iam implementing a rag chatbot, but it's always answeri...

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iam implementing a rag chatbot, but it's always answering from first retrieved documents only. If I can any other questions retrieved documents are same . It's not able to retrieve different set of documents

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kkapa.ai

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kkapa.ai

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It seems like you're facing an issue with the retrieval part of your Retrieval-Augmented Generation (RAG) system. The retrieval part is responsible for finding relevant information from an existing knowledge base, such as a database or corpus of text. If it's always retrieving the same documents, it might be due to the way your retrieval system is set up.

In a naive RAG system, the set of input documents are chunked, embedded, and dumped to a vector database collection. Retrieval would just fetch the top-k documents by embedding similarity. This can fail if the set of documents is large - it can be hard to disambiguate raw chunks, and you're not guaranteed to filter for the set of documents that contain relevant context [source] ().

To improve the retrieval process, you might want to consider implementing structured retrieval, which is a more advanced query algorithm that takes advantage of structure within your documents for higher-precision retrieval. There are two main methods for structured retrieval:

Metadata Filters + Auto Retrieval: Tag each document with metadata and then store in a vector database. During inference time, use the LLM to infer the right metadata filters to query the vector db in addition to the semantic query string [source] ().

Store Document Hierarchies (summaries -> raw chunks) + Recursive Retrieval: Embed document summaries and map to chunks per document. Fetch at the document-level first before chunk level [source] ().

Here's an example of how you can create an embedding store and index:

kkapa.ai

Plain Text

index = VectorStoreIndex.from_documents(
    documents, transformations=[splitter], embed_model=embed_model
)
query_engine = index.as_query_engine(llm=llm)

[source] ()

Remember to install the necessary packages:

Plain Text

%pip install llama-index-llms-openai
%pip install llama-index-vector-stores-weaviate

[source] ()

Please note that these are just suggestions and the actual implementation might vary depending on your specific use case and the structure of your data.
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TTech explorer

@kapa.ai how can I add some unique text as metadata to each document while doing vector store index

kkapa.ai

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kkapa.ai

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You can add unique metadata to each document while creating a vector store index in LlamaIndex. The metadata should be added under the metadata key of each document. Here's an example of how you can do this:

Plain Text

# load your documents normally, then add your metadata
documents = SimpleDirectoryReader("../data/paul_graham").load_data()

for document in documents:
    document.metadata = {"unique_text": "Your unique text here"}

storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

[source] ()

In this example, "Your unique text here" is the unique text that you want to add as metadata. You can replace this with any unique text that you want to add for each document. The SimpleDirectoryReader is used to load the documents, and then a loop is used to add the metadata to each document.

Please note that the metadata must be declared when creating the index. If you do not do this, your queries will come back empty. There is no way to modify an existing index after it had already been created due to a limitation in Redis [source] ().
----------------------
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TTech explorer

@kapa.ai also it's showing embedding none .

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

----------------------
If you're seeing embedding=None for your documents, it means that the embeddings for your documents have not been generated or set. In LlamaIndex, embeddings are vector representations of your documents that are used for similarity search in the vector store.

To generate embeddings for your documents, you need to use an embedding model. Here's an example of how you can do this:

Plain Text

from llama_index.embeddings.dashscope import (
    DashScopeEmbedding,
    DashScopeTextEmbeddingModels,
    DashScopeTextEmbeddingType,
)

# Create embeddings
embedder = DashScopeEmbedding(
    model_name=DashScopeTextEmbeddingModels.TEXT_EMBEDDING_V2,
    text_type=DashScopeTextEmbeddingType.TEXT_TYPE_QUERY,
)

# Call text Embedding
embedding = embedder.get_text_embedding("Your text here")

[source] ()

In this example, the DashScopeEmbedding model is used to generate embeddings for a text. You can replace "Your text here" with the text of your document.

After generating the embeddings, you can set them for your documents like this:

Plain Text

for document in documents:
    document.embedding = embedder.get_text_embedding(document.text)

In this example, the text of each document is used to generate an embedding, which is then set as the embedding of the document.

Please note that the actual implementation might vary depending on your specific use case and the structure of your data.
----------------------
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----------------------

TTech explorer

@kapa.ai how to add file name without extension as metadata

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

----------------------
You can add the file name without extension as metadata to each document in LlamaIndex. Here's an example of how you can do this:

Plain Text

from pathlib import Path

# load your documents normally, then add your metadata
documents = SimpleDirectoryReader("../data/paul_graham").load_data()

for document in documents:
    file_path = Path(document.metadata['filename'])
    document.metadata['filename_without_extension'] = file_path.stem

In this example, the Path class from the pathlib module is used to get the file name without extension (also known as the stem of the file path). The stem attribute of a Path object returns the file name without the extension.

Please note that this assumes that the filename metadata is already set for each document and contains the full file name including the extension. If this is not the case, you will need to adjust the code accordingly.

Also, remember to replace "../data/paul_graham" with the path to your actual data.
----------------------
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TTech explorer

@kapa.ai even after adding metadata, my vector store index is returning wrong source documents

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

----------------------
If your vector store index is returning incorrect source documents even after adding metadata, there could be several reasons for this. Here are a few things you might want to check:

Metadata Fields: When creating the index, you must declare the metadata fields. If you do not do this, your queries will come back empty. There is no way to modify an existing index after it had already been created. This is a limitation of Redis [source] ().

Prefix Issues: If you first create your index with a specific prefix but later change that prefix in your code, your query will come back empty. Redis saves the prefix you originally created your index with and expects it to be consistent [source] ().

Metadata Query: If you add metadata to the index after it has already been created and then try to query over that metadata, your queries will come back empty. Redis indexes fields upon index creation only [source] ().

Metadata Filters: If you're using metadata filters, make sure they are set correctly. The filters should match the metadata of the documents you want to retrieve [source] ().

If you're still having issues, you might want to consider deleting the index and recreating it. Here's how you can delete an index:

Plain Text

vector_store.delete_index()

[source] ()

kkapa.ai

Please note that this will delete all the documents and the index, but it will not drop your actual data. After deleting the index, you can recreate it and make sure to add the metadata correctly.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------

TTech explorer

@kapa.ai my vector store index is returning wrong source documents. How to improve

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

----------------------
If your vector store index is returning incorrect source documents, there could be several reasons for this. Here are a few things you might want to check:

Metadata Fields: When creating the index, you must declare the metadata fields. If you do not do this, your queries will come back empty. There is no way to modify an existing index after it had already been created. This is a limitation of Redis [source] ().

Prefix Issues: If you first create your index with a specific prefix but later change that prefix in your code, your query will come back empty. Redis saves the prefix you originally created your index with and expects it to be consistent [source] ().

Metadata Query: If you add metadata to the index after it has already been created and then try to query over that metadata, your queries will come back empty. Redis indexes fields upon index creation only [source] ().

Metadata Filters: If you're using metadata filters, make sure they are set correctly. The filters should match the metadata of the documents you want to retrieve [source] ().

If you're still having issues, you might want to consider deleting the index and recreating it. Here's how you can delete an index:

Plain Text

vector_store.delete_index()

[source] ()

kkapa.ai

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