hotdata-langchain

Connect LangChain to Hotdata — tools that let an agent run SQL against your workspace connections, full-text search indexed columns and work with managed databases, plus a VectorStore implementation so Hotdata can back any LangChain retriever or chain.

Install

pip install hotdata-langchain

Authentication

Set HOTDATA_API_KEY in your environment. Optionally set HOTDATA_WORKSPACE to pin a specific workspace (the first available workspace is used if unset).

Quickstart

Of the LangChain packages, this one needs only langchain-core, and works with any tool-calling model. Running an agent additionally needs the langchain package and the integration for whichever model provider you use; using HotdataVectorStore needs an embedding provider's integration, such as langchain-openai.

from langchain.agents import create_agent
import hotdata_langchain as hl

client = hl.from_env()
tools = hl.make_hotdata_tools(client, database_id="dbid...")

agent = create_agent(model=your_model, tools=tools)
result = agent.invoke(
    {"messages": [{"role": "user", "content": "Which product categories have the most orders?"}]}
)
print(result["messages"][-1].content)

Queries run against a database scope, so pass database_id= (a managed database id). hl.from_env().list_managed_databases() shows what is available in the workspace, with the id of each.

Tools

make_hotdata_tools(client) returns a list of LangChain StructuredTool objects ready to pass to any agent:

Tool	What it does
hotdata_execute_sql	Run a SQL query and return rows as JSON
hotdata_list_managed_databases	List available managed databases, with the id of each
hotdata_create_managed_database	Create a new managed database and return its id
hotdata_load_managed_table	Load a parquet file into a managed table, addressed by database id
hotdata_describe_tables	List tables, or one table's columns and types
hotdata_search_text	Full-text search an indexed column, ranked by relevance (opt-in — see below)

The descriptions carry the engine's contract — dialect, what SQL can and cannot do, and where to look things up — so an agent does not need a system prompt explaining the query engine.

Letting the agent discover the schema

hotdata_describe_tables is registered by default. Called with no arguments it lists every table with its column count; called with a table name it returns that table's columns and types. Without it an agent has to guess column names, and a guess that misses fails the query.

tools = hl.make_hotdata_tools(client, database_id="dbid...")            # included
tools = hl.make_hotdata_tools(client, database_id="dbid...", describe_tables=False)  # omitted

It reads information_schema in whichever database the tools are scoped to, so it needs no extra permissions. With it turned off, the SQL tool's description tells the agent to query information_schema directly instead.

Calling tools directly

You can also invoke tools outside of an agent loop:

import json

tools = {t.name: t for t in hl.make_hotdata_tools(client, database_id="dbid...")}

result = tools["hotdata_execute_sql"].invoke({"sql": "SELECT * FROM orders LIMIT 10"})
print(result)  # JSON rows

created = tools["hotdata_create_managed_database"].invoke({
    "name": "sales",            # a display label, not an identifier
    "schema_name": "public",
    "tables": "orders,customers",
})

tools["hotdata_load_managed_table"].invoke({
    "database_id": json.loads(created)["id"],
    "table": "orders",
    "file": "/path/to/orders.parquet",
})

Full-text search

Point the agent at a text column carrying a BM25 index and it gets a search tool alongside SQL:

tools = hl.make_hotdata_tools(
    client,
    database_id="dbid...",
    search_table="default.public.listings",   # catalog.schema.table
    search_column="description",              # must have a BM25 index
    search_columns=["id", "name", "price", "description"],  # what each hit returns
    search_k=5,
)

hits = {t.name: t for t in tools}["hotdata_search_text"].invoke(
    {"query": "cozy apartment with a view"}
)

Rows come back ranked, each with a score. The agent supplies only query and an optional k; the table and column are fixed when you build the tool. That is deliberate — nothing in the tool surface lets an agent discover which columns are indexed, and the engine errors outright rather than falling back to a scan when a column has no BM25 index.

Inside a managed database the built-in catalog is always default, so a managed table reads as default.<schema>.<table> when database_id= scopes the query to it. Write all three parts: a two-part schema.table reference resolves and returns the same rows, but the engine matches its index lookup on the reference as written, so the short form can quietly forfeit an index. The SQL tool's description tells the model this; HotdataVectorStore and the search tool emit the full form themselves.

For more than one searchable corpus, build the tools yourself and give each a distinct name and description — the agent then routes on the descriptions:

tools = [
    # Configure the first corpus here, so the SQL tool's description still names a search
    # tool to defer text matching to. Passing no search_table/search_column drops that,
    # and the agent goes back to trying to match text in SQL.
    *hl.make_hotdata_tools(
        client,
        database_id="dbid...",
        search_table="default.public.listings",
        search_column="description",
        search_tool_name="search_listings",
    ),
    hl.make_hotdata_search_tool(
        client, table="default.public.reviews", column="comments",
        name="search_reviews", database_id="dbid...",
    ),
]

Provisioning the index itself is not yet part of this package; create it through the Hotdata API or CLI. demo/ has a script that does the whole flow — managed database, data load, BM25 index, then an agent that picks between search and SQL.

Vector store

HotdataVectorStore implements LangChain's VectorStore, so Hotdata works as the retrieval backend for any retriever, chain or eval built on that interface.

It is a primitive rather than a tool: it is not part of make_hotdata_tools, and a model cannot call it directly because it has no name, description or argument schema. You compose it into a chain, hand as_retriever() to anything expecting a retriever, or wrap it as a tool so an agent can call it — see below.

from langchain_openai import OpenAIEmbeddings

store = hl.HotdataVectorStore(
    client,
    OpenAIEmbeddings(model="text-embedding-3-small"),
    database_id="dbid...",
    table="documents",
)

store.add_texts(
    ["Cozy studio with great light", "Two-bedroom near the park"],
    [{"city": "sf"}, {"city": "nyc"}],
)

docs = store.similarity_search("somewhere bright to stay", k=3)
retriever = store.as_retriever(search_kwargs={"k": 3})   # composes into any chain

Rows are stored in one managed table keyed on id, so re-adding a document with an existing id replaces it rather than duplicating it. delete(ids=[...]) requires ids — there is no delete-everything call.

The store declares that table itself. If you pre-create the database, leave the table out of tables=[...] and let the store declare it, or declare it with key=["id"] yourself — a managed table with no key takes writes as appends, so re-adding a document would duplicate it, and an existing table's key cannot be read back to warn you.

Searches run as a single SQL query using the engine's scalar distance functions:

SELECT id, content, metadata_json,
       cosine_distance(embedding, ARRAY[...]) AS dist
FROM "default"."public"."documents"
ORDER BY dist ASC
LIMIT 4

That query is correct with no index at all — it brute-forces the table — so a store is usable the moment you create it, before any indexing exists.

Once a vector index built on the same metric exists on the embedding column, the engine rewrites that identical query into an index lookup, with nothing in your code changing. This is confirmed against a live engine: the query plan switches to a USearchExec node, and a WHERE filter is pushed into the index lookup rather than costing you the fast path. See docs/engine-contract.md for the observed plans.

Three things forfeit the rewrite and fall back to a full scan, silently and without error: projecting the raw embedding column, querying with a distance function the index was not built for, and omitting LIMIT. Similarity search does none of them; MMR does the first, by necessity.

The store builds that index for you:

store.create_index()                       # or, in one step:
store = hl.HotdataVectorStore.from_texts(
    texts, embeddings, client=client, database_id="dbid...", create_index=True
)

Build it after the first write. The engine reads the vector width off stored data, so there is nothing to measure before then. The metric always comes from this store's distance, which is what earns the rewrite — leaving it to the server would build an l2 index, its default, that never serves a cosine search. Calling create_index() when a matching index already exists does nothing and returns None, so it is safe on every start-up; an index that already exists under a different metric raises, since only you know whether the index or the distance= is the mistake.

Builds are polled to completion, up to timeout_s=900. Pass wait=False to return as soon as the build is accepted and check the job yourself.

distance= accepts "cosine" (default), "l2" and "dot". Prefer cosine: its relevance score is exact, whereas the engine's l2_distance is squared L2 and LangChain's Euclidean relevance score expects true Euclidean distance, so similarity_search_with_relevance_scores under l2 returns scores on the wrong scale. Ranking is correct under all three.

Diverse results with MMR

The k nearest documents are often near-duplicates of each other — all genuinely close to the query, all making the same point. Maximal marginal relevance ranks a wider pool by distance, then picks k from it one at a time, scoring each candidate against both the query and what it has already picked:

docs = store.max_marginal_relevance_search("somewhere bright to stay", k=3, fetch_k=20)

retriever = store.as_retriever(search_type="mmr", search_kwargs={"k": 3, "fetch_k": 20})

lambda_mult is the balance: 1.0 is pure relevance, 0.0 is pure variety. fetch_k is the candidate pool, and is raised to k if you pass less. filter= works the same as it does on similarity_search. Results come back in selection order — only the first is the nearest to the query, and a later pick is often further away than one it was chosen over.

This is the one search that reads the stored vectors, which is what MMR needs and what forfeits the index lookup — the candidate fetch is a full scan even where an index exists, bounded by fetch_k. Use it where variety in the retrieved set matters more than the cost of scanning; similarity_search stays the fast path.

Both halves of that score use cosine similarity whatever distance= is set to. That is LangChain's own convention, shared by every implementation of this interface: under l2 the candidate pool is L2-nearest while the selection among those candidates is cosine-based. So lambda_mult=1.0 gives back this store's similarity ranking under cosine only — under l2 and dot it reorders the candidate pool by cosine instead.

Expect to tune lambda_mult upward. The 0.5 default is LangChain's, kept so code ported from another vector store behaves identically. It weights relevance and variety equally, and those two terms rarely have equal spread: an embedding model that packs its distances into a narrow band leaves the variety term varying far more than the relevance term, so variety quietly decides most picks. On the demo corpus through text-embedding-3-small every distance fell between 0.60 and 0.67, and 0.5 promoted a listing that did not answer the question at all, while 0.7 and 0.8 both dropped a near-duplicate for a genuine alternative. That is one corpus and one model — a reason to sweep the value on your own data, not a number to copy.

Letting an agent search the store

The store is not a tool, but a retriever becomes one with LangChain's own create_retriever_tool — so an agent decides whether to search and what to search for, alongside the SQL tools:

from langchain_core.tools.retriever import create_retriever_tool

search_docs = create_retriever_tool(
    store.as_retriever(search_kwargs={"k": 4}),
    name="search_listings",
    description="Find listings whose description matches what the guest is describing.",
)

tools = [*hl.make_hotdata_tools(client, database_id="dbid..."), search_docs]

Use a chain when every question needs the corpus — one retrieval, predictable cost. Wrap it as a tool when the model should choose, reformulate a query, or search more than once.

Note the two return different things: create_retriever_tool gives the model concatenated document text, whereas hotdata_search_text returns the {"metadata", "rows"} envelope the other Hotdata tools use, so values from a hit can be carried into a follow-up SQL query.

Filtering on metadata

Metadata always round-trips in full. To filter on a key, declare it up front so it is stored as a real typed column:

store = hl.HotdataVectorStore(
    client,
    embeddings,
    database_id="dbid...",
    metadata_columns={"city": "string", "beds": "int"},
)

store.similarity_search("bright and quiet", k=3, filter={"city": "sf"})

Equality only, for now. Filtering on an undeclared key raises ValueError rather than quietly returning unfiltered results. The predicate goes into the search query itself, not around it — filtering after a top-k selection can only shrink the result, never re-fill it back to k.

metadata_columns has to match the table it points at. An upsert must carry every column the table has, so opening an existing store with different promoted columns fails on the first write with upload is missing column '<name>'.

Scoping queries to a managed database

database_id= scopes all SQL the agent runs to one managed database. The API requires a database scope, so queries fail with a database is required without it:

tools = hl.make_hotdata_tools(client, database_id="dbid...")

Databases are addressed by id, never by name. A database name is a display label and is not unique, so a name lookup can silently resolve to the wrong database — and the agent's hotdata_load_managed_table overwrites the table it loads into. Passing a name raises KeyError. Ids come from client.list_managed_databases(), the hotdata_list_managed_databases tool, or the response of a create.

The id is resolved once when the tools are built, so a bad id fails there rather than on the agent's first query, and no query pays a repeat lookup. If you already hold a ManagedDatabase — from list_managed_databases() or create_managed_database() — pass it instead of its id to skip the lookup entirely:

db = client.create_managed_database(description="sales", schema="public", tables=["orders"])
tools = hl.make_hotdata_tools(client, database_id=db)

Controlling result size

Limit how many rows are returned to the LLM. Useful for keeping responses within context limits (default: 100):

tools = hl.make_hotdata_tools(client, max_rows=50)

Run the examples

uv run python examples/langchain_basic.py
uv run python examples/langchain_managed_db.py

# needs an embedding provider key and the langchain-openai integration
uv run --group demo python examples/langchain_vectorstore.py

For full end-to-end runs against a real workspace, see demo/: one takes a workspace from empty through a data load and BM25 index build to an agent choosing between search and SQL; the other writes embedded documents into a managed table and answers a question with a stock LangChain retrieval chain over HotdataVectorStore.

Development

uv sync --locked
uv run pytest