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docs/snippets/c/utils/vector_database_integration.md
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docs/snippets/c/utils/vector_database_integration.md
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```c title="C"
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#include "kreuzberg.h"
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#include <stdio.h>
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#include <stdlib.h>
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int main(void) {
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const char *document_path = "document.pdf";
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const char *document_id = "doc-001";
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const char *config_json =
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"{"
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"\"chunking\": {"
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"\"chunker_type\": \"character\","
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"\"max_characters\": 512,"
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"\"overlap\": 50,"
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"\"embedding\": {"
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"\"model\": {\"preset\": {\"name\": \"balanced\"}},"
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"\"normalize\": true,"
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"\"batch_size\": 32"
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"}"
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"}"
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"}";
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KREUZBERGExtractionConfig *config = kreuzberg_extraction_config_from_json(config_json);
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if (!config) {
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fprintf(stderr, "config parse failed (code %d): %s\n",
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kreuzberg_last_error_code(),
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kreuzberg_last_error_context());
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return 1;
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}
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KREUZBERGExtractionResult *result =
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kreuzberg_extract_file_sync(document_path, NULL, config);
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if (!result) {
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fprintf(stderr, "extraction failed (code %d): %s\n",
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kreuzberg_last_error_code(),
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kreuzberg_last_error_context());
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kreuzberg_extraction_config_free(config);
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return 1;
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}
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/* The chunks JSON array carries content + embedding + metadata for each
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chunk. Pass this directly to your vector database client (pgvector,
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Qdrant, Pinecone, etc.) along with the document_id as a metadata field. */
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char *chunks_json = kreuzberg_extraction_result_chunks(result);
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printf("document_id: %s\n", document_id);
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printf("chunks (JSON, ready to upsert into a vector DB):\n%s\n",
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chunks_json ? chunks_json : "[]");
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kreuzberg_free_string(chunks_json);
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kreuzberg_extraction_result_free(result);
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kreuzberg_extraction_config_free(config);
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return 0;
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}
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```
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