MilvusRetriever

Setup:

Install langchain-milvus and other dependencies:

.. code-block:: bash

pip install -U pymilvus[model] langchain-milvus

Key init args:

collection: Milvus Collection

Instantiate:

.. code-block:: python

retriever = MilvusCollectionHybridSearchRetriever(collection=collection)

Usage:

.. code-block:: python

query = "What are the story about ventures?"

retriever.invoke(query)

.. code-block:: none

[Document(page_content="In 'The Lost Expedition' by Caspian Grey...", metadata={'doc_id': '449281835035545843'}),
Document(page_content="In 'The Phantom Pilgrim' by Rowan Welles...", metadata={'doc_id': '449281835035545845'}),
Document(page_content="In 'The Dreamwalker's Journey' by Lyra Snow..", metadata={'doc_id': '449281835035545846'})]

Use within a chain:

.. code-block:: python

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

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

Context: {context}

Question: {question}"""
)

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

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

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

chain.invoke("What novels has Lila written and what are their contents?")

.. code-block:: none

 "Lila Rose has written 'The Memory Thief,' which follows a charismatic thief..."