Spaces:
Runtime error
Runtime error
File size: 6,300 Bytes
105b369 |
1 2 3 4 5 6 7 8 9 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 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 |
from hashlib import md5
from typing import List, Optional
import json
try:
import lancedb
import pyarrow as pa
except ImportError:
raise ImportError("`lancedb` not installed.")
from phi.document import Document
from phi.embedder import Embedder
from phi.embedder.openai import OpenAIEmbedder
from phi.vectordb.base import VectorDb
from phi.vectordb.distance import Distance
from phi.utils.log import logger
class LanceDb(VectorDb):
def __init__(
self,
embedder: Embedder = OpenAIEmbedder(),
distance: Distance = Distance.cosine,
connection: Optional[lancedb.db.LanceTable] = None,
uri: Optional[str] = "/tmp/lancedb",
table_name: Optional[str] = "phi",
nprobes: Optional[int] = 20,
**kwargs,
):
# Embedder for embedding the document contents
self.embedder: Embedder = embedder
self.dimensions: int = self.embedder.dimensions
# Distance metric
self.distance: Distance = distance
# Connection to lancedb table, can also be provided to use an existing connection
self.uri = uri
self.client = lancedb.connect(self.uri)
self.nprobes = nprobes
if connection:
if not isinstance(connection, lancedb.db.LanceTable):
raise ValueError(
"connection should be an instance of lancedb.db.LanceTable, ",
f"got {type(connection)}",
)
self.connection = connection
self.table_name = self.connection.name
self._vector_col = self.connection.schema.names[0]
self._id = self.tbl.schema.names[1] # type: ignore
else:
self.table_name = table_name
self.connection = self._init_table()
# Lancedb kwargs
self.kwargs = kwargs
def create(self) -> lancedb.db.LanceTable:
return self._init_table()
def _init_table(self) -> lancedb.db.LanceTable:
self._id = "id"
self._vector_col = "vector"
schema = pa.schema(
[
pa.field(
self._vector_col,
pa.list_(
pa.float32(),
len(self.embedder.get_embedding("test")), # type: ignore
),
),
pa.field(self._id, pa.string()),
pa.field("payload", pa.string()),
]
)
logger.info(f"Creating table: {self.table_name}")
tbl = self.client.create_table(self.table_name, schema=schema, mode="overwrite")
return tbl
def doc_exists(self, document: Document) -> bool:
"""
Validating if the document exists or not
Args:
document (Document): Document to validate
"""
if self.client:
cleaned_content = document.content.replace("\x00", "\ufffd")
doc_id = md5(cleaned_content.encode()).hexdigest()
result = self.connection.search().where(f"{self._id}='{doc_id}'").to_arrow()
return len(result) > 0
return False
def insert(self, documents: List[Document]) -> None:
logger.debug(f"Inserting {len(documents)} documents")
data = []
for document in documents:
document.embed(embedder=self.embedder)
cleaned_content = document.content.replace("\x00", "\ufffd")
doc_id = str(md5(cleaned_content.encode()).hexdigest())
payload = {
"name": document.name,
"meta_data": document.meta_data,
"content": cleaned_content,
"usage": document.usage,
}
data.append(
{
"id": doc_id,
"vector": document.embedding,
"payload": json.dumps(payload),
}
)
logger.debug(f"Inserted document: {document.name} ({document.meta_data})")
self.connection.add(data)
logger.debug(f"Upsert {len(data)} documents")
def upsert(self, documents: List[Document]) -> None:
"""
Upsert documents into the database.
Args:
documents (List[Document]): List of documents to upsert
"""
logger.debug("Redirecting the request to insert")
self.insert(documents)
def search(self, query: str, limit: int = 5) -> List[Document]:
query_embedding = self.embedder.get_embedding(query)
if query_embedding is None:
logger.error(f"Error getting embedding for Query: {query}")
return []
results = (
self.connection.search(
query=query_embedding,
vector_column_name=self._vector_col,
)
.limit(limit)
.nprobes(self.nprobes)
.to_pandas()
)
# Build search results
search_results: List[Document] = []
try:
for _, item in results.iterrows():
payload = json.loads(item["payload"])
search_results.append(
Document(
name=payload["name"],
meta_data=payload["meta_data"],
content=payload["content"],
embedder=self.embedder,
embedding=item["vector"],
usage=payload["usage"],
)
)
except Exception as e:
logger.error(f"Error building search results: {e}")
return search_results
def delete(self) -> None:
if self.exists():
logger.debug(f"Deleting collection: {self.table_name}")
self.client.drop(self.table_name)
def exists(self) -> bool:
if self.client:
if self.table_name in self.client.table_names():
return True
return False
def get_count(self) -> int:
if self.exists():
return self.client.table(self.table_name).count_rows()
return 0
def optimize(self) -> None:
pass
def clear(self) -> bool:
return False
def name_exists(self, name: str) -> bool:
raise NotImplementedError
|