langchain-vectoramp 0.2.0

LangChain VectorStore integration for VectorAmp

langchain-vectoramp

LangChain VectorStore, retriever, and document loader integration for VectorAmp, backed by VectorAmp-hosted embeddings and SABLE datasets.

VectorAmp embeds your text server-side, so you never have to wire up a LangChain Embeddings object: text add and search call VectorAmp's hosted model and SABLE index directly.

Installation

pip install langchain-vectoramp

Set your API key once in the environment (every class reads it automatically):

export VECTORAMP_API_KEY="vsk_..."

Quickstart

from langchain_vectoramp import VectorAmpVectorStore

# API key comes from VECTORAMP_API_KEY; base_url defaults to production.
store = VectorAmpVectorStore(dataset_name="docs")   # or dataset_id="..."

store.add_texts(
    ["VectorAmp is built for billion-scale vector search."],
    metadatas=[{"source": "readme"}],
)

docs = store.similarity_search(
    "What is VectorAmp built for?",
    k=3,
    filter={"source": "readme"},
    rerank=True,                # expands to the VectorAmp-Rerank-v1 rerank object
)

add_texts embeds each text with the dataset's hosted model, copies the source text into metadata.text, generates ids when you omit them, and inserts the vectors in one call.

Vector ids: strings or integers

ids accepts strings or integers. Integer ids are preserved and sent as JSON numbers, so the server stores exactly what you passed:

store.add_texts(["a", "b"], ids=[1, 2])      # -> [1, 2], stored as numbers
store.add_texts(["c"], ids=["doc-c"])        # -> ["doc-c"]

# Delete vectors by id through the LangChain VectorStore API.
store.delete(ids=["doc-c"])

Retriever

as_retriever() is inherited from the LangChain VectorStore base class and works out of the box:

retriever = store.as_retriever(
    search_kwargs={"k": 5, "filter": {"tenant": "acme"}},
)
results = retriever.invoke("find relevant docs")

Intelligence (RAG answers)

VectorAmpVectorStore retrieves documents; VectorAmpIntelligence returns a generated answer with sources. The simplest usage needs only a question, and queries run across all your datasets by default:

from langchain_vectoramp import VectorAmpIntelligence

intel = VectorAmpIntelligence()   # uses VECTORAMP_API_KEY, all datasets, sources on
print(intel.ask("What changed in the Q4 planning docs?"))

Multi-turn "just works": pass prior LangChain messages and they are forwarded as conversation history automatically. You decide how many previous turns to include simply by how many you pass.

from langchain_core.messages import HumanMessage, AIMessage

history = [
    HumanMessage("What is VectorAmp?"),
    AIMessage("A vector database platform."),
]
intel.ask("Does it support hybrid search?", history=history)

It is also a Runnable, so it composes in LCEL chains and works with RunnableWithMessageHistory — pass a list of messages and the last one is taken as the question:

intel.invoke("Summarize the docs")
intel.invoke([HumanMessage("hi"), AIMessage("hello"), HumanMessage("and reranking?")])

# Need the sources/metadata too?
intel.ask_with_sources("What are the contract termination terms?")

Scope to one dataset or tune retrieval only when you need to:

intel = VectorAmpIntelligence(dataset_name="contracts", top_k=8)

Loader

from langchain_vectoramp import VectorAmpLoader

loader = VectorAmpLoader(
    dataset_id="dataset-id",
    metadata={"loaded_from": "vectoramp"},
)

documents = loader.load()
# or stream lazily:
for document in loader.lazy_load():
    print(document.page_content)

When query is omitted the loader lists the dataset's retained source documents via GET /datasets/{id}/documents (downloading originals as needed). Provide a query to switch to semantic search and return the top k matching documents:

loader = VectorAmpLoader(
    dataset_id="dataset-id",
    query="contract renewal terms",
    filter={"source": "contracts"},
    k=10,
)

A note on embeddings

VectorAmp embeds server-side. Every class accepts an optional LangChain Embeddings object (embedding=... on the vector store) for constructor compatibility only — it is intentionally ignored for add and search, which always use VectorAmp's hosted model. You never need to provide one.

Method reference

VectorAmpVectorStore

Constructor:

VectorAmpVectorStore(
    *,
    api_key: str | None = None,        # default: env VECTORAMP_API_KEY
    base_url: str = "https://api.vectoramp.com",
    dataset_id: str | None = None,     # provide exactly one of dataset_id / dataset_name
    dataset_name: str | None = None,   # resolved to an id via GET /datasets
    client: VectorAmpHTTPClient | None = None,
    embedding: Embeddings | None = None,   # accepted but ignored (server-side embedding)
    timeout: float = 30.0,
)

Method	Required	Optional (defaults)	Returns
add_texts(texts, metadatas=None, *, ids=None, **kwargs)	texts	metadatas, ids (auto-generated UUIDs; str or int)	list[str | int] ids
aadd_texts(texts, metadatas=None, *, ids=None, **kwargs)	texts	metadatas, ids	list[str | int]
similarity_search(query, k=4, **kwargs)	query	k (4), filter/filters, hybrid, sparse_query, alpha, rerank, advanced_filters, include_metadata, embedding_provider, embedding_model, nprobe_override, rerank_depth_override	list[Document]
similarity_search_with_score(query, k=4, **kwargs)	query	same as above	list[tuple[Document, float]]
asimilarity_search(query, k=4, **kwargs)	query	same as above	list[Document]
asimilarity_search_with_score(query, k=4, **kwargs)	query	same as above	list[tuple[Document, float]]
delete(ids=[...])	ids	—	True
adelete(ids=[...])	ids	—	True
as_retriever(**kwargs)	—	search_kwargs (e.g. {"k": 5, "filter": {...}}), search_type	VectorStoreRetriever (inherited)
from_texts(texts, embedding=None, metadatas=None, *, ids=None, **kwargs) (classmethod)	texts, plus dataset_id or dataset_name in kwargs	embedding (ignored), metadatas, ids	VectorAmpVectorStore
from_documents(documents, embedding=None, **kwargs) (classmethod)	documents, plus dataset_id or dataset_name	embedding (ignored)	VectorAmpVectorStore
afrom_texts_instance(texts, metadatas=None, *, ids=None)	texts	metadatas, ids	list[str | int]
close() / aclose()	—	—	None

Properties: dataset_id (resolved id), dataset_name (original name, if given), embeddings (the unused embedding arg).

Filtering: pass filter={...} (the LangChain convention, preferred) or filters={...} (accepted for SDK parity) — use only one.

VectorAmpIntelligence

Constructor:

VectorAmpIntelligence(
    *,
    dataset_id: str | None = None,     # default: query across all datasets ("all")
    dataset_name: str | None = None,
    api_key: str | None = None,        # default: env VECTORAMP_API_KEY
    base_url: str = "https://api.vectoramp.com",
    client: VectorAmpHTTPClient | None = None,
    top_k: int | None = None,          # default: server default (5)
    include_sources: bool = True,
)

Method	Required	Optional (defaults)	Returns
ask(query, *, history=None, **overrides)	query	history, dataset_id, include_sources, top_k	str answer
aask(query, *, history=None, **overrides)	query	same as ask	str
ask_with_sources(query, *, history=None, **overrides)	query	same as ask	dict (answer, sources, metadata)
invoke(input, config=None, **kwargs)	input	—	str
ainvoke(input, config=None, **kwargs)	input	—	str

invoke/ainvoke accept a plain string, a dict ({query|question|input, history|chat_history|messages}), or a list of LangChain messages (the last is the question, the rest become conversation history). LangChain message types map to API roles: human → user, ai → assistant, system → system, tool → tool.

VectorAmpLoader

Constructor:

VectorAmpLoader(
    *,
    dataset_id: str,                   # required
    api_key: str | None = None,        # default: env VECTORAMP_API_KEY
    base_url: str = "https://api.vectoramp.com",
    client: VectorAmpHTTPClient | None = None,
    query: str | None = None,          # omit -> list documents; provide -> semantic search
    filter: Mapping | None = None,
    k: int = 10,
    metadata: Mapping | None = None,   # merged into every loaded Document's metadata
    timeout: float = 30.0,
    **search_kwargs,                   # forwarded to search when query is set
)

Method	Returns	Behavior
load()	list[Document]	Materializes all documents
lazy_load()	Iterator[Document]	Streams documents (paginates the documents API or runs one search)
close()	None	Closes owned HTTP resources

Development

pip install -e '.[dev]'
pytest
ruff check .
mypy

Tests use mocked clients/transports only; no live VectorAmp API calls are made.

License

Apache License 2.0. See LICENSE and NOTICE.