Chroma and Pinecone provide totally different results even when created with the same document set.
Labels: name: area: vector store, name: auto:bug, name: integration: chroma

### Checked other resources

- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.

### Example Code
```
def createAgent():
    pinecone.init(environment="gcp-starter")
    llm = ChatOpenAI(temperature=0, model_name="gpt-4-1106-preview")

    embeddings = OpenAIEmbeddings()
    docsearch_ch = Chroma(persist_directory="../chroma_db", embedding_function=embeddings,
                          collection_name="company")
    docsearch = Pinecone.from_existing_index(index_name="company", embedding=embeddings)

    companyQa = RetrievalQA.from_chain_type(
        llm=llm,
        chain_type="stuff",
        retriever=docsearch_ch.as_retriever(kwargs={"k": 15})
    )
   
    tools = [
        Tool(
            name="Company QA",
            description="Useful to get informations about Company",
            func=companyQa.run
        )
    ]
    return initialize_agent(
        tools=tools, llm=llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
    )
```
### Description

I have Chroma index and Pinecone index created from exactly the same documents. It is a company website scraped through Apify. Below code is used to create both indices:
```
splitter = RecursiveCharacterTextSplitter(
    chunk_size=500,
    chunk_overlap=50,
    separators= ["\n", "", " ", "\n\n"])

docs = splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()

Pinecone.from_documents(docs, embeddings, index_name="company")
Chroma.from_documents(docs, embeddings, persist_directory="./chroma_db", collection_name="company")
```

The problem is that Chroma seems very limited in capabilities compared to Pinecone.I have provided the example agent code that I am using.
When we use the `docsearch_ch` and ask the following prompt: `What are some clients of the Company"?`. We are going to receive `To find out the clients of the Company, you would need to contact Company directly or visit their official website or check their press releases for announcements of new partnerships or clients. Company QA does not have access to this information.`
When we use the `docsearch` that is based on Pinecone. The same prompt returns `Some clients of the company include industry leaders such as Demandbase, New Relic, Nubank, Rapid7, and Skyscanner.`

If we try to do the similarity search manually using the below code:
```
print(docsearch_ch.similarity_search("List of the company clients.", k =10))
print(docsearch.similarity_search("List of the company clients.", k =10))
```
Both of the vector stores will return more or less correct results (altough seems that Pinecone results are better), but more importantly both of them will return the Document with following content:
```
page_content='by industry leaders worldwide, such as Demandbase, New Relic, Nubank, Rapid7, and Skyscanner.'
```

When I try to debug the code and setup the breakpoint in the [BaseCombineDocumentsChain](https://github.com/langchain-ai/langchain/blob/ca014d5b04b1d73fd8f0fe224def98a82600c991/libs/langchain/langchain/chains/combine_documents/base.py#L136) the inputs for Chroma based retriever are empty. Seems like this isn't the expected behaviour.

### System Info

langchain==0.0.342
langchain-community==0.0.13
langchain-core==0.0.7
langchain-experimental==0.0.49

Mac OS M1 Ventura 13.6
Python 3.9.6

### Related Components

- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async