mirror of
https://github.com/infiniflow/ragflow.git
synced 2025-12-24 23:46:52 +08:00
Feat: message manage (#12083)
### What problem does this PR solve? Message CRUD. Issue #4213 ### Type of change - [x] New Feature (non-breaking change which adds functionality)
This commit is contained in:
Lynn
467
memory/utils/infinity_conn.py
Normal file
467
memory/utils/infinity_conn.py
Normal file
@ -0,0 +1,467 @@
|
||||
#
|
||||
# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
|
||||
import re
|
||||
import json
|
||||
import copy
|
||||
from infinity.common import InfinityException, SortType
|
||||
from infinity.errors import ErrorCode
|
||||
|
||||
from common.decorator import singleton
|
||||
import pandas as pd
|
||||
from common.constants import PAGERANK_FLD, TAG_FLD
|
||||
from common.doc_store.doc_store_base import MatchExpr, MatchTextExpr, MatchDenseExpr, FusionExpr, OrderByExpr
|
||||
from common.doc_store.infinity_conn_base import InfinityConnectionBase
|
||||
from common.time_utils import date_string_to_timestamp
|
||||
|
||||
|
||||
@singleton
|
||||
class InfinityConnection(InfinityConnectionBase):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.mapping_file_name = "message_infinity_mapping.json"
|
||||
|
||||
"""
|
||||
Dataframe and fields convert
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
def field_keyword(field_name: str):
|
||||
# no keywords right now
|
||||
return False
|
||||
|
||||
@staticmethod
|
||||
def convert_message_field_to_infinity(field_name: str):
|
||||
match field_name:
|
||||
case "message_type":
|
||||
return "message_type_kwd"
|
||||
case "status":
|
||||
return "status_int"
|
||||
case _:
|
||||
return field_name
|
||||
|
||||
@staticmethod
|
||||
def convert_infinity_field_to_message(field_name: str):
|
||||
if field_name.startswith("message_type"):
|
||||
return "message_type"
|
||||
if field_name.startswith("status"):
|
||||
return "status"
|
||||
if re.match(r"q_\d+_vec", field_name):
|
||||
return "content_embed"
|
||||
return field_name
|
||||
|
||||
def convert_select_fields(self, output_fields: list[str]) -> list[str]:
|
||||
return list({self.convert_message_field_to_infinity(f) for f in output_fields})
|
||||
|
||||
@staticmethod
|
||||
def convert_matching_field(field_weight_str: str) -> str:
|
||||
tokens = field_weight_str.split("^")
|
||||
field = tokens[0]
|
||||
if field == "content":
|
||||
field = "content@ft_contentm_rag_fine"
|
||||
tokens[0] = field
|
||||
return "^".join(tokens)
|
||||
|
||||
@staticmethod
|
||||
def convert_condition_and_order_field(field_name: str):
|
||||
match field_name:
|
||||
case "message_type":
|
||||
return "message_type_kwd"
|
||||
case "status":
|
||||
return "status_int"
|
||||
case "valid_at":
|
||||
return "valid_at_flt"
|
||||
case "invalid_at":
|
||||
return "invalid_at_flt"
|
||||
case "forget_at":
|
||||
return "forget_at_flt"
|
||||
case _:
|
||||
return field_name
|
||||
|
||||
"""
|
||||
CRUD operations
|
||||
"""
|
||||
|
||||
def search(
|
||||
self,
|
||||
select_fields: list[str],
|
||||
highlight_fields: list[str],
|
||||
condition: dict,
|
||||
match_expressions: list[MatchExpr],
|
||||
order_by: OrderByExpr,
|
||||
offset: int,
|
||||
limit: int,
|
||||
index_names: str | list[str],
|
||||
memory_ids: list[str],
|
||||
agg_fields: list[str] | None = None,
|
||||
rank_feature: dict | None = None,
|
||||
hide_forgotten: bool = True,
|
||||
) -> tuple[pd.DataFrame, int]:
|
||||
"""
|
||||
BUG: Infinity returns empty for a highlight field if the query string doesn't use that field.
|
||||
"""
|
||||
if isinstance(index_names, str):
|
||||
index_names = index_names.split(",")
|
||||
assert isinstance(index_names, list) and len(index_names) > 0
|
||||
inf_conn = self.connPool.get_conn()
|
||||
db_instance = inf_conn.get_database(self.dbName)
|
||||
df_list = list()
|
||||
table_list = list()
|
||||
if hide_forgotten:
|
||||
condition.update({"must_not": {"exists": "forget_at_flt"}})
|
||||
output = select_fields.copy()
|
||||
output = self.convert_select_fields(output)
|
||||
if agg_fields is None:
|
||||
agg_fields = []
|
||||
for essential_field in ["id"] + agg_fields:
|
||||
if essential_field not in output:
|
||||
output.append(essential_field)
|
||||
score_func = ""
|
||||
score_column = ""
|
||||
for matchExpr in match_expressions:
|
||||
if isinstance(matchExpr, MatchTextExpr):
|
||||
score_func = "score()"
|
||||
score_column = "SCORE"
|
||||
break
|
||||
if not score_func:
|
||||
for matchExpr in match_expressions:
|
||||
if isinstance(matchExpr, MatchDenseExpr):
|
||||
score_func = "similarity()"
|
||||
score_column = "SIMILARITY"
|
||||
break
|
||||
if match_expressions:
|
||||
if score_func not in output:
|
||||
output.append(score_func)
|
||||
if PAGERANK_FLD not in output:
|
||||
output.append(PAGERANK_FLD)
|
||||
output = [f for f in output if f != "_score"]
|
||||
if limit <= 0:
|
||||
# ElasticSearch default limit is 10000
|
||||
limit = 10000
|
||||
|
||||
# Prepare expressions common to all tables
|
||||
filter_cond = None
|
||||
filter_fulltext = ""
|
||||
if condition:
|
||||
condition_dict = {self.convert_condition_and_order_field(k): v for k, v in condition.items()}
|
||||
table_found = False
|
||||
for indexName in index_names:
|
||||
for mem_id in memory_ids:
|
||||
table_name = f"{indexName}_{mem_id}"
|
||||
try:
|
||||
filter_cond = self.equivalent_condition_to_str(condition_dict, db_instance.get_table(table_name))
|
||||
table_found = True
|
||||
break
|
||||
except Exception:
|
||||
pass
|
||||
if table_found:
|
||||
break
|
||||
if not table_found:
|
||||
self.logger.error(f"No valid tables found for indexNames {index_names} and memoryIds {memory_ids}")
|
||||
return pd.DataFrame(), 0
|
||||
|
||||
for matchExpr in match_expressions:
|
||||
if isinstance(matchExpr, MatchTextExpr):
|
||||
if filter_cond and "filter" not in matchExpr.extra_options:
|
||||
matchExpr.extra_options.update({"filter": filter_cond})
|
||||
matchExpr.fields = [self.convert_matching_field(field) for field in matchExpr.fields]
|
||||
fields = ",".join(matchExpr.fields)
|
||||
filter_fulltext = f"filter_fulltext('{fields}', '{matchExpr.matching_text}')"
|
||||
if filter_cond:
|
||||
filter_fulltext = f"({filter_cond}) AND {filter_fulltext}"
|
||||
minimum_should_match = matchExpr.extra_options.get("minimum_should_match", 0.0)
|
||||
if isinstance(minimum_should_match, float):
|
||||
str_minimum_should_match = str(int(minimum_should_match * 100)) + "%"
|
||||
matchExpr.extra_options["minimum_should_match"] = str_minimum_should_match
|
||||
|
||||
# Add rank_feature support
|
||||
if rank_feature and "rank_features" not in matchExpr.extra_options:
|
||||
# Convert rank_feature dict to Infinity's rank_features string format
|
||||
# Format: "field^feature_name^weight,field^feature_name^weight"
|
||||
rank_features_list = []
|
||||
for feature_name, weight in rank_feature.items():
|
||||
# Use TAG_FLD as the field containing rank features
|
||||
rank_features_list.append(f"{TAG_FLD}^{feature_name}^{weight}")
|
||||
if rank_features_list:
|
||||
matchExpr.extra_options["rank_features"] = ",".join(rank_features_list)
|
||||
|
||||
for k, v in matchExpr.extra_options.items():
|
||||
if not isinstance(v, str):
|
||||
matchExpr.extra_options[k] = str(v)
|
||||
self.logger.debug(f"INFINITY search MatchTextExpr: {json.dumps(matchExpr.__dict__)}")
|
||||
elif isinstance(matchExpr, MatchDenseExpr):
|
||||
if filter_fulltext and "filter" not in matchExpr.extra_options:
|
||||
matchExpr.extra_options.update({"filter": filter_fulltext})
|
||||
for k, v in matchExpr.extra_options.items():
|
||||
if not isinstance(v, str):
|
||||
matchExpr.extra_options[k] = str(v)
|
||||
similarity = matchExpr.extra_options.get("similarity")
|
||||
if similarity:
|
||||
matchExpr.extra_options["threshold"] = similarity
|
||||
del matchExpr.extra_options["similarity"]
|
||||
self.logger.debug(f"INFINITY search MatchDenseExpr: {json.dumps(matchExpr.__dict__)}")
|
||||
elif isinstance(matchExpr, FusionExpr):
|
||||
self.logger.debug(f"INFINITY search FusionExpr: {json.dumps(matchExpr.__dict__)}")
|
||||
|
||||
order_by_expr_list = list()
|
||||
if order_by.fields:
|
||||
for order_field in order_by.fields:
|
||||
order_field_name = self.convert_condition_and_order_field(order_field[0])
|
||||
if order_field[1] == 0:
|
||||
order_by_expr_list.append((order_field_name, SortType.Asc))
|
||||
else:
|
||||
order_by_expr_list.append((order_field_name, SortType.Desc))
|
||||
|
||||
total_hits_count = 0
|
||||
# Scatter search tables and gather the results
|
||||
for indexName in index_names:
|
||||
for memory_id in memory_ids:
|
||||
table_name = f"{indexName}_{memory_id}"
|
||||
try:
|
||||
table_instance = db_instance.get_table(table_name)
|
||||
except Exception:
|
||||
continue
|
||||
table_list.append(table_name)
|
||||
builder = table_instance.output(output)
|
||||
if len(match_expressions) > 0:
|
||||
for matchExpr in match_expressions:
|
||||
if isinstance(matchExpr, MatchTextExpr):
|
||||
fields = ",".join(matchExpr.fields)
|
||||
builder = builder.match_text(
|
||||
fields,
|
||||
matchExpr.matching_text,
|
||||
matchExpr.topn,
|
||||
matchExpr.extra_options.copy(),
|
||||
)
|
||||
elif isinstance(matchExpr, MatchDenseExpr):
|
||||
builder = builder.match_dense(
|
||||
matchExpr.vector_column_name,
|
||||
matchExpr.embedding_data,
|
||||
matchExpr.embedding_data_type,
|
||||
matchExpr.distance_type,
|
||||
matchExpr.topn,
|
||||
matchExpr.extra_options.copy(),
|
||||
)
|
||||
elif isinstance(matchExpr, FusionExpr):
|
||||
builder = builder.fusion(matchExpr.method, matchExpr.topn, matchExpr.fusion_params)
|
||||
else:
|
||||
if filter_cond and len(filter_cond) > 0:
|
||||
builder.filter(filter_cond)
|
||||
if order_by.fields:
|
||||
builder.sort(order_by_expr_list)
|
||||
builder.offset(offset).limit(limit)
|
||||
mem_res, extra_result = builder.option({"total_hits_count": True}).to_df()
|
||||
if extra_result:
|
||||
total_hits_count += int(extra_result["total_hits_count"])
|
||||
self.logger.debug(f"INFINITY search table: {str(table_name)}, result: {str(mem_res)}")
|
||||
df_list.append(mem_res)
|
||||
self.connPool.release_conn(inf_conn)
|
||||
res = self.concat_dataframes(df_list, output)
|
||||
if match_expressions:
|
||||
res["_score"] = res[score_column] + res[PAGERANK_FLD]
|
||||
res = res.sort_values(by="_score", ascending=False).reset_index(drop=True)
|
||||
res = res.head(limit)
|
||||
self.logger.debug(f"INFINITY search final result: {str(res)}")
|
||||
return res, total_hits_count
|
||||
|
||||
def get_forgotten_messages(self, select_fields: list[str], index_name: str, memory_id: str, limit: int=2000):
|
||||
condition = {"memory_id": memory_id, "exists": "forget_at_flt"}
|
||||
order_by = OrderByExpr()
|
||||
order_by.asc("forget_at_flt")
|
||||
# query
|
||||
inf_conn = self.connPool.get_conn()
|
||||
db_instance = inf_conn.get_database(self.dbName)
|
||||
table_name = f"{index_name}_{memory_id}"
|
||||
table_instance = db_instance.get_table(table_name)
|
||||
output_fields = [self.convert_message_field_to_infinity(f) for f in select_fields]
|
||||
builder = table_instance.output(output_fields)
|
||||
filter_cond = self.equivalent_condition_to_str(condition, db_instance.get_table(table_name))
|
||||
builder.filter(filter_cond)
|
||||
order_by_expr_list = list()
|
||||
if order_by.fields:
|
||||
for order_field in order_by.fields:
|
||||
order_field_name = self.convert_condition_and_order_field(order_field[0])
|
||||
if order_field[1] == 0:
|
||||
order_by_expr_list.append((order_field_name, SortType.Asc))
|
||||
else:
|
||||
order_by_expr_list.append((order_field_name, SortType.Desc))
|
||||
builder.sort(order_by_expr_list)
|
||||
builder.offset(0).limit(limit)
|
||||
mem_res, _ = builder.option({"total_hits_count": True}).to_df()
|
||||
res = self.concat_dataframes(mem_res, output_fields)
|
||||
res.head(limit)
|
||||
self.connPool.release_conn(inf_conn)
|
||||
return res
|
||||
|
||||
def get(self, message_id: str, index_name: str, memory_ids: list[str]) -> dict | None:
|
||||
inf_conn = self.connPool.get_conn()
|
||||
db_instance = inf_conn.get_database(self.dbName)
|
||||
df_list = list()
|
||||
assert isinstance(memory_ids, list)
|
||||
table_list = list()
|
||||
for memoryId in memory_ids:
|
||||
table_name = f"{index_name}_{memoryId}"
|
||||
table_list.append(table_name)
|
||||
try:
|
||||
table_instance = db_instance.get_table(table_name)
|
||||
except Exception:
|
||||
self.logger.warning(f"Table not found: {table_name}, this memory isn't created in Infinity. Maybe it is created in other document engine.")
|
||||
continue
|
||||
mem_res, _ = table_instance.output(["*"]).filter(f"id = '{message_id}'").to_df()
|
||||
self.logger.debug(f"INFINITY get table: {str(table_list)}, result: {str(mem_res)}")
|
||||
df_list.append(mem_res)
|
||||
self.connPool.release_conn(inf_conn)
|
||||
res = self.concat_dataframes(df_list, ["id"])
|
||||
fields = set(res.columns.tolist())
|
||||
res_fields = self.get_fields(res, list(fields))
|
||||
return res_fields.get(message_id, None)
|
||||
|
||||
def insert(self, documents: list[dict], index_name: str, memory_id: str = None) -> list[str]:
|
||||
if not documents:
|
||||
return []
|
||||
inf_conn = self.connPool.get_conn()
|
||||
db_instance = inf_conn.get_database(self.dbName)
|
||||
table_name = f"{index_name}_{memory_id}"
|
||||
vector_size = int(len(documents[0]["content_embed"]))
|
||||
try:
|
||||
table_instance = db_instance.get_table(table_name)
|
||||
except InfinityException as e:
|
||||
# src/common/status.cppm, kTableNotExist = 3022
|
||||
if e.error_code != ErrorCode.TABLE_NOT_EXIST:
|
||||
raise
|
||||
if vector_size == 0:
|
||||
raise ValueError("Cannot infer vector size from documents")
|
||||
self.create_idx(index_name, memory_id, vector_size)
|
||||
table_instance = db_instance.get_table(table_name)
|
||||
|
||||
# embedding fields can't have a default value....
|
||||
embedding_columns = []
|
||||
table_columns = table_instance.show_columns().rows()
|
||||
for n, ty, _, _ in table_columns:
|
||||
r = re.search(r"Embedding\([a-z]+,([0-9]+)\)", ty)
|
||||
if not r:
|
||||
continue
|
||||
embedding_columns.append((n, int(r.group(1))))
|
||||
|
||||
docs = copy.deepcopy(documents)
|
||||
for d in docs:
|
||||
assert "_id" not in d
|
||||
assert "id" in d
|
||||
for k, v in list(d.items()):
|
||||
field_name = self.convert_message_field_to_infinity(k)
|
||||
if field_name in ["valid_at", "invalid_at", "forget_at"]:
|
||||
d[f"{field_name}_flt"] = date_string_to_timestamp(v) if v else 0
|
||||
if v is None:
|
||||
d[field_name] = ""
|
||||
elif self.field_keyword(k):
|
||||
if isinstance(v, list):
|
||||
d[k] = "###".join(v)
|
||||
else:
|
||||
d[k] = v
|
||||
elif k == "memory_id":
|
||||
if isinstance(d[k], list):
|
||||
d[k] = d[k][0] # since d[k] is a list, but we need a str
|
||||
elif field_name == "content_embed":
|
||||
d[f"q_{vector_size}_vec"] = d["content_embed"]
|
||||
d.pop("content_embed")
|
||||
else:
|
||||
d[field_name] = v
|
||||
if k != field_name:
|
||||
d.pop(k)
|
||||
|
||||
for n, vs in embedding_columns:
|
||||
if n in d:
|
||||
continue
|
||||
d[n] = [0] * vs
|
||||
ids = ["'{}'".format(d["id"]) for d in docs]
|
||||
str_ids = ", ".join(ids)
|
||||
str_filter = f"id IN ({str_ids})"
|
||||
table_instance.delete(str_filter)
|
||||
table_instance.insert(docs)
|
||||
self.connPool.release_conn(inf_conn)
|
||||
self.logger.debug(f"INFINITY inserted into {table_name} {str_ids}.")
|
||||
return []
|
||||
|
||||
def update(self, condition: dict, new_value: dict, index_name: str, memory_id: str) -> bool:
|
||||
inf_conn = self.connPool.get_conn()
|
||||
db_instance = inf_conn.get_database(self.dbName)
|
||||
table_name = f"{index_name}_{memory_id}"
|
||||
table_instance = db_instance.get_table(table_name)
|
||||
|
||||
columns = {}
|
||||
if table_instance:
|
||||
for n, ty, de, _ in table_instance.show_columns().rows():
|
||||
columns[n] = (ty, de)
|
||||
condition_dict = {self.convert_condition_and_order_field(k): v for k, v in condition.items()}
|
||||
filter = self.equivalent_condition_to_str(condition_dict, table_instance)
|
||||
update_dict = {self.convert_message_field_to_infinity(k): v for k, v in new_value.items()}
|
||||
date_floats = {}
|
||||
for k, v in update_dict.items():
|
||||
if k in ["valid_at", "invalid_at", "forget_at"]:
|
||||
date_floats[f"{k}_flt"] = date_string_to_timestamp(v) if v else 0
|
||||
elif self.field_keyword(k):
|
||||
if isinstance(v, list):
|
||||
update_dict[k] = "###".join(v)
|
||||
else:
|
||||
update_dict[k] = v
|
||||
elif k == "memory_id":
|
||||
if isinstance(update_dict[k], list):
|
||||
update_dict[k] = update_dict[k][0] # since d[k] is a list, but we need a str
|
||||
else:
|
||||
update_dict[k] = v
|
||||
if date_floats:
|
||||
update_dict.update(date_floats)
|
||||
|
||||
self.logger.debug(f"INFINITY update table {table_name}, filter {filter}, newValue {new_value}.")
|
||||
table_instance.update(filter, update_dict)
|
||||
self.connPool.release_conn(inf_conn)
|
||||
return True
|
||||
|
||||
"""
|
||||
Helper functions for search result
|
||||
"""
|
||||
|
||||
def get_fields(self, res: tuple[pd.DataFrame, int] | pd.DataFrame, fields: list[str]) -> dict[str, dict]:
|
||||
if isinstance(res, tuple):
|
||||
res = res[0]
|
||||
if not fields:
|
||||
return {}
|
||||
fields_all = fields.copy()
|
||||
fields_all.append("id")
|
||||
fields_all = {self.convert_message_field_to_infinity(f) for f in fields_all}
|
||||
|
||||
column_map = {col.lower(): col for col in res.columns}
|
||||
matched_columns = {column_map[col.lower()]: col for col in fields_all if col.lower() in column_map}
|
||||
none_columns = [col for col in fields_all if col.lower() not in column_map]
|
||||
|
||||
res2 = res[matched_columns.keys()]
|
||||
res2 = res2.rename(columns=matched_columns)
|
||||
res2.drop_duplicates(subset=["id"], inplace=True)
|
||||
|
||||
for column in list(res2.columns):
|
||||
k = column.lower()
|
||||
if self.field_keyword(k):
|
||||
res2[column] = res2[column].apply(lambda v: [kwd for kwd in v.split("###") if kwd])
|
||||
else:
|
||||
pass
|
||||
for column in ["content"]:
|
||||
if column in res2:
|
||||
del res2[column]
|
||||
for column in none_columns:
|
||||
res2[column] = None
|
||||
|
||||
res_dict = res2.set_index("id").to_dict(orient="index")
|
||||
return {_id: {self.convert_infinity_field_to_message(k): v for k, v in doc.items()} for _id, doc in res_dict.items()}
|
||||
Reference in New Issue
Block a user