Feat/llamaindex: adding llamaindex (#999)

fixes: #557 


its been long 🙂

src/ragas/embeddings/__init__.py

@@ -2,12 +2,14 @@
     BaseRagasEmbeddings,
     HuggingfaceEmbeddings,
     LangchainEmbeddingsWrapper,
+    LlamaIndexEmbeddingsWrapper,
     embedding_factory,
 )
 
 __all__ = [
     "HuggingfaceEmbeddings",
     "BaseRagasEmbeddings",
     "LangchainEmbeddingsWrapper",
+    "LlamaIndexEmbeddingsWrapper",
     "embedding_factory",
 ]

src/ragas/embeddings/base.py

@@ -13,6 +13,9 @@
 
 from ragas.run_config import RunConfig, add_async_retry, add_retry
 
+if t.TYPE_CHECKING:
+    from llama_index.core.base.embeddings.base import BaseEmbedding
+
 DEFAULT_MODEL_NAME = "BAAI/bge-small-en-v1.5"
 
 
@@ -153,6 +156,28 @@ def predict(self, texts: List[List[str]]) -> List[List[float]]:
         return predictions.tolist()
 
 
+class LlamaIndexEmbeddingsWrapper(BaseRagasEmbeddings):
+    def __init__(
+        self, embeddings: BaseEmbedding, run_config: t.Optional[RunConfig] = None
+    ):
+        self.embeddings = embeddings
+        if run_config is None:
+            run_config = RunConfig()
+        self.set_run_config(run_config)
+
+    def embed_query(self, text: str) -> t.List[float]:
+        return self.embeddings.get_query_embedding(text)
+
+    def embed_documents(self, texts: t.List[str]) -> t.List[t.List[float]]:
+        return self.embeddings.get_text_embedding_batch(texts)
+
+    async def aembed_query(self, text: str) -> t.List[float]:
+        return await self.embeddings.aget_query_embedding(text)
+
+    async def aembed_documents(self, texts: t.List[str]) -> t.List[t.List[float]]:
+        return await self.embeddings.aget_text_embedding_batch(texts)
+
+
 def embedding_factory(
     model: str = "text-embedding-ada-002", run_config: t.Optional[RunConfig] = None
 ) -> BaseRagasEmbeddings:

src/ragas/executor.py

@@ -17,7 +17,6 @@
 
 
 def runner_exception_hook(args: threading.ExceptHookArgs):
-    print(args)
     raise args.exc_type
 
 

src/ragas/integrations/llama_index.py

@@ -0,0 +1,113 @@
+from __future__ import annotations
+
+import logging
+import typing as t
+from copy import copy
+from uuid import uuid4
+
+from datasets import Dataset
+
+from ragas.embeddings import LlamaIndexEmbeddingsWrapper
+from ragas.evaluation import evaluate as ragas_evaluate
+from ragas.exceptions import ExceptionInRunner
+from ragas.executor import Executor
+from ragas.llms import LlamaIndexLLMWrapper
+from ragas.validation import EVALMODE_TO_COLUMNS, validate_evaluation_modes
+
+if t.TYPE_CHECKING:
+    from llama_index.core.base.embeddings.base import (
+        BaseEmbedding as LlamaIndexEmbeddings,
+    )
+    from llama_index.core.base.llms.base import BaseLLM as LlamaindexLLM
+
+    from ragas.evaluation import Result
+    from ragas.metrics.base import Metric
+
+
+logger = logging.getLogger(__name__)
+
+
+def validate_dataset(dataset: dict, metrics: list[Metric]):
+    # change EVALMODE_TO_COLUMNS for usecase with no contexts and answer
+    evalmod_to_columns_llamaindex = copy(EVALMODE_TO_COLUMNS)
+    for mode in EVALMODE_TO_COLUMNS:
+        if "answer" in EVALMODE_TO_COLUMNS[mode]:
+            EVALMODE_TO_COLUMNS[mode].remove("answer")
+        if "contexts" in EVALMODE_TO_COLUMNS[mode]:
+            EVALMODE_TO_COLUMNS[mode].remove("contexts")
+
+    hf_dataset = Dataset.from_dict(dataset)
+    validate_evaluation_modes(hf_dataset, metrics, evalmod_to_columns_llamaindex)
+
+
+def evaluate(
+    query_engine,
+    dataset: dict,
+    metrics: list[Metric],
+    llm: t.Optional[LlamaindexLLM] = None,
+    embeddings: t.Optional[LlamaIndexEmbeddings] = None,
+    raise_exceptions: bool = True,
+    column_map: t.Optional[t.Dict[str, str]] = None,
+) -> Result:
+    column_map = column_map or {}
+
+    # wrap llms and embeddings
+    li_llm = None
+    if llm is not None:
+        li_llm = LlamaIndexLLMWrapper(llm)
+    li_embeddings = None
+    if embeddings is not None:
+        li_embeddings = LlamaIndexEmbeddingsWrapper(embeddings)
+
+    # validate and transform dataset
+    if dataset is None:
+        raise ValueError("Provide dataset!")
+
+    exec = Executor(
+        desc="Running Query Engine",
+        keep_progress_bar=True,
+        raise_exceptions=raise_exceptions,
+    )
+
+    # get query
+    queries = dataset["question"]
+    for i, q in enumerate(queries):
+        exec.submit(query_engine.aquery, q, name=f"query-{i}")
+
+    answers: t.List[str] = []
+    contexts: t.List[t.List[str]] = []
+    try:
+        results = exec.results()
+        if results == []:
+            raise ExceptionInRunner()
+    except Exception as e:
+        raise e
+    else:
+        for r in results:
+            answers.append(r.response)
+            contexts.append([n.node.text for n in r.source_nodes])
+
+    # create HF dataset
+    hf_dataset = Dataset.from_dict(
+        {
+            "question": queries,
+            "contexts": contexts,
+            "answer": answers,
+        }
+    )
+    if "ground_truth" in dataset:
+        hf_dataset = hf_dataset.add_column(
+            name="ground_truth",
+            column=dataset["ground_truth"],
+            new_fingerprint=str(uuid4()),
+        )
+
+    results = ragas_evaluate(
+        dataset=hf_dataset,
+        metrics=metrics,
+        llm=li_llm,
+        embeddings=li_embeddings,
+        raise_exceptions=raise_exceptions,
+    )
+
+    return results

src/ragas/llms/__init__.py

@@ -1,7 +1,13 @@
-from ragas.llms.base import BaseRagasLLM, LangchainLLMWrapper, llm_factory
+from ragas.llms.base import (
+    BaseRagasLLM,
+    LangchainLLMWrapper,
+    LlamaIndexLLMWrapper,
+    llm_factory,
+)
 
 __all__ = [
     "BaseRagasLLM",
     "LangchainLLMWrapper",
+    "LlamaIndexLLMWrapper",
     "llm_factory",
 ]

src/ragas/llms/base.py

@@ -10,7 +10,7 @@
 from langchain_community.chat_models.vertexai import ChatVertexAI
 from langchain_community.llms import VertexAI
 from langchain_core.language_models import BaseLanguageModel
-from langchain_core.outputs import LLMResult
+from langchain_core.outputs import Generation, LLMResult
 from langchain_openai.chat_models import AzureChatOpenAI, ChatOpenAI
 from langchain_openai.llms import AzureOpenAI, OpenAI
 from langchain_openai.llms.base import BaseOpenAI
@@ -19,6 +19,7 @@
 
 if t.TYPE_CHECKING:
     from langchain_core.callbacks import Callbacks
+    from llama_index.core.base.llms.base import BaseLLM
 
     from ragas.llms.prompt import PromptValue
 
@@ -203,6 +204,78 @@ def set_run_config(self, run_config: RunConfig):
             self.run_config.exception_types = RateLimitError
 
 
+class LlamaIndexLLMWrapper(BaseRagasLLM):
+    """
+    A Adaptor for LlamaIndex LLMs
+    """
+
+    def __init__(
+        self,
+        llm: BaseLLM,
+        run_config: t.Optional[RunConfig] = None,
+    ):
+        self.llm = llm
+
+        self._signature = ""
+        if type(self.llm).__name__.lower() == "bedrock":
+            self._signature = "bedrock"
+        if run_config is None:
+            run_config = RunConfig()
+        self.set_run_config(run_config)
+
+    def check_args(
+        self,
+        n: int,
+        temperature: float,
+        stop: t.Optional[t.List[str]],
+        callbacks: Callbacks,
+    ) -> dict[str, t.Any]:
+        if n != 1:
+            logger.warning("n values greater than 1 not support for LlamaIndex LLMs")
+        if temperature != 1e-8:
+            logger.info("temperature kwarg passed to LlamaIndex LLM")
+        if stop is not None:
+            logger.info("stop kwarg passed to LlamaIndex LLM")
+        if callbacks is not None:
+            logger.info(
+                "callbacks not supported for LlamaIndex LLMs, ignoring callbacks"
+            )
+        if self._signature == "bedrock":
+            return {"temperature": temperature}
+        else:
+            return {
+                "n": n,
+                "temperature": temperature,
+                "stop": stop,
+            }
+
+    def generate_text(
+        self,
+        prompt: PromptValue,
+        n: int = 1,
+        temperature: float = 1e-8,
+        stop: t.Optional[t.List[str]] = None,
+        callbacks: Callbacks = None,
+    ) -> LLMResult:
+        kwargs = self.check_args(n, temperature, stop, callbacks)
+        li_response = self.llm.complete(prompt.to_string(), **kwargs)
+
+        return LLMResult(generations=[[Generation(text=li_response.text)]])
+
+    async def agenerate_text(
+        self,
+        prompt: PromptValue,
+        n: int = 1,
+        temperature: float = 1e-8,
+        stop: t.Optional[t.List[str]] = None,
+        callbacks: Callbacks = None,
+    ) -> LLMResult:
+        kwargs = self.check_args(n, temperature, stop, callbacks)
+        li_response = await self.llm.acomplete(prompt.to_string(), **kwargs)
+
+        return LLMResult(generations=[[Generation(text=li_response.text)]])
+
+
 def llm_factory(
     model: str = "gpt-3.5-turbo", run_config: t.Optional[RunConfig] = None
 ) -> BaseRagasLLM:

src/ragas/llms/prompt.py

@@ -44,12 +44,12 @@ class Prompt(BaseModel):
         language (str): The language of the prompt (default: "english").
     """
 
-    name: str
+    name: str = ""
     instruction: str
     output_format_instruction: str = ""
     examples: t.List[Example] = []
-    input_keys: t.List[str]
-    output_key: str
+    input_keys: t.List[str] = [""]
+    output_key: str = ""
     output_type: t.Literal["json", "str"] = "json"
     language: str = "english"
 

src/ragas/metrics/_context_relevancy.py

@@ -60,7 +60,6 @@ def _compute_score(self, response: str, row: t.Dict) -> float:
             if response.lower() != "insufficient information."
             else []
         )
-        # print(len(indices))
         if len(context_sents) == 0:
             return 0
         else:

src/ragas/testset/generator.py

@@ -7,16 +7,18 @@
 
 import pandas as pd
 from datasets import Dataset
-from langchain_core.embeddings import Embeddings
-from langchain_core.language_models import BaseLanguageModel
 from langchain_openai.chat_models import ChatOpenAI
 from langchain_openai.embeddings import OpenAIEmbeddings
 
 from ragas._analytics import TestsetGenerationEvent, track
-from ragas.embeddings.base import BaseRagasEmbeddings, LangchainEmbeddingsWrapper
+from ragas.embeddings.base import (
+    BaseRagasEmbeddings,
+    LangchainEmbeddingsWrapper,
+    LlamaIndexEmbeddingsWrapper,
+)
 from ragas.exceptions import ExceptionInRunner
 from ragas.executor import Executor
-from ragas.llms import BaseRagasLLM, LangchainLLMWrapper
+from ragas.llms import BaseRagasLLM, LangchainLLMWrapper, LlamaIndexLLMWrapper
 from ragas.run_config import RunConfig
 from ragas.testset.docstore import Document, DocumentStore, InMemoryDocumentStore
 from ragas.testset.evolutions import (
@@ -34,6 +36,12 @@
 
 if t.TYPE_CHECKING:
     from langchain_core.documents import Document as LCDocument
+    from langchain_core.embeddings import Embeddings as LangchainEmbeddings
+    from langchain_core.language_models import BaseLanguageModel as LangchainLLM
+    from llama_index.core.base.embeddings.base import (
+        BaseEmbedding as LlamaIndexEmbeddings,
+    )
+    from llama_index.core.base.llms.base import BaseLLM as LlamaindexLLM
     from llama_index.core.schema import Document as LlamaindexDocument
 
 logger = logging.getLogger(__name__)
@@ -75,9 +83,9 @@ class TestsetGenerator:
     @classmethod
     def from_langchain(
         cls,
-        generator_llm: BaseLanguageModel,
-        critic_llm: BaseLanguageModel,
-        embeddings: Embeddings,
+        generator_llm: LangchainLLM,
+        critic_llm: LangchainLLM,
+        embeddings: LangchainEmbeddings,
         docstore: t.Optional[DocumentStore] = None,
         run_config: t.Optional[RunConfig] = None,
         chunk_size: int = 1024,
@@ -104,6 +112,36 @@ def from_langchain(
             docstore=docstore,
         )
 
+    @classmethod
+    def from_llama_index(
+        cls,
+        generator_llm: LlamaindexLLM,
+        critic_llm: LlamaindexLLM,
+        embeddings: LlamaIndexEmbeddings,
+        docstore: t.Optional[DocumentStore] = None,
+        run_config: t.Optional[RunConfig] = None,
+    ) -> "TestsetGenerator":
+        generator_llm_model = LlamaIndexLLMWrapper(generator_llm)
+        critic_llm_model = LlamaIndexLLMWrapper(critic_llm)
+        embeddings_model = LlamaIndexEmbeddingsWrapper(embeddings)
+        keyphrase_extractor = KeyphraseExtractor(llm=generator_llm_model)
+        if docstore is None:
+            from langchain.text_splitter import TokenTextSplitter
+
+            splitter = TokenTextSplitter(chunk_size=1024, chunk_overlap=0)
+            docstore = InMemoryDocumentStore(
+                splitter=splitter,
+                embeddings=embeddings_model,
+                extractor=keyphrase_extractor,
+                run_config=run_config,
+            )
+        return cls(
+            generator_llm=generator_llm_model,
+            critic_llm=critic_llm_model,
+            embeddings=embeddings_model,
+            docstore=docstore,
+        )
+
     @classmethod
     @deprecated("0.1.4", removal="0.2.0", alternative="from_langchain")
     def with_openai(