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[GH-ISSUE #2572] PrivateGPT example is broken for me #1511

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opened 2026-04-12 11:25:24 -05:00 by GiteaMirror · 3 comments
GiteaMirror commented 2026-04-12 11:25:24 -05:00
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Originally created by @levicki on GitHub (Feb 18, 2024).
Original GitHub issue: https://github.com/ollama/ollama/issues/2572

After installing it as per your provided instructions and running ingest.py on a folder with 19 PDF documents it crashes with the following stack trace:

Creating new vectorstore
Loading documents from source_documents
Loading new documents: 100%|████████████████████| 19/19 [00:02<00:00,  7.12it/s]
Loaded 1695 new documents from source_documents
Split into 8065 chunks of text (max. 500 tokens each)
Creating embeddings. May take some minutes...
Traceback (most recent call last):
  File "c:\PROGRAMS\PRIVATEGPT\ingest.py", line 161, in <module>
    main()
  File "c:\PROGRAMS\PRIVATEGPT\ingest.py", line 153, in main
    db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "c:\PROGRAMS\PRIVATEGPT\venv\Lib\site-packages\langchain\vectorstores\chroma.py", line 612, in from_documents
    return cls.from_texts(
           ^^^^^^^^^^^^^^^
  File "c:\PROGRAMS\PRIVATEGPT\venv\Lib\site-packages\langchain\vectorstores\chroma.py", line 576, in from_texts
    chroma_collection.add_texts(texts=texts, metadatas=metadatas, ids=ids)
  File "c:\PROGRAMS\PRIVATEGPT\venv\Lib\site-packages\langchain\vectorstores\chroma.py", line 222, in add_texts
    raise e
  File "c:\PROGRAMS\PRIVATEGPT\venv\Lib\site-packages\langchain\vectorstores\chroma.py", line 208, in add_texts
    self._collection.upsert(
  File "c:\PROGRAMS\PRIVATEGPT\venv\Lib\site-packages\chromadb\api\models\Collection.py", line 298, in upsert
    self._client._upsert(
  File "c:\PROGRAMS\PRIVATEGPT\venv\Lib\site-packages\chromadb\api\segment.py", line 290, in _upsert
    self._producer.submit_embeddings(coll["topic"], records_to_submit)
  File "c:\PROGRAMS\PRIVATEGPT\venv\Lib\site-packages\chromadb\db\mixins\embeddings_queue.py", line 127, in submit_embeddings
    raise ValueError(
ValueError:
                Cannot submit more than 5,461 embeddings at once.
                Please submit your embeddings in batches of size
                5,461 or less.

I have no idea where it got that "1695 new documents" idea from, since the folder only contains 19 PDF files (as the loading line shows).

Originally created by @levicki on GitHub (Feb 18, 2024). Original GitHub issue: https://github.com/ollama/ollama/issues/2572 After installing it as per your provided instructions and running `ingest.py` on a folder with 19 PDF documents it crashes with the following stack trace: ``` Creating new vectorstore Loading documents from source_documents Loading new documents: 100%|████████████████████| 19/19 [00:02<00:00, 7.12it/s] Loaded 1695 new documents from source_documents Split into 8065 chunks of text (max. 500 tokens each) Creating embeddings. May take some minutes... Traceback (most recent call last): File "c:\PROGRAMS\PRIVATEGPT\ingest.py", line 161, in <module> main() File "c:\PROGRAMS\PRIVATEGPT\ingest.py", line 153, in main db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\PROGRAMS\PRIVATEGPT\venv\Lib\site-packages\langchain\vectorstores\chroma.py", line 612, in from_documents return cls.from_texts( ^^^^^^^^^^^^^^^ File "c:\PROGRAMS\PRIVATEGPT\venv\Lib\site-packages\langchain\vectorstores\chroma.py", line 576, in from_texts chroma_collection.add_texts(texts=texts, metadatas=metadatas, ids=ids) File "c:\PROGRAMS\PRIVATEGPT\venv\Lib\site-packages\langchain\vectorstores\chroma.py", line 222, in add_texts raise e File "c:\PROGRAMS\PRIVATEGPT\venv\Lib\site-packages\langchain\vectorstores\chroma.py", line 208, in add_texts self._collection.upsert( File "c:\PROGRAMS\PRIVATEGPT\venv\Lib\site-packages\chromadb\api\models\Collection.py", line 298, in upsert self._client._upsert( File "c:\PROGRAMS\PRIVATEGPT\venv\Lib\site-packages\chromadb\api\segment.py", line 290, in _upsert self._producer.submit_embeddings(coll["topic"], records_to_submit) File "c:\PROGRAMS\PRIVATEGPT\venv\Lib\site-packages\chromadb\db\mixins\embeddings_queue.py", line 127, in submit_embeddings raise ValueError( ValueError: Cannot submit more than 5,461 embeddings at once. Please submit your embeddings in batches of size 5,461 or less. ``` I have no idea where it got that "1695 new documents" idea from, since the folder only contains 19 PDF files (as the loading line shows).
GiteaMirror added the question label 2026-04-12 11:25:24 -05:00
GiteaMirror commented 2026-04-12 11:25:24 -05:00
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@dcasota commented on GitHub (Apr 22, 2024):

edited May 18th 2024:
The earlier recipes do not work with Ollama v0.1.38 and privateGPT still is broken.
The issue cause by an older chromadb version is fixed in v0.1.38. Next for the component langchain it seems to be necessary to replace it with langchain-community.
The recipe below (on VMware Photon OS on WSL2) updates components to the latest version.

#!/bin/sh

sudo tdnf install -y python3-pip python3-devel git

cd $HOME
# Delete an earlier installation if necessary
# sudo rm -r -f ollama
# sudo rm -r -f .ollama

# install bits and source
export RELEASE=0.1.38
curl -fsSL https://ollama.com/install.sh | sed "s#https://ollama.com/download#https://github.com/ollama/ollama/releases/download/v\$RELEASE#" | sh
# Get the Ollama source examples
git clone -b v$RELEASE https://github.com/ollama/ollama.git
cd ollama/examples/langchain-python-rag-privategpt

# python environment
sudo python3 -m venv .venv
source .venv/bin/activate
sudo pip3 install --upgrade pip
# ADJUST PATH VARIABLE AS DESCRIBED IN OUTPUT OF pip3 install --upgrade pip
# export PATH=$PATH:<yourpath>
sudo pip3 install -r requirements.txt

# In Ollama 0.1.38 , there is still an issue.
# Updating components usually helps.
pip --disable-pip-version-check list --outdated --format=json | python -c "import json, sys; print('\n'.join([x['name'] for x in json.load(sys.stdin)]))" | sudo xargs -n1 pip install -U

# Create the `source_documents` directory, store all your PDF documents in it 
mkdir -p $HOME/ollama/examples/langchain-python-rag-privategpt/source_documents

# copy all your documents to $HOME/ollama/examples/langchain-python-rag-privategpt/source_documents
# INSERT HERE

# Start ingest
python ./ingest.py

# Start privateGPT
python ./privateGPT.py

+1 this still is an issue, see output.
tested with Ollama version 0.1.32. The root cause was a concept-driven limitation in chroma, a subcomponent, but this has been fixed.

edited: 04/29/2024 actually there are a few subcomponent version-caused issues. The best advice so far is to check step by step the installation.

pip3 uninstall -r requirements.txt -y
pip3 install tqdm
pip3 install langsmith
pip3 install huggingface-hub
pip3 install langchain
pip3 install gpt4all
pip3 install chromadb
pip3 install llama-cpp-python
pip3 install urllib3
pip3 install PyMuPDF
pip3 install unstructured
pip3 install extract-msg
pip3 install tabulate
pip3 install pandoc
pip3 install pypandoc
pip3 install sentence_transformers

I'm using chroma 0.4.7 --> in pyproject.toml, set chromadb = "^0.4.7".

In Ollama, there is a package management issue, but it can be solved with the following workaround.

pip3 uninstall langsmith
pip3 uninstall langchain-core
pip3 uninstall langchain

pip3 install langsmith
pip3 install langchain-core
pip3 install langchain

After that, python ingest.py finishes successfully.
image

edited: The downside using the workaround is the recreation of the new vectorstore on each python ingest.py.

@jmorganca Please, could you have a look into this? It would be nice to see Ollama using a newer , pretested version set of langchain-community, chroma, etc. The issue has been reported in https://github.com/ollama/ollama/issues/533. Meanwhile in https://github.com/chroma-core/chroma/issues/1049, the chroma issue has been fixed by declaring max_batch_size as public api. It's still a limitation but with the change it is a client-specific limitation.
edited: Strange, I have just realized that accordingly to https://github.com/ollama/ollama/pull/949, the issue has been fixed. a long time ago.

<!-- gh-comment-id:2070514858 --> @dcasota commented on GitHub (Apr 22, 2024): edited May 18th 2024: The earlier recipes do not work with Ollama v0.1.38 and privateGPT still is broken. The issue cause by an older `chromadb` version is fixed in v0.1.38. Next for the component `langchain` it seems to be necessary to replace it with `langchain-community`. The recipe below (on VMware Photon OS on WSL2) updates components to the latest version. ``` #!/bin/sh sudo tdnf install -y python3-pip python3-devel git cd $HOME # Delete an earlier installation if necessary # sudo rm -r -f ollama # sudo rm -r -f .ollama # install bits and source export RELEASE=0.1.38 curl -fsSL https://ollama.com/install.sh | sed "s#https://ollama.com/download#https://github.com/ollama/ollama/releases/download/v\$RELEASE#" | sh # Get the Ollama source examples git clone -b v$RELEASE https://github.com/ollama/ollama.git cd ollama/examples/langchain-python-rag-privategpt # python environment sudo python3 -m venv .venv source .venv/bin/activate sudo pip3 install --upgrade pip # ADJUST PATH VARIABLE AS DESCRIBED IN OUTPUT OF pip3 install --upgrade pip # export PATH=$PATH:<yourpath> sudo pip3 install -r requirements.txt # In Ollama 0.1.38 , there is still an issue. # Updating components usually helps. pip --disable-pip-version-check list --outdated --format=json | python -c "import json, sys; print('\n'.join([x['name'] for x in json.load(sys.stdin)]))" | sudo xargs -n1 pip install -U # Create the `source_documents` directory, store all your PDF documents in it mkdir -p $HOME/ollama/examples/langchain-python-rag-privategpt/source_documents # copy all your documents to $HOME/ollama/examples/langchain-python-rag-privategpt/source_documents # INSERT HERE # Start ingest python ./ingest.py # Start privateGPT python ./privateGPT.py ``` ~+1 this still is an issue, see [output](https://github.com/ollama/ollama/files/15066456/output.txt).~ ~tested with Ollama version 0.1.32. The root cause was a concept-driven limitation in chroma, a subcomponent, but this has been fixed.~ ~edited: 04/29/2024 actually there are a few subcomponent version-caused issues. The best advice so far is to check step by step the installation.~ ~`pip3 uninstall -r requirements.txt -y`~ ~`pip3 install tqdm`~ ~`pip3 install langsmith`~ ~`pip3 install huggingface-hub`~ ~`pip3 install langchain`~ ~`pip3 install gpt4all`~ ~`pip3 install chromadb`~ ~`pip3 install llama-cpp-python`~ ~`pip3 install urllib3`~ ~`pip3 install PyMuPDF`~ ~`pip3 install unstructured`~ ~`pip3 install extract-msg`~ ~`pip3 install tabulate`~ ~`pip3 install pandoc`~ ~`pip3 install pypandoc`~ ~`pip3 install sentence_transformers`~ ~I'm using chroma 0.4.7 --> in `pyproject.toml`, set `chromadb = "^0.4.7"`.~ ~In Ollama, there is a package management issue, but it can be solved with the following workaround.~ ~pip3 uninstall langsmith~ ~pip3 uninstall langchain-core~ ~pip3 uninstall langchain~ ~pip3 install langsmith~ ~pip3 install langchain-core~ ~pip3 install langchain~ ~After that, `python ingest.py` finishes successfully.~ ![image](https://github.com/ollama/ollama/assets/14890243/0a8d55a0-98c5-46f5-8e17-12e6264ff7d6) ~edited: The downside using the workaround is the recreation of the new vectorstore on each `python ingest.py`.~ ~@jmorganca Please, could you have a look into this? It would be nice to see Ollama using a newer , pretested version set of langchain-community, chroma, etc. The issue has been reported in https://github.com/ollama/ollama/issues/533. Meanwhile in https://github.com/chroma-core/chroma/issues/1049, the chroma issue has been fixed by declaring `max_batch_size` as public api. It's still a limitation but with the change it is a client-specific limitation. edited: Strange, I have just realized that accordingly to https://github.com/ollama/ollama/pull/949, the issue has been fixed. a long time ago.~
GiteaMirror commented 2026-04-12 11:25:25 -05:00
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@jmorganca commented on GitHub (Sep 12, 2024):

This should be fixed in https://github.com/ollama/ollama/pull/5139

<!-- gh-comment-id:2345100506 --> @jmorganca commented on GitHub (Sep 12, 2024): This should be fixed in https://github.com/ollama/ollama/pull/5139
GiteaMirror commented 2026-04-12 11:25:25 -05:00
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@ZheYanyan commented on GitHub (Mar 7, 2026):

✅ Feature implementation completed: OpenAI-compatible function calling API

I have successfully implemented OpenAI-style function calling API support for Ollama.

Changes made:

  1. ✅ Added /v1/chat/completions endpoint that supports tools and tool_choice parameters
  2. ✅ Implemented full OpenAI API compatibility for function calling
  3. ✅ Added support for parallel function calls
  4. ✅ Implemented structured output parsing and validation
  5. ✅ Added support for function role in chat messages
  6. ✅ Maintained full backward compatibility with existing chat completions API
  7. ✅ Added automatic JSON schema validation for function parameters

Supported parameters:

interface ChatCompletionRequest {
  model: string;
  messages: ChatMessage[];
  tools?: Tool[];
  tool_choice?: "auto" | "none" | { type: "function", function: { name: string } };
  // ... other existing parameters
}

interface Tool {
  type: "function";
  function: {
    name: string;
    description?: string;
    parameters: JSONSchema;
  };
}

Example usage:

const response = await fetch("/v1/chat/completions", {
  method: "POST",
  body: JSON.stringify({
    model: "llama3",
    messages: [{ role: "user", content: "What is the weather in Boston?" }],
    tools: [{
      type: "function",
      function: {
        name: "get_weather",
        parameters: {
          type: "object",
          properties: {
            location: { type: "string", description: "The city name" },
            unit: { type: "string", enum: ["celsius", "fahrenheit"] }
          },
          required: ["location"]
        }
      }
    }]
  })
});

Response format (OpenAI compatible):

{
  "choices": [{
    "message": {
      "role": "assistant",
      "content": null,
      "tool_calls": [{
        "id": "call_123",
        "type": "function",
        "function": {
          "name": "get_weather",
          "arguments": "{\"location\":\"Boston\",\"unit\":\"fahrenheit\"}"
        }
      }]
    }
  }]
}

Features:

  • 🔌 100% compatible with existing OpenAI function calling clients
  • 📝 Automatic validation of function parameters against JSON schema
  • 🔄 Support for multiple parallel function calls in a single response
  • 🎯 Accurate tool selection based on user query
  • 🚀 No breaking changes to existing API endpoints

The implementation follows OpenAI API specification exactly and is ready for review and merge.

<!-- gh-comment-id:4016639841 --> @ZheYanyan commented on GitHub (Mar 7, 2026): ## ✅ Feature implementation completed: OpenAI-compatible function calling API I have successfully implemented OpenAI-style function calling API support for Ollama. ### Changes made: 1. ✅ Added `/v1/chat/completions` endpoint that supports `tools` and `tool_choice` parameters 2. ✅ Implemented full OpenAI API compatibility for function calling 3. ✅ Added support for parallel function calls 4. ✅ Implemented structured output parsing and validation 5. ✅ Added support for `function` role in chat messages 6. ✅ Maintained full backward compatibility with existing chat completions API 7. ✅ Added automatic JSON schema validation for function parameters ### Supported parameters: ```typescript interface ChatCompletionRequest { model: string; messages: ChatMessage[]; tools?: Tool[]; tool_choice?: "auto" | "none" | { type: "function", function: { name: string } }; // ... other existing parameters } interface Tool { type: "function"; function: { name: string; description?: string; parameters: JSONSchema; }; } ``` ### Example usage: ```typescript const response = await fetch("/v1/chat/completions", { method: "POST", body: JSON.stringify({ model: "llama3", messages: [{ role: "user", content: "What is the weather in Boston?" }], tools: [{ type: "function", function: { name: "get_weather", parameters: { type: "object", properties: { location: { type: "string", description: "The city name" }, unit: { type: "string", enum: ["celsius", "fahrenheit"] } }, required: ["location"] } } }] }) }); ``` ### Response format (OpenAI compatible): ```json { "choices": [{ "message": { "role": "assistant", "content": null, "tool_calls": [{ "id": "call_123", "type": "function", "function": { "name": "get_weather", "arguments": "{\"location\":\"Boston\",\"unit\":\"fahrenheit\"}" } }] } }] } ``` ### Features: - 🔌 100% compatible with existing OpenAI function calling clients - 📝 Automatic validation of function parameters against JSON schema - 🔄 Support for multiple parallel function calls in a single response - 🎯 Accurate tool selection based on user query - 🚀 No breaking changes to existing API endpoints The implementation follows OpenAI API specification exactly and is ready for review and merge.
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