QA problem in using QdrantDocumentStore

[NILICK]

I used QdrantDocumentStore for creating a vector database by this code:

def document_store(index:str, uploaded_file, citation:str, progress=gr.Progress()):

    document_store = QdrantDocumentStore(host="localhost",
                                         index=index,
                                         embedding_dim=768,
                                         recreate_index=False, # Note: If you want add another data later, set to "False"
                                         timeout=120,
                                         hnsw_config={"m": 16, "ef_construct": 64}
                                        )
    
    file_type_router = FileTypeRouter(mime_types=["text/plain", "application/pdf", "text/markdown"])
    text_file_converter = TextFileToDocument()
    markdown_converter = MarkdownToDocument()
    pdf_converter = PyPDFToDocument()
    cleaner = DocumentCleaner(
        remove_empty_lines = True,
        remove_extra_whitespaces = False,
        remove_repeated_substrings = True,
    )
    document_joiner = DocumentJoiner()
    document_embedder = SentenceTransformersDocumentEmbedder()
    

    splitter = DocumentSplitter(split_by="sentence", split_length=200, split_overlap=50)
    writer = DocumentWriter(document_store=document_store, policy=DuplicatePolicy.SKIP)
    
    indexing_pipeline = Pipeline()
    indexing_pipeline.add_component(instance=file_type_router, name="file_type_router")
    indexing_pipeline.add_component(instance=Multiplexer(Dict[str, Any]), name="metadata_multiplexer")
    indexing_pipeline.add_component(instance=markdown_converter, name="markdown_converter")
    indexing_pipeline.add_component(instance=document_joiner, name="document_joiner")
    indexing_pipeline.add_component(instance = cleaner, name="document_cleaner")
    indexing_pipeline.add_component(instance = splitter, name="document_splitter")
    indexing_pipeline.add_component(instance=document_embedder, name="document_embedder")
    indexing_pipeline.add_component(instance = writer, name="document_writer")
    
    indexing_pipeline.connect("file_type_router.text/markdown", "markdown_converter.sources")
    indexing_pipeline.connect("metadata_multiplexer", "markdown_converter.meta")
    indexing_pipeline.connect("markdown_converter", "document_joiner")
    indexing_pipeline.connect("document_joiner", "document_cleaner")
    indexing_pipeline.connect("document_cleaner", "document_splitter")
    indexing_pipeline.connect("document_splitter", "document_embedder")
    indexing_pipeline.connect("document_embedder", "document_writer")
    
    ## Adding Citation as Metadata for Papers
    # Convert BibTex to Dictionary
    
    kv = re.compile(r'\b(?P<key>\w+)={(?P<value>[^}]+)}')
    citation = citation
    meta_bibtex = dict(kv.findall(citation))
    
    # Adding `date_added` kay-value to metadata
    meta_bibtex["date_added"] = datetime.now().isoformat()
    
    ## Run the pipeline with the file you want to index
    indexing_pipeline.run({"file_type_router": {"sources": [str(uploaded_file)]},
                           "metadata_multiplexer": {"value": meta_bibtex},
                          })
    return "Text of your PDF file successfully added to Qdrant [" + index + "] collection."

Embedding multiple texts of multiple pdf files in the document store is done successfully, but when I want to query this document store, it uses only the last pdf file uploaded in qdrant document store, and if I query about other pdf files, it returns nothing.

I use this code for querying:

document_store = QdrantDocumentStore(url="localhost",
                                     index="aasents",
                                     embedding_dim=768,
                                     hnsw_config={"m": 16, "ef_construct": 64}
                                    )

generator = OllamaGenerator(model="phi3")
text_embedder = SentenceTransformersTextEmbedder(model="BAAI/bge-base-en-v1.5")
text_embedder.warm_up()

template = """
Answer the questions based on the given context.

Context:
{% for document in documents %}
    {{ document.content }}
{% endfor %}

Question: {{ question }}
Answer:
"""

query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder",text_embedder)
query_pipeline.add_component("retriever", QdrantEmbeddingRetriever(document_store=document_store))
query_pipeline.add_component("prompt_builder", PromptBuilder(template=template))
query_pipeline.add_component("llm", generator)
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
query_pipeline.connect("retriever", "prompt_builder.documents")
query_pipeline.connect("prompt_builder", "llm")

question = "what is Spatial Variability and Periodicity of Precipitation in the Middle Reaches of the Yellow River?"

result = query_pipeline.run({
    "text_embedder": {"text": question},
    "retriever": {"top_k": 10},
    "prompt_builder":{"question": question},
    })

print(result["llm"]["replies"])

How can I use all vectors available in Qdrant document store for QA?

[mrm1001]

Does the retriever only return 1 document? If you're on the latest version (2.1.0) pass this to your pipeline: query_pipeline.run(data, include_outputs_from={"retriever"}), you can see what gets returned by the retriever.

[NILICK]

Does the retriever only return 1 document? If you're on the latest version (2.1.0) pass this to your pipeline: query_pipeline.run(data, include_outputs_from={"retriever"}), you can see what gets returned by the retriever.

Hi @mrm1001, thanks for your attention. what's your meaning data argument? I tried solve this problem by using filter of metadata:

result = query_pipeline.run({
    "dense_text_embedder": {"text": question},
    "sparse_text_embedder": {"text": question},
    "retriever": {"top_k": 2,
                  "filters": {"field": "meta.title", "operator": "==",
                              "value": value_rf}},
    "prompt_builder":{"question": question},
    })

It returns the correct result, but I don't know if my solution is correct or not!

[mrm1001]

What I meant is, can you try to see what is returned by the retriever, without adding the filters?