Dynamic Context Window Size for Ollama Chat (#6582)

# Dynamic Context Window Size for Ollama Chat

## Problem Statement
Previously, the Ollama chat implementation used a fixed context window
size of 32768 tokens. This caused two main issues:
1. Performance degradation due to unnecessarily large context windows
for small conversations
2. Potential business logic failures when using smaller fixed sizes
(e.g., 2048 tokens)

## Solution
Implemented a dynamic context window size calculation that:
1. Uses a base context size of 8192 tokens
2. Applies a 1.2x buffer ratio to the total token count
3. Adds multiples of 8192 tokens based on the buffered token count
4. Implements a smart context size update strategy

## Implementation Details

### Token Counting Logic
```python
def count_tokens(text):
    """Calculate token count for text"""
    # Simple calculation: 1 token per ASCII character
    # 2 tokens for non-ASCII characters (Chinese, Japanese, Korean, etc.)
    total = 0
    for char in text:
        if ord(char) < 128:  # ASCII characters
            total += 1
        else:  # Non-ASCII characters
            total += 2
    return total
```

### Dynamic Context Calculation
```python
def _calculate_dynamic_ctx(self, history):
    """Calculate dynamic context window size"""
    # Calculate total tokens for all messages
    total_tokens = 0
    for message in history:
        content = message.get("content", "")
        content_tokens = count_tokens(content)
        role_tokens = 4  # Role marker token overhead
        total_tokens += content_tokens + role_tokens

    # Apply 1.2x buffer ratio
    total_tokens_with_buffer = int(total_tokens * 1.2)
    
    # Calculate context size in multiples of 8192
    if total_tokens_with_buffer <= 8192:
        ctx_size = 8192
    else:
        ctx_multiplier = (total_tokens_with_buffer // 8192) + 1
        ctx_size = ctx_multiplier * 8192
    
    return ctx_size
```

### Integration in Chat Method
```python
def chat(self, system, history, gen_conf):
    if system:
        history.insert(0, {"role": "system", "content": system})
    if "max_tokens" in gen_conf:
        del gen_conf["max_tokens"]
    try:
        # Calculate new context size
        new_ctx_size = self._calculate_dynamic_ctx(history)
        
        # Prepare options with context size
        options = {
            "num_ctx": new_ctx_size
        }
        # Add other generation options
        if "temperature" in gen_conf:
            options["temperature"] = gen_conf["temperature"]
        if "max_tokens" in gen_conf:
            options["num_predict"] = gen_conf["max_tokens"]
        if "top_p" in gen_conf:
            options["top_p"] = gen_conf["top_p"]
        if "presence_penalty" in gen_conf:
            options["presence_penalty"] = gen_conf["presence_penalty"]
        if "frequency_penalty" in gen_conf:
            options["frequency_penalty"] = gen_conf["frequency_penalty"]
            
        # Make API call with dynamic context size
        response = self.client.chat(
            model=self.model_name,
            messages=history,
            options=options,
            keep_alive=60
        )
        return response["message"]["content"].strip(), response.get("eval_count", 0) + response.get("prompt_eval_count", 0)
    except Exception as e:
        return "**ERROR**: " + str(e), 0
```

## Benefits
1. **Improved Performance**: Uses appropriate context windows based on
conversation length
2. **Better Resource Utilization**: Context window size scales with
content
3. **Maintained Compatibility**: Works with existing business logic
4. **Predictable Scaling**: Context growth in 8192-token increments
5. **Smart Updates**: Context size updates are optimized to reduce
unnecessary model reloads

## Future Considerations
1. Fine-tune buffer ratio based on usage patterns
2. Add monitoring for context window utilization
3. Consider language-specific token counting optimizations
4. Implement adaptive threshold based on conversation patterns
5. Add metrics for context size update frequency

---------

Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>

rag/llm/chat_model.py

@@ -179,7 +179,41 @@ class Base(ABC):
         except Exception:
             pass
         return 0
-
+        
+    def _calculate_dynamic_ctx(self, history):
+        """Calculate dynamic context window size"""
+        def count_tokens(text):
+            """Calculate token count for text"""
+            # Simple calculation: 1 token per ASCII character
+            # 2 tokens for non-ASCII characters (Chinese, Japanese, Korean, etc.)
+            total = 0
+            for char in text:
+                if ord(char) < 128:  # ASCII characters
+                    total += 1
+                else:  # Non-ASCII characters (Chinese, Japanese, Korean, etc.)
+                    total += 2
+            return total
+
+        # Calculate total tokens for all messages
+        total_tokens = 0
+        for message in history:
+            content = message.get("content", "")
+            # Calculate content tokens
+            content_tokens = count_tokens(content)
+            # Add role marker token overhead
+            role_tokens = 4
+            total_tokens += content_tokens + role_tokens
+
+        # Apply 1.2x buffer ratio
+        total_tokens_with_buffer = int(total_tokens * 1.2)
+            
+        if total_tokens_with_buffer <= 8192:
+            ctx_size = 8192
+        else:
+            ctx_multiplier = (total_tokens_with_buffer // 8192) + 1
+            ctx_size = ctx_multiplier * 8192
+        
+        return ctx_size
 
 class GptTurbo(Base):
     def __init__(self, key, model_name="gpt-3.5-turbo", base_url="https://api.openai.com/v1"):
@@ -469,7 +503,7 @@ class ZhipuChat(Base):
 
 class OllamaChat(Base):
     def __init__(self, key, model_name, **kwargs):
-        self.client = Client(host=kwargs["base_url"]) if not key or key == "x" else Client(host=kwargs["base_url"], headers={"Authorization": f"Bear {key}"})
+        self.client = Client(host=kwargs["base_url"]) if not key or key == "x" else Client(host=kwargs["base_url"], headers={"Authorization": f"Bearer {key}"})
         self.model_name = model_name
 
     def chat(self, system, history, gen_conf):
@@ -478,7 +512,12 @@ class OllamaChat(Base):
         if "max_tokens" in gen_conf:
             del gen_conf["max_tokens"]
         try:
-            options = {"num_ctx": 32768}
+            # Calculate context size
+            ctx_size = self._calculate_dynamic_ctx(history)
+            
+            options = {
+                "num_ctx": ctx_size
+            }
             if "temperature" in gen_conf:
                 options["temperature"] = gen_conf["temperature"]
             if "max_tokens" in gen_conf:
@@ -489,9 +528,11 @@ class OllamaChat(Base):
                 options["presence_penalty"] = gen_conf["presence_penalty"]
             if "frequency_penalty" in gen_conf:
                 options["frequency_penalty"] = gen_conf["frequency_penalty"]
-            response = self.client.chat(model=self.model_name, messages=history, options=options, keep_alive=-1)
+
+            response = self.client.chat(model=self.model_name, messages=history, options=options, keep_alive=10)
             ans = response["message"]["content"].strip()
-            return ans, response.get("eval_count", 0) + response.get("prompt_eval_count", 0)
+            token_count = response.get("eval_count", 0) + response.get("prompt_eval_count", 0)
+            return ans, token_count
         except Exception as e:
             return "**ERROR**: " + str(e), 0
 
@@ -500,28 +541,38 @@ class OllamaChat(Base):
             history.insert(0, {"role": "system", "content": system})
         if "max_tokens" in gen_conf:
             del gen_conf["max_tokens"]
-        options = {}
-        if "temperature" in gen_conf:
-            options["temperature"] = gen_conf["temperature"]
-        if "max_tokens" in gen_conf:
-            options["num_predict"] = gen_conf["max_tokens"]
-        if "top_p" in gen_conf:
-            options["top_p"] = gen_conf["top_p"]
-        if "presence_penalty" in gen_conf:
-            options["presence_penalty"] = gen_conf["presence_penalty"]
-        if "frequency_penalty" in gen_conf:
-            options["frequency_penalty"] = gen_conf["frequency_penalty"]
-        ans = ""
         try:
-            response = self.client.chat(model=self.model_name, messages=history, stream=True, options=options, keep_alive=-1)
-            for resp in response:
-                if resp["done"]:
-                    yield resp.get("prompt_eval_count", 0) + resp.get("eval_count", 0)
-                ans = resp["message"]["content"]
-                yield ans
+            # Calculate context size
+            ctx_size = self._calculate_dynamic_ctx(history)
+            options = {
+                "num_ctx": ctx_size
+            }
+            if "temperature" in gen_conf:
+                options["temperature"] = gen_conf["temperature"]
+            if "max_tokens" in gen_conf:
+                options["num_predict"] = gen_conf["max_tokens"]
+            if "top_p" in gen_conf:
+                options["top_p"] = gen_conf["top_p"]
+            if "presence_penalty" in gen_conf:
+                options["presence_penalty"] = gen_conf["presence_penalty"]
+            if "frequency_penalty" in gen_conf:
+                options["frequency_penalty"] = gen_conf["frequency_penalty"]
+
+            ans = ""
+            try:
+                response = self.client.chat(model=self.model_name, messages=history, stream=True, options=options, keep_alive=10 )
+                for resp in response:
+                    if resp["done"]:
+                        token_count = resp.get("prompt_eval_count", 0) + resp.get("eval_count", 0)
+                        yield token_count
+                    ans = resp["message"]["content"]
+                    yield ans
+            except Exception as e:
+                yield ans + "\n**ERROR**: " + str(e)
+            yield 0
         except Exception as e:
-            yield ans + "\n**ERROR**: " + str(e)
-        yield 0
+            yield "**ERROR**: " + str(e)
+            yield 0
 
 
 class LocalAIChat(Base):