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You can use SynapCores with LlamaIndex through two separate Python adapters: one for vector retrieval and another for property-graph retrieval. Both can point to the same SynapCores service, while LlamaIndex uses them through VectorStoreIndex and PropertyGraphIndex. The choice depends on whether your application needs similarity search alone or also needs to retrieve entities and their relationships.
How the two SynapCores adapters fit into LlamaIndex
The integrations are separate packages, each connecting a SynapCores store to a different LlamaIndex abstraction:
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llama-index-vector-stores-synapcoresprovides the vector-store adapter used withVectorStoreIndex.llama-index-graph-stores-synapcoresprovides the property-graph adapter used withPropertyGraphIndex.
SynapCores’ June 17, 2026 integration article shows both adapters configured against http://localhost:8080. The vector store is given a table name and embedding dimension; the graph store is given a graph name and embedding dimension. That is an example configuration, not a requirement that every deployment use those names or run locally. See the SynapCores integration article.
At the framework level, LlamaIndex accepts a PropertyGraphStore when constructing a PropertyGraphIndex for insertion and querying. The SynapCores package supplies that adapter; the general pattern is documented in LlamaIndex’s graph-store guide.
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Choose vector retrieval or property-graph retrieval
| Approach | LlamaIndex component | What it is for | SynapCores adapter details |
|---|---|---|---|
| Vector retrieval | VectorStoreIndex |
Finding text or other indexed content by embedding similarity, with metadata filters where appropriate. | The SynapCores team describes vector queries, HNSW indexing, cosine, Euclidean and dot-product distance options, metadata filters, and upserts. These are vendor claims, not independent validation. |
| Property-graph retrieval | PropertyGraphIndex |
Representing entities and their relationships so an application can retrieve or expand along connections as well as use vector search. | The SynapCores team describes entity and chunk nodes, typed relations, filtered retrieval, depth-bounded relation-map expansion, structured Cypher queries and vector queries over chunk embeddings. These are vendor claims, not independent validation. |
Choose vector-only retrieval when the task is principally finding semantically similar content. Choose the graph adapter when relationships between entities matter to the question or downstream reasoning. A property graph can also use vector queries according to the adapter author, so the distinction is not simply “vectors or no vectors.”
SynapCores’ product documentation describes its version 1.5 graph engine and Cypher surface, including MATCH, WHERE, RETURN, ORDER BY, LIMIT and MERGE. That product-level documentation is separate from validation of the Python adapter: see SynapCores features.
Install and configure the adapter you need
Vector-store example pattern
The SynapCores article demonstrates creating a vector store with a service URI, table name and embedding dimension, then supplying it to LlamaIndex through a storage context for VectorStoreIndex.from_documents. In outline, the pattern is:
vector_store = SynapCoresVectorStore(uri="http://localhost:8080", table_name="documents", dimension=1536)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)Use the adapter’s current package documentation for the exact import and constructor signature for the version you install; the example above conveys the configuration pattern rather than a version-pinned, verified code listing. The vendor also describes reusing an existing table through VectorStoreIndex.from_vector_store.
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Property-graph example pattern
The graph package’s PyPI quickstart installs llama-index and llama-index-graph-stores-synapcores, starts a SynapCores Community container, constructs SynapCoresPropertyGraphStore, passes it to PropertyGraphIndex.from_documents, and retrieves through the resulting index. The article’s example uses a graph name and embedding dimension for the store. Check the graph package page on PyPI for its current installation and quickstart instructions.
These are the vendor’s demonstrated configurations, not the only valid LlamaIndex configurations. The framework exposes other ways to construct and query indexes; consult the PropertyGraphIndex API reference for the current API surface.
Do you need to install a model in SynapCores?
No engine-side model installation is required for the integration path described by SynapCores. The vendor says LlamaIndex computes embeddings using the model configured in LlamaIndex, then writes those vectors to a SynapCores VECTOR(N) column. The integration article names OpenAI, Hugging Face and Cohere as example embedding configurations; that does not establish that any provider is best for a particular application.
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This is the SynapCores team’s wording in its June 17, 2026 article. Configure an embedding model in LlamaIndex and ensure its output dimension matches the vector column or store configuration.
What the vendor says each adapter implements
Vector adapter
The SynapCores team describes the vector adapter as supporting add(nodes), delete(ref_doc_id) and query(VectorStoreQuery), along with optional delete_nodes, clear and get_nodes methods. It also reports asynchronous wrappers, HNSW indexing over a VECTOR(N) column, cosine, Euclidean and dot-product distance metrics, metadata filtering, upsert behavior, and reuse of existing tables. Treat this list as the adapter author’s description rather than independently audited capability coverage.
Graph adapter
The adapter author says SynapCoresPropertyGraphStore implements the required property-graph-store methods and an async surface. Described operations include entity and chunk upserts, typed relations, filtered gets, depth-bounded relation-map expansion, structured Cypher queries and vector search over chunk embeddings. The article states that supports_structured_queries and supports_vector_queries are true. Confirm the behavior you depend on against the installed release and your own queries.
What to verify before relying on it in production
The integration article is dated June 17, 2026 and describes package version 0.1.0. Its roadmap statements are time-specific, not confirmation of current release status: it listed a hybrid retriever for v0.2.0, native httpx async support for v0.3.0, and an official LlamaIndex integration listing as a future goal. Check the package release history and current documentation to establish what has shipped before designing around any of those items.
For async calls in the described v0.1.0 snapshot, the article says methods used asyncio.to_thread over the synchronous SDK. That is a different implementation approach from a native asynchronous HTTP client and may matter if your application depends heavily on concurrent I/O.
The SynapCores team reported 48/48 tests against a stock synapcores/community:latest container in 2026, with an empty models directory and no model pulls. This is a vendor-reported test count for its test pass, not an independent reliability audit, a guarantee for your environment, or a performance benchmark.
- Check the installed package versions and whether their documented methods match your required insert, delete, filter and query paths.
- Confirm that embedding dimensions match between LlamaIndex’s configured embedding model and the SynapCores vector configuration.
- Test the Cypher queries, relation expansion depth and metadata filters your application will actually use.
- Exercise async workloads under your own concurrency and failure conditions, especially if the application depends on nonblocking network operations.
- Measure latency, throughput, resource use and recovery behavior in your deployment rather than inferring them from the vendor’s test count.
One SynapCores instance or separate backends?
Pointing both adapters at one SynapCores service can consolidate the vector and graph integrations within one database system. Separate vector and graph backends may fit applications that already operate distinct services or have requirements the shared instance does not meet. The available product and integration descriptions do not establish that either arrangement is faster, cheaper or more scalable.
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