Can I retrieve metadata along with the retrieved documents from a Vectorstore by modifying the parameters in asRetriever()?

gon-martinam commented 7 months ago

Hi 👋

For my use case I would need to return not only the text of the most similar documents in the vector database, but also one of the metadata fields of each returned document.

Currently I have a chain created, passing the vector database as vectordb.asRetriever(5, customFilter) so that it returns the 5 most similar documents and I also apply a filter.

After reading the documentation and source code of the asRetriever() method,

asRetriever(
    kOrFields?: number | Partial<VectorStoreRetrieverInput<this>>,
    filter?: this["FilterType"],
    callbacks?: Callbacks,
    tags?: string[],
    metadata?: Record<string, unknown>,
    verbose?: boolean
  ): VectorStoreRetriever<this> {
    if (typeof kOrFields === "number") {
      return new VectorStoreRetriever({
        vectorStore: this,
        k: kOrFields,
        filter,
        tags: [...(tags ?? []), this._vectorstoreType()],
        metadata,
        verbose,
        callbacks,
      });
    } else {
      const params = {
        vectorStore: this,
        k: kOrFields?.k,
        filter: kOrFields?.filter,
        tags: [...(kOrFields?.tags ?? []), this._vectorstoreType()],
        metadata: kOrFields?.metadata,
        verbose: kOrFields?.verbose,
        callbacks: kOrFields?.callbacks,
        searchType: kOrFields?.searchType,
      };
      if (kOrFields?.searchType === "mmr") {
        return new VectorStoreRetriever({
          ...params,
          searchKwargs: kOrFields.searchKwargs,
        });
      }
      return new VectorStoreRetriever({ ...params });
    }

it is not clear to me what the function of the "metadata" parameter is and that is why I would like to know if in this parameter I can indicate the metadata that I want to be returned along with the text of the most similar documents to be passed together as context to the LLM.

Thanks in advance 😄

P.S. The definition of the Retriever() method can be found in the following link: https://github.com/langchain-ai/langchainjs/blob/54beee324444f1915f99a34a6b7c66539d74202e/langchain-core/src/vectorstores.ts#L340C3-L376

dosubot[bot] commented 7 months ago

🤖

¡Hola de nuevo, @gon-martinam! 😄 Es genial verte por aquí otra vez.

Yes, you can modify the parameters in the asRetriever() method to retrieve metadata along with the retrieved documents from a Vectorstore. The asRetriever() method accepts a number of parameters, including kOrFields, filter, callbacks, tags, metadata, and verbose.

The metadata parameter is used to store additional information about the retriever. It is an optional parameter and its type is a record of string keys and values of any type. This metadata is then used in the getRelevantDocuments method. It is passed to the CallbackManager's configure method along with other parameters. This suggests that the metadata could be used in the configuration of callbacks, possibly to provide additional context or configuration options.

However, the metadata parameter in the asRetriever() method does not directly control the metadata fields returned with the documents. The returned metadata fields are determined by the Vectorstore implementation and the specific document fields stored in the Vectorstore.

If you want to filter the documents based on metadata fields, you can use the filter parameter in the asRetriever() method. The filter is a function that takes a document and returns a boolean indicating whether the document should be included in the results. You can define a custom filter function to check the metadata fields of the documents.

Here is an example:

const customFilter = (doc) => doc.metadataField === 'value';
const retriever = vectorStore.asRetriever(5, customFilter);

In this example, the customFilter function checks if the metadataField of the document is equal to 'value'. The asRetriever() method is then called with the customFilter function as the filter parameter. This will return the 5 most similar documents where the metadataField is 'value'.

Please note that the actual implementation may vary depending on the specific Vectorstore and the structure of the documents stored in it.

For more information, you can refer to the Vectorstore documentation and the BaseRetriever class in the langchainjs codebase.

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

Sources

#### 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. If you want to continue the conversation, start your reply with @dosu-bot.

gon-martinam commented 7 months ago

@dosu-bot

What I would like to achieve would be to pass as context to the LLM in a RAG not only the text of the documents returned by the vector database, but also certain metadata fields that each returned document contains.

How could I pass this context with text + metadata to the LLM in the RAG?

dosubot[bot] commented 7 months ago

🤖

To pass both the text and certain metadata fields from the retrieved documents as context to the Language Model in the Retrieval-Augmented Generation model using the LangChain JavaScript framework, you can modify the formatDocumentsAsString function. This function is used to format the retrieved documents into a string that can be passed as context to the language model.

Here's an example of how you can modify the formatDocumentsAsString function to include metadata:

function formatDocumentsWithMetadataAsString(documents) {
  return documents.map(doc => `${doc.pageContent} ${JSON.stringify(doc.metadata)}`).join('\n');
}

In this function, we're mapping over the documents, and for each document, we're returning a string that includes the pageContent and a stringified version of the metadata object. We then join all these strings with a newline character.

You can then use this function in your RunnableSequence:

const chain = RunnableSequence.from([
  {
    context: retriever.pipe(formatDocumentsWithMetadataAsString),
    question: new RunnablePassthrough(),
  },
  prompt,
  model,
  new StringOutputParser(),
]);

Please note that this is a simple example and might not suit your needs exactly. You might need to adjust the function to format the metadata in a way that's more suitable for your specific use case.

For more information, you can refer to the examples/src/use_cases/local_retrieval_qa/chain.ts file in the langchainjs repository.

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

Sources

#### 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. If you want to continue the conversation, start your reply with @dosu-bot.

gon-martinam commented 7 months ago

@dosu-bot

What's the difference in "creating a chain" by using a RunnableSequence or by using a, for example, RetrievalQAChain?

Is one way or another more recommended or better practice?

dosubot[bot] commented 7 months ago

🤖

The choice between using a RunnableSequence and a RetrievalQAChain in the LangChain JavaScript framework largely depends on the specific requirements of your application.

The RunnableSequence is a sequence of runnables, where the output of each runnable is the input of the next. This allows for chaining of operations where the output of one operation is required as the input for the next operation. It's a more general-purpose tool that can be used to chain together any operations that can be represented as runnables. You can find more details about RunnableSequence in the langchain-core/src/runnables/base.ts file.

On the other hand, the RetrievalQAChain is specifically designed for question-answering tasks with a retrieval component. It retrieves relevant documents based on a given query and then uses a language model to generate an answer from the retrieved documents. It's a more specialized tool that's specifically designed for retrieval-based question answering. You can find more details about RetrievalQAChain in the langchain/src/chains/retrieval_qa.ts file.

As for which one is more recommended or considered better practice, it really depends on the specific use case. If you're performing a sequence of operations where the output of one operation is the input of the next, then RunnableSequence would be the appropriate choice. If you're performing a retrieval-based question answering task, then RetrievalQAChain would be the appropriate choice.

Regarding performance differences, I wasn't able to find specific information within the repository. The performance of each method would likely depend on the specific operations being performed and the resources available. If you have specific performance concerns, it might be helpful to conduct some benchmarking tests to compare the performance of each method in your specific use case.

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

Sources

#### 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. If you want to continue the conversation, start your reply with @dosu-bot.

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