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db15762 733bea7 db15762 a5081b6 db15762 a5081b6 db15762 a5081b6 1863cbe a5081b6 db15762 1863cbe a5081b6 db15762 a5081b6 db15762 3fdc088 1863cbe 3fdc088 733bea7 3fdc088 733bea7 db15762 1863cbe db15762 a5081b6 db15762 a5081b6 db15762 a5081b6 db15762 1863cbe db15762 a5081b6 db15762 a5081b6 7a65857 b63d6c4 de93b1d b63d6c4 a5081b6 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 | import os
import pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Pinecone
from typing import TYPE_CHECKING, Any, Callable, Iterable, List, Optional, Tuple, Union
from langchain.docstore.document import Document
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.utils import DistanceStrategy, maximal_marginal_relevance
import numpy as np
import json
import logging
import uuid
from langchain.utils.iter import batch_iterate
try:
from script import export
except:
pass
logger = logging.getLogger(__name__)
class VectorStore(Pinecone):
REQUEST_TIMEOUT=10
INDEX_NAME = "jarvis"
NAMESPACE = "filecoin"
def __init__(self) -> None:
# pinecone.init(
# api_key=os.getenv("PINECONE_API_KEY"),
# environment=os.getenv("PINECONE_ENV"),
# )
pc = pinecone.Pinecone(api_key=os.getenv("PINECONE_API_KEY"))
self.dims = 1536
index = pc.Index(self.INDEX_NAME)
super().__init__(index, OpenAIEmbeddings(),"text")
def add_docs(self,docs):
Pinecone.from_documents(docs, self.embeddings, index_name=self.INDEX_NAME)
def search(self,
query: str,
k: int = 4,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Return pinecone documents most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Dictionary of argument(s) to filter on metadata
namespace: Namespace to search in. Default will search in '' namespace.
Returns:
List of Documents most similar to the query and score for each
"""
docs_and_scores = self.similarity_search_by_vector_with_score(
self._embed_query(query), k=k, filter=filter, namespace=namespace
)
return [doc for doc, _ in docs_and_scores]
def marginal_search(self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
**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.
"""
embedding = self._embed_query(query)
return self.max_marginal_relevance_search_by_vector(
embedding, k, fetch_k, lambda_mult, filter, namespace
)
def jsonfy(self,docs):
docs = [doc.dict() for doc in docs]
docs = json.dumps(docs)
return docs
def similarity_search_by_vector_with_score(
self,
embedding: List[float],
*,
k: int = 4,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
) -> List[Tuple[Document, float]]:
"""Return pinecone documents most similar to embedding, along with scores."""
if namespace is None:
namespace = self._namespace
docs = []
results = self._index.query(
vector=[embedding],
top_k=k,
include_metadata=True,
namespace=namespace,
filter=filter,
_request_timeout=self.REQUEST_TIMEOUT
)
for res in results["matches"]:
metadata = res["metadata"]
if self._text_key in metadata:
text = metadata.pop(self._text_key)
score = res["score"]
metadata['score'] = score
# print(f"metadata {metadata}")
docs.append((Document(page_content=text, metadata=metadata), score))
else:
logger.warning(
f"Found document with no `{self._text_key}` key. Skipping."
)
return docs
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 5,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
**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 namespace is None:
namespace = self._namespace
results = self._index.query(
vector=[embedding],
top_k=fetch_k,
include_values=True,
include_metadata=True,
namespace=namespace,
filter=filter,
_request_timeout=self.REQUEST_TIMEOUT
)
mmr_selected = maximal_marginal_relevance(
np.array([embedding], dtype=np.float32),
[item["values"] for item in results["matches"]],
k=k,
lambda_mult=lambda_mult,
)
selected = []
for i in mmr_selected:
metadata = results["matches"][i]["metadata"]
score = results["matches"][i]["score"]
metadata['score'] = score
selected.append(metadata)
# selected = [results["matches"][i]["metadata"] for i in mmr_selected]
return [
Document(page_content=metadata.pop((self._text_key)), metadata=metadata)
for metadata in selected
]
def upsert(self,text,id,source=''):
embed = self.embeddings.embed_query(text)
metadata = {"description":text,"text":text,"source":source}
vector_store._index.update(id=id,values=embed,set_metadata=metadata)
# vector_store._index.upsert(vectors=[
# {'id':id,'values':embed,'metadata':metadata}]
# )
print(f"upsert text:{text} with meta:{metadata}")
vector_store = VectorStore() |