Qdrant retriver not working with some collection

[kientv]

Checked other resources

• I added a very descriptive title to this question.
• I searched the LangChain documentation with the integrated search.
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Example Code

self.vectorStore = Qdrant(
            client=qdrantClient,
            collection_name=collection_name,
            embeddings=embeddings,
            metadata_payload_key="metadata",
            content_payload_key="page_content",
            vector_name="custom_vector",
        )

Description

Qdrant retriever is not working with some collection, some collection still work:

@lru_cache()
def as_retriever(self) -> BaseRetriever:
if self.collection_name != "":
return self.vectorStore.as_retriever(
search_type="similarity",
search_kwargs=dict(k=self.top_k)
).with_config(
run_name="as_retriever"
)
else:
raise NotImplementedError(
f"No retriever for collection {self.collection_name}"
)

System Info

langchain==0.2.16
langchain-community==0.2.17
langchain-core==0.2.41
langchain-google-genai==1.0.10
langchain-google-vertexai==1.0.10
langchain-openai==0.1.25
langchain-qdrant==0.1.1
langchain-text-splitters==0.2.0

Ubuntu

python3.11 -m uvicorn main:app --host 0.0.0.0 --port

[dosubot[bot]]

I found a similar issue that might help you resolve the problem with your Qdrant retriever. In the issue Qdrant from LangChain failed, the suggested solution involves using a specific payload structure for Qdrant:

{
    "content_payload_key": "page content from the document",
    "metadata_payload_key": {
        "meta 1": "something",
        "meta 2": "something",
        etc
    }
}

This structure is different from other vector stores like Pinecone and Chroma, but it reflects the structure of the Document in LangChain ^[1].

Additionally, ensure that your Qdrant client and collection are properly initialized and accessible. If the problem persists, you might want to check if there are any specific errors or logs that could provide more insight into why some collections are not working.

To continue talking to Dosu, mention @dosu.

[kientv]

@dosu my chatbot still working, but some other chatbot is not retrieve result from qdrant db

[dosubot[bot]]

Your chatbot is retrieving results from the Qdrant database because it has been set up with a retrieval mechanism that uses a vector database to store and retrieve relevant documents. This setup involves creating a retriever from the vectorstore, which is then used to fetch documents that can be used as context for answering questions. Here is the relevant code snippet:

# Creating a retriever from the initialized vectorstore
retriever = vectorstore.as_retriever(k=4)

docs = retriever.invoke("Can LangSmith help test my LLM applications?")

docs

This retriever fetches relevant documents from the vectorstore, which can then be used by the chatbot to provide more informed responses. Other chatbots that do not have this retrieval mechanism set up will not be able to fetch and use external documents in the same way ^[1][2].

Additionally, ensure that the metadata_payload_key and content_payload_key parameters are correctly set in your Qdrant vector store configuration, as these are crucial for proper document storage and retrieval ^[3][4].

To continue talking to Dosu, mention @dosu.

[kientv]

Now it's work, but I don't understand why. Thanks :D