Ollama Embeddings

pip install aiohttp requests

from upsonic import Agent, Task, KnowledgeBase
from upsonic.embeddings import OllamaEmbedding, OllamaEmbeddingConfig
from upsonic.vectordb import ChromaProvider, ChromaConfig, ConnectionConfig, Mode

# Create embedding provider
embedding = OllamaEmbedding(OllamaEmbeddingConfig(
    model_name="nomic-embed-text",
    base_url="http://localhost:11434",
    auto_pull_model=True
))

# Setup KnowledgeBase
vectordb = ChromaProvider(ChromaConfig(
    collection_name="ollama_docs",
    vector_size=768,
    connection=ConnectionConfig(mode=Mode.IN_MEMORY)
))

kb = KnowledgeBase(
    sources=["document.txt"],
    embedding_provider=embedding,
    vectordb=vectordb
)

# Query with Agent
agent = Agent("openai/gpt-4o")
task = Task("What is this document about?", context=[kb])
result = agent.do(task)
print(result)