Merge pull request stanford-oval#189 from xiaobeicn/main

fix encoder documentation; add costorm-runner instance dumping in the example script

Author: Yucheng-Jiang

README.md

@@ -255,7 +255,7 @@ python examples/storm_examples/run_storm_wiki_gpt.py \
 
 To run Co-STORM with `gpt` family models with default configurations,
 
-1. Add `BING_SEARCH_API_KEY="xxx"`to `secrets.toml`
+1. Add `BING_SEARCH_API_KEY="xxx"` and `ENCODER_API_TYPE="xxx"` to `secrets.toml`
 2. Run the following command
 
 ```bash

examples/costorm_examples/run_costorm_gpt.py

@@ -143,6 +143,11 @@ def main(args):
     with open(os.path.join(args.output_dir, "report.md"), "w") as f:
         f.write(article)
 
+    # Save instance dump
+    instance_copy = costorm_runner.to_dict()
+    with open(os.path.join(args.output_dir, "instance_dump.json"), "w") as f:
+        json.dump(instance_copy, f, indent=2)
+
     # Save logging
     log_dump = costorm_runner.dump_logging_and_reset()
     with open(os.path.join(args.output_dir, "log.json"), "w") as f:

knowledge_storm/encoder.py

@@ -7,38 +7,13 @@
 
 
 class EmbeddingModel:
-    def __init__():
+    def __init__(self):
         pass
 
     def get_embedding(self, text: str) -> Tuple[np.ndarray, int]:
         raise Exception("Not implemented")
 
 
-class OpenAIEmbeddingModel(EmbeddingModel):
-    def __init__(self, model: str = "text-embedding-3-small", api_key: str = None):
-        if not api_key:
-            self.api_key = os.getenv("OPENAI_API_KEY")
-
-        self.url = "https://api.openai.com/v1/embeddings"
-        self.headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {self.api_key}",
-        }
-        self.model = model
-
-    def get_embedding(self, text: str) -> Tuple[np.ndarray, int]:
-        data = {"input": text, "model": "text-embedding-3-small"}
-
-        response = requests.post(self.url, headers=self.headers, json=data)
-        if response.status_code == 200:
-            data = response.json()
-            embedding = np.array(data["data"][0]["embedding"])
-            token = data["usage"]["prompt_tokens"]
-            return embedding, token
-        else:
-            response.raise_for_status()
-
-
 class OpenAIEmbeddingModel(EmbeddingModel):
     def __init__(self, model: str = "text-embedding-3-small", api_key: str = None):
         if not api_key: