mirror of https://github.com/hwchase17/langchain
Add template for self-query-qdrant (#12795)
This PR adds a self-querying template using Qdrant as a vector store. The template uses an artificial dataset and was implemented in a way that simplifies passing different components and choosing LLM and embedding providers. --------- Co-authored-by: Erick Friis <erick@langchain.dev>pull/12975/head v0.0.330
Kacper Łukawski
7 months ago
committed by
GitHub
parent
f41f4c5e37
commit
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.idea
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tests
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# self-query-qdrant
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This template performs [self-querying](https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/)
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using Qdrant and OpenAI. By default, it uses an artificial dataset of 10 documents, but you can replace it with your own dataset.
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## Environment Setup
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Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
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Set the `QDRANT_URL` to the URL of your Qdrant instance. If you use [Qdrant Cloud](https://cloud.qdrant.io)
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you have to set the `QDRANT_API_KEY` environment variable as well. If you do not set any of them,
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the template will try to connect a local Qdrant instance at `http://localhost:6333`.
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```shell
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export QDRANT_URL=
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export QDRANT_API_KEY=
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export OPENAI_API_KEY=
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```
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## Usage
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To use this package, install the LangChain CLI first:
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```shell
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pip install -U "langchain-cli[serve]"
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```
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Create a new LangChain project and install this package as the only one:
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```shell
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langchain app new my-app --package self-query-qdrant
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```
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To add this to an existing project, run:
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```shell
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langchain app add self-query-qdrant
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```
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### Defaults
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Before you launch the server, you need to create a Qdrant collection and index the documents.
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It can be done by running the following command:
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```python
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from self_query_qdrant.chain import initialize
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initialize()
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```
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Add the following code to your `app/server.py` file:
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```python
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from self_query_qdrant.chain import chain
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add_routes(app, chain, path="/self-query-qdrant")
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```
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The default dataset consists 10 documents about dishes, along with their price and restaurant information.
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You can find the documents in the `packages/self-query-qdrant/self_query_qdrant/defaults.py` file.
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Here is one of the documents:
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```python
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from langchain.schema import Document
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Document(
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page_content="Spaghetti with meatballs and tomato sauce",
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metadata={
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"price": 12.99,
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"restaurant": {
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"name": "Olive Garden",
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"location": ["New York", "Chicago", "Los Angeles"],
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},
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},
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)
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```
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The self-querying allows performing semantic search over the documents, with some additional filtering
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based on the metadata. For example, you can search for the dishes that cost less than $15 and are served in New York.
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### Customization
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All the examples above assume that you want to launch the template with just the defaults.
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If you want to customize the template, you can do it by passing the parameters to the `create_chain` function
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in the `app/server.py` file:
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```python
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from langchain.llms import Cohere
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from langchain.embeddings import HuggingFaceEmbeddings
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from langchain.chains.query_constructor.schema import AttributeInfo
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from self_query_qdrant.chain import create_chain
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chain = create_chain(
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llm=Cohere(),
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embeddings=HuggingFaceEmbeddings(),
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document_contents="Descriptions of cats, along with their names and breeds.",
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metadata_field_info=[
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AttributeInfo(name="name", description="Name of the cat", type="string"),
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AttributeInfo(name="breed", description="Cat's breed", type="string"),
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],
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collection_name="cats",
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)
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```
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The same goes for the `initialize` function that creates a Qdrant collection and indexes the documents:
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```python
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from langchain.schema import Document
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from langchain.embeddings import HuggingFaceEmbeddings
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from self_query_qdrant.chain import initialize
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initialize(
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embeddings=HuggingFaceEmbeddings(),
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collection_name="cats",
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documents=[
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Document(
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page_content="A mean lazy old cat who destroys furniture and eats lasagna",
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metadata={"name": "Garfield", "breed": "Tabby"},
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),
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...
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]
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)
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```
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The template is flexible and might be used for different sets of documents easily.
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### LangSmith
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(Optional) If you have access to LangSmith, configure it to help trace, monitor and debug LangChain applications. If you don't have access, skip this section.
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```shell
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export LANGCHAIN_TRACING_V2=true
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export LANGCHAIN_API_KEY=<your-api-key>
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export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
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```
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If you are inside this directory, then you can spin up a LangServe instance directly by:
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```shell
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langchain serve
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```
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### Local Server
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This will start the FastAPI app with a server running locally at
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[http://localhost:8000](http://localhost:8000)
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You can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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Access the playground at [http://127.0.0.1:8000/self-query-qdrant/playground](http://127.0.0.1:8000/self-query-qdrant/playground)
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Access the template from code with:
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```python
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from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/self-query-qdrant")
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```
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File diff suppressed because it is too large
Load Diff
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[tool.poetry]
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name = "self-query-qdrant"
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version = "0.1.0"
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description = "Self-querying retriever using Qdrant"
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authors = ["Kacper Łukawski <lukawski.kacper@gmail.com>"]
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license = "Apache 2.0"
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readme = "README.md"
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packages = [{include = "self_query_qdrant"}]
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[tool.poetry.dependencies]
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python = ">=3.9,<3.13"
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langchain = ">=0.0.325"
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openai = "^0.28.1"
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qdrant-client = ">=1.6"
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lark = "^1.1.8"
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tiktoken = "^0.5.1"
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[tool.poetry.group.dev.dependencies]
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langchain-cli = ">=0.0.15"
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[tool.poetry.group.dev.dependencies.python-dotenv]
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extras = [
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"cli",
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]
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version = "^1.0.0"
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[tool.langserve]
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export_module = "self_query_qdrant"
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export_attr = "chain"
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[build-system]
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requires = ["poetry-core"]
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build-backend = "poetry.core.masonry.api"
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from self_query_qdrant.chain import chain
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__all__ = ["chain"]
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import os
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from typing import List, Optional
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from langchain.chains.query_constructor.schema import AttributeInfo
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from langchain.embeddings import OpenAIEmbeddings
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from langchain.llms import BaseLLM
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from langchain.llms.openai import OpenAI
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from langchain.pydantic_v1 import BaseModel
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from langchain.retrievers import SelfQueryRetriever
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from langchain.schema import Document, StrOutputParser
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from langchain.schema.embeddings import Embeddings
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from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
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from langchain.vectorstores.qdrant import Qdrant
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from qdrant_client import QdrantClient
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from self_query_qdrant import defaults, helper, prompts
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class Query(BaseModel):
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__root__: str
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def create_chain(
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llm: Optional[BaseLLM] = None,
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embeddings: Optional[Embeddings] = None,
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document_contents: str = defaults.DEFAULT_DOCUMENT_CONTENTS,
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metadata_field_info: List[AttributeInfo] = defaults.DEFAULT_METADATA_FIELD_INFO,
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collection_name: str = defaults.DEFAULT_COLLECTION_NAME,
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):
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"""
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Create a chain that can be used to query a Qdrant vector store with a self-querying
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capability. By default, this chain will use the OpenAI LLM and OpenAIEmbeddings, and
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work with the default document contents and metadata field info. You can override
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these defaults by passing in your own values.
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:param llm: an LLM to use for generating text
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:param embeddings: an Embeddings to use for generating queries
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:param document_contents: a description of the document set
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:param metadata_field_info: list of metadata attributes
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:param collection_name: name of the Qdrant collection to use
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:return:
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"""
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llm = llm or OpenAI()
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embeddings = embeddings or OpenAIEmbeddings()
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# Set up a vector store to store your vectors and metadata
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client = QdrantClient(
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url=os.environ.get("QDRANT_URL", "http://localhost:6333"),
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api_key=os.environ.get("QDRANT_API_KEY"),
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)
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vectorstore = Qdrant(
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client=client,
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collection_name=collection_name,
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embeddings=embeddings,
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)
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# Set up a retriever to query your vector store with self-querying capabilities
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retriever = SelfQueryRetriever.from_llm(
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llm, vectorstore, document_contents, metadata_field_info, verbose=True
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)
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context = RunnableParallel(
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context=retriever | helper.combine_documents,
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query=RunnablePassthrough(),
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)
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pipeline = context | prompts.LLM_CONTEXT_PROMPT | llm | StrOutputParser()
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return pipeline.with_types(input_type=Query)
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def initialize(
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embeddings: Optional[Embeddings] = None,
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collection_name: str = defaults.DEFAULT_COLLECTION_NAME,
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documents: List[Document] = defaults.DEFAULT_DOCUMENTS,
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):
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"""
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Initialize a vector store with a set of documents. By default, the documents will be
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compatible with the default metadata field info. You can override these defaults by
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passing in your own values.
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:param embeddings: an Embeddings to use for generating queries
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:param collection_name: name of the Qdrant collection to use
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:param documents: a list of documents to initialize the vector store with
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:return:
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"""
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embeddings = embeddings or OpenAIEmbeddings()
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# Set up a vector store to store your vectors and metadata
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Qdrant.from_documents(
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documents, embedding=embeddings, collection_name=collection_name
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)
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# Create the default chain
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chain = create_chain()
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from langchain.chains.query_constructor.schema import AttributeInfo
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from langchain.schema import Document
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# Qdrant collection name
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DEFAULT_COLLECTION_NAME = "restaurants"
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# Here is a description of the dataset and metadata attributes. Metadata attributes will
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# be used to filter the results of the query beyond the semantic search.
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DEFAULT_DOCUMENT_CONTENTS = (
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"Dishes served at different restaurants, along with the restaurant information"
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)
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DEFAULT_METADATA_FIELD_INFO = [
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AttributeInfo(
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name="price",
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description="The price of the dish",
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type="float",
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),
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AttributeInfo(
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name="restaurant.name",
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description="The name of the restaurant",
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type="string",
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),
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AttributeInfo(
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name="restaurant.location",
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description="Name of the city where the restaurant is located",
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type="string or list[string]",
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),
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]
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# A default set of documents to use for the vector store. This is a list of Document
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# objects, which have a page_content field and a metadata field. The metadata field is a
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# dictionary of metadata attributes compatible with the metadata field info above.
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DEFAULT_DOCUMENTS = [
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Document(
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page_content="Pepperoni pizza with extra cheese, crispy crust",
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metadata={
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"price": 10.99,
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"restaurant": {
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"name": "Pizza Hut",
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"location": ["New York", "Chicago"],
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},
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},
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),
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Document(
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page_content="Spaghetti with meatballs and tomato sauce",
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metadata={
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"price": 12.99,
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"restaurant": {
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"name": "Olive Garden",
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"location": ["New York", "Chicago", "Los Angeles"],
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},
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},
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),
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Document(
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page_content="Chicken tikka masala with naan",
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metadata={
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"price": 14.99,
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"restaurant": {
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"name": "Indian Oven",
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"location": ["New York", "Los Angeles"],
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},
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},
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),
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Document(
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page_content="Chicken teriyaki with rice",
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metadata={
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"price": 11.99,
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"restaurant": {
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"name": "Sakura",
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"location": ["New York", "Chicago", "Los Angeles"],
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},
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},
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),
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Document(
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page_content="Scabbard fish with banana and passion fruit sauce",
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metadata={
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"price": 19.99,
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"restaurant": {
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"name": "A Concha",
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"location": ["San Francisco"],
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},
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},
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),
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Document(
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page_content="Pielmieni with sour cream",
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metadata={
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"price": 13.99,
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"restaurant": {
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"name": "Russian House",
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"location": ["New York", "Chicago"],
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},
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},
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),
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Document(
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page_content="Chicken biryani with raita",
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metadata={
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"price": 14.99,
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"restaurant": {
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"name": "Indian Oven",
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"location": ["Los Angeles"],
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},
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},
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),
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Document(
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page_content="Tomato soup with croutons",
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metadata={
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"price": 7.99,
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"restaurant": {
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"name": "Olive Garden",
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"location": ["New York", "Chicago", "Los Angeles"],
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},
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},
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),
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Document(
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page_content="Vegan burger with sweet potato fries",
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metadata={
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"price": 12.99,
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"restaurant": {
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"name": "Burger King",
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"location": ["New York", "Los Angeles"],
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},
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},
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),
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Document(
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page_content="Chicken nuggets with french fries",
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metadata={
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"price": 9.99,
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"restaurant": {
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"name": "McDonald's",
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"location": ["San Francisco", "New York", "Los Angeles"],
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},
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},
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),
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]
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@ -0,0 +1,27 @@
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from string import Formatter
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from typing import List
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from langchain.schema import Document
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document_template = """
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PASSAGE: {page_content}
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METADATA: {metadata}
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"""
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def combine_documents(documents: List[Document]) -> str:
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"""
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Combine a list of documents into a single string that might be passed further down
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to a language model.
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:param documents: list of documents to combine
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:return:
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"""
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formatter = Formatter()
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return "\n\n".join(
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formatter.format(
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document_template,
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page_content=document.page_content,
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metadata=document.metadata,
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)
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for document in documents
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)
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@ -0,0 +1,16 @@
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from langchain.prompts import PromptTemplate
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llm_context_prompt_template = """
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Answer the user query using provided passages. Each passage has metadata given as
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a nested JSON object you can also use. When answering, cite source name of the passages
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you are answering from below the answer in a unique bullet point list.
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If you don't know the answer, just say that you don't know, don't try to make up an answer.
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----
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{context}
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----
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Query: {query}
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""" # noqa: E501
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LLM_CONTEXT_PROMPT = PromptTemplate.from_template(llm_context_prompt_template)
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