document querying

[Juhaina]

I was querying multiple PDFs using Gemini-1.5-Pro within ConversationalRetrievalChain, but the accuracy is very poor. Is there any alternative solution or approach to improve accuracy?

[ruthseki]

Hi @Juhaina,

Welcome to Google Cloud Community!

Directly querying multiple PDFs with Gemini-1.5-Pro within ConversationalRetrievalChain can be challenging and often leads to inaccurate results. 

Here are some of the challenges with direct PDF querying:

• Structure and Formatting: PDFs are complex documents with varying structures and formatting. Gemini might struggle to understand the context and relationships between different sections, tables, and figures.
• Text Extraction Errors: PDF parsing can be error-prone, leading to inaccurate text extraction. This can result in misinterpretations and irrelevant responses.
• Document Length: Long PDFs can overwhelm Gemini's capacity to process and retain information effectively.
• Contextual Understanding: Gemini might fail to connect the query to the appropriate context within the document, leading to irrelevant or incomplete answers.

And here are some alternative solutions and approaches to improve accuracy:

1. Pre-Processing and Structuring:

• PDF to Text Conversion: Convert PDFs to plain text using libraries. This removes formatting and facilitates text analysis.
• Chunking: Split large PDFs into smaller chunks to improve processing speed and reduce the cognitive load on Gemini.
• Structured Extraction: Use a PDF parser or OCR (Optical Character Recognition) to extract key information, such as headings, tables, and lists, and store them in a structured format (e.g., JSON).

2. Semantic Embedding and Similarity Search:

• Embed Documents: Use a language model like Gemini to generate vector representations (embeddings) of each document chunk or extracted information.
• Embed Queries: Embed the user's query into a vector space.
• Similarity Search: Calculate the cosine similarity between the query embedding and document embeddings. This identifies the most relevant document chunks.

3. Fine-tuning and Specialization:

• Fine-tune Gemini: Fine-tune a smaller version of Gemini on your specific PDF collection. This helps the model learn domain-specific language and concepts.
• Use Specialized Models: Explore models specifically designed for document understanding.

4. Hybrid Approaches:

• Retrieval + Generation: Combine retrieval-based methods (finding relevant documents) with generative models (like Gemini) to provide a more informative and fluent response.
• Conversational Chain with Structured Data: Integrate structured data extracted from PDFs into your ConversationalRetrievalChain. This allows Gemini to access relevant information directly and avoid relying solely on unstructured text.

Key Considerations:

Data Size: Adjust your approach based on the size and complexity of your PDF collection.

Resource Constraints: Consider the computational resources available for embedding generation and similarity search.

Evaluation: Implement a robust evaluation methodology to assess the accuracy of your solution.

By carefully combining pre-processing, semantic embedding, and fine-tuning techniques, you can significantly improve the accuracy of your PDF querying system with Gemini-1.5-Pro.

I hope the above information is helpful.