milvus: New langchain_milvus package and new milvus features (#21077)

New features:

- New langchain_milvus package in partner
- Milvus collection hybrid search retriever
- Zilliz cloud pipeline retriever
- Milvus Local guid
- Rag-milvus template

---------

Signed-off-by: ChengZi <chen.zhang@zilliz.com>
Signed-off-by: Jael Gu <mengjia.gu@zilliz.com>
Co-authored-by: Jael Gu <mengjia.gu@zilliz.com>
Co-authored-by: Jackson <jacksonxie612@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Erick Friis <erickfriis@gmail.com>

templates/rag-milvus/.gitignore

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+__pycache__

templates/rag-milvus/LICENSE

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+MIT License
+
+Copyright (c) 2024 LangChain, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

templates/rag-milvus/README.md

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+# rag-milvus
+
+This template performs RAG using Milvus and OpenAI.
+
+## Environment Setup
+
+Start the milvus server instance, and get the host ip and port.
+
+Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
+
+## Usage
+
+To use this package, you should first have the LangChain CLI installed:
+
+```shell
+pip install -U langchain-cli
+```
+
+To create a new LangChain project and install this as the only package, you can do:
+
+```shell
+langchain app new my-app --package rag-milvus
+```
+
+If you want to add this to an existing project, you can just run:
+
+```shell
+langchain app add rag-milvus
+```
+
+And add the following code to your `server.py` file:
+```python
+from rag_milvus import chain as rag_milvus_chain
+
+add_routes(app, rag_milvus_chain, path="/rag-milvus")
+```
+
+(Optional) Let's now configure LangSmith. 
+LangSmith will help us trace, monitor and debug LangChain applications. 
+You can sign up for LangSmith [here](https://smith.langchain.com/). 
+If you don't have access, you can skip this section
+
+
+```shell
+export LANGCHAIN_TRACING_V2=true
+export LANGCHAIN_API_KEY=<your-api-key>
+export LANGCHAIN_PROJECT=<your-project>  # if not specified, defaults to "default"
+```
+
+If you are inside this directory, then you can spin up a LangServe instance directly by:
+
+```shell
+langchain serve
+```
+
+This will start the FastAPI app with a server is running locally at 
+[http://localhost:8000](http://localhost:8000)
+
+We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
+We can access the playground at [http://127.0.0.1:8000/rag-milvus/playground](http://127.0.0.1:8000/rag-milvus/playground)  
+
+We can access the template from code with:
+
+```python
+from langserve.client import RemoteRunnable
+
+runnable = RemoteRunnable("http://localhost:8000/rag-milvus")
+```

templates/rag-milvus/pyproject.toml

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+[tool.poetry]
+name = "rag-milvus"
+version = "0.1.0"
+description = "RAG using Milvus"
+authors = []
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.8.1,<4.0"
+langchain = "^0.1"
+langchain-core = "^0.1"
+langchain-openai = "^0.1"
+langchain-community = "^0.0.30"
+pymilvus = "^2.4"
+scipy = "^1.9"
+
+[tool.poetry.group.dev.dependencies]
+langchain-cli = ">=0.0.4"
+fastapi = "^0.104.0"
+sse-starlette = "^1.6.5"
+
+[tool.langserve]
+export_module = "rag_milvus"
+export_attr = "chain"
+
+[tool.templates-hub]
+use-case = "rag"
+author = "LangChain"
+integrations = ["OpenAI", "Milvus"]
+tags = ["vectordbs"]
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

templates/rag-milvus/rag_milvus/__init__.py

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+from rag_milvus.chain import chain
+
+__all__ = ["chain"]

templates/rag-milvus/rag_milvus/chain.py

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+from langchain_community.vectorstores import Milvus
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.pydantic_v1 import BaseModel
+from langchain_core.runnables import RunnableParallel, RunnablePassthrough
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+
+# Example for document loading (from url), splitting, and creating vectorstore
+
+""" 
+# Load
+from langchain_community.document_loaders import WebBaseLoader
+
+loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
+data = loader.load()
+
+# Split
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
+all_splits = text_splitter.split_documents(data)
+
+# Add to vectorDB
+vectorstore = Milvus.from_documents(documents=all_splits,
+                                    collection_name="rag_milvus",
+                                    embedding=OpenAIEmbeddings(),
+                                    drop_old=True,
+                                    )
+retriever = vectorstore.as_retriever()
+"""
+
+# Embed a single document as a test
+vectorstore = Milvus.from_texts(
+    ["harrison worked at kensho"],
+    collection_name="rag_milvus",
+    embedding=OpenAIEmbeddings(),
+    drop_old=True,
+    connection_args={
+        "uri": "http://127.0.0.1:19530",
+    },
+)
+retriever = vectorstore.as_retriever()
+
+# RAG prompt
+template = """Answer the question based only on the following context:
+{context}
+
+Question: {question}
+"""
+prompt = ChatPromptTemplate.from_template(template)
+
+# LLM
+model = ChatOpenAI()
+
+# RAG chain
+chain = (
+    RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
+    | prompt
+    | model
+    | StrOutputParser()
+)
+
+
+# Add typing for input
+class Question(BaseModel):
+    __root__: str
+
+
+chain = chain.with_types(input_type=Question)