Qdrant - Load the saved vector db/store ?

[VpkPrasanna]

Issue you'd like to raise.

I use Qdrant through langchain to store vectors. But I can't find any example in docs where the dataset is searched based on a previously created collection. How to load the data? I found that Pinecone has "from_existing_index" function, which probably does the thing. But Qdrant doesn't have such a function. I created my own solution using qdrant_client, but I would like to use Langchain to simplify the script. How to do it?

Suggestion:

No response

[dosubot]

🤖

Hello,

Thank you for your question. Currently, the Qdrant class in LangChain does not have a method similar to Pinecone's "from_existing_index" function for loading a previously created collection. However, you can use the construct_instance or aconstruct_instance class methods of the Qdrant class to create a new instance and connect to the existing collection. Here's an example:

from qdrant_client import QdrantClient
from langchain.vectorstores import Qdrant
from langchain.embeddings import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
collection_name = "MyExistingCollection"  # replace with your collection name

qdrant = Qdrant.construct_instance(
    texts=[],  # no texts to add
    embedding=embeddings,
    collection_name=collection_name,
)

In this code, embeddings is an instance of the Embeddings class that you want to use. If you don't have it, you can create it using the appropriate model. For example, if you're using OpenAI's GPT-3 model, you can do:

from langchain.embeddings import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()

Please note that this will not check if the collection actually exists in the Qdrant server. If the collection does not exist, any operation that requires the collection (like add_texts, similarity_search, etc.) will fail.

This approach was derived from the source code of the Qdrant class in the LangChain repository here.

I hope this helps! If you have any other questions, feel free to ask.

Sources

• Documentation should point out how to retrieve a vectorstore already uploaded in a database
• [Q] How to re-use QDrant collection data that are created separatly with non-default vector name?
• libs/langchain/langchain/vectorstores/qdrant.py

---

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[VpkPrasanna]

hi

🤖

Hello,

Thank you for your question. Currently, the Qdrant class in LangChain does not have a method similar to Pinecone's "from_existing_index" function for loading a previously created collection. However, you can use the construct_instance or aconstruct_instance class methods of the Qdrant class to create a new instance and connect to the existing collection. Here's an example:

from qdrant_client import QdrantClient
from langchain.vectorstores import Qdrant
from langchain.embeddings import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
collection_name = "MyExistingCollection"  # replace with your collection name

qdrant = Qdrant.construct_instance(
    texts=[],  # no texts to add
    embedding=embeddings,
    collection_name=collection_name,
)

In this code, embeddings is an instance of the Embeddings class that you want to use. If you don't have it, you can create it using the appropriate model. For example, if you're using OpenAI's GPT-3 model, you can do:

from langchain.embeddings import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()

Please note that this will not check if the collection actually exists in the Qdrant server. If the collection does not exist, any operation that requires the collection (like add_texts, similarity_search, etc.) will fail.

This approach was derived from the source code of the Qdrant class in the LangChain repository here.

I hope this helps! If you have any other questions, feel free to ask.

Sources

• Documentation should point out how to retrieve a vectorstore already uploaded in a database
• [Q] How to re-use QDrant collection data that are created separatly with non-default vector name?
• libs/langchain/langchain/vectorstores/qdrant.py

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.
@dosu-bot the above mentioned method does not work out for RAG based implementations

[dosubot]

🤖

Hello,

You're correct in your understanding of the construct_instance and aconstruct_instance methods of the Qdrant class in the LangChain Python framework. These methods do check if a collection exists in the Qdrant server.

Here's a snippet from the LangChain source code that shows this:

try:
    # Skip any validation in case of forced collection recreate.
    if force_recreate:
        raise ValueError
    # Get the vector configuration of the existing collection and vector, if it
    # was specified. If the old configuration does not match the current one,
    # an exception is being thrown.
    collection_info = client.get_collection(collection_name=collection_name)
    current_vector_config = collection_info.config.params.vectors
    ...

In this code, client.get_collection(collection_name=collection_name) is used to get the collection information from the Qdrant server. If the collection does not exist, an exception will be thrown.

If the collection does not exist and operations such as add_texts or similarity_search are attempted, they will indeed fail. This is because these operations are performed on the collection, and if the collection does not exist, there is no place to perform these operations.

Regarding the behavior of these methods when the specified collection does not exist in the Qdrant server:

1. If the force_recreate parameter is set to True, the methods will skip any validation and attempt to recreate the collection. This is done by calling the recreate_collection method on the client object.

2. If the force_recreate parameter is not set or is False, the methods will attempt to get the vector configuration of the existing collection and vector. If the old configuration does not match the current one, an exception is thrown.

3. If the specified collection does not exist, the get_collection method will likely throw an exception, which should be handled by the calling code.

In summary, if the specified collection does not exist in the Qdrant server, these methods will either attempt to recreate the collection (if force_recreate is True), or throw an exception (if force_recreate is False or not set).

I hope this clarifies your question. If you have any more questions or need further clarification, feel free to ask.

Best regards,
Dosu

Sources

• libs/langchain/langchain/vectorstores/qdrant.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[karrtikiyer-tw]

Hi @VpkPrasanna : Did you find a solution to your requirement? I am also in the need of the same, where I need to load an existing collection?

[VpkPrasanna]

here is it
from qdrant_client import QdrantClient
client = QdrantClient(path=saved_db_path) db = Qdrant( client=client, collection_name=self.collection_name, embeddings=embeddings, )

the above piece of code helps you to load the saved qdrant path

[karrtikiyer-tw]

🙏 thanks

…

On Mon, 8 Jan 2024 at 12:28, V.Prasanna kumar ***@***.***> wrote: here is it from qdrant_client import QdrantClient client = QdrantClient(path=saved_db_path) db = Qdrant( client=client, collection_name=self.collection_name, embeddings=embeddings, ) the above piece of code helps you to load the saved qdrant path — Reply to this email directly, view it on GitHub <#11471 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AESCYLPP37EKL4Q6GLF2S2DYNOKKFAVCNFSM6AAAAAA5VTWVXCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTQOBQGQ3TCMZTGM> . You are receiving this because you commented.Message ID: ***@***.***>