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Built a Document question answering chatbot form National Health Services London. NHS London is the controller of all the NHS agencies across Europe. The chatbot is used to fetch information from different documents (PDF), Website or even from database.
The process starts by extracting text content from documents. This involves parsing the documents to convert them into a text format that can be processed further.
The extracted text is then divided into smaller, manageable pieces or "chunks" to facilitate more accurate embedding and search.
Each chunk of text is converted into embedding vectors. These vectors represent the semantic meaning of the text, allowing for more effective search and matching.
Users interact with the chatbot by inputting their questions. The system captures these questions and processes them.
The user’s question is converted into an embedding vector using LangChain. This step ensures that the system understands the semantic context of the query.
The query embedding is compared against the indexed vectors stored in Pinecone. This semantic search identifies the most relevant chunks of text that match the user’s query
The matched results are ranked based on relevance. The LangChain agent facilitates this ranking process by evaluating the context and significance of each result.
For generating the final response, the LangChain agent integrates with Generative AI models and external Data APIs. This integration ensures that the response is not only accurate but also enriched with the latest information and context.
The embedding vectors are indexed and stored in Pinecone, a vector database. Pinecone enables fast and scalable similarity searches, which is crucial for real-time information retrieval.
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