EntityRetriever module

EntityRetriever

Bases: Module

Retrieve entities from a knowledge base, based on the embedding vector.

This module is useful to implement vector-only (retrieval augmented generation) RAG systems, for KAG (knowledge augmented generation) systems see the KnowledgeRetriever module.

If you give multiple entity models to this module, the LM will select the most suitable one to perform the search. Having multiple entity models to search for is an easy way to enhance the performance of you RAG system by having multiple indexes (one per entity model).

import synalinks
import asyncio
from typing import Literal

class Query(synalinks.DataModel):
    query: str = synalinks.Field(
        description="The user query",
    )

class Answer(synalinks.DataModel):
    query: str = synalinks.Field(
        description="The answer to the user query",
    )

class Document(synalinks.Entity):
    label: Literal["Document"]
    content: str = synalinks.Field(
        description="The document's content",
    )

class Chunk(synalinks.Entity):
    label: Literal["Chunk"]
    content: str = synalinks.Field(
        description="The chunk's content",
    )

class IsPartOf(synalinks.Relation):
    subj: Chunk
    label: Literal["IsPartOf"]
    obj: Document

knowledge_base = synalinks.KnowledgeBase(
    index_name="neo4j://localhost:7687",
    entity_models=[Document, Chunk],
    relation_models=[IsPartOf],
    embedding_model=embedding_model,
    metric="cosine",
    wipe_on_start=False,
)

language_model = synalinks.LanguageModel(
    model="ollama/mistral",
)

async def main():
    inputs = synalinks.Input(data_model=Query)
    x = await synalinks.EntityRetriever(
        entity_models=[Chunk],
        language_model=language_model,
        knowledge_base=knowledge_base,
    )(inputs)
    outputs = await synalinks.Generator(
        data_model=Answer,
        language_model=language_model,
    )(x)

    program = synalinks.Program(
        inputs=inputs,
        outputs=outputs,
        name="rag_program",
        description="A naive RAG program",
    )

if __name__ == "__main__":
    asyncio.run(main())

Parameters:

Name	Type	Description	Default
knowledge_base	KnowledgeBase	The knowledge base to use.	None
language_model	LanguageModel	The language model to use.	None
entity_models	list	The list of entities models to search for being a list of Entity data models.	None
k	int	Maximum number of similar entities to return (Defaults to 10).	10
threshold	float	Minimum similarity score for results. Entities with similarity below this threshold are excluded. Should be between 0.0 and 1.0 (Defaults to 0.7).	0.7
prompt_template	str	The default jinja2 prompt template to use (see Generator).	None
examples	list	The default examples to use in the prompt (see Generator).	None
instructions	list	The default instructions to use (see Generator).	None
use_inputs_schema	bool	Optional. Whether or not use the inputs schema in the prompt (Default to False) (see Generator).	False
use_outputs_schema	bool	Optional. Whether or not use the outputs schema in the prompt (Default to False) (see Generator).	False
return_inputs	bool	Optional. Whether or not to concatenate the inputs to the outputs (Default to True).	True
return_query	bool	Optional. Whether or not to concatenate the search query to the outputs (Default to True).	True
name	str	Optional. The name of the module.	None
description	str	Optional. The description of the module.	None
trainable	bool	Whether the module's variables should be trainable.	True

Source code in synalinks/src/modules/knowledge/entity_retriever.py

@synalinks_export(
    [
        "synalinks.modules.EntityRetriever",
        "synalinks.EntityRetriever",
    ]
)
class EntityRetriever(Module):
    """Retrieve entities from a knowledge base, based on the embedding vector.

    This module is useful to implement vector-only (retrieval augmented generation) RAG
    systems, for KAG (knowledge augmented generation) systems see the
    `KnowledgeRetriever` module.

    If you give multiple entity models to this module, the LM will select the most
    suitable one to perform the search. Having multiple entity models to search
    for is an easy way to enhance the performance of you RAG system by having
    multiple indexes (one per entity model).

    ```python
    import synalinks
    import asyncio
    from typing import Literal

    class Query(synalinks.DataModel):
        query: str = synalinks.Field(
            description="The user query",
        )

    class Answer(synalinks.DataModel):
        query: str = synalinks.Field(
            description="The answer to the user query",
        )

    class Document(synalinks.Entity):
        label: Literal["Document"]
        content: str = synalinks.Field(
            description="The document's content",
        )

    class Chunk(synalinks.Entity):
        label: Literal["Chunk"]
        content: str = synalinks.Field(
            description="The chunk's content",
        )

    class IsPartOf(synalinks.Relation):
        subj: Chunk
        label: Literal["IsPartOf"]
        obj: Document

    knowledge_base = synalinks.KnowledgeBase(
        index_name="neo4j://localhost:7687",
        entity_models=[Document, Chunk],
        relation_models=[IsPartOf],
        embedding_model=embedding_model,
        metric="cosine",
        wipe_on_start=False,
    )

    language_model = synalinks.LanguageModel(
        model="ollama/mistral",
    )

    async def main():
        inputs = synalinks.Input(data_model=Query)
        x = await synalinks.EntityRetriever(
            entity_models=[Chunk],
            language_model=language_model,
            knowledge_base=knowledge_base,
        )(inputs)
        outputs = await synalinks.Generator(
            data_model=Answer,
            language_model=language_model,
        )(x)

        program = synalinks.Program(
            inputs=inputs,
            outputs=outputs,
            name="rag_program",
            description="A naive RAG program",
        )

    if __name__ == "__main__":
        asyncio.run(main())
    ```

    Args:
        knowledge_base (KnowledgeBase): The knowledge base to use.
        language_model (LanguageModel): The language model to use.
        entity_models (list): The list of entities models to search for
            being a list of `Entity` data models.
        k (int): Maximum number of similar entities to return
            (Defaults to 10).
        threshold (float): Minimum similarity score for results.
            Entities with similarity below this threshold are excluded.
            Should be between 0.0 and 1.0 (Defaults to 0.7).
        prompt_template (str): The default jinja2 prompt template
            to use (see `Generator`).
        examples (list): The default examples to use in the prompt
            (see `Generator`).
        instructions (list): The default instructions to use (see `Generator`).
        use_inputs_schema (bool): Optional. Whether or not use the inputs schema in
            the prompt (Default to False) (see `Generator`).
        use_outputs_schema (bool): Optional. Whether or not use the outputs schema in
            the prompt (Default to False) (see `Generator`).
        return_inputs (bool): Optional. Whether or not to concatenate the inputs to
            the outputs (Default to True).
        return_query (bool): Optional. Whether or not to concatenate the search query to
            the outputs (Default to True).
        name (str): Optional. The name of the module.
        description (str): Optional. The description of the module.
        trainable (bool): Whether the module's variables should be trainable.
    """

    def __init__(
        self,
        knowledge_base=None,
        language_model=None,
        entity_models=None,
        k=10,
        threshold=0.7,
        prompt_template=None,
        examples=None,
        instructions=None,
        use_inputs_schema=False,
        use_outputs_schema=False,
        return_inputs=True,
        return_query=True,
        name=None,
        description=None,
        trainable=True,
    ):
        super().__init__(
            name=name,
            description=description,
            trainable=trainable,
        )
        self.knowledge_base = knowledge_base
        self.language_model = language_model
        self.k = k
        self.threshold = threshold
        self.prompt_template = prompt_template
        self.examples = examples
        self.instructions = instructions
        self.use_inputs_schema = use_inputs_schema
        self.use_outputs_schema = use_outputs_schema
        self.return_inputs = return_inputs
        self.return_query = return_query

        self.schema = SimilaritySearch.get_schema()

        if entity_models:
            node_labels = [
                entity_model.get_schema().get("title") for entity_model in entity_models
            ]
        else:
            node_labels = []
        node_labels.append("*")

        self.schema = dynamic_enum(
            schema=self.schema,
            prop_to_update="entity_label",
            labels=node_labels,
            description=("The entity label to search for (use `*` to match them all)"),
        )

        self.query_generator = Generator(
            schema=self.schema,
            language_model=self.language_model,
            prompt_template=self.prompt_template,
            examples=self.examples,
            instructions=self.instructions,
            use_inputs_schema=self.use_inputs_schema,
            use_outputs_schema=self.use_outputs_schema,
            return_inputs=False,
            name=self.name + "_query_generator",
        )

    async def call(self, inputs, training=False):
        if not inputs:
            return None
        similarity_search_query = await self.query_generator(
            inputs,
            training=training,
        )
        if self.return_inputs:
            if self.return_query:
                return await ops.concat(
                    inputs,
                    await ops.concat(
                        similarity_search_query,
                        await ops.similarity_search(
                            similarity_search_query,
                            knowledge_base=self.knowledge_base,
                            k=self.k,
                            threshold=self.threshold,
                            name=self.name + "_similarity_search",
                        ),
                        name=self.name + "_similarity_search_with_query_and_inputs",
                    ),
                )
            else:
                return await ops.concat(
                    inputs,
                    await ops.similarity_search(
                        similarity_search_query,
                        knowledge_base=self.knowledge_base,
                        k=self.k,
                        threshold=self.threshold,
                        name=self.name + "_similarity_search",
                    ),
                    name=self.name + "_similarity_search_with_inputs",
                )
        else:
            if self.return_query:
                return await ops.concat(
                    similarity_search_query,
                    await ops.similarity_search(
                        similarity_search_query,
                        knowledge_base=self.knowledge_base,
                        k=self.k,
                        threshold=self.threshold,
                        name=self.name + "_similarity_search",
                    ),
                    name=self.name + "_similarity_search_with_query",
                )
            else:
                return await ops.similarity_search(
                    similarity_search_query,
                    knowledge_base=self.knowledge_base,
                    k=self.k,
                    threshold=self.threshold,
                    name=self.name + "_similarity_search",
                )