Vector Databases

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llmware supports the following vector databases:

• Milvus and Milvus-Lite - milvus
• Postgres (PG Vector) - postgres
• Qdrant - qdrant
• ChromaDB - chromadb
• Redis - redis
• Neo4j - neo4j
• LanceDB - lancedb
• FAISS - faiss
• Mongo-Atlas - mongo-atlas
• Pinecone - pinecone

In llmware, unstructured content is ingested and organized into a Library, and then embeddings are created against the Library object, and usually, handled by implicitly through the Library method .install_new_embedding.

All embedding models are implemented through the embeddings.py module, and the EmbeddingHandler class, which routes the embedding process to the vector db specific handler and provides a common set of utility functions.
In most cases, it is not necessarily to explicitly call the vector db class.

The design is intended to promote code re-use and to make it easy to experiment with different endpoint vector databases without significant code changes, as well as to leverage the Library as the core organizing construct.

Select Vector DB

To select a vector database in llmware is generally done is one of two ways:

1. Explicit Setting - LLMWareConfig().set_vector_db("postgres")

2. Pass the name of the vector database at the time of installing the embeddings:

   library.install_new_embedding(embedding_model_name=embedding_model, vector_db='milvus',batch_size=100)

Install Vector DB

No-install options: chromadb, lancedb, faiss, and milvus-lite

API-based options: mongo-atlas, pinecone

Install server options:

Generally, we have found that Docker (and Docker-Compose) are the easiest and most consistent ways to install vector db across different platforms.

1. milvus - we provide a docker-compose script in the main repository root folder path, which installs mongodb as well.

curl -o docker-compose.yaml https://raw.githubusercontent.com/llmware-ai/llmware/main/docker-compose_mongo_milvus.yaml
docker compose up -d

1. qdrant

curl -o docker-compose.yaml https://raw.githubusercontent.com/llmware-ai/llmware/main/docker-compose-qdrant.yaml
docker compose up -d  

1. postgres and pgvector

curl -o docker-compose.yaml https://raw.githubusercontent.com/llmware-ai/llmware/main/docker-compose-pgvector.yaml
docker compose up -d  

1. redis

   # scripts to deploy other options
   curl -o docker-compose.yaml https://raw.githubusercontent.com/llmware-ai/llmware/main/docker-compose-redis-stack.yaml
   

2. neo4j

curl -o docker-compose.yaml https://raw.githubusercontent.com/llmware-ai/llmware/main/docker-compose-neo4j.yaml
docker compose up -d  

Configure Vector DB

To configure a vector database in llmware, we provide configuration objects in the configs.py module to adjust authentication, port/host information, and other common configurations. To use the configuration, the pattern is as follows through simple get_config and set_config methods:

from llmware.configs import MilvusConfig
MilvusConfig().set_config("lite", True)

from llmware.configs import ChromaDBConfig
current_config = ChromaDBConfig().get_config("persistent_path")  
ChromaDBConfig().set_config("persistent_path", "/new/local/path")

Configuration objects are provided for the following vector DB: MilvusConfig, ChromaDBConfig, QdrantConfig, Neo4jConfig, LanceDBConfig, PineConeConfig, MongoConfig, PostgresConfig.

For ‘out-of-the-box’ testing and development, for most use cases, you will not need to change these configs.

Need help or have questions?

Check out the llmware videos and GitHub repository.

Reach out to us on GitHub Discussions.

About the project

llmware is © 2023-2024 by AI Bloks.

Contributing

Please first discuss any change you want to make publicly, for example on GitHub via raising an issue or starting a new discussion. You can also write an email or start a discussion on our Discrod channel. Read more about becoming a contributor in the GitHub repo.

Code of conduct

We welcome everyone into the llmware community. View our Code of Conduct in our GitHub repository.

llmware and AI Bloks

llmware is an open source project from AI Bloks - the company behind llmware. The company offers a Software as a Service (SaaS) Retrieval Augmented Generation (RAG) service. AI Bloks was founded by Namee Oberst and Darren Oberst in Oktober 2022.

License

llmware is distributed by an Apache-2.0 license.

Thank you to the contributors of llmware!

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