unexpected behavior: retriever.get_relevant_documents 
Labels: name: area: vector store, name: auto:bug, name: area: models

### System Info

As stated in the title, the query is returning, but not the relevant documents. The code snippet below illustrates the issue:
```python
query = 'building bridges'
filter = Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='construction_material', value='steel')
limit = None
[]

```

I know that the documents and data are stored correctly because the query I am using works fine with similarity_search, and it returns the appropriate text. After splitting the PDF, I had to recreate the metadata and added it along with the documents. The meta_data field prints off without any problems when I access it in the similarity_search as well.

### Who can help?

@hwchase17
@agola11

### Information

- [ ] The official example notebooks/scripts
- [X] My own modified scripts

### Related Components

- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [X] Callbacks/Tracing
- [ ] Async

### Reproduction

**### Steps to Reproduce the Behavior:**

1. **Load PDF:** Begin by loading the PDF file that you will be working with.
2. **Append page_content:** Next, append the content from the PDF pages into an empty string called text.
3. **Split text Recursively:** Split the text string recursively to segment the content.
4. **Create Metadata for Split Text:** Use the following function to create metadata for the split text.

```python
def create_metadata(title: str, section: int, material: str) -> dict:
    metadata = {
        "title": title,
        "section": section,
        "material": material,
    }
    return metadata

```

5. **Loop Over Split Text:** Iterate through the split text, appending custom metadata to a list.
6. **Add Docs, Embeddings, Metadata:** Utilize the Chroma.from_texts method with the following parameters:
```python
vectordb = Chroma.from_texts(
    texts=docs,
    embedding=embedding,
    persist_directory=persist_directory,
    metadatas=metadatas_list,
)
```
**Proceed with SelfQueryRetriever:** Finally, proceed to use the SelfQueryRetriever.from_llm method as described in the documentation.

---------------------------------------------------------------------------------------------------------------------
### Note:
Everything works as intended with the similarity_search. The SelfQueryRetriever is returning as expected minus the relevant documents.

My suspicion is that the issue may be related to the Documents() class, but I recreated the object/class without any success regarding output. It should still function properly if the data is inserting fine into the database and all the other queries are working fine. What has lead me to this place early on is an issue arises with PDFs when they are split; thus, the workarounds are either:

**Appending into an Empty String:** This is necessary because metadata becomes distorted, and page break behavior takes precedence over separators and chunk size.

**Converting PDF to Image and Then to Text:** The process is PDF -> IMG -> Tesseract -> Text, which still requires metadata to be recreated.

### Expected behavior

Expected behavior:
Output the query and the data related to it, not just the query.