代码拉取完成，页面将自动刷新
---
layout: default
title: "Agentic Coding"
---
# Agentic Coding: Humans Design, Agents code!
> If you are an AI agent involved in building LLM Systems, read this guide **VERY, VERY** carefully! This is the most important chapter in the entire document. Throughout development, you should always (1) start with a small and simple solution, (2) design at a high level (`docs/design.md`) before implementation, and (3) frequently ask humans for feedback and clarification.
{: .warning }
## Agentic Coding Steps
Agentic Coding should be a collaboration between Human System Design and Agent Implementation:
| Steps | Human | AI | Comment |
|:-----------------------|:----------:|:---------:|:------------------------------------------------------------------------|
| 1. Requirements | ★★★ High | ★☆☆ Low | Humans understand the requirements and context. |
| 2. Flow | ★★☆ Medium | ★★☆ Medium | Humans specify the high-level design, and the AI fills in the details. |
| 3. Utilities | ★★☆ Medium | ★★☆ Medium | Humans provide available external APIs and integrations, and the AI helps with implementation. |
| 4. Node | ★☆☆ Low | ★★★ High | The AI helps design the node types and data handling based on the flow. |
| 5. Implementation | ★☆☆ Low | ★★★ High | The AI implements the flow based on the design. |
| 6. Optimization | ★★☆ Medium | ★★☆ Medium | Humans evaluate the results, and the AI helps optimize. |
| 7. Reliability | ★☆☆ Low | ★★★ High | The AI writes test cases and addresses corner cases. |
1. **Requirements**: Clarify the requirements for your project, and evaluate whether an AI system is a good fit.
- Understand AI systems' strengths and limitations:
- **Good for**: Routine tasks requiring common sense (filling forms, replying to emails)
- **Good for**: Creative tasks with well-defined inputs (building slides, writing SQL)
- **Not good for**: Ambiguous problems requiring complex decision-making (business strategy, startup planning)
- **Keep It User-Centric:** Explain the "problem" from the user's perspective rather than just listing features.
- **Balance complexity vs. impact**: Aim to deliver the highest value features with minimal complexity early.
2. **Flow Design**: Outline at a high level, describe how your AI system orchestrates nodes.
- Identify applicable design patterns (e.g., [Map Reduce](./design_pattern/mapreduce.md), [Agent](./design_pattern/agent.md), [RAG](./design_pattern/rag.md)).
- For each node in the flow, start with a high-level one-line description of what it does.
- If using **Map Reduce**, specify how to map (what to split) and how to reduce (how to combine).
- If using **Agent**, specify what are the inputs (context) and what are the possible actions.
- If using **RAG**, specify what to embed, noting that there's usually both offline (indexing) and online (retrieval) workflows.
- Outline the flow and draw it in a mermaid diagram. For example:
```mermaid
flowchart LR
start[Start] --> batch[Batch]
batch --> check[Check]
check -->|OK| process
check -->|Error| fix[Fix]
fix --> check
subgraph process[Process]
step1[Step 1] --> step2[Step 2]
end
process --> endNode[End]
```
- > **If Humans can't specify the flow, AI Agents can't automate it!** Before building an LLM system, thoroughly understand the problem and potential solution by manually solving example inputs to develop intuition.
{: .best-practice }
3. **Utilities**: Based on the Flow Design, identify and implement necessary utility functions.
- Think of your AI system as the brain. It needs a body—these *external utility functions*—to interact with the real world:
<div align="center"><img src="https://github.com/the-pocket/.github/raw/main/assets/utility.png?raw=true" width="400"/></div>
- Reading inputs (e.g., retrieving Slack messages, reading emails)
- Writing outputs (e.g., generating reports, sending emails)
- Using external tools (e.g., calling LLMs, searching the web)
- **NOTE**: *LLM-based tasks* (e.g., summarizing text, analyzing sentiment) are **NOT** utility functions; rather, they are *core functions* internal in the AI system.
- For each utility function, implement it and write a simple test.
- Document their input/output, as well as why they are necessary. For example:
- `name`: `get_embedding` (`utils/get_embedding.py`)
- `input`: `str`
- `output`: a vector of 3072 floats
- `necessity`: Used by the second node to embed text
- Example utility implementation:
```python
# utils/call_llm.py
from openai import OpenAI
def call_llm(prompt):
client = OpenAI(api_key="YOUR_API_KEY_HERE")
r = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": prompt}]
)
return r.choices[0].message.content
if __name__ == "__main__":
prompt = "What is the meaning of life?"
print(call_llm(prompt))
```
- > **Sometimes, design Utilies before Flow:** For example, for an LLM project to automate a legacy system, the bottleneck will likely be the available interface to that system. Start by designing the hardest utilities for interfacing, and then build the flow around them.
{: .best-practice }
4. **Node Design**: Plan how each node will read and write data, and use utility functions.
- One core design principle for PocketFlow is to use a [shared store](./core_abstraction/communication.md), so start with a shared store design:
- For simple systems, use an in-memory dictionary.
- For more complex systems or when persistence is required, use a database.
- **Don't Repeat Yourself**: Use in-memory references or foreign keys.
- Example shared store design:
```python
shared = {
"user": {
"id": "user123",
"context": { # Another nested dict
"weather": {"temp": 72, "condition": "sunny"},
"location": "San Francisco"
}
},
"results": {} # Empty dict to store outputs
}
```
- For each [Node](./core_abstraction/node.md), describe its type, how it reads and writes data, and which utility function it uses. Keep it specific but high-level without codes. For example:
- `type`: Regular (or Batch, or Async)
- `prep`: Read "text" from the shared store
- `exec`: Call the embedding utility function
- `post`: Write "embedding" to the shared store
5. **Implementation**: Implement the initial nodes and flows based on the design.
- 🎉 If you've reached this step, humans have finished the design. Now *Agentic Coding* begins!
- **"Keep it simple, stupid!"** Avoid complex features and full-scale type checking.
- **FAIL FAST**! Avoid `try` logic so you can quickly identify any weak points in the system.
- Add logging throughout the code to facilitate debugging.
7. **Optimization**:
- **Use Intuition**: For a quick initial evaluation, human intuition is often a good start.
- **Redesign Flow (Back to Step 3)**: Consider breaking down tasks further, introducing agentic decisions, or better managing input contexts.
- If your flow design is already solid, move on to micro-optimizations:
- **Prompt Engineering**: Use clear, specific instructions with examples to reduce ambiguity.
- **In-Context Learning**: Provide robust examples for tasks that are difficult to specify with instructions alone.
- > **You'll likely iterate a lot!** Expect to repeat Steps 3–6 hundreds of times.
>
> <div align="center"><img src="https://github.com/the-pocket/.github/raw/main/assets/success.png?raw=true" width="400"/></div>
{: .best-practice }
8. **Reliability**
- **Node Retries**: Add checks in the node `exec` to ensure outputs meet requirements, and consider increasing `max_retries` and `wait` times.
- **Logging and Visualization**: Maintain logs of all attempts and visualize node results for easier debugging.
- **Self-Evaluation**: Add a separate node (powered by an LLM) to review outputs when results are uncertain.
## Example LLM Project File Structure
```
my_project/
├── main.py
├── nodes.py
├── flow.py
├── utils/
│ ├── __init__.py
│ ├── call_llm.py
│ └── search_web.py
├── requirements.txt
└── docs/
└── design.md
```
- **`docs/design.md`**: Contains project documentation for each step above. This should be *high-level* and *no-code*.
- **`utils/`**: Contains all utility functions.
- It's recommended to dedicate one Python file to each API call, for example `call_llm.py` or `search_web.py`.
- Each file should also include a `main()` function to try that API call
- **`nodes.py`**: Contains all the node definitions.
```python
# nodes.py
from pocketflow import Node
from utils.call_llm import call_llm
class GetQuestionNode(Node):
def exec(self, _):
# Get question directly from user input
user_question = input("Enter your question: ")
return user_question
def post(self, shared, prep_res, exec_res):
# Store the user's question
shared["question"] = exec_res
return "default" # Go to the next node
class AnswerNode(Node):
def prep(self, shared):
# Read question from shared
return shared["question"]
def exec(self, question):
# Call LLM to get the answer
return call_llm(question)
def post(self, shared, prep_res, exec_res):
# Store the answer in shared
shared["answer"] = exec_res
```
- **`flow.py`**: Implements functions that create flows by importing node definitions and connecting them.
```python
# flow.py
from pocketflow import Flow
from nodes import GetQuestionNode, AnswerNode
def create_qa_flow():
"""Create and return a question-answering flow."""
# Create nodes
get_question_node = GetQuestionNode()
answer_node = AnswerNode()
# Connect nodes in sequence
get_question_node >> answer_node
# Create flow starting with input node
return Flow(start=get_question_node)
```
- **`main.py`**: Serves as the project's entry point.
```python
# main.py
from flow import create_qa_flow
# Example main function
# Please replace this with your own main function
def main():
shared = {
"question": None, # Will be populated by GetQuestionNode from user input
"answer": None # Will be populated by AnswerNode
}
# Create the flow and run it
qa_flow = create_qa_flow()
qa_flow.run(shared)
print(f"Question: {shared['question']}")
print(f"Answer: {shared['answer']}")
if __name__ == "__main__":
main()
```
================================================
File: docs/index.md
================================================
---
layout: default
title: "Home"
nav_order: 1
---
# Pocket Flow
A [100-line](https://github.com/the-pocket/PocketFlow/blob/main/pocketflow/__init__.py) minimalist LLM framework for *Agents, Task Decomposition, RAG, etc*.
- **Lightweight**: Just the core graph abstraction in 100 lines. ZERO dependencies, and vendor lock-in.
- **Expressive**: Everything you love from larger frameworks—([Multi-](./design_pattern/multi_agent.html))[Agents](./design_pattern/agent.html), [Workflow](./design_pattern/workflow.html), [RAG](./design_pattern/rag.html), and more.
- **Agentic-Coding**: Intuitive enough for AI agents to help humans build complex LLM applications.
<div align="center">
<img src="https://github.com/the-pocket/.github/raw/main/assets/meme.jpg?raw=true" width="400"/>
</div>
## Core Abstraction
We model the LLM workflow as a **Graph + Shared Store**:
- [Node](./core_abstraction/node.md) handles simple (LLM) tasks.
- [Flow](./core_abstraction/flow.md) connects nodes through **Actions** (labeled edges).
- [Shared Store](./core_abstraction/communication.md) enables communication between nodes within flows.
- [Batch](./core_abstraction/batch.md) nodes/flows allow for data-intensive tasks.
- [Async](./core_abstraction/async.md) nodes/flows allow waiting for asynchronous tasks.
- [(Advanced) Parallel](./core_abstraction/parallel.md) nodes/flows handle I/O-bound tasks.
<div align="center">
<img src="https://github.com/the-pocket/.github/raw/main/assets/abstraction.png" width="500"/>
</div>
## Design Pattern
From there, it’s easy to implement popular design patterns:
- [Agent](./design_pattern/agent.md) autonomously makes decisions.
- [Workflow](./design_pattern/workflow.md) chains multiple tasks into pipelines.
- [RAG](./design_pattern/rag.md) integrates data retrieval with generation.
- [Map Reduce](./design_pattern/mapreduce.md) splits data tasks into Map and Reduce steps.
- [Structured Output](./design_pattern/structure.md) formats outputs consistently.
- [(Advanced) Multi-Agents](./design_pattern/multi_agent.md) coordinate multiple agents.
<div align="center">
<img src="https://github.com/the-pocket/.github/raw/main/assets/design.png" width="500"/>
</div>
## Utility Function
We **do not** provide built-in utilities. Instead, we offer *examples*—please *implement your own*:
- [LLM Wrapper](./utility_function/llm.md)
- [Viz and Debug](./utility_function/viz.md)
- [Web Search](./utility_function/websearch.md)
- [Chunking](./utility_function/chunking.md)
- [Embedding](./utility_function/embedding.md)
- [Vector Databases](./utility_function/vector.md)
- [Text-to-Speech](./utility_function/text_to_speech.md)
**Why not built-in?**: I believe it's a *bad practice* for vendor-specific APIs in a general framework:
- *API Volatility*: Frequent changes lead to heavy maintenance for hardcoded APIs.
- *Flexibility*: You may want to switch vendors, use fine-tuned models, or run them locally.
- *Optimizations*: Prompt caching, batching, and streaming are easier without vendor lock-in.
## Ready to build your Apps?
Check out [Agentic Coding Guidance](./guide.md), the fastest way to develop LLM projects with Pocket Flow!
================================================
File: docs/core_abstraction/async.md
================================================
---
layout: default
title: "(Advanced) Async"
parent: "Core Abstraction"
nav_order: 5
---
# (Advanced) Async
**Async** Nodes implement `prep_async()`, `exec_async()`, `exec_fallback_async()`, and/or `post_async()`. This is useful for:
1. **prep_async()**: For *fetching/reading data (files, APIs, DB)* in an I/O-friendly way.
2. **exec_async()**: Typically used for async LLM calls.
3. **post_async()**: For *awaiting user feedback*, *coordinating across multi-agents* or any additional async steps after `exec_async()`.
**Note**: `AsyncNode` must be wrapped in `AsyncFlow`. `AsyncFlow` can also include regular (sync) nodes.
### Example
```python
class SummarizeThenVerify(AsyncNode):
async def prep_async(self, shared):
# Example: read a file asynchronously
doc_text = await read_file_async(shared["doc_path"])
return doc_text
async def exec_async(self, prep_res):
# Example: async LLM call
summary = await call_llm_async(f"Summarize: {prep_res}")
return summary
async def post_async(self, shared, prep_res, exec_res):
# Example: wait for user feedback
decision = await gather_user_feedback(exec_res)
if decision == "approve":
shared["summary"] = exec_res
return "approve"
return "deny"
summarize_node = SummarizeThenVerify()
final_node = Finalize()
# Define transitions
summarize_node - "approve" >> final_node
summarize_node - "deny" >> summarize_node # retry
flow = AsyncFlow(start=summarize_node)
async def main():
shared = {"doc_path": "document.txt"}
await flow.run_async(shared)
print("Final Summary:", shared.get("summary"))
asyncio.run(main())
```
================================================
File: docs/core_abstraction/batch.md
================================================
---
layout: default
title: "Batch"
parent: "Core Abstraction"
nav_order: 4
---
# Batch
**Batch** makes it easier to handle large inputs in one Node or **rerun** a Flow multiple times. Example use cases:
- **Chunk-based** processing (e.g., splitting large texts).
- **Iterative** processing over lists of input items (e.g., user queries, files, URLs).
## 1. BatchNode
A **BatchNode** extends `Node` but changes `prep()` and `exec()`:
- **`prep(shared)`**: returns an **iterable** (e.g., list, generator).
- **`exec(item)`**: called **once** per item in that iterable.
- **`post(shared, prep_res, exec_res_list)`**: after all items are processed, receives a **list** of results (`exec_res_list`) and returns an **Action**.
### Example: Summarize a Large File
```python
class MapSummaries(BatchNode):
def prep(self, shared):
# Suppose we have a big file; chunk it
content = shared["data"]
chunk_size = 10000
chunks = [content[i:i+chunk_size] for i in range(0, len(content), chunk_size)]
return chunks
def exec(self, chunk):
prompt = f"Summarize this chunk in 10 words: {chunk}"
summary = call_llm(prompt)
return summary
def post(self, shared, prep_res, exec_res_list):
combined = "\n".join(exec_res_list)
shared["summary"] = combined
return "default"
map_summaries = MapSummaries()
flow = Flow(start=map_summaries)
flow.run(shared)
```
---
## 2. BatchFlow
A **BatchFlow** runs a **Flow** multiple times, each time with different `params`. Think of it as a loop that replays the Flow for each parameter set.
### Example: Summarize Many Files
```python
class SummarizeAllFiles(BatchFlow):
def prep(self, shared):
# Return a list of param dicts (one per file)
filenames = list(shared["data"].keys()) # e.g., ["file1.txt", "file2.txt", ...]
return [{"filename": fn} for fn in filenames]
# Suppose we have a per-file Flow (e.g., load_file >> summarize >> reduce):
summarize_file = SummarizeFile(start=load_file)
# Wrap that flow into a BatchFlow:
summarize_all_files = SummarizeAllFiles(start=summarize_file)
summarize_all_files.run(shared)
```
### Under the Hood
1. `prep(shared)` returns a list of param dicts—e.g., `[{filename: "file1.txt"}, {filename: "file2.txt"}, ...]`.
2. The **BatchFlow** loops through each dict. For each one:
- It merges the dict with the BatchFlow’s own `params`.
- It calls `flow.run(shared)` using the merged result.
3. This means the sub-Flow is run **repeatedly**, once for every param dict.
---
## 3. Nested or Multi-Level Batches
You can nest a **BatchFlow** in another **BatchFlow**. For instance:
- **Outer** batch: returns a list of diretory param dicts (e.g., `{"directory": "/pathA"}`, `{"directory": "/pathB"}`, ...).
- **Inner** batch: returning a list of per-file param dicts.
At each level, **BatchFlow** merges its own param dict with the parent’s. By the time you reach the **innermost** node, the final `params` is the merged result of **all** parents in the chain. This way, a nested structure can keep track of the entire context (e.g., directory + file name) at once.
```python
class FileBatchFlow(BatchFlow):
def prep(self, shared):
directory = self.params["directory"]
# e.g., files = ["file1.txt", "file2.txt", ...]
files = [f for f in os.listdir(directory) if f.endswith(".txt")]
return [{"filename": f} for f in files]
class DirectoryBatchFlow(BatchFlow):
def prep(self, shared):
directories = [ "/path/to/dirA", "/path/to/dirB"]
return [{"directory": d} for d in directories]
# MapSummaries have params like {"directory": "/path/to/dirA", "filename": "file1.txt"}
inner_flow = FileBatchFlow(start=MapSummaries())
outer_flow = DirectoryBatchFlow(start=inner_flow)
```
================================================
File: docs/core_abstraction/communication.md
================================================
---
layout: default
title: "Communication"
parent: "Core Abstraction"
nav_order: 3
---
# Communication
Nodes and Flows **communicate** in 2 ways:
1. **Shared Store (for almost all the cases)**
- A global data structure (often an in-mem dict) that all nodes can read ( `prep()`) and write (`post()`).
- Great for data results, large content, or anything multiple nodes need.
- You shall design the data structure and populate it ahead.
- > **Separation of Concerns:** Use `Shared Store` for almost all cases to separate *Data Schema* from *Compute Logic*! This approach is both flexible and easy to manage, resulting in more maintainable code. `Params` is more a syntax sugar for [Batch](./batch.md).
{: .best-practice }
2. **Params (only for [Batch](./batch.md))**
- Each node has a local, ephemeral `params` dict passed in by the **parent Flow**, used as an identifier for tasks. Parameter keys and values shall be **immutable**.
- Good for identifiers like filenames or numeric IDs, in Batch mode.
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).
---
## 1. Shared Store
### Overview
A shared store is typically an in-mem dictionary, like:
```python
shared = {"data": {}, "summary": {}, "config": {...}, ...}
```
It can also contain local file handlers, DB connections, or a combination for persistence. We recommend deciding the data structure or DB schema first based on your app requirements.
### Example
```python
class LoadData(Node):
def post(self, shared, prep_res, exec_res):
# We write data to shared store
shared["data"] = "Some text content"
return None
class Summarize(Node):
def prep(self, shared):
# We read data from shared store
return shared["data"]
def exec(self, prep_res):
# Call LLM to summarize
prompt = f"Summarize: {prep_res}"
summary = call_llm(prompt)
return summary
def post(self, shared, prep_res, exec_res):
# We write summary to shared store
shared["summary"] = exec_res
return "default"
load_data = LoadData()
summarize = Summarize()
load_data >> summarize
flow = Flow(start=load_data)
shared = {}
flow.run(shared)
```
Here:
- `LoadData` writes to `shared["data"]`.
- `Summarize` reads from `shared["data"]`, summarizes, and writes to `shared["summary"]`.
---
## 2. Params
**Params** let you store *per-Node* or *per-Flow* config that doesn't need to live in the shared store. They are:
- **Immutable** during a Node's run cycle (i.e., they don't change mid-`prep->exec->post`).
- **Set** via `set_params()`.
- **Cleared** and updated each time a parent Flow calls it.
> Only set the uppermost Flow params because others will be overwritten by the parent Flow.
>
> If you need to set child node params, see [Batch](./batch.md).
{: .warning }
Typically, **Params** are identifiers (e.g., file name, page number). Use them to fetch the task you assigned or write to a specific part of the shared store.
### Example
```python
# 1) Create a Node that uses params
class SummarizeFile(Node):
def prep(self, shared):
# Access the node's param
filename = self.params["filename"]
return shared["data"].get(filename, "")
def exec(self, prep_res):
prompt = f"Summarize: {prep_res}"
return call_llm(prompt)
def post(self, shared, prep_res, exec_res):
filename = self.params["filename"]
shared["summary"][filename] = exec_res
return "default"
# 2) Set params
node = SummarizeFile()
# 3) Set Node params directly (for testing)
node.set_params({"filename": "doc1.txt"})
node.run(shared)
# 4) Create Flow
flow = Flow(start=node)
# 5) Set Flow params (overwrites node params)
flow.set_params({"filename": "doc2.txt"})
flow.run(shared) # The node summarizes doc2, not doc1
```
================================================
File: docs/core_abstraction/flow.md
================================================
---
layout: default
title: "Flow"
parent: "Core Abstraction"
nav_order: 2
---
# Flow
A **Flow** orchestrates a graph of Nodes. You can chain Nodes in a sequence or create branching depending on the **Actions** returned from each Node's `post()`.
## 1. Action-based Transitions
Each Node's `post()` returns an **Action** string. By default, if `post()` doesn't return anything, we treat that as `"default"`.
You define transitions with the syntax:
1. **Basic default transition**: `node_a >> node_b`
This means if `node_a.post()` returns `"default"`, go to `node_b`.
(Equivalent to `node_a - "default" >> node_b`)
2. **Named action transition**: `node_a - "action_name" >> node_b`
This means if `node_a.post()` returns `"action_name"`, go to `node_b`.
It's possible to create loops, branching, or multi-step flows.
## 2. Creating a Flow
A **Flow** begins with a **start** node. You call `Flow(start=some_node)` to specify the entry point. When you call `flow.run(shared)`, it executes the start node, looks at its returned Action from `post()`, follows the transition, and continues until there's no next node.
### Example: Simple Sequence
Here's a minimal flow of two nodes in a chain:
```python
node_a >> node_b
flow = Flow(start=node_a)
flow.run(shared)
```
- When you run the flow, it executes `node_a`.
- Suppose `node_a.post()` returns `"default"`.
- The flow then sees `"default"` Action is linked to `node_b` and runs `node_b`.
- `node_b.post()` returns `"default"` but we didn't define `node_b >> something_else`. So the flow ends there.
### Example: Branching & Looping
Here's a simple expense approval flow that demonstrates branching and looping. The `ReviewExpense` node can return three possible Actions:
- `"approved"`: expense is approved, move to payment processing
- `"needs_revision"`: expense needs changes, send back for revision
- `"rejected"`: expense is denied, finish the process
We can wire them like this:
```python
# Define the flow connections
review - "approved" >> payment # If approved, process payment
review - "needs_revision" >> revise # If needs changes, go to revision
review - "rejected" >> finish # If rejected, finish the process
revise >> review # After revision, go back for another review
payment >> finish # After payment, finish the process
flow = Flow(start=review)
```
Let's see how it flows:
1. If `review.post()` returns `"approved"`, the expense moves to the `payment` node
2. If `review.post()` returns `"needs_revision"`, it goes to the `revise` node, which then loops back to `review`
3. If `review.post()` returns `"rejected"`, it moves to the `finish` node and stops
```mermaid
flowchart TD
review[Review Expense] -->|approved| payment[Process Payment]
review -->|needs_revision| revise[Revise Report]
review -->|rejected| finish[Finish Process]
revise --> review
payment --> finish
```
### Running Individual Nodes vs. Running a Flow
- `node.run(shared)`: Just runs that node alone (calls `prep->exec->post()`), returns an Action.
- `flow.run(shared)`: Executes from the start node, follows Actions to the next node, and so on until the flow can't continue.
> `node.run(shared)` **does not** proceed to the successor.
> This is mainly for debugging or testing a single node.
>
> Always use `flow.run(...)` in production to ensure the full pipeline runs correctly.
{: .warning }
## 3. Nested Flows
A **Flow** can act like a Node, which enables powerful composition patterns. This means you can:
1. Use a Flow as a Node within another Flow's transitions.
2. Combine multiple smaller Flows into a larger Flow for reuse.
3. Node `params` will be a merging of **all** parents' `params`.
### Flow's Node Methods
A **Flow** is also a **Node**, so it will run `prep()` and `post()`. However:
- It **won't** run `exec()`, as its main logic is to orchestrate its nodes.
- `post()` always receives `None` for `exec_res` and should instead get the flow execution results from the shared store.
### Basic Flow Nesting
Here's how to connect a flow to another node:
```python
# Create a sub-flow
node_a >> node_b
subflow = Flow(start=node_a)
# Connect it to another node
subflow >> node_c
# Create the parent flow
parent_flow = Flow(start=subflow)
```
When `parent_flow.run()` executes:
1. It starts `subflow`
2. `subflow` runs through its nodes (`node_a->node_b`)
3. After `subflow` completes, execution continues to `node_c`
### Example: Order Processing Pipeline
Here's a practical example that breaks down order processing into nested flows:
```python
# Payment processing sub-flow
validate_payment >> process_payment >> payment_confirmation
payment_flow = Flow(start=validate_payment)
# Inventory sub-flow
check_stock >> reserve_items >> update_inventory
inventory_flow = Flow(start=check_stock)
# Shipping sub-flow
create_label >> assign_carrier >> schedule_pickup
shipping_flow = Flow(start=create_label)
# Connect the flows into a main order pipeline
payment_flow >> inventory_flow >> shipping_flow
# Create the master flow
order_pipeline = Flow(start=payment_flow)
# Run the entire pipeline
order_pipeline.run(shared_data)
```
This creates a clean separation of concerns while maintaining a clear execution path:
```mermaid
flowchart LR
subgraph order_pipeline[Order Pipeline]
subgraph paymentFlow["Payment Flow"]
A[Validate Payment] --> B[Process Payment] --> C[Payment Confirmation]
end
subgraph inventoryFlow["Inventory Flow"]
D[Check Stock] --> E[Reserve Items] --> F[Update Inventory]
end
subgraph shippingFlow["Shipping Flow"]
G[Create Label] --> H[Assign Carrier] --> I[Schedule Pickup]
end
paymentFlow --> inventoryFlow
inventoryFlow --> shippingFlow
end
```
================================================
File: docs/core_abstraction/node.md
================================================
---
layout: default
title: "Node"
parent: "Core Abstraction"
nav_order: 1
---
# Node
A **Node** is the smallest building block. Each Node has 3 steps `prep->exec->post`:
<div align="center">
<img src="https://github.com/the-pocket/.github/raw/main/assets/node.png?raw=true" width="400"/>
</div>
1. `prep(shared)`
- **Read and preprocess data** from `shared` store.
- Examples: *query DB, read files, or serialize data into a string*.
- Return `prep_res`, which is used by `exec()` and `post()`.
2. `exec(prep_res)`
- **Execute compute logic**, with optional retries and error handling (below).
- Examples: *(mostly) LLM calls, remote APIs, tool use*.
- ⚠️ This shall be only for compute and **NOT** access `shared`.
- ⚠️ If retries enabled, ensure idempotent implementation.
- Return `exec_res`, which is passed to `post()`.
3. `post(shared, prep_res, exec_res)`
- **Postprocess and write data** back to `shared`.
- Examples: *update DB, change states, log results*.
- **Decide the next action** by returning a *string* (`action = "default"` if *None*).
> **Why 3 steps?** To enforce the principle of *separation of concerns*. The data storage and data processing are operated separately.
>
> All steps are *optional*. E.g., you can only implement `prep` and `post` if you just need to process data.
{: .note }
### Fault Tolerance & Retries
You can **retry** `exec()` if it raises an exception via two parameters when define the Node:
- `max_retries` (int): Max times to run `exec()`. The default is `1` (**no** retry).
- `wait` (int): The time to wait (in **seconds**) before next retry. By default, `wait=0` (no waiting).
`wait` is helpful when you encounter rate-limits or quota errors from your LLM provider and need to back off.
```python
my_node = SummarizeFile(max_retries=3, wait=10)
```
When an exception occurs in `exec()`, the Node automatically retries until:
- It either succeeds, or
- The Node has retried `max_retries - 1` times already and fails on the last attempt.
You can get the current retry times (0-based) from `self.cur_retry`.
```python
class RetryNode(Node):
def exec(self, prep_res):
print(f"Retry {self.cur_retry} times")
raise Exception("Failed")
```
### Graceful Fallback
To **gracefully handle** the exception (after all retries) rather than raising it, override:
```python
def exec_fallback(self, prep_res, exc):
raise exc
```
By default, it just re-raises exception. But you can return a fallback result instead, which becomes the `exec_res` passed to `post()`.
### Example: Summarize file
```python
class SummarizeFile(Node):
def prep(self, shared):
return shared["data"]
def exec(self, prep_res):
if not prep_res:
return "Empty file content"
prompt = f"Summarize this text in 10 words: {prep_res}"
summary = call_llm(prompt) # might fail
return summary
def exec_fallback(self, prep_res, exc):
# Provide a simple fallback instead of crashing
return "There was an error processing your request."
def post(self, shared, prep_res, exec_res):
shared["summary"] = exec_res
# Return "default" by not returning
summarize_node = SummarizeFile(max_retries=3)
# node.run() calls prep->exec->post
# If exec() fails, it retries up to 3 times before calling exec_fallback()
action_result = summarize_node.run(shared)
print("Action returned:", action_result) # "default"
print("Summary stored:", shared["summary"])
```
================================================
File: docs/core_abstraction/parallel.md
================================================
---
layout: default
title: "(Advanced) Parallel"
parent: "Core Abstraction"
nav_order: 6
---
# (Advanced) Parallel
**Parallel** Nodes and Flows let you run multiple **Async** Nodes and Flows **concurrently**—for example, summarizing multiple texts at once. This can improve performance by overlapping I/O and compute.
> 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**:
```python
class ParallelSummaries(AsyncParallelBatchNode):
async def prep_async(self, shared):
# e.g., multiple texts
return shared["texts"]
async def exec_async(self, text):
prompt = f"Summarize: {text}"
return await call_llm_async(prompt)
async def post_async(self, shared, prep_res, exec_res_list):
shared["summary"] = "\n\n".join(exec_res_list)
return "default"
node = ParallelSummaries()
flow = AsyncFlow(start=node)
```
## AsyncParallelBatchFlow
Parallel version of **BatchFlow**. Each iteration of the sub-flow runs **concurrently** using different parameters:
```python
class SummarizeMultipleFiles(AsyncParallelBatchFlow):
async def prep_async(self, shared):
return [{"filename": f} for f in shared["files"]]
sub_flow = AsyncFlow(start=LoadAndSummarizeFile())
parallel_flow = SummarizeMultipleFiles(start=sub_flow)
await parallel_flow.run_async(shared)
```
================================================
File: docs/design_pattern/agent.md
================================================
---
layout: default
title: "Agent"
parent: "Design Pattern"
nav_order: 1
---
# Agent
Agent is a powerful design pattern in which nodes can take dynamic actions based on the context.
<div align="center">
<img src="https://github.com/the-pocket/.github/raw/main/assets/agent.png?raw=true" width="350"/>
</div>
## Implement Agent with Graph
1. **Context and Action:** Implement nodes that supply context and perform actions.
2. **Branching:** Use branching to connect each action node to an agent node. Use action to allow the agent to direct the [flow](../core_abstraction/flow.md) between nodes—and potentially loop back for multi-step.
3. **Agent Node:** Provide a prompt to decide action—for example:
```python
f"""
### CONTEXT
Task: {task_description}
Previous Actions: {previous_actions}
Current State: {current_state}
### ACTION SPACE
[1] search
Description: Use web search to get results
Parameters:
- query (str): What to search for
[2] answer
Description: Conclude based on the results
Parameters:
- result (str): Final answer to provide
### NEXT ACTION
Decide the next action based on the current context and available action space.
Return your response in the following format:
```yaml
thinking: |
<your step-by-step reasoning process>
action: <action_name>
parameters:
<parameter_name>: <parameter_value>
```"""
```
The core of building **high-performance** and **reliable** agents boils down to:
1. **Context Management:** Provide *relevant, minimal context.* For example, rather than including an entire chat history, retrieve the most relevant via [RAG](./rag.md). Even with larger context windows, LLMs still fall victim to ["lost in the middle"](https://arxiv.org/abs/2307.03172), overlooking mid-prompt content.
2. **Action Space:** Provide *a well-structured and unambiguous* set of actions—avoiding overlap like separate `read_databases` or `read_csvs`. Instead, import CSVs into the database.
## Example Good Action Design
- **Incremental:** Feed content in manageable chunks (500 lines or 1 page) instead of all at once.
- **Overview-zoom-in:** First provide high-level structure (table of contents, summary), then allow drilling into details (raw texts).
- **Parameterized/Programmable:** Instead of fixed actions, enable parameterized (columns to select) or programmable (SQL queries) actions, for example, to read CSV files.
- **Backtracking:** Let the agent undo the last step instead of restarting entirely, preserving progress when encountering errors or dead ends.
## Example: Search Agent
This agent:
1. Decides whether to search or answer
2. If searches, loops back to decide if more search needed
3. Answers when enough context gathered
```python
class DecideAction(Node):
def prep(self, shared):
context = shared.get("context", "No previous search")
query = shared["query"]
return query, context
def exec(self, inputs):
query, context = inputs
prompt = f"""
Given input: {query}
Previous search results: {context}
Should I: 1) Search web for more info 2) Answer with current knowledge
Output in yaml:
```yaml
action: search/answer
reason: why this action
search_term: search phrase if action is search
```"""
resp = call_llm(prompt)
yaml_str = resp.split("```yaml")[1].split("```")[0].strip()
result = yaml.safe_load(yaml_str)
assert isinstance(result, dict)
assert "action" in result
assert "reason" in result
assert result["action"] in ["search", "answer"]
if result["action"] == "search":
assert "search_term" in result
return result
def post(self, shared, prep_res, exec_res):
if exec_res["action"] == "search":
shared["search_term"] = exec_res["search_term"]
return exec_res["action"]
class SearchWeb(Node):
def prep(self, shared):
return shared["search_term"]
def exec(self, search_term):
return search_web(search_term)
def post(self, shared, prep_res, exec_res):
prev_searches = shared.get("context", [])
shared["context"] = prev_searches + [
{"term": shared["search_term"], "result": exec_res}
]
return "decide"
class DirectAnswer(Node):
def prep(self, shared):
return shared["query"], shared.get("context", "")
def exec(self, inputs):
query, context = inputs
return call_llm(f"Context: {context}\nAnswer: {query}")
def post(self, shared, prep_res, exec_res):
print(f"Answer: {exec_res}")
shared["answer"] = exec_res
# Connect nodes
decide = DecideAction()
search = SearchWeb()
answer = DirectAnswer()
decide - "search" >> search
decide - "answer" >> answer
search - "decide" >> decide # Loop back
flow = Flow(start=decide)
flow.run({"query": "Who won the Nobel Prize in Physics 2024?"})
```
================================================
File: docs/design_pattern/mapreduce.md
================================================
---
layout: default
title: "Map Reduce"
parent: "Design Pattern"
nav_order: 4
---
# Map Reduce
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.
<div align="center">
<img src="https://github.com/the-pocket/.github/raw/main/assets/mapreduce.png?raw=true" width="400"/>
</div>
You first break down the task using [BatchNode](../core_abstraction/batch.md) in the map phase, followed by aggregation in the reduce phase.
### Example: Document Summarization
```python
class SummarizeAllFiles(BatchNode):
def prep(self, shared):
files_dict = shared["files"] # e.g. 10 files
return list(files_dict.items()) # [("file1.txt", "aaa..."), ("file2.txt", "bbb..."), ...]
def exec(self, one_file):
filename, file_content = one_file
summary_text = call_llm(f"Summarize the following file:\n{file_content}")
return (filename, summary_text)
def post(self, shared, prep_res, exec_res_list):
shared["file_summaries"] = dict(exec_res_list)
class CombineSummaries(Node):
def prep(self, shared):
return shared["file_summaries"]
def exec(self, file_summaries):
# format as: "File1: summary\nFile2: summary...\n"
text_list = []
for fname, summ in file_summaries.items():
text_list.append(f"{fname} summary:\n{summ}\n")
big_text = "\n---\n".join(text_list)
return call_llm(f"Combine these file summaries into one final summary:\n{big_text}")
def post(self, shared, prep_res, final_summary):
shared["all_files_summary"] = final_summary
batch_node = SummarizeAllFiles()
combine_node = CombineSummaries()
batch_node >> combine_node
flow = Flow(start=batch_node)
shared = {
"files": {
"file1.txt": "Alice was beginning to get very tired of sitting by her sister...",
"file2.txt": "Some other interesting text ...",
# ...
}
}
flow.run(shared)
print("Individual Summaries:", shared["file_summaries"])
print("\nFinal Summary:\n", shared["all_files_summary"])
```
================================================
File: docs/design_pattern/rag.md
================================================
---
layout: default
title: "RAG"
parent: "Design Pattern"
nav_order: 3
---
# RAG (Retrieval Augmented Generation)
For certain LLM tasks like answering questions, providing relevant context is essential. One common architecture is a **two-stage** RAG pipeline:
<div align="center">
<img src="https://github.com/the-pocket/.github/raw/main/assets/rag.png?raw=true" width="400"/>
</div>
1. **Offline stage**: Preprocess and index documents ("building the index").
2. **Online stage**: Given a question, generate answers by retrieving the most relevant context.
---
## Stage 1: Offline Indexing
We create three Nodes:
1. `ChunkDocs` – [chunks](../utility_function/chunking.md) raw text.
2. `EmbedDocs` – [embeds](../utility_function/embedding.md) each chunk.
3. `StoreIndex` – stores embeddings into a [vector database](../utility_function/vector.md).
```python
class ChunkDocs(BatchNode):
def prep(self, shared):
# A list of file paths in shared["files"]. We process each file.
return shared["files"]
def exec(self, filepath):
# read file content. In real usage, do error handling.
with open(filepath, "r", encoding="utf-8") as f:
text = f.read()
# chunk by 100 chars each
chunks = []
size = 100
for i in range(0, len(text), size):
chunks.append(text[i : i + size])
return chunks
def post(self, shared, prep_res, exec_res_list):
# exec_res_list is a list of chunk-lists, one per file.
# flatten them all into a single list of chunks.
all_chunks = []
for chunk_list in exec_res_list:
all_chunks.extend(chunk_list)
shared["all_chunks"] = all_chunks
class EmbedDocs(BatchNode):
def prep(self, shared):
return shared["all_chunks"]
def exec(self, chunk):
return get_embedding(chunk)
def post(self, shared, prep_res, exec_res_list):
# Store the list of embeddings.
shared["all_embeds"] = exec_res_list
print(f"Total embeddings: {len(exec_res_list)}")
class StoreIndex(Node):
def prep(self, shared):
# We'll read all embeds from shared.
return shared["all_embeds"]
def exec(self, all_embeds):
# Create a vector index (faiss or other DB in real usage).
index = create_index(all_embeds)
return index
def post(self, shared, prep_res, index):
shared["index"] = index
# Wire them in sequence
chunk_node = ChunkDocs()
embed_node = EmbedDocs()
store_node = StoreIndex()
chunk_node >> embed_node >> store_node
OfflineFlow = Flow(start=chunk_node)
```
Usage example:
```python
shared = {
"files": ["doc1.txt", "doc2.txt"], # any text files
}
OfflineFlow.run(shared)
```
---
## Stage 2: Online Query & Answer
We have 3 nodes:
1. `EmbedQuery` – embeds the user’s question.
2. `RetrieveDocs` – retrieves top chunk from the index.
3. `GenerateAnswer` – calls the LLM with the question + chunk to produce the final answer.
```python
class EmbedQuery(Node):
def prep(self, shared):
return shared["question"]
def exec(self, question):
return get_embedding(question)
def post(self, shared, prep_res, q_emb):
shared["q_emb"] = q_emb
class RetrieveDocs(Node):
def prep(self, shared):
# We'll need the query embedding, plus the offline index/chunks
return shared["q_emb"], shared["index"], shared["all_chunks"]
def exec(self, inputs):
q_emb, index, chunks = inputs
I, D = search_index(index, q_emb, top_k=1)
best_id = I[0][0]
relevant_chunk = chunks[best_id]
return relevant_chunk
def post(self, shared, prep_res, relevant_chunk):
shared["retrieved_chunk"] = relevant_chunk
print("Retrieved chunk:", relevant_chunk[:60], "...")
class GenerateAnswer(Node):
def prep(self, shared):
return shared["question"], shared["retrieved_chunk"]
def exec(self, inputs):
question, chunk = inputs
prompt = f"Question: {question}\nContext: {chunk}\nAnswer:"
return call_llm(prompt)
def post(self, shared, prep_res, answer):
shared["answer"] = answer
print("Answer:", answer)
embed_qnode = EmbedQuery()
retrieve_node = RetrieveDocs()
generate_node = GenerateAnswer()
embed_qnode >> retrieve_node >> generate_node
OnlineFlow = Flow(start=embed_qnode)
```
Usage example:
```python
# Suppose we already ran OfflineFlow and have:
# shared["all_chunks"], shared["index"], etc.
shared["question"] = "Why do people like cats?"
OnlineFlow.run(shared)
# final answer in shared["answer"]
```
================================================
File: docs/design_pattern/structure.md
================================================
---
layout: default
title: "Structured Output"
parent: "Design Pattern"
nav_order: 5
---
# Structured Output
In many use cases, you may want the LLM to output a specific structure, such as a list or a dictionary with predefined keys.
There are several approaches to achieve a structured output:
- **Prompting** the LLM to strictly return a defined structure.
- Using LLMs that natively support **schema enforcement**.
- **Post-processing** the LLM's response to extract structured content.
In practice, **Prompting** is simple and reliable for modern LLMs.
### Example Use Cases
- Extracting Key Information
```yaml
product:
name: Widget Pro
price: 199.99
description: |
A high-quality widget designed for professionals.
Recommended for advanced users.
```
- Summarizing Documents into Bullet Points
```yaml
summary:
- This product is easy to use.
- It is cost-effective.
- Suitable for all skill levels.
```
- Generating Configuration Files
```yaml
server:
host: 127.0.0.1
port: 8080
ssl: true
```
## Prompt Engineering
When prompting the LLM to produce **structured** output:
1. **Wrap** the structure in code fences (e.g., `yaml`).
2. **Validate** that all required fields exist (and let `Node` handles retry).
### Example Text Summarization
```python
class SummarizeNode(Node):
def exec(self, prep_res):
# Suppose `prep_res` is the text to summarize.
prompt = f"""
Please summarize the following text as YAML, with exactly 3 bullet points
{prep_res}
Now, output:
```yaml
summary:
- bullet 1
- bullet 2
- bullet 3
```"""
response = call_llm(prompt)
yaml_str = response.split("```yaml")[1].split("```")[0].strip()
import yaml
structured_result = yaml.safe_load(yaml_str)
assert "summary" in structured_result
assert isinstance(structured_result["summary"], list)
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.
**In JSON**
```json
{
"dialogue": "Alice said: \"Hello Bob.\\nHow are you?\\nI am good.\""
}
```
- Every double quote inside the string must be escaped with `\"`.
- Each newline in the dialogue must be represented as `\n`.
**In YAML**
```yaml
dialogue: |
Alice said: "Hello Bob.
How are you?
I am good."
```
- No need to escape interior quotes—just place the entire text under a block literal (`|`).
- Newlines are naturally preserved without needing `\n`.
================================================
File: docs/design_pattern/workflow.md
================================================
---
layout: default
title: "Workflow"
parent: "Design Pattern"
nav_order: 2
---
# Workflow
Many real-world tasks are too complex for one LLM call. The solution is to **Task Decomposition**: decompose them into a [chain](../core_abstraction/flow.md) of multiple Nodes.
<div align="center">
<img src="https://github.com/the-pocket/.github/raw/main/assets/workflow.png?raw=true" width="400"/>
</div>
> - 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
```python
class GenerateOutline(Node):
def prep(self, shared): return shared["topic"]
def exec(self, topic): return call_llm(f"Create a detailed outline for an article about {topic}")
def post(self, shared, prep_res, exec_res): shared["outline"] = exec_res
class WriteSection(Node):
def prep(self, shared): return shared["outline"]
def exec(self, outline): return call_llm(f"Write content based on this outline: {outline}")
def post(self, shared, prep_res, exec_res): shared["draft"] = exec_res
class ReviewAndRefine(Node):
def prep(self, shared): return shared["draft"]
def exec(self, draft): return call_llm(f"Review and improve this draft: {draft}")
def post(self, shared, prep_res, exec_res): shared["final_article"] = exec_res
# Connect nodes
outline = GenerateOutline()
write = WriteSection()
review = ReviewAndRefine()
outline >> write >> review
# Create and run flow
writing_flow = Flow(start=outline)
shared = {"topic": "AI Safety"}
writing_flow.run(shared)
```
For *dynamic cases*, consider using [Agents](./agent.md).
================================================
File: docs/utility_function/llm.md
================================================
---
layout: default
title: "LLM Wrapper"
parent: "Utility Function"
nav_order: 1
---
# LLM Wrappers
Check out libraries like [litellm](https://github.com/BerriAI/litellm).
Here, we provide some minimal example implementations:
1. OpenAI
```python
def call_llm(prompt):
from openai import OpenAI
client = OpenAI(api_key="YOUR_API_KEY_HERE")
r = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": prompt}]
)
return r.choices[0].message.content
# Example usage
call_llm("How are you?")
```
> Store the API key in an environment variable like OPENAI_API_KEY for security.
{: .best-practice }
2. Claude (Anthropic)
```python
def call_llm(prompt):
from anthropic import Anthropic
client = Anthropic(api_key="YOUR_API_KEY_HERE")
response = client.messages.create(
model="claude-2",
messages=[{"role": "user", "content": prompt}],
max_tokens=100
)
return response.content
```
3. Google (Generative AI Studio / PaLM API)
```python
def call_llm(prompt):
import google.generativeai as genai
genai.configure(api_key="YOUR_API_KEY_HERE")
response = genai.generate_text(
model="models/text-bison-001",
prompt=prompt
)
return response.result
```
4. Azure (Azure OpenAI)
```python
def call_llm(prompt):
from openai import AzureOpenAI
client = AzureOpenAI(
azure_endpoint="https://<YOUR_RESOURCE_NAME>.openai.azure.com/",
api_key="YOUR_API_KEY_HERE",
api_version="2023-05-15"
)
r = client.chat.completions.create(
model="<YOUR_DEPLOYMENT_NAME>",
messages=[{"role": "user", "content": prompt}]
)
return r.choices[0].message.content
```
5. Ollama (Local LLM)
```python
def call_llm(prompt):
from ollama import chat
response = chat(
model="llama2",
messages=[{"role": "user", "content": prompt}]
)
return response.message.content
```
## Improvements
Feel free to enhance your `call_llm` function as needed. Here are examples:
- Handle chat history:
```python
def call_llm(messages):
from openai import OpenAI
client = OpenAI(api_key="YOUR_API_KEY_HERE")
r = client.chat.completions.create(
model="gpt-4o",
messages=messages
)
return r.choices[0].message.content
```
- Add in-memory caching
```python
from functools import lru_cache
@lru_cache(maxsize=1000)
def call_llm(prompt):
# Your implementation here
pass
```
> ⚠️ Caching conflicts with Node retries, as retries yield the same result.
>
> To address this, you could use cached results only if not retried.
{: .warning }
```python
from functools import lru_cache
@lru_cache(maxsize=1000)
def cached_call(prompt):
pass
def call_llm(prompt, use_cache):
if use_cache:
return cached_call(prompt)
# Call the underlying function directly
return cached_call.__wrapped__(prompt)
class SummarizeNode(Node):
def exec(self, text):
return call_llm(f"Summarize: {text}", self.cur_retry==0)
```
- Enable logging:
```python
def call_llm(prompt):
import logging
logging.info(f"Prompt: {prompt}")
response = ... # Your implementation here
logging.info(f"Response: {response}")
return response
```
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