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@ -1,155 +1,24 @@
================================================
File: docs/guide.md
================================================
---
layout: default
title: "Design Guidance"
parent: "Apps"
nav_order: 1
---
# LLM System Design Guidance
## Example LLM Project File Structure
```
my_project/
├── main.py
├── flow.py
├── utils/
│ ├── __init__.py
│ ├── call_llm.py
│ └── search_web.py
├── tests/
│ ├── __init__.py
│ ├── test_flow.py
│ └── test_nodes.py
├── requirements.txt
└── docs/
└── design.md
```
### `docs/`
Store the documentation of the project.
It should include a `design.md` file, which describes
- Project requirements
- Required utility functions
- High-level flow with a mermaid diagram
- Shared memory data structure
- For each node, discuss
- Node purpose and design (e.g., should it be a batch or async node?)
- How the data shall be read (for `prep`) and written (for `post`)
- How the data shall be processed (for `exec`)
### `utils/`
Houses functions for external API calls (e.g., LLMs, web searches, etc.).
It’s recommended to dedicate one Python file per API call, with names like `call_llm.py` or `search_web.py`. Each file should include:
- The function to call the API
- A main function to run that API call
For instance, here’s a simplified `call_llm.py` example:
```python
from openai import OpenAI
def call_llm(prompt):
client = OpenAI(api_key="YOUR_API_KEY_HERE")
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": prompt}]
)
return response.choices[0].message.content
def main():
prompt = "Hello, how are you?"
print(call_llm(prompt))
if __name__ == "__main__":
main()
```
### `main.py`
Serves as the project’s entry point.
### `flow.py`
Implements the application’s flow, starting with node followed by the flow structure.
### `tests/`
Optionally contains all tests. Use `pytest` for testing flows, nodes, and utility functions.
For example, `test_call_llm.py` might look like:
```python
from utils.call_llm import call_llm
def test_call_llm():
prompt = "Hello, how are you?"
assert call_llm(prompt) is not None
```
## System Design Steps
1. **Project Requirements**
- Identify the project's core entities.
- Define each functional requirement and map out how these entities interact step by step.
2. **Utility Functions**
- Determine the low-level utility functions you’ll need (e.g., for LLM calls, web searches, file handling).
- Implement these functions and write basic tests to confirm they work correctly.
3. **Flow Design**
- Develop a high-level process flow that meets the project’s requirements.
- Specify which utility functions are used at each step.
- Identify possible decision points for *Node Actions* and data-intensive operations for *Batch* tasks.
- Illustrate the flow with a Mermaid diagram.
4. **Data Structure**
- Decide how to store and update state, whether in memory (for smaller applications) or a database (for larger or persistent needs).
- Define data schemas or models that detail how information is stored, accessed, and updated.
5. **Implementation**
- Start coding with a simple, direct approach (avoid over-engineering at first).
- For each node in your flow:
- **prep**: Determine how data is accessed or retrieved.
- **exec**: Outline the actual processing or logic needed.
- **post**: Handle any final updates or data persistence tasks.
6. **Optimization**
- **Prompt Engineering**: Use clear and specific instructions with illustrative examples to reduce ambiguity.
- **Task Decomposition**: Break large, complex tasks into manageable, logical steps.
7. **Reliability**
- **Structured Output**: Verify outputs conform to the required format. Consider increasing `max_retries` if needed.
- **Test Cases**: Develop clear, reproducible tests for each part of the flow.
- **Self-Evaluation**: Introduce an additional Node (powered by LLMs) to review outputs when the results are uncertain.
================================================
File: docs/agent.md
================================================
---
layout: default
title: "Agent"
parent: "Paradigm"
parent: "Design"
nav_order: 6
---
# Agent
For many tasks, we need agents that take dynamic and recursive actions based on the inputs they receive.
You can create these agents as **Nodes** connected by *Actions* in a directed graph using [Flow](./flow.md).
Agent is a powerful design pattern, where node can take dynamic actions based on the context it receives.
To express an agent, create a Node (the agent) with [branching](./flow.md) to other nodes (Actions).
> The core of build **performant** and **reliable** agents boils down to:
>
> 1. **Context Management:** Provide *clear, relevant context* so agents can understand the problem.E.g., Rather than dumping an entire chat history or entire files, use a [Workflow](./decomp.md) that filters out and includes only the most relevant information.
>
> 2. **Action Space:** Define *a well-structured, unambiguous, and easy-to-use* set of actions. For instance, avoid creating overlapping actions like `read_databases` and `read_csvs`. Instead, unify data sources (e.g., move CSVs into a database) and design a single action. The action can be parameterized (e.g., string for search) or programmable (e.g., SQL queries).
{: .best-practice }
### Example: Search Agent
@ -234,8 +103,6 @@ flow = Flow(start=decide)
flow.run({"query": "Who won the Nobel Prize in Physics 2024?"})
```
================================================
File: docs/async.md
================================================
@ -436,10 +303,8 @@ Nodes and Flows **communicate** in two ways:
If you know memory management, think of the **Shared Store** like a **heap** (shared by all function calls), and **Params** like a **stack** (assigned by the caller).
> **Best Practice:** Use `Shared Store` for almost all cases. It's flexible and easy to manage. It separates data storage from data processing, making the code more readable and easier to maintain.
>
> `Params` is more a syntax sugar for [Batch](./batch.md).
{: .note }
> Use `Shared Store` for almost all cases. It's flexible and easy to manage. It separates *Data Schema* from *Compute Logic*, making the code easier to maintain. `Params` is more a syntax sugar for [Batch](./batch.md).
{: .best-practice }
---
@ -551,14 +416,21 @@ File: docs/decomp.md
================================================
---
layout: default
title: "Task Decomposition"
parent: "Paradigm"
title: "Workflow"
parent: "Design"
nav_order: 2
---
# Task Decomposition
# Workflow
Many real-world tasks are too complex for one LLM call. The solution is to decompose them into multiple calls as a [Flow](./flow.md) of Nodes.
Many real-world tasks are too complex for one LLM call. The solution is to decompose them into a [chain](./flow.md) of multiple Nodes.
> - You don't want to make each task **too coarse**, because it may be *too complex for one LLM call*.
> - You don't want to make each task **too granular**, because then *the LLM call doesn't have enough context* and results are *not consistent across nodes*.
>
> You usually need multiple *iterations* to find the *sweet spot*. If the task has too many *edge cases*, consider using [Agents](./agent.md).
{: .best-practice }
### Example: Article Writing
@ -932,6 +804,123 @@ flowchart LR
================================================
File: docs/guide.md
================================================
---
layout: default
title: "Design Guidance"
parent: "Apps"
nav_order: 1
---
# LLM System Design Guidance
## System Design Steps
1. **Project Requirements**
- Identify the project's core entities, and provide a step-by-step user story.
- Define a list of both functional and non-functional requirements.
2. **Utility Functions**
- Determine the utility functions on which this project depends (e.g., for LLM calls, web searches, file handling).
- Implement these functions and write basic tests to confirm they work correctly.
> After this step, don't jump straight into building an LLM system.
>
> First, make sure you clearly understand the problem by manually solving it using some example inputs.
>
> It's always easier to first build a solid intuition about the problem and its solution, then focus on automating the process.
{: .warning }
3. **Flow Design**
- Build a high-level design of the flow of nodes (for example, using a Mermaid diagram) to automate the solution.
- For each node in your flow, specify:
- **prep**: How data is accessed or retrieved.
- **exec**: The specific utility function to use (ideally one function per node).
- **post**: How data is updated or persisted.
- Identify potential design patterns, such as Batch, Agent, or RAG.
4. **Data Structure**
- Decide how you will store and update state (in memory for smaller applications or in a database for larger, persistent needs).
- If it isn’t straightforward, define data schemas or models detailing how information is stored, accessed, and updated.
- As you finalize your data structure, you may need to refine your flow design.
5. **Implementation**
- For each node, implement the **prep**, **exec**, and **post** functions based on the flow design.
- Start coding with a simple, direct approach (avoid over-engineering at first).
- Add logging throughout the code to facilitate debugging.
6. **Optimization**
- **Prompt Engineering**: Use clear, specific instructions with illustrative examples to reduce ambiguity.
- **Task Decomposition**: Break large or complex tasks into manageable, logical steps.
7. **Reliability**
- **Structured Output**: Ensure outputs conform to the required format. Consider increasing `max_retries` if needed.
- **Test Cases**: Develop clear, reproducible tests for each part of the flow.
- **Self-Evaluation**: Introduce an additional node (powered by LLMs) to review outputs when results are uncertain.
## Example LLM Project File Structure
```
my_project/
├── main.py
├── flow.py
├── utils/
│ ├── __init__.py
│ ├── call_llm.py
│ └── search_web.py
├── requirements.txt
└── docs/
└── design.md
```
### `docs/`
Holds all project documentation. Include a `design.md` file covering:
- Project requirements
- Utility functions
- High-level flow (with a Mermaid diagram)
- Shared memory data structure
- Node designs:
- Purpose and design (e.g., batch or async)
- Data read (prep) and write (post)
- Data processing (exec)
### `utils/`
Houses functions for external API calls (e.g., LLMs, web searches, etc.). It’s recommended to dedicate one Python file per API call, with names like `call_llm.py` or `search_web.py`. Each file should include:
- The function to call the API
- A main function to run that API call for testing
For instance, here’s a simplified `call_llm.py` example:
```python
from openai import OpenAI
def call_llm(prompt):
client = OpenAI(api_key="YOUR_API_KEY_HERE")
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": prompt}]
)
return response.choices[0].message.content
if __name__ == "__main__":
prompt = "Hello, how are you?"
print(call_llm(prompt))
```
### `main.py`
Serves as the project’s entry point.
### `flow.py`
Implements the application’s flow, starting with node followed by the flow structure.
================================================
File: docs/index.md
================================================
@ -956,7 +945,7 @@ We model the LLM workflow as a **Nested Directed Graph**:
<div align="center">
<img src="https://github.com/the-pocket/PocketFlow/raw/main/assets/minillmflow.jpg?raw=true" width="400"/>
<img src="https://github.com/the-pocket/PocketFlow/raw/main/assets/meme.jpg?raw=true" width="400"/>
</div>
@ -974,23 +963,21 @@ We model the LLM workflow as a **Nested Directed Graph**:
- [(Advanced) Async](./async.md)
- [(Advanced) Parallel](./parallel.md)
## Low-Level Details
## Utility Functions
- [LLM Wrapper](./llm.md)
- [Tool](./tool.md)
- [Viz and Debug](./viz.md)
- Chunking
> We do not provide built-in implementations.
>
> Example implementations are provided as reference.
> We do not provide built-in utility functions. Example implementations are provided as reference.
{: .warning }
## High-Level Paradigm
## Design Patterns
- [Structured Output](./structure.md)
- [Task Decomposition](./decomp.md)
- [Workflow](./decomp.md)
- [Map Reduce](./mapreduce.md)
- [RAG](./rag.md)
- [Chat Memory](./memory.md)
@ -1012,7 +999,7 @@ File: docs/llm.md
---
layout: default
title: "LLM Wrapper"
parent: "Details"
parent: "Utility"
nav_order: 1
---
@ -1113,13 +1100,19 @@ File: docs/mapreduce.md
---
layout: default
title: "Map Reduce"
parent: "Paradigm"
parent: "Design"
nav_order: 3
---
# Map Reduce
Process large inputs by splitting them into chunks using [BatchNode](./batch.md), then combining results.
MapReduce is a design pattern suitable when you have either:
- Large input data (e.g., multiple files to process), or
- Large output data (e.g., multiple forms to fill)
and there is a logical way to break the task into smaller, ideally independent parts.
You first break down the task using [BatchNode](./batch.md) in the map phase, followed by aggregation in the reduce phase.
### Example: Document Summarization
@ -1151,7 +1144,7 @@ File: docs/memory.md
---
layout: default
title: "Chat Memory"
parent: "Paradigm"
parent: "Design"
nav_order: 5
---
@ -1197,59 +1190,81 @@ We can:
2. Use [vector search](./tool.md) to retrieve relevant exchanges beyond the last 4.
```python
class ChatWithMemory(Node):
################################
# Node A: Retrieve user input & relevant messages
################################
class ChatRetrieve(Node):
def prep(self, s):
# Initialize shared dict
s.setdefault("history", [])
s.setdefault("memory_index", None)
user_input = input("You: ")
# Retrieve relevant past if we have enough history and an index
return user_input
def exec(self, user_input):
emb = get_embedding(user_input)
relevant = []
if len(s["history"]) > 8 and s["memory_index"]:
idx, _ = search_index(s["memory_index"], get_embedding(user_input), top_k=2)
relevant = [s["history"][i[0]] for i in idx]
if len(shared["history"]) > 8 and shared["memory_index"]:
idx, _ = search_index(shared["memory_index"], emb, top_k=2)
relevant = [shared["history"][i[0]] for i in idx]
return (user_input, relevant)
return {"user_input": user_input, "recent": s["history"][-8:], "relevant": relevant}
def post(self, s, p, r):
user_input, relevant = r
s["user_input"] = user_input
s["relevant"] = relevant
return "continue"
def exec(self, c):
messages = [{"role": "system", "content": "You are a helpful assistant."}]
# Include relevant history if any
if c["relevant"]:
messages.append({"role": "system", "content": f"Relevant: {c['relevant']}"})
# Add recent history and the current user input
messages += c["recent"] + [{"role": "user", "content": c["user_input"]}]
return call_llm(messages)
################################
# Node B: Call LLM, update history + index
################################
class ChatReply(Node):
def prep(self, s):
user_input = s["user_input"]
recent = s["history"][-8:]
relevant = s.get("relevant", [])
return user_input, recent, relevant
def exec(self, inputs):
user_input, recent, relevant = inputs
msgs = [{"role":"system","content":"You are a helpful assistant."}]
if relevant:
msgs.append({"role":"system","content":f"Relevant: {relevant}"})
msgs.extend(recent)
msgs.append({"role":"user","content":user_input})
ans = call_llm(msgs)
return ans
def post(self, s, pre, ans):
# Update chat history
s["history"] += [
{"role": "user", "content": pre["user_input"]},
{"role": "assistant", "content": ans}
]
user_input, _, _ = pre
s["history"].append({"role":"user","content":user_input})
s["history"].append({"role":"assistant","content":ans})
# When first reaching 8 messages, create index
# Manage memory index
if len(s["history"]) == 8:
embeddings = []
embs = []
for i in range(0, 8, 2):
e = s["history"][i]["content"] + " " + s["history"][i+1]["content"]
embeddings.append(get_embedding(e))
s["memory_index"] = create_index(embeddings)
# Embed older exchanges once we exceed 8 messages
text = s["history"][i]["content"] + " " + s["history"][i+1]["content"]
embs.append(get_embedding(text))
s["memory_index"] = create_index(embs)
elif len(s["history"]) > 8:
pair = s["history"][-10:-8]
embedding = get_embedding(pair[0]["content"] + " " + pair[1]["content"])
s["memory_index"].add(np.array([embedding]).astype('float32'))
text = s["history"][-2]["content"] + " " + s["history"][-1]["content"]
new_emb = np.array([get_embedding(text)]).astype('float32')
s["memory_index"].add(new_emb)
print(f"Assistant: {ans}")
return "continue"
chat = ChatWithMemory()
chat - "continue" >> chat
flow = Flow(start=chat)
flow.run({})
################################
# Flow wiring
################################
retrieve = ChatRetrieve()
reply = ChatReply()
retrieve - "continue" >> reply
reply - "continue" >> retrieve
flow = Flow(start=retrieve)
shared = {}
flow.run(shared)
```
@ -1259,7 +1274,7 @@ File: docs/multi_agent.md
---
layout: default
title: "(Advanced) Multi-Agents"
parent: "Paradigm"
parent: "Design"
nav_order: 7
---
@ -1268,6 +1283,8 @@ nav_order: 7
Multiple [Agents](./flow.md) can work together by handling subtasks and communicating the progress.
Communication between agents is typically implemented using message queues in shared storage.
> Most of time, you don't need Multi-Agents. Start with a simple solution first.
{: .best-practice }
### Example Agent Communication: Message Queue
@ -1548,18 +1565,6 @@ print("Action returned:", action_result) # "default"
print("Summary stored:", shared["summary"])
```
================================================
File: docs/paradigm.md
================================================
---
layout: default
title: "Paradigm"
nav_order: 4
has_children: true
---
================================================
File: docs/parallel.md
================================================
@ -1577,6 +1582,14 @@ nav_order: 6
> Because of Python’s GIL, parallel nodes and flows can’t truly parallelize CPU-bound tasks (e.g., heavy numerical computations). However, they excel at overlapping I/O-bound work—like LLM calls, database queries, API requests, or file I/O.
{: .warning }
> - **Ensure Tasks Are Independent**: If each item depends on the output of a previous item, **do not** parallelize.
>
> - **Beware of Rate Limits**: Parallel calls can **quickly** trigger rate limits on LLM services. You may need a **throttling** mechanism (e.g., semaphores or sleep intervals).
>
> - **Consider Single-Node Batch APIs**: Some LLMs offer a **batch inference** API where you can send multiple prompts in a single call. This is more complex to implement but can be more efficient than launching many parallel requests and mitigates rate limits.
{: .best-practice }
## AsyncParallelBatchNode
Like **AsyncBatchNode**, but run `exec_async()` in **parallel**:
@ -1613,33 +1626,13 @@ parallel_flow = SummarizeMultipleFiles(start=sub_flow)
await parallel_flow.run_async(shared)
```
## Best Practices
- **Ensure Tasks Are Independent**: If each item depends on the output of a previous item, **do not** parallelize.
- **Beware of Rate Limits**: Parallel calls can **quickly** trigger rate limits on LLM services. You may need a **throttling** mechanism (e.g., semaphores or sleep intervals).
- **Consider Single-Node Batch APIs**: Some LLMs offer a **batch inference** API where you can send multiple prompts in a single call. This is more complex to implement but can be more efficient than launching many parallel requests and mitigates rate limits.
================================================
File: docs/preparation.md
================================================
---
layout: default
title: "Details"
nav_order: 3
has_children: true
---
================================================
File: docs/rag.md
================================================
---
layout: default
title: "RAG"
parent: "Paradigm"
parent: "Design"
nav_order: 4
---
@ -1653,34 +1646,44 @@ Use [vector search](./tool.md) to find relevant context for LLM responses.
```python
class PrepareEmbeddings(Node):
def prep(self, shared):
texts = shared["texts"]
embeddings = [get_embedding(text) for text in texts]
shared["search_index"] = create_index(embeddings)
return shared["texts"]
def exec(self, texts):
# Embed each text chunk
embs = [get_embedding(t) for t in texts]
return embs
def post(self, shared, prep_res, exec_res):
shared["search_index"] = create_index(exec_res)
# no action string means "default"
class AnswerQuestion(Node):
def prep(self, shared):
question = input("Enter question: ")
query_embedding = get_embedding(question)
indices, _ = search_index(shared["search_index"], query_embedding, top_k=1)
relevant_text = shared["texts"][indices[0][0]]
return question, relevant_text
return question
def exec(self, inputs):
question, context = inputs
prompt = f"Question: {question}\nContext: {context}\nAnswer: "
def exec(self, question):
q_emb = get_embedding(question)
idx, _ = search_index(shared["search_index"], q_emb, top_k=1)
best_id = idx[0][0]
relevant_text = shared["texts"][best_id]
prompt = f"Question: {question}\nContext: {relevant_text}\nAnswer:"
return call_llm(prompt)
def post(self, shared, prep_res, exec_res):
print(f"Answer: {exec_res}")
def post(self, shared, p, answer):
print("Answer:", answer)
# Connect nodes
############################################
# Wire up the flow
prep = PrepareEmbeddings()
qa = AnswerQuestion()
prep >> qa
# Create flow
qa_flow = Flow(start=prep)
qa_flow.run(shared)
flow = Flow(start=prep)
# Example usage
shared = {"texts": ["I love apples", "Cats are great", "The sky is blue"]}
flow.run(shared)
```
================================================
@ -1689,7 +1692,7 @@ File: docs/structure.md
---
layout: default
title: "Structured Output"
parent: "Paradigm"
parent: "Design"
nav_order: 1
---
@ -1771,6 +1774,9 @@ summary:
return structured_result
```
> Besides using `assert` statements, another popular way to validate schemas is [Pydantic](https://github.com/pydantic/pydantic)
{: .note }
### Why YAML instead of JSON?
Current LLMs struggle with escaping. YAML is easier with strings since they don't always need quotes.
@ -1804,7 +1810,7 @@ File: docs/tool.md
---
layout: default
title: "Tool"
parent: "Details"
parent: "Utility"
nav_order: 2
---
@ -1814,7 +1820,6 @@ Similar to LLM wrappers, we **don't** provide built-in tools. Here, we recommend
---
## 1. Embedding Calls
```python
@ -2025,7 +2030,7 @@ File: docs/viz.md
---
layout: default
title: "Viz and Debug"
parent: "Details"
parent: "Utility"
nav_order: 3
---
@ -2162,4 +2167,3 @@ data_science_flow.run({})
```
The output would be: `Call stack: ['EvaluateModelNode', 'ModelFlow', 'DataScienceFlow']`

2
README.md
View File

@ -36,7 +36,7 @@ For a new development paradigmn: **Build LLM Apps by Chatting with LLM agents, N
- **For quick questions**: Use the [GPT assistant](https://chatgpt.com/g/g-677464af36588191b9eba4901946557b-pocket-flow-assistant) (note: it uses older models not ideal for coding).
- **For one-time LLM task**: Create a [ChatGPT](https://help.openai.com/en/articles/10169521-using-projects-in-chatgpt) or [Claude](https://www.anthropic.com/news/projects) project; upload the [docs](docs) to project knowledge.
- **For LLM App development**: Use [Cursor AI](https://www.cursor.com/). Copy [.cursorrules](assets/.cursorrules) to your project root as **[Cursor Rules](https://docs.cursor.com/context/rules-for-ai)**.
- **For LLM App development**: Use [Cursor AI](https://www.cursor.com/). Copy [.cursorrules](.cursorrules) to your project root as **[Cursor Rules](https://docs.cursor.com/context/rules-for-ai)**.
</details>