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docs: updated langchain usage (#948)

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Co-authored-by: Anush <anushshetty90@gmail.com>
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JonZeolla
2024-06-07 08:19:50 +05:30
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co-authored by Anush
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qdrant-landing/content/documentation/frameworks/langchain.md
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@@ -8,22 +8,21 @@ aliases:
# LangChain
LangChain is a library that makes developing Large Language Models based applications much easier. It unifies the interfaces
to different libraries, including major embedding providers and Qdrant. Using LangChain, you can focus on the business value
LangChain is a library that makes developing Large Language Model-based applications much easier. It unifies the interfaces
to different libraries, including major embedding providers and Qdrant. Using LangChain, you can focus on the business value
instead of writing the boilerplate.
Langchain comes with the Qdrant integration by default. It might be installed with pip:
Langchain distributes their Qdrant integration in their community package. It might be installed with pip:
```bash
pip install langchain
pip install langchain-community langchain-qdrant
```
Qdrant acts as a vector index that may store the embeddings with the documents used to generate them. There are various ways
how to use it, but calling `Qdrant.from_texts` is probably the most straightforward way how to get started:
Qdrant acts as a vector index that may store the embeddings with the documents used to generate them. There are various ways to use it, but calling `Qdrant.from_texts` or `Qdrant.from_documents` is probably the most straightforward way to get started:
```python
from langchain.vectorstores import Qdrant
from langchain.embeddings import HuggingFaceEmbeddings
from langchain_qdrant import Qdrant
from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings
embeddings = HuggingFaceEmbeddings(
model_name="sentence-transformers/all-mpnet-base-v2"
@@ -33,43 +32,34 @@ doc_store = Qdrant.from_texts(
)
```
Calling `Qdrant.from_documents` or `Qdrant.from_texts` will always recreate the collection and remove all the existing points.
That's fine for some experiments, but you'll prefer not to start from scratch every single time in a real-world scenario.
If you prefer reusing an existing collection, you can create an instance of Qdrant on your own:
## Using an existing collection
To get an instance of `langchain_qdrant.Qdrant` without loading any new documents or texts, you can use the `Qdrant.from_existing_collection()` method.
```python
import qdrant_client
embeddings = HuggingFaceEmbeddings(
model_name="sentence-transformers/all-mpnet-base-v2"
)
client = qdrant_client.QdrantClient(
"<qdrant-url>",
api_key="<qdrant-api-key>", # For Qdrant Cloud, None for local instance
)
doc_store = Qdrant(
client=client, collection_name="texts",
doc_store = Qdrant.from_existing_collection(
embeddings=embeddings,
collection_name="my_documents",
url="<qdrant-url>",
api_key="<qdrant-api-key>",
)
```
## Local mode
Python client allows you to run the same code in local mode without running the Qdrant server. That's great for testing things
out and debugging or if you plan to store just a small amount of vectors. The embeddings might be fully kept in memory or
Python client allows you to run the same code in local mode without running the Qdrant server. That's great for testing things
out and debugging or if you plan to store just a small amount of vectors. The embeddings might be fully kept in memory or
persisted on disk.
### In-memory
For some testing scenarios and quick experiments, you may prefer to keep all the data in memory only, so it gets lost when the
For some testing scenarios and quick experiments, you may prefer to keep all the data in memory only, so it gets lost when the
client is destroyed - usually at the end of your script/notebook.
```python
qdrant = Qdrant.from_documents(
docs,
embeddings,
docs,
embeddings,
location=":memory:", # Local mode with in-memory storage only
collection_name="my_documents",
)
@@ -81,8 +71,8 @@ Local mode, without using the Qdrant server, may also store your vectors on disk
```python
qdrant = Qdrant.from_documents(
docs,
embeddings,
docs,
embeddings,
path="/tmp/local_qdrant",
collection_name="my_documents",
)
@@ -90,24 +80,24 @@ qdrant = Qdrant.from_documents(
### On-premise server deployment
No matter if you choose to launch Qdrant locally with [a Docker container](/documentation/guides/installation/), or
select a Kubernetes deployment with [the official Helm chart](https://github.com/qdrant/qdrant-helm), the way you're
No matter if you choose to launch Qdrant locally with [a Docker container](/documentation/guides/installation/), or
select a Kubernetes deployment with [the official Helm chart](https://github.com/qdrant/qdrant-helm), the way you're
going to connect to such an instance will be identical. You'll need to provide a URL pointing to the service.
```python
url = "<---qdrant url here --->"
qdrant = Qdrant.from_documents(
docs,
embeddings,
url,
prefer_grpc=True,
docs,
embeddings,
url,
prefer_grpc=True,
collection_name="my_documents",
)
```
## Next steps
If you'd like to know more about running Qdrant in a LangChain-based application, please read our article
If you'd like to know more about running Qdrant in a LangChain-based application, please read our article
[Question Answering with LangChain and Qdrant without boilerplate](/articles/langchain-integration/). Some more information
might also be found in the [LangChain documentation](https://python.langchain.com/docs/integrations/vectorstores/qdrant).