88 lines
3.2 KiB
Markdown
88 lines
3.2 KiB
Markdown
---
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layout: default
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title: "Batch"
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parent: "Core Abstraction"
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nav_order: 4
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---
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# Batch
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**Batch** makes it easier to handle large inputs in one Node or **rerun** a Flow multiple times. Handy for:
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- **Chunk-based** processing (e.g., splitting large texts).
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- **Multi-file** processing.
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- **Iterating** over lists of params (e.g., user queries, documents, URLs).
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## 1. BatchNode
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A **BatchNode** extends `Node` but changes `prep()` and `exec()`:
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- **`prep(shared)`**: returns an **iterable** (e.g., list, generator).
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- **`exec(item)`**: called **once** per item in that iterable.
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- **`post(shared, prep_res, exec_res_list)`**: after all items are processed, receives a **list** of results (`exec_res_list`) and returns an **Action**.
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### Example: Summarize a Large File
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``python
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class MapSummaries(BatchNode):
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def prep(self, shared):
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# Suppose we have a big file; chunk it
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content = shared["data"].get("large_text.txt", "")
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chunk_size = 10000
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chunks = [content[i:i+chunk_size] for i in range(0, len(content), chunk_size)]
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return chunks
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def exec(self, chunk):
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prompt = f"Summarize this chunk in 10 words: {chunk}"
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summary = call_llm(prompt)
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return summary
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def post(self, shared, prep_res, exec_res_list):
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combined = "\n".join(exec_res_list)
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shared["summary"]["large_text.txt"] = combined
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return "default"
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map_summaries = MapSummaries()
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flow = Flow(start=map_summaries)
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flow.run(shared)
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``python
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---
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## 2. BatchFlow
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.
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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.
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### Example: Summarize Many Files
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``python
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class SummarizeAllFiles(BatchFlow):
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def prep(self, shared):
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# Return a list of param dicts (one per file)
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filenames = list(shared["data"].keys()) # e.g., ["file1.txt", "file2.txt", ...]
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return [{"filename": fn} for fn in filenames]
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# Suppose we have a per-file Flow (e.g., load_file >> summarize >> reduce):
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summarize_file = SummarizeFile(start=load_file)
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# Wrap that flow into a BatchFlow:
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summarize_all_files = SummarizeAllFiles(start=summarize_file)
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summarize_all_files.run(shared)
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``python
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**Under the Hood**:
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1. `prep(shared)` returns a list of param dicts—e.g., `[{filename: "file1.txt"}, {filename: "file2.txt"}, ...]`.
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2. The **BatchFlow** loops through each dict. For each one:
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- It merges the dict with the BatchFlow’s own `params`.
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- It calls `flow.run(shared)` using the merged result.
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3. This means the sub-Flow is run **repeatedly**, once for every param dict.
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---
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### Nested or Multi-Level Batches
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You can nest a **BatchFlow** in another **BatchFlow**. For instance:
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- **Outer** batch: returns a list of diretory param dicts (e.g., `{"directory": "/pathA"}`, `{"directory": "/pathB"}`, ...).
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- **Inner** batch: returning a list of per-file param dicts.
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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. |