Base Service (trustgraph-base/trustgraph/base/embeddings_service.py):
- Changed on_request to use request.texts
FastEmbed Processor
(trustgraph-flow/trustgraph/embeddings/fastembed/processor.py):
- on_embeddings(texts, model=None) now processes full batch efficiently
- Returns [[v.tolist()] for v in vecs] - list of vector sets
Ollama Processor (trustgraph-flow/trustgraph/embeddings/ollama/processor.py):
- on_embeddings(texts, model=None) passes list directly to Ollama
- Returns [[embedding] for embedding in embeds.embeddings]
EmbeddingsClient (trustgraph-base/trustgraph/base/embeddings_client.py):
- embed(texts, timeout=300) accepts list of texts
Tests Updated:
- test_fastembed_dynamic_model.py - 4 tests updated for new interface
- test_ollama_dynamic_model.py - 4 tests updated for new interface
Updated CLI, SDK and APIs
Tech spec
BlobStore (trustgraph-flow/trustgraph/librarian/blob_store.py):
- get_stream() - yields document content in chunks for streaming retrieval
- create_multipart_upload() - initializes S3 multipart upload, returns
upload_id
- upload_part() - uploads a single part, returns etag
- complete_multipart_upload() - finalizes upload with part etags
- abort_multipart_upload() - cancels and cleans up
Cassandra schema (trustgraph-flow/trustgraph/tables/library.py):
- New upload_session table with 24-hour TTL
- Index on user for listing sessions
- Prepared statements for all operations
- Methods: create_upload_session(), get_upload_session(),
update_upload_session_chunk(), delete_upload_session(),
list_upload_sessions()
- Schema extended with UploadSession, UploadProgress, and new
request/response fields
- Librarian methods: begin_upload, upload_chunk, complete_upload,
abort_upload, get_upload_status, list_uploads
- Service routing for all new operations
- Python SDK with transparent chunked upload:
- add_document() auto-switches to chunked for files > 10MB
- Progress callback support (on_progress)
- get_pending_uploads(), get_upload_status(), abort_upload(),
resume_upload()
- Document table: Added parent_id and document_type columns with index
- Document schema (knowledge/document.py): Added document_id field for
streaming retrieval
- Librarian operations:
- add-child-document for extracted PDF pages
- list-children to get child documents
- stream-document for chunked content retrieval
- Cascade delete removes children when parent is deleted
- list-documents filters children by default
- PDF decoder (decoding/pdf/pdf_decoder.py): Updated to stream large
documents from librarian API to temp file
- Librarian service (librarian/service.py): Sends document_id instead of
content for large PDFs (>2MB)
- Deprecated tools (load_pdf.py, load_text.py): Added deprecation
warnings directing users to tg-add-library-document +
tg-start-library-processing
Remove load_pdf and load_text utils
Move chunker/librarian comms to base class
Updating tests
* Changed schema for Value -> Term, majorly breaking change
* Following the schema change, Value -> Term into all processing
* Updated Cassandra for g, p, s, o index patterns (7 indexes)
* Reviewed and updated all tests
* Neo4j, Memgraph and FalkorDB remain broken, will look at once settled down
* Removed legacy storage management cruft. Tidied tech specs.
* Fix deletion of last collection
* Storage processor ignores data on the queue which is for a deleted collection
* Updated tests
* Tech spec
* Address multi-tenant queue option problems in CLI
* Modified collection service to use config
* Changed storage management to use the config service definition
* Tech spec
* Python CLI utilities updated to use the API including streaming features
* Added type safety to Python API
* Completed missing auth token support in CLI
* Tidy up duplicate tech specs in doc directory
* Streaming LLM text-completion service tech spec.
* text-completion and prompt interfaces
* streaming change applied to all LLMs, so far tested with VertexAI
* Skip Pinecone unit tests, upstream module issue is affecting things, tests are passing again
* Added agent streaming, not working and has broken tests
* Remove some 'unnecessary' parameters from OpenAI invocation. The OpenAI
API is getting complicated with the API and SDK changing on OpenAI's end,
but this not getting mapped through to other services which are 'compatible'
with OpenAI.
* Update OpenAI test for this change
* Trying running tests with Python 3.13
* Fixed hard-coded embeddings store size
* Vector store lazy-creates collections, different collections for
different dimension lengths.
* Added tech spec for vector store lifecycle
* Fixed some tests for the new spec
