Response Synthesizers

Response Synthesizers are advanced nodes in InnoSynth-Forjinn designed to combine, summarize, or otherwise process multiple agent, retriever, or tool outputs into a single, coherent answer. They're critical for multi-document QA, ensemble flows, and workflows requiring aggregation, voting, or higher-level synthesis of many results.

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Why Use Response Synthesizers?

• Multi-Document/Multi-Answer QA: Combine context chunks into one clear answer.
• Voting/Merging: Aggregate multiple agent/tool outputs (e.g., best of N, consensus).
• Dynamic Aggregation: Build workflows that adapt synthesis logic to response content.

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Types of Response Synthesizers

1. Simple Concatenation

• Joins outputs from multiple nodes (e.g., context or results) with separator/prefix
• Used for collating search hits, ensemble model results

2. LLM Summarization

• Passes all results to an LLM with an instruction (“summarize these findings,” “choose the best answer,” etc)
• Useful for producing a human-consumable or just-right-length answer

3. Voting/Judging

• Each path produces a candidate answer; Synthesizer uses another node (VotingAgent, etc.) to select or summarize
• Supports confidence scoring, tie breaking, explainability

4. Custom Logic

• Use custom function node to script your own synthesis logic: e.g., fuzzy matching, stats, or outside data

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Example: Multi-Document QA

1. Start Node → Retriever: Find 5 context docs
2. LLM Node: Each context is sent to LLM for answer generation
3. Response Synthesizer Node: Receives 5 answers, passes all to LLM with instruction:

   "Here are several proposed answers. Please produce the most accurate and concise summary, using only facts present in the answers."
   

4. Output: One unified answer, references all source docs.

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Configuration

• Aggregation Logic: Choose concat, LLM summarization, voting, or custom
• Input Paths: Connect each candidate output
• Prompt/Config: (For LLM summarizer or judge)

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Troubleshooting

• Contradictory/Conflicted Answers: Tune instruction prompt, use more sophisticated summarization.
• LLM hallucinations: Limit summarizer to only aggregate original passages/candidates.

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Best Practices

• Always log candidate/inputs in workflow trace for debugging/audit
• Use with retriever+ensemble flows for highest accuracy
• Annotate with source doc ID for explainability/compliance

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Response Synthesizers turn many moving parts into a single clear action—key for multi-modal, multi-path, and high-stakes flows.