Feature/cog 1358 local ollama model support for cognee (#555)

<!-- .github/pull_request_template.md -->

This PR contains the ollama specific llm adapter together with the
embedding engine.

Tested with the following models:

`LLM_API_KEY="ollama"
llm_model = "llama3.1:8b"
LLM_PROVIDER = "ollama"
llm_endpoint = "http://localhost:11434/v1"
EMBEDDING_PROVIDER="ollama"
EMBEDDING_MODEL="avr/sfr-embedding-mistral:latest"
EMBEDDING_ENDPOINT="http://localhost:11434/api/embeddings"
EMBEDDING_DIMENSIONS=4096
HUGGINGFACE_TOKENIZER="Salesforce/SFR-Embedding-Mistral"`

## DCO Affirmation
I affirm that all code in every commit of this pull request conforms to
the terms of the Topoteretes Developer Certificate of Origin


<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

- **New Features**
- Introduced a new embedding option that leverages an external provider
for asynchronous text processing.
- Added enhanced language model integration using a dedicated adapter to
improve interaction quality.

- **Enhancements**
  - Expanded configuration settings to include a new tokenizer option.
- Updated provider selection logic to incorporate the additional
embedding and language model features.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Co-authored-by: Vasilije <8619304+Vasilije1990@users.noreply.github.com>
Co-authored-by: vasilije <vas.markovic@gmail.com>

cognee/infrastructure/databases/vector/embeddings/OllamaEmbeddingEngine.py

@@ -0,0 +1,101 @@
+import asyncio
+import httpx
+import logging
+from typing import List, Optional
+import os
+
+from cognee.infrastructure.databases.vector.embeddings.EmbeddingEngine import EmbeddingEngine
+from cognee.infrastructure.databases.exceptions.EmbeddingException import EmbeddingException
+from cognee.infrastructure.llm.tokenizer.HuggingFace import HuggingFaceTokenizer
+
+logger = logging.getLogger("OllamaEmbeddingEngine")
+
+
+class OllamaEmbeddingEngine(EmbeddingEngine):
+    model: str
+    dimensions: int
+    max_tokens: int
+    endpoint: str
+    mock: bool
+    huggingface_tokenizer_name: str
+
+    MAX_RETRIES = 5
+
+    def __init__(
+        self,
+        model: Optional[str] = "avr/sfr-embedding-mistral:latest",
+        dimensions: Optional[int] = 1024,
+        max_tokens: int = 512,
+        endpoint: Optional[str] = "http://localhost:11434/api/embeddings",
+        huggingface_tokenizer: str = "Salesforce/SFR-Embedding-Mistral",
+    ):
+        self.model = model
+        self.dimensions = dimensions
+        self.max_tokens = max_tokens
+        self.endpoint = endpoint
+        self.huggingface_tokenizer_name = huggingface_tokenizer
+        self.tokenizer = self.get_tokenizer()
+
+        enable_mocking = os.getenv("MOCK_EMBEDDING", "false")
+        if isinstance(enable_mocking, bool):
+            enable_mocking = str(enable_mocking).lower()
+        self.mock = enable_mocking in ("true", "1", "yes")
+
+    async def embed_text(self, text: List[str]) -> List[List[float]]:
+        """
+        Given a list of text prompts, returns a list of embedding vectors.
+        """
+        if self.mock:
+            return [[0.0] * self.dimensions for _ in text]
+
+        embeddings = []
+        async with httpx.AsyncClient() as client:
+            for prompt in text:
+                embedding = await self._get_embedding(client, prompt)
+                embeddings.append(embedding)
+        return embeddings
+
+    async def _get_embedding(self, client: httpx.AsyncClient, prompt: str) -> List[float]:
+        """
+        Internal method to call the Ollama embeddings endpoint for a single prompt.
+        """
+        payload = {
+            "model": self.model,
+            "prompt": prompt,
+        }
+        headers = {}
+        api_key = os.getenv("LLM_API_KEY")
+        if api_key:
+            headers["Authorization"] = f"Bearer {api_key}"
+
+        retries = 0
+        while retries < self.MAX_RETRIES:
+            try:
+                response = await client.post(
+                    self.endpoint, json=payload, headers=headers, timeout=60.0
+                )
+                response.raise_for_status()
+                data = response.json()
+                return data["embedding"]
+            except httpx.HTTPStatusError as e:
+                logger.error(f"HTTP error on attempt {retries + 1}: {e}")
+                retries += 1
+                await asyncio.sleep(min(2**retries, 60))
+            except Exception as e:
+                logger.error(f"Error on attempt {retries + 1}: {e}")
+                retries += 1
+                await asyncio.sleep(min(2**retries, 60))
+        raise EmbeddingException(
+            f"Failed to embed text using model {self.model} after {self.MAX_RETRIES} retries"
+        )
+
+    def get_vector_size(self) -> int:
+        return self.dimensions
+
+    def get_tokenizer(self):
+        logger.debug("Loading HuggingfaceTokenizer for OllamaEmbeddingEngine...")
+        tokenizer = HuggingFaceTokenizer(
+            model=self.huggingface_tokenizer_name, max_tokens=self.max_tokens
+        )
+        logger.debug("Tokenizer loaded for OllamaEmbeddingEngine")
+        return tokenizer

cognee/infrastructure/databases/vector/embeddings/config.py

@@ -11,6 +11,7 @@ class EmbeddingConfig(BaseSettings):
     embedding_api_key: Optional[str] = None
     embedding_api_version: Optional[str] = None
     embedding_max_tokens: Optional[int] = 8191
+    huggingface_tokenizer: Optional[str] = None
     model_config = SettingsConfigDict(env_file=".env", extra="allow")
 
 

cognee/infrastructure/databases/vector/embeddings/get_embedding_engine.py

@@ -16,10 +16,19 @@ def get_embedding_engine() -> EmbeddingEngine:
             max_tokens=config.embedding_max_tokens,
         )
 
+    if config.embedding_provider == "ollama":
+        from .OllamaEmbeddingEngine import OllamaEmbeddingEngine
+
+        return OllamaEmbeddingEngine(
+            model=config.embedding_model,
+            dimensions=config.embedding_dimensions,
+            max_tokens=config.embedding_max_tokens,
+            huggingface_tokenizer=config.huggingface_tokenizer,
+        )
+
     from .LiteLLMEmbeddingEngine import LiteLLMEmbeddingEngine
 
     return LiteLLMEmbeddingEngine(
-        # If OpenAI API is used for embeddings, litellm needs only the api_key.
         provider=config.embedding_provider,
         api_key=config.embedding_api_key or llm_config.llm_api_key,
         endpoint=config.embedding_endpoint,

cognee/infrastructure/llm/get_llm_client.py

@@ -4,6 +4,7 @@ from enum import Enum
 
 from cognee.exceptions import InvalidValueError
 from cognee.infrastructure.llm import get_llm_config
+from cognee.infrastructure.llm.ollama.adapter import OllamaAPIAdapter
 
 
 # Define an Enum for LLM Providers
@@ -52,7 +53,7 @@ def get_llm_client():
 
         from .generic_llm_api.adapter import GenericAPIAdapter
 
-        return GenericAPIAdapter(
+        return OllamaAPIAdapter(
             llm_config.llm_endpoint,
             llm_config.llm_api_key,
             llm_config.llm_model,

cognee/infrastructure/llm/ollama/adapter.py

@@ -0,0 +1,44 @@
+from typing import Type
+from pydantic import BaseModel
+import instructor
+from cognee.infrastructure.llm.llm_interface import LLMInterface
+from cognee.infrastructure.llm.config import get_llm_config
+from openai import OpenAI
+
+
+class OllamaAPIAdapter(LLMInterface):
+    """Adapter for a Generic API LLM provider using instructor with an OpenAI backend."""
+
+    def __init__(self, endpoint: str, api_key: str, model: str, name: str, max_tokens: int):
+        self.name = name
+        self.model = model
+        self.api_key = api_key
+        self.endpoint = endpoint
+        self.max_tokens = max_tokens
+
+        self.aclient = instructor.from_openai(
+            OpenAI(base_url=self.endpoint, api_key=self.api_key), mode=instructor.Mode.JSON
+        )
+
+    async def acreate_structured_output(
+        self, text_input: str, system_prompt: str, response_model: Type[BaseModel]
+    ) -> BaseModel:
+        """Generate a structured output from the LLM using the provided text and system prompt."""
+
+        response = self.aclient.chat.completions.create(
+            model=self.model,
+            messages=[
+                {
+                    "role": "user",
+                    "content": f"Use the given format to extract information from the following input: {text_input}",
+                },
+                {
+                    "role": "system",
+                    "content": system_prompt,
+                },
+            ],
+            max_retries=5,
+            response_model=response_model,
+        )
+
+        return response