Can we use Synonym filter retrieval option which is available in Elastic Search in llama index framework

[amitguptadumka]

Hi,
I have my embedding stored in Elastic search vector db created via Llama index framework. Now ES has native synonym filter retrieval functionality.
In llama index framework can i use that functionality, if yes how?

[dosubot[bot]]

🤖

Hello @amitguptadumka! Great to see you back and interacting with the LlamaIndex repository again. Hope you're doing well.

Based on the information available in the LlamaIndex repository, it appears that the framework does have built-in functionality for interacting with Elastic Search's vector database. This is evident from the ElasticsearchStore class defined in the elasticsearch.py file under the llama_index/vector_stores directory. This class includes methods for adding, deleting, and querying nodes in an Elasticsearch index.

However, there is no explicit mention or evidence of support for Elastic Search's native synonym filter retrieval functionality within the LlamaIndex framework. The ElasticsearchStore class does not seem to include any methods or functionality specifically designed to integrate with this feature of Elastic Search.

It's possible that this functionality could be implemented manually by extending the ElasticsearchStore class or by using Elastic Search's API in conjunction with LlamaIndex, but this would likely require a deep understanding of both LlamaIndex and Elastic Search, as well as significant development effort.

If you have more specific information about how you're hoping to use the synonym filter retrieval functionality with LlamaIndex, I might be able to provide a more detailed or helpful response. For example, are you looking to use this functionality as part of the process of adding or querying nodes in the Elasticsearch index, or in some other context?

Here is the link to the ElasticsearchStore class for your reference.

Sources

• llama_index/vector_stores/elasticsearch.py

---

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[amitguptadumka]

🤖

Hello @amitguptadumka! Great to see you back and interacting with the LlamaIndex repository again. Hope you're doing well.

Based on the information available in the LlamaIndex repository, it appears that the framework does have built-in functionality for interacting with Elastic Search's vector database. This is evident from the ElasticsearchStore class defined in the elasticsearch.py file under the llama_index/vector_stores directory. This class includes methods for adding, deleting, and querying nodes in an Elasticsearch index.

However, there is no explicit mention or evidence of support for Elastic Search's native synonym filter retrieval functionality within the LlamaIndex framework. The ElasticsearchStore class does not seem to include any methods or functionality specifically designed to integrate with this feature of Elastic Search.

It's possible that this functionality could be implemented manually by extending the ElasticsearchStore class or by using Elastic Search's API in conjunction with LlamaIndex, but this would likely require a deep understanding of both LlamaIndex and Elastic Search, as well as significant development effort.

If you have more specific information about how you're hoping to use the synonym filter retrieval functionality with LlamaIndex, I might be able to provide a more detailed or helpful response. For example, are you looking to use this functionality as part of the process of adding or querying nodes in the Elasticsearch index, or in some other context?

Here is the link to the ElasticsearchStore class for your reference.

Sources

• llama_index/vector_stores/elasticsearch.py

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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@dosu-bot.
So i am hoping to use this functionality for retrieval where synonyms are present in the query instead of actual word in document/index.
For example a query can be "What is the population of England" can be asked as "What is the population of UK". So in both cases same set of chunks should be retrieved, that's the goal of my use case. Not sure if llama index has something to tackle this.

[dosubot[bot]]

🤖

Thank you for your detailed explanation of the issue. It seems like you're trying to leverage the synonym filter functionality of Elastic Search within the LlamaIndex framework. While there's no explicit support for this in the ElasticsearchStore class, it might be possible to extend this class or use Elastic Search's API to achieve your goal.

However, to provide a more accurate guidance, I need a bit more information. Could you please provide the following details:

1. The version of Elastic Search you're using.
2. The specific synonym filter you're referring to (if it's a built-in Elastic Search feature or a custom one).
3. Any specific requirements or constraints you have regarding the implementation.

Once I have this information, I'll be able to provide a more detailed and accurate guidance. Dosu

---

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Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

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[amitguptadumka]

🤖

Thank you for your detailed explanation of the issue. It seems like you're trying to leverage the synonym filter functionality of Elastic Search within the LlamaIndex framework. While there's no explicit support for this in the ElasticsearchStore class, it might be possible to extend this class or use Elastic Search's API to achieve your goal.

However, to provide a more accurate guidance, I need a bit more information. Could you please provide the following details:

1. The version of Elastic Search you're using.
2. The specific synonym filter you're referring to (if it's a built-in Elastic Search feature or a custom one).
3. Any specific requirements or constraints you have regarding the implementation.

Once I have this information, I'll be able to provide a more detailed and accurate guidance. 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.

@dosu-bot
i am using ES Version 8.11.1
its synonym api functionality in ES
No specific constraint.

[dosubot[bot]]

🤖

Based on the current implementation of the LlamaIndex framework, it does not natively support Elastic Search's synonym filter retrieval functionality. However, it is possible to extend the ElasticsearchStore class to include this feature.

To achieve this, you would need to modify the Elasticsearch index settings during the index creation process in the _create_index_if_not_exists method. Specifically, you would need to add a synonym filter to the analyzer used for the text_field in the index settings.

Here is a basic example of how you might do this:

index_settings = {
    "settings": {
        "analysis": {
            "filter": {
                "synonym_filter": {
                    "type": "synonym",
                    "synonyms": [
                        "synonym1, synonym2",
                        "synonym3, synonym4"
                    ]
                }
            },
            "analyzer": {
                "synonym_analyzer": {
                    "tokenizer": "standard",
                    "filter": ["lowercase", "synonym_filter"]
                }
            }
        }
    },
    "mappings": {
        "properties": {
            self.vector_field: {
                "type": "dense_vector",
                "dims": dims_length,
                "index": True,
                "similarity": similarityAlgo,
            },
            self.text_field: {
                "type": "text",
                "analyzer": "synonym_analyzer"
            },
            "metadata": {
                "properties": {
                    "document_id": {"type": "keyword"},
                    "doc_id": {"type": "keyword"},
                    "ref_doc_id": {"type": "keyword"},
                }
            },
        }
    }
}

In this example, a new analyzer called synonym_analyzer is created, which uses the synonym_filter. The synonym_filter is defined with a list of synonyms. The text_field is then set to use this synonym_analyzer.

Please note that this is a basic example and the actual implementation may vary based on your specific requirements. For instance, you might want to load the synonyms from a file or an external source, or use a different tokenizer. You should refer to the Elasticsearch documentation for more details on how to configure the synonym filter and analyzer.

As for the retrieval of chunks from the Elasticsearch index, the LlamaIndex framework handles this through the use of the MultiModalVectorStoreIndex class. This class is responsible for managing the storage and retrieval of data from the Elasticsearch index. The retrieval process is handled by the as_retriever method, which returns an instance of MultiModalVectorIndexRetriever. This retriever is responsible for fetching the data from the Elasticsearch index.

The as_query_engine method is also related to the retrieval process. It returns an instance of SimpleMultiModalQueryEngine which uses the retriever to fetch the data.

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

For more details, you can refer to the source code here:

• llama_index/vector_stores/elasticsearch.py
• llama_index/indices/multi_modal/base.py

Sources

• llama_index/vector_stores/elasticsearch.py
• llama_index/indices/multi_modal/base.py

---

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[amitguptadumka]

@dosu-bot
The above code looks fine for implementing synonym filter. Can you provide me step by step guide where and how to plug this in the llama index framework to bring in the functionality.
Thanks.

[dosubot[bot]]

🤖

Based on the information available in the LlamaIndex repository, there doesn't seem to be a specific location in the codebase where the Elasticsearch index settings are configured. The codebase primarily deals with the creation, deletion, and updating of documents and nodes within the LlamaIndex, but it does not contain any explicit references to Elasticsearch or methods for setting up an Elasticsearch index.

As such, integrating the synonym filter functionality of Elasticsearch into the LlamaIndex framework might require a significant modification of the existing codebase. This could involve creating a new method or function that sets up the Elasticsearch index with your desired settings, and then calling this method or function at the appropriate point in the code.

However, without more information about the structure and functionality of the LlamaIndex framework, it's difficult to provide a more detailed guide on how to implement this. If you could provide more context or information about the LlamaIndex codebase, such as where the Elasticsearch client is initialized or how the Elasticsearch index is currently being set up, it would be easier to provide a step-by-step guide on how to integrate your code.

Also, it's worth noting that the compatibility of the LlamaIndex framework with Elastic Search version 8.11.1 is not explicitly mentioned in the repository. Therefore, you might need to ensure that the LlamaIndex framework is compatible with this version of Elasticsearch before proceeding with the integration.

For more detailed guidance, you might want to consider reaching out to the developers of the LlamaIndex framework directly, or consulting the Elasticsearch documentation for more information on how to set up and customize an Elasticsearch index.

I hope this helps, and please let me know if you have any other questions or need further clarification. Dosu

Sources

• llama_index/indices/base.py

---

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[amitguptadumka]

@dosu-bot
I have made below changes to the elasticsearch.py codebase.
"settings": {
"analysis": {
"filter": {
"synonym_filter": {
"type": "synonym",
"synonyms": [
"cdi, clouddataintegration",
"cdi => clouddataintegration"
]
}
},
"analyzer": {
"synonym_analyzer": {
"tokenizer": "standard",
"filter": ["lowercase", "synonym_filter"]
}
}
}
},
"mappings": {
"properties": {
self.vector_field: {
"type": "dense_vector",
"dims": dims_length,
"index": True,
"similarity": similarityAlgo,
},
self.text_field: {
"type": "text",
"analyzer": "synonym_analyzer"
},
"metadata": {
"properties": {
"document_id": {"type": "keyword"},
"doc_id": {"type": "keyword"},
"ref_doc_id": {"type": "keyword"},
}
},
}
}

Now can you please guide my if i have to do any further modifications to any code to achieve synonym retrieval ?

[dosubot[bot]]

🤖

Based on the current structure of the elasticsearch.py file in the LlamaIndex codebase, it seems that the ElasticsearchEmbedding class does not have any method to update the Elasticsearch index settings. The class provides methods for creating an instance of the class from an existing Elasticsearch connection or from credentials, and methods for generating embeddings for a single query text. However, there are no methods for updating the Elasticsearch index settings.

Your modifications to include a synonym filter in the Elasticsearch index settings seem to be correct according to the Elasticsearch documentation for synonym token filters. However, you would need to ensure that these settings are applied when the Elasticsearch index is created.

Since the ElasticsearchEmbedding class does not provide a method to update the index settings, you might need to create the index with the desired settings before using the class. This could be done using the Elasticsearch Python client's create method, as shown in the Elasticsearch Python client documentation.

Here is an example:

from elasticsearch import Elasticsearch

es = Elasticsearch()

body = {
    "settings": {
        "analysis": {
            "filter": {
                "synonym_filter": {
                    "type": "synonym",
                    "synonyms": [
                        "cdi, clouddataintegration",
                        "cdi => clouddataintegration"
                    ]
                }
            },
            "analyzer": {
                "synonym_analyzer": {
                    "tokenizer": "standard",
                    "filter": ["lowercase", "synonym_filter"]
                }
            }
        }
    },
    "mappings": {
        "properties": {
            "vector_field": {
                "type": "dense_vector",
                "dims": dims_length,
                "index": True,
                "similarity": similarityAlgo,
            },
            "text_field": {
                "type": "text",
                "analyzer": "synonym_analyzer"
            },
            "metadata": {
                "properties": {
                    "document_id": {"type": "keyword"},
                    "doc_id": {"type": "keyword"},
                    "ref_doc_id": {"type": "keyword"},
                }
            },
        }
    }
}

es.indices.create(index="your_index_name", body=body)

Please replace "your_index_name" with the name of your index, and ensure that dims_length and similarityAlgo are defined.

After creating the index with the desired settings, you can then use the ElasticsearchEmbedding class to generate embeddings.

Please note that this is a workaround and might not be the most efficient solution if you need to frequently update the index settings. If that's the case, you might need to extend the ElasticsearchEmbedding class to include a method for updating the index settings.

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

Source

Sources

• llama_index/embeddings/elasticsearch.py

---

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