fenecdb for Python

A client for fenec-pg's HTTP endpoint (fenec-pg --http <address>), and vector stores for LangChain and LlamaIndex on top of it.

pip install "fenecdb[langchain] @ git+https://github.com/fenecdb/fenec#subdirectory=integrations/python"
# or fenecdb[llama-index]

from fenecdb import Client

db = Client("http://127.0.0.1:8080", token="...")
db.query("get articles select title near embed $1 limit 5", [[0.1, 0.2, 0.3]])

from fenecdb.langchain import FenecVectorStore

store = FenecVectorStore(embeddings, "docs", url="http://127.0.0.1:8080", token="...",
                         metadata_fields={"source": "text"})
store.add_documents(documents)
store.similarity_search("how do I compact", k=4, filter={"source": "handbook"})

from fenecdb.llama_index import FenecVectorStore
from llama_index.core import StorageContext, VectorStoreIndex

store = FenecVectorStore("docs", url="http://127.0.0.1:8080", token="...")
index = VectorStoreIndex.from_documents(
    documents, storage_context=StorageContext.from_defaults(vector_store=store)
)

A document or node is a row -- its id, text, metadata as JSON and vector under @hnsw -- in a collection created on the first write. Fields named in metadata_fields get columns of their own under @hash, and filters over them are answered by the index before the search runs.

With full_text=True the text is indexed for BM25 too, and both stores search by the words alone or by the words and the vector fused -- LangChain's mode="text" and mode="hybrid", LlamaIndex's TEXT_SEARCH and HYBRID. quant="int8" or "bit" keeps the graph over codes.

store = FenecVectorStore(embeddings, "docs", url=..., token=..., full_text=True)
store.similarity_search("how do I compact", k=4, mode="hybrid")

./run-tests.sh runs LangChain's standard vector store suite and the tests LlamaIndex's own integrations run against a fenec-pg it builds and starts. Full reference: https://fenecdb.com/docs/integrations