Skip to content

Awadb

AwadbReader #

Bases: BaseReader

Awadb reader.

Retrieves documents through an existing awadb client. These documents can then be used in a downstream LlamaIndex data structure.

Parameters:

Name Type Description Default
client client

An awadb client.

 required
Source code in llama-index-integrations/readers/llama-index-readers-awadb/llama_index/readers/awadb/base.py 
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
class AwadbReader(BaseReader):
    """Awadb reader.

    Retrieves documents through an existing awadb client.
    These documents can then be used in a downstream LlamaIndex data structure.

    Args:
        client (awadb.client): An awadb client.

    """

    def __init__(self, client: Any):
        """Initialize with parameters."""
        import_err_msg = "`awadb` package not found, please run `pip install awadb`"
        try:
            pass
        except ImportError:
            raise ImportError(import_err_msg)

        self.awadb_client = client

    def load_data(
        self,
        query: np.ndarray,
        k: int = 4,
        separate_documents: bool = True,
    ) -> List[Document]:
        """Load data from Faiss.

        Args:
            query (np.ndarray): A 2D numpy array of query vectors.
            k (int): Number of nearest neighbors to retrieve. Defaults to 4.
            separate_documents (Optional[bool]): Whether to return separate
                documents. Defaults to True.

        Returns:
            List[Document]: A list of documents.

        """
        results = self.awadb_client.Search(
            query,
            k,
            text_in_page_content=None,
            meta_filter=None,
            not_include_fields=None,
        )
        documents = []
        for item_detail in results[0]["ResultItems"]:
            documents.append(Document(text=item_detail["embedding_text"]))

        if not separate_documents:
            # join all documents into one
            text_list = [doc.get_content() for doc in documents]
            text = "\n\n".join(text_list)
            documents = [Document(text=text)]

        return documents

load_data #

load_data(query: ndarray, k: int = 4, separate_documents: bool = True) -> List[Document]

Load data from Faiss.

Parameters:

Name Type Description Default
query ndarray

A 2D numpy array of query vectors.

 required
k int

Number of nearest neighbors to retrieve. Defaults to 4.

 4
separate_documents Optional[bool]

Whether to return separate documents. Defaults to True.

 True

Returns:

Type Description
List[Document]

List[Document]: A list of documents.
Source code in llama-index-integrations/readers/llama-index-readers-awadb/llama_index/readers/awadb/base.py 
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
def load_data(
    self,
    query: np.ndarray,
    k: int = 4,
    separate_documents: bool = True,
) -> List[Document]:
    """Load data from Faiss.

    Args:
        query (np.ndarray): A 2D numpy array of query vectors.
        k (int): Number of nearest neighbors to retrieve. Defaults to 4.
        separate_documents (Optional[bool]): Whether to return separate
            documents. Defaults to True.

    Returns:
        List[Document]: A list of documents.

    """
    results = self.awadb_client.Search(
        query,
        k,
        text_in_page_content=None,
        meta_filter=None,
        not_include_fields=None,
    )
    documents = []
    for item_detail in results[0]["ResultItems"]:
        documents.append(Document(text=item_detail["embedding_text"]))

    if not separate_documents:
        # join all documents into one
        text_list = [doc.get_content() for doc in documents]
        text = "\n\n".join(text_list)
        documents = [Document(text=text)]

    return documents