LlamaIndex¶

writerslogic_warm_integrations.llamaindex puts WARM behind LlamaIndex's vector store, key-value/doc/index stores, chat stores and memory interfaces.

Tested: llama-index-core==0.14.25, Python 3.12, against a real warm-server.

pip install "writerslogic-warm-integrations[llamaindex]"
WARM_API_KEY=... warm-server --input-dim 1536 --data-dir ./hms-data   # input-dim = your embedding model's dimension

Classes¶

Class	LlamaIndex interface	Notes
`WarmVectorStore`	`BasePydanticVectorStore`	stores text; sync and async `add/delete/delete_nodes/get_nodes/clear/query`
`WarmMemoryBlock`	`BaseMemoryBlock[str]`	message recall for `Memory`, encoded by the WARM encoder, scoped by `session_id`
`WarmAsyncChatStore`	`AsyncDBChatStore`	the FIFO store for `Memory.from_defaults(chat_store=...)`
`WarmChatStore`	`BaseChatStore`	for `ChatMemoryBuffer`
`WarmKVStore`	`BaseKVStore`	any KV consumer
`WarmDocumentStore`, `WarmIndexStore`	`KVDocumentStore`, `KVIndexStore`	persist a full `StorageContext` in WARM

llama-index-core's own VectorMemoryBlock also works unchanged on WarmVectorStore.

Quickstart¶

from llama_index.core import StorageContext, VectorStoreIndex
from writerslogic_warm_integrations.llamaindex import WarmVectorStore

store = WarmVectorStore(url="http://127.0.0.1:8080", api_key="...", tenant_id="acme", input_dim=1536)
index = VectorStoreIndex.from_documents(docs, storage_context=StorageContext.from_defaults(vector_store=store))
index = VectorStoreIndex.from_vector_store(store)          # reopen later

Runnable end to end (index, filters, reload from storage, memory block): integrations/python/examples/llamaindex_quickstart.py.

Configuration (the WARM knobs)¶

Every class takes an WarmConfig or the same fields as keywords.

Field	Meaning
`url`, `api_key`, `tenant_id`	service, bearer key, store selection (hard isolation)
`collection`	scope inside a tenant; ids and queries are restricted to it. Use different collections for vector, doc and index stores sharing a tenant
`input_dim`	must equal the server's `--input-dim` and your embedding model
`embedder`	the WARM encoder, used only for records or queries that arrive without an embedding (default: lexical `HashingEmbedder`). The vector store uses the embeddings LlamaIndex computes
`text_queries`	the service embeds and ranks text itself (BM25 + cosine, needs `warm-server` with `local-models`); enables TEXT_SEARCH, SPARSE, HYBRID and SEMANTIC_HYBRID modes
`zero_trust_key`	client-side vector masking (obfuscation, not encryption)
`overfetch`, `max_fetch`	candidates examined for client-side filtering (`overfetch * k`, capped at `max_fetch`); set `max_fetch` to the server's `--max-top-k`

The server's BM25 parameters, title weight and at-rest encryption are engine options, not part of the HTTP service; use the Node surface or Rust for those.

Metadata filters¶

The service evaluates equality on top-level keys only. MetadataFilters are converted to an AST: top-level ANDed equalities on strings, ints and bools are sent to the service (applied before ranking); everything else is evaluated in the adapter over overfetch * k candidates (at most max_fetch, default 100), so a filter with a non-equality term can return fewer than similarity_top_k results. Float equality is always client-side.

Semantics follow SimpleVectorStore (verified differentially in tests/llamaindex/test_vector_store.py: every case below returns the same ids from both stores).

Operator	Mapping
`==`	equality (pushed when str/int/bool)
`!=`, `nin`	client-side; a missing key matches
`>`, `>=`, `<`, `<=`	client-side; numbers or strings only, bool excluded
`in`	client-side
`any`, `all`, `contains`	client-side membership of list metadata (substring for string metadata)
`text_match`, `text_match_insensitive`	client-side substring
`is_empty`	missing, null, `""` or `[]`
`FilterCondition.AND/OR`	conjunction/disjunction, nested `MetadataFilters` supported (an extension over the reference store, which rejects nesting)
`FilterCondition.NOT`	none of the listed filters match (reference semantics)

Deviation: text_match on list-valued metadata does membership instead of raising.

VectorStoreQuery.node_ids and doc_ids become client-side in filters; an empty list means no restriction (the index passes [] for stores that hold their own nodes).

Query modes¶

default only, unless text_queries=True; then TEXT_SEARCH, SPARSE, HYBRID, SEMANTIC_HYBRID send query_str (and the embedding if given) to the service, which fuses BM25 and cosine; alpha is ignored and score is then a fused rank score, not a cosine. MMR, SVM and the learner modes raise ValueError. Text mode is tested at the wire level only (mock transport), because the model-backed server is not part of the automated environment.

Limits (inherited from `warm-server`)¶

No list/get endpoint: get_nodes is per-id queries; get_nodes() without ids and clear() page through at most max_fetch records per pass (clear and delete loop until empty).
similarity_top_k > max_fetch raises ValueError (the server caps top_k). Boundary tested at cap-1, cap, cap+1.
get_nodes does not return embeddings. Node text must fit one record (64 KiB, 4096 words) and metadata 64 KiB; the serialized node excluding text is stored in metadata, so keep node metadata small.
delete_nodes() with neither ids nor filters raises; use clear() (removes only this collection).
Async methods create one httpx.AsyncClient on first use, bound to that event loop; call aclose().

Storage, chat and memory classes¶

KV/doc/index stores: each value is the metadata of one record (JSON, 64 KiB max). get_all reads buckets hash partitions (default 8) of at most max_fetch entries and raises WarmError rather than return a partial collection, so capacity is about buckets * max_fetch entries per collection (tested at the boundary). Raise buckets for bigger docstores. Note that with a text-storing vector store the docstore stays empty unless you add to it.
Chat stores: messages are paged by sequence number so histories longer than max_fetch work; page_size <= max_fetch. One writer per key (appends read-modify-write a header). delete_message(key, idx) takes the sequence number. Boundary tested around a page edge.
WarmMemoryBlock: Memory hands blocks the chat history plus the new input, and the block queries with the last retrieval_context_window messages (default 5, like VectorMemoryBlock). A long window dilutes the query with the assistant's replies; set it to 1 to query by the latest turn only.

Conformance¶

No published third-party conformance suite exists for LlamaIndex vector stores, so the oracle is SimpleVectorStore: 21 filter cases compared id-for-id, plus nested filters, VectorStoreIndex.from_documents / from_vector_store / retriever / delete_ref_doc, the full StorageContext (vector + doc + index store) reload through load_index_from_storage, SimpleDocumentStore and SimpleChatStore differential tests, Memory.from_defaults(chat_store=WarmAsyncChatStore, memory_blocks=[...]), and the stock VectorMemoryBlock.
Run: WARM_SERVER_BIN=<warm-server> pytest integrations/python/tests/llamaindex in an environment with writerslogic-warm-integrations[llamaindex]. 51 tests pass against llama-index-core 0.14.25 and the real server.