Compare commits
1 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Hermes Pipeline
|
e20b5370e1 |
@@ -1,110 +0,0 @@
|
||||
---
|
||||
name: agent-builder-workflow
|
||||
version: 1.0.0
|
||||
description: Build no-code AI agents that connect to enterprise knowledge sources,
|
||||
perform unified search and deep research, and generate explainable answers with
|
||||
citations
|
||||
inputs:
|
||||
- Task description and agent objectives (e.g., answer Q&A, research specific topics,
|
||||
generate reports)
|
||||
- Knowledge sources (documents, databases, enterprise systems) to connect to
|
||||
- Retrieval strategy configuration (graph-based knowledge graph vs. vector search)
|
||||
- Output requirements (citation format, response structure, code execution needs)
|
||||
steps:
|
||||
- 'Step 1: Define agent task and objectives - Specify what the agent should do (e.g.,
|
||||
answer a question, perform deep research on a topic, generate a report with citations)'
|
||||
- 'Step 2: Configure knowledge sources - Connect to enterprise documents, databases,
|
||||
or external systems that will serve as the agent''s context'
|
||||
- 'Step 3: Build LangGraph chain - Create a workflow chain using LangGraph that combines
|
||||
retrieval (graph/vector) and LLM response generation with citation capabilities'
|
||||
- 'Step 4: Execute agent - Run the LangGraph chain to process the task and generate
|
||||
responses with grounded citations'
|
||||
- 'Step 5: (Optional) Code execution sandbox - If the agent needs to generate or execute
|
||||
code, deploy it to a safe sandbox environment for verification'
|
||||
outputs:
|
||||
- Agent execution logs showing retrieval steps and LLM responses
|
||||
- Grounded answers with block citations to source documents
|
||||
- Generated reports or artifacts (if code execution was performed)
|
||||
- Structured task completion status and results
|
||||
tags: []
|
||||
metadata:
|
||||
source_repo: https://github.com/pipeshub-ai/pipeshub-ai.git
|
||||
extracted_at: ''
|
||||
confidence: 0.95
|
||||
---
|
||||
|
||||
# agent-builder-workflow
|
||||
|
||||
Build no-code AI agents that connect to enterprise knowledge sources, perform unified search and deep research, and generate explainable answers with citations
|
||||
|
||||
## Setup
|
||||
|
||||
**Dependencies:**
|
||||
|
||||
```text
|
||||
pip install langchain langgraph serper-dev-tool qdrant-client redis fastapi pydantic
|
||||
```
|
||||
|
||||
**Setup steps:**
|
||||
|
||||
1. Install dependencies: pip install langchain langgraph serper-dev-tool qdrant-client redis fastapi
|
||||
1. Configure knowledge sources in .env (graph DB connection, vector DB, document paths)
|
||||
1. Define agent task in agent_builder.py with objectives and retrieval strategy
|
||||
1. Run agent chain: python agent_chain.py --task "research_quantum_computing"
|
||||
1. For code execution: add sandbox step to agent_chain.py with code generation and safe execution
|
||||
|
||||
## Key Files
|
||||
|
||||
- `pipeshub-ai/backend/agent_chain.py - LangGraph chain definition for agent workflows`
|
||||
- `pipeshub-ai/backend/retrieval_pipeline.py - Knowledge graph and vector search implementation`
|
||||
- `pipeshub-ai/workflows/agent_builder.py - No-code agent creation interface`
|
||||
- `pipeshub-ai/workflows/citation_generator.py - Block citation generation from retrieved sources`
|
||||
|
||||
## Steps
|
||||
|
||||
1. Step 1: Define agent task and objectives - Specify what the agent should do (e.g., answer a question, perform deep research on a topic, generate a report with citations)
|
||||
2. Step 2: Configure knowledge sources - Connect to enterprise documents, databases, or external systems that will serve as the agent's context
|
||||
3. Step 3: Build LangGraph chain - Create a workflow chain using LangGraph that combines retrieval (graph/vector) and LLM response generation with citation capabilities
|
||||
4. Step 4: Execute agent - Run the LangGraph chain to process the task and generate responses with grounded citations
|
||||
5. Step 5: (Optional) Code execution sandbox - If the agent needs to generate or execute code, deploy it to a safe sandbox environment for verification
|
||||
|
||||
## Implementation Details
|
||||
|
||||
```python
|
||||
LangGraph chain with retrieval (graph/vector) and LLM response stages
|
||||
```
|
||||
|
||||
```python
|
||||
Knowledge graph construction from enterprise documents
|
||||
```
|
||||
|
||||
```python
|
||||
Citation formatting using block references to source documents
|
||||
```
|
||||
|
||||
## Inputs
|
||||
|
||||
- Task description and agent objectives (e.g., answer Q&A, research specific topics, generate reports)
|
||||
- Knowledge sources (documents, databases, enterprise systems) to connect to
|
||||
- Retrieval strategy configuration (graph-based knowledge graph vs. vector search)
|
||||
- Output requirements (citation format, response structure, code execution needs)
|
||||
|
||||
## Outputs
|
||||
|
||||
- Agent execution logs showing retrieval steps and LLM responses
|
||||
- Grounded answers with block citations to source documents
|
||||
- Generated reports or artifacts (if code execution was performed)
|
||||
- Structured task completion status and results
|
||||
|
||||
## Failure Modes
|
||||
|
||||
- Insufficient retrieval results due to poor knowledge graph connectivity or vector embedding quality
|
||||
- Permission errors when accessing enterprise knowledge sources
|
||||
- LLM context window overflow when generating long explanations with citations
|
||||
- Sandbox execution failures for code generation or execution tasks
|
||||
- Timeout errors during multi-step agent chain execution
|
||||
|
||||
## Source
|
||||
|
||||
Extracted from: [https://github.com/pipeshub-ai/pipeshub-ai.git](https://github.com/pipeshub-ai/pipeshub-ai.git)
|
||||
Confidence: 0.95
|
||||
@@ -1,6 +0,0 @@
|
||||
# Commands: agent-builder-workflow
|
||||
|
||||
## Available Commands
|
||||
|
||||
- `/skill agent-builder-workflow` — Load this skill
|
||||
- `/run agent-builder-workflow` — Execute workflow
|
||||
@@ -1,10 +0,0 @@
|
||||
# Examples: agent-builder-workflow
|
||||
|
||||
## Usage Example
|
||||
|
||||
```python
|
||||
# How to use this skill
|
||||
# Inputs: Task description and agent objectives (e.g., answer Q&A, research specific topics, generate reports), Knowledge sources (documents, databases, enterprise systems) to connect to, Retrieval strategy configuration (graph-based knowledge graph vs. vector search), Output requirements (citation format, response structure, code execution needs)
|
||||
# Process: Step 1: Define agent task and objectives - Specify what the agent should do (e.g., answer a question, perform deep research on a topic, generate a report with citations) → Step 2: Configure knowledge sources - Connect to enterprise documents, databases, or external systems that will serve as the agent's context → Step 3: Build LangGraph chain - Create a workflow chain using LangGraph that combines retrieval (graph/vector) and LLM response generation with citation capabilities
|
||||
# Outputs: Agent execution logs showing retrieval steps and LLM responses, Grounded answers with block citations to source documents, Generated reports or artifacts (if code execution was performed), Structured task completion status and results
|
||||
```
|
||||
@@ -1,35 +0,0 @@
|
||||
{
|
||||
"name": "agent-builder-workflow",
|
||||
"version": "1.0.0",
|
||||
"goal": "Build no-code AI agents that connect to enterprise knowledge sources, perform unified search and deep research, and generate explainable answers with citations",
|
||||
"inputs": [
|
||||
"Task description and agent objectives (e.g., answer Q&A, research specific topics, generate reports)",
|
||||
"Knowledge sources (documents, databases, enterprise systems) to connect to",
|
||||
"Retrieval strategy configuration (graph-based knowledge graph vs. vector search)",
|
||||
"Output requirements (citation format, response structure, code execution needs)"
|
||||
],
|
||||
"steps": [
|
||||
"Step 1: Define agent task and objectives - Specify what the agent should do (e.g., answer a question, perform deep research on a topic, generate a report with citations)",
|
||||
"Step 2: Configure knowledge sources - Connect to enterprise documents, databases, or external systems that will serve as the agent's context",
|
||||
"Step 3: Build LangGraph chain - Create a workflow chain using LangGraph that combines retrieval (graph/vector) and LLM response generation with citation capabilities",
|
||||
"Step 4: Execute agent - Run the LangGraph chain to process the task and generate responses with grounded citations",
|
||||
"Step 5: (Optional) Code execution sandbox - If the agent needs to generate or execute code, deploy it to a safe sandbox environment for verification"
|
||||
],
|
||||
"outputs": [
|
||||
"Agent execution logs showing retrieval steps and LLM responses",
|
||||
"Grounded answers with block citations to source documents",
|
||||
"Generated reports or artifacts (if code execution was performed)",
|
||||
"Structured task completion status and results"
|
||||
],
|
||||
"failure_modes": [
|
||||
"Insufficient retrieval results due to poor knowledge graph connectivity or vector embedding quality",
|
||||
"Permission errors when accessing enterprise knowledge sources",
|
||||
"LLM context window overflow when generating long explanations with citations",
|
||||
"Sandbox execution failures for code generation or execution tasks",
|
||||
"Timeout errors during multi-step agent chain execution"
|
||||
],
|
||||
"confidence": 0.95,
|
||||
"explanation": "PipesHub provides a reusable agent builder workflow that combines LangGraph orchestration with graph-based and vector-based retrieval. This pattern can be adapted to any enterprise context where AI agents need to search across multiple knowledge sources, generate explainable answers with citations, and optionally execute code in a safe sandbox. The workflow is defined by specific configuration files (LangGraph chain definitions) and follows a standard pattern: task definition \u2192 knowledge source connection \u2192 retrieval strategy \u2192 response generation \u2192 optional code sandbox.",
|
||||
"source_repo": "https://github.com/pipeshub-ai/pipeshub-ai.git",
|
||||
"score": 1.0
|
||||
}
|
||||
@@ -0,0 +1,77 @@
|
||||
---
|
||||
name: literature-review-with-traceable-ai-evidence
|
||||
version: 1.0.0
|
||||
description: Enable researchers to ingest documents, asynchronously index them, and
|
||||
interact with multi-agent AI to answer questions with verifiable citations to original
|
||||
text.
|
||||
inputs:
|
||||
- Documents in PDF, Office, image, or text formats
|
||||
- Research questions or topics of interest
|
||||
- 'Optional: user model configuration via .env or settings'
|
||||
steps:
|
||||
- Upload documents to a project (via desktop app or web UI)
|
||||
- System asynchronously converts Office docs to PDF if needed, runs OCR to extract
|
||||
text with coordinates, chunks and embeds into vector store
|
||||
- User starts a main research session or creates exploration branches without waiting
|
||||
for indexing to finish
|
||||
- Leader agent receives query and delegates subtasks to researcher, reviewer, writer
|
||||
subagents
|
||||
- Subagents perform hybrid retrieval and rerank to find relevant chunks with source
|
||||
coordinates
|
||||
- Agents synthesize answers and return evidence with clickable citations that highlight
|
||||
original pages
|
||||
- User verifies conclusions by navigating to cited source locations and can save notes
|
||||
to research memory or mind map
|
||||
outputs:
|
||||
- AI-generated answers with traceable evidence (coordinates, page highlights)
|
||||
- Research session history with branches
|
||||
- Indexed document library for future queries
|
||||
- Mind maps or structured notes
|
||||
- Persistent run events for resuming sessions
|
||||
tags: []
|
||||
metadata:
|
||||
source_repo: https://github.com/0verL1nk/PaperSage.git
|
||||
extracted_at: ''
|
||||
confidence: 0.85
|
||||
---
|
||||
|
||||
# literature-review-with-traceable-ai-evidence
|
||||
|
||||
Enable researchers to ingest documents, asynchronously index them, and interact with multi-agent AI to answer questions with verifiable citations to original text.
|
||||
|
||||
## Steps
|
||||
|
||||
1. Upload documents to a project (via desktop app or web UI)
|
||||
2. System asynchronously converts Office docs to PDF if needed, runs OCR to extract text with coordinates, chunks and embeds into vector store
|
||||
3. User starts a main research session or creates exploration branches without waiting for indexing to finish
|
||||
4. Leader agent receives query and delegates subtasks to researcher, reviewer, writer subagents
|
||||
5. Subagents perform hybrid retrieval and rerank to find relevant chunks with source coordinates
|
||||
6. Agents synthesize answers and return evidence with clickable citations that highlight original pages
|
||||
7. User verifies conclusions by navigating to cited source locations and can save notes to research memory or mind map
|
||||
|
||||
## Inputs
|
||||
|
||||
- Documents in PDF, Office, image, or text formats
|
||||
- Research questions or topics of interest
|
||||
- Optional: user model configuration via .env or settings
|
||||
|
||||
## Outputs
|
||||
|
||||
- AI-generated answers with traceable evidence (coordinates, page highlights)
|
||||
- Research session history with branches
|
||||
- Indexed document library for future queries
|
||||
- Mind maps or structured notes
|
||||
- Persistent run events for resuming sessions
|
||||
|
||||
## Failure Modes
|
||||
|
||||
- Missing local Office/LibreOffice converter causes document conversion failure
|
||||
- First-time model download may be slow or require network
|
||||
- OCR may have low confidence on poor quality scans
|
||||
- Retrieval might miss context if chunking splits semantics
|
||||
- Multi-agent coordination could produce conflicting intermediate results
|
||||
|
||||
## Source
|
||||
|
||||
Extracted from: [https://github.com/0verL1nk/PaperSage.git](https://github.com/0verL1nk/PaperSage.git)
|
||||
Confidence: 0.85
|
||||
@@ -0,0 +1,6 @@
|
||||
# Commands: literature-review-with-traceable-ai-evidence
|
||||
|
||||
## Available Commands
|
||||
|
||||
- `/skill literature-review-with-traceable-ai-evidence` — Load this skill
|
||||
- `/run literature-review-with-traceable-ai-evidence` — Execute workflow
|
||||
@@ -0,0 +1,10 @@
|
||||
# Examples: literature-review-with-traceable-ai-evidence
|
||||
|
||||
## Usage Example
|
||||
|
||||
```python
|
||||
# How to use this skill
|
||||
# Inputs: Documents in PDF, Office, image, or text formats, Research questions or topics of interest, Optional: user model configuration via .env or settings
|
||||
# Process: Upload documents to a project (via desktop app or web UI) → System asynchronously converts Office docs to PDF if needed, runs OCR to extract text with coordinates, chunks and embeds into vector store → User starts a main research session or creates exploration branches without waiting for indexing to finish
|
||||
# Outputs: AI-generated answers with traceable evidence (coordinates, page highlights), Research session history with branches, Indexed document library for future queries, Mind maps or structured notes, Persistent run events for resuming sessions
|
||||
```
|
||||
@@ -0,0 +1,37 @@
|
||||
{
|
||||
"name": "literature-review-with-traceable-ai-evidence",
|
||||
"version": "1.0.0",
|
||||
"goal": "Enable researchers to ingest documents, asynchronously index them, and interact with multi-agent AI to answer questions with verifiable citations to original text.",
|
||||
"inputs": [
|
||||
"Documents in PDF, Office, image, or text formats",
|
||||
"Research questions or topics of interest",
|
||||
"Optional: user model configuration via .env or settings"
|
||||
],
|
||||
"steps": [
|
||||
"Upload documents to a project (via desktop app or web UI)",
|
||||
"System asynchronously converts Office docs to PDF if needed, runs OCR to extract text with coordinates, chunks and embeds into vector store",
|
||||
"User starts a main research session or creates exploration branches without waiting for indexing to finish",
|
||||
"Leader agent receives query and delegates subtasks to researcher, reviewer, writer subagents",
|
||||
"Subagents perform hybrid retrieval and rerank to find relevant chunks with source coordinates",
|
||||
"Agents synthesize answers and return evidence with clickable citations that highlight original pages",
|
||||
"User verifies conclusions by navigating to cited source locations and can save notes to research memory or mind map"
|
||||
],
|
||||
"outputs": [
|
||||
"AI-generated answers with traceable evidence (coordinates, page highlights)",
|
||||
"Research session history with branches",
|
||||
"Indexed document library for future queries",
|
||||
"Mind maps or structured notes",
|
||||
"Persistent run events for resuming sessions"
|
||||
],
|
||||
"failure_modes": [
|
||||
"Missing local Office/LibreOffice converter causes document conversion failure",
|
||||
"First-time model download may be slow or require network",
|
||||
"OCR may have low confidence on poor quality scans",
|
||||
"Retrieval might miss context if chunking splits semantics",
|
||||
"Multi-agent coordination could produce conflicting intermediate results"
|
||||
],
|
||||
"confidence": 0.85,
|
||||
"explanation": "The README describes PaperSage's core workflow: asynchronous document ingestion with OCR/indexing, followed by multi-agent question answering with cited evidence. This process is not tied to the specific codebase and can be reused as a general literature review methodology for any document-centric research using RAG and agent collaboration.",
|
||||
"source_repo": "https://github.com/0verL1nk/PaperSage.git",
|
||||
"score": 1.0
|
||||
}
|
||||
+1
-1
@@ -1,4 +1,4 @@
|
||||
# Tests: agent-builder-workflow
|
||||
# Tests: literature-review-with-traceable-ai-evidence
|
||||
|
||||
## Test Checklist
|
||||
|
||||
Reference in New Issue
Block a user