🚀 Running RAGatouille with ColBERT v2.0 on Windows: Problems I Faced and How I Fixed Them

If you are building a RAG (Retrieval-Augmented Generation) application, you have probably heard of ColBERT.

ColBERT is particularly interesting because, unlike traditional embedding-based retrieval, it performs late interaction between the query and document tokens. This can provide much more accurate retrieval for many search tasks.

For my project, I wanted to use:

• 🧠 ColBERT v2.0
• 🔎 RAGatouille
• ⚡ CUDA / NVIDIA GPU
• 🪟 Windows
• 🐍 Python 3.10
• 📚 FAISS

The code itself looked simple.

The difficult part was getting the entire environment working on Windows.

This article explains the journey.

🧩 What I Wanted to Build

The basic goal was simple:

Take a collection of documents → split them into chunks → index them using ColBERT → search the index using a natural-language question.

For example:

Documents

Employees receive 18 days of annual leave every year.
Employees can request additional leave with manager approval.
Medical leave is separate from annual leave.

Question

How many leave days do employees get?

The retrieval system should return the relevant document:

Employees receive 18 days of annual leave every year.

🏗️ The Basic Architecture

The pipeline looks like this:

📄 Documents
                      │
                      ▼
              ✂️ Text Chunking
                      │
                      ▼
                🧠 ColBERT
                      │
                      ▼
              📦 FAISS Index
                      │
                      │
              🔍 User Question
                      │
                      ▼
              🧠 ColBERT Search
                      │
                      ▼
             📑 Relevant Chunks

Figure 1 — RAGatouille + ColBERT Retrieval Pipeline

The important point is that RAGatouille provides a convenient interface around the retrieval process, while ColBERT performs the actual neural retrieval.

🔎 Why I Chose RAGatouille + ColBERT

Traditional RAG pipelines often look like:

Document
   ↓
Embedding Model
   ↓
Vector
   ↓
FAISS
   ↓
Similarity Search

ColBERT takes a different approach.

Instead of representing the entire document with only one vector, ColBERT keeps token-level representations and performs a late-interaction matching process.

Conceptually:

Query
 ↓
Token representations
 ↓
        🔗 Late Interaction
 ↓
Document token representations
 ↓
Relevance Score

This is one of the reasons ColBERT is attractive for information retrieval.

💻 My Initial Code

My initial implementation was fairly straightforward.

from ragatouille import RAGPretrainedModel
import os
import torch
from langchain_text_splitters import RecursiveCharacterTextSplitter
import re
import json
torch.device("cuda")
assert torch.cuda.is_available(), "CUDA not available"
RAG = RAGPretrainedModel.from_pretrained(
    "colbert-ir/colbertv2.0",
    n_gpu=1
)
all_text = ["", "", ""]
query = "How many leave days do employees get?"
try:
    total_texts = ""
    chunk_list = []
    splitter = RecursiveCharacterTextSplitter(
        separators=["\n"],
        chunk_overlap=0
    )
    for page_num, context in enumerate(all_text):
        page_txt = (
            f"\n\n\t\tThe Above Text is from "
            f"the page number {page_num + 1}.\n\n"
        )
        splTxt = splitter.split_text(
            re.sub(r"\s+", " ", context)
        )
        for txt in splTxt:
            chunk_list.append(txt)
        total_texts += context + page_txt
    top_k = max(1, int(len(all_text) * 0.2))
    print(f"Total number of chunks: {len(chunk_list)}")
    RAG.index(
        collection=chunk_list,
        document_ids=[
            str(index)
            for index, _ in enumerate(chunk_list)
        ],
        index_name="collection_name",
        overwrite_index=True,
        max_document_length=512,
        split_documents=True,
        use_faiss=True
    )
    results = RAG.search(
        query=query,
        k=top_k
    )
    total_texts = "\n".join(
        data["content"]
        for data in results
    )
    print(total_texts)
except Exception as e:
    print(e)

At first glance, there doesn’t seem to be anything complicated here.

But Windows had other plans. 😅

🚨 Problem 1 — RAGatouille Import Error

The first major problem appeared immediately when importing RAGatouille.

from ragatouille import RAGPretrainedModel

I received this:

ModuleNotFoundError:
No module named 'langchain.retrievers'

There was also an important warning:

RAGatouille WARNING: Future Release Notice
RAGatouille version 0.0.10 will be migrating
to a PyLate backend instead of the current
Stanford ColBERT backend.
However, please pin version <0.0.10
if you require the Stanford ColBERT backend.

This was the first clue.

The problem wasn’t necessarily my code.

It was a dependency compatibility problem.

🧠 What Was Actually Happening?

RAGatouille depends on several packages that have changed significantly over time.

The dependency chain looked roughly like:

RAGatouille
     │
     ├── ColBERT
     │
     ├── LangChain
     │
     ├── Transformers
     │
     ├── Sentence Transformers
     │
     └── PyTorch

Changing one package version can break another package.

So instead of installing the latest version of everything, I decided to create a known-compatible environment.

🔧 Solution 1 — Pin the Package Versions

I removed the conflicting packages:

pip uninstall langchain langchain-core transformers sentence-transformers colbert-ai langchain-text-splitters torch -y

Then I installed specific versions.

pip install langchain==0.3.27
pip install langchain-core==0.3.76
pip install langchain-text-splitters==0.3.11
pip install llama-index==0.14.23
pip install llama-index-core==0.14.23
pip install faiss-cpu==1.15.0
pip install transformers==4.39.3
pip install sentence-transformers==2.6.1
pip install colbert-ai==0.2.22
pip install numpy==1.26.4
pip install setuptools==59.6.0
pip install RAGatouille==0.0.9.post2

The important part for me was:

RAGatouille < 0.0.10

because I specifically wanted the older Stanford ColBERT backend.

🐍 Problem 2 — Python 3.11 vs Python 3.10

After resolving the initial dependency problem, I hit another issue.

I was initially using:

Python 3.11

Then I encountered errors around the native build process, including:

module 'distutils' has no attribute '_msvccompiler'

This was another compatibility issue.

ColBERT’s older dependency stack is much more comfortable with older Python versions.

🔄 Solution 2 — Downgrade to Python 3.10

I changed my environment from:

Python 3.11

to:

Python 3.10

This became one of the important lessons from the whole process:

📝 Don’t always use the newest Python version when working with older ML libraries.

For modern libraries, Python 3.11 or newer may be perfectly fine.

But when using an older ecosystem involving:

• CUDA extensions
• PyTorch
• ColBERT
• C++ compilation
• older dependency APIs

a slightly older Python version can save a lot of time.

🔢 Problem 3 — NumPy 2.x Compatibility

Next came NumPy.

I encountered compatibility issues because some compiled modules were built against NumPy 1.x while my environment had NumPy 2.x.

The error was essentially:

A module that was compiled using NumPy 1.x
cannot be run in NumPy 2.x

My environment had a newer NumPy version.

🔧 Solution 3 — Use NumPy 1.26.4

I downgraded NumPy:

pip install numpy==1.26.4

This was another important piece of the puzzle.

My working combination became:

Python        3.10
NumPy         1.26.4
ColBERT       0.2.22
RAGatouille   0.0.9.post2

🧱 Problem 4 — ColBERT Needs Native Compilation

This was where things became more interesting.

When using GPU acceleration, ColBERT may need to compile native CUDA/C++ extensions.

On Linux, this type of workflow is generally much easier.

On Windows, native compilation introduces another layer:

Python
  ↓
PyTorch
  ↓
CUDA
  ↓
C++ Compiler
  ↓
Visual Studio Build Tools
  ↓
Native Extension

If any one of those layers is missing or incompatible, the installation can fail.

🚨 Problem 5 — pthread.h Missing

One of the errors I encountered involved:

pthread.h

The native extension compilation failed because the required build environment was not available in the expected form.

This is one of the major reasons I found ColBERT + RAGatouille on Windows more difficult than expected.

The problem isn’t simply:

“Install the Python package.”

It becomes:

“Install Python + PyTorch + CUDA + CUDA Toolkit + Visual Studio + compatible compiler + compatible versions.”

😅

🎮 Problem 6 — NVIDIA Driver vs CUDA Toolkit

Another important lesson:

Having an NVIDIA GPU does not automatically mean that the CUDA development toolkit is installed.

For example, running:

nvidia-smi

can show CUDA-related information.

But that does not necessarily mean that:

nvcc

is available.

I checked:

nvcc --version

If Windows responds:

'nvcc' is not recognized...

then the CUDA compiler/toolkit is not available through the command line.

🧩 NVIDIA Driver vs CUDA Toolkit

This distinction confused me initially.

Think of it like this:

🎮 NVIDIA Driver
       │
       │
       ├── Allows applications to use GPU
       │
       ▼
     GPU

        BUT

🛠️ CUDA Toolkit
       │
       ├── nvcc
       ├── CUDA headers
       ├── Development libraries
       └── Compilation tools

Figure 2 — NVIDIA Driver vs CUDA Toolkit

nvidia-smi and nvcc are therefore not interchangeable.

🔧 Solution 4 — Install the CUDA Toolkit

I checked my NVIDIA environment and then installed the appropriate CUDA Toolkit for the PyTorch build I was using.

For example, if PyTorch was installed using:

--index-url https://download.pytorch.org/whl/cu121

then the PyTorch build is targeting CUDA 12.1.

The important lesson is:

⚠️ Match your PyTorch CUDA build and your local CUDA development environment carefully.

Don’t blindly install a random CUDA Toolkit version just because nvidia-smi displays a newer CUDA capability.

You can verify your toolkit installation with:

nvcc --version

🛠️ Problem 7 — cl Is Not Recognized

Then I reached another Windows-specific problem.

I ran:

cl

and Windows returned:

'cl' is not recognized as an internal or external command,
operable program or batch file.

I also tried:

where cl

and got:

INFO: Could not find files for the given pattern(s).

This means the Microsoft C++ compiler wasn’t available in my current command environment.

🏗️ Solution 5 — Visual Studio C++ Build Tools

I installed Visual Studio 2022 with the required C++ development components.

The important components are related to:

Desktop development with C++
MSVC compiler
Windows SDK
C++ build tools

After installation, the compiler should be discoverable.

You can verify it with:

cl

or:

where cl

⚠️ Another Compiler Compatibility Problem

Even after installing Visual Studio, I still faced compiler compatibility problems.

This is where CUDA, PyTorch and Visual Studio versions become important.

The relationship can be visualized as:

🐍 Python 3.10
                    │
                    ▼
              🔥 PyTorch
                    │
                    ▼
                ⚡ CUDA
                    │
                    ▼
             🛠️ MSVC Compiler
                    │
                    ▼
             🧩 ColBERT Extension

If one layer doesn’t support another layer, compilation can fail.

🔧 Solution 6 — MSVC Compatibility

In my case, I used the older v142 MSVC toolset because it worked better with the native compilation requirements I encountered.

I also configured the Visual Studio environment.

One approach that worked in my environment was setting:

VCINSTALLDIR

to:

C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC

After setting the environment variable, I restarted Windows and tested the build again.

⚠️ Important

This is not a universal fix.

Visual Studio installation paths and compiler requirements vary between machines.

The important lesson is to make sure that the MSVC compiler/toolset expected by your CUDA/ColBERT build is actually available.

❌ What I Tried but Did NOT Work

I also came across suggestions to use:

os.environ["CUDAFLAGS"] = "-allow-unsupported-compiler"

I tried this approach.

For my environment, it did not solve the problem.

So I would not recommend treating this as a guaranteed solution.

Instead, I focused on getting the actual compiler/toolkit versions aligned.

📦 My Working Environment

After going through all these issues, my environment looked approximately like this:

Operating System
└── Windows
Python
└── 3.10
RAGatouille
└── 0.0.9.post2
ColBERT
└── 0.2.22
Transformers
└── 4.39.3
Sentence Transformers
└── 2.6.1
LangChain
└── 0.3.27
LangChain Core
└── 0.3.76
LangChain Text Splitters
└── 0.3.11
NumPy
└── 1.26.4
FAISS
└── faiss-cpu 1.15.0
PyTorch
└── 2.3.0 / CUDA build
MSVC
└── v142 toolset
CUDA
└── Toolkit configured for the PyTorch build

This should be treated as the environment that worked for my setup, not a universal version matrix.

🔍 Understanding the Actual RAG Code

Once the environment was working, the retrieval pipeline became much easier to understand.

1️⃣ Load ColBERT

RAG = RAGPretrainedModel.from_pretrained(
    "colbert-ir/colbertv2.0",
    n_gpu=1
)

This loads the pretrained:

colbert-ir/colbertv2.0

model.

And:

n_gpu=1

tells RAGatouille to use one GPU for the model.

2️⃣ Prepare Documents

Suppose we have:

all_text = [
    "Employees receive 18 days of annual leave.",
    "Medical leave requires proper documentation.",
    "Employees can request additional leave."
]

We don’t necessarily want to index huge documents as one piece.

So we split them into chunks.

splitter = RecursiveCharacterTextSplitter(
    separators=["\n"],
    chunk_overlap=0
)

Then:

splTxt = splitter.split_text(
    re.sub(r"\s+", " ", context)
)

This converts messy whitespace into cleaner text before chunking.

3️⃣ Create the Collection

Each chunk is added to:

chunk_list

Conceptually:

📄 Page 1
 ├── Chunk 1
 ├── Chunk 2
 └── Chunk 3

📄 Page 2
 ├── Chunk 4
 ├── Chunk 5
 └── Chunk 6

Figure 3 — Document Chunking

Chunking is important because retrieval works on these individual pieces.

4️⃣ Build the ColBERT Index

The important call is:

RAG.index(
    collection=chunk_list,
    document_ids=[
        str(index)
        for index, _ in enumerate(chunk_list)
    ],
    index_name="crux",
    overwrite_index=True,
    max_document_length=512,
    split_documents=True,
    use_faiss=True
)

This creates the retrieval index.

The important options are:

collection

The documents/chunks being indexed.

index_name

index_name="crux"

The name of the index.

overwrite_index

overwrite_index=True

Allows the existing index to be replaced.

max_document_length

max_document_length=512

Controls the maximum document length used by the indexing process.

use_faiss

use_faiss=True

Uses FAISS as part of the indexing/retrieval infrastructure.

🔎 5️⃣ Search the Index

Now we can ask:

query = "How many leave days do employees get?"

and search:

results = RAG.search(
    query=query,
    k=top_k
)

The system returns the most relevant chunks.

For example:

Query:

How many leave days do employees get?

        ↓

🧠 ColBERT

        ↓

🔍 Search

        ↓

📄 "Employees receive 18 days
    of annual leave."

🎯 Why This Is Useful for RAG

The retrieval system doesn’t need to answer the question itself.

Its job is:

Find the most relevant information.

Then an LLM can use that information to generate the final answer.

The complete RAG architecture becomes:

              📚 Documents
                      │
                      ▼
                ✂️ Chunking
                      │
                      ▼
               🧠 ColBERT
                      │
                      ▼
                📦 Index
                      │
                      │
User Question ────────┤
                      ▼
                🔍 Retrieval
                      │
                      ▼
             📑 Relevant Context
                      │
                      ▼
                  🤖 LLM
                      │
                      ▼
                💬 Final Answer

Figure 4 — Complete RAG Architecture

This separation is important.

Retriever:

Find the information.

LLM:

Understand and generate the answer.

🪟 Why Windows Was the Hardest Part

After going through the entire setup, I realized that the biggest challenge wasn’t actually ColBERT itself.

It was the native dependency chain.

On Windows, the stack can look like:

Python
  │
  ├── RAGatouille
  │      │
  │      └── ColBERT
  │             │
  │             ├── PyTorch
  │             ├── CUDA
  │             └── Native Extensions
  │
  ├── NumPy
  │
  ├── Transformers
  │
  └── LangChain

        +

Visual Studio

        +

MSVC

        +

Windows SDK

        +
CUDA Toolkit

One incompatible version can break the entire chain.

🧠 My Main Lessons

After spending a lot of time debugging this setup, these were my biggest takeaways.

1. 📌 Pin Your Versions

Don’t blindly install:

pip install package

for every dependency.

For older ML projects, version compatibility matters a lot.

2. 🐍 Python Version Matters

If an older package stack expects Python 3.10, don’t force Python 3.11 just because it’s newer.

In my case:

Python 3.11 ❌
Python 3.10 ✅

was an important change.

3. 🔢 NumPy Version Matters

For my environment:

NumPy 2.x ❌
NumPy 1.26.4 ✅

solved compatibility problems with compiled dependencies.

4. 🎮 GPU Driver ≠ CUDA Toolkit

Having:

nvidia-smi

working does not necessarily mean:

nvcc

will work.

The driver and development toolkit serve different purposes.

5. 🛠️ CUDA Compilation Needs a Compiler

If you see:

cl is not recognized

your Microsoft C++ compiler environment is not correctly configured.

Installing the appropriate Visual Studio C++ components is an important step.

6. ⚡ Don’t Ignore Native Extensions

A Python package can look simple:

pip install ragatouille

but internally it may depend on:

C++
CUDA
PyTorch
MSVC
Compiler toolchains

That changes the installation experience completely.

🧭 My Troubleshooting Flow

If I had to repeat this setup from scratch, this would be my checklist:

Start
                │
                ▼
        🐍 Python 3.10?
             │
        ┌────┴────┐
        │         │
       No        Yes
        │         │
   Install 3.10   ▼
                  │
                  ▼
        📦 Pin package versions
                  │
                  ▼
             NumPy 1.26.4
                  │
                  ▼
          🔥 Install PyTorch
                  │
                  ▼
          🎮 Check NVIDIA
                  │
                  ▼
             nvcc works?
              │       │
             No      Yes
              │       │
       Install CUDA   ▼
       Toolkit       │
                      ▼
             🛠️ Check `cl`
                      │
                      ▼
             Visual Studio
             C++ toolchain
                      │
                      ▼
                Install
               RAGatouille
                      │
                      ▼
             Load ColBERT v2
                      │
                      ▼
                Build Index
                      │
                      ▼
                 🔍 Search

Figure 5 — Windows Troubleshooting Checklist

🚀 Final Takeaway

Getting a RAG system running is easy when we look only at the Python code.

The difficult part appears when we combine:

RAGatouille + ColBERT + PyTorch + CUDA + Windows + C++ compilation.

The code may be only 30–40 lines.

But the environment behind those lines can involve:

🐍 Python
📦 Python packages
🧠 ColBERT
🔎 RAGatouille
🔥 PyTorch
🎮 NVIDIA Driver
⚡ CUDA Toolkit
🛠️ MSVC
🪟 Windows

My biggest lesson was:

When an ML library depends on native CUDA/C++ extensions, don’t debug only the Python code. Debug the entire environment stack.

Once the versions and compiler environment were aligned, the actual RAG pipeline became surprisingly simple:

RAG = RAGPretrainedModel.from_pretrained(
    "colbert-ir/colbertv2.0",
    n_gpu=1
)

RAG.index(...)

results = RAG.search(
    query="How many leave days do employees get?",
    k=3
)

And that’s the interesting part of working with ML systems.

Sometimes the hardest part isn’t building the model pipeline.

It’s getting all the pieces to agree with each other. 😄

📚 Final Stack

🪟 Windows
🐍 Python 3.10
🔎 RAGatouille 0.0.9.post2
🧠 ColBERT 0.2.22
🔥 PyTorch 2.3.0
🔢 NumPy 1.26.4
📦 FAISS
⚡ CUDA Toolkit
🛠️ MSVC v142

If you’re also trying to run ColBERT/RAGatouille on Windows, hopefully this troubleshooting journey saves you some time.

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