

mapping = Mapping.load("mapping.aecp")
base = OpenAIEmbeddings(model="text-embedding-3-small")
ae = AECPEmbeddings(mapping, base)

from langchain_chroma import Chroma
db = Chroma.from_documents(docs, embedding=ae)
results = db.similarity_search("query", k=10)

Quality gate

Before migrating anything, verify the transform preserves retrieval quality:

aecp gate --mapping mapping.aecp \
          --source-vectors X_sample.npy \
          --target-vectors Y_sample.npy

Output: retention table (Recall@1/5/10, MRR), bootstrap confidence intervals, per-metric pass/fail, and a one-line verdict. Exit code 0 for PASS, 1 for WARN/FAIL — use it in CI.

Full migration

# 1. Plan cost
aecp plan --source-model ada-002 --target-model te3-large --corpus-size 1000000

# 2. Calibrate
aecp calibrate --source-vectors X.npy --target-vectors Y.npy -o mapping.aecp

# 3. Gate
aecp gate --mapping mapping.aecp --source-vectors X.npy --target-vectors Y.npy

# 4. Migrate
aecp transform --mapping mapping.aecp --source-dir ./old_store --target-dir ./new_store

Serve mode (zero corpus writes)

Map queries on-the-fly without touching stored data:

from aecp.serve import QueryAdapter

qa = QueryAdapter.load("mapping.aecp")
legacy_vec = qa.map_query(new_model_embed(query))

Adapter status

Claims policy

Every quantitative claim in this README or docs references a committed artifact in benchmarks/results/ and a row in aecp-python/CLAIMS.md. No exceptions. If a number isn't in CLAIMS.md, it isn't a claim.

Adapter comparison (SciFact, MiniLM→bge-large, K=4000, 3 seeds)

K-sweep (all adapters averaged, SciFact, 3 seeds)

Same-dim pair (bge-large→e5-large, 1024→1024)

Same dimension ≠ same space. e5 models require "query: "/"passage: " prefixes; without them ceiling drops to 0.36.

Confidence flags (predictive across both pairs)

Score recalibration (MiniLM→bge, rectangular)

When NOT to use AECP

• Maximum retrieval quality matters more than cost → re-embed
• Calibration domain mismatches corpus (e.g., code index calibrated on prose)
• Quality gate returns FAIL → do not migrate; re-embed
• You need unsupervised migration (AECP requires paired calibration)
• K < 2000 (quality degrades significantly below this)

Anti-patterns

• Do not mix vectors from different models in one collection
• Do not assume same dimensionality means compatibility
• Do not skip the quality gate
• Do not use MLP adapter (0.727 vs 0.871 for Ridge, same cost)

How it works

1. Embed K texts with source and target models → matrices X, Y
2. Fit ridge map Y = [X | 1] W (handles unequal dims)
3. Hold out 10% to estimate quality
4. Transform corpus: V' = normalize(V @ W) (streaming batches)
5. Write to new collection; keep old as rollback

Prior art

Engineering, not research. Built on:

• vec2vec (Jha et al., 2025)
• Drift-Adapter (EMNLP 2025)
• Platonic Representation Hypothesis (Huh et al., 2024)

Security

Embedding translation enables inversion-style attacks. Treat mapped vectors with same sensitivity as source text.

License

Apache-2.0