> ## 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.
# Azure Cognitive Search
> Batch process all your records using `unstructured-ingest` to store structured outputs locally on your filesystem and upload those local files to an Azure Cognitive Search index.
First you’ll need to install the azure cognitive search dependencies as shown here.
```bash
pip install "unstructured[azure-cognitive-search]"
```
## 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=${EMBEDDING_PROVIDER:-"langchain-huggingface"}
unstructured-ingest \
local \
--input-path example-docs/book-war-and-peace-1225p.txt \
--output-dir local-output-to-azure-cog-search \
--strategy fast \
--chunk-elements \
--embedding-provider "$EMBEDDING_PROVIDER" \
--num-processes 2 \
--verbose \
azure-cognitive-search \
--key "$AZURE_SEARCH_API_KEY" \
--endpoint "$AZURE_SEARCH_ENDPOINT" \
--index utic-test-ingest-fixtures-output
```
```python Python
import os
from unstructured.ingest.connector.azure_cognitive_search import (
AzureCognitiveSearchAccessConfig,
AzureCognitiveSearchWriteConfig,
SimpleAzureCognitiveSearchStorageConfig,
)
from unstructured.ingest.connector.local import SimpleLocalConfig
from unstructured.ingest.interfaces import (
ChunkingConfig,
EmbeddingConfig,
PartitionConfig,
ProcessorConfig,
ReadConfig,
)
from unstructured.ingest.runner import LocalRunner
from unstructured.ingest.runner.writers.azure_cognitive_search import (
AzureCognitiveSearchWriter,
)
from unstructured.ingest.runner.writers.base_writer import Writer
def get_writer() -> Writer:
return AzureCognitiveSearchWriter(
connector_config=SimpleAzureCognitiveSearchStorageConfig(
access_config=AzureCognitiveSearchAccessConfig(key=os.getenv("AZURE_SEARCH_API_KEY")),
endpoint=os.getenv("$AZURE_SEARCH_ENDPOINT"),
),
write_config=AzureCognitiveSearchWriteConfig(index="utic-test-ingest-fixtures-output"),
)
if __name__ == "__main__":
writer = get_writer()
runner = LocalRunner(
processor_config=ProcessorConfig(
verbose=True,
output_dir="local-output-to-azure-cog-search",
num_processes=2,
),
connector_config=SimpleLocalConfig(
input_path="example-docs/book-war-and-peace-1225p.txt",
),
read_config=ReadConfig(),
partition_config=PartitionConfig(),
chunking_config=ChunkingConfig(chunk_elements=True),
embedding_config=EmbeddingConfig(
provider="langchain-huggingface",
),
writer=writer,
writer_kwargs={},
)
runner.run()
```
For a full list of the options the CLI accepts check `unstructured-ingest azure-cognitive-search --help`.
NOTE: Keep in mind that you will need to have all the appropriate extras and dependencies for the file types of the documents contained in your data storage platform if you’re running this locally. You can find more information about this in the [installation guide](/open-source/installation/overview).
## Sample Index Schema
To make sure the schema of the index matches the data being written to it, a sample schema json can be used:
```json Object description
1{
2 "@odata.context": "https://utic-test-ingest-fixtures.search.windows.net/$metadata#indexes/$entity",
3 "@odata.etag": "\"0x8DBB93E09C8F4BD\"",
4 "name": "your-index-here",
5 "fields": [
6 {
7 "name": "id",
8 "type": "Edm.String",
9 "key": true
10 },
11 {
12 "name": "element_id",
13 "type": "Edm.String"
14 },
15 {
16 "name": "text",
17 "type": "Edm.String"
18 },
19 {
20 "name": "embeddings",
21 "type": "Collection(Edm.Single)",
22 "dimensions": 400,
23 "vectorSearchConfiguration": "embeddings-config"
24 },
25 {
26 "name": "type",
27 "type": "Edm.String"
28 },
29 {
30 "name": "metadata",
31 "type": "Edm.ComplexType",
32 "fields": [
33 {
34 "name": "category_depth",
35 "type": "Edm.Int32"
36 },
37 {
38 "name": "parent_id",
39 "type": "Edm.String"
40 },
41 {
42 "name": "attached_to_filename",
43 "type": "Edm.String"
44 },
45 {
46 "name": "filetype",
47 "type": "Edm.String"
48 },
49 {
50 "name": "last_modified",
51 "type": "Edm.DateTimeOffset"
52 },
53 {
54 "name": "file_directory",
55 "type": "Edm.String"
56 },
57 {
58 "name": "filename",
59 "type": "Edm.String"
60 },
61 {
62 "name": "data_source",
63 "type": "Edm.ComplexType",
64 "fields": [
65 {
66 "name": "url",
67 "type": "Edm.String"
68 },
69 {
70 "name": "version",
71 "type": "Edm.String"
72 },
73 {
74 "name": "date_created",
75 "type": "Edm.DateTimeOffset"
76 },
77 {
78 "name": "date_modified",
79 "type": "Edm.DateTimeOffset"
80 },
81 {
82 "name": "date_processed",
83 "type": "Edm.DateTimeOffset"
84 },
85 {
86 "name": "permissions_data",
87 "type": "Edm.String"
88 },
89 {
90 "name": "record_locator",
91 "type": "Edm.String"
92 }
93 ]
94 },
95 {
96 "name": "coordinates",
97 "type": "Edm.ComplexType",
98 "fields": [
99 {
100 "name": "system",
101 "type": "Edm.String"
102 },
103 {
104 "name": "layout_width",
105 "type": "Edm.Double"
106 },
107 {
108 "name": "layout_height",
109 "type": "Edm.Double"
110 },
111 {
112 "name": "points",
113 "type": "Edm.String"
114 }
115 ]
116 },
117 {
118 "name": "page_number",
119 "type": "Edm.String"
120 },
121 {
122 "name": "links",
123 "type": "Collection(Edm.String)"
124 },
125 {
126 "name": "url",
127 "type": "Edm.String"
128 },
129 {
130 "name": "link_urls",
131 "type": "Collection(Edm.String)"
132 },
133 {
134 "name": "link_texts",
135 "type": "Collection(Edm.String)"
136 },
137 {
138 "name": "sent_from",
139 "type": "Collection(Edm.String)"
140 },
141 {
142 "name": "sent_to",
143 "type": "Collection(Edm.String)"
144 },
145 {
146 "name": "subject",
147 "type": "Edm.String"
148 },
149 {
150 "name": "section",
151 "type": "Edm.String"
152 },
153 {
154 "name": "header_footer_type",
155 "type": "Edm.String"
156 },
157 {
158 "name": "emphasized_text_contents",
159 "type": "Collection(Edm.String)"
160 },
161 {
162 "name": "emphasized_text_tags",
163 "type": "Collection(Edm.String)"
164 },
165 {
166 "name": "text_as_html",
167 "type": "Edm.String"
168 },
169 {
170 "name": "regex_metadata",
171 "type": "Edm.String"
172 },
173 {
174 "name": "detection_class_prob",
175 "type": "Edm.Double"
176 }
177 ]
178 }
179 ],
180 "vectorSearch": {
181 "algorithmConfigurations": [
182 {
183 "name": "embeddings-config",
184 "kind": "hnsw",
185 "hnswParameters": {
186 "metric": "cosine",
187 "m": 4,
188 "efConstruction": 400,
189 "efSearch": 500
190 }
191 }
192 ]
193 }
194}
```