๐Advanced Usage
Embedding functions are rarely used alone.
Its used in two way
In ETL and Vectordb to convert all the raw data extracted by the ETL into embeddings to be stored in the Vectordb. It also helps in converting the query to embeddings.
In Retrieval it converts the user query into an embedding to search against the other data in the vectordb index
Usage
Imports:
from genai_stack.etl.langchain import LangchainETL
from genai_stack.stack.stack import Stack
from genai_stack.vectordb.chromadb import ChromaDB
from genai_stack.etl.utils import get_config_from_source_kwargs
from genai_stack.embedding.utils import get_default_embeddings
Configuration:
config = {
"name": "HuggingFaceEmbeddings",
"fields": {
"model_name": "sentence-transformers/all-mpnet-base-v2",
"model_kwargs": {"device": "cpu"},
"encode_kwargs": {"normalize_embeddings": False},
}
}
Using with ETL
Once you have defined your configuration as a Python dictionary, you can use it with the LangchainEmbedding.from_kwargs()
method:
embeddings = LangchainETL.from_kwargs(**config)
etl = LangchainETL.from_config(get_config_from_source_kwargs("pdf", "path/to/pdf"))
# Connect the ETL, Embedding and Vectordb component using Stack
stack = Stack(model=None, embedding=get_default_embeddings(), etl=etl, vectordb=ChromaDB.from_kwargs())
etl.run()
Using with retriever
# Initialise all your components
etl = LangchainETL.from_kwargs(name="CSVLoader", fields={"file_path": "addresses.csv"})
embedding = LangchainEmbedding.from_kwargs(**config)
chromadb = ChromaDB.from_kwargs()
llm = OpenAIGpt35Model.from_kwargs(parameters={"openai_api_key": "<OPENAI-API-KEY>"})
prompt_engine = PromptEngine.from_kwargs(should_validate=False)
retriever = LangChainRetriever.from_kwargs()
memory = ConversationBufferMemory.from_kwargs()
# Initialise your stack by connecting the components end-to-end
stack = Stack(
etl=etl,
embedding=embedding,
vectordb=chromadb,
model=llm,
prompt_engine=prompt_engine,
retriever=retriever,
memory=memory
)
# Query to get RAG based results
response = retriever.retrieve("Where does John live?")
Last updated