Finding your next book
In this notebook we will be going over generating embeddings of book descriptions with OpenAI and using those embeddings within Milvus to find relevant books. The dataset in this example is sourced from HuggingFace datasets, and contains a little over 1 million title-description pairs.
Lets begin by first downloading the required libraries for this notebook:
openaiis used for communicating with the OpenAI embedding servicepymilvusis used for communicating with the Milvus serverdatasetsis used for downloading the datasettqdmis used for the progress bars
! pip install openai pymilvus datasets tqdmLooking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
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[0m[?25hWith Milvus running we can setup our global variables:
- HOST: The Milvus host address
- PORT: The Milvus port number
- COLLECTION_NAME: What to name the collection within Milvus
- DIMENSION: The dimension of the embeddings
- OPENAI_ENGINE: Which embedding model to use
- openai.api_key: Your OpenAI account key
- INDEX_PARAM: The index settings to use for the collection
- QUERY_PARAM: The search parameters to use
- BATCH_SIZE: How many texts to embed and insert at once
import openai
HOST = 'localhost'
PORT = 19530
COLLECTION_NAME = 'book_search'
DIMENSION = 1536
OPENAI_ENGINE = 'text-embedding-3-small'
openai.api_key = 'sk-your_key'
INDEX_PARAM = {
'metric_type':'L2',
'index_type':"HNSW",
'params':{'M': 8, 'efConstruction': 64}
}
QUERY_PARAM = {
"metric_type": "L2",
"params": {"ef": 64},
}
BATCH_SIZE = 1000Milvus
This segment deals with Milvus and setting up the database for this use case. Within Milvus we need to setup a collection and index the collection.
from pymilvus import connections, utility, FieldSchema, Collection, CollectionSchema, DataType
# Connect to Milvus Database
connections.connect(host=HOST, port=PORT)# Remove collection if it already exists
if utility.has_collection(COLLECTION_NAME):
utility.drop_collection(COLLECTION_NAME)# Create collection which includes the id, title, and embedding.
fields = [
FieldSchema(name='id', dtype=DataType.INT64, is_primary=True, auto_id=True),
FieldSchema(name='title', dtype=DataType.VARCHAR, max_length=64000),
FieldSchema(name='description', dtype=DataType.VARCHAR, max_length=64000),
FieldSchema(name='embedding', dtype=DataType.FLOAT_VECTOR, dim=DIMENSION)
]
schema = CollectionSchema(fields=fields)
collection = Collection(name=COLLECTION_NAME, schema=schema)# Create the index on the collection and load it.
collection.create_index(field_name="embedding", index_params=INDEX_PARAM)
collection.load()Dataset
With Milvus up and running we can begin grabbing our data. Hugging Face Datasets is a hub that holds many different user datasets, and for this example we are using Skelebor’s book dataset. This dataset contains title-description pairs for over 1 million books. We are going to embed each description and store it within Milvus along with its title.
import datasets
# Download the dataset and only use the `train` portion (file is around 800Mb)
dataset = datasets.load_dataset('Skelebor/book_titles_and_descriptions_en_clean', split='train')/Users/filiphaltmayer/miniconda3/envs/haystack/lib/python3.9/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
from .autonotebook import tqdm as notebook_tqdm
Found cached dataset parquet (/Users/filiphaltmayer/.cache/huggingface/datasets/Skelebor___parquet/Skelebor--book_titles_and_descriptions_en_clean-3596935b1d8a7747/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)Insert the Data
Now that we have our data on our machine we can begin embedding it and inserting it into Milvus. The embedding function takes in text and returns the embeddings in a list format.
# Simple function that converts the texts to embeddings
def embed(texts):
embeddings = openai.Embedding.create(
input=texts,
engine=OPENAI_ENGINE
)
return [x['embedding'] for x in embeddings['data']]This next step does the actual inserting. Due to having so many datapoints, if you want to immidiately test it out you can stop the inserting cell block early and move along. Doing this will probably decrease the accuracy of the results due to less datapoints, but it should still be good enough.
from tqdm import tqdm
data = [
[], # title
[], # description
]
# Embed and insert in batches
for i in tqdm(range(0, len(dataset))):
data[0].append(dataset[i]['title'])
data[1].append(dataset[i]['description'])
if len(data[0]) % BATCH_SIZE == 0:
data.append(embed(data[1]))
collection.insert(data)
data = [[],[]]
# Embed and insert the remainder
if len(data[0]) != 0:
data.append(embed(data[1]))
collection.insert(data)
data = [[],[]] 0%| | 1999/1032335 [00:06<57:22, 299.31it/s]KeyboardInterrupt
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[1;32m 5[0m engine[39m=[39;49mOPENAI_ENGINE
[1;32m 6[0m )
[1;32m 7[0m [39mreturn[39;00m [x[[39m'[39m[39membedding[39m[39m'[39m] [39mfor[39;00m x [39min[39;00m embeddings[[39m'[39m[39mdata[39m[39m'[39m]]
File [0;32m~/miniconda3/envs/haystack/lib/python3.9/site-packages/openai/api_resources/embedding.py:33[0m, in [0;36mEmbedding.create[0;34m(cls, *args, **kwargs)[0m
[1;32m 31[0m [39mwhile[39;00m [39mTrue[39;00m:
[1;32m 32[0m [39mtry[39;00m:
[0;32m---> 33[0m response [39m=[39m [39msuper[39;49m()[39m.[39;49mcreate([39m*[39;49margs, [39m*[39;49m[39m*[39;49mkwargs)
[1;32m 35[0m [39m# If a user specifies base64, we'll just return the encoded string.[39;00m
[1;32m 36[0m [39m# This is only for the default case.[39;00m
[1;32m 37[0m [39mif[39;00m [39mnot[39;00m user_provided_encoding_format:
File [0;32m~/miniconda3/envs/haystack/lib/python3.9/site-packages/openai/api_resources/abstract/engine_api_resource.py:153[0m, in [0;36mEngineAPIResource.create[0;34m(cls, api_key, api_base, api_type, request_id, api_version, organization, **params)[0m
[1;32m 127[0m [39m@classmethod[39m
[1;32m 128[0m [39mdef[39;00m [39mcreate[39m(
[1;32m 129[0m [39mcls[39m,
[0;32m (...)[0m
[1;32m 136[0m [39m*[39m[39m*[39mparams,
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[0;32m--> 153[0m response, _, api_key [39m=[39m requestor[39m.[39;49mrequest(
[1;32m 154[0m [39m"[39;49m[39mpost[39;49m[39m"[39;49m,
[1;32m 155[0m url,
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[1;32m 161[0m )
[1;32m 163[0m [39mif[39;00m stream:
[1;32m 164[0m [39m# must be an iterator[39;00m
[1;32m 165[0m [39massert[39;00m [39mnot[39;00m [39misinstance[39m(response, OpenAIResponse)
File [0;32m~/miniconda3/envs/haystack/lib/python3.9/site-packages/openai/api_requestor.py:216[0m, in [0;36mAPIRequestor.request[0;34m(self, method, url, params, headers, files, stream, request_id, request_timeout)[0m
[1;32m 205[0m [39mdef[39;00m [39mrequest[39m(
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[1;32m 215[0m ) [39m-[39m[39m>[39m Tuple[Union[OpenAIResponse, Iterator[OpenAIResponse]], [39mbool[39m, [39mstr[39m]:
[0;32m--> 216[0m result [39m=[39m [39mself[39;49m[39m.[39;49mrequest_raw(
[1;32m 217[0m method[39m.[39;49mlower(),
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[1;32m 227[0m [39mreturn[39;00m resp, got_stream, [39mself[39m[39m.[39mapi_key
File [0;32m~/miniconda3/envs/haystack/lib/python3.9/site-packages/openai/api_requestor.py:516[0m, in [0;36mAPIRequestor.request_raw[0;34m(self, method, url, params, supplied_headers, files, stream, request_id, request_timeout)[0m
[1;32m 514[0m _thread_context[39m.[39msession [39m=[39m _make_session()
[1;32m 515[0m [39mtry[39;00m:
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[1;32m 517[0m method,
[1;32m 518[0m abs_url,
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[1;32m 523[0m timeout[39m=[39;49mrequest_timeout [39mif[39;49;00m request_timeout [39melse[39;49;00m TIMEOUT_SECS,
[1;32m 524[0m )
[1;32m 525[0m [39mexcept[39;00m requests[39m.[39mexceptions[39m.[39mTimeout [39mas[39;00m e:
[1;32m 526[0m [39mraise[39;00m error[39m.[39mTimeout([39m"[39m[39mRequest timed out: [39m[39m{}[39;00m[39m"[39m[39m.[39mformat(e)) [39mfrom[39;00m [39me[39;00m
File [0;32m~/miniconda3/envs/haystack/lib/python3.9/site-packages/requests/sessions.py:587[0m, in [0;36mSession.request[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)[0m
[1;32m 582[0m send_kwargs [39m=[39m {
[1;32m 583[0m [39m"[39m[39mtimeout[39m[39m"[39m: timeout,
[1;32m 584[0m [39m"[39m[39mallow_redirects[39m[39m"[39m: allow_redirects,
[1;32m 585[0m }
[1;32m 586[0m send_kwargs[39m.[39mupdate(settings)
[0;32m--> 587[0m resp [39m=[39m [39mself[39;49m[39m.[39;49msend(prep, [39m*[39;49m[39m*[39;49msend_kwargs)
[1;32m 589[0m [39mreturn[39;00m resp
File [0;32m~/miniconda3/envs/haystack/lib/python3.9/site-packages/requests/sessions.py:701[0m, in [0;36mSession.send[0;34m(self, request, **kwargs)[0m
[1;32m 698[0m start [39m=[39m preferred_clock()
[1;32m 700[0m [39m# Send the request[39;00m
[0;32m--> 701[0m r [39m=[39m adapter[39m.[39;49msend(request, [39m*[39;49m[39m*[39;49mkwargs)
[1;32m 703[0m [39m# Total elapsed time of the request (approximately)[39;00m
[1;32m 704[0m elapsed [39m=[39m preferred_clock() [39m-[39m start
File [0;32m~/miniconda3/envs/haystack/lib/python3.9/site-packages/requests/adapters.py:489[0m, in [0;36mHTTPAdapter.send[0;34m(self, request, stream, timeout, verify, cert, proxies)[0m
[1;32m 487[0m [39mtry[39;00m:
[1;32m 488[0m [39mif[39;00m [39mnot[39;00m chunked:
[0;32m--> 489[0m resp [39m=[39m conn[39m.[39;49murlopen(
[1;32m 490[0m method[39m=[39;49mrequest[39m.[39;49mmethod,
[1;32m 491[0m url[39m=[39;49murl,
[1;32m 492[0m body[39m=[39;49mrequest[39m.[39;49mbody,
[1;32m 493[0m headers[39m=[39;49mrequest[39m.[39;49mheaders,
[1;32m 494[0m redirect[39m=[39;49m[39mFalse[39;49;00m,
[1;32m 495[0m assert_same_host[39m=[39;49m[39mFalse[39;49;00m,
[1;32m 496[0m preload_content[39m=[39;49m[39mFalse[39;49;00m,
[1;32m 497[0m decode_content[39m=[39;49m[39mFalse[39;49;00m,
[1;32m 498[0m retries[39m=[39;49m[39mself[39;49m[39m.[39;49mmax_retries,
[1;32m 499[0m timeout[39m=[39;49mtimeout,
[1;32m 500[0m )
[1;32m 502[0m [39m# Send the request.[39;00m
[1;32m 503[0m [39melse[39;00m:
[1;32m 504[0m [39mif[39;00m [39mhasattr[39m(conn, [39m"[39m[39mproxy_pool[39m[39m"[39m):
File [0;32m~/miniconda3/envs/haystack/lib/python3.9/site-packages/urllib3/connectionpool.py:703[0m, in [0;36mHTTPConnectionPool.urlopen[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)[0m
[1;32m 700[0m [39mself[39m[39m.[39m_prepare_proxy(conn)
[1;32m 702[0m [39m# Make the request on the httplib connection object.[39;00m
[0;32m--> 703[0m httplib_response [39m=[39m [39mself[39;49m[39m.[39;49m_make_request(
[1;32m 704[0m conn,
[1;32m 705[0m method,
[1;32m 706[0m url,
[1;32m 707[0m timeout[39m=[39;49mtimeout_obj,
[1;32m 708[0m body[39m=[39;49mbody,
[1;32m 709[0m headers[39m=[39;49mheaders,
[1;32m 710[0m chunked[39m=[39;49mchunked,
[1;32m 711[0m )
[1;32m 713[0m [39m# If we're going to release the connection in ``finally:``, then[39;00m
[1;32m 714[0m [39m# the response doesn't need to know about the connection. Otherwise[39;00m
[1;32m 715[0m [39m# it will also try to release it and we'll have a double-release[39;00m
[1;32m 716[0m [39m# mess.[39;00m
[1;32m 717[0m response_conn [39m=[39m conn [39mif[39;00m [39mnot[39;00m release_conn [39melse[39;00m [39mNone[39;00m
File [0;32m~/miniconda3/envs/haystack/lib/python3.9/site-packages/urllib3/connectionpool.py:449[0m, in [0;36mHTTPConnectionPool._make_request[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)[0m
[1;32m 444[0m httplib_response [39m=[39m conn[39m.[39mgetresponse()
[1;32m 445[0m [39mexcept[39;00m [39mBaseException[39;00m [39mas[39;00m e:
[1;32m 446[0m [39m# Remove the TypeError from the exception chain in[39;00m
[1;32m 447[0m [39m# Python 3 (including for exceptions like SystemExit).[39;00m
[1;32m 448[0m [39m# Otherwise it looks like a bug in the code.[39;00m
[0;32m--> 449[0m six[39m.[39;49mraise_from(e, [39mNone[39;49;00m)
[1;32m 450[0m [39mexcept[39;00m (SocketTimeout, BaseSSLError, SocketError) [39mas[39;00m e:
[1;32m 451[0m [39mself[39m[39m.[39m_raise_timeout(err[39m=[39me, url[39m=[39murl, timeout_value[39m=[39mread_timeout)
File [0;32m<string>:3[0m, in [0;36mraise_from[0;34m(value, from_value)[0m
File [0;32m~/miniconda3/envs/haystack/lib/python3.9/site-packages/urllib3/connectionpool.py:444[0m, in [0;36mHTTPConnectionPool._make_request[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)[0m
[1;32m 441[0m [39mexcept[39;00m [39mTypeError[39;00m:
[1;32m 442[0m [39m# Python 3[39;00m
[1;32m 443[0m [39mtry[39;00m:
[0;32m--> 444[0m httplib_response [39m=[39m conn[39m.[39;49mgetresponse()
[1;32m 445[0m [39mexcept[39;00m [39mBaseException[39;00m [39mas[39;00m e:
[1;32m 446[0m [39m# Remove the TypeError from the exception chain in[39;00m
[1;32m 447[0m [39m# Python 3 (including for exceptions like SystemExit).[39;00m
[1;32m 448[0m [39m# Otherwise it looks like a bug in the code.[39;00m
[1;32m 449[0m six[39m.[39mraise_from(e, [39mNone[39;00m)
File [0;32m~/miniconda3/envs/haystack/lib/python3.9/http/client.py:1377[0m, in [0;36mHTTPConnection.getresponse[0;34m(self)[0m
[1;32m 1375[0m [39mtry[39;00m:
[1;32m 1376[0m [39mtry[39;00m:
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[1;32m 1378[0m [39mexcept[39;00m [39mConnectionError[39;00m:
[1;32m 1379[0m [39mself[39m[39m.[39mclose()
File [0;32m~/miniconda3/envs/haystack/lib/python3.9/http/client.py:320[0m, in [0;36mHTTPResponse.begin[0;34m(self)[0m
[1;32m 318[0m [39m# read until we get a non-100 response[39;00m
[1;32m 319[0m [39mwhile[39;00m [39mTrue[39;00m:
[0;32m--> 320[0m version, status, reason [39m=[39m [39mself[39;49m[39m.[39;49m_read_status()
[1;32m 321[0m [39mif[39;00m status [39m!=[39m CONTINUE:
[1;32m 322[0m [39mbreak[39;00m
File [0;32m~/miniconda3/envs/haystack/lib/python3.9/http/client.py:281[0m, in [0;36mHTTPResponse._read_status[0;34m(self)[0m
[1;32m 280[0m [39mdef[39;00m [39m_read_status[39m([39mself[39m):
[0;32m--> 281[0m line [39m=[39m [39mstr[39m([39mself[39;49m[39m.[39;49mfp[39m.[39;49mreadline(_MAXLINE [39m+[39;49m [39m1[39;49m), [39m"[39m[39miso-8859-1[39m[39m"[39m)
[1;32m 282[0m [39mif[39;00m [39mlen[39m(line) [39m>[39m _MAXLINE:
[1;32m 283[0m [39mraise[39;00m LineTooLong([39m"[39m[39mstatus line[39m[39m"[39m)
File [0;32m~/miniconda3/envs/haystack/lib/python3.9/socket.py:704[0m, in [0;36mSocketIO.readinto[0;34m(self, b)[0m
[1;32m 702[0m [39mwhile[39;00m [39mTrue[39;00m:
[1;32m 703[0m [39mtry[39;00m:
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[1;32m 705[0m [39mexcept[39;00m timeout:
[1;32m 706[0m [39mself[39m[39m.[39m_timeout_occurred [39m=[39m [39mTrue[39;00m
File [0;32m~/miniconda3/envs/haystack/lib/python3.9/ssl.py:1242[0m, in [0;36mSSLSocket.recv_into[0;34m(self, buffer, nbytes, flags)[0m
[1;32m 1238[0m [39mif[39;00m flags [39m!=[39m [39m0[39m:
[1;32m 1239[0m [39mraise[39;00m [39mValueError[39;00m(
[1;32m 1240[0m [39m"[39m[39mnon-zero flags not allowed in calls to recv_into() on [39m[39m%s[39;00m[39m"[39m [39m%[39m
[1;32m 1241[0m [39mself[39m[39m.[39m[39m__class__[39m)
[0;32m-> 1242[0m [39mreturn[39;00m [39mself[39;49m[39m.[39;49mread(nbytes, buffer)
[1;32m 1243[0m [39melse[39;00m:
[1;32m 1244[0m [39mreturn[39;00m [39msuper[39m()[39m.[39mrecv_into(buffer, nbytes, flags)
File [0;32m~/miniconda3/envs/haystack/lib/python3.9/ssl.py:1100[0m, in [0;36mSSLSocket.read[0;34m(self, len, buffer)[0m
[1;32m 1098[0m [39mtry[39;00m:
[1;32m 1099[0m [39mif[39;00m buffer [39mis[39;00m [39mnot[39;00m [39mNone[39;00m:
[0;32m-> 1100[0m [39mreturn[39;00m [39mself[39;49m[39m.[39;49m_sslobj[39m.[39;49mread([39mlen[39;49m, buffer)
[1;32m 1101[0m [39melse[39;00m:
[1;32m 1102[0m [39mreturn[39;00m [39mself[39m[39m.[39m_sslobj[39m.[39mread([39mlen[39m)
[0;31mKeyboardInterrupt[0m:Query the Database
With our data safely inserted in Milvus, we can now perform a query. The query takes in a string or a list of strings and searches them. The resuts print out your provided description and the results that include the result score, the result title, and the result book description.
import textwrap
def query(queries, top_k = 5):
if type(queries) != list:
queries = [queries]
res = collection.search(embed(queries), anns_field='embedding', param=QUERY_PARAM, limit = top_k, output_fields=['title', 'description'])
for i, hit in enumerate(res):
print('Description:', queries[i])
print('Results:')
for ii, hits in enumerate(hit):
print('\t' + 'Rank:', ii + 1, 'Score:', hits.score, 'Title:', hits.entity.get('title'))
print(textwrap.fill(hits.entity.get('description'), 88))
print()query('Book about a k-9 from europe')RPC error: [search], <MilvusException: (code=1, message=code: UnexpectedError, reason: code: CollectionNotExists, reason: can't find collection: book_search)>, <Time:{'RPC start': '2023-03-17 14:22:18.368461', 'RPC error': '2023-03-17 14:22:18.382086'}>MilvusException
<MilvusException: (code=1, message=code: UnexpectedError, reason: code: CollectionNotExists, reason: can't find collection: book_search)>
[0;31m---------------------------------------------------------------------------[0m[0;31mMilvusException[0m Traceback (most recent call last)Cell [0;32mIn[32], line 1[0m
[0;32m----> 1[0m query([39m'[39;49m[39mBook about a k-9 from europe[39;49m[39m'[39;49m)
Cell [0;32mIn[31], line 6[0m, in [0;36mquery[0;34m(queries, top_k)[0m
[1;32m 4[0m [39mif[39;00m [39mtype[39m(queries) [39m!=[39m [39mlist[39m:
[1;32m 5[0m queries [39m=[39m [queries]
[0;32m----> 6[0m res [39m=[39m collection[39m.[39;49msearch(embed(queries), anns_field[39m=[39;49m[39m'[39;49m[39membedding[39;49m[39m'[39;49m, param[39m=[39;49mQUERY_PARAM, limit [39m=[39;49m top_k, output_fields[39m=[39;49m[[39m'[39;49m[39mtitle[39;49m[39m'[39;49m, [39m'[39;49m[39mdescription[39;49m[39m'[39;49m])
[1;32m 7[0m [39mfor[39;00m i, hit [39min[39;00m [39menumerate[39m(res):
[1;32m 8[0m [39mprint[39m([39m'[39m[39mDescription:[39m[39m'[39m, queries[i])
File [0;32m~/miniconda3/envs/haystack/lib/python3.9/site-packages/pymilvus/orm/collection.py:614[0m, in [0;36mCollection.search[0;34m(self, data, anns_field, param, limit, expr, partition_names, output_fields, timeout, round_decimal, **kwargs)[0m
[1;32m 611[0m [39mraise[39;00m DataTypeNotMatchException(message[39m=[39mExceptionsMessage[39m.[39mExprType [39m%[39m [39mtype[39m(expr))
[1;32m 613[0m conn [39m=[39m [39mself[39m[39m.[39m_get_connection()
[0;32m--> 614[0m res [39m=[39m conn[39m.[39;49msearch([39mself[39;49m[39m.[39;49m_name, data, anns_field, param, limit, expr,
[1;32m 615[0m partition_names, output_fields, round_decimal, timeout[39m=[39;49mtimeout,
[1;32m 616[0m schema[39m=[39;49m[39mself[39;49m[39m.[39;49m_schema_dict, [39m*[39;49m[39m*[39;49mkwargs)
[1;32m 617[0m [39mif[39;00m kwargs[39m.[39mget([39m"[39m[39m_async[39m[39m"[39m, [39mFalse[39;00m):
[1;32m 618[0m [39mreturn[39;00m SearchFuture(res)
File [0;32m~/miniconda3/envs/haystack/lib/python3.9/site-packages/pymilvus/decorators.py:109[0m, in [0;36merror_handler.<locals>.wrapper.<locals>.handler[0;34m(*args, **kwargs)[0m
[1;32m 107[0m record_dict[[39m"[39m[39mRPC error[39m[39m"[39m] [39m=[39m [39mstr[39m(datetime[39m.[39mdatetime[39m.[39mnow())
[1;32m 108[0m LOGGER[39m.[39merror([39mf[39m[39m"[39m[39mRPC error: [[39m[39m{[39;00minner_name[39m}[39;00m[39m], [39m[39m{[39;00me[39m}[39;00m[39m, <Time:[39m[39m{[39;00mrecord_dict[39m}[39;00m[39m>[39m[39m"[39m)
[0;32m--> 109[0m [39mraise[39;00m e
[1;32m 110[0m [39mexcept[39;00m grpc[39m.[39mFutureTimeoutError [39mas[39;00m e:
[1;32m 111[0m record_dict[[39m"[39m[39mgRPC timeout[39m[39m"[39m] [39m=[39m [39mstr[39m(datetime[39m.[39mdatetime[39m.[39mnow())
File [0;32m~/miniconda3/envs/haystack/lib/python3.9/site-packages/pymilvus/decorators.py:105[0m, in [0;36merror_handler.<locals>.wrapper.<locals>.handler[0;34m(*args, **kwargs)[0m
[1;32m 103[0m [39mtry[39;00m:
[1;32m 104[0m record_dict[[39m"[39m[39mRPC start[39m[39m"[39m] [39m=[39m [39mstr[39m(datetime[39m.[39mdatetime[39m.[39mnow())
[0;32m--> 105[0m [39mreturn[39;00m func([39m*[39;49margs, [39m*[39;49m[39m*[39;49mkwargs)
[1;32m 106[0m [39mexcept[39;00m MilvusException [39mas[39;00m e:
[1;32m 107[0m record_dict[[39m"[39m[39mRPC error[39m[39m"[39m] [39m=[39m [39mstr[39m(datetime[39m.[39mdatetime[39m.[39mnow())
File [0;32m~/miniconda3/envs/haystack/lib/python3.9/site-packages/pymilvus/decorators.py:136[0m, in [0;36mtracing_request.<locals>.wrapper.<locals>.handler[0;34m(self, *args, **kwargs)[0m
[1;32m 134[0m [39mif[39;00m req_id:
[1;32m 135[0m [39mself[39m[39m.[39mset_onetime_request_id(req_id)
[0;32m--> 136[0m ret [39m=[39m func([39mself[39;49m, [39m*[39;49margs, [39m*[39;49m[39m*[39;49mkwargs)
[1;32m 137[0m [39mreturn[39;00m ret
File [0;32m~/miniconda3/envs/haystack/lib/python3.9/site-packages/pymilvus/decorators.py:85[0m, in [0;36mretry_on_rpc_failure.<locals>.wrapper.<locals>.handler[0;34m(self, *args, **kwargs)[0m
[1;32m 83[0m back_off [39m=[39m [39mmin[39m(back_off [39m*[39m back_off_multiplier, max_back_off)
[1;32m 84[0m [39melse[39;00m:
[0;32m---> 85[0m [39mraise[39;00m e
[1;32m 86[0m [39mexcept[39;00m [39mException[39;00m [39mas[39;00m e:
[1;32m 87[0m [39mraise[39;00m e
File [0;32m~/miniconda3/envs/haystack/lib/python3.9/site-packages/pymilvus/decorators.py:50[0m, in [0;36mretry_on_rpc_failure.<locals>.wrapper.<locals>.handler[0;34m(self, *args, **kwargs)[0m
[1;32m 48[0m [39mwhile[39;00m [39mTrue[39;00m:
[1;32m 49[0m [39mtry[39;00m:
[0;32m---> 50[0m [39mreturn[39;00m func([39mself[39;49m, [39m*[39;49margs, [39m*[39;49m[39m*[39;49mkwargs)
[1;32m 51[0m [39mexcept[39;00m grpc[39m.[39mRpcError [39mas[39;00m e:
[1;32m 52[0m [39m# DEADLINE_EXCEEDED means that the task wat not completed[39;00m
[1;32m 53[0m [39m# UNAVAILABLE means that the service is not reachable currently[39;00m
[1;32m 54[0m [39m# Reference: https://grpc.github.io/grpc/python/grpc.html#grpc-status-code[39;00m
[1;32m 55[0m [39mif[39;00m e[39m.[39mcode() [39m!=[39m grpc[39m.[39mStatusCode[39m.[39mDEADLINE_EXCEEDED [39mand[39;00m e[39m.[39mcode() [39m!=[39m grpc[39m.[39mStatusCode[39m.[39mUNAVAILABLE:
File [0;32m~/miniconda3/envs/haystack/lib/python3.9/site-packages/pymilvus/client/grpc_handler.py:472[0m, in [0;36mGrpcHandler.search[0;34m(self, collection_name, data, anns_field, param, limit, expression, partition_names, output_fields, round_decimal, timeout, schema, **kwargs)[0m
[1;32m 467[0m requests [39m=[39m Prepare[39m.[39msearch_requests_with_expr(collection_name, data, anns_field, param, limit, schema,
[1;32m 468[0m expression, partition_names, output_fields, round_decimal,
[1;32m 469[0m [39m*[39m[39m*[39mkwargs)
[1;32m 471[0m auto_id [39m=[39m schema[[39m"[39m[39mauto_id[39m[39m"[39m]
[0;32m--> 472[0m [39mreturn[39;00m [39mself[39;49m[39m.[39;49m_execute_search_requests(requests, timeout, round_decimal[39m=[39;49mround_decimal, auto_id[39m=[39;49mauto_id, [39m*[39;49m[39m*[39;49mkwargs)
File [0;32m~/miniconda3/envs/haystack/lib/python3.9/site-packages/pymilvus/client/grpc_handler.py:441[0m, in [0;36mGrpcHandler._execute_search_requests[0;34m(self, requests, timeout, **kwargs)[0m
[1;32m 439[0m [39mif[39;00m kwargs[39m.[39mget([39m"[39m[39m_async[39m[39m"[39m, [39mFalse[39;00m):
[1;32m 440[0m [39mreturn[39;00m SearchFuture([39mNone[39;00m, [39mNone[39;00m, [39mTrue[39;00m, pre_err)
[0;32m--> 441[0m [39mraise[39;00m pre_err
File [0;32m~/miniconda3/envs/haystack/lib/python3.9/site-packages/pymilvus/client/grpc_handler.py:432[0m, in [0;36mGrpcHandler._execute_search_requests[0;34m(self, requests, timeout, **kwargs)[0m
[1;32m 429[0m response [39m=[39m [39mself[39m[39m.[39m_stub[39m.[39mSearch(request, timeout[39m=[39mtimeout)
[1;32m 431[0m [39mif[39;00m response[39m.[39mstatus[39m.[39merror_code [39m!=[39m [39m0[39m:
[0;32m--> 432[0m [39mraise[39;00m MilvusException(response[39m.[39mstatus[39m.[39merror_code, response[39m.[39mstatus[39m.[39mreason)
[1;32m 434[0m raws[39m.[39mappend(response)
[1;32m 435[0m round_decimal [39m=[39m kwargs[39m.[39mget([39m"[39m[39mround_decimal[39m[39m"[39m, [39m-[39m[39m1[39m)
[0;31mMilvusException[0m: <MilvusException: (code=1, message=code: UnexpectedError, reason: code: CollectionNotExists, reason: can't find collection: book_search)>