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Upgrade the AwaDB from 0.3.5 to 0.3.6 (#7363)

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pull/7123/head^2
ljeagle 11 months ago committed by GitHub
parent c5edbea34a
commit fb6e63dc36
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2 changed files with 814 additions and 72 deletions
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langchain/vectorstores/awadb.py
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@ -3,11 +3,14 @@ from __future__ import annotations
import logging
import uuid
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Type
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Set, Tuple, Type
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance
# from pydantic import BaseModel, Field, root_validator
@ -30,9 +33,19 @@ class AwaDB(VectorStore):
embedding: Optional[Embeddings] = None,
log_and_data_dir: Optional[str] = None,
client: Optional[awadb.Client] = None,
**kwargs: Any,
) -> None:
"""Initialize with AwaDB client."""
"""Initialize with AwaDB client.
Args:
table_name: Iterable of strings to add to the vectorstore.
embedding: Optional list of metadatas associated with the texts.
log_and_data_dir: Optional whether to duplicate texts.
client: Optional AwaDB client.
kwargs: any possible extend parameters in the future.
Returns:
None.
"""
try:
import awadb
except ImportError:
@ -71,7 +84,7 @@ class AwaDB(VectorStore):
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
is_duplicate_texts: Optional whether to duplicate texts.
kwargs: vectorstore specific parameters.
kwargs: any possible extend parameters in the future.
Returns:
List of ids from adding the texts into the vectorstore.
@ -99,6 +112,16 @@ class AwaDB(VectorStore):
table_name: str,
**kwargs: Any,
) -> bool:
"""Load the local specified table.
Args:
table_name: Table name
kwargs: Any possible extend parameters in the future.
Returns:
Success or failure of loading the local specified table
"""
if self.awadb_client is None:
raise ValueError("AwaDB client is None!!!")
@ -110,7 +133,17 @@ class AwaDB(VectorStore):
k: int = DEFAULT_TOPN,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query."""
"""Return docs most similar to query.
Args:
query: Text query.
k: The maximum number of documents to return.
kwargs: Any possible extend parameters in the future.
Returns:
Returns the k most similar documents to the specified text query.
"""
if self.awadb_client is None:
raise ValueError("AwaDB client is None!!!")
@ -123,7 +156,10 @@ class AwaDB(VectorStore):
llm = llm_embedding.LLMEmbedding()
embedding = llm.Embedding(query)
return self.similarity_search_by_vector(embedding, k)
not_include_fields: Set[str] = {"text_embedding", "_id", "score"}
return self.similarity_search_by_vector(
embedding, k, not_include_fields_in_metadata=not_include_fields
)
def similarity_search_with_score(
self,
@ -131,9 +167,16 @@ class AwaDB(VectorStore):
k: int = DEFAULT_TOPN,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and relevance scores, normalized on a scale from 0 to 1.
"""The most k similar documents and scores of the specified query.
Args:
query: Text query.
k: The k most similar documents to the text query.
kwargs: Any possible extend parameters in the future.
0 is dissimilar, 1 is most similar.
Returns:
The k most similar documents to the specified text query.
0 is dissimilar, 1 is the most similar.
"""
if self.awadb_client is None:
@ -150,17 +193,18 @@ class AwaDB(VectorStore):
results: List[Tuple[Document, float]] = []
scores: List[float] = []
retrieval_docs = self.similarity_search_by_vector(embedding, k, scores)
L2_Norm = 0.0
for score in scores:
L2_Norm = L2_Norm + score * score
dists: List[float] = []
not_include_fields: Set[str] = {"text_embedding", "_id", "score"}
retrieval_docs = self.similarity_search_by_vector(
embedding,
k,
scores=dists,
not_include_fields_in_metadata=not_include_fields,
)
L2_Norm = pow(L2_Norm, 0.5)
doc_no = 0
for doc in retrieval_docs:
doc_tuple = (doc, 1 - (scores[doc_no] / L2_Norm))
doc_tuple = (doc, dists[doc_no])
results.append(doc_tuple)
doc_no = doc_no + 1
@ -172,9 +216,17 @@ class AwaDB(VectorStore):
k: int = DEFAULT_TOPN,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and relevance scores, normalized on a scale from 0 to 1.
"""Return docs and relevance scores
which denote the InnerProduct distance, range from 0 to 1.
Args:
query: Text query.
k: Number of the most similar documents to return. Defaults to 4.
0 is dissimilar, 1 is most similar.
Returns:
List of (Document, relevance_score) tuples similar to the text query.
Note that relevance_score ranged from 0 to 1.
0 is dissimilar, 1 is the most similar.
"""
if self.awadb_client is None:
@ -191,17 +243,18 @@ class AwaDB(VectorStore):
if show_results.__len__() == 0:
return results
scores: List[float] = []
retrieval_docs = self.similarity_search_by_vector(embedding, k, scores)
L2_Norm = 0.0
for score in scores:
L2_Norm = L2_Norm + score * score
dists: List[float] = []
not_include_fields: Set[str] = {"text_embedding", "_id", "score"}
retrieval_docs = self.similarity_search_by_vector(
embedding,
k,
scores=dists,
not_include_fields_in_metadata=not_include_fields,
)
L2_Norm = pow(L2_Norm, 0.5)
doc_no = 0
for doc in retrieval_docs:
doc_tuple = (doc, 1 - scores[doc_no] / L2_Norm)
doc_tuple = (doc, dists[doc_no])
results.append(doc_tuple)
doc_no = doc_no + 1
@ -212,6 +265,7 @@ class AwaDB(VectorStore):
embedding: Optional[List[float]] = None,
k: int = DEFAULT_TOPN,
scores: Optional[list] = None,
not_include_fields_in_metadata: Optional[Set[str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
@ -219,9 +273,11 @@ class AwaDB(VectorStore):
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
scores: Scores for retrieved docs.
not_incude_fields_in_metadata: Not include meta fields of each document.
Returns:
List of Documents most similar to the query vector.
List of Documents which are the most similar to the query vector.
"""
if self.awadb_client is None:
@ -232,7 +288,9 @@ class AwaDB(VectorStore):
if embedding is None:
return results
show_results = self.awadb_client.Search(embedding, k)
show_results = self.awadb_client.Search(
embedding, k, not_include_fields=not_include_fields_in_metadata
)
if show_results.__len__() == 0:
return results
@ -241,26 +299,200 @@ class AwaDB(VectorStore):
content = ""
meta_data = {}
for item_key in item_detail:
if (
item_key == "Field@0"
and self.using_table_name in self.table2embeddings
): # text for the document
content = item_detail[item_key]
elif item_key == "embedding_text":
if item_key == "embedding_text":
content = item_detail[item_key]
elif (
item_key == "Field@1" or item_key == "text_embedding"
): # embedding field for the document
continue
elif item_key == "score": # L2 distance
elif item_key == "score":
if scores is not None:
score = item_detail[item_key]
scores.append(score)
else:
meta_data[item_key] = item_detail[item_key]
scores.append(item_detail[item_key])
continue
elif not_include_fields_in_metadata is not None:
if item_key in not_include_fields_in_metadata:
continue
meta_data[item_key] = item_detail[item_key]
results.append(Document(page_content=content, metadata=meta_data))
return results
def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
if self.awadb_client is None:
raise ValueError("AwaDB client is None!!!")
embedding: List[float] = []
if self.using_table_name in self.table2embeddings:
embedding = self.table2embeddings[self.using_table_name].embed_query(query)
else:
from awadb import llm_embedding
llm = llm_embedding.LLMEmbedding()
embedding = llm.Embedding(query)
if embedding.__len__() == 0:
return []
results = self.max_marginal_relevance_search_by_vector(
embedding, k, fetch_k, lambda_mult=lambda_mult
)
return results
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
if self.awadb_client is None:
raise ValueError("AwaDB client is None!!!")
results: List[Document] = []
if embedding is None:
return results
not_include_fields: set = {"_id", "score"}
retrieved_docs = self.similarity_search_by_vector(
embedding, fetch_k, not_include_fields_in_metadata=not_include_fields
)
top_embeddings = []
for doc in retrieved_docs:
top_embeddings.append(doc.metadata["text_embedding"])
selected_docs = maximal_marginal_relevance(
np.array(embedding, dtype=np.float32), embedding_list=top_embeddings
)
for s_id in selected_docs:
if "text_embedding" in retrieved_docs[s_id].metadata:
del retrieved_docs[s_id].metadata["text_embedding"]
results.append(retrieved_docs[s_id])
return results
def get(
self,
ids: List[str],
not_include_fields: Optional[Set[str]] = None,
**kwargs: Any,
) -> Dict[str, Document]:
"""Return docs according ids.
Args:
ids: The ids of the embedding vectors.
Returns:
Documents which have the ids.
"""
if self.awadb_client is None:
raise ValueError("AwaDB client is None!!!")
docs_detail = self.awadb_client.Get(ids, not_include_fields=not_include_fields)
results: Dict[str, Document] = {}
for doc_detail in docs_detail:
content = ""
meta_info = {}
for field in doc_detail:
if field == "embeddint_text":
content = doc_detail[field]
continue
elif field == "text_embedding" or field == "_id":
continue
meta_info[field] = doc_detail[field]
doc = Document(page_content=content, metadata=meta_info)
results[doc_detail["_id"]] = doc
return results
def delete(
self,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> Optional[bool]:
"""Delete the documents which have the specified ids.
Args:
ids: The ids of the embedding vectors.
**kwargs: Other keyword arguments that subclasses might use.
Returns:
Optional[bool]: True if deletion is successful.
False otherwise, None if not implemented.
"""
if self.awadb_client is None:
raise ValueError("AwaDB client is None!!!")
ret: Optional[bool] = None
if ids is None or ids.__len__() == 0:
return ret
ret = self.awadb_client.Delete(ids)
return ret
def update(
self,
ids: List[str],
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Update the documents which have the specified ids.
Args:
ids: The id list of the updating embedding vector.
texts: The texts of the updating documents.
metadatas: The metadatas of the updating documents.
Returns:
the ids of the updated documents.
"""
if self.awadb_client is None:
raise ValueError("AwaDB client is None!!!")
return self.awadb_client.UpdateTexts(
ids=ids, text_field_name="embedding_text", texts=texts, metadatas=metadatas
)
def create_table(
self,
table_name: str,
@ -364,7 +596,8 @@ class AwaDB(VectorStore):
embedding (Optional[Embeddings]): Embedding function. Defaults to None.
table_name (str): Name of the table to create.
log_and_data_dir (Optional[str]): Directory to persist the table.
client (Optional[awadb.Client]): AwaDB client
client (Optional[awadb.Client]): AwaDB client.
Any: Any possible parameters in the future
Returns:
AwaDB: AwaDB vectorstore.

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