configure project and remove analytics

README.md

@@ -91,9 +91,3 @@ Refer to our [documentation](https://docs.ragas.io/) to learn more.
 ## 🫂 Community
 
 If you want to get more involved with Ragas, check out our [discord server](https://discord.gg/5qGUJ6mh7C). It's a fun community where we geek out about LLM, Retrieval, Production issues, and more.
-
-## 🔍 Open Analytics
-
-We track very basic usage metrics to guide us to figure out what our users want, what is working, and what's not. As a young startup, we have to be brutally honest about this which is why we are tracking these metrics. But as an Open Startup, we open-source all the data we collect. You can read more about this [here](https://github.com/explodinggradients/ragas/issues/49). **Ragas does not track any information that can be used to identify you or your company**. You can take a look at exactly what we track in the [code](./src/ragas/_analytics.py)
-
-To disable usage-tracking you set the `RAGAS_DO_NOT_TRACK` flag to true.

pyproject.toml

@@ -1,26 +1,25 @@
 [project]
-name = "ragas"
+name = "gigaragas"
 dependencies = [
     "numpy",
     "datasets",
     "tiktoken",
-    "langchain",
-    "langchain-core",
-    "langchain-community",
-    "langchain_openai",
-    "nest-asyncio",
-    "appdirs",
-    "pydantic>=2",
+    "gigachain<0.3",
+    "gigachain-core<0.3",
+    "gigachain-community",
+    "gigachain-openai",
     "openai>1",
     "pysbd>=0.3.4",
+    "nest-asyncio",
+    "appdirs",
+    "nltk",
 ]
 dynamic = ["version", "readme"]
 
 [project.optional-dependencies]
 all = [
     "sentence-transformers",
     "transformers",
-    "nltk",
     "rouge_score",
     "rapidfuzz",
     "pandas",

src/ragas/evaluation.py

@@ -9,7 +9,7 @@
 from langchain_core.embeddings import Embeddings as LangchainEmbeddings
 from langchain_core.language_models import BaseLanguageModel as LangchainLLM
 
-from ragas._analytics import EvaluationEvent, track, track_was_completed
+# from ragas._analytics import EvaluationEvent, track, track_was_completed
 from ragas.callbacks import new_group
 from ragas.cost import TokenUsage
 from ragas.dataset_schema import EvaluationDataset, MultiTurnSample, SingleTurnSample
@@ -54,7 +54,6 @@
 RAGAS_EVALUATION_CHAIN_NAME = "ragas evaluation"
 
 
-@track_was_completed
 def evaluate(
     dataset: t.Union[Dataset, EvaluationDataset],
     metrics: list[Metric] | None = None,
@@ -347,20 +346,6 @@ def evaluate(
         for i in reproducable_metrics:
             metrics[i].reproducibility = 1  # type: ignore
 
-    # log the evaluation event
-    metrics_names = [m.name for m in metrics]
-    metric_lang = [get_feature_language(m) for m in metrics]
-    metric_lang = np.unique([m for m in metric_lang if m is not None])
-    track(
-        EvaluationEvent(
-            event_type="evaluation",
-            metrics=metrics_names,
-            evaluation_mode="",
-            num_rows=len(dataset),
-            language=metric_lang[0] if len(metric_lang) > 0 else "",
-            in_ci=in_ci,
-        )
-    )
     return result