two version of R2R are here HEAD master

1 files changed, 194 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/core/providers/embeddings/ollama.py b/.venv/lib/python3.12/site-packages/core/providers/embeddings/ollama.py
new file mode 100644
index 00000000..297d9167
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/core/providers/embeddings/ollama.py
@@ -0,0 +1,194 @@
+import logging
+import os
+from typing import Any
+
+from ollama import AsyncClient, Client
+
+from core.base import (
+    ChunkSearchResult,
+    EmbeddingConfig,
+    EmbeddingProvider,
+    EmbeddingPurpose,
+    R2RException,
+)
+
+logger = logging.getLogger()
+
+
+class OllamaEmbeddingProvider(EmbeddingProvider):
+    def __init__(self, config: EmbeddingConfig):
+        super().__init__(config)
+        provider = config.provider
+        if not provider:
+            raise ValueError(
+                "Must set provider in order to initialize `OllamaEmbeddingProvider`."
+            )
+        if provider != "ollama":
+            raise ValueError(
+                "OllamaEmbeddingProvider must be initialized with provider `ollama`."
+            )
+        if config.rerank_model:
+            raise ValueError(
+                "OllamaEmbeddingProvider does not support separate reranking."
+            )
+
+        self.base_model = config.base_model
+        self.base_dimension = config.base_dimension
+        self.base_url = os.getenv("OLLAMA_API_BASE")
+        logger.info(
+            f"Using Ollama API base URL: {self.base_url or 'http://127.0.0.1:11434'}"
+        )
+        self.client = Client(host=self.base_url)
+        self.aclient = AsyncClient(host=self.base_url)
+
+        self.set_prefixes(config.prefixes or {}, self.base_model)
+        self.batch_size = config.batch_size or 32
+
+    def _get_embedding_kwargs(self, **kwargs):
+        embedding_kwargs = {
+            "model": self.base_model,
+        }
+        embedding_kwargs.update(kwargs)
+        return embedding_kwargs
+
+    async def _execute_task(self, task: dict[str, Any]) -> list[list[float]]:
+        texts = task["texts"]
+        purpose = task.get("purpose", EmbeddingPurpose.INDEX)
+        kwargs = self._get_embedding_kwargs(**task.get("kwargs", {}))
+
+        try:
+            embeddings = []
+            for i in range(0, len(texts), self.batch_size):
+                batch = texts[i : i + self.batch_size]
+                prefixed_batch = [
+                    self.prefixes.get(purpose, "") + text for text in batch
+                ]
+                response = await self.aclient.embed(
+                    input=prefixed_batch, **kwargs
+                )
+                embeddings.extend(response["embeddings"])
+            return embeddings
+        except Exception as e:
+            error_msg = f"Error getting embeddings: {str(e)}"
+            logger.error(error_msg)
+            raise R2RException(error_msg, 400) from e
+
+    def _execute_task_sync(self, task: dict[str, Any]) -> list[list[float]]:
+        texts = task["texts"]
+        purpose = task.get("purpose", EmbeddingPurpose.INDEX)
+        kwargs = self._get_embedding_kwargs(**task.get("kwargs", {}))
+
+        try:
+            embeddings = []
+            for i in range(0, len(texts), self.batch_size):
+                batch = texts[i : i + self.batch_size]
+                prefixed_batch = [
+                    self.prefixes.get(purpose, "") + text for text in batch
+                ]
+                response = self.client.embed(input=prefixed_batch, **kwargs)
+                embeddings.extend(response["embeddings"])
+            return embeddings
+        except Exception as e:
+            error_msg = f"Error getting embeddings: {str(e)}"
+            logger.error(error_msg)
+            raise R2RException(error_msg, 400) from e
+
+    async def async_get_embedding(
+        self,
+        text: str,
+        stage: EmbeddingProvider.Step = EmbeddingProvider.Step.BASE,
+        purpose: EmbeddingPurpose = EmbeddingPurpose.INDEX,
+        **kwargs,
+    ) -> list[float]:
+        if stage != EmbeddingProvider.Step.BASE:
+            raise ValueError(
+                "OllamaEmbeddingProvider only supports search stage."
+            )
+
+        task = {
+            "texts": [text],
+            "stage": stage,
+            "purpose": purpose,
+            "kwargs": kwargs,
+        }
+        result = await self._execute_with_backoff_async(task)
+        return result[0]
+
+    def get_embedding(
+        self,
+        text: str,
+        stage: EmbeddingProvider.Step = EmbeddingProvider.Step.BASE,
+        purpose: EmbeddingPurpose = EmbeddingPurpose.INDEX,
+        **kwargs,
+    ) -> list[float]:
+        if stage != EmbeddingProvider.Step.BASE:
+            raise ValueError(
+                "OllamaEmbeddingProvider only supports search stage."
+            )
+
+        task = {
+            "texts": [text],
+            "stage": stage,
+            "purpose": purpose,
+            "kwargs": kwargs,
+        }
+        result = self._execute_with_backoff_sync(task)
+        return result[0]
+
+    async def async_get_embeddings(
+        self,
+        texts: list[str],
+        stage: EmbeddingProvider.Step = EmbeddingProvider.Step.BASE,
+        purpose: EmbeddingPurpose = EmbeddingPurpose.INDEX,
+        **kwargs,
+    ) -> list[list[float]]:
+        if stage != EmbeddingProvider.Step.BASE:
+            raise ValueError(
+                "OllamaEmbeddingProvider only supports search stage."
+            )
+
+        task = {
+            "texts": texts,
+            "stage": stage,
+            "purpose": purpose,
+            "kwargs": kwargs,
+        }
+        return await self._execute_with_backoff_async(task)
+
+    def get_embeddings(
+        self,
+        texts: list[str],
+        stage: EmbeddingProvider.Step = EmbeddingProvider.Step.BASE,
+        purpose: EmbeddingPurpose = EmbeddingPurpose.INDEX,
+        **kwargs,
+    ) -> list[list[float]]:
+        if stage != EmbeddingProvider.Step.BASE:
+            raise ValueError(
+                "OllamaEmbeddingProvider only supports search stage."
+            )
+
+        task = {
+            "texts": texts,
+            "stage": stage,
+            "purpose": purpose,
+            "kwargs": kwargs,
+        }
+        return self._execute_with_backoff_sync(task)
+
+    def rerank(
+        self,
+        query: str,
+        results: list[ChunkSearchResult],
+        stage: EmbeddingProvider.Step = EmbeddingProvider.Step.RERANK,
+        limit: int = 10,
+    ) -> list[ChunkSearchResult]:
+        return results[:limit]
+
+    async def arerank(
+        self,
+        query: str,
+        results: list[ChunkSearchResult],
+        stage: EmbeddingProvider.Step = EmbeddingProvider.Step.RERANK,
+        limit: int = 10,
+    ):
+        return results[:limit]