Qwen series models are provided by Alibaba Cloud. They are much better
in Asia languages then other LLMs.
DashScope is a model service platform. Qwen models are its primary
supported models. But it also supports other series like LLaMA2, Dolly,
ChatGLM, BiLLa(based on LLaMA)...These may be integrated sometime in the
future.
- Added ChatMemoryStore and InMemoryChatMemoryStore
- Changed MessageWindowChatMemory and TokenWindowChatMemory to use
ChatMemoryStore
- Changed Supplier<ChatMemory> into ChatMemoryProvider
- Small improvements
- did some refactorings
- added javadoc
- fixed NPE in PineconeEmbeddingStoreImpl when adding embedding by id
- PineconeEmbeddingStoreImpl now takes into account minScore and returns
score in EmbeddingMatch
- InMemoryEmbeddingStore now returns score instead of cosine similarity
- all-minilm-l6-v2
- all-minilm-l6-v2-q
- e5-small-v2
- e5-small-v2-q
The idea is to give users an option to embed documents/texts in the same
Java process without any external dependencies.
ONNX Runtime is used to run models inside JVM.
Each model resides in it's own maven module (inside the jar).
Sorry for a huge PR...
- added retries to OpenAiChatModel
- added @UserName: an option to define a name of a user as a parameter
in AI Services API
- added an option to split multiple documents at once (see
DocumentSplitter)
- redesigned document loaders (see FileSystemDocumentLoader)
- renamed DocumentSegment into TextSegment
- redesigned ConversationalRetrievalChain
- added EmbeddingStoreIngestor
- misc refactorings/fixes
---------
Co-authored-by: deep-learning-dynamo <deep-learning-dynamo@gmail.com>
Have a parent pom that contains most/all common things for the
sub-projects.
Note that it is separate from the root aggregator pom: not mixing the
aggregator and the parents makes things slightly easier.
If this change makes it harder to do releases, there might be a
possibility to generate the effective poms for each subproject, but on
the other hand releasing everything should not be too problematic.
LangChain4j