Community: RankLLMRerank AttributeError (Handle list-based rerank results) (#29840)

# community: Fix AttributeError in RankLLMRerank (`list` object has no
attribute `candidates`)

## **Description**
This PR fixes an issue in `RankLLMRerank` where reranking fails with the
following error:

```
AttributeError: 'list' object has no attribute 'candidates'
```

The issue arises because `rerank_batch()` returns a `List[Result]`
instead of an object containing `.candidates`.

### **Changes Introduced**
- Adjusted `compress_documents()` to support both:
  - Old API format: `rerank_results.candidates`
  - New API format: `rerank_results` as a list
  - Also fix wrong .txt location parsing while I was at it.

---

## **Issue**
Fixes **AttributeError** in `RankLLMRerank` when using
`compression_retriever.invoke()`. The issue is observed when
`rerank_batch()` returns a list instead of an object with `.candidates`.

**Relevant log:**
```
AttributeError: 'list' object has no attribute 'candidates'
```

## **Dependencies**
- No additional dependencies introduced.

---

## **Checklist**
- [x] **Backward compatible** with previous API versions
- [x] **Tested** locally with different RankLLM models
- [x] **No new dependencies introduced**
- [x] **Linted** with `make format && make lint`
- [x] **Ready for review**

---

## **Testing**
- Ran `compression_retriever.invoke(query)`

## **Reviewers**
If no review within a few days, please **@mention** one of:
- @baskaryan
- @efriis
- @eyurtsev
- @ccurme
- @vbarda
- @hwchase17

libs/community/langchain_community/document_compressors/rankllm_rerank.py

@@ -130,10 +130,17 @@ class RankLLMRerank(BaseDocumentCompressor):
         )
 
         final_results = []
-        for res in rerank_results.candidates:
-            doc = documents[int(res.docid)]
-            doc_copy = Document(doc.page_content, metadata=deepcopy(doc.metadata))
-            final_results.append(doc_copy)
+        if hasattr(rerank_results, "candidates"):
+            # Old API format
+            for res in rerank_results.candidates:
+                doc = documents[int(res.docid)]
+                doc_copy = Document(doc.page_content, metadata=deepcopy(doc.metadata))
+                final_results.append(doc_copy)
+        else:
+            for res in rerank_results:
+                doc = documents[int(res.docid)]
+                doc_copy = Document(doc.page_content, metadata=deepcopy(doc.metadata))
+                final_results.append(doc_copy)
 
         return final_results[: self.top_n]