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Documentation Index

Fetch the complete documentation index at: https://docs.langchain.com/llms.txt

Use this file to discover all available pages before exploring further.

Galaxia is GraphRAG solution, which automates document processing, knowledge base (Graph Language Model) creation and retrieval: galaxia-rag To use Galaxia first upload your texts and create a Graph Language Model here: smabbler-cloud After the model is built and activated, you will be able to use this integration to retrieve what you need. The module repository is located here: github

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Integration details

RetrieverSelf-hostCloud offeringPackage
Galaxia Retriever❌✅langchain-galaxia-retriever

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Setup

Before you can retrieve anything you need to create your Graph Language Model here: smabbler-cloud following these 3 simple steps: rag-instruction Don’t forget to activate the model after building it!

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Installation

The retriever is implemented in the following package: pypi 
pip install -qU langchain-galaxia-retriever

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Instantiation

from langchain_galaxia_retriever.retriever import GalaxiaRetriever

gr = GalaxiaRetriever(
    api_url="beta.api.smabbler.com",
    api_key="<key>",  # you can find it here: https://beta.cloud.smabbler.com/user/account
    knowledge_base_id="<knowledge_base_id>",  # you can find it in https://beta.cloud.smabbler.com , in the model table
    n_retries=10,
    wait_time=5,
)

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Usage

result = gr.invoke("<test question>")
print(result)

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Use within a chain

# | output: false
# | echo: false

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough

prompt = ChatPromptTemplate.from_template(
    """Answer the question based only on the context provided.

Context: {context}

Question: {question}"""
)


def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)


chain = (
    {"context": gr | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

chain.invoke("<test question>")

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API reference

For more information about Galaxia Retriever check its implementation on GitHub
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