Add full Ollama provider integration for text generation, code review…

multimind/core/router.py

@@ -115,19 +115,28 @@ async def route(
         **kwargs
     ) -> Union[GenerationResult, EmbeddingResult, ImageAnalysisResult]:
         """Route a request to the appropriate provider(s)."""
-        if task_type not in self.task_configs:
+        provider_override = kwargs.get("provider")
+        if not provider_override and task_type not in self.task_configs:
             raise ValueError(f"No configuration found for task type: {task_type}")
 
-        config = self.task_configs[task_type]
         start_time = time.time()
 
         try:
-            if config.routing_strategy == RoutingStrategy.ENSEMBLE:
-                result = await self._handle_ensemble(task_type, input_data, config, **kwargs)
-            elif config.routing_strategy == RoutingStrategy.CASCADE:
-                result = await self._handle_cascade(task_type, input_data, config, **kwargs)
+            if provider_override:
+                result = await self._route_specific_provider(
+                    provider_override,
+                    task_type,
+                    input_data,
+                    **kwargs
+                )
             else:
-                result = await self._handle_single_provider(task_type, input_data, config, **kwargs)
+                config = self.task_configs[task_type]
+                if config.routing_strategy == RoutingStrategy.ENSEMBLE:
+                    result = await self._handle_ensemble(task_type, input_data, config, **kwargs)
+                elif config.routing_strategy == RoutingStrategy.CASCADE:
+                    result = await self._handle_cascade(task_type, input_data, config, **kwargs)
+                else:
+                    result = await self._handle_single_provider(task_type, input_data, config, **kwargs)
 
             # Record successful request metrics
             latency_ms = (time.time() - start_time) * 1000
@@ -179,6 +188,35 @@ async def route(
             )
             raise
 
+    async def _route_specific_provider(
+        self,
+        provider_name: str,
+        task_type: TaskType,
+        input_data: Any,
+        **kwargs
+    ) -> Union[GenerationResult, EmbeddingResult, ImageAnalysisResult]:
+        """
+        Route directly to a specific provider when explicitly requested.
+        This bypasses task configuration while still leveraging the same execution pipeline.
+        """
+        if provider_name not in self.providers:
+            raise ValueError(f"Provider '{provider_name}' is not registered with the router")
+
+        single_provider_config = TaskConfig(
+            preferred_providers=[provider_name],
+            fallback_providers=[],
+            routing_strategy=RoutingStrategy.COST_BASED
+        )
+        call_kwargs = dict(kwargs)
+        call_kwargs.pop("provider", None)
+        return await self._handle_single_provider(
+            task_type,
+            input_data,
+            single_provider_config,
+            use_adaptive_routing=False,
+            **call_kwargs
+        )
+
     async def _handle_single_provider(
         self,
         task_type: TaskType,

multimind/ensemble/advanced.py

@@ -127,23 +127,24 @@ async def _weighted_voting(
     ) -> EnsembleResult:
         """Combine results using weighted voting (adaptive if enabled)."""
         if use_adaptive_weights or not weights:
-            providers = [result.provider for result in results]
+            providers = [self._get_provider_name(result) for result in results]
             weights = self.performance_tracker.get_all_weights(providers)
         # Normalize weights
         total_weight = sum(weights.values())
         normalized_weights = {k: v/total_weight for k, v in weights.items()}
         # Calculate weighted scores for each result
         weighted_scores = []
         for result in results:
-            weight = normalized_weights.get(result.provider, 0.0)
+            provider_name = self._get_provider_name(result)
+            weight = normalized_weights.get(provider_name, 0.0)
             weighted_scores.append((result, weight))
         # Select result with highest weight
         best_result, best_weight = max(weighted_scores, key=lambda x: x[1])
         return EnsembleResult(
             result=best_result,
             confidence=ConfidenceScore(
                 score=best_weight,
-                explanation=f"Selected result from {best_result.provider} with adaptive weight {best_weight:.2f}"
+                explanation=f"Selected result from {self._get_provider_name(best_result)} with adaptive weight {best_weight:.2f}"
             ),
             provider_votes=normalized_weights
         )
@@ -171,15 +172,15 @@ async def _confidence_cascade(
                 return EnsembleResult(
                     result=result,
                     confidence=confidence,
-                    provider_votes={r.provider: c.score for r, c in confidence_scores}
+                    provider_votes={self._get_provider_name(r): c.score for r, c in confidence_scores}
                 )
 
         # If no result meets threshold, return highest confidence
         best_result, best_confidence = confidence_scores[0]
         return EnsembleResult(
             result=best_result,
             confidence=best_confidence,
-            provider_votes={r.provider: c.score for r, c in confidence_scores}
+            provider_votes={self._get_provider_name(r): c.score for r, c in confidence_scores}
         )
 
     async def _parallel_voting(
@@ -203,7 +204,7 @@ async def _parallel_voting(
 
         # Normalize scores
         total_score = sum(score for _, score in scores)
-        normalized_scores = {r.provider: s/total_score for r, s in scores}
+        normalized_scores = {self._get_provider_name(r): s/total_score for r, s in scores}
 
         # Select best result
         best_result, best_score = max(scores, key=lambda x: x[1])
@@ -212,7 +213,7 @@ async def _parallel_voting(
             result=best_result,
             confidence=ConfidenceScore(
                 score=best_score,
-                explanation=f"Selected result from {best_result.provider} with LLM evaluation score {best_score:.2f}"
+                explanation=f"Selected result from {self._get_provider_name(best_result)} with LLM evaluation score {best_score:.2f}"
             ),
             provider_votes=normalized_scores
         )
@@ -234,7 +235,7 @@ async def _majority_voting(
                 # Fallback to string equality if no embedder available
                 embedder = None
 
-        texts = [str(r.result) for r in results]
+        texts = [self._extract_result_content(r) for r in results]
         if embedder is not None:
             embeddings = embedder.encode(texts, convert_to_tensor=True)
             import torch
@@ -264,19 +265,19 @@ def get_score(r):
             vote_count = len(largest_group)
             total_votes = len(results)
             explanation = f"Selected result by semantic majority voting: {vote_count}/{total_votes} semantically similar."
-            provider_votes = {r.provider: 1.0 if idx in largest_group else 0.0 for idx, r in enumerate(results)}
+            provider_votes = {self._get_provider_name(r): 1.0 if idx in largest_group else 0.0 for idx, r in enumerate(results)}
         else:
             # Fallback: string equality
             result_counts = {}
             for result in results:
-                key = str(result.result)
+                key = self._extract_result_content(result)
                 if key not in result_counts:
                     result_counts[key] = (result, 0)
                 result_counts[key] = (result, result_counts[key][1] + 1)
             best_result, vote_count = max(result_counts.values(), key=lambda x: x[1])
             total_votes = len(results)
             explanation = f"Selected result with {vote_count}/{total_votes} votes (string equality fallback)"
-            provider_votes = {r.provider: 1.0 for r in results}
+            provider_votes = {self._get_provider_name(r): 1.0 for r in results}
         return EnsembleResult(
             result=best_result,
             confidence=ConfidenceScore(
@@ -303,15 +304,15 @@ async def _rank_based(
         borda_scores = {}
         for result, ranking in zip(results, rankings):
             score = self._calculate_borda_score(ranking, len(results))
-            borda_scores[result.provider] = score
+            borda_scores[self._get_provider_name(result)] = score
 
         # Normalize scores
         total_score = sum(borda_scores.values())
         normalized_scores = {k: v/total_score for k, v in borda_scores.items()}
 
         # Select result with highest Borda score
         best_provider = max(borda_scores.items(), key=lambda x: x[1])[0]
-        best_result = next(r for r in results if r.provider == best_provider)
+        best_result = next(r for r in results if self._get_provider_name(r) == best_provider)
 
         return EnsembleResult(
             result=best_result,
@@ -322,16 +323,40 @@ async def _rank_based(
             provider_votes=normalized_scores
         )
 
+    def _extract_result_content(self, result: Union[GenerationResult, EmbeddingResult, ImageAnalysisResult]) -> str:
+        """Extract text content from any result type for evaluation."""
+        if isinstance(result, GenerationResult):
+            return result.text
+        elif isinstance(result, EmbeddingResult):
+            return f"Embedding vector of length {len(result.embedding)}"
+        elif isinstance(result, ImageAnalysisResult):
+            # Combine text, captions, and objects for evaluation
+            parts = []
+            if result.text:
+                parts.append(f"Text: {result.text}")
+            if result.captions:
+                parts.append(f"Captions: {', '.join(result.captions)}")
+            if result.objects:
+                parts.append(f"Objects: {len(result.objects)} detected")
+            return " | ".join(parts) if parts else "No content extracted"
+        else:
+            return str(result)
+
+    def _get_provider_name(self, result: Union[GenerationResult, EmbeddingResult, ImageAnalysisResult]) -> str:
+        """Extract provider name from any result type."""
+        return getattr(result, 'provider', None) or getattr(result, 'provider_name', 'unknown')
+
     async def _evaluate_confidence(
         self,
         result: Union[GenerationResult, EmbeddingResult, ImageAnalysisResult],
         task_type: TaskType,
         **kwargs
     ) -> ConfidenceScore:
         """Evaluate confidence in a result using LLM."""
+        content = self._extract_result_content(result)
         prompt = f"""
         Evaluate the confidence in this {task_type} result:
-        {result.result}
+        {content}
 
         Consider:
         1. Completeness of the response
@@ -342,21 +367,33 @@ async def _evaluate_confidence(
         Provide a confidence score (0.0 to 1.0) and explanation.
         """
 
+        provider_name = self._get_provider_name(result)
+        evaluation_models = kwargs.get("evaluation_models", {})
+        evaluation_providers = kwargs.get("evaluation_providers", {})
+        default_model = kwargs.get("evaluation_model", "gpt-4")
+        eval_model = evaluation_models.get(provider_name, default_model)
+        eval_provider = evaluation_providers.get(provider_name, kwargs.get("evaluation_provider", provider_name))
+        route_kwargs = {
+            k: v for k, v in kwargs.items()
+            if k not in {"evaluation_models", "evaluation_providers", "evaluation_model", "evaluation_provider"}
+        }
         evaluation = await self.router.route(
             TaskType.TEXT_GENERATION,
             prompt,
-            model="gpt-4",
-            **kwargs
+            provider=eval_provider,
+            model=eval_model,
+            **route_kwargs
         )
 
         # Parse confidence score from evaluation
-        score = self._parse_confidence_score(evaluation.result)
-        explanation = self._parse_confidence_explanation(evaluation.result)
+        eval_content = self._extract_result_content(evaluation)
+        score = self._parse_confidence_score(eval_content)
+        explanation = self._parse_confidence_explanation(eval_content)
 
         return ConfidenceScore(
             score=score,
             explanation=explanation,
-            metadata={"raw_evaluation": evaluation.result}
+            metadata={"raw_evaluation": eval_content}
         )
 
     async def _evaluate_with_llm(
@@ -366,9 +403,10 @@ async def _evaluate_with_llm(
         **kwargs
     ) -> str:
         """Evaluate a result using LLM."""
+        content = self._extract_result_content(result)
         prompt = f"""
         Evaluate this {task_type} result:
-        {result.result}
+        {content}
 
         Consider:
         1. Accuracy and correctness
@@ -379,14 +417,25 @@ async def _evaluate_with_llm(
         Provide a detailed evaluation with a numerical score (0-100).
         """
 
+        provider_name = self._get_provider_name(result)
+        evaluation_models = kwargs.get("evaluation_models", {})
+        evaluation_providers = kwargs.get("evaluation_providers", {})
+        default_model = kwargs.get("evaluation_model", "gpt-4")
+        eval_model = evaluation_models.get(provider_name, default_model)
+        eval_provider = evaluation_providers.get(provider_name, kwargs.get("evaluation_provider", provider_name))
+        route_kwargs = {
+            k: v for k, v in kwargs.items()
+            if k not in {"evaluation_models", "evaluation_providers", "evaluation_model", "evaluation_provider"}
+        }
         evaluation = await self.router.route(
             TaskType.TEXT_GENERATION,
             prompt,
-            model="gpt-4",
-            **kwargs
+            provider=eval_provider,
+            model=eval_model,
+            **route_kwargs
         )
 
-        return evaluation.result
+        return self._extract_result_content(evaluation)
 
     async def _get_provider_ranking(
         self,
@@ -395,9 +444,10 @@ async def _get_provider_ranking(
         **kwargs
     ) -> List[str]:
         """Get ranking of results from a provider."""
+        content = self._extract_result_content(result)
         prompt = f"""
         Rank the following {task_type} results from best to worst:
-        {result.result}
+        {content}
 
         Consider:
         1. Quality and accuracy
@@ -408,14 +458,36 @@ async def _get_provider_ranking(
         Provide a ranked list of provider names.
         """
 
+        provider_name = self._get_provider_name(result)
+        evaluation_models = kwargs.get("evaluation_models", {})
+        evaluation_providers = kwargs.get("evaluation_providers", {})
+        default_model = kwargs.get("evaluation_model", "gpt-4")
+
+        # Use provider-specific model if available, otherwise use smart defaults
+        eval_model = evaluation_models.get(provider_name)
+        if not eval_model:
+            # Use provider-appropriate default models
+            if provider_name == "ollama":
+                eval_model = "mistral"  # Ollama doesn't have gpt-4
+            elif provider_name == "anthropic" or provider_name == "claude":
+                eval_model = "claude-3-sonnet"
+            else:
+                eval_model = default_model  # Use gpt-4 for OpenAI and others
+
+        eval_provider = evaluation_providers.get(provider_name, kwargs.get("evaluation_provider", provider_name))
+        route_kwargs = {
+            k: v for k, v in kwargs.items()
+            if k not in {"evaluation_models", "evaluation_providers", "evaluation_model", "evaluation_provider"}
+        }
         ranking = await self.router.route(
             TaskType.TEXT_GENERATION,
             prompt,
-            model="gpt-4",
-            **kwargs
+            provider=eval_provider,
+            model=eval_model,
+            **route_kwargs
         )
 
-        return self._parse_ranking(ranking.result)
+        return self._parse_ranking(self._extract_result_content(ranking))
 
     def _parse_confidence_score(self, evaluation: str) -> float:
         """Parse confidence score from evaluation text."""
@@ -519,7 +591,7 @@ def tune_weights_with_optuna(self, results, task_type, eval_fn, n_trials=30):
         if not OPTUNA_AVAILABLE:
             raise ImportError("Optuna is required for hyperparameter tuning. Please install optuna.")
 
-        providers = [r.provider for r in results]
+        providers = [self._get_provider_name(r) for r in results]
         def objective(trial):
             weights = {p: trial.suggest_float(f"weight_{p}", 0.01, 1.0) for p in providers}
             # Normalize

multimind/providers/__init__.py

@@ -6,8 +6,10 @@
 
 from .claude import ClaudeProvider
 from .openai import OpenAIProvider
+from .ollama import OllamaProvider
 
 __all__ = [
     "ClaudeProvider",
-    "OpenAIProvider"
+    "OpenAIProvider",
+    "OllamaProvider"
 ] 

multimind/providers/claude.py

@@ -97,10 +97,10 @@ async def chat(
             ) / 1000  # Convert to USD
 
             return GenerationResult(
+                text=result,
+                tokens_used=tokens_used,
                 provider_name="claude",
                 model_name=model,
-                result=result,
-                tokens_used=tokens_used,
                 latency_ms=latency_ms,
                 cost_estimate_usd=cost
             )