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Updated 6 days ago

I want to create custom response syntehsizer because i have metadata that I want to inject

ccmosguy
·
@kapa.ai I want to create custom response syntehsizer because i have metadata that I want to inject into the context window to help with answering the prompt how do I do this? I want to add the metadata to what is synthesized
k
c
11 comments
kkapa.ai
Please stand by @cmosguy as I search the knowledge base. I normally take about 15 seconds or so. :wave:
kkapa.ai
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To create a custom response synthesizer that injects metadata into the context window for answering prompts in LlamaIndex, you can follow these steps:

  1. Create a custom response synthesizer by subclassing the BaseSynthesizer class.
  2. Override the get_response method to include your custom logic for injecting metadata.
Here's an example of how you can implement this:

Plain Text
from llama_index.core.response_synthesizers import BaseSynthesizer
from llama_index.core import PromptTemplate

class CustomMetadataSynthesizer(BaseSynthesizer):
    def get_response(
        self,
        query_str: str,
        text_chunks: Sequence[str],
        **response_kwargs: Any,
    ) -> RESPONSE_TEXT_TYPE:
        # Inject metadata into the context
        metadata = response_kwargs.get('metadata', {})
        context_with_metadata = f"Metadata: {metadata}\n\nContext:\n" + "\n".join(text_chunks)
        
        # Create a custom prompt template that includes metadata
        custom_prompt = PromptTemplate(
            "Given the following context and metadata, please answer the question:\n"
            "Context and Metadata: {context_str}\n"
            "Question: {query_str}\n"
            "Answer: "
        )
        
        # Format the prompt with the context (including metadata) and query
        formatted_prompt = custom_prompt.format(
            context_str=context_with_metadata,
            query_str=query_str
        )
        
        # Use the LLM to generate a response
        response = self._llm.predict(formatted_prompt)
        
        return response


To use this custom synthesizer:

  1. Create an instance of your custom synthesizer.
  2. Use it when creating a query engine or directly for synthesis.
Here's how you can use it:

```python

Create an instance of your custom synthesizer

custom_synthesizer = CustomMetadataSynthesizer()
kkapa.ai

Use it when creating a query engine

query_engine = index.as_query_engine(
response_synthesizer=custom_synthesizer
)

Or use it directly

response = custom_synthesizer.get_response(
"Your question here",
text_chunks=["chunk1", "chunk2"],
metadata={"key": "value"}
)
```

This approach allows you to inject metadata into the context window, which will be used when synthesizing the response. The metadata will be available to the LLM when generating the answer, potentially improving the relevance and accuracy of the response.

For more information on customizing response synthesizers, you can refer to the LlamaIndex documentation on Response Synthesis Modules.
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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ccmosguy
@kapa.ai i see that a query bundle specifies an image path as input, how is this used what do we do with it?
kkapa.ai
Let me check the knowledge base for you again. One moment! :books:
kkapa.ai
----------------------
To answer your question about how the image path specified in a QueryBundle is used in LlamaIndex, let's look at the relevant information from the knowledge sources.

The QueryBundle class in LlamaIndex can indeed include an image path as input. This is primarily used for multimodal queries, where both text and images are involved in the query process. Here's how it's utilized:

  1. Image Retrieval:
    The image path in the QueryBundle is used for image-to-image retrieval. According to the BaseImageRetriever documentation, when you call the image_to_image_retrieve method with a string or QueryBundle, it creates a QueryBundle with an empty query string and the provided image path:
Plain Text
   if isinstance(str_or_query_bundle, str):
       # leave query_str as empty since we are using image_path for image retrieval
       str_or_query_bundle = QueryBundle(
           query_str="", image_path=str_or_query_bundle
       )


  1. Multimodal Query Engines:
    In multimodal query engines, the image path is used to load the image for processing alongside the text query. For example, in the SimpleMultiModalQueryEngine, the image documents are extracted from the nodes and passed to the multimodal LLM:
Plain Text
   llm_response = self._multi_modal_llm.complete(
       prompt=fmt_prompt,
       image_documents=[
           image_node.node
           for image_node in image_nodes
           if isinstance(image_node.node, ImageNode)
       ],
   )
kkapa.ai
  1. Custom Query Engines:
    You can also create custom query engines that make use of the image path. For instance, in the MultimodalQueryEngine example, the image path is used to load image documents for the multimodal LLM:
Plain Text
   llm_response = self.multi_modal_llm.complete(
       prompt=fmt_prompt,
       image_documents=[n.node for n in reranked_image_nodes],
   )


In summary, the image path in a QueryBundle is primarily used to load and process images alongside text in multimodal queries. It allows LlamaIndex to handle both text and image inputs in various retrieval and query scenarios, enabling more complex and diverse types of queries that involve visual information.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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ccmosguy
@kapa.ai how are images stored in the index?
kkapa.ai
Let me check the knowledge base for you again. One moment! :books:
kkapa.ai
----------------------
To answer your question about how images are stored in the index in LlamaIndex, let's look at the relevant information from the knowledge sources.

Images in LlamaIndex are typically stored as part of a MultiModalVectorStoreIndex. Here's how it works:

  1. Image Storage:
    Images are stored as ImageNodes in the index. These ImageNodes contain metadata about the image, including the file path. According to the MultiModalVectorStoreIndex documentation, you can create an index that includes both text and image data:
Plain Text
   documents = SimpleDirectoryReader("./mixed_wiki/").load_data()
   index = MultiModalVectorStoreIndex.from_documents(
       documents,
       storage_context=storage_context,
   )


  1. Vector Store:
    The actual image data is typically not stored directly in the index. Instead, the images are processed to create embeddings, which are then stored in a vector store. This allows for efficient similarity search and retrieval. For example, you can use different vector stores for text and images:
Plain Text
   text_store = QdrantVectorStore(
       client=client, collection_name="text_collection"
   )
   image_store = QdrantVectorStore(
       client=client, collection_name="image_collection"
   )

   storage_context = StorageContext.from_defaults(
       vector_store=text_store, image_store=image_store
   )


  1. Image Embeddings:
    The images are converted into embeddings using multimodal embedding models. For instance, the Cohere MultiModal Embedding model can be used to create embeddings for both text and images:
kkapa.ai
Plain Text
   Settings.embed_model = CohereEmbedding(
       api_key=os.environ["COHERE_API_KEY"],
       model_name="embed-english-v3.0",  # current v3 models support multimodal embeddings
   )


  1. Retrieval:
    When retrieving images, the index uses these embeddings to find similar images. The actual image data is typically accessed using the file path stored in the ImageNode metadata. For example:
Plain Text
   retriever_engine = index.as_retriever(image_similarity_top_k=4)
   retrieval_results = retriever_engine.image_to_image_retrieve(
       "./mixed_wiki/2.jpg"
   )


  1. Image Processing:
    It's worth noting that some preprocessing may be done on the images before storage. For instance, large images might be deleted or resized to meet size constraints of the embedding model:
Plain Text
   delete_large_images(data_path)


In summary, images in LlamaIndex are not stored directly in the index but are represented as ImageNodes with metadata including the file path. The actual image data is processed into embeddings and stored in a vector store, allowing for efficient similarity search and retrieval. The original image files remain in their original location and are accessed when needed using the stored file paths.

LlamaIndex documentation
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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