Hermes Pipeline
bd457c0b6d
Extracted from: https://github.com/pipeshub-ai/pipeshub-ai.git Score: 1.0
111 lines
4.9 KiB
Markdown
111 lines
4.9 KiB
Markdown
---
|
|
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
|