[{"data":1,"prerenderedAt":3688},["ShallowReactive",2],{"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Fpinecone-app":3,"navigation-developers":995,"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Fpinecone":2965,"surr-\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Fpinecone":3685},{"id":4,"title":5,"author":6,"body":7,"description":986,"extension":987,"meta":988,"navigation":698,"path":990,"seo":991,"sitemap":992,"stem":993,"__hash__":994},"content\u002F2.developers\u002F5.API-docs\u002F7.pathway-io\u002F355.pinecone.md","pw.io.pinecone",null,{"type":8,"value":9,"toc":982},"minimark",[10,14,28,48,51,115,130,145,163,174,187,204,207,395,398,401,412,590,597,827,833,934,943,978],[11,12,5],"h1",{"id":13},"pwiopinecone",[15,16,17,21,22,27],"p",{},[18,19,20],"strong",{},"This module is available when using one of the following licenses only:"," ",[23,24,26],"a",{"href":25},"\u002Fpricing","Pathway Scale, Pathway Enterprise",".",[15,29,30,31,35,36,39,40,43,44,47],{},"Pathway writes each row of the table as a single Pinecone record: the value of the\n",[32,33,34],"code",{},"primary_key"," column becomes the record id (converted to a string), the\n",[32,37,38],{},"vector"," column (",[32,41,42],{},"list[float]"," or a 1-D ",[32,45,46],{},"numpy.ndarray",", with length equal\nto the index dimension) becomes the dense vector, and the metadata columns become\nthe record metadata.",[15,49,50],{},"Metadata columns are converted by type as follows:",[52,53,54,67],"table",{},[55,56,57],"thead",{},[58,59,60,64],"tr",{},[61,62,63],"th",{},"Pathway type",[61,65,66],{},"Pinecone type",[68,69,70,85,95,105],"tbody",{},[58,71,72,82],{},[73,74,75,78,79],"td",{},[32,76,77],{},"int",", ",[32,80,81],{},"float",[73,83,84],{},"number",[58,86,87,92],{},[73,88,89],{},[32,90,91],{},"bool",[73,93,94],{},"boolean",[58,96,97,102],{},[73,98,99],{},[32,100,101],{},"str",[73,103,104],{},"string",[58,106,107,112],{},[73,108,109],{},[32,110,111],{},"list[str]",[73,113,114],{},"list of strings",[15,116,117,118,121,122,125,126,129],{},"A ",[32,119,120],{},"None"," metadata value is omitted, as Pinecone rejects null metadata. A\nmetadata column of any other type (for example ",[32,123,124],{},"bytes",", a nested ",[32,127,128],{},"pw.Json","\nobject, or a numeric array) is not supported and raises an error that names the\noffending column.",[131,132,134,137,138,144],"h2",{"id":133},"pathway.io.pinecone.write",[18,135,136],{},"write","(",[139,140,143],"span",{"className":141},[142],"sphinx-paramslist","table, index_name, *, primary_key=None, vector, api_key=None, host=None, namespace='', metadata_columns=None, batch_size=100, name=None, sort_by=None",")",[15,146,147,155,156,162],{},[23,148,152],{"className":149,"href":151},[150],"sphinx-source-url","https:\u002F\u002Fgithub.com\u002Fpathwaycom\u002Fpathway\u002Ftree\u002Fmain\u002Fpython\u002Fpathway\u002Fio\u002Fpinecone\u002F__init__.py#L104-L333",[139,153,154],{},"source","Writes a Pathway Live Data Framework table to a ",[23,157,161],{"href":158,"rel":159},"https:\u002F\u002Fwww.pinecone.io\u002F",[160],"nofollow","Pinecone"," index.",[15,164,165,166,169,170,173],{},"The Pinecone index is kept in sync with the current state of the table. When a\nrow is added to the table it is upserted into the index, and when a row is\nremoved from the table the corresponding record is deleted; an update replaces\nthe previous record for that id. Only the current state is reflected — the\nconnector does not append a change log, so no ",[32,167,168],{},"time"," or ",[32,171,172],{},"diff"," columns are\nwritten.",[15,175,176,177,179,180,183,184,186],{},"The Pinecone record id is taken from the ",[32,178,34],{}," column when given. By\ndefault (",[32,181,182],{},"primary_key=None",") the table’s internal row key is used as the id:\nit is unique by construction and matches the way the dataflow shards rows, so\nwriting runs in parallel across workers. A ",[32,185,34],{}," column instead lets\nyou choose a meaningful id, but its values must uniquely identify rows; to\nguarantee that, writing then runs on a single worker and a collision (two rows\nwith the same id) raises an error rather than silently overwriting.",[15,188,189,190,192,193,196,197,199,200,203],{},"The ",[32,191,38],{}," column provides the dense embedding and every remaining column —\nor just the columns listed in ",[32,194,195],{},"metadata_columns"," — is stored as record\nmetadata. ",[32,198,120],{}," metadata values are dropped; a metadata value of a type\nPinecone cannot store raises an error naming the offending column. See the\nconnector documentation for the full type-conversion table. For\nhigh-dimensional vectors, ",[32,201,202],{},"batch_size"," may be reduced automatically so a\nsingle request stays within Pinecone’s size limit.",[15,205,206],{},"The target index must already exist before the pipeline starts and its\ndimension must match the length of the produced vectors; the connector does not\ncreate it.",[208,209,210,387],"ul",{},[211,212,213,216],"li",{},[18,214,215],{},"Parameters",[208,217,218,230,238,262,279,296,317,325,352,363,373],{},[211,219,220,222,223,229],{},[18,221,52],{}," (",[23,224,226],{"href":225},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-table#pathway.Table",[32,227,228],{},"Table",") – The table to write.",[211,231,232,222,235,237],{},[18,233,234],{},"index_name",[32,236,101],{},") – Name of the Pinecone index to write to.",[211,239,240,222,242,248,249,251,252,255,256,258,259,261],{},[18,241,34],{},[23,243,245],{"href":244},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway#pathway.ColumnReference",[32,246,247],{},"ColumnReference"," | ",[32,250,120],{},") – A column reference (e.g. ",[32,253,254],{},"table.doc_id",") whose values are\nused as the Pinecone record id. The column must belong to ",[32,257,52],{}," and\nits values must uniquely identify rows (the id selects the record to\nupsert or delete); a collision raises an error at runtime and writing\nruns on a single worker. When ",[32,260,120],{}," (the default), the table’s\ninternal row key is used as the id — always unique and written in\nparallel across workers — but the ids are then Pathway’s internal\nkeys rather than your own values.",[211,263,264,222,266,270,271,273,274,276,277,27],{},[18,265,38],{},[23,267,268],{"href":244},[32,269,247],{},") – A column reference holding the dense embedding (",[32,272,42],{}," or\na 1-D ",[32,275,46],{},"). The column must belong to ",[32,278,52],{},[211,280,281,222,284,248,286,288,289,291,292,295],{},[18,282,283],{},"api_key",[32,285,101],{},[32,287,120],{},") – Pinecone API key. When ",[32,290,120],{},", the ",[32,293,294],{},"PINECONE_API_KEY","\nenvironment variable is used.",[211,297,298,222,301,248,303,305,306,308,309,312,313,316],{},[18,299,300],{},"host",[32,302,101],{},[32,304,120],{},") – Control-plane host. Leave as ",[32,307,120],{}," for Pinecone cloud\n(",[32,310,311],{},"https:\u002F\u002Fapi.pinecone.io","); set it to a local URL such as\n",[32,314,315],{},"\"http:\u002F\u002Flocalhost:5080\""," to target Pinecone Local.",[211,318,319,222,322,324],{},[18,320,321],{},"namespace",[32,323,101],{},") – Pinecone namespace to write to. Defaults to the index’s default\nnamespace.",[211,326,327,222,329,332,333,332,336,340,341,343,344,346,347,349,350,27],{},[18,328,195],{},[32,330,331],{},"Optional","[",[32,334,335],{},"Iterable",[23,337,338],{"href":244},[32,339,247],{},"]]) – Column references for the columns to store as record\nmetadata. When ",[32,342,120],{},", every column except ",[32,345,34],{}," and\n",[32,348,38],{}," is stored. All columns must belong to ",[32,351,52],{},[211,353,354,222,356,358,359,362],{},[18,355,202],{},[32,357,77],{},") – Maximum number of records sent to Pinecone per ",[32,360,361],{},"upsert"," call\n(capped at Pinecone’s per-request limit of 1000).",[211,364,365,222,368,248,370,372],{},[18,366,367],{},"name",[32,369,101],{},[32,371,120],{},") – A unique name for the connector. If provided, this name will be used\nin logs and monitoring dashboards.",[211,374,375,222,378,332,380,332,382,386],{},[18,376,377],{},"sort_by",[32,379,331],{},[32,381,335],{},[23,383,384],{"href":244},[32,385,247],{},"]]) – If specified, the output within each mini-batch will be sorted in\nascending order by the given columns.",[211,388,389,392,120],{},[18,390,391],{},"Returns",[393,394],"br",{},[15,396,397],{},"Example:",[15,399,400],{},"Suppose you have a couple of documents, each already turned into a\n4-dimensional embedding, and you want them to be searchable in Pinecone — and\nto stay in sync as the documents change.",[15,402,403,404,407,408,411],{},"The first thing to take care of is the index itself: the connector never\ncreates it, so it has to exist before the pipeline starts, and its\n",[32,405,406],{},"dimension"," must match the length of your vectors (",[32,409,410],{},"4"," here).",[413,414,419],"pre",{"className":415,"code":416,"language":417,"meta":418,"style":418},"language-python shiki shiki-themes material-theme-palenight","from pinecone import Pinecone, ServerlessSpec  \npc = Pinecone(api_key=\"YOUR_API_KEY\")  \npc.create_index(  \n    name=\"docs\",\n    dimension=4,\n    metric=\"cosine\",\n    spec=ServerlessSpec(cloud=\"aws\", region=\"us-east-1\"),\n)\n","python","",[32,420,421,446,479,494,512,525,542,584],{"__ignoreMap":418},[139,422,425,429,433,436,439,443],{"class":423,"line":424},"line",1,[139,426,428],{"class":427},"s6cf3","from",[139,430,432],{"class":431},"s0W1g"," pinecone ",[139,434,435],{"class":427},"import",[139,437,438],{"class":431}," Pinecone",[139,440,442],{"class":441},"sAklC",",",[139,444,445],{"class":431}," ServerlessSpec  \n",[139,447,449,452,455,458,460,463,465,468,472,474,476],{"class":423,"line":448},2,[139,450,451],{"class":431},"pc ",[139,453,454],{"class":441},"=",[139,456,438],{"class":457},"sdLwU",[139,459,137],{"class":441},[139,461,283],{"class":462},"s7ZW3",[139,464,454],{"class":441},[139,466,467],{"class":441},"\"",[139,469,471],{"class":470},"sfyAc","YOUR_API_KEY",[139,473,467],{"class":441},[139,475,144],{"class":441},[139,477,478],{"class":431},"  \n",[139,480,482,485,487,490,492],{"class":423,"line":481},3,[139,483,484],{"class":431},"pc",[139,486,27],{"class":441},[139,488,489],{"class":457},"create_index",[139,491,137],{"class":441},[139,493,478],{"class":457},[139,495,497,500,502,504,507,509],{"class":423,"line":496},4,[139,498,499],{"class":462},"    name",[139,501,454],{"class":441},[139,503,467],{"class":441},[139,505,506],{"class":470},"docs",[139,508,467],{"class":441},[139,510,511],{"class":441},",\n",[139,513,515,518,520,523],{"class":423,"line":514},5,[139,516,517],{"class":462},"    dimension",[139,519,454],{"class":441},[139,521,410],{"class":522},"sx098",[139,524,511],{"class":441},[139,526,528,531,533,535,538,540],{"class":423,"line":527},6,[139,529,530],{"class":462},"    metric",[139,532,454],{"class":441},[139,534,467],{"class":441},[139,536,537],{"class":470},"cosine",[139,539,467],{"class":441},[139,541,511],{"class":441},[139,543,545,548,550,553,555,558,560,562,565,567,569,572,574,576,579,581],{"class":423,"line":544},7,[139,546,547],{"class":462},"    spec",[139,549,454],{"class":441},[139,551,552],{"class":457},"ServerlessSpec",[139,554,137],{"class":441},[139,556,557],{"class":462},"cloud",[139,559,454],{"class":441},[139,561,467],{"class":441},[139,563,564],{"class":470},"aws",[139,566,467],{"class":441},[139,568,442],{"class":441},[139,570,571],{"class":462}," region",[139,573,454],{"class":441},[139,575,467],{"class":441},[139,577,578],{"class":470},"us-east-1",[139,580,467],{"class":441},[139,582,583],{"class":441},"),\n",[139,585,587],{"class":423,"line":586},8,[139,588,589],{"class":441},")\n",[15,591,592,593,596],{},"With the index in place, you describe the data. 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