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community[minor]: Add support for Upstash Vector (#20824)
## Description Adding `UpstashVectorStore` to utilize [Upstash Vector](https://upstash.com/docs/vector/overall/getstarted)! #17012 was opened to add Upstash Vector to langchain but was closed to wait for filtering. Now filtering is added to Upstash vector and we open a new PR. Additionally, [embedding feature](https://upstash.com/docs/vector/features/embeddingmodels) was added and we add this to our vectorstore aswell. ## Dependencies [upstash-vector](https://pypi.org/project/upstash-vector/) should be installed to use `UpstashVectorStore`. Didn't update dependencies because of [this comment in the previous PR](https://github.com/langchain-ai/langchain/pull/17012#pullrequestreview-1876522450). ## Tests Tests are added and they pass. Tests are naturally network bound since Upstash Vector is offered through an API. There was [a discussion in the previous PR about mocking the unittests](https://github.com/langchain-ai/langchain/pull/17012#pullrequestreview-1891820567). We didn't make changes to this end yet. We can update the tests if you can explain how the tests should be mocked. --------- Co-authored-by: ytkimirti <yusuftaha9@gmail.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
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Cahid Arda Öz
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# Upstash Redis
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Upstash offers developers serverless databases and messaging
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platforms to build powerful applications without having to worry
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about the operational complexity of running databases at scale.
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Upstash offers developers serverless databases and messaging platforms to build powerful applications without having to worry about the operational complexity of running databases at scale.
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One significant advantage of Upstash is that their databases support HTTP and all of their SDKs use HTTP.
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This means that you can run this in serverless platforms, edge or any platform that does not support TCP connections.
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Currently, there are two Upstash integrations available for LangChain:
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Upstash Vector as a vector embedding database and Upstash Redis as a cache and memory store.
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# Upstash Vector
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Upstash Vector is a serverless vector database that can be used to store and query vectors.
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## Installation
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Create a new serverless vector database at the [Upstash Console](https://console.upstash.com/vector).
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Select your preferred distance metric and dimension count according to your model.
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Install the Upstash Vector Python SDK with `pip install upstash-vector`.
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The Upstash Vector integration in langchain is a wrapper for the Upstash Vector Python SDK. That's why the `upstash-vector` package is required.
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## Integrations
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Create a `UpstashVectorStore` object using credentials from the Upstash Console.
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You also need to pass in an `Embeddings` object which can turn text into vector embeddings.
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```python
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from langchain_community.vectorstores.upstash import UpstashVectorStore
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import os
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os.environ["UPSTASH_VECTOR_REST_URL"] = "<UPSTASH_VECTOR_REST_URL>"
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os.environ["UPSTASH_VECTOR_REST_TOKEN"] = "<UPSTASH_VECTOR_REST_TOKEN>"
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store = UpstashVectorStore(
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embedding=embeddings
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)
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```
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An alternative way of `UpstashVectorStore` is to pass `embedding=True`. This is a unique
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feature of the `UpstashVectorStore` thanks to the ability of the Upstash Vector indexes
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to have an associated embedding model. In this configuration, documents we want to insert or
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queries we want to search for are simply sent to Upstash Vector as text. In the background,
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Upstash Vector embeds these text and executes the request with these embeddings. To use this
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feature, [create an Upstash Vector index by selecting a model](https://upstash.com/docs/vector/features/embeddingmodels#using-a-model)
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and simply pass `embedding=True`:
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```python
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from langchain_community.vectorstores.upstash import UpstashVectorStore
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import os
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os.environ["UPSTASH_VECTOR_REST_URL"] = "<UPSTASH_VECTOR_REST_URL>"
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os.environ["UPSTASH_VECTOR_REST_TOKEN"] = "<UPSTASH_VECTOR_REST_TOKEN>"
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store = UpstashVectorStore(
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embedding=True
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)
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```
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See [Upstash Vector documentation](https://upstash.com/docs/vector/features/embeddingmodels)
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for more detail on embedding models.
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### Inserting Vectors
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```python
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from langchain.text_splitter import CharacterTextSplitter
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from langchain_community.document_loaders import TextLoader
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from langchain_openai import OpenAIEmbeddings
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loader = TextLoader("../../modules/state_of_the_union.txt")
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documents = loader.load()
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text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
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docs = text_splitter.split_documents(documents)
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# Create a new embeddings object
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embeddings = OpenAIEmbeddings()
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# Create a new UpstashVectorStore object
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store = UpstashVectorStore(
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embedding=embeddings
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)
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# Insert the document embeddings into the store
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store.add_documents(docs)
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```
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When inserting documents, first they are embedded using the `Embeddings` object.
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Most embedding models can embed multiple documents at once, so the documents are batched and embedded in parallel.
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The size of the batch can be controlled using the `embedding_chunk_size` parameter.
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The embedded vectors are then stored in the Upstash Vector database. When they are sent, multiple vectors are batched together to reduce the number of HTTP requests.
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The size of the batch can be controlled using the `batch_size` parameter. Upstash Vector has a limit of 1000 vectors per batch in the free tier.
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```python
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store.add_documents(
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documents,
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batch_size=100,
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embedding_chunk_size=200
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)
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```
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### Querying Vectors
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Vectors can be queried using a text query or another vector.
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The returned value is a list of Document objects.
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```python
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result = store.similarity_search(
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"The United States of America",
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k=5
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)
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```
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Or using a vector:
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```python
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vector = embeddings.embed_query("Hello world")
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result = store.similarity_search_by_vector(
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vector,
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k=5
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)
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```
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When searching, you can also utilize the `filter` parameter which will allow you to filter by metadata:
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```python
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result = store.similarity_search(
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"The United States of America",
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k=5,
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filter="type = 'country'"
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)
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```
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See [Upstash Vector documentation](https://upstash.com/docs/vector/features/filtering)
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for more details on metadata filtering.
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### Deleting Vectors
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Vectors can be deleted by their IDs.
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```python
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store.delete(["id1", "id2"])
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```
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### Getting information about the store
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You can get information about your database like the distance metric dimension using the info function.
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When an insert happens, the database an indexing takes place. While this is happening new vectors can not be queried. `pendingVectorCount` represents the number of vector that are currently being indexed.
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```python
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info = store.info()
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print(info)
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# Output:
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# {'vectorCount': 44, 'pendingVectorCount': 0, 'indexSize': 2642412, 'dimension': 1536, 'similarityFunction': 'COSINE'}
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```
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# Upstash Redis
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This page covers how to use [Upstash Redis](https://upstash.com/redis) with LangChain.
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@ -12,7 +172,6 @@ This page covers how to use [Upstash Redis](https://upstash.com/redis) with Lang
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## Integrations
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All of Upstash-LangChain integrations are based on `upstash-redis` Python SDK being utilized as wrappers for LangChain.
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This SDK utilizes Upstash Redis DB by giving UPSTASH_REDIS_REST_URL and UPSTASH_REDIS_REST_TOKEN parameters from the console.
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One significant advantage of this is that, this SDK uses a REST API. This means, you can run this in serverless platforms, edge or any platform that does not support TCP connections.
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### Cache
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389
docs/docs/integrations/vectorstores/upstash.ipynb
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docs/docs/integrations/vectorstores/upstash.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Upstash Vector\n",
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"\n",
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"> [Upstash Vector](https://upstash.com/docs/vector/overall/whatisvector) is a serverless vector database designed for working with vector embeddings.\n",
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">\n",
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"> The vector langchain integration is a wrapper around the [upstash-vector](https://github.com/upstash/vector-py) package.\n",
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">\n",
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"> The python package uses the [vector rest api](https://upstash.com/docs/vector/api/get-started) behind the scenes."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Installation\n",
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"\n",
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"Create a free vector database from [upstash console](https://console.upstash.com/vector) with the desired dimensions and distance metric.\n",
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"\n",
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"You can then create an `UpstashVectorStore` instance by:\n",
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"\n",
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"- Providing the environment variables `UPSTASH_VECTOR_URL` and `UPSTASH_VECTOR_TOKEN`\n",
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"\n",
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"- Giving them as parameters to the constructor\n",
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"\n",
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"- Passing an Upstash Vector `Index` instance to the constructor\n",
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"\n",
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"Also, an `Embeddings` instance is required to turn given texts into embeddings. Here we use `OpenAIEmbeddings` as an example"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install langchain-openai langchain upstash-vector"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"from langchain_community.vectorstores.upstash import UpstashVectorStore\n",
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"from langchain_openai import OpenAIEmbeddings\n",
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"\n",
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"os.environ[\"OPENAI_API_KEY\"] = \"<YOUR_OPENAI_KEY>\"\n",
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"os.environ[\"UPSTASH_VECTOR_REST_URL\"] = \"<YOUR_UPSTASH_VECTOR_URL>\"\n",
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"os.environ[\"UPSTASH_VECTOR_REST_TOKEN\"] = \"<YOUR_UPSTASH_VECTOR_TOKEN>\"\n",
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"\n",
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"# Create an embeddings instance\n",
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"embeddings = OpenAIEmbeddings()\n",
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"\n",
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"# Create a vector store instance\n",
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"store = UpstashVectorStore(embedding=embeddings)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"An alternative way of creating `UpstashVectorStore` is to [create an Upstash Vector index by selecting a model](https://upstash.com/docs/vector/features/embeddingmodels#using-a-model) and passing `embedding=True`. In this configuration, documents or queries will be sent to Upstash as text and embedded there.\n",
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"\n",
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"```python\n",
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"store = UpstashVectorStore(embedding=True)\n",
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"```\n",
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"\n",
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"If you are interested in trying out this approach, you can update the initialization of `store` like above and run the rest of the tutorial."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load documents\n",
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"\n",
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"Load an example text file and split it into chunks which can be turned into vector embeddings."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[Document(page_content='Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans. \\n\\nLast year COVID-19 kept us apart. This year we are finally together again. \\n\\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \\n\\nWith a duty to one another to the American people to the Constitution. \\n\\nAnd with an unwavering resolve that freedom will always triumph over tyranny. \\n\\nSix days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. \\n\\nHe thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. \\n\\nHe met the Ukrainian people. \\n\\nFrom President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.', metadata={'source': '../../modules/state_of_the_union.txt'}),\n",
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" Document(page_content='Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. \\n\\nIn this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United States is here tonight. \\n\\nLet each of us here tonight in this Chamber send an unmistakable signal to Ukraine and to the world. \\n\\nPlease rise if you are able and show that, Yes, we the United States of America stand with the Ukrainian people. \\n\\nThroughout our history we’ve learned this lesson when dictators do not pay a price for their aggression they cause more chaos. \\n\\nThey keep moving. \\n\\nAnd the costs and the threats to America and the world keep rising. \\n\\nThat’s why the NATO Alliance was created to secure peace and stability in Europe after World War 2. \\n\\nThe United States is a member along with 29 other nations. \\n\\nIt matters. American diplomacy matters. American resolve matters.', metadata={'source': '../../modules/state_of_the_union.txt'}),\n",
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" Document(page_content='Putin’s latest attack on Ukraine was premeditated and unprovoked. \\n\\nHe rejected repeated efforts at diplomacy. \\n\\nHe thought the West and NATO wouldn’t respond. And he thought he could divide us at home. Putin was wrong. We were ready. Here is what we did. \\n\\nWe prepared extensively and carefully. \\n\\nWe spent months building a coalition of other freedom-loving nations from Europe and the Americas to Asia and Africa to confront Putin. \\n\\nI spent countless hours unifying our European allies. We shared with the world in advance what we knew Putin was planning and precisely how he would try to falsely justify his aggression. \\n\\nWe countered Russia’s lies with truth. \\n\\nAnd now that he has acted the free world is holding him accountable. \\n\\nAlong with twenty-seven members of the European Union including France, Germany, Italy, as well as countries like the United Kingdom, Canada, Japan, Korea, Australia, New Zealand, and many others, even Switzerland.', metadata={'source': '../../modules/state_of_the_union.txt'})]"
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]
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},
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain_community.document_loaders import TextLoader\n",
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"\n",
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"loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n",
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"documents = loader.load()\n",
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
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"docs = text_splitter.split_documents(documents)\n",
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"\n",
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"docs[:3]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Inserting documents\n",
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"\n",
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"The vectorstore embeds text chunks using the embedding object and batch inserts them into the database. This returns an id array of the inserted vectors."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['82b3781b-817c-4a4d-8f8b-cbd07c1d005a',\n",
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" 'a20e0a49-29d8-465e-8eae-0bc5ac3d24dc',\n",
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" 'c19f4108-b652-4890-873e-d4cad00f1b1a',\n",
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" '23d1fcf9-6ee1-4638-8c70-0f5030762301',\n",
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" '2d775784-825d-4627-97a3-fee4539d8f58']"
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]
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},
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"execution_count": 35,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"inserted_vectors = store.add_documents(docs)\n",
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"\n",
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"inserted_vectors[:5]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"store"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['fe1f7a7b-42e2-4828-88b0-5b449c49fe86',\n",
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" '154a0021-a99c-427e-befb-f0b2b18ed83c',\n",
|
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" 'a8218226-18a9-4ab5-ade5-5a71b19a7831',\n",
|
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" '62b7ef97-83bf-4b6d-8c93-f471796244dc',\n",
|
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" 'ab43fd2e-13df-46d4-8cf7-e6e16506e4bb',\n",
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" '6841e7f9-adaa-41d9-af3d-0813ee52443f',\n",
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" '45dda5a1-f0c1-4ac7-9acb-50253e4ee493']"
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]
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},
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"execution_count": 27,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"store.add_texts(\n",
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" [\n",
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" \"A timeless tale set in the Jazz Age, this novel delves into the lives of affluent socialites, their pursuits of wealth, love, and the elusive American Dream. Amidst extravagant parties and glittering opulence, the story unravels the complexities of desire, ambition, and the consequences of obsession.\",\n",
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" \"Set in a small Southern town during the 1930s, this novel explores themes of racial injustice, moral growth, and empathy through the eyes of a young girl. It follows her father, a principled lawyer, as he defends a black man accused of assaulting a white woman, confronting deep-seated prejudices and challenging societal norms along the way.\",\n",
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" \"A chilling portrayal of a totalitarian regime, this dystopian novel offers a bleak vision of a future world dominated by surveillance, propaganda, and thought control. Through the eyes of a disillusioned protagonist, it explores the dangers of totalitarianism and the erosion of individual freedom in a society ruled by fear and oppression.\",\n",
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" \"Set in the English countryside during the early 19th century, this novel follows the lives of the Bennet sisters as they navigate the intricate social hierarchy of their time. Focusing on themes of marriage, class, and societal expectations, the story offers a witty and insightful commentary on the complexities of romantic relationships and the pursuit of happiness.\",\n",
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" \"Narrated by a disillusioned teenager, this novel follows his journey of self-discovery and rebellion against the phoniness of the adult world. Through a series of encounters and reflections, it explores themes of alienation, identity, and the search for authenticity in a society marked by conformity and hypocrisy.\",\n",
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" \"In a society where emotion is suppressed and individuality is forbidden, one man dares to defy the oppressive regime. Through acts of rebellion and forbidden love, he discovers the power of human connection and the importance of free will.\",\n",
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" \"Set in a future world devastated by environmental collapse, this novel follows a group of survivors as they struggle to survive in a harsh, unforgiving landscape. Amidst scarcity and desperation, they must confront moral dilemmas and question the nature of humanity itself.\",\n",
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" ],\n",
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||||
" [\n",
|
||||
" {\"title\": \"The Great Gatsby\", \"author\": \"F. Scott Fitzgerald\", \"year\": 1925},\n",
|
||||
" {\"title\": \"To Kill a Mockingbird\", \"author\": \"Harper Lee\", \"year\": 1960},\n",
|
||||
" {\"title\": \"1984\", \"author\": \"George Orwell\", \"year\": 1949},\n",
|
||||
" {\"title\": \"Pride and Prejudice\", \"author\": \"Jane Austen\", \"year\": 1813},\n",
|
||||
" {\"title\": \"The Catcher in the Rye\", \"author\": \"J.D. Salinger\", \"year\": 1951},\n",
|
||||
" {\"title\": \"Brave New World\", \"author\": \"Aldous Huxley\", \"year\": 1932},\n",
|
||||
" {\"title\": \"The Road\", \"author\": \"Cormac McCarthy\", \"year\": 2006},\n",
|
||||
" ],\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Querying\n",
|
||||
"\n",
|
||||
"The database can be queried using a vector or a text prompt.\n",
|
||||
"If a text prompt is used, it's first converted into embedding and then queried.\n",
|
||||
"\n",
|
||||
"The `k` parameter specifies how many results to return from the query."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[Document(page_content='And my report is this: the State of the Union is strong—because you, the American people, are strong. \\n\\nWe are stronger today than we were a year ago. \\n\\nAnd we will be stronger a year from now than we are today. \\n\\nNow is our moment to meet and overcome the challenges of our time. \\n\\nAnd we will, as one people. \\n\\nOne America. \\n\\nThe United States of America. \\n\\nMay God bless you all. May God protect our troops.', metadata={'source': '../../modules/state_of_the_union.txt'}),\n",
|
||||
" Document(page_content='And built the strongest, freest, and most prosperous nation the world has ever known. \\n\\nNow is the hour. \\n\\nOur moment of responsibility. \\n\\nOur test of resolve and conscience, of history itself. \\n\\nIt is in this moment that our character is formed. Our purpose is found. Our future is forged. \\n\\nWell I know this nation. \\n\\nWe will meet the test. \\n\\nTo protect freedom and liberty, to expand fairness and opportunity. \\n\\nWe will save democracy. \\n\\nAs hard as these times have been, I am more optimistic about America today than I have been my whole life. \\n\\nBecause I see the future that is within our grasp. \\n\\nBecause I know there is simply nothing beyond our capacity. \\n\\nWe are the only nation on Earth that has always turned every crisis we have faced into an opportunity. \\n\\nThe only nation that can be defined by a single word: possibilities. \\n\\nSo on this night, in our 245th year as a nation, I have come to report on the State of the Union.', metadata={'source': '../../modules/state_of_the_union.txt'}),\n",
|
||||
" Document(page_content='Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. \\n\\nIn this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United States is here tonight. \\n\\nLet each of us here tonight in this Chamber send an unmistakable signal to Ukraine and to the world. \\n\\nPlease rise if you are able and show that, Yes, we the United States of America stand with the Ukrainian people. \\n\\nThroughout our history we’ve learned this lesson when dictators do not pay a price for their aggression they cause more chaos. \\n\\nThey keep moving. \\n\\nAnd the costs and the threats to America and the world keep rising. \\n\\nThat’s why the NATO Alliance was created to secure peace and stability in Europe after World War 2. \\n\\nThe United States is a member along with 29 other nations. \\n\\nIt matters. American diplomacy matters. American resolve matters.', metadata={'source': '../../modules/state_of_the_union.txt'}),\n",
|
||||
" Document(page_content='When we use taxpayer dollars to rebuild America – we are going to Buy American: buy American products to support American jobs. \\n\\nThe federal government spends about $600 Billion a year to keep the country safe and secure. \\n\\nThere’s been a law on the books for almost a century \\nto make sure taxpayers’ dollars support American jobs and businesses. \\n\\nEvery Administration says they’ll do it, but we are actually doing it. \\n\\nWe will buy American to make sure everything from the deck of an aircraft carrier to the steel on highway guardrails are made in America. \\n\\nBut to compete for the best jobs of the future, we also need to level the playing field with China and other competitors. \\n\\nThat’s why it is so important to pass the Bipartisan Innovation Act sitting in Congress that will make record investments in emerging technologies and American manufacturing. \\n\\nLet me give you one example of why it’s so important to pass it.', metadata={'source': '../../modules/state_of_the_union.txt'}),\n",
|
||||
" Document(page_content='Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans. \\n\\nLast year COVID-19 kept us apart. This year we are finally together again. \\n\\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \\n\\nWith a duty to one another to the American people to the Constitution. \\n\\nAnd with an unwavering resolve that freedom will always triumph over tyranny. \\n\\nSix days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. \\n\\nHe thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. \\n\\nHe met the Ukrainian people. \\n\\nFrom President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.', metadata={'source': '../../modules/state_of_the_union.txt'})]"
|
||||
]
|
||||
},
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"result = store.similarity_search(\"The United States of America\", k=5)\n",
|
||||
"result"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[Document(page_content='A chilling portrayal of a totalitarian regime, this dystopian novel offers a bleak vision of a future world dominated by surveillance, propaganda, and thought control. Through the eyes of a disillusioned protagonist, it explores the dangers of totalitarianism and the erosion of individual freedom in a society ruled by fear and oppression.', metadata={'title': '1984', 'author': 'George Orwell', 'year': 1949}),\n",
|
||||
" Document(page_content='Narrated by a disillusioned teenager, this novel follows his journey of self-discovery and rebellion against the phoniness of the adult world. Through a series of encounters and reflections, it explores themes of alienation, identity, and the search for authenticity in a society marked by conformity and hypocrisy.', metadata={'title': 'The Catcher in the Rye', 'author': 'J.D. Salinger', 'year': 1951}),\n",
|
||||
" Document(page_content='Set in the English countryside during the early 19th century, this novel follows the lives of the Bennet sisters as they navigate the intricate social hierarchy of their time. Focusing on themes of marriage, class, and societal expectations, the story offers a witty and insightful commentary on the complexities of romantic relationships and the pursuit of happiness.', metadata={'title': 'Pride and Prejudice', 'author': 'Jane Austen', 'year': 1813})]"
|
||||
]
|
||||
},
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"result = store.similarity_search(\"dystopia\", k=3, filter=\"year < 2000\")\n",
|
||||
"result"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Querying with score\n",
|
||||
"\n",
|
||||
"The score of the query can be included for every result. \n",
|
||||
"\n",
|
||||
"> The score returned in the query requests is a normalized value between 0 and 1, where 1 indicates the highest similarity and 0 the lowest regardless of the similarity function used. For more information look at the [docs](https://upstash.com/docs/vector/overall/features#vector-similarity-functions)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'source': '../../modules/state_of_the_union.txt'} - 0.87391514\n",
|
||||
"{'source': '../../modules/state_of_the_union.txt'} - 0.8549463\n",
|
||||
"{'source': '../../modules/state_of_the_union.txt'} - 0.847913\n",
|
||||
"{'source': '../../modules/state_of_the_union.txt'} - 0.84328896\n",
|
||||
"{'source': '../../modules/state_of_the_union.txt'} - 0.832347\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"result = store.similarity_search_with_score(\"The United States of America\", k=5)\n",
|
||||
"\n",
|
||||
"for doc, score in result:\n",
|
||||
" print(f\"{doc.metadata} - {score}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Deleting vectors\n",
|
||||
"\n",
|
||||
"Vectors can be deleted by their ids"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"store.delete(inserted_vectors)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Clearing the vector database\n",
|
||||
"\n",
|
||||
"This will clear the vector database"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"store.delete(delete_all=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Getting info about vector database\n",
|
||||
"\n",
|
||||
"You can get information about your database like the distance metric dimension using the info function.\n",
|
||||
"\n",
|
||||
"> When an insert happens, the database an indexing takes place. While this is happening new vectors can not be queried. `pendingVectorCount` represents the number of vector that are currently being indexed. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 36,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"InfoResult(vector_count=42, pending_vector_count=0, index_size=6470, dimension=384, similarity_function='COSINE')"
|
||||
]
|
||||
},
|
||||
"execution_count": 36,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"store.info()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "ai",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@ -60,7 +60,7 @@
|
||||
" * document addition by id (`add_documents` method with `ids` argument)\n",
|
||||
" * delete by id (`delete` method with `ids` argument)\n",
|
||||
"\n",
|
||||
"Compatible Vectorstores: `AnalyticDB`, `AstraDB`, `AwaDB`, `Bagel`, `Cassandra`, `Chroma`, `CouchbaseVectorStore`, `DashVector`, `DatabricksVectorSearch`, `DeepLake`, `Dingo`, `ElasticVectorSearch`, `ElasticsearchStore`, `FAISS`, `HanaDB`, `LanceDB`, `Milvus`, `MyScale`, `OpenSearchVectorSearch`, `PGVector`, `Pinecone`, `Qdrant`, `Redis`, `Rockset`, `ScaNN`, `SupabaseVectorStore`, `SurrealDBStore`, `TimescaleVector`, `Vald`, `VDMS`, `Vearch`, `VespaStore`, `Weaviate`, `ZepVectorStore`, `TencentVectorDB`, `OpenSearchVectorSearch`.\n",
|
||||
"Compatible Vectorstores: `AnalyticDB`, `AstraDB`, `AwaDB`, `Bagel`, `Cassandra`, `Chroma`, `CouchbaseVectorStore`, `DashVector`, `DatabricksVectorSearch`, `DeepLake`, `Dingo`, `ElasticVectorSearch`, `ElasticsearchStore`, `FAISS`, `HanaDB`, `LanceDB`, `Milvus`, `MyScale`, `OpenSearchVectorSearch`, `PGVector`, `Pinecone`, `Qdrant`, `Redis`, `Rockset`, `ScaNN`, `SupabaseVectorStore`, `SurrealDBStore`, `TimescaleVector`, `UpstashVectorStore`, `Vald`, `VDMS`, `Vearch`, `VespaStore`, `Weaviate`, `ZepVectorStore`, `TencentVectorDB`, `OpenSearchVectorSearch`.\n",
|
||||
" \n",
|
||||
"## Caution\n",
|
||||
"\n",
|
||||
|
||||
@ -250,6 +250,9 @@ if TYPE_CHECKING:
|
||||
from langchain_community.vectorstores.typesense import (
|
||||
Typesense, # noqa: F401
|
||||
)
|
||||
from langchain_community.vectorstores.upstash import (
|
||||
UpstashVectorStore, # noqa: F401
|
||||
)
|
||||
from langchain_community.vectorstores.usearch import (
|
||||
USearch, # noqa: F401
|
||||
)
|
||||
@ -364,6 +367,7 @@ __all__ = [
|
||||
"TileDB",
|
||||
"TimescaleVector",
|
||||
"Typesense",
|
||||
"UpstashVectorStore",
|
||||
"USearch",
|
||||
"VDMS",
|
||||
"Vald",
|
||||
@ -458,6 +462,7 @@ _module_lookup = {
|
||||
"TileDB": "langchain_community.vectorstores.tiledb",
|
||||
"TimescaleVector": "langchain_community.vectorstores.timescalevector",
|
||||
"Typesense": "langchain_community.vectorstores.typesense",
|
||||
"UpstashVectorStore": "langchain_community.vectorstores.upstash",
|
||||
"USearch": "langchain_community.vectorstores.usearch",
|
||||
"Vald": "langchain_community.vectorstores.vald",
|
||||
"VDMS": "langchain_community.vectorstores.vdms",
|
||||
|
||||
956
libs/community/langchain_community/vectorstores/upstash.py
Normal file
956
libs/community/langchain_community/vectorstores/upstash.py
Normal file
@ -0,0 +1,956 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import uuid
|
||||
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Union
|
||||
|
||||
import numpy as np
|
||||
from langchain_core.documents import Document
|
||||
from langchain_core.embeddings import Embeddings
|
||||
from langchain_core.utils.iter import batch_iterate
|
||||
from langchain_core.vectorstores import VectorStore
|
||||
|
||||
from langchain_community.vectorstores.utils import (
|
||||
maximal_marginal_relevance,
|
||||
)
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from upstash_vector import AsyncIndex, Index
|
||||
from upstash_vector.types import InfoResult
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class UpstashVectorStore(VectorStore):
|
||||
"""Upstash Vector vector store
|
||||
|
||||
To use, the ``upstash-vector`` python package must be installed.
|
||||
|
||||
Also an Upstash Vector index is required. First create a new Upstash Vector index
|
||||
and copy the `index_url` and `index_token` variables. Then either pass
|
||||
them through the constructor or set the environment
|
||||
variables `UPSTASH_VECTOR_REST_URL` and `UPSTASH_VECTOR_REST_TOKEN`.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_openai import OpenAIEmbeddings
|
||||
from langchain_community.vectorstores import UpstashVectorStore
|
||||
|
||||
embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
|
||||
vectorstore = UpstashVectorStore(
|
||||
embedding=embeddings,
|
||||
index_url="...",
|
||||
index_token="..."
|
||||
)
|
||||
|
||||
# or
|
||||
|
||||
import os
|
||||
|
||||
os.environ["UPSTASH_VECTOR_REST_URL"] = "..."
|
||||
os.environ["UPSTASH_VECTOR_REST_TOKEN"] = "..."
|
||||
|
||||
vectorstore = UpstashVectorStore(
|
||||
embedding=embeddings
|
||||
)
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
text_key: str = "text",
|
||||
index: Optional[Index] = None,
|
||||
async_index: Optional[AsyncIndex] = None,
|
||||
index_url: Optional[str] = None,
|
||||
index_token: Optional[str] = None,
|
||||
embedding: Optional[Union[Embeddings, bool]] = None,
|
||||
):
|
||||
"""
|
||||
Constructor for UpstashVectorStore.
|
||||
|
||||
If index or index_url and index_token are not provided, the constructor will
|
||||
attempt to create an index using the environment variables
|
||||
`UPSTASH_VECTOR_REST_URL`and `UPSTASH_VECTOR_REST_TOKEN`.
|
||||
|
||||
Args:
|
||||
text_key: Key to store the text in metadata.
|
||||
index: UpstashVector Index object.
|
||||
async_index: UpstashVector AsyncIndex object, provide only if async
|
||||
functions are needed
|
||||
index_url: URL of the UpstashVector index.
|
||||
index_token: Token of the UpstashVector index.
|
||||
embedding: Embeddings object or a boolean. When false, no embedding
|
||||
is applied. If true, Upstash embeddings are used. When Upstash
|
||||
embeddings are used, text is sent directly to Upstash and
|
||||
embedding is applied there instead of embedding in Langchain.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_community.vectorstores.upstash import UpstashVectorStore
|
||||
from langchain_community.embeddings.openai import OpenAIEmbeddings
|
||||
|
||||
embeddings = OpenAIEmbeddings()
|
||||
vectorstore = UpstashVectorStore(
|
||||
embedding=embeddings,
|
||||
index_url="...",
|
||||
index_token="..."
|
||||
)
|
||||
|
||||
# With an existing index
|
||||
from upstash_vector import Index
|
||||
|
||||
index = Index(url="...", token="...")
|
||||
vectorstore = UpstashVectorStore(
|
||||
embedding=embeddings,
|
||||
index=index
|
||||
)
|
||||
"""
|
||||
|
||||
try:
|
||||
from upstash_vector import AsyncIndex, Index
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"Could not import upstash_vector python package. "
|
||||
"Please install it with `pip install upstash_vector`."
|
||||
)
|
||||
|
||||
if index:
|
||||
if not isinstance(index, Index):
|
||||
raise ValueError(
|
||||
"Passed index object should be an "
|
||||
"instance of upstash_vector.Index, "
|
||||
f"got {type(index)}"
|
||||
)
|
||||
self._index = index
|
||||
logger.info("Using the index passed as parameter")
|
||||
if async_index:
|
||||
if not isinstance(async_index, AsyncIndex):
|
||||
raise ValueError(
|
||||
"Passed index object should be an "
|
||||
"instance of upstash_vector.AsyncIndex, "
|
||||
f"got {type(async_index)}"
|
||||
)
|
||||
self._async_index = async_index
|
||||
logger.info("Using the async index passed as parameter")
|
||||
|
||||
if index_url and index_token:
|
||||
self._index = Index(url=index_url, token=index_token)
|
||||
self._async_index = AsyncIndex(url=index_url, token=index_token)
|
||||
logger.info("Created index from the index_url and index_token parameters")
|
||||
elif not index and not async_index:
|
||||
self._index = Index.from_env()
|
||||
self._async_index = AsyncIndex.from_env()
|
||||
logger.info("Created index using environment variables")
|
||||
|
||||
self._embeddings = embedding
|
||||
self._text_key = text_key
|
||||
|
||||
@property
|
||||
def embeddings(self) -> Optional[Union[Embeddings, bool]]: # type: ignore
|
||||
"""Access the query embedding object if available."""
|
||||
return self._embeddings
|
||||
|
||||
def _embed_documents(
|
||||
self, texts: Iterable[str]
|
||||
) -> Union[List[List[float]], List[str]]:
|
||||
"""Embed strings using the embeddings object"""
|
||||
if not self._embeddings:
|
||||
raise ValueError(
|
||||
"No embeddings object provided. "
|
||||
"Pass an embeddings object to the constructor."
|
||||
)
|
||||
if isinstance(self._embeddings, Embeddings):
|
||||
return self._embeddings.embed_documents(list(texts))
|
||||
|
||||
# using self._embeddings is True, Upstash embeddings will be used.
|
||||
# returning list of text as List[str]
|
||||
return list(texts)
|
||||
|
||||
def _embed_query(self, text: str) -> Union[List[float], str]:
|
||||
"""Embed query text using the embeddings object."""
|
||||
if not self._embeddings:
|
||||
raise ValueError(
|
||||
"No embeddings object provided. "
|
||||
"Pass an embeddings object to the constructor."
|
||||
)
|
||||
if isinstance(self._embeddings, Embeddings):
|
||||
return self._embeddings.embed_query(text)
|
||||
|
||||
# using self._embeddings is True, Upstash embeddings will be used.
|
||||
# returning query as it is
|
||||
return text
|
||||
|
||||
def add_documents(
|
||||
self,
|
||||
documents: List[Document],
|
||||
ids: Optional[List[str]] = None,
|
||||
batch_size: int = 32,
|
||||
embedding_chunk_size: int = 1000,
|
||||
**kwargs: Any,
|
||||
) -> List[str]:
|
||||
"""
|
||||
Get the embeddings for the documents and add them to the vectorstore.
|
||||
|
||||
Documents are sent to the embeddings object
|
||||
in batches of size `embedding_chunk_size`.
|
||||
The embeddings are then upserted into the vectorstore
|
||||
in batches of size `batch_size`.
|
||||
|
||||
Args:
|
||||
documents: Iterable of Documents to add to the vectorstore.
|
||||
batch_size: Batch size to use when upserting the embeddings.
|
||||
Upstash supports at max 1000 vectors per request.
|
||||
embedding_batch_size: Chunk size to use when embedding the texts.
|
||||
|
||||
Returns:
|
||||
List of ids from adding the texts into the vectorstore.
|
||||
|
||||
"""
|
||||
texts = [doc.page_content for doc in documents]
|
||||
metadatas = [doc.metadata for doc in documents]
|
||||
|
||||
return self.add_texts(
|
||||
texts,
|
||||
metadatas=metadatas,
|
||||
batch_size=batch_size,
|
||||
ids=ids,
|
||||
embedding_chunk_size=embedding_chunk_size,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
async def aadd_documents(
|
||||
self,
|
||||
documents: Iterable[Document],
|
||||
ids: Optional[List[str]] = None,
|
||||
batch_size: int = 32,
|
||||
embedding_chunk_size: int = 1000,
|
||||
**kwargs: Any,
|
||||
) -> List[str]:
|
||||
"""
|
||||
Get the embeddings for the documents and add them to the vectorstore.
|
||||
|
||||
Documents are sent to the embeddings object
|
||||
in batches of size `embedding_chunk_size`.
|
||||
The embeddings are then upserted into the vectorstore
|
||||
in batches of size `batch_size`.
|
||||
|
||||
Args:
|
||||
documents: Iterable of Documents to add to the vectorstore.
|
||||
batch_size: Batch size to use when upserting the embeddings.
|
||||
Upstash supports at max 1000 vectors per request.
|
||||
embedding_batch_size: Chunk size to use when embedding the texts.
|
||||
|
||||
Returns:
|
||||
List of ids from adding the texts into the vectorstore.
|
||||
|
||||
"""
|
||||
texts = [doc.page_content for doc in documents]
|
||||
metadatas = [doc.metadata for doc in documents]
|
||||
|
||||
return await self.aadd_texts(
|
||||
texts,
|
||||
metadatas=metadatas,
|
||||
ids=ids,
|
||||
batch_size=batch_size,
|
||||
embedding_chunk_size=embedding_chunk_size,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
def add_texts(
|
||||
self,
|
||||
texts: Iterable[str],
|
||||
metadatas: Optional[List[dict]] = None,
|
||||
ids: Optional[List[str]] = None,
|
||||
batch_size: int = 32,
|
||||
embedding_chunk_size: int = 1000,
|
||||
**kwargs: Any,
|
||||
) -> List[str]:
|
||||
"""
|
||||
Get the embeddings for the texts and add them to the vectorstore.
|
||||
|
||||
Texts are sent to the embeddings object
|
||||
in batches of size `embedding_chunk_size`.
|
||||
The embeddings are then upserted into the vectorstore
|
||||
in batches of size `batch_size`.
|
||||
|
||||
Args:
|
||||
texts: Iterable of strings to add to the vectorstore.
|
||||
metadatas: Optional list of metadatas associated with the texts.
|
||||
ids: Optional list of ids to associate with the texts.
|
||||
batch_size: Batch size to use when upserting the embeddings.
|
||||
Upstash supports at max 1000 vectors per request.
|
||||
embedding_batch_size: Chunk size to use when embedding the texts.
|
||||
|
||||
Returns:
|
||||
List of ids from adding the texts into the vectorstore.
|
||||
|
||||
"""
|
||||
texts = list(texts)
|
||||
ids = ids or [str(uuid.uuid4()) for _ in texts]
|
||||
|
||||
# Copy metadatas to avoid modifying the original documents
|
||||
if metadatas:
|
||||
metadatas = [m.copy() for m in metadatas]
|
||||
else:
|
||||
metadatas = [{} for _ in texts]
|
||||
|
||||
# Add text to metadata
|
||||
for metadata, text in zip(metadatas, texts):
|
||||
metadata[self._text_key] = text
|
||||
|
||||
for i in range(0, len(texts), embedding_chunk_size):
|
||||
chunk_texts = texts[i : i + embedding_chunk_size]
|
||||
chunk_ids = ids[i : i + embedding_chunk_size]
|
||||
chunk_metadatas = metadatas[i : i + embedding_chunk_size]
|
||||
embeddings = self._embed_documents(chunk_texts)
|
||||
|
||||
for batch in batch_iterate(
|
||||
batch_size, zip(chunk_ids, embeddings, chunk_metadatas)
|
||||
):
|
||||
self._index.upsert(vectors=batch, **kwargs)
|
||||
|
||||
return ids
|
||||
|
||||
async def aadd_texts(
|
||||
self,
|
||||
texts: Iterable[str],
|
||||
metadatas: Optional[List[dict]] = None,
|
||||
ids: Optional[List[str]] = None,
|
||||
batch_size: int = 32,
|
||||
embedding_chunk_size: int = 1000,
|
||||
**kwargs: Any,
|
||||
) -> List[str]:
|
||||
"""
|
||||
Get the embeddings for the texts and add them to the vectorstore.
|
||||
|
||||
Texts are sent to the embeddings object
|
||||
in batches of size `embedding_chunk_size`.
|
||||
The embeddings are then upserted into the vectorstore
|
||||
in batches of size `batch_size`.
|
||||
|
||||
Args:
|
||||
texts: Iterable of strings to add to the vectorstore.
|
||||
metadatas: Optional list of metadatas associated with the texts.
|
||||
ids: Optional list of ids to associate with the texts.
|
||||
batch_size: Batch size to use when upserting the embeddings.
|
||||
Upstash supports at max 1000 vectors per request.
|
||||
embedding_batch_size: Chunk size to use when embedding the texts.
|
||||
|
||||
Returns:
|
||||
List of ids from adding the texts into the vectorstore.
|
||||
|
||||
"""
|
||||
texts = list(texts)
|
||||
ids = ids or [str(uuid.uuid4()) for _ in texts]
|
||||
|
||||
# Copy metadatas to avoid modifying the original documents
|
||||
if metadatas:
|
||||
metadatas = [m.copy() for m in metadatas]
|
||||
else:
|
||||
metadatas = [{} for _ in texts]
|
||||
|
||||
# Add text to metadata
|
||||
for metadata, text in zip(metadatas, texts):
|
||||
metadata[self._text_key] = text
|
||||
|
||||
for i in range(0, len(texts), embedding_chunk_size):
|
||||
chunk_texts = texts[i : i + embedding_chunk_size]
|
||||
chunk_ids = ids[i : i + embedding_chunk_size]
|
||||
chunk_metadatas = metadatas[i : i + embedding_chunk_size]
|
||||
embeddings = self._embed_documents(chunk_texts)
|
||||
|
||||
for batch in batch_iterate(
|
||||
batch_size, zip(chunk_ids, embeddings, chunk_metadatas)
|
||||
):
|
||||
await self._async_index.upsert(vectors=batch, **kwargs)
|
||||
|
||||
return ids
|
||||
|
||||
def similarity_search_with_score(
|
||||
self,
|
||||
query: str,
|
||||
k: int = 4,
|
||||
filter: Optional[str] = None,
|
||||
**kwargs: Any,
|
||||
) -> List[Tuple[Document, float]]:
|
||||
"""Retrieve texts most similar to query and
|
||||
convert the result to `Document` objects.
|
||||
|
||||
Args:
|
||||
query: Text to look up documents similar to.
|
||||
k: Number of Documents to return. Defaults to 4.
|
||||
filter: Optional metadata filter in str format
|
||||
|
||||
Returns:
|
||||
List of Documents most similar to the query and score for each
|
||||
"""
|
||||
return self.similarity_search_by_vector_with_score(
|
||||
self._embed_query(query), k=k, filter=filter, **kwargs
|
||||
)
|
||||
|
||||
async def asimilarity_search_with_score(
|
||||
self,
|
||||
query: str,
|
||||
k: int = 4,
|
||||
filter: Optional[str] = None,
|
||||
**kwargs: Any,
|
||||
) -> List[Tuple[Document, float]]:
|
||||
"""Retrieve texts most similar to query and
|
||||
convert the result to `Document` objects.
|
||||
|
||||
Args:
|
||||
query: Text to look up documents similar to.
|
||||
k: Number of Documents to return. Defaults to 4.
|
||||
filter: Optional metadata filter in str format
|
||||
|
||||
Returns:
|
||||
List of Documents most similar to the query and score for each
|
||||
"""
|
||||
return await self.asimilarity_search_by_vector_with_score(
|
||||
self._embed_query(query), k=k, filter=filter, **kwargs
|
||||
)
|
||||
|
||||
def _process_results(self, results: List) -> List[Tuple[Document, float]]:
|
||||
docs = []
|
||||
for res in results:
|
||||
metadata = res.metadata
|
||||
if metadata and self._text_key in metadata:
|
||||
text = metadata.pop(self._text_key)
|
||||
doc = Document(page_content=text, metadata=metadata)
|
||||
docs.append((doc, res.score))
|
||||
else:
|
||||
logger.warning(
|
||||
f"Found document with no `{self._text_key}` key. Skipping."
|
||||
)
|
||||
return docs
|
||||
|
||||
def similarity_search_by_vector_with_score(
|
||||
self,
|
||||
embedding: Union[List[float], str],
|
||||
k: int = 4,
|
||||
filter: Optional[str] = None,
|
||||
**kwargs: Any,
|
||||
) -> List[Tuple[Document, float]]:
|
||||
"""Return texts whose embedding is closest to the given embedding"""
|
||||
|
||||
filter = filter or ""
|
||||
|
||||
if isinstance(embedding, str):
|
||||
results = self._index.query(
|
||||
data=embedding, top_k=k, include_metadata=True, filter=filter, **kwargs
|
||||
)
|
||||
else:
|
||||
results = self._index.query(
|
||||
vector=embedding,
|
||||
top_k=k,
|
||||
include_metadata=True,
|
||||
filter=filter,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
return self._process_results(results)
|
||||
|
||||
async def asimilarity_search_by_vector_with_score(
|
||||
self,
|
||||
embedding: Union[List[float], str],
|
||||
k: int = 4,
|
||||
filter: Optional[str] = None,
|
||||
**kwargs: Any,
|
||||
) -> List[Tuple[Document, float]]:
|
||||
"""Return texts whose embedding is closest to the given embedding"""
|
||||
|
||||
filter = filter or ""
|
||||
|
||||
if isinstance(embedding, str):
|
||||
results = await self._async_index.query(
|
||||
data=embedding, top_k=k, include_metadata=True, filter=filter, **kwargs
|
||||
)
|
||||
else:
|
||||
results = await self._async_index.query(
|
||||
vector=embedding,
|
||||
top_k=k,
|
||||
include_metadata=True,
|
||||
filter=filter,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
return self._process_results(results)
|
||||
|
||||
def similarity_search(
|
||||
self,
|
||||
query: str,
|
||||
k: int = 4,
|
||||
filter: Optional[str] = None,
|
||||
**kwargs: Any,
|
||||
) -> List[Document]:
|
||||
"""Return documents most similar to query.
|
||||
|
||||
Args:
|
||||
query: Text to look up documents similar to.
|
||||
k: Number of Documents to return. Defaults to 4.
|
||||
filter: Optional metadata filter in str format
|
||||
|
||||
Returns:
|
||||
List of Documents most similar to the query and score for each
|
||||
"""
|
||||
docs_and_scores = self.similarity_search_with_score(
|
||||
query, k=k, filter=filter, **kwargs
|
||||
)
|
||||
return [doc for doc, _ in docs_and_scores]
|
||||
|
||||
async def asimilarity_search(
|
||||
self,
|
||||
query: str,
|
||||
k: int = 4,
|
||||
filter: Optional[str] = None,
|
||||
**kwargs: Any,
|
||||
) -> List[Document]:
|
||||
"""Return documents most similar to query.
|
||||
|
||||
Args:
|
||||
query: Text to look up documents similar to.
|
||||
k: Number of Documents to return. Defaults to 4.
|
||||
filter: Optional metadata filter in str format
|
||||
|
||||
Returns:
|
||||
List of Documents most similar to the query
|
||||
"""
|
||||
docs_and_scores = await self.asimilarity_search_with_score(
|
||||
query, k=k, filter=filter, **kwargs
|
||||
)
|
||||
return [doc for doc, _ in docs_and_scores]
|
||||
|
||||
def similarity_search_by_vector(
|
||||
self,
|
||||
embedding: Union[List[float], str],
|
||||
k: int = 4,
|
||||
filter: Optional[str] = None,
|
||||
**kwargs: Any,
|
||||
) -> List[Document]:
|
||||
"""Return documents closest to the given embedding.
|
||||
|
||||
Args:
|
||||
embedding: Embedding to look up documents similar to.
|
||||
k: Number of Documents to return. Defaults to 4.
|
||||
filter: Optional metadata filter in str format
|
||||
|
||||
Returns:
|
||||
List of Documents most similar to the query
|
||||
"""
|
||||
docs_and_scores = self.similarity_search_by_vector_with_score(
|
||||
embedding, k=k, filter=filter, **kwargs
|
||||
)
|
||||
return [doc for doc, _ in docs_and_scores]
|
||||
|
||||
async def asimilarity_search_by_vector(
|
||||
self,
|
||||
embedding: Union[List[float], str],
|
||||
k: int = 4,
|
||||
filter: Optional[str] = None,
|
||||
**kwargs: Any,
|
||||
) -> List[Document]:
|
||||
"""Return documents closest to the given embedding.
|
||||
|
||||
Args:
|
||||
embedding: Embedding to look up documents similar to.
|
||||
k: Number of Documents to return. Defaults to 4.
|
||||
filter: Optional metadata filter in str format
|
||||
|
||||
Returns:
|
||||
List of Documents most similar to the query
|
||||
"""
|
||||
docs_and_scores = await self.asimilarity_search_by_vector_with_score(
|
||||
embedding, k=k, filter=filter, **kwargs
|
||||
)
|
||||
return [doc for doc, _ in docs_and_scores]
|
||||
|
||||
def _similarity_search_with_relevance_scores(
|
||||
self,
|
||||
query: str,
|
||||
k: int = 4,
|
||||
filter: Optional[str] = None,
|
||||
**kwargs: Any,
|
||||
) -> List[Tuple[Document, float]]:
|
||||
"""
|
||||
Since Upstash always returns relevance scores, default implementation is used.
|
||||
"""
|
||||
return self.similarity_search_with_score(query, k=k, filter=filter, **kwargs)
|
||||
|
||||
async def _asimilarity_search_with_relevance_scores(
|
||||
self,
|
||||
query: str,
|
||||
k: int = 4,
|
||||
filter: Optional[str] = None,
|
||||
**kwargs: Any,
|
||||
) -> List[Tuple[Document, float]]:
|
||||
"""
|
||||
Since Upstash always returns relevance scores, default implementation is used.
|
||||
"""
|
||||
return await self.asimilarity_search_with_score(
|
||||
query, k=k, filter=filter, **kwargs
|
||||
)
|
||||
|
||||
def max_marginal_relevance_search_by_vector(
|
||||
self,
|
||||
embedding: Union[List[float], str],
|
||||
k: int = 4,
|
||||
fetch_k: int = 20,
|
||||
lambda_mult: float = 0.5,
|
||||
filter: Optional[str] = None,
|
||||
**kwargs: Any,
|
||||
) -> List[Document]:
|
||||
"""Return docs selected using the maximal marginal relevance.
|
||||
|
||||
Maximal marginal relevance optimizes for similarity to query AND diversity
|
||||
among selected documents.
|
||||
|
||||
Args:
|
||||
embedding: Embedding to look up documents similar to.
|
||||
k: Number of Documents to return. Defaults to 4.
|
||||
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
|
||||
lambda_mult: Number between 0 and 1 that determines the degree
|
||||
of diversity among the results with 0 corresponding
|
||||
to maximum diversity and 1 to minimum diversity.
|
||||
Defaults to 0.5.
|
||||
filter: Optional metadata filter in str format
|
||||
|
||||
Returns:
|
||||
List of Documents selected by maximal marginal relevance.
|
||||
"""
|
||||
assert isinstance(self.embeddings, Embeddings)
|
||||
if isinstance(embedding, str):
|
||||
results = self._index.query(
|
||||
data=embedding,
|
||||
top_k=fetch_k,
|
||||
include_vectors=True,
|
||||
include_metadata=True,
|
||||
filter=filter or "",
|
||||
**kwargs,
|
||||
)
|
||||
else:
|
||||
results = self._index.query(
|
||||
vector=embedding,
|
||||
top_k=fetch_k,
|
||||
include_vectors=True,
|
||||
include_metadata=True,
|
||||
filter=filter or "",
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
mmr_selected = maximal_marginal_relevance(
|
||||
np.array([embedding], dtype=np.float32),
|
||||
[item.vector for item in results],
|
||||
k=k,
|
||||
lambda_mult=lambda_mult,
|
||||
)
|
||||
selected = [results[i].metadata for i in mmr_selected]
|
||||
return [
|
||||
Document(page_content=metadata.pop((self._text_key)), metadata=metadata) # type: ignore
|
||||
for metadata in selected
|
||||
]
|
||||
|
||||
async def amax_marginal_relevance_search_by_vector(
|
||||
self,
|
||||
embedding: Union[List[float], str],
|
||||
k: int = 4,
|
||||
fetch_k: int = 20,
|
||||
lambda_mult: float = 0.5,
|
||||
filter: Optional[str] = None,
|
||||
**kwargs: Any,
|
||||
) -> List[Document]:
|
||||
"""Return docs selected using the maximal marginal relevance.
|
||||
|
||||
Maximal marginal relevance optimizes for similarity to query AND diversity
|
||||
among selected documents.
|
||||
|
||||
Args:
|
||||
embedding: Embedding to look up documents similar to.
|
||||
k: Number of Documents to return. Defaults to 4.
|
||||
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
|
||||
lambda_mult: Number between 0 and 1 that determines the degree
|
||||
of diversity among the results with 0 corresponding
|
||||
to maximum diversity and 1 to minimum diversity.
|
||||
Defaults to 0.5.
|
||||
filter: Optional metadata filter in str format
|
||||
|
||||
Returns:
|
||||
List of Documents selected by maximal marginal relevance.
|
||||
"""
|
||||
assert isinstance(self.embeddings, Embeddings)
|
||||
if isinstance(embedding, str):
|
||||
results = await self._async_index.query(
|
||||
data=embedding,
|
||||
top_k=fetch_k,
|
||||
include_vectors=True,
|
||||
include_metadata=True,
|
||||
filter=filter or "",
|
||||
**kwargs,
|
||||
)
|
||||
else:
|
||||
results = await self._async_index.query(
|
||||
vector=embedding,
|
||||
top_k=fetch_k,
|
||||
include_vectors=True,
|
||||
include_metadata=True,
|
||||
filter=filter or "",
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
mmr_selected = maximal_marginal_relevance(
|
||||
np.array([embedding], dtype=np.float32),
|
||||
[item.vector for item in results],
|
||||
k=k,
|
||||
lambda_mult=lambda_mult,
|
||||
)
|
||||
selected = [results[i].metadata for i in mmr_selected]
|
||||
return [
|
||||
Document(page_content=metadata.pop((self._text_key)), metadata=metadata) # type: ignore
|
||||
for metadata in selected
|
||||
]
|
||||
|
||||
def max_marginal_relevance_search(
|
||||
self,
|
||||
query: str,
|
||||
k: int = 4,
|
||||
fetch_k: int = 20,
|
||||
lambda_mult: float = 0.5,
|
||||
filter: Optional[str] = None,
|
||||
**kwargs: Any,
|
||||
) -> List[Document]:
|
||||
"""Return docs selected using the maximal marginal relevance.
|
||||
|
||||
Maximal marginal relevance optimizes for similarity to query AND diversity
|
||||
among selected documents.
|
||||
|
||||
Args:
|
||||
query: Text to look up documents similar to.
|
||||
k: Number of Documents to return. Defaults to 4.
|
||||
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
|
||||
lambda_mult: Number between 0 and 1 that determines the degree
|
||||
of diversity among the results with 0 corresponding
|
||||
to maximum diversity and 1 to minimum diversity.
|
||||
Defaults to 0.5.
|
||||
filter: Optional metadata filter in str format
|
||||
|
||||
Returns:
|
||||
List of Documents selected by maximal marginal relevance.
|
||||
"""
|
||||
embedding = self._embed_query(query)
|
||||
return self.max_marginal_relevance_search_by_vector(
|
||||
embedding=embedding,
|
||||
k=k,
|
||||
fetch_k=fetch_k,
|
||||
lambda_mult=lambda_mult,
|
||||
filter=filter,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
async def amax_marginal_relevance_search(
|
||||
self,
|
||||
query: str,
|
||||
k: int = 4,
|
||||
fetch_k: int = 20,
|
||||
lambda_mult: float = 0.5,
|
||||
filter: Optional[str] = None,
|
||||
**kwargs: Any,
|
||||
) -> List[Document]:
|
||||
"""Return docs selected using the maximal marginal relevance.
|
||||
|
||||
Maximal marginal relevance optimizes for similarity to query AND diversity
|
||||
among selected documents.
|
||||
|
||||
Args:
|
||||
query: Text to look up documents similar to.
|
||||
k: Number of Documents to return. Defaults to 4.
|
||||
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
|
||||
lambda_mult: Number between 0 and 1 that determines the degree
|
||||
of diversity among the results with 0 corresponding
|
||||
to maximum diversity and 1 to minimum diversity.
|
||||
Defaults to 0.5.
|
||||
filter: Optional metadata filter in str format
|
||||
|
||||
Returns:
|
||||
List of Documents selected by maximal marginal relevance.
|
||||
"""
|
||||
embedding = self._embed_query(query)
|
||||
return await self.amax_marginal_relevance_search_by_vector(
|
||||
embedding=embedding,
|
||||
k=k,
|
||||
fetch_k=fetch_k,
|
||||
lambda_mult=lambda_mult,
|
||||
filter=filter,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def from_texts(
|
||||
cls,
|
||||
texts: List[str],
|
||||
embedding: Embeddings,
|
||||
metadatas: Optional[List[dict]] = None,
|
||||
ids: Optional[List[str]] = None,
|
||||
embedding_chunk_size: int = 1000,
|
||||
batch_size: int = 32,
|
||||
text_key: str = "text",
|
||||
index: Optional[Index] = None,
|
||||
async_index: Optional[AsyncIndex] = None,
|
||||
index_url: Optional[str] = None,
|
||||
index_token: Optional[str] = None,
|
||||
**kwargs: Any,
|
||||
) -> UpstashVectorStore:
|
||||
"""Create a new UpstashVectorStore from a list of texts.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
from langchain_community.vectorstores.upstash import UpstashVectorStore
|
||||
from langchain_community.embeddings import OpenAIEmbeddings
|
||||
|
||||
embeddings = OpenAIEmbeddings()
|
||||
vector_store = UpstashVectorStore.from_texts(
|
||||
texts,
|
||||
embeddings,
|
||||
)
|
||||
"""
|
||||
vector_store = cls(
|
||||
embedding=embedding,
|
||||
text_key=text_key,
|
||||
index=index,
|
||||
async_index=async_index,
|
||||
index_url=index_url,
|
||||
index_token=index_token,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
vector_store.add_texts(
|
||||
texts,
|
||||
metadatas=metadatas,
|
||||
ids=ids,
|
||||
batch_size=batch_size,
|
||||
embedding_chunk_size=embedding_chunk_size,
|
||||
)
|
||||
return vector_store
|
||||
|
||||
@classmethod
|
||||
async def afrom_texts(
|
||||
cls,
|
||||
texts: List[str],
|
||||
embedding: Embeddings,
|
||||
metadatas: Optional[List[dict]] = None,
|
||||
ids: Optional[List[str]] = None,
|
||||
embedding_chunk_size: int = 1000,
|
||||
batch_size: int = 32,
|
||||
text_key: str = "text",
|
||||
index: Optional[Index] = None,
|
||||
async_index: Optional[AsyncIndex] = None,
|
||||
index_url: Optional[str] = None,
|
||||
index_token: Optional[str] = None,
|
||||
**kwargs: Any,
|
||||
) -> UpstashVectorStore:
|
||||
"""Create a new UpstashVectorStore from a list of texts.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
from langchain_community.vectorstores.upstash import UpstashVectorStore
|
||||
from langchain_community.embeddings import OpenAIEmbeddings
|
||||
|
||||
embeddings = OpenAIEmbeddings()
|
||||
vector_store = UpstashVectorStore.from_texts(
|
||||
texts,
|
||||
embeddings,
|
||||
)
|
||||
"""
|
||||
vector_store = cls(
|
||||
embedding=embedding,
|
||||
text_key=text_key,
|
||||
index=index,
|
||||
async_index=async_index,
|
||||
index_url=index_url,
|
||||
index_token=index_token,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
await vector_store.aadd_texts(
|
||||
texts,
|
||||
metadatas=metadatas,
|
||||
ids=ids,
|
||||
batch_size=batch_size,
|
||||
embedding_chunk_size=embedding_chunk_size,
|
||||
)
|
||||
return vector_store
|
||||
|
||||
def delete(
|
||||
self,
|
||||
ids: Optional[List[str]] = None,
|
||||
delete_all: Optional[bool] = None,
|
||||
batch_size: Optional[int] = 1000,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
"""Delete by vector IDs
|
||||
|
||||
Args:
|
||||
ids: List of ids to delete.
|
||||
delete_all: Delete all vectors in the index.
|
||||
batch_size: Batch size to use when deleting the embeddings.
|
||||
Upstash supports at max 1000 deletions per request.
|
||||
"""
|
||||
|
||||
if delete_all:
|
||||
self._index.reset()
|
||||
elif ids is not None:
|
||||
for batch in batch_iterate(batch_size, ids):
|
||||
self._index.delete(ids=batch)
|
||||
else:
|
||||
raise ValueError("Either ids or delete_all should be provided")
|
||||
|
||||
return None
|
||||
|
||||
async def adelete(
|
||||
self,
|
||||
ids: Optional[List[str]] = None,
|
||||
delete_all: Optional[bool] = None,
|
||||
batch_size: Optional[int] = 1000,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
"""Delete by vector IDs
|
||||
|
||||
Args:
|
||||
ids: List of ids to delete.
|
||||
delete_all: Delete all vectors in the index.
|
||||
batch_size: Batch size to use when deleting the embeddings.
|
||||
Upstash supports at max 1000 deletions per request.
|
||||
"""
|
||||
|
||||
if delete_all:
|
||||
await self._async_index.reset()
|
||||
elif ids is not None:
|
||||
for batch in batch_iterate(batch_size, ids):
|
||||
await self._async_index.delete(ids=batch)
|
||||
else:
|
||||
raise ValueError("Either ids or delete_all should be provided")
|
||||
|
||||
return None
|
||||
|
||||
def info(self) -> InfoResult:
|
||||
"""Get statistics about the index.
|
||||
|
||||
Returns:
|
||||
- total number of vectors
|
||||
- total number of vectors waiting to be indexed
|
||||
- total size of the index on disk in bytes
|
||||
- dimension count for the index
|
||||
- similarity function selected for the index
|
||||
"""
|
||||
return self._index.info()
|
||||
|
||||
async def ainfo(self) -> InfoResult:
|
||||
"""Get statistics about the index.
|
||||
|
||||
Returns:
|
||||
- total number of vectors
|
||||
- total number of vectors waiting to be indexed
|
||||
- total size of the index on disk in bytes
|
||||
- dimension count for the index
|
||||
- similarity function selected for the index
|
||||
"""
|
||||
return await self._async_index.info()
|
||||
@ -56,3 +56,12 @@ MONGODB_ATLAS_URI=your_mongodb_atlas_connection_string
|
||||
KINETICA_URL = _db_connection_url_here
|
||||
KINETICA_USER = _login_user_here
|
||||
KINETICA_PASSWD = _password_here
|
||||
|
||||
|
||||
# Upstash Vector
|
||||
# Create two Upstash Vector instances. First one should have dimensionality of 10.
|
||||
# Second one should be created with embedding model BAA/bge-small-en-V1.5
|
||||
UPSTASH_VECTOR_URL=your_upstash_vector_url
|
||||
UPSTASH_VECTOR_TOKEN=your_upstash_vector_token
|
||||
UPSTASH_VECTOR_URL_EMBEDDING=your_upstash_vector_embedding_url
|
||||
UPSTASH_VECTOR_TOKEN_EMBEDDING=your_upstash_vector_embedding_token
|
||||
|
||||
@ -0,0 +1,433 @@
|
||||
interactions:
|
||||
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|
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body: ''
|
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headers:
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|
||||
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|
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|
||||
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|
||||
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|
||||
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response:
|
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content: "{\n \"result\" : {\n \"vectorCount\" : 3,\n \"pendingVectorCount\"
|
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: 3,\n \"indexSize\" : 295,\n \"dimension\" : 10,\n \"similarityFunction\"
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|
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method: POST
|
||||
uri: https://fond-mutt-96644-eu1-vector.upstash.io/query
|
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|
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content: "{\n \"result\" : [ {\n \"id\" : \"deb53135-eb87-4d69-8e59-7243bce077dc\",\n
|
||||
\ \"score\" : 1.0,\n \"metadata\" : {\"page\":\"0\",\"text\":\"foo\"}\n
|
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version: 1
|
||||
@ -0,0 +1,584 @@
|
||||
"""Test Upstash Vector functionality."""
|
||||
|
||||
import os
|
||||
from time import sleep
|
||||
from typing import List, Tuple
|
||||
|
||||
# to fix the following error in test with vcr and asyncio
|
||||
#
|
||||
# RuntimeError: asyncio.run() cannot be called from a running event loop
|
||||
import pytest
|
||||
from langchain_core.documents import Document
|
||||
|
||||
from langchain_community.vectorstores.upstash import UpstashVectorStore
|
||||
from tests.integration_tests.vectorstores.fake_embeddings import (
|
||||
FakeEmbeddings,
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
def vcr_cassette_dir() -> str:
|
||||
# save under cassettes/test_upstash/{item}.yaml
|
||||
return os.path.join("cassettes", "test_upstash")
|
||||
|
||||
|
||||
@pytest.fixture(scope="function", autouse=True)
|
||||
def fixture() -> None:
|
||||
store = UpstashVectorStore()
|
||||
embedding_store = UpstashVectorStore(
|
||||
index_url=os.environ["UPSTASH_VECTOR_URL_EMBEDDING"],
|
||||
index_token=os.environ["UPSTASH_VECTOR_TOKEN_EMBEDDING"],
|
||||
)
|
||||
|
||||
store.delete(delete_all=True)
|
||||
embedding_store.delete(delete_all=True)
|
||||
|
||||
wait_for_indexing(store)
|
||||
wait_for_indexing(embedding_store)
|
||||
|
||||
|
||||
def wait_for_indexing(store: UpstashVectorStore) -> None:
|
||||
while store.info().pending_vector_count != 0:
|
||||
# Wait for indexing to complete
|
||||
sleep(0.5)
|
||||
|
||||
|
||||
def check_response_with_score(
|
||||
result: List[Tuple[Document, float]],
|
||||
expected: List[Tuple[Document, float]],
|
||||
) -> None:
|
||||
"""
|
||||
check the result of a search with scores with an expected value
|
||||
|
||||
scores in result will be rounded by two digits
|
||||
"""
|
||||
result = list(map(lambda result: (result[0], round(result[1], 2)), result))
|
||||
|
||||
assert result == expected
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_upstash_simple_insert() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
store = UpstashVectorStore.from_texts(texts=texts, embedding=FakeEmbeddings())
|
||||
wait_for_indexing(store)
|
||||
output = store.similarity_search("foo", k=1)
|
||||
assert output == [Document(page_content="foo")]
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
@pytest.mark.asyncio
|
||||
async def test_upstash_simple_insert_async() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
store = UpstashVectorStore.from_texts(texts=texts, embedding=FakeEmbeddings())
|
||||
wait_for_indexing(store)
|
||||
output = await store.asimilarity_search("foo", k=1)
|
||||
assert output == [Document(page_content="foo")]
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_upstash_with_metadatas() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
store = UpstashVectorStore.from_texts(
|
||||
texts=texts,
|
||||
embedding=FakeEmbeddings(),
|
||||
metadatas=metadatas,
|
||||
)
|
||||
wait_for_indexing(store)
|
||||
output = store.similarity_search("foo", k=1)
|
||||
assert output == [Document(page_content="foo", metadata={"page": "0"})]
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
@pytest.mark.asyncio
|
||||
async def test_upstash_with_metadatas_async() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
store = UpstashVectorStore.from_texts(
|
||||
texts=texts,
|
||||
embedding=FakeEmbeddings(),
|
||||
metadatas=metadatas,
|
||||
)
|
||||
wait_for_indexing(store)
|
||||
output = await store.asimilarity_search("foo", k=1)
|
||||
assert output == [Document(page_content="foo", metadata={"page": "0"})]
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_upstash_with_metadatas_with_scores() -> None:
|
||||
"""Test end to end construction and scored search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
store = UpstashVectorStore.from_texts(
|
||||
texts=texts,
|
||||
embedding=FakeEmbeddings(),
|
||||
metadatas=metadatas,
|
||||
)
|
||||
wait_for_indexing(store)
|
||||
output = store.similarity_search_with_score("foo", k=1)
|
||||
assert output == [(Document(page_content="foo", metadata={"page": "0"}), 1.0)]
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
@pytest.mark.asyncio
|
||||
async def test_upstash_with_metadatas_with_scores_async() -> None:
|
||||
"""Test end to end construction and scored search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
store = UpstashVectorStore.from_texts(
|
||||
texts=texts,
|
||||
embedding=FakeEmbeddings(),
|
||||
metadatas=metadatas,
|
||||
)
|
||||
wait_for_indexing(store)
|
||||
output = await store.asimilarity_search_with_score("foo", k=1)
|
||||
assert output == [(Document(page_content="foo", metadata={"page": "0"}), 1.0)]
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_upstash_with_metadatas_with_scores_using_vector() -> None:
|
||||
"""Test end to end construction and scored search, using embedding vector."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
embeddings = FakeEmbeddings()
|
||||
|
||||
store = UpstashVectorStore.from_texts(
|
||||
texts=texts,
|
||||
embedding=embeddings,
|
||||
metadatas=metadatas,
|
||||
)
|
||||
wait_for_indexing(store)
|
||||
embedded_query = embeddings.embed_query("foo")
|
||||
output = store.similarity_search_by_vector_with_score(embedding=embedded_query, k=1)
|
||||
assert output == [(Document(page_content="foo", metadata={"page": "0"}), 1.0)]
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
@pytest.mark.asyncio
|
||||
async def test_upstash_with_metadatas_with_scores_using_vector_async() -> None:
|
||||
"""Test end to end construction and scored search, using embedding vector."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
embeddings = FakeEmbeddings()
|
||||
|
||||
store = UpstashVectorStore.from_texts(
|
||||
texts=texts,
|
||||
embedding=embeddings,
|
||||
metadatas=metadatas,
|
||||
)
|
||||
wait_for_indexing(store)
|
||||
embedded_query = embeddings.embed_query("foo")
|
||||
output = await store.asimilarity_search_by_vector_with_score(
|
||||
embedding=embedded_query, k=1
|
||||
)
|
||||
assert output == [(Document(page_content="foo", metadata={"page": "0"}), 1.0)]
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_upstash_mmr() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
store = UpstashVectorStore.from_texts(texts=texts, embedding=FakeEmbeddings())
|
||||
wait_for_indexing(store)
|
||||
output = store.max_marginal_relevance_search("foo", k=1)
|
||||
assert output == [Document(page_content="foo")]
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
@pytest.mark.asyncio
|
||||
async def test_upstash_mmr_async() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
store = UpstashVectorStore.from_texts(texts=texts, embedding=FakeEmbeddings())
|
||||
wait_for_indexing(store)
|
||||
output = await store.amax_marginal_relevance_search("foo", k=1)
|
||||
assert output == [Document(page_content="foo")]
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_upstash_mmr_by_vector() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
embeddings = FakeEmbeddings()
|
||||
store = UpstashVectorStore.from_texts(texts=texts, embedding=embeddings)
|
||||
wait_for_indexing(store)
|
||||
embedded_query = embeddings.embed_query("foo")
|
||||
output = store.max_marginal_relevance_search_by_vector(embedded_query, k=1)
|
||||
assert output == [Document(page_content="foo")]
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
@pytest.mark.asyncio
|
||||
async def test_upstash_mmr_by_vector_async() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
embeddings = FakeEmbeddings()
|
||||
store = UpstashVectorStore.from_texts(texts=texts, embedding=embeddings)
|
||||
wait_for_indexing(store)
|
||||
embedded_query = embeddings.embed_query("foo")
|
||||
output = await store.amax_marginal_relevance_search_by_vector(embedded_query, k=1)
|
||||
assert output == [Document(page_content="foo")]
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_init_from_index() -> None:
|
||||
from upstash_vector import Index
|
||||
|
||||
index = Index.from_env()
|
||||
|
||||
store = UpstashVectorStore(index=index)
|
||||
|
||||
assert store.info() is not None
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
@pytest.mark.asyncio
|
||||
async def test_init_from_async_index() -> None:
|
||||
from upstash_vector import AsyncIndex
|
||||
|
||||
index = AsyncIndex.from_env()
|
||||
|
||||
store = UpstashVectorStore(async_index=index)
|
||||
|
||||
assert await store.ainfo() is not None
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_init_from_credentials() -> None:
|
||||
store = UpstashVectorStore(
|
||||
index_url=os.environ["UPSTASH_VECTOR_REST_URL"],
|
||||
index_token=os.environ["UPSTASH_VECTOR_REST_TOKEN"],
|
||||
)
|
||||
|
||||
assert store.info() is not None
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
@pytest.mark.asyncio
|
||||
async def test_init_from_credentials_async() -> None:
|
||||
store = UpstashVectorStore(
|
||||
index_url=os.environ["UPSTASH_VECTOR_REST_URL"],
|
||||
index_token=os.environ["UPSTASH_VECTOR_REST_TOKEN"],
|
||||
)
|
||||
|
||||
assert await store.ainfo() is not None
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_upstash_add_documents_no_metadata() -> None:
|
||||
store = UpstashVectorStore(embedding=FakeEmbeddings())
|
||||
store.add_documents([Document(page_content="foo")])
|
||||
wait_for_indexing(store)
|
||||
|
||||
search = store.similarity_search("foo")
|
||||
assert search == [Document(page_content="foo")]
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_upstash_add_documents_mixed_metadata() -> None:
|
||||
store = UpstashVectorStore(embedding=FakeEmbeddings())
|
||||
docs = [
|
||||
Document(page_content="foo"),
|
||||
Document(page_content="bar", metadata={"baz": 1}),
|
||||
]
|
||||
ids = ["0", "1"]
|
||||
actual_ids = store.add_documents(docs, ids=ids)
|
||||
wait_for_indexing(store)
|
||||
assert actual_ids == ids
|
||||
search = store.similarity_search("foo bar")
|
||||
assert sorted(search, key=lambda d: d.page_content) == sorted(
|
||||
docs, key=lambda d: d.page_content
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_upstash_similarity_search_with_metadata() -> None:
|
||||
store = UpstashVectorStore(embedding=FakeEmbeddings())
|
||||
docs = [
|
||||
Document(page_content="foo"),
|
||||
Document(page_content="bar", metadata={"waldo": 1}),
|
||||
Document(page_content="baz", metadata={"waldo": 1}),
|
||||
Document(page_content="fred", metadata={"waldo": 2}),
|
||||
]
|
||||
ids = ["0", "1", "3", "4"]
|
||||
store.add_documents(docs, ids=ids)
|
||||
wait_for_indexing(store)
|
||||
|
||||
result = store.similarity_search(query="foo", k=5, filter="waldo = 1")
|
||||
|
||||
assert result == [
|
||||
Document(page_content="bar", metadata={"waldo": 1}),
|
||||
Document(page_content="baz", metadata={"waldo": 1}),
|
||||
]
|
||||
|
||||
search_result = store.similarity_search_with_score(
|
||||
query="foo", k=5, filter="waldo = 2"
|
||||
)
|
||||
|
||||
check_response_with_score(
|
||||
search_result, [(Document(page_content="fred", metadata={"waldo": 2}), 0.85)]
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
@pytest.mark.asyncio
|
||||
async def test_upstash_similarity_search_with_metadata_async() -> None:
|
||||
store = UpstashVectorStore(embedding=FakeEmbeddings())
|
||||
docs = [
|
||||
Document(page_content="foo"),
|
||||
Document(page_content="bar", metadata={"waldo": 1}),
|
||||
Document(page_content="baz", metadata={"waldo": 1}),
|
||||
Document(page_content="fred", metadata={"waldo": 2}),
|
||||
]
|
||||
ids = ["0", "1", "3", "4"]
|
||||
store.add_documents(docs, ids=ids)
|
||||
wait_for_indexing(store)
|
||||
|
||||
result = await store.asimilarity_search(query="foo", k=5, filter="waldo = 1")
|
||||
|
||||
assert result == [
|
||||
Document(page_content="bar", metadata={"waldo": 1}),
|
||||
Document(page_content="baz", metadata={"waldo": 1}),
|
||||
]
|
||||
|
||||
search_result = await store.asimilarity_search_with_score(
|
||||
query="foo", k=5, filter="waldo = 2"
|
||||
)
|
||||
|
||||
check_response_with_score(
|
||||
search_result, [(Document(page_content="fred", metadata={"waldo": 2}), 0.85)]
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_upstash_similarity_search_by_vector_with_metadata() -> None:
|
||||
store = UpstashVectorStore(embedding=FakeEmbeddings())
|
||||
docs = [
|
||||
Document(page_content="foo"),
|
||||
Document(page_content="bar", metadata={"waldo": 1}),
|
||||
Document(page_content="baz", metadata={"waldo": 1}),
|
||||
Document(page_content="fred", metadata={"waldo": 2}),
|
||||
]
|
||||
ids = ["0", "1", "3", "4"]
|
||||
store.add_documents(docs, ids=ids)
|
||||
wait_for_indexing(store)
|
||||
|
||||
result = store.similarity_search_by_vector_with_score(
|
||||
embedding=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
|
||||
k=5,
|
||||
filter="waldo = 1",
|
||||
)
|
||||
|
||||
check_response_with_score(
|
||||
result,
|
||||
[
|
||||
(Document(page_content="bar", metadata={"waldo": 1}), 1.0),
|
||||
(Document(page_content="baz", metadata={"waldo": 1}), 0.98),
|
||||
],
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
@pytest.mark.asyncio
|
||||
async def test_upstash_similarity_search_by_vector_with_metadata_async() -> None:
|
||||
store = UpstashVectorStore(embedding=FakeEmbeddings())
|
||||
docs = [
|
||||
Document(page_content="foo"),
|
||||
Document(page_content="bar", metadata={"waldo": 1}),
|
||||
Document(page_content="baz", metadata={"waldo": 1}),
|
||||
Document(page_content="fred", metadata={"waldo": 2}),
|
||||
]
|
||||
ids = ["0", "1", "3", "4"]
|
||||
store.add_documents(docs, ids=ids)
|
||||
wait_for_indexing(store)
|
||||
|
||||
result = await store.asimilarity_search_by_vector_with_score(
|
||||
embedding=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
|
||||
k=5,
|
||||
filter="waldo = 1",
|
||||
)
|
||||
|
||||
check_response_with_score(
|
||||
result,
|
||||
[
|
||||
(Document(page_content="bar", metadata={"waldo": 1}), 1.0),
|
||||
(Document(page_content="baz", metadata={"waldo": 1}), 0.98),
|
||||
],
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_upstash_max_marginal_relevance_search_with_metadata() -> None:
|
||||
store = UpstashVectorStore(embedding=FakeEmbeddings())
|
||||
docs = [
|
||||
Document(page_content="foo"),
|
||||
Document(page_content="bar", metadata={"waldo": 1}),
|
||||
Document(page_content="baz", metadata={"waldo": 1}),
|
||||
Document(page_content="fred", metadata={"waldo": 2}),
|
||||
]
|
||||
ids = ["0", "1", "3", "4"]
|
||||
store.add_documents(docs, ids=ids)
|
||||
wait_for_indexing(store)
|
||||
|
||||
result = store.max_marginal_relevance_search(query="foo", k=3, filter="waldo = 1")
|
||||
|
||||
assert result == [
|
||||
Document(page_content="bar", metadata={"waldo": 1}),
|
||||
Document(page_content="baz", metadata={"waldo": 1}),
|
||||
]
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
@pytest.mark.asyncio
|
||||
async def test_upstash_max_marginal_relevance_search_with_metadata_async() -> None:
|
||||
store = UpstashVectorStore(embedding=FakeEmbeddings())
|
||||
docs = [
|
||||
Document(page_content="foo"),
|
||||
Document(page_content="bar", metadata={"waldo": 1}),
|
||||
Document(page_content="baz", metadata={"waldo": 1}),
|
||||
Document(page_content="fred", metadata={"waldo": 2}),
|
||||
]
|
||||
ids = ["0", "1", "3", "4"]
|
||||
store.add_documents(docs, ids=ids)
|
||||
wait_for_indexing(store)
|
||||
|
||||
result = await store.amax_marginal_relevance_search(
|
||||
query="foo", k=3, filter="waldo = 1"
|
||||
)
|
||||
|
||||
assert result == [
|
||||
Document(page_content="bar", metadata={"waldo": 1}),
|
||||
Document(page_content="baz", metadata={"waldo": 1}),
|
||||
]
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_embeddings_configurations() -> None:
|
||||
"""
|
||||
test the behavior of the vector store for different `embeddings` parameter
|
||||
values
|
||||
"""
|
||||
# case 1: use FakeEmbeddings, a subclass of Embeddings
|
||||
store = UpstashVectorStore(embedding=FakeEmbeddings())
|
||||
query_embedding = store._embed_query("query")
|
||||
assert query_embedding == [1, 1, 1, 1, 1, 1, 1, 1, 1, 0]
|
||||
|
||||
document_embedding = store._embed_documents(["doc1", "doc2"])
|
||||
assert document_embedding == [
|
||||
[1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
|
||||
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
|
||||
]
|
||||
|
||||
# case 2: pass False as embedding
|
||||
store = UpstashVectorStore(embedding=False)
|
||||
with pytest.raises(ValueError):
|
||||
query_embedding = store._embed_query("query")
|
||||
|
||||
with pytest.raises(ValueError):
|
||||
document_embedding = store._embed_documents(["doc1", "doc2"])
|
||||
|
||||
# case 3: pass True as embedding
|
||||
# Upstash embeddings will be used
|
||||
store = UpstashVectorStore(
|
||||
index_url=os.environ["UPSTASH_VECTOR_URL_EMBEDDING"],
|
||||
index_token=os.environ["UPSTASH_VECTOR_TOKEN_EMBEDDING"],
|
||||
embedding=True,
|
||||
)
|
||||
query_embedding = store._embed_query("query")
|
||||
assert query_embedding == "query"
|
||||
document_embedding = store._embed_documents(["doc1", "doc2"])
|
||||
assert document_embedding == ["doc1", "doc2"]
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_embedding_index() -> None:
|
||||
store = UpstashVectorStore(
|
||||
index_url=os.environ["UPSTASH_VECTOR_URL_EMBEDDING"],
|
||||
index_token=os.environ["UPSTASH_VECTOR_TOKEN_EMBEDDING"],
|
||||
embedding=True,
|
||||
)
|
||||
|
||||
# add documents
|
||||
store.add_documents(
|
||||
[
|
||||
Document(page_content="penguin", metadata={"topic": "bird"}),
|
||||
Document(page_content="albatros", metadata={"topic": "bird"}),
|
||||
Document(page_content="beethoven", metadata={"topic": "composer"}),
|
||||
Document(page_content="rachmaninoff", metadata={"topic": "composer"}),
|
||||
]
|
||||
)
|
||||
wait_for_indexing(store)
|
||||
|
||||
# similarity search
|
||||
search_result = store.similarity_search_with_score(query="eagle", k=2)
|
||||
check_response_with_score(
|
||||
search_result,
|
||||
[
|
||||
(Document(page_content="penguin", metadata={"topic": "bird"}), 0.82),
|
||||
(Document(page_content="albatros", metadata={"topic": "bird"}), 0.78),
|
||||
],
|
||||
)
|
||||
|
||||
# similarity search with relevance score
|
||||
search_result = store.similarity_search_with_relevance_scores(query="mozart", k=2)
|
||||
check_response_with_score(
|
||||
search_result,
|
||||
[
|
||||
(Document(page_content="beethoven", metadata={"topic": "composer"}), 0.88),
|
||||
(
|
||||
Document(page_content="rachmaninoff", metadata={"topic": "composer"}),
|
||||
0.84,
|
||||
),
|
||||
],
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
@pytest.mark.asyncio
|
||||
async def test_embedding_index_async() -> None:
|
||||
store = UpstashVectorStore(
|
||||
index_url=os.environ["UPSTASH_VECTOR_URL_EMBEDDING"],
|
||||
index_token=os.environ["UPSTASH_VECTOR_TOKEN_EMBEDDING"],
|
||||
embedding=True,
|
||||
)
|
||||
|
||||
# add documents
|
||||
await store.aadd_documents(
|
||||
[
|
||||
Document(page_content="penguin", metadata={"topic": "bird"}),
|
||||
Document(page_content="albatros", metadata={"topic": "bird"}),
|
||||
Document(page_content="beethoven", metadata={"topic": "composer"}),
|
||||
Document(page_content="rachmaninoff", metadata={"topic": "composer"}),
|
||||
]
|
||||
)
|
||||
wait_for_indexing(store)
|
||||
|
||||
# similarity search
|
||||
search_result = await store.asimilarity_search_with_score(query="eagle", k=2)
|
||||
check_response_with_score(
|
||||
search_result,
|
||||
[
|
||||
(Document(page_content="penguin", metadata={"topic": "bird"}), 0.82),
|
||||
(Document(page_content="albatros", metadata={"topic": "bird"}), 0.78),
|
||||
],
|
||||
)
|
||||
|
||||
# similarity search with relevance score
|
||||
search_result = await store.asimilarity_search_with_relevance_scores(
|
||||
query="mozart", k=2
|
||||
)
|
||||
check_response_with_score(
|
||||
search_result,
|
||||
[
|
||||
(Document(page_content="beethoven", metadata={"topic": "composer"}), 0.88),
|
||||
(
|
||||
Document(page_content="rachmaninoff", metadata={"topic": "composer"}),
|
||||
0.84,
|
||||
),
|
||||
],
|
||||
)
|
||||
@ -83,6 +83,7 @@ EXPECTED_ALL = [
|
||||
"TileDB",
|
||||
"TimescaleVector",
|
||||
"Typesense",
|
||||
"UpstashVectorStore",
|
||||
"USearch",
|
||||
"VDMS",
|
||||
"Vald",
|
||||
|
||||
@ -85,6 +85,7 @@ def test_compatible_vectorstore_documentation() -> None:
|
||||
"TileDB",
|
||||
"TimescaleVector",
|
||||
"TencentVectorDB",
|
||||
"UpstashVectorStore",
|
||||
"EcloudESVectorStore",
|
||||
"Vald",
|
||||
"VDMS",
|
||||
|
||||
@ -77,6 +77,7 @@ _EXPECTED = [
|
||||
"Tigris",
|
||||
"TimescaleVector",
|
||||
"Typesense",
|
||||
"UpstashVectorStore",
|
||||
"USearch",
|
||||
"Vald",
|
||||
"VDMS",
|
||||
|
||||
Loading…
Reference in New Issue
Block a user