chore: import upstream snapshot with attribution

docs-website/docs/pipeline-components/caching/cachechecker.mdx

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+---
+title: "CacheChecker"
+id: cachechecker
+slug: "/cachechecker"
+description: "This component checks for the presence of documents in a Document Store based on a specified cache field."
+---
+
+# CacheChecker
+
+This component checks for the presence of documents in a Document Store based on a specified cache field.
+
+<div className="key-value-table">
+
+|  |  |
+| --- | --- |
+| **Most common position in a pipeline** | Flexible |
+| **Mandatory init variables** | `document_store`: A Document Store instance  <br /> <br />`cache_field`: Name of the document's metadata field |
+| **Mandatory run variables** | `items`: A list of values associated with the `cache_field` in documents |
+| **Output variables** | `hits`: A list of documents that were found with the specified value in cache  <br /> <br />`misses`: A list of values that could not be found |
+| **API reference** | [Caching](/reference/caching-api) |
+| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/caching/cache_checker.py |
+| **Package name** | `haystack-ai` |
+
+</div>
+
+## Overview
+
+`CacheChecker` checks if a Document Store contains any document with a value in the `cache_field` that matches any of the values provided in the `items` input variable. It returns a dictionary with two keys: `"hits"` and `"misses"`. The values are lists of documents that were found in the cache and items that were not, respectively.
+
+## Usage
+
+### On its own
+
+```python
+from haystack.components.caching import CacheChecker
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+
+my_doc_store = InMemoryDocumentStore()
+
+# For URL-based caching
+cache_checker = CacheChecker(document_store=my_doc_store, cache_field="url")
+cache_check_results = cache_checker.run(
+    items=[
+        "https://example.com/resource",
+        "https://another_example.com/other_resources",
+    ],
+)
+print(
+    cache_check_results["hits"],
+)  # List of Documents that were found in the cache: all of these have 'url': <one of the above> in the metadata
+print(
+    cache_check_results["misses"],
+)  # URLs that were not found in the cache, like ["https://example.com/resource"]
+
+# For caching based on a custom identifier
+cache_checker = CacheChecker(document_store=my_doc_store, cache_field="metadata_field")
+cache_check_results = cache_checker.run(items=["12345", "ABCDE"])
+print(
+    cache_check_results["hits"],
+)  # Documents that were found in the cache: all of these have 'metadata_field': <one of the above> in the metadata
+print(
+    cache_check_results["misses"],
+)  # Values that were not found in the cache, like: ["ABCDE"]
+```
+
+### In a pipeline
+
+```python
+from haystack import Pipeline
+from haystack.components.converters import TextFileToDocument
+from haystack.components.preprocessors import DocumentCleaner, DocumentSplitter
+from haystack.components.writers import DocumentWriter
+from haystack.components.caching import CacheChecker
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+
+pipeline = Pipeline()
+document_store = InMemoryDocumentStore()
+pipeline.add_component(
+    instance=CacheChecker(document_store, cache_field="meta.file_path"),
+    name="cache_checker",
+)
+pipeline.add_component(instance=TextFileToDocument(), name="text_file_converter")
+pipeline.add_component(instance=DocumentCleaner(), name="cleaner")
+pipeline.add_component(
+    instance=DocumentSplitter(split_by="sentence", split_length=250, split_overlap=30),
+    name="splitter",
+)
+pipeline.add_component(
+    instance=DocumentWriter(document_store=document_store),
+    name="writer",
+)
+pipeline.connect("cache_checker.misses", "text_file_converter.sources")
+pipeline.connect("text_file_converter.documents", "cleaner.documents")
+pipeline.connect("cleaner.documents", "splitter.documents")
+pipeline.connect("splitter.documents", "writer.documents")
+
+pipeline.draw("pipeline.png")
+
+# Take the current directory as input and run the pipeline
+result = pipeline.run({"cache_checker": {"items": ["code_of_conduct_1.txt"]}})
+print(result)
+
+# The second execution skips the files that were already processed
+result = pipeline.run({"cache_checker": {"items": ["code_of_conduct_1.txt"]}})
+print(result)
+```