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pip install "unstructured[elasticsearch]"
Run Locally
The upstream connector can be any of the ones supported, but for convenience here, showing a sample command using the upstream local connector.Copy
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#!/usr/bin/env bash
EMBEDDING_PROVIDER=${EMBEDDING_PROVIDER:-"langchain-huggingface"}
unstructured-ingest \
local \
--input-path example-docs/book-war-and-peace-1225p.txt \
--output-dir local-output-to-elasticsearch \
--strategy fast \
--chunk-elements \
--embedding-provider "$EMBEDDING_PROVIDER" \
--num-processes 4 \
--verbose \
elasticsearch \
--hosts "$ELASTICSEARCH_HOSTS" \
--username "$ELASTICSEARCH_USERNAME" \
--password "$ELASTICSEARCH_PASSWORD" \
--index-name "$ELASTICSEARCH_INDEX_NAME" \
--num-processes 2
unstructured-ingest <upstream connector> elasticsearch --help.
NOTE: Keep in mind that you will need to have all the appropriate extras and dependencies for the file types of the documents contained in your data storage platform if you’re running this locally. You can find more information about this in the installation guide.
Vector Search Sample Mapping
To make sure the schema of the index matches the data being written to it, a sample mapping json can be used.Object description
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1 {
2 "properties": {
3 "element_id": {
4 "type": "keyword"
5 },
6 "text": {
7 "type": "text",
8 "analyzer": "english"
9 },
10 "type": {
11 "type": "text"
12 },
13 "embeddings": {
14 "type": "dense_vector",
15 "dims": 384
16 },
17 "metadata": {
18 "type": "object",
19 "properties": {
20 "category_depth": {
21 "type": "integer"
22 },
23 "parent_id": {
24 "type": "keyword"
25 },
26 "attached_to_filename": {
27 "type": "keyword"
28 },
29 "filetype": {
30 "type": "keyword"
31 },
32 "last_modified": {
33 "type": "date"
34 },
35 "file_directory": {
36 "type": "keyword"
37 },
38 "filename": {
39 "type": "keyword"
40 },
41 "data_source": {
42 "type": "object",
43 "properties": {
44 "url": {
45 "type": "text",
46 "analyzer": "standard"
47 },
48 "version": {
49 "type": "keyword"
50 },
51 "date_created": {
52 "type": "date"
53 },
54 "date_modified": {
55 "type": "date"
56 },
57 "date_processed": {
58 "type": "date"
59 },
60 "record_locator": {
61 "type": "keyword"
62 },
63 "permissions_data": {
64 "type": "object"
65 }
66 }
67 },
68 "coordinates": {
69 "type": "object",
70 "properties": {
71 "system": {
72 "type": "keyword"
73 },
74 "layout_width": {
75 "type": "float"
76 },
77 "layout_height": {
78 "type": "float"
79 },
80 "points": {
81 "type": "float"
82 }
83 }
84 },
85 "languages": {
86 "type": "keyword"
87 },
88 "page_number": {
89 "type": "integer"
90 },
91 "page_name": {
92 "type": "keyword"
93 },
94 "url": {
95 "type": "text",
96 "analyzer": "standard"
97 },
98 "links": {
99 "type": "object"
100 },
101 "link_urls": {
102 "type": "text"
103 },
104 "link_texts": {
105 "type": "text"
106 },
107 "sent_from": {
108 "type": "text",
109 "analyzer": "standard"
110 },
111 "sent_to": {
112 "type": "text",
113 "analyzer": "standard"
114 },
115 "subject": {
116 "type": "text",
117 "analyzer": "standard"
118 },
119 "section": {
120 "type": "text",
121 "analyzer": "standard"
122 },
123 "header_footer_type": {
124 "type": "keyword"
125 },
126 "emphasized_text_contents": {
127 "type": "text"
128 },
129 "emphasized_text_tags": {
130 "type": "keyword"
131 },
132 "text_as_html": {
133 "type": "text",
134 "analyzer": "standard"
135 },
136 "regex_metadata": {
137 "type": "object"
138 },
139 "detection_class_prob": {
140 "type": "float"
141 }
142 }
143 }
144 }
145}

