Working methodologies in vector query

[PradiptaAtByteIQMind-Graph2023]

Hi

How in case of vector search the intermediate query string is getting formed in case of autovector query engine or any other query engine for vector search? Please explain briefly

Thanks

[dosubot[bot]]

Hey there, @PradiptaAtByteIQMind-Graph2023! Good to see you diving into another intriguing topic.

In the context of vector search, the intermediate query string is formed through a process that involves translating an original question into a SQL query, executing the SQL query to get a response, and then transforming that SQL query and response into a vector store query. This process is facilitated by the use of a synthesis prompt template, specifically DEFAULT_SQL_VECTOR_SYNTHESIS_PROMPT, which outlines how the original question, SQL query, SQL response, and the transformed vector store query and its response are synthesized to form a comprehensive response to the original question.

Here is the synthesis prompt template used for forming the intermediate query string:

DEFAULT_SQL_VECTOR_SYNTHESIS_PROMPT_TMPL = """
The original question is given below.
This question has been translated into a SQL query. \
Both the SQL query and the response are given below.
Given the SQL response, the question has also been translated into a vector store query.
The vector store query and response is given below.
Given SQL query, SQL response, transformed vector store query, and vector store \
response, please synthesize a response to the original question.

Original question: {query_str}
SQL query: {sql_query_str}
SQL response: {sql_response_str}
Transformed vector store query: {query_engine_query_str}
Vector store response: {query_engine_response_str}
Response:
"""

This template is then instantiated as DEFAULT_SQL_VECTOR_SYNTHESIS_PROMPT and used within the SQLAutoVectorQueryEngine class to guide the synthesis of the intermediate query string. The process involves taking the original question, translating it into a SQL query, executing the SQL query to obtain a response, and then using both the SQL query and its response to form a transformed vector store query. This transformed query is then executed against the vector store, and the results are synthesized along with the original inputs to form a comprehensive response to the original question.

You can find more details about this process in the SQLAutoVectorQueryEngine class in the LlamaIndex codebase. This class is designed to query both a SQL database and a vector database, deciding whether to query the SQL database or the vector store and, if querying the SQL database, whether to augment the information with results from the vector store using the VectorIndexAutoRetriever.

I hope this explanation helps! If you have any more questions or need further clarification, feel free to ask.

Sources

• llama-index-core/llama_index/core/query_engine/sql_vector_query_engine.py
• llama-index-legacy/llama_index/legacy/query_engine/sql_vector_query_engine.py

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[dosubot]

@PradiptaAtByteIQMind-Graph2023

I apologize for the confusion. Let me provide a more focused explanation for vector queries.

In the context of vector queries, the intermediate query string is formed through a process that involves translating the original question directly into a vector store query. This process bypasses the translation into a SQL query and the subsequent execution against a SQL database.

The vector query engine, specifically designed for vector queries, takes the original question and directly transforms it into a query against the vector store. This query is then executed against the vector store, and the results are synthesized to form a comprehensive response to the original question.

You can find more details about the vector query process in the relevant classes within the LlamaIndex codebase, specifically designed for vector queries.

I hope this explanation aligns more closely with your query. If you have any more questions or need further clarification, feel free to ask.

About Dosu

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.

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[PradiptaAtByteIQMind-Graph2023]

@dosu-bot

Suppose I have 100 documents each having one document id. I want to build a chat bot where it can answer anything like how many documents are there or for specific name how many documents has in the chroma like. how can i achieve this using llamaindex

[dosubot]

Sure, I can guide you on how to build a chatbot using LlamaIndex that can answer queries about a set of 100 documents. Here are the steps you can follow:

1. Initialize the VectorStoreIndexWithCitationsChat: First, you need to initialize the VectorStoreIndexWithCitationsChat with your documents. This involves embedding your documents using a suitable model and adding them to the index. The documents should be pre-processed to include the necessary metadata, such as the presence of specific names in the chroma.

2. Set Up the Chat Engine: Use the as_chat_engine method of VectorStoreIndexWithCitationsChat to set up the chat engine. You should specify the ChatModeCitations.COHERE_CITATIONS_CONTEXT as the chat mode to enable the chat engine to use citations and documents in generating responses.

3. Handle Queries: Implement a function to handle user queries. This function should:

   • Accept a query as input.
   • Use the chat, stream_chat, achat, or astream_chat method of the CitationsContextChatEngine class to generate a response based on the query. These methods support synchronous and asynchronous handling of chat responses, including streaming responses for real-time interaction.
   • Extract and format the response, citations, and documents from the AgentCitationsChatResponse or StreamingAgentCitationsChatResponse object returned by the chat method.

4. Special Queries Handling: For queries specifically asking for the total number of documents or the number of documents containing a specific name in the chroma, you can directly query the index or the documents list maintained by the chat engine. Implement custom logic to count the documents or search for documents containing the specific name in the chroma.

Here's a simplified example of how you might set up and use the chat engine:

from llama_index.packs.cohere_citation_chat.citations_context_chat_engine import VectorStoreIndexWithCitationsChat, ChatModeCitations

# Initialize the VectorStoreIndexWithCitationsChat with your documents
vector_store = VectorStoreIndexWithCitationsChat(documents=your_documents_list)
vector_store.set_embed_model_input_type('search_query')

# Set up the chat engine
chat_engine = vector_store.as_chat_engine(chat_mode=ChatModeCitations.COHERE_CITATIONS_CONTEXT)

# Function to handle queries
def handle_query(query):
    if query.lower() == "how many documents are there?":
        # Custom logic to return the total number of documents
        return f"There are {len(your_documents_list)} documents."
    elif "name in the chroma" in query.lower():
        # Custom logic to search documents for a specific name in the chroma
        # This is a placeholder for actual search logic
        matching_docs_count = search_documents_for_name_in_chroma(your_documents_list, query)
        return f"There are {matching_docs_count} documents containing the name in the chroma."
    else:
        # Use the chat engine for other queries
        response = chat_engine.chat(query)
        return response.response  # Extract the text response

# Example usage
query = "Tell me about the chroma feature."
response = handle_query(query)
print(response)

This example assumes you have a function search_documents_for_name_in_chroma to search the documents for a specific name in the chroma, and your_documents_list is a list of your documents prepared for indexing. Adjust the logic according to your specific requirements and the structure of your documents.

For more details, you can refer to the CitationsContextChatEngine class in the LlamaIndex codebase.

Regarding the initialization of VectorStoreIndexWithCitationsChat with a set of documents, you would typically convert your documents into a format expected by the CitationsContextChatEngine, such as a list of Document objects or another format expected by the underlying embedding or retrieval system. Then, you would add or index these documents using a method provided by the parent class or the embedding system. However, the exact methods for indexing documents and generating responses may vary based on the implementation details of VectorStoreIndex and CitationsContextChatEngine, which are not fully detailed in the provided context.

As for the structure of the documents to be indexed, they typically need to conform to the structure of BaseNode or its derivatives like TextNode. These nodes are expected to have embeddings associated with them when being added to a vector store. However, without the explicit definition of VectorStoreIndexWithCitationsChat and the specific fields of BaseNode or TextNode in the provided context, we cannot determine the exact fields and structure required for the documents. The exact structure of the documents depends on the implementation of these classes or data structures.

I hope this helps! If you have any more questions or need further clarification, feel free to ask.

Sources

• llama-index-packs/llama-index-packs-cohere-citation-chat/llama_index/packs/cohere_citation_chat/citations_context_chat_engine.py
• llama-index-legacy/llama_index/legacy/vector_stores/types.py

About Dosu

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[PradiptaAtByteIQMind-Graph2023]

@dosu-bot

I am unable to understand how the intermeddiate query string is forming. Is it done by LLM or how exactly it is forming?

[dosubot]

The LlamaIndex system generates the intermediate query string for vector search by using embeddings to represent both the query and the documents in a vector space. This process is facilitated by an embedding mechanism, specifically through the use of a mock embedding class (MockEmbedding) in the provided context, rather than directly by the Llama Language Model (LLM) or another language model.

The MockEmbedding class is responsible for converting text (both queries and documents) into vector representations. This is evident in the methods _aget_query_embedding and _aget_text_embedding, which simulate the process of obtaining embeddings for queries and texts, respectively. These embeddings are then used in the vector search process to find the most relevant documents based on the similarity of their vector representations to the query vector.

For example, the method _aget_query_embedding returns predefined embeddings for specific queries ("Foo?", "Orange?", "Cat?"), and similarly, _aget_text_embedding returns embeddings for given texts. These embeddings are lists of floats representing the vectorized form of the texts or queries.

The vector search and the generation of intermediate query strings are demonstrated in the test functions, where a query string is passed to a query engine (query_engine.query(query_str)), and the engine returns a response based on the vector similarity between the query and the documents in the index.

In summary, the intermediate query string generation in the context of vector search is facilitated by an embedding mechanism (demonstrated by MockEmbedding in the provided context), which converts queries and documents into vector representations for similarity comparison. This process does not directly involve the Llama Language Model (LLM) or another language model within the given context.

You can find more details in the source code here: llama-index-core/tests/indices/query/test_compose_vector.py

As for the MockEmbedding class, it converts text into vector representations by returning a list of floats where each element in the list is 0.5, and the length of the list is equal to the embedding dimension (embed_dim) specified during the initialization of the MockEmbedding instance. This behavior is implemented in the _get_vector method, which is called by both _get_text_embedding and _get_query_embedding (and their asynchronous counterparts _aget_text_embedding and _aget_query_embedding) to generate the mock embedding vectors for any given text or query string.

You can find more details in the source code here: llama-index-core/llama_index/core/embeddings/mock_embed_model.py

After the intermediate query string has been generated, the LlamaIndex system handles the vector search process by performing several steps:

1. Check Collection Initialization: It first checks if the collection is initialized by verifying if the _collection attribute is an instance of DocumentCollection. If not, it raises a ValueError indicating that the collection is not initialized.

2. Validate Query: It then validates the query by ensuring that either a query string (query.query_str) or a query embedding (query.query_embedding) is provided. If neither is present, it raises a ValueError.

3. Query String and Embedding Handling: If a query embedding is provided, the query string is set to None to ensure that Zep, the underlying search engine, which does not allow both a query string and a query embedding to be set simultaneously, can process the query correctly.

4. Metadata Filters Conversion: If metadata filters are provided in the query (query.filters), these are converted to a format that Zep can understand using the _to_zep_filters method. This method translates the metadata filters into a dictionary with a structure that specifies the conditions for filtering.

5. Performing the Search: The system then performs the search using the Zep client's search method (or asearch for asynchronous queries) on the collection. This search method takes the query string or embedding, the metadata filters (if any), and the limit for the number of top similar results to return (query.similarity_top_k). The search method returns a list of documents that match the query criteria.

6. Parsing Query Results: Finally, the results from the Zep search are parsed using the _parse_query_result method. This method iterates over the returned documents, converting each document's metadata back into a TextNode object, extracting the document content, and collecting the document IDs and similarity scores. The method returns a VectorStoreQueryResult object containing the nodes (as TextNode objects), their similarities, and their IDs.

You can find more details in the source code here: llama-index-integrations/vector_stores/llama-index-vector-stores-zep/llama_index/vector_stores/zep/base.py

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

Sources

• llama-index-core/tests/indices/query/test_compose_vector.py
• llama-index-core/llama_index/core/embeddings/mock_embed_model.py
• llama-index-integrations/vector_stores/llama-index-vector-stores-zep/llama_index/vector_stores/zep/base.py

About Dosu

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.

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