llama_demos.llama_rag_demo_node Namespace Reference

Functions
	format_docs (docs)

Variables
	loader
	docs = loader.load()
	text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
	splits = text_splitter.split_documents(docs)
	vectorstore = Chroma.from_documents(documents=splits, embedding=LlamaROSEmbeddings())
	retriever = vectorstore.as_retriever(search_kwargs={"k": 20})
	prompt
	compressor = LlamaROSReranker(top_n=3)
	compression_retriever
tuple	rag_chain
	c
	flush
	True
	end

Function Documentation

format_docs()

llama_demos.llama_rag_demo_node.format_docs

(

docs

)

Variable Documentation

c

llama_demos.llama_rag_demo_node.c

compression_retriever

llama_demos.llama_rag_demo_node.compression_retriever

Initial value:

=  ContextualCompressionRetriever(
    base_compressor=compressor, base_retriever=retriever
)

compressor

llama_demos.llama_rag_demo_node.compressor = LlamaROSReranker(top_n=3)

docs

llama_demos.llama_rag_demo_node.docs = loader.load()

end

llama_demos.llama_rag_demo_node.end

flush

llama_demos.llama_rag_demo_node.flush

loader

llama_demos.llama_rag_demo_node.loader

Initial value:

=  WebBaseLoader(
    web_paths=("https://lilianweng.github.io/posts/2023-06-23-agent/",),
    bs_kwargs=dict(
        parse_only=bs4.SoupStrainer(class_=("post-content", "post-title", "post-header"))
    ),
)

prompt

llama_demos.llama_rag_demo_node.prompt

Initial value:

=  ChatPromptTemplate.from_messages(
    [
        SystemMessage("You are an AI assistant that answer questions briefly."),
        HumanMessagePromptTemplate.from_template(
            "Taking into account the following information:{context}\n\n{question}"
        ),
    ]
)

rag_chain

tuple llama_demos.llama_rag_demo_node.rag_chain

Initial value:

=  (
    {"context": compression_retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | ChatLlamaROS(temp=0.0)
    | StrOutputParser()
)

retriever

llama_demos.llama_rag_demo_node.retriever = vectorstore.as_retriever(search_kwargs={"k": 20})

splits

llama_demos.llama_rag_demo_node.splits = text_splitter.split_documents(docs)

text_splitter

llama_demos.llama_rag_demo_node.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)

True

llama_demos.llama_rag_demo_node.True

vectorstore

llama_demos.llama_rag_demo_node.vectorstore = Chroma.from_documents(documents=splits, embedding=LlamaROSEmbeddings())