S3 Text Knowledge Base - Phidata

The S3TextKnowledgeBase reads text files from an S3 bucket, converts them into vector embeddings and loads them to a vector databse.

Usage

We are using a local PgVector database for this example. Make sure it’s running

pip install textract

from phi.knowledge.s3.text import S3TextKnowledgeBase
from phi.vectordb.pgvector import PgVector

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

knowledge_base = S3TextKnowledgeBase(
    bucket_name="phi-public",
    key="recipes/recipes.docx",
    vector_db=PgVector(table_name="recipes", db_url=db_url),
)

Then use the knowledge_base with an Agent:

from phi.agent import Agent
from knowledge_base import knowledge_base

agent = Agent(
    knowledge=knowledge_base,
    search_knowledge=True,
)
agent.knowledge.load(recreate=False)

agent.print_response("How to make Hummus?")

Params

Parameter	Type	Default	Description
`formats`	`List[str]`	`[".doc", ".docx"]`	Formats accepted by this knowledge base.
`reader`	`S3TextReader`	`S3TextReader()`	A `S3TextReader` that converts the `Text` files into `Documents` for the vector database.
`vector_db`	`VectorDb`	-	Vector Database for the Knowledge Base.
`num_documents`	`int`	`5`	Number of documents to return on search.
`optimize_on`	`int`	-	Number of documents to optimize the vector db on.
`chunking_strategy`	`ChunkingStrategy`	`FixedSizeChunking`	The chunking strategy to use.