update available extractors with descriptions (tensorlakeai#289)

docs/docs/apis/available_extractors.md

@@ -3,20 +3,56 @@
 Indexify comes with some pre-built extractors that you can download and deploy with your installation. We keep these extractors always updated with new releases of models and our internal code.
 
 ## Embedding Extractors
-* ColBERT
-* E5
-* Hash
-* Jina
-* Sentence Transformers - MiniLML6, MPnet
-* Scibert
+#### [ColBERT](https://github.com/tensorlakeai/indexify-extractors/tree/main/embedding-extractors/colbert)
+This ColBERTv2-based extractor is a Python class that encapsulates the functionality to convert text inputs into vector embeddings using the ColBERTv2 model. It leverages ColBERTv2's transformer-based architecture to generate context-aware embeddings suitable for various natural language processing tasks.
+
+
+#### [E5](https://github.com/tensorlakeai/indexify-extractors/tree/main/embedding-extractors/e5_embedding)
+A good small and fast general model for similarity search or downstream enrichments.
+Based on [E5_Small_V2](https://huggingface.co/intfloat/e5-small-v2) which only works for English texts. Long texts will be truncated to at most 512 tokens.
+
+
+#### [Hash](https://github.com/tensorlakeai/indexify-extractors/tree/main/embedding-extractors/hash-embedding)
+This extractor extractors an "identity-"embedding for a piece of text, or file. It uses the sha256 to calculate the unique embeding for a given text, or file. This can be used to quickly search for duplicates within a large set of data.
+
+
+#### [Jina](https://github.com/tensorlakeai/indexify-extractors/tree/main/embedding-extractors/jina_base_en)
+This extractor extractors an embedding for a piece of text.
+It uses the huggingface [Jina model](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) which is an English, monolingual embedding model supporting 8192 sequence length. It is based on a Bert architecture (JinaBert) that supports the symmetric bidirectional variant of ALiBi to allow longer sequence length.
+
+#### [MiniLML6 - Sentence Transformer](https://github.com/tensorlakeai/indexify-extractors/tree/main/embedding-extractors/minilm-l6)
+This extractor extractors an embedding for a piece of text.
+It uses the huggingface [MiniLM-6 model](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2), which is a tiny but very robust emebdding model for text.
+
+#### [MPnet - Sentence Transformer](https://github.com/tensorlakeai/indexify-extractors/tree/main/embedding-extractors/mpnet)
+This is a sentence embedding extractor based on the [MPNET Multilingual Base V2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2).
+This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+It's best use case is paraphrasing, but it can also be used for other tasks.
+
+#### [OpenAI Embedding](https://github.com/tensorlakeai/indexify-extractors/tree/main/embedding-extractors/openai-embedding)
+This extractor extracts an embedding for a piece of text.
+It uses the OpenAI text-embedding-ada-002 model.
+
+#### [SciBERT Uncased](https://github.com/tensorlakeai/indexify-extractors/tree/main/embedding-extractors/scibert)
+This is the pretrained model presented in [SciBERT: A Pretrained Language Model for Scientific Text](https://www.aclweb.org/anthology/D19-1371/), which is a BERT model trained on scientific text.
+Works best with scientific text embedding extraction.
+
 
 ## Audio
-* Whisper
+#### [Whisper](https://github.com/tensorlakeai/indexify-extractors/tree/main/whisper-asr)
+This extractor converts extracts transcriptions from audio. The entire text and
+chunks with timestamps are represented as metadata of the content.
 
 ## Invoice
-* Chord 
-* Donut 
-* Nougat
+#### [Donut CORD](https://github.com/tensorlakeai/indexify-extractors/tree/main/invoice-extractor/donut_cord)
+This extractor parses pdf or image form of invoice which is provided in JSON format. It uses the pre-trained [donut cord fine-tune model from huggingface](https://huggingface.co/naver-clova-ix/donut-base-finetuned-cord-v2).
+This model is specially good at extracting list of product and its price from invoice.
+
+#### [Donut Invoice](https://github.com/tensorlakeai/indexify-extractors/tree/main/invoice-extractor/donut_invoice)
+This extractor parses some invoice-related data from a PDF.
+It uses the pre-trained [donut model from huggingface](https://huggingface.co/docs/transformers/model_doc/donut).
 
 ## Web Extractors
-* Wikipedia
+
+#### [Wikipedia](https://github.com/tensorlakeai/indexify-extractors/tree/main/web-extractors/wikipedia)
+Extract text content from wikipedia html pages.