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@@ -6,7 +6,7 @@ It abstracts the enitre process of loading dataset, chunking it, creating embedd
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You can add a single or multiple dataset using `.add` function and then use `.query` function to find an answer from the added datasets.
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-* If you want to create a Naval Ravikant bot which has 1 youtube video, 1 book as pdf and 2 of his blog posts, all you need to do is add the links to the videos, pdf and blog posts and embedchain will create a bot for you.
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+If you want to create a Naval Ravikant bot which has 1 youtube video, 1 book as pdf and 2 of his blog posts, all you need to do is add the links to the videos, pdf and blog posts and embedchain will create a bot for you.
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```python
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@@ -95,7 +95,7 @@ To add any pdf file, use the data_type as `pdf_file`. Eg:
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app.add('pdf_file', 'a_valid_url_where_pdf_file_can_be_accessed')
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```
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-Note that we do not support password protected pdfs as of now.
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+Note that we do not support password protected pdfs.
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### Web Page
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@@ -138,7 +138,7 @@ In the first release, we are making it easier for anyone to get a chatbot over a
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embedchain is built on the following stack:
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-- [langchain](https://github.com/hwchase17/langchain) as an LLM framework to load, chunk and index data
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+- [Langchain](https://github.com/hwchase17/langchain) as an LLM framework to load, chunk and index data
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- [OpenAI's Ada embedding model](https://platform.openai.com/docs/guides/embeddings) to create embeddings
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- [OpenAI's ChatGPT API](https://platform.openai.com/docs/guides/gpt/chat-completions-api) as LLM to get answers given the context
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- [Chroma](https://github.com/chroma-core/chroma) as the vector database to store embeddings
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Taranjeet Singh