def _load_similar_data(
        self,
        embedding: List[float],
        k: int = 10,
        metadata_fields: Optional[List[str]] = None,
        where: Optional[str] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Load data by similarity search from the jaguar store."""
        ### args is additional search conditions, such as time decay
        args = kwargs.get("args")
        fetch_k = kwargs.get("fetch_k", -1)

        vcol = self._vector_index
        vtype = self._vector_type
        str_embeddings = [str(f) for f in embedding]
        qv_comma = ",".join(str_embeddings)
        podstore = self._pod + "." + self._store
        q = (
            "select similarity("
            + vcol
            + ",'"
            + qv_comma
            + "','topk="
            + str(k)
            + ",fetch_k="
            + str(fetch_k)
            + ",type="
            + vtype
        )
        q += ",with_score,with_text"
        if args is not None:
            q += "," + args

        if metadata_fields is not None:
            x = "&".join(metadata_fields)
            q += ",metadata=" + x

        q += "') from " + podstore

        if where is not None:
            q += " where " + where

        jarr = self.run(q)
        if jarr is None:
            return []

        docs = []
        for js in jarr:
            score = js["score"]
            text = js["text"]
            zid = js["zid"]

            md = {}
            md["zid"] = zid
            md["score"] = score
            if metadata_fields is not None:
                for m in metadata_fields:
                    md[m] = js[m]

            doc = Document(
                id_=zid,
                text=text,
                metadata=md,
            )
            docs.append(doc)

        return docs
Load data by similarity search from the jaguar store.
llama-index-integrations/readers/llama-index-readers-jaguar/llama_index/readers/jaguar/base.py