lmsyz-genai-ie-rfs 0.1.2

Concurrent and batch LLM extraction over a pandas DataFrame, no schema boilerplate.

GenAI Information Extraction for DataFrames

A general-purpose library for prompt-based information extraction over DataFrames, with concurrent and batch execution against OpenAI, Anthropic, and models on OpenRouter.

If you find this library useful in your research, please cite:

Li, Mai, Shen, Yang, Zhang (2026), "Dissecting Corporate Culture Using Generative AI," Review of Financial Studies 39(1):253–296, doi.org/10.1093/rfs/hhaf081.

---

Install

pip install -U lmsyz_genai_ie_rfs

Set at least one provider key (or drop them in a .env file next to your notebook):

import os
os.environ["OPENAI_API_KEY"] = "sk-..."
# os.environ["ANTHROPIC_API_KEY"] = "sk-ant-..."
# os.environ["OPENROUTER_API_KEY"] = "sk-or-..."

---

Quickstart: entity + relation extraction

Three short news sentences in:

id	text
1	Apple CEO Tim Cook announced the iPhone 17 at WWDC in June 2025.
2	Tesla acquired SolarCity in 2016 for $2.6 billion to enter the solar market.
3	Pfizer's decision to spin off its consumer health unit was driven by activist pressure from Trian Partners.

Structured DataFrame out:

input_id	entities	causal_triples	sentiment
1	[{Tim Cook, PERSON}, {Apple, ORG}, {iPhone 17, PRODUCT}, {WWDC, EVENT}, {June 2025, DATE}]	[]	neutral
2	[{Tesla, ORG}, {SolarCity, ORG}, {2016, DATE}, {$2.6 billion, MONEY}]	[[acquisition, enabled, market entry]]	positive
3	[{Pfizer, ORG}, {Trian Partners, ORG}]	[[activist pressure, drove, spin-off]]	neutral

One function call:

import pandas as pd
from lmsyz_genai_ie_rfs import extract_df

df = pd.DataFrame({
    "id": [1, 2, 3],
    "text": [
        "Apple CEO Tim Cook announced the iPhone 17 at WWDC in June 2025.",
        "Tesla’s $2.6 billion acquisition of SolarCity in 2016 enabled its entry into the solar market.",
        "Pfizer's decision to spin off its consumer health unit was driven by activist pressure from Trian Partners.",
    ],
})

prompt = """
You are an information-extraction assistant. For each input row, analyze the text and extract structured information.

Step-by-step instructions:

1. input_id: Copy the input_id from the row verbatim.
2. entities: List every named entity mentioned in the text. For each entity give:
   - name: the surface form as it appears in the text.
   - type: one of "PERSON", "ORG", "PRODUCT", "DATE", "MONEY".
3. causal_triples: If the text explicitly states a cause and effect, list each as a
   three-element array ["cause", "relation", "effect"]. If there is no explicit
   causation, return an empty list []. All elements should be concisely summarized, in three words or less. 
4. sentiment: One of "positive", "neutral", or "negative".

Return a JSON object with this EXACT structure:

{
  "all_results": [
    {
      "input_id": "1",
      "entities": [
        {"name": "Apple",    "type": "ORG"},
        {"name": "Tim Cook", "type": "PERSON"}
      ],
      "causal_triples": [[cause_1, relation_1, effect_1], [cause_2, relation_2, effect_2], ...],
      "sentiment": "positive/neutral/negative"
    }
  ]
}

Do not include any fields besides input_id, entities, causal_triples, and sentiment.
"""

out = extract_df(
    df, prompt=prompt,
    chunk_size=5, 
    max_workers=20,
    backend="openai", 
    model="gpt-4.1-mini",
    cache_path="demo.sqlite",
    id_col="id", text_col="text",
    api_key=os.environ["OPENAI_API_KEY"],
)

print(out)
entities = out[['input_id', 'entities']].explode("entities")
print(entities)

Save:

out.to_csv("extraction.csv", index=False)                          

---

Drafting a prompt with draft_prompt

Pass a sentence describing what you want to extract; draft_prompt returns a candidate prompt in the house style for you to edit:

import os
from lmsyz_genai_ie_rfs import draft_prompt

prompt = draft_prompt(
    goal="Tag short product reviews with sentiment (positive/neutral/negative) and a confidence score 0-1.",
    api_key=os.environ["OPENAI_API_KEY"],
)

Prints and returns:

You are an information-extraction assistant. For each input row, analyze the product review and extract structured information.

Step-by-step instructions:

1. input_id: Copy the input_id from the row verbatim.
2. sentiment: Tag the review's overall sentiment as one of "positive", "neutral", or "negative".
3. confidence: Provide a confidence score for the sentiment tag as a float between 0 and 1 inclusive.

Return a JSON object with this EXACT structure:

{
  "all_results": [
    {
      "input_id": "42",
      "sentiment": "positive",
      "confidence": 0.87
    }
  ]
}

Do not include any fields besides input_id, sentiment, and confidence.

Read the result, tighten enums, add any domain-specific instructions, then pass it to extract_df(prompt=prompt, ...). Defaults are backend="openai", model="gpt-4.1-mini"; pass backend="anthropic" to use Claude instead. The temperature is fixed at 0, so the same goal reproduces the same starting prompt.

---

Speed up: chunk_size and max_workers

max_workers sets the size of the threadpool that issues API calls in parallel. chunk_size sets how many DataFrame rows are packed into each call (one shared system prompt, chunk_size user inputs). Combining them can speed up execution by 100× compared to a naive for loop with one row per call.

out = extract_df(
    df, prompt=...,
    chunk_size=5,        # rows packed into one API call (amortizes the system prompt)
    max_workers=20,      # API calls in flight at once (a thread pool)
    backend="openai", model="gpt-4.1-mini",
    cache_path="big.sqlite",
)

At ~1 second per call, 100,000 rows:

Approach	API calls	Wall-clock
One row per call, serial (a plain for loop)	100,000	~28 hours
One row per call, max_workers=20	100,000	~83 minutes
chunk_size=5, max_workers=20	20,000	~17 minutes

Tuning: start chunk_size=5, max_workers=20. Raise max_workers until the log shows rate-limit retries (OpenAI tier-3 handles 60–100; Anthropic tier-2 handles 20–50).

For ~50% lower per-token cost in exchange for asynchronous execution, see the batch path below.

---

The results cache DB (cache_path)

API calls cost money and time. The cache exists so that an interrupted, edited, or replayed run does not re-send rows that already returned.

cache_path is required. It is the path to a SQLite file. Each completed row is written to it as soon as it returns from the model. The file is not auto-deleted.

On rerun with the same cache_path, rows already in the cache are skipped; only missing rows go to the model.

The schema is three columns:

results(row_id TEXT PRIMARY KEY, json_result TEXT, prompt_hash TEXT)

Open in any SQLite browser or sqlite3 my_experiment.sqlite to inspect. Force a full re-run: fresh=True.

What if I change the prompt?

Each cached row is stamped with sha256(prompt)[:16]. On rerun, rows whose stamp matches the current prompt are skipped; rows stamped with a different prompt are re-executed and the old row is overwritten (INSERT OR REPLACE on row_id).

Three escape hatches:

• Compare prompts side by side: use a different cache_path for each prompt version.
• Fixed a typo and want to keep the cached rows: pass ignore_prompt_hash=True.
• Wipe and redo: fresh=True, or delete the .sqlite file.

---

Optional: a JSON schema file

By default, output structure is enforced only by the prompt; the model returns a JSON object whose shape it infers from the instructions.

Pass schema= to enforce structure at the API layer. OpenAI consumes it as response_format={"type": "json_schema", ...}; Anthropic consumes it as the input_schema of a forced tool_use.

out = extract_df(
    df, prompt=my_prompt, schema="my_schema.json",
    backend="openai", model="gpt-4.1-mini",
    cache_path="strict.sqlite",
)

The file contains a standard OpenAI response_format object. Here is one matching the Quickstart task above:

{
  "type": "json_schema",
  "json_schema": {
    "name": "news_extraction",
    "strict": true,
    "schema": {
      "type": "object",
      "additionalProperties": false,
      "required": ["all_results"],
      "properties": {
        "all_results": {
          "type": "array",
          "items": {
            "type": "object",
            "additionalProperties": false,
            "required": ["input_id", "entities", "causal_triples", "sentiment"],
            "properties": {
              "input_id": {"type": "string"},
              "entities": {
                "type": "array",
                "items": {
                  "type": "object",
                  "additionalProperties": false,
                  "required": ["name", "type"],
                  "properties": {
                    "name": {"type": "string"},
                    "type": {"type": "string", "enum": ["PERSON", "ORG", "PRODUCT", "DATE", "MONEY", "EVENT"]}
                  }
                }
              },
              "causal_triples": {
                "type": "array",
                "items": {
                  "type": "array",
                  "minItems": 3, "maxItems": 3,
                  "items": {"type": "string"}
                }
              },
              "sentiment": {"type": "string", "enum": ["positive", "neutral", "negative"]}
            }
          }
        }
      }
    }
  }
}

The same file works on Anthropic: the library extracts the inner schema object and passes it as the input_schema of a tool_use.

schema= accepts a file path, a dict with the contents above, or None (the default).

---

Model providers

Two native backends (OpenAI, Anthropic), plus any OpenAI-compatible endpoint reachable by setting base_url=. The same extract_df call shape works for all of them; only model, base_url, and api_key change.

Capability	OpenAI	Anthropic	OpenAI-compatible (OpenRouter, Gemini, Together, vLLM, ...)
Concurrent, no schema	yes: json_object	yes: plain text (parsed tolerantly)	model-dependent
Concurrent, with schema	yes: structured outputs	yes: tool_use + prompt caching	model-dependent (gateway forwards response_format)
Batch API (~50% cheaper)	yes: OpenAIBatchExtractor	yes: AnthropicBatchExtractor	use the gateway's native batch API
Long-prompt caching	automatic (1024+ tokens)	explicit cache_control on system	upstream-model-dependent

OpenRouter: one key, hundreds of models

OpenRouter is a gateway: a single key + base_url puts you in front of Llama, Mistral, DeepSeek, Qwen, Gemini, Claude, and many more. Swap model= for whichever you want.

out = extract_df(
    df, prompt=my_prompt,
    backend="openai",
    base_url="https://openrouter.ai/api/v1",
    api_key=os.environ["OPENROUTER_API_KEY"],
    model="qwen/qwen-2.5-72b-instruct",          # Qwen
    # model="deepseek/deepseek-chat",            # DeepSeek
    # model="meta-llama/llama-3.3-70b-instruct", # Llama
    # model="mistralai/mistral-large",           # Mistral
    # model="anthropic/claude-haiku-4.5",        # Claude (proxied)
    cache_path="openrouter.sqlite",
)

JSON-mode support varies by model on OpenRouter. If a model returns malformed JSON, switch to one that supports response_format, or pass a strict schema= so the gateway routes via JSON-schema mode.

Gemini direct

out = extract_df(
    df, prompt=my_prompt,
    backend="openai", model="gemini-2.5-flash",
    base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
    api_key=os.environ["GEMINI_API_KEY"],
    cache_path="gemini.sqlite",
)

---

The batch path

OpenAI and Anthropic both expose a Batch API at approximately 50% of the per-token cost. Execution is asynchronous with a stated 24-hour SLA (typical completion is faster). Lifecycle: build request files, submit, poll status, retrieve.

OpenAI batch

from lmsyz_genai_ie_rfs import OpenAIBatchExtractor

ext = OpenAIBatchExtractor(batch_root_dir="my_job/")

ext.create_batch_jsonl(               # 1. write JSONL input files
    dataframe=df, id_col="id", text_col="text",
    prompt=prompt, job_id="my_job",
    model_name="gpt-4.1-mini",
    schema_dict=None,                 # or a strict json_schema dict
)
ext.submit_batches("my_job")          # 2. upload + submit
ext.check_batch_status("my_job",      # 3. poll
                       continuous=True, interval=60)
out = ext.retrieve_results_as_dataframe("my_job")   # 4. results as DataFrame

Anthropic batch

Same lifecycle, different wire format (a JSON request body, not a JSONL file upload):

from lmsyz_genai_ie_rfs import AnthropicBatchExtractor

ext = AnthropicBatchExtractor(batch_root_dir="my_job/")
ext.create_batch_requests(..., schema_dict=my_schema)
ext.submit_batch("my_job")
ext.check_batch_status("my_job", continuous=True)
out = ext.retrieve_results_as_dataframe("my_job")

All intermediate files (JSONL input, submission manifest, raw results) are written under batch_root_dir/<job_id>/ and can be inspected directly.

---

All knobs

extract_df(
    df,
    prompt=...,                 # required
    cache_path="path.sqlite",   # required: results DB on disk
    backend="openai",           # or "anthropic"
    model="gpt-4.1-mini",       # required
    schema=None,                # optional: path to JSON schema file, dict, or None
    id_col="id",
    text_col="text",
    chunk_size=5,               # rows per API call; 5-20 is typical
    max_workers=20,             # parallel threads; tune to your rate limit
    fresh=False,                # True to ignore prior DB contents
    ignore_prompt_hash=False,   # True to reuse rows produced by a different prompt
    api_key=None,               # overrides .env / environment
    base_url=None,              # for OpenRouter / Gemini compat
    client=None,                # BYO preconfigured SDK client
)

Failed API calls are retried automatically (tenacity, exponential backoff, up to 5 attempts) on RateLimitError and APIError. If a chunk still fails after retries, the failure is logged and the chunk's rows are omitted from the returned DataFrame.

draft_prompt knobs

draft_prompt(
    goal,                       # required: plain-English description of what to extract
    backend="openai",           # or "anthropic"
    model="gpt-4.1-mini",       # model for the meta-call; any chat model works
    api_key=None,               # overrides .env / environment
    base_url=None,              # for OpenRouter / Gemini compat
    print_prompt=True,          # print result to stdout (useful in notebooks)
)

draft_prompt returns the generated prompt string. Set print_prompt=False to suppress the automatic print and capture only the return value.

---

FAQ

Why does my DataFrame have fewer rows than I expect? A chunk failed after retries. Check the log for the stack trace.

How do I restart a batch job when I lost the job_id? Look under batch_root_dir/. The directories ARE the job_ids.

Is there retry logic? Yes: tenacity, 5 attempts with exponential backoff 2-30s for RateLimitError and APIError.

What about nested lists and dicts in the output? Flatten with df.explode("entities") (each list item becomes its own row), followed by pd.json_normalize(df["entities"]) (each dict's keys become columns). CSV stringifies nested fields; save as JSONL (df.to_json(..., orient="records", lines=True)) for clean round-tripping.

---

License

MIT.