Update README.md

README.md

@@ -12,12 +12,9 @@ configs:
   - split: train
     path: "**/*.parquet"
 ---
-# Important Notice
-**This dataset is just a sample. The real dataset will be uploaded after New Year's 2024. This early release is intended for Agent Search launching today, but the data is not yet finalized.**
-
 ### Getting Started
 
-The AgentSearch-V1 dataset includes over one billion embeddings sourced from over 50 million high-quality documents. This extensive collection encompasses the majority of content from sources like Arxiv, Wikipedia, Project Gutenberg, and includes quality-filtered CC data.
+The AgentSearch-V1 dataset includes over one billion embeddings sourced from over 50 million high-quality documents. This extensive collection encompasses the majority of content from sources like Arxiv, Wikipedia, Project Gutenberg, and includes quality-filtered CC data. We are actively working to expand the corpus and improve the search experience, if you have any thoughts or suggestions please reach out!
 
 To access and utilize the AgentSearch-V1 dataset, you can stream it via HuggingFace with the following Python code:
 
@@ -27,27 +24,22 @@ from datasets import load_dataset
 ds = load_dataset("SciPhi/AgentSearch-V1", data_files="**/*", streaming=True)
 
 # Optional, stream just the "arxiv" dataset
-ds = load_dataset("SciPhi/AgentSearch-V1", data_files="arxiv/*", streaming=True)
+# ds = load_dataset("SciPhi/AgentSearch-V1", data_files="arxiv/*", streaming=True)
+
+# To process the entries:
+for entry in ds:
+    embeddings = np.frombuffer(
+        entry['embeddings'], dtype=np.float32
+    ).reshape(-1, 768)
+    text_chunks = json.loads(entry['text_chunks'])
+    metadata = json.loads(entry['metadata'])
+    print(f'Embeddings:\n{embeddings}\n\nChunks:\n{text_chunks}\n\nMetadata:\n{metadata}')
+    break
 ```
 
 ---
 
-A full set of scripts to recreate the dataset from scratch can be found [here](https://github.com/SciPhi-AI/agent-search). [Synthesizer](https://github.com/SciPhi-AI/synthesizer) offers direct integration with AgentSearch and top LLM providers.
-
-### Dataset Summary
-
-We take a similar approach to RedPajama-v1 and divide AgentSearch into a number of categories.
-
-
-| Dataset        | Token Count |
-|----------------|-------------|
-| Books          | TBD   |
-| ArXiv          | TBD   |
-| Wikipedia      | TBD   |
-| StackExchange  | TBD   |
-| OpenMath       | TBD   |
-| Filtered Crawl | TBD   |
-| Total          | TBD   |
+A full set of scripts to recreate the dataset from scratch can be found [here](https://github.com/SciPhi-AI/agent-search). Further, you may check the docs for details on how to perform RAG over AgentSearch.
 
 ### Languages
 
@@ -64,15 +56,14 @@ The raw dataset structure is as follows:
     "metadata": {"url": "...", "timestamp": "...", "source": "...", "language": "...", ...},
     "text_chunks": ...,
     "embeddings": ...,
-    "dataset": "github" | "books" | "arxiv" | "wikipedia" | "stackexchange" | "open-math" | "filtered-rp2"
+    "dataset": "book" | "arxiv" | "wikipedia" | "stack-exchange" | "open-math" | "RedPajama-Data-V2"
 }
 ```
 
-The indexed dataset can be downloaded directly and is structured as a qdrant database dump, each entry has meta data {"url", "vector"}. In addition, there is a corresponding sqlite dataset which contains the mapping from urls onto embeddings, text chunks, and other metadata.
 
 ## Dataset Creation
 
-This dataset was created as a step towards making humanities most important knowledge locally searchable and LLM optimal. It was created by filtering, cleaning, and augmenting locally publicly available datasets.
+This dataset was created as a step towards making humanities most important knowledge openly searchable and LLM optimal. It was created by filtering, cleaning, and augmenting locally publicly available datasets.
 
 To cite our work, please use the following: