Solved: OpenAI Assistants API v2 'File Search' Not Returning Results

You have successfully uploaded a file. You have the file_id. You created an Assistant with the file_search tool enabled. Yet, when you query the Assistant about the document, it apologizes and claims it doesn't have access to that information, or worse, it hallucinates an answer.

This is the most common frustration with the OpenAI Assistants API v2.

The issue is rarely with the file itself. It usually stems from a misunderstanding of how the v2 Vector Store architecture decouples files from Assistants, or how the run orchestration handles tool selection.

This guide provides a rigorous root cause analysis and a production-grade Python solution to ensure your RAG (Retrieval-Augmented Generation) pipeline actually retrieves data.

The Root Cause: Why "Attached" Doesn't Mean "Indexed"

In the deprecated v1 API, you simply attached a file to an Assistant. In v2, OpenAI introduced a strictly managed RAG pipeline involving Vector Stores.

When you experience silent failures or empty results, it is almost always due to one of these three architectural gaps:

Asynchronous Indexing Latency: Uploading a file and immediately starting a Run guarantees failure. The file must be processed, chunked, and embedded into the Vector Store before it is queryable. This process is asynchronous.
Missing tool_resources Mapping: Adding a file to a Vector Store is insufficient. That Vector Store must be explicitly mapped to the Assistant's tool_resources object.
Ambiguous tool_choice: By default, the model uses auto to decide if it should search files. If the user prompt is conversational (e.g., "Hello"), the model may skip the search to save tokens.

The Technical Solution

To fix this, we must build a robust initialization routine that handles the full lifecycle: Upload $\rightarrow$ Vector Store Creation $\rightarrow$ Polling for Completion $\rightarrow$ Assistant Association.

We will use the official openai Python SDK (v1.x+).

Prerequisites

Ensure you have the latest library version to avoid legacy endpoint issues:

pip install --upgrade openai

The Implementation

This script demonstrates the "Safe RAG" pattern. It enforces index completion checks and explicit tool binding.

import time
import os
from openai import OpenAI

# Initialize client
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

def setup_knowledge_base(file_path):
    """
    Uploads a file and ensures it is fully indexed in a Vector Store
    before returning the store ID.
    """
    print(f"--- 1. Uploading file: {file_path} ---")
    
    # Upload the file to OpenAI
    file_object = client.files.create(
        file=open(file_path, "rb"),
        purpose="assistants"
    )
    print(f"File uploaded. ID: {file_object.id}")

    # Create a Vector Store
    print("--- 2. Creating Vector Store ---")
    vector_store = client.beta.vector_stores.create(
        name="Financial_Reports_Store"
    )
    
    # Add file to Vector Store
    # We use a batch operation as it's more robust for future scaling
    file_batch = client.beta.vector_stores.file_batches.create_and_poll(
        vector_store_id=vector_store.id,
        file_ids=[file_object.id]
    )

    # CRITICAL: Verify Indexing Status
    # The SDK's 'create_and_poll' helps, but explicit status checking is vital
    # for debugging silent failures.
    if file_batch.status == "completed":
        print(f"Indexing complete. File count: {file_batch.file_counts.completed}")
    else:
        raise Exception(f"Vector Store indexing failed with status: {file_batch.status}")

    return vector_store.id

def query_assistant(vector_store_id, user_query):
    """
    Creates an assistant linked to the vector store and forces a search.
    """
    print("--- 3. Creating Assistant with Vector Store Link ---")
    
    assistant = client.beta.assistants.create(
        name="Fiscal Analyst",
        instructions="You are a financial analyst. Use the provided documents to answer questions.",
        model="gpt-4o", # Use a high-intelligence model for better tool logic
        tools=[{"type": "file_search"}],
        tool_resources={
            "file_search": {
                "vector_store_ids": [vector_store_id]
            }
        }
    )

    # Create Thread
    thread = client.beta.threads.create(
        messages=[
            {
                "role": "user",
                "content": user_query
            }
        ]
    )

    print("--- 4. Executing Run ---")
    
    # Execute Run
    # We create and poll to wait for the result synchronously
    run = client.beta.threads.runs.create_and_poll(
        thread_id=thread.id,
        assistant_id=assistant.id,
        # OPTIONAL: Force the tool use if you are getting "I don't know"
        # usually 'auto' is fine, but 'required' forces the search.
        tool_choice={"type": "file_search"} 
    )

    if run.status == 'completed':
        messages = client.beta.threads.messages.list(
            thread_id=thread.id
        )
        # The latest message is at index 0
        answer = messages.data[0].content[0].text.value
        
        # Check for annotations (citations)
        annotations = messages.data[0].content[0].text.annotations
        if not annotations:
            print("WARNING: No citations found. The model might have hallucinated or ignored the file.")
            
        return answer
    else:
        return f"Run failed with status: {run.status}"

# --- Execution ---
if __name__ == "__main__":
    # Ensure you have a dummy PDF named 'report.pdf' in your directory
    try:
        vs_id = setup_knowledge_base("report.pdf")
        response = query_assistant(vs_id, "What is the net profit margin mentioned in the document?")
        print(f"\nASSISTANT RESPONSE:\n{response}")
    except Exception as e:
        print(f"Error: {e}")

Deep Dive: Why This Code Fixes the Issue

1. The `create_and_poll` Method

In previous SDK versions, developers had to write while loops to check file status. The v2 SDK introduced helper methods like file_batches.create_and_poll. This blocks execution until OpenAI's backend confirms that the embeddings are generated. Without this, your Assistant attempts to query a Vector Store that is technically empty, returning zero results.

2. The `tool_resources` Injection

Many developers mistakenly try to pass file IDs directly to the Thread or the Assistant message.

In v2, the hierarchy is strict: Assistant $\rightarrow$ tool_resources $\rightarrow$ file_search $\rightarrow$ vector_store_ids.

If you miss this nesting, the Assistant has the tool enabled (the capability) but no data source (the memory).

3. Enforcing `tool_choice`

In the code above, notice the optional tool_choice={"type": "file_search"} param in the Run creation.

If your query is ambiguous, GPT-4 may opt to rely on its internal training data. By setting this to explicit or required (depending on implementation context), you force the model to query the vector store before attempting to generate an answer. This dramatically reduces hallucinations.

Troubleshooting Common Edge Cases

Even with the correct code, you might face edge cases. Here is how to handle them.

"I cannot read the file" (Format Issues)

The Assistants API supports a specific list of file extensions (PDF, MD, DOCX, etc.). It does not support scanning images inside PDFs (OCR) by default. If your PDF is a scanned image, the Vector Store will index it as blank text.

Fix: Ensure documents are text-selectable. Use a library like pytesseract to pre-process scanned PDFs into .txt files before uploading to OpenAI.

High Latency on First Query

Vector Stores are persistent. If you create a new Vector Store for every user query, you are paying for re-embedding and waiting for indexing every time.

Fix: Create the Vector Store once (e.g., during your app's admin setup or user onboarding), save the vector_store_id in your database, and simply reference that ID when creating threads.

The "I don't know" Loop

If the model searches but still claims ignorance, the chunking strategy might be failing. OpenAI automatically handles chunking, but it isn't perfect for data-dense CSVs or JSONs.

Fix: For structured data (CSV/JSON), the code_interpreter tool is often superior to file_search. File Search is for semantic retrieval (text); Code Interpreter is for data analysis.

Conclusion

The "empty result" error in the Assistants API v2 is rarely a bug in the platform. It is a synchronization issue. By ensuring your application awaits the completed status of the file batch and correctly maps the vector_store_id into the tool_resources object, you ensure the LLM has actual access to your data.

Stop relying on implicit file attachment. Be explicit with your Vector Store orchestration, and your RAG pipeline will become reliable.

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