chroma: [Bug]: querying a persisted index returns an empty array

What happened?

The following example uses langchain to successfully load documents into chroma and to successfully persist the data. Querying works as expected.

However, when we restart the notebook and attempt to query again without ingesting data and instead reading the persisted directory, we get [] when querying both using the langchain wrapper’s method and chromadb’s client (accessed from langchain wrapper). I’ve concluded that there is either a deep bug in chromadb or I am doing something wrong. Please help!

Code to reproduce

In the notebook paste:

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
import json

assembly_ai_output_file = "data_auto_chapters.json"

# Read the JSON file
with open(assembly_ai_output_file, "r") as json_file:
    json_data = json.load(json_file)

utterances = json_data['utterances']
data = list(map(lambda x: {key: value for key, value in x.items() if key != "words"}, utterances))
transcription_id = json_data['id']
docs = list(map(lambda x: Document(page_content=x['text'], 
                                   metadata={"transcription_id": transcription_id,"confidence": x['confidence'], "start": x['start'], "end": x['end'], "speaker": x['speaker']}),
                            utterances))

embedding = OpenAIEmbeddings()

persist_directory = 'db'
vectordb = Chroma.from_documents(documents=docs, embedding=embedding, persist_directory=persist_directory, collection_name="condense_demo")
vectordb.persist()

query = "what does the speaker say about raytheon?"

retrieved_docs = vectordb.similarity_search(query) # filter={"speaker": "B"}
retrieved_docs # data comes back

Now lets restart the notebook, and run the following code which I believe should work.

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
embedding = OpenAIEmbeddings()

vectordb = Chroma(persist_directory="db", embedding_function=embedding, collection_name="condense_demo")
query = "what does the speaker say about raytheon?"

retrieved_docs = vectordb.similarity_search(query) # filter={"speaker": "B"}
retrieved_docs # returns []

To make sure its not langchain issue, lets get the chroma client from the Chroma class and query the data at a lower level.

vectordb._client.list_collections() # looks good
vectordb._client.get_collection("condense_demo").peek() # yes there is data there!
query_vector = embedding.embed_query(query) # use langchain implementation of openai embedding algorithm
vectordb._client.get_collection("condense_demo").query(query_texts=[query], n_results=4) 
# returns {'ids': [[]],
# 'embeddings': None,
# 'documents': [[]],
# 'metadatas': [[]],
# 'distances': [[]]}

Am I doing something wrong? I am attaching the JSON that is fed into the data as a zip file because GitHub doesn’t allow json to make it easier to reproduce but i believe you can reproduce using any document (my assumption).

data_auto_chapters.json.zip

Versions

Python 3.8.8 langchain 0.0.184 chroma 0.3.25

Relevant log output

No response