Haystack llm and embedding wrapper

pyproject.toml

@@ -60,6 +60,8 @@ dev = [
     "rapidfuzz",
     "pandas",
     "datacompy",
+    "haystack-ai",
+    "sacrebleu",
     "r2r",
 ]
 test = [

requirements/dev.txt

@@ -17,3 +17,4 @@ nltk
 rapidfuzz
 pandas
 datacompy
+haystack-ai

src/ragas/embeddings/__init__.py

@@ -5,11 +5,13 @@
     LlamaIndexEmbeddingsWrapper,
     embedding_factory,
 )
+from ragas.embeddings.haystack_wrapper import HaystackEmbeddingsWrapper
 
 __all__ = [
     "BaseRagasEmbeddings",
+    "HaystackEmbeddingsWrapper",
+    "HuggingfaceEmbeddings",
     "LangchainEmbeddingsWrapper",
     "LlamaIndexEmbeddingsWrapper",
-    "HuggingfaceEmbeddings",
     "embedding_factory",
 ]

src/ragas/embeddings/base.py

@@ -4,7 +4,6 @@
 import typing as t
 from abc import ABC, abstractmethod
 from dataclasses import field
-from typing import List
 
 import numpy as np
 from langchain_core.embeddings import Embeddings
@@ -51,15 +50,15 @@ def __init__(self, cache: t.Optional[CacheInterface] = None):
                 self.aembed_documents
             )
 
-    async def embed_text(self, text: str, is_async=True) -> List[float]:
+    async def embed_text(self, text: str, is_async=True) -> t.List[float]:
         """
         Embed a single text string.
         """
         embs = await self.embed_texts([text], is_async=is_async)
         return embs[0]
 
     async def embed_texts(
-        self, texts: List[str], is_async: bool = True
+        self, texts: t.List[str], is_async: bool = True
     ) -> t.List[t.List[float]]:
         """
         Embed multiple texts.
@@ -77,10 +76,10 @@ async def embed_texts(
             return await loop.run_in_executor(None, embed_documents_with_retry, texts)
 
     @abstractmethod
-    async def aembed_query(self, text: str) -> List[float]: ...
+    async def aembed_query(self, text: str) -> t.List[float]: ...
 
     @abstractmethod
-    async def aembed_documents(self, texts: List[str]) -> t.List[t.List[float]]: ...
+    async def aembed_documents(self, texts: t.List[str]) -> t.List[t.List[float]]: ...
 
     def set_run_config(self, run_config: RunConfig):
         """
@@ -117,25 +116,25 @@ def __init__(
             run_config = RunConfig()
         self.set_run_config(run_config)
 
-    def embed_query(self, text: str) -> List[float]:
+    def embed_query(self, text: str) -> t.List[float]:
         """
         Embed a single query text.
         """
         return self.embeddings.embed_query(text)
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_documents(self, texts: t.List[str]) -> t.List[t.List[float]]:
         """
         Embed multiple documents.
         """
         return self.embeddings.embed_documents(texts)
 
-    async def aembed_query(self, text: str) -> List[float]:
+    async def aembed_query(self, text: str) -> t.List[float]:
         """
         Asynchronously embed a single query text.
         """
         return await self.embeddings.aembed_query(text)
 
-    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
+    async def aembed_documents(self, texts: t.List[str]) -> t.List[t.List[float]]:
         """
         Asynchronously embed multiple documents.
         """
@@ -256,13 +255,13 @@ def __post_init__(self):
         if self.cache is not None:
             self.predict = cacher(cache_backend=self.cache)(self.predict)
 
-    def embed_query(self, text: str) -> List[float]:
+    def embed_query(self, text: str) -> t.List[float]:
         """
         Embed a single query text.
         """
         return self.embed_documents([text])[0]
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_documents(self, texts: t.List[str]) -> t.List[t.List[float]]:
         """
         Embed multiple documents.
         """
@@ -279,7 +278,7 @@ def embed_documents(self, texts: List[str]) -> List[List[float]]:
         assert isinstance(embeddings, Tensor)
         return embeddings.tolist()
 
-    def predict(self, texts: List[List[str]]) -> List[List[float]]:
+    def predict(self, texts: t.List[t.List[str]]) -> t.List[t.List[float]]:
         """
         Make predictions using a cross-encoder model.
         """

src/ragas/embeddings/haystack_wrapper.py

@@ -0,0 +1,113 @@
+import asyncio
+import typing as t
+
+from ragas.cache import CacheInterface
+from ragas.embeddings.base import BaseRagasEmbeddings
+from ragas.run_config import RunConfig
+
+
+class HaystackEmbeddingsWrapper(BaseRagasEmbeddings):
+    """
+    A wrapper for using Haystack embedders within the Ragas framework.
+
+    This class allows you to use both synchronous and asynchronous methods
+    (`embed_query`/`embed_documents` and `aembed_query`/`aembed_documents`)
+    for generating embeddings through a Haystack embedder.
+
+    Parameters
+    ----------
+    embedder : AzureOpenAITextEmbedder | HuggingFaceAPITextEmbedder | OpenAITextEmbedder | SentenceTransformersTextEmbedder
+        An instance of a supported Haystack embedder class.
+    run_config : RunConfig, optional
+        A configuration object to manage embedding execution settings, by default None.
+    cache : CacheInterface, optional
+        A cache instance for storing and retrieving embedding results, by default None.
+    """
+
+    def __init__(
+        self,
+        embedder: t.Any,
+        run_config: t.Optional[RunConfig] = None,
+        cache: t.Optional[CacheInterface] = None,
+    ):
+        super().__init__(cache=cache)
+
+        # Lazy Import of required Haystack components
+        try:
+            from haystack import AsyncPipeline
+            from haystack.components.embedders import (
+                AzureOpenAITextEmbedder,
+                HuggingFaceAPITextEmbedder,
+                OpenAITextEmbedder,
+                SentenceTransformersTextEmbedder,
+            )
+        except ImportError as exc:
+            raise ImportError(
+                "Haystack is not installed. Please install it with `pip install haystack-ai`."
+            ) from exc
+
+        # Validate embedder type
+        if not isinstance(
+            embedder,
+            (
+                AzureOpenAITextEmbedder,
+                HuggingFaceAPITextEmbedder,
+                OpenAITextEmbedder,
+                SentenceTransformersTextEmbedder,
+            ),
+        ):
+            raise TypeError(
+                "Expected 'embedder' to be one of: AzureOpenAITextEmbedder, "
+                "HuggingFaceAPITextEmbedder, OpenAITextEmbedder, or "
+                f"SentenceTransformersTextEmbedder, but got {type(embedder).__name__}."
+            )
+
+        self.embedder = embedder
+
+        # Initialize an asynchronous pipeline and add the embedder component
+        self.async_pipeline = AsyncPipeline()
+        self.async_pipeline.add_component("embedder", self.embedder)
+
+        # Set or create the run configuration
+        if run_config is None:
+            run_config = RunConfig()
+        self.set_run_config(run_config)
+
+    def embed_query(self, text: str) -> t.List[float]:
+        result = self.embedder.run(text=text)
+        return result["embedding"]
+
+    def embed_documents(self, texts: t.List[str]) -> t.List[t.List[float]]:
+        return [self.embed_query(text) for text in texts]
+
+    async def aembed_query(self, text: str) -> t.List[float]:
+        # Run the async pipeline with the input text
+        output = await self.async_pipeline.run_async({"embedder": {"text": text}})
+        return output.get("embedder", {}).get("embedding", [])
+
+    async def aembed_documents(self, texts: t.List[str]) -> t.List[t.List[float]]:
+        tasks = (self.aembed_query(text) for text in texts)
+        results = await asyncio.gather(*tasks)
+        return results
+
+    def __repr__(self) -> str:
+        try:
+            from haystack.components.embedders import (
+                AzureOpenAITextEmbedder,
+                HuggingFaceAPITextEmbedder,
+                OpenAITextEmbedder,
+                SentenceTransformersTextEmbedder,
+            )
+        except ImportError:
+            return f"{self.__class__.__name__}(embeddings=Unknown(...))"
+
+        if isinstance(self.embedder, (OpenAITextEmbedder, SentenceTransformersTextEmbedder)):  # type: ignore
+            model_info = self.embedder.model
+        elif isinstance(self.embedder, AzureOpenAITextEmbedder):  # type: ignore
+            model_info = self.embedder.azure_deployment
+        elif isinstance(self.embedder, HuggingFaceAPITextEmbedder):  # type: ignore
+            model_info = self.embedder.api_params
+        else:
+            model_info = "Unknown"
+
+        return f"{self.__class__.__name__}(embeddings={model_info}(...))"

src/ragas/llms/__init__.py

@@ -4,9 +4,11 @@
     LlamaIndexLLMWrapper,
     llm_factory,
 )
+from ragas.llms.haystack_wrapper import HaystackLLMWrapper
 
 __all__ = [
     "BaseRagasLLM",
+    "HaystackLLMWrapper",
     "LangchainLLMWrapper",
     "LlamaIndexLLMWrapper",
     "llm_factory",

src/ragas/llms/base.py

@@ -24,6 +24,7 @@
     from langchain_core.prompt_values import PromptValue
     from llama_index.core.base.llms.base import BaseLLM
 
+
 logger = logging.getLogger(__name__)
 
 MULTIPLE_COMPLETION_SUPPORTED = [

src/ragas/llms/haystack_wrapper.py

@@ -0,0 +1,141 @@
+import typing as t
+
+from langchain_core.callbacks import Callbacks
+from langchain_core.outputs import Generation, LLMResult
+from langchain_core.prompt_values import PromptValue
+
+from ragas.cache import CacheInterface
+from ragas.llms import BaseRagasLLM
+from ragas.run_config import RunConfig
+
+
+class HaystackLLMWrapper(BaseRagasLLM):
+    """
+    A wrapper class for using Haystack LLM generators within the Ragas framework.
+
+    This class integrates Haystack's LLM components (e.g., `OpenAIGenerator`,
+    `HuggingFaceAPIGenerator`, etc.) into Ragas, enabling both synchronous and
+    asynchronous text generation.
+
+    Parameters
+    ----------
+    haystack_generator : AzureOpenAIGenerator | HuggingFaceAPIGenerator | HuggingFaceLocalGenerator | OpenAIGenerator
+        An instance of a Haystack generator.
+    run_config : RunConfig, optional
+        Configuration object to manage LLM execution settings, by default None.
+    cache : CacheInterface, optional
+        A cache instance for storing results, by default None.
+    """
+
+    def __init__(
+        self,
+        haystack_generator: t.Any,
+        run_config: t.Optional[RunConfig] = None,
+        cache: t.Optional[CacheInterface] = None,
+    ):
+        super().__init__(cache=cache)
+
+        # Lazy Import of required Haystack components
+        try:
+            from haystack import AsyncPipeline
+            from haystack.components.generators import (
+                AzureOpenAIGenerator,
+                HuggingFaceAPIGenerator,
+                HuggingFaceLocalGenerator,
+                OpenAIGenerator,
+            )
+        except ImportError as exc:
+            raise ImportError(
+                "Haystack is not installed. Please install it using `pip install haystack-ai`."
+            ) from exc
+
+        # Validate haystack_generator type
+        if not isinstance(
+            haystack_generator,
+            (
+                AzureOpenAIGenerator,
+                HuggingFaceAPIGenerator,
+                HuggingFaceLocalGenerator,
+                OpenAIGenerator,
+            ),
+        ):
+            raise TypeError(
+                "Expected 'haystack_generator' to be one of: "
+                "AzureOpenAIGenerator, HuggingFaceAPIGenerator, "
+                "HuggingFaceLocalGenerator, or OpenAIGenerator, but received "
+                f"{type(haystack_generator).__name__}."
+            )
+
+        # Set up Haystack pipeline and generator
+        self.generator = haystack_generator
+        self.async_pipeline = AsyncPipeline()
+        self.async_pipeline.add_component("llm", self.generator)
+
+        if run_config is None:
+            run_config = RunConfig()
+        self.set_run_config(run_config)
+
+    def is_finished(self, response: LLMResult) -> bool:
+        return True
+
+    def generate_text(
+        self,
+        prompt: PromptValue,
+        n: int = 1,
+        temperature: float = 1e-8,
+        stop: t.Optional[t.List[str]] = None,
+        callbacks: t.Optional[Callbacks] = None,
+    ) -> LLMResult:
+
+        component_output: t.Dict[str, t.Any] = self.generator.run(prompt.to_string())
+        replies = component_output.get("llm", {}).get("replies", [])
+        output_text = replies[0] if replies else ""
+
+        return LLMResult(generations=[[Generation(text=output_text)]])
+
+    async def agenerate_text(
+        self,
+        prompt: PromptValue,
+        n: int = 1,
+        temperature: t.Optional[float] = None,
+        stop: t.Optional[t.List[str]] = None,
+        callbacks: t.Optional[Callbacks] = None,
+    ) -> LLMResult:
+        # Prepare input parameters for the LLM component
+        llm_input = {
+            "prompt": prompt.to_string(),
+            "generation_kwargs": {"temperature": temperature},
+        }
+
+        # Run the async pipeline with the LLM input
+        pipeline_output = await self.async_pipeline.run_async(data={"llm": llm_input})
+        replies = pipeline_output.get("llm", {}).get("replies", [])
+        output_text = replies[0] if replies else ""
+
+        return LLMResult(generations=[[Generation(text=output_text)]])
+
+    def __repr__(self) -> str:
+        try:
+            from haystack.components.generators import (
+                AzureOpenAIGenerator,
+                HuggingFaceAPIGenerator,
+                HuggingFaceLocalGenerator,
+                OpenAIGenerator,
+            )
+        except ImportError:
+            return f"{self.__class__.__name__}(llm=Unknown(...))"
+
+        generator = self.generator
+
+        if isinstance(generator, OpenAIGenerator):
+            model_info = generator.model
+        elif isinstance(generator, HuggingFaceLocalGenerator):
+            model_info = generator.huggingface_pipeline_kwargs.get("model")
+        elif isinstance(generator, HuggingFaceAPIGenerator):
+            model_info = generator.api_params.get("model")
+        elif isinstance(generator, AzureOpenAIGenerator):
+            model_info = generator.azure_deployment
+        else:
+            model_info = "Unknown"
+
+        return f"{self.__class__.__name__}(llm={model_info}(...))"