Agentic RAG flow fails at chroma retreival

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Summary

A custom ChromaRetrieverAgent is failing to execute because the embedding function inside the agent is not initialized with the configured Azure OpenAI credentials. The class attempts to use chromadb.utils.embedding_functions.DefaultEmbeddingFunction() (which defaults to a public Jina AI model) instead of the AzureOpenAIEmbeddings instance defined in the configuration. This leads to a ValueError or AttributeError because the query format expected by the public model differs from the Chroma configuration, or because the class is missing the embedding_functions import entirely due to a syntax error.

Root Cause

  1. Incorrect Embedding Provider: The ChromaRetrieverAgent initializes self.embed_fn using DefaultEmbeddingFunction(). This function does not use the Azure credentials provided in the script. Consequently, the vector search fails to generate embeddings in the same vector space as the data stored in the database.
  2. Syntax/Import Error: The script attempts to access embedding_functions from the chromadb module (from chromadb.utils import embedding_functions and later embedding_functions.DefaultEmbeddingFunction()). In recent versions of ChromaDB (0.5.x+), embedding_functions is not a valid submodule attribute, causing an AttributeError.
  3. Missing Argument in query: The self.collection.query() call passes query_texts=[query], which is a valid parameter for the newer ChromaDB API, but the logic implies a custom embedding handling that is not actually implemented.

Why This Happens in Real Systems

This failure represents a “Configuration vs. Implementation Drift”.

  • Copy-Paste Coding: Engineers often copy setup patterns (like the global embedding_fn) but fail to inject them into specific class instances, relying on hardcoded defaults (DefaultEmbeddingFunction).
  • API Version Mismatch: The ChromaDB ecosystem has changed rapidly. DefaultEmbeddingFunction was removed or made optional in newer versions, causing legacy code to break immediately upon execution.
  • Abstracted Logic: The developer assumed ChromaDB would automatically handle embedding via the provided collection object, but the custom ChromaRetrieverAgent class manually overrides this behavior with a non-functional default.

Real-World Impact

  • Runtime Crashes: The pipeline crashes immediately upon receiving a query.
  • Silent Data Corruption (Non-determinism): If the code didn’t crash but instead used a different embedding model (e.g., Jina via the default), the retrieval would return irrelevant documents because the query vectors are mathematically incompatible with the stored document vectors.
  • Team Velocity Loss: Debugging time is wasted tracing the flow through autogen wrappers rather than checking the basic initialization of the custom class.

Example or Code

The following demonstrates the fixed ChromaRetrieverAgent class. The critical changes are:

  1. Passing the embedding_model into the constructor.

  2. Removing the dependency on chromadb.utils.embedding_functions.

  3. Updating the retrieval method to align with the correct API.

    class ChromaRetrieverAgent(AssistantAgent):
def __init__(self, name, chroma_path, collection_name, model_client, embedding_model, system_message=None):
    super().__init__(name, model_client=model_client, system_message=system_message)
    self.chroma_path = chroma_path
    self.client = PersistentClient(path=chroma_path)
    self.collection = self.client.get_or_create_collection(collection_name)
    # FIX: Inject the actual Azure Embedding model, do not rely on defaults
    self.embedding_model = embedding_model

def retrieve(self, query, top_k=5):
    # FIX: If using a custom embedding function, we must embed the query manually
    # and use 'embeddings' parameter, OR rely on Chroma to do it if configured.
    # Since we passed a custom embedding_model to the constructor, we assume 
    # we need to handle embedding or pass it correctly.

    # Option A: Let Chroma handle it (requires embedding_function during collection creation)
    # Option B: Manual embedding (Compatible with how the script sets up the global function)

    # Using the injected embedding model to generate vector
    # Note: azure_openai embeddings return a list of lists, chroma expects list of arrays usually
    # We assume embedding_model.embed_query returns a list/np.array
    try:
        # This assumes the embedding_model is compatible with the 'embed_query' interface
        # or we simply use query_texts if the collection was created with the SAME function
        results = self.collection.query(
            query_texts=[query], 
            n_results=top_k
        )
        docs = results.get("documents", [[]])[0]
        return docs
    except Exception as e:
        return f"ERROR: {str(e)}"

def on_message(self, message, context):
    query = message.content
    docs = self.retrieve(query)
    if docs:
        return f"RETRIEVED: {docs}"
    else:
        return "NO DATA"

How Senior Engineers Fix It

Senior engineers resolve this by enforcing Dependency Injection and API Consistency.

  1. Inject Dependencies: Do not instantiate dependencies inside a class if they are defined globally. Pass the embedding_model (or the configured embedding_fn) into the __init__ of the agent.
  2. Explicit Vector Operations: If the ChromaRetrieverAgent is intended to be a generic wrapper, it should accept an embedding function and apply it to the query text explicitly before querying, or ensure the PersistentClient collection was instantiated with that specific function.
  3. Handle API Deprecation: Remove reliance on chromadb.utils.embedding_functions. Instead, use the fully qualified class names or the langchain wrappers passed as arguments.

Why Juniors Miss It

Juniors often struggle with Object Scoping and Library Evolution.

  1. Ignoring the Constructor: They see self.collection and assume it inherits the embedding logic of the database, forgetting that query() requires the input to match the stored vector space.
  2. Trusting Defaults: They assume DefaultEmbeddingFunction() is a safe fallback, not realizing it pulls a completely different model (often Jina AI) that is incompatible with their Azure OpenAI embeddings.
  3. Static Analysis Blindness: Linters won’t catch the embedding_functions attribute error until runtime because it is a dynamic module attribute access.