add ollama embeddings and test them out

config.toml

@@ -25,8 +25,9 @@ chunks_collection = "document_chunks"
 
 # Vector Store Configuration
 [vector_store.mongodb]
-dimensions = 1536
+dimensions = 1536 # 768 for nomic-embed-text
 index_name = "vector_index"
+similarity_metric = "dotProduct"
 
 # Model Configurations
 [models]
@@ -38,6 +39,9 @@ model_name = "gpt-4o-mini"
 default_max_tokens = 1000
 default_temperature = 0.7
 
+[models.ollama]
+base_url = "http://localhost:11434"
+
 # Document Processing
 [processing]
 [processing.text]

core/api.py

@@ -6,6 +6,7 @@ from fastapi.middleware.cors import CORSMiddleware
 import jwt
 import logging
 from core.completion.openai_completion import OpenAICompletionModel
+from core.embedding.ollama_embedding_model import OllamaEmbeddingModel
 from core.models.request import (
     IngestTextRequest,
     RetrieveRequest,
@@ -103,6 +104,11 @@ match settings.EMBEDDING_PROVIDER:
             api_key=settings.OPENAI_API_KEY,
             model_name=settings.EMBEDDING_MODEL,
         )
+    case "ollama":
+        embedding_model = OllamaEmbeddingModel(
+            model_name=settings.EMBEDDING_MODEL,
+            base_url=settings.OLLAMA_BASE_URL,
+        )
     case _:
         raise ValueError(
             f"Unsupported embedding provider: {settings.EMBEDDING_PROVIDER}"
@@ -113,6 +119,7 @@ match settings.COMPLETION_PROVIDER:
     case "ollama":
         completion_model = OllamaCompletionModel(
             model_name=settings.COMPLETION_MODEL,
+            base_url=settings.OLLAMA_BASE_URL,
         )
     case "openai":
         completion_model = OpenAICompletionModel(

core/completion/ollama_completion.py

@@ -3,14 +3,15 @@ from core.completion.base_completion import (
     CompletionRequest,
     CompletionResponse,
 )
-import ollama
+from ollama import AsyncClient
 
 
 class OllamaCompletionModel(BaseCompletionModel):
     """Ollama completion model implementation"""
 
-    def __init__(self, model_name: str):
+    def __init__(self, model_name: str, base_url: str):
         self.model_name = model_name
+        self.client = AsyncClient(host=base_url)
 
     async def complete(self, request: CompletionRequest) -> CompletionResponse:
         """Generate completion using Ollama API"""
@@ -24,7 +25,7 @@ Context:
 Question: {request.query}"""
 
         # Call Ollama API
-        response = ollama.chat(
+        response = await self.client.chat(
             model=self.model_name,
             messages=[{"role": "user", "content": prompt}],
             options={
@@ -36,7 +37,6 @@ Question: {request.query}"""
         # Ollama doesn't provide token usage info, so we'll estimate based on characters
         completion_text = response["message"]["content"]
         char_to_token_ratio = 4  # Rough estimate
-        total_chars = len(prompt) + len(completion_text)
         estimated_prompt_tokens = len(prompt) // char_to_token_ratio
         estimated_completion_tokens = len(completion_text) // char_to_token_ratio
 

core/config.py

@@ -48,6 +48,7 @@ class Settings(BaseSettings):
     COMPLETION_MODEL: str = "llama3.1"
     COMPLETION_MAX_TOKENS: int = 1000
     COMPLETION_TEMPERATURE: float = 0.7
+    OLLAMA_BASE_URL: str = "http://localhost:11434"
 
     # Processing settings
     CHUNK_SIZE: int = 1000
@@ -96,6 +97,7 @@ def get_settings() -> Settings:
         "COMPLETION_MODEL": config["models"]["completion"]["model_name"],
         "COMPLETION_MAX_TOKENS": config["models"]["completion"]["default_max_tokens"],
         "COMPLETION_TEMPERATURE": config["models"]["completion"]["default_temperature"],
+        "OLLAMA_BASE_URL": config["models"]["ollama"]["base_url"],
         # Processing settings
         "CHUNK_SIZE": config["processing"]["text"]["chunk_size"],
         "CHUNK_OVERLAP": config["processing"]["text"]["chunk_overlap"],

core/embedding/ollama_embedding_model.py

@@ -0,0 +1,27 @@
+from typing import List, Union
+from ollama import AsyncClient
+from core.embedding.base_embedding_model import BaseEmbeddingModel
+
+
+class OllamaEmbeddingModel(BaseEmbeddingModel):
+    def __init__(self, model_name, base_url: str = "http://localhost:11434"):
+        self.model_name = model_name
+        self.client = AsyncClient(host=base_url)
+
+    async def embed_for_ingestion(
+        self, text: Union[str, List[str]]
+    ) -> List[List[float]]:
+        if isinstance(text, str):
+            text = [text]
+
+        embeddings: List[List[float]] = []
+        for t in text:
+            response = await self.client.embeddings(model=self.model_name, prompt=t)
+            embedding = list(response["embedding"])
+            embeddings.append(embedding)
+
+        return embeddings
+
+    async def embed_for_query(self, text: str) -> List[float]:
+        response = await self.client.embeddings(model=self.model_name, prompt=text)
+        return response["embedding"]

quick_setup.py

@@ -43,13 +43,14 @@ with open(config_path, "rb") as f:
     LOGGER.info("Loaded configuration from config.toml")
 
 # Extract configuration values
-DEFAULT_REGION = CONFIG["aws"]["default_region"]
+DEFAULT_REGION = CONFIG["storage"]["aws"]["region"]
-DEFAULT_BUCKET_NAME = CONFIG["aws"]["default_bucket_name"]
+DEFAULT_BUCKET_NAME = CONFIG["storage"]["aws"]["bucket_name"]
-DATABASE_NAME = CONFIG["mongodb"]["database_name"]
+DATABASE_NAME = CONFIG["database"]["mongodb"]["database_name"]
-DOCUMENTS_COLLECTION = CONFIG["mongodb"]["documents_collection"]
+DOCUMENTS_COLLECTION = CONFIG["database"]["mongodb"]["documents_collection"]
-CHUNKS_COLLECTION = CONFIG["mongodb"]["chunks_collection"]
+CHUNKS_COLLECTION = CONFIG["database"]["mongodb"]["chunks_collection"]
-VECTOR_DIMENSIONS = CONFIG["mongodb"]["vector"]["dimensions"]
+VECTOR_DIMENSIONS = CONFIG["vector_store"]["mongodb"]["dimensions"]
-VECTOR_INDEX_NAME = CONFIG["mongodb"]["vector"]["index_name"]
+VECTOR_INDEX_NAME = CONFIG["vector_store"]["mongodb"]["index_name"]
+SIMILARITY_METRIC = CONFIG["vector_store"]["mongodb"]["similarity_metric"]
 
 
 def create_s3_bucket(bucket_name, region=DEFAULT_REGION):
@@ -142,7 +143,7 @@ def setup_mongodb():
                 {
                     "numDimensions": VECTOR_DIMENSIONS,
                     "path": "embedding",
-                    "similarity": "dotProduct",
+                    "similarity": SIMILARITY_METRIC,
                     "type": "vector",
                 },
                 {"path": "document_id", "type": "filter"},