ChromaDB embedding error solution

Prepared: 20250711_124457_AI_in_Creative_Industries.md (3 chunks) Error processing output/2025/07/20250711_124457_AI_in_Creative_Industries.md: You must provide an embedding function to compute embeddings.https://docs.trychroma.com/guides/embeddings in add. Prepared: 20250703_095259_Quantitative_Persona_vs_MCP.md (7 chunks) Error processing output/2025/07/20250703_095259_Quantitative_Persona_vs_MCP.md: You must provide an embedding function to compute embeddings.https://docs.trychroma.com/guides/embeddings in add. Prepared: 20250712_043845_Retail_Analytics_Dashboard.md (7 chunks) Error processing output/2025/07/20250712_043845_Retail_Analytics_Dashboard.md: You must provide an embedding function to compute embeddings.https://docs.trychroma.com/guides/embeddings in add. Prepared: 20250714_090204_Glitch_Art_mit_Expressionismus.md (2 chunks) Error processing output/2025/07/20250714_090204_Glitch_Art_mit_Expressionismus.md: You must provide an embedding function to compute embeddings.https://docs.trychroma.com/guides/embeddings in add. Prepared: 20250706_162332_Digital_Product_Ideas_LLM.md (5 chunks) Error processing output/2025/07/20250706_162332_Digital_Product_Ideas_LLM.md: You must provide an embedding function to compute embeddings.https://docs.trychroma.com/guides/embeddings in add. Prepared: 20250708_115701_Netlify_Build_Errors.md (12 chunks) Error processing output/2025/07/20250708_115701_Netlify_Build_Errors.md: You must provide an embedding function to compute embeddings.https://docs.trychroma.com/guides/embeddings in add. Prepared: 20250714_152322_Productivity_Update_for_Psychi.md (4 chunks) Error processing output/2025/07/20250714_152322_Productivity_Update_for_Psychi.md: You must provide an embedding function to compute embeddings.https://docs.trychroma.com/guides/embeddings in add. Prepared: 20250726_062137_Personality-driven_writing_ana.md (5 chunks) ^CTraceback (most recent call last): File "/Users/danielkliewer/agentsearch01/agent_search.py", line 1164, in <module> main() ~~~~^^ File "/Users/danielkliewer/agentsearch01/agent_search.py", line 1121, in main search_system.process_files_batch(batch_size=args.batch_size) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/danielkliewer/agentsearch01/agent_search.py", line 798, in process_files_batch self.collection.add( ~~~~~~~~~~~~~~~~~~~^ documents=batch_documents, ^^^^^^^^^^^^^^^^^^^^^^^^^^ metadatas=batch_metadatas, ^^^^^^^^^^^^^^^^^^^^^^^^^^ ids=batch_ids ^^^^^^^^^^^^^ ) ^ File "/Users/danielkliewer/agentsearch01/venv/lib/python3.13/site-packages/chromadb/api/models/Collection.py", line 80, in add add_request = self._validate_and_prepare_add_request( ids=ids, ...<4 lines>... uris=uris, ) File "/Users/danielkliewer/agentsearch01/venv/lib/python3.13/site-packages/chromadb/api/models/CollectionCommon.py", line 95, in wrapper return func(self, *args, **kwargs) File "/Users/danielkliewer/agentsearch01/venv/lib/python3.13/site-packages/chromadb/api/models/CollectionCommon.py", line 219, in _validate_and_prepare_add_request validate_insert_record_set(record_set=add_records) ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/danielkliewer/agentsearch01/venv/lib/python3.13/site-packages/chromadb/api/types.py", line 314, in validate_insert_record_set validate_metadatas(record_set["metadatas"]) ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/danielkliewer/agentsearch01/venv/lib/python3.13/site-packages/chromadb/api/types.py", line 791, in validate_metadatas validate_metadata(metadata) ~~~~~~~~~~~~~~~~~^^^^^^^^^^ File "/Users/danielkliewer/agentsearch01/venv/lib/python3.13/site-packages/chromadb/api/types.py", line 754, in validate_metadata if not isinstance(value, bool) and not isinstance( ~~~~~~~~~~^ value, (str, int, float, type(None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ): ^ KeyboardInterrupt (venv) danielkliewer@Daniels-MacBook-Pro agentsearch01 %

You're getting this error from ChromaDB: > `You must provide an embedding function to compute embeddings.` This means you haven’t passed an **embedding function** when calling `collection.add(...)`. --- ### ✅ Solution When you use a **ChromaDB `Collection`**, and you try to `.add()` documents **without passing explicit embeddings**, Chroma needs to know **how to generate them**. You must either: 1. **Pass the `embedding_function` when creating the client**, or 2. **Provide embeddings manually in `collection.add(embeddings=...)`**. --- ### ✅ Fix Option 1: Add an embedding function on client/collection creation If you’re using LangChain’s embedding wrapper (e.g., `OllamaEmbeddings`, `HuggingFaceEmbeddings`, etc.), do something like this: ```python from langchain_community.embeddings import OllamaEmbeddings from chromadb import PersistentClient embedding_function = OllamaEmbeddings(model="nomic-embed-text") client = PersistentClient(path="./chroma_db") collection = client.get_or_create_collection( name="your_collection_name", embedding_function=embedding_function # <- This is the key line ) ``` If you're not using LangChain, you can provide a **custom embedding function**: ```python def embed_texts(texts: List[str]) -> List[List[float]]: # return your own list of vector embeddings here ... ``` Then pass: ```python collection = client.get_or_create_collection( name="your_collection", embedding_function=embed_texts ) ``` --- ### ✅ Fix Option 2: Precompute and pass `embeddings` manually If you don't want Chroma to call the embedding function for you: ```python collection.add( documents=your_docs, metadatas=your_metadatas, ids=your_ids, embeddings=precomputed_embeddings # <- must be List[List[float]] ) ``` --- ### 🧠 Final Tip To confirm whether you're using the `embedding_function`, print it out: ```python print(collection._embedding_function) ``` If it’s `None`, Chroma can’t embed your docs, and you’ll get the exact error you're seeing. --- Let me know if you're using a specific embedding model or stack (e.g., Ollama locally, Hugging Face Transformers, etc.), and I can tailor the fix.

The main issues from your error messages and traceback boil down to: ### 1. **Missing embedding function in your ChromaDB `add` calls** > `You must provide an embedding function to compute embeddings.` ChromaDB requires an embedding function or precomputed embeddings when you add documents to a collection. The error says you are calling `.add()` without providing embeddings or a function that generates embeddings. - You need to instantiate your collection with an embedding function or provide embeddings explicitly. - If you have raw documents/text, you must embed them before adding. - See [ChromaDB embedding guide](https://docs.trychroma.com/guides/embeddings). ### 2. **Metadata validation failure** From the stack trace, the error appears while validating metadata in your `add` call: ```python if not isinstance(value, bool) and not isinstance(value, (str, int, float, type(None))): ``` This suggests that some metadata values have invalid types. Valid types for metadata values are: - `bool` - `str` - `int` - `float` - `None` Check your metadata dictionaries to make sure all values are one of these types. If you have lists, dicts, or other objects as metadata values, you will get this error. --- # How to fix both: ### A. Fix embedding function usage If you are using ChromaDB with Python, a minimal example for setting an embedding function: ```python from chromadb.config import Settings import chromadb # Example embedding function (must return vector list) def dummy_embed(texts): return [[0.0] * 768 for _ in texts] # replace with real embedding calls client = chromadb.Client(Settings(chroma_db_impl="duckdb+parquet", persist_directory="./chroma_db")) collection = client.get_or_create_collection( name="my_collection", embedding_function=dummy_embed ) ``` Then when you add documents: ```python collection.add( documents=["My doc 1", "My doc 2"], metadatas=[{"source": "doc1"}, {"source": "doc2"}], ids=["doc1", "doc2"], ) ``` - If you do not specify `embedding_function` at collection creation, you must provide precomputed embeddings during `.add()` like: ```python embeddings = my_embedding_model.encode(["My doc 1", "My doc 2"]) collection.add( documents=["My doc 1", "My doc 2"], metadatas=[{"source": "doc1"}, {"source": "doc2"}], ids=["doc1", "doc2"], embeddings=embeddings ) ``` ### B. Fix metadata types Review your code that creates `batch_metadatas`. Make sure every metadata dictionary contains only primitive types (`bool`, `str`, `int`, `float`, or `None`). Example correction: ```python batch_metadatas = [ { "filename": "file1.md", "chunk_index": 0, "is_processed": True, # "tags": ["tag1", "tag2"] # This would be invalid if tags is a list. # Instead store as a string or remove "tags": "tag1,tag2" }, ... ] ``` --- # Summary checklist 1. Ensure your collection is created with a valid embedding function or provide embeddings on `.add()`. 2. Validate all metadata values are `bool`, `str`, `int`, `float`, or `None`. No lists/dicts. 3. If needed, convert any complex metadata fields (lists, dicts) into JSON strings or remove them. --- If you want, share your snippet for how you create your Chroma client and add documents, and I can help rewrite it to fix these issues.