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qdrant: init package (#21146)

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## Description

This PR introduces the new `langchain-qdrant` partner package, intending
to deprecate the community package.

## Changes

- Moved the Qdrant vector store implementation `/libs/partners/qdrant`
with integration tests.
- The conditional imports of the client library are now regular with
minor implementation improvements.
- Added a deprecation warning to
`langchain_community.vectorstores.qdrant.Qdrant`.
- Replaced references/imports from `langchain_community` with either
`langchain_core` or by moving the definitions to the `langchain_qdrant`
package itself.
- Updated the Qdrant vector store documentation to reflect the changes.

## Testing
- `QDRANT_URL` and
[`QDRANT_API_KEY`](583e36bf6b)
env values need to be set to [run integration
tests](d608c93d1f)
in the [cloud](https://cloud.qdrant.tech).
- If a Qdrant instance is running at `http://localhost:6333`, the
integration tests will use it too.
- By default, tests use an
[`in-memory`](https://github.com/qdrant/qdrant-client?tab=readme-ov-file#local-mode)
instance(Not comprehensive).

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Erick Friis <erickfriis@gmail.com>
This commit is contained in:
Anush
2024-05-14 06:50:03 +05:30
committed by GitHub
parent fe8c9d621a
commit edd68e4ad4
35 changed files with 5650 additions and 47 deletions
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89
docs/docs/integrations/vectorstores/qdrant.ipynb
View File

@@ -30,7 +30,7 @@
},
"outputs": [],
"source": [
"%pip install --upgrade --quiet qdrant-client"
"%pip install --upgrade --quiet langchain-qdrant langchain-openai langchain"
]
},
{
@@ -79,8 +79,8 @@
"outputs": [],
"source": [
"from langchain_community.document_loaders import TextLoader\n",
"from langchain_community.vectorstores import Qdrant\n",
"from langchain_openai import OpenAIEmbeddings\n",
"from langchain_qdrant import Qdrant\n",
"from langchain_text_splitters import CharacterTextSplitter"
]
},
@@ -216,7 +216,7 @@
"source": [
"### Qdrant Cloud\n",
"\n",
"If you prefer not to keep yourself busy with managing the infrastructure, you can choose to set up a fully-managed Qdrant cluster on [Qdrant Cloud](https://cloud.qdrant.io/). There is a free forever 1GB cluster included for trying out. The main difference with using a managed version of Qdrant is that you'll need to provide an API key to secure your deployment from being accessed publicly."
"If you prefer not to keep yourself busy with managing the infrastructure, you can choose to set up a fully-managed Qdrant cluster on [Qdrant Cloud](https://cloud.qdrant.io/). There is a free forever 1GB cluster included for trying out. The main difference with using a managed version of Qdrant is that you'll need to provide an API key to secure your deployment from being accessed publicly. The value can also be set in a `QDRANT_API_KEY` environment variable."
]
},
{
@@ -243,6 +243,36 @@
")"
]
},
{
"cell_type": "markdown",
"id": "825c7903",
"metadata": {},
"source": [
"## Using an existing collection"
]
},
{
"cell_type": "markdown",
"id": "3f772575",
"metadata": {},
"source": [
"To get an instance of `langchain_qdrant.Qdrant` without loading any new documents or texts, you can use the `Qdrant.from_existing_collection()` method."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "daf7a6e5",
"metadata": {},
"outputs": [],
"source": [
"qdrant = Qdrant.from_existing_collection(\n",
" embeddings=embeddings,\n",
" collection_name=\"my_documents\",\n",
" url=\"http://localhost:6333\",\n",
")"
]
},
{
"attachments": {},
"cell_type": "markdown",
@@ -251,7 +281,7 @@
"source": [
"## Recreating the collection\n",
"\n",
"Both `Qdrant.from_texts` and `Qdrant.from_documents` methods are great to start using Qdrant with Langchain. In the previous versions the collection was recreated every time you called any of them. That behaviour has changed. Currently, the collection is going to be reused if it already exists. Setting `force_recreate` to `True` allows to remove the old collection and start from scratch."
"The collection is reused if it already exists. Setting `force_recreate` to `True` allows to remove the old collection and start from scratch."
]
},
{
@@ -520,7 +550,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": null,
"id": "9427195f",
"metadata": {
"ExecuteTime": {
@@ -528,21 +558,9 @@
"start_time": "2023-04-04T10:51:26.018763Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"VectorStoreRetriever(vectorstore=<langchain_community.vectorstores.qdrant.Qdrant object at 0x7fc4e5720a00>, search_type='similarity', search_kwargs={})"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"retriever = qdrant.as_retriever()\n",
"retriever"
"retriever = qdrant.as_retriever()"
]
},
{
@@ -556,7 +574,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": null,
"id": "64348f1b",
"metadata": {
"ExecuteTime": {
@@ -564,21 +582,9 @@
"start_time": "2023-04-04T10:51:26.034284Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"VectorStoreRetriever(vectorstore=<langchain_community.vectorstores.qdrant.Qdrant object at 0x7fc4e5720a00>, search_type='mmr', search_kwargs={})"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"retriever = qdrant.as_retriever(search_type=\"mmr\")\n",
"retriever"
"retriever = qdrant.as_retriever(search_type=\"mmr\")"
]
},
{
@@ -678,7 +684,7 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": null,
"id": "e4d6baf9",
"metadata": {
"ExecuteTime": {
@@ -686,18 +692,7 @@
"start_time": "2023-04-04T11:08:30.229748Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<langchain_community.vectorstores.qdrant.Qdrant at 0x7fc4e2baa230>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"Qdrant.from_documents(\n",
" docs,\n",