> ## Documentation Index
> Fetch the complete documentation index at: https://unstructured-53-kapa-ai.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Opensearch
# OpenSearch - Unstructured 0.13.0 documentation
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Batch process all your records using `unstructured-ingest` to store structured outputs locally on your filesystem and upload those local files to an OpenSearch index.
First you’ll need to install OpenSearch dependencies as shown here.
```bash
pip install "unstructured[opensearch]"
```
## 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.
```bash Shell
#!/usr/bin/env bash
EMBEDDING_PROVIDER=${EMBEDDING_PROVIDER:-"langchain-huggingface"}
unstructured-ingest \
local \
--input-path example-docs/book-war-and-peace-1225p.txt \
--output-dir local-output-to-opensearch \
--strategy fast \
--chunk-elements \
--embedding-provider "$EMBEDDING_PROVIDER" \
--num-processes 4 \
--verbose \
opensearch \
--hosts "$OPENSEARCH_HOSTS" \
--username "$OPENSEARCH_USERNAME" \
--password "$OPENSEARCH_PASSWORD" \
--index-name "$OPENSEARCH_INDEX_NAME" \
--num-processes 2
```
```python Python
import os
from unstructured.ingest.connector.elasticsearch import (
ElasticsearchWriteConfig,
)
from unstructured.ingest.connector.local import SimpleLocalConfig
from unstructured.ingest.connector.opensearch import (
OpenSearchAccessConfig,
SimpleOpenSearchConfig,
)
from unstructured.ingest.interfaces import (
ChunkingConfig,
EmbeddingConfig,
PartitionConfig,
ProcessorConfig,
ReadConfig,
)
from unstructured.ingest.runner import LocalRunner
from unstructured.ingest.runner.writers.base_writer import Writer
from unstructured.ingest.runner.writers.opensearch import (
OpenSearchWriter,
)
def get_writer() -> Writer:
return OpenSearchWriter(
connector_config=SimpleOpenSearchConfig(
access_config=OpenSearchAccessConfig(
hosts=os.getenv("OPENSEARCH_HOSTS"),
username=os.getenv("OPENSEARCH_USERNAME"),
password=os.getenv("OPENSEARCH_PASSWORD"),
),
index_name=os.getenv("OPENSEARCH_INDEX_NAME"),
),
write_config=ElasticsearchWriteConfig(
batch_size_bytes=15_000_000,
num_processes=2,
),
)
if __name__ == "__main__":
writer = get_writer()
runner = LocalRunner(
processor_config=ProcessorConfig(
verbose=True,
output_dir="local-output-to-opensearch",
num_processes=2,
),
connector_config=SimpleLocalConfig(
input_path="example-docs/book-war-and-peace-1225p.txt",
),
read_config=ReadConfig(),
partition_config=PartitionConfig(),
chunking_config=ChunkingConfig(chunk_elements=True),
embedding_config=EmbeddingConfig(
provider="langchain-huggingface",
),
writer=writer,
writer_kwargs={},
)
runner.run()
```
For a full list of the options the CLI accepts check `unstructured-ingest opensearch --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](/open-source/installation/overview).
## 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.
```json Object description
1 {"settings": {
2 "index": {
3 "knn": true,
4 "knn.algo_param.ef_search": 100
5 }
6 },
7 "mappings": {
8 "properties": {
9 "element_id": {
10 "type": "keyword"
11 },
12 "text": {
13 "type": "text",
14 "analyzer": "english"
15 },
16 "type": {
17 "type": "text"
18 },
19 "embeddings": {
20 "type": "knn_vector",
21 "dimension": 384
22 },
23 "metadata": {
24 "type": "object",
25 "properties": {
26 "category_depth": {
27 "type": "integer"
28 },
29 "parent_id": {
30 "type": "keyword"
31 },
32 "attached_to_filename": {
33 "type": "keyword"
34 },
35 "filetype": {
36 "type": "keyword"
37 },
38 "last_modified": {
39 "type": "date"
40 },
41 "file_directory": {
42 "type": "keyword"
43 },
44 "filename": {
45 "type": "keyword"
46 },
47 "data_source": {
48 "type": "object",
49 "properties": {
50 "url": {
51 "type": "text",
52 "analyzer": "standard"
53 },
54 "version": {
55 "type": "keyword"
56 },
57 "date_created": {
58 "type": "date"
59 },
60 "date_modified": {
61 "type": "date"
62 },
63 "date_processed": {
64 "type": "date"
65 },
66 "record_locator": {
67 "type": "keyword"
68 },
69 "permissions_data": {
70 "type": "object"
71 }
72 }
73 },
74 "coordinates": {
75 "type": "object",
76 "properties": {
77 "system": {
78 "type": "keyword"
79 },
80 "layout_width": {
81 "type": "float"
82 },
83 "layout_height": {
84 "type": "float"
85 },
86 "points": {
87 "type": "float"
88 }
89 }
90 },
91 "languages": {
92 "type": "keyword"
93 },
94 "page_number": {
95 "type": "integer"
96 },
97 "page_name": {
98 "type": "keyword"
99 },
100 "url": {
101 "type": "text",
102 "analyzer": "standard"
103 },
104 "links": {
105 "type": "object"
106 },
107 "link_urls": {
108 "type": "text"
109 },
110 "link_texts": {
111 "type": "text"
112 },
113 "sent_from": {
114 "type": "text",
115 "analyzer": "standard"
116 },
117 "sent_to": {
118 "type": "text",
119 "analyzer": "standard"
120 },
121 "subject": {
122 "type": "text",
123 "analyzer": "standard"
124 },
125 "section": {
126 "type": "text",
127 "analyzer": "standard"
128 },
129 "header_footer_type": {
130 "type": "keyword"
131 },
132 "emphasized_text_contents": {
133 "type": "text"
134 },
135 "emphasized_text_tags": {
136 "type": "keyword"
137 },
138 "text_as_html": {
139 "type": "text",
140 "analyzer": "standard"
141 },
142 "regex_metadata": {
143 "type": "object"
144 },
145 "detection_class_prob": {
146 "type": "float"
147 }
148 }
149 }
150 }
151 }
152}
```