Ollama with Python - Chat is stuck on the first prompt

Many users have reported issues with Ollama-based chatbots implemented with Python getting stuck on the first prompt. This problem can manifest in several ways:


1. The chatbot responds correctly to the initial prompt but fails to continue the conversation.

2. The chatbot repeats the same response over and over.

3. The chatbot seems to lose context after the first interaction.


These issues can be frustrating to debug, especially when they occur unexpectedly after changes to the configuration or code.


### Key Points to Consider


1. Ollama is designed to run as a standalone server, which can complicate integration with Python applications.

2. The way prompts are formatted and passed to Ollama can significantly impact the chatbot's behavior.

3. Context management is crucial for maintaining a coherent conversation.

4. Proper error handling and logging can help identify the root cause of the issue.


### Step-by-Step Thought Process


1. Verify the Ollama installation and configuration.

2. Examine the Python code responsible for interacting with Ollama.

3. Check how prompts are formatted and sent to Ollama.

4. Investigate context management strategies.

5. Implement detailed logging to capture more information during the chat process.

6. Consider using a more robust chat framework that handles context automatically.

7. Test the chat functionality with a simple prompt to isolate the issue.

8. Gradually add complexity to the prompts and observe the behavior.


### Implementation Steps


#### 1. Verify Ollama Installation


First, ensure that Ollama is correctly installed and running:


```bash

ollama run llama3.2

```


This command should start the Ollama server and allow you to interact with it directly.


#### 2. Examine Python Code


Review your Python code that interacts with Ollama. Here's a basic example of how to use Ollama with Python:


```python

import subprocess

import json


def call_ollama(prompt):

    command = [

        "ollama",

        "run",

        "--prompt", prompt,

        "--temperature", "0.7",

        "--max-tokens", "100",

        "--top-k", "40",

        "--top-p", "0.95",

        "--stop-sequence", "<|endoftext|>",

        "--model", "llama3.2"

    ]

    

    process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)

    output, error = process.communicate()

    

    if process.returncode != 0:

        raise Exception(f"Error executing Ollama: {error.decode('utf-8')}")

    

    return output.decode('utf-8')


# Example usage

prompt = "Tell me a joke."

response = call_ollama(prompt)

print(response)

```


#### 3. Format Prompts Correctly


Ensure that your prompts are properly formatted. Ollama expects a specific format for prompts, which typically starts with "system:" followed by the system message, and then the user message.


```python

prompt = f"""

system:

You are a helpful assistant. Answer questions to the best of your ability.


User: Tell me a joke.

"""

response = call_ollama(prompt)

print(response)

```


#### 4. Implement Context Management


To maintain context throughout the conversation, you can append the previous response to the current prompt:


```python

context = ""

while True:

    user_input = input("User: ")

    prompt = f"""

    system:

    You are a helpful assistant. Continue the conversation based on the previous exchange.


    Previous conversation:

    {context}


    User: {user_input}

    """

    response = call_ollama(prompt)

    print("Assistant:", response)

    context += f"\nAssistant: {response}\n"

```


#### 5. Add Detailed Logging


Implement detailed logging to capture more information during the chat process:


```python

import logging


logging.basicConfig(level=logging.DEBUG)

logger = logging.getLogger(__name__)


def call_ollama(prompt):

    logger.debug(f"Sending prompt: {prompt}")

    command = [

        "ollama",

        "run",

        "--prompt", prompt,

        "--temperature", "0.7",

        "--max-tokens", "100",

        "--top-k", "40",

        "--top-p", "0.95",

        "--stop-sequence", "<|endoftext|>",

        "--model", "llama3.2"

    ]

    

    process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)

    output, error = process.communicate()

    

    if process.returncode != 0:

        logger.error(f"Error executing Ollama: {error.decode('utf-8')}")

        raise Exception(f"Error executing Ollama: {error.decode('utf-8')}")

    

    logger.debug(f"Received response: {output.decode('utf-8')}")

    return output.decode('utf-8')

```


#### 6. Use a Robust Chat Framework


Consider using a more robust chat framework that handles context automatically. One option is to use LangChain with its built-in tools:


```python

from langchain.callbacks import CallbackHandler

from langchain.prompts import PromptTemplate

from langchain.schema import HumanMessage, SystemMessage

from langchain.tools import Tool

from langchain.chains import LLMChain

from langchain.llms.base import BaseLanguageModel


class CustomTool(Tool):

    def __init__(self, tool):

        self.tool = tool


    async def execute(self, messages):

        return await self.tool.execute(messages)


async def chat_with_tool(tool):

    messages = []

    while True:

        user_message = input("User: ")

        messages.append(HumanMessage(user_message))

        

        # Execute the tool

        tool_response = await tool.execute(messages)

        

        # Append the tool's response to the messages

        messages.append(SystemMessage(tool_response))

        

        print("Assistant:", tool_response)


# Usage

tool = CustomTool(MyCustomTool())  # Replace with your actual tool

await chat_with_tool(tool)

```


#### 7. Test with Simple Prompts


Isolate the issue by testing with a simple prompt:


```python

simple_prompt = "Hello, world!"

try:

    response = call_ollama(simple_prompt)

    print("Response:", response)

except Exception as e:

    print("Error:", str(e))

```


#### 8. Gradually Increase Complexity


Once the simple prompt works, gradually increase the complexity of your prompts to identify where the issue arises:


```python

complex_prompt = f"""

system:

You are a helpful assistant. Answer questions to the best of your ability.


User: Explain quantum computing in simple terms.

"""

try:

    response = call_ollama(complex_prompt)

    print("Response:", response)

except Exception as e:

    print("Error:", str(e))

```


### Best Practices Followed


1. **Detailed Logging**: Implement comprehensive logging to aid in troubleshooting.

2. **Context Management**: Use techniques like appending previous responses to maintain context.

3. **Prompt Formatting**: Ensure prompts are correctly formatted according to Ollama's expectations.

4. **Error Handling**: Implement robust error handling and reporting.

5. **Modular Design**: Separate concerns by using dedicated functions for Ollama interactions.

6. **Testing**: Implement unit tests for critical components of the chat functionality.


### Troubleshooting Tips


1. **Check Ollama Logs**: Examine Ollama's logs for any errors or warnings.

2. **Verify Model Compatibility**: Ensure the chosen model is compatible with your setup.

3. **Network Issues**: Check for any network connectivity problems between your Python application and Ollama.

4. **Timeouts**: Implement timeout mechanisms to prevent indefinite waiting.

5. **Version Compatibility**: Verify that all dependencies (Ollama, Python libraries) are compatible with each other.


### Summary


Troubleshooting issues with Ollama and Python chatbots getting stuck on the first prompt involves a systematic approach:


1. Verify Ollama installation and configuration.

2. Examine and correct the Python code interacting with Ollama.

3. Implement proper prompt formatting and context management.

4. Add detailed logging to capture more information during the chat process.

5. Consider using a more robust chat framework like LangChain.

6. Test with simple prompts and gradually increase complexity.

7. Implement error handling and reporting mechanisms.


By following these steps and considering the best practices outlined above, you should be able to effectively diagnose and resolve issues with Ollama-based chatbots in Python. Remember that maintaining context and proper prompt formatting are crucial for achieving natural-sounding conversations.

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