Copilot commented on code in PR #250:
URL:
https://github.com/apache/incubator-hugegraph-ai/pull/250#discussion_r2103998263
##########
hugegraph-llm/src/hugegraph_llm/models/embeddings/ollama.py:
##########
@@ -63,8 +96,20 @@ def get_texts_embeddings(
A list of embedding vectors, where each vector is a list of floats.
The order of embeddings matches the order of input texts.
"""
- response = self.client.embed(model=self.model,
input=texts)["embeddings"]
- return [list(inner_sequence) for inner_sequence in response]
+ if hasattr(self.client, "embed"): # pylint: disable=no-else-return
+ response = self.client.embed(model=self.model,
input=texts)["embeddings"]
+ return [list(inner_sequence) for inner_sequence in response]
+ elif hasattr(self.client, "embeddings"):
+ embeddings_list = []
+ for text_item in texts:
+ response_item = self.client.embeddings(model=self.model,
prompt=text_item)
+ embeddings_list.append(list(response_item["embedding"]))
+ return embeddings_list
Review Comment:
[nitpick] When using the 'embeddings' method, the code makes individual API
calls for each text. Consider checking if a batch API is available or batching
these calls to reduce potential network overhead.
```suggestion
response = self.client.embeddings(model=self.model, prompt=texts)
try:
return [list(embedding) for embedding in
response["embeddings"]]
except KeyError as e:
raise RuntimeError(
"Failed to extract embeddings from Ollama client
'embeddings' response. "
f"Response: {response}. Error: {e}"
) from e
```
##########
hugegraph-llm/src/hugegraph_llm/models/embeddings/ollama.py:
##########
@@ -39,8 +39,41 @@ def get_text_embedding(
self,
text: str
) -> List[float]:
- """Comment"""
- return list(self.client.embed(model=self.model,
input=text)["embeddings"][0])
+ """Get embedding for a single text.
+
+ This method handles different Ollama client API versions by checking
for
+ the presence of 'embed' or 'embeddings' methods.
+ """
+ if hasattr(self.client, "embed"):
+ response = self.client.embed(model=self.model, input=text)
+ try:
+ # First, try the structure typically seen for single embeddings
+ # or newer batch responses that might return a single
"embedding" key.
+ return list(response["embedding"])
+ except KeyError:
+ # Fallback for older batch-like response for single item,
+ # or if "embeddings" is a list with one item.
+ try:
+ return list(response["embeddings"][0])
+ except (KeyError, IndexError) as e:
+ raise RuntimeError(
+ "Failed to extract embedding from Ollama client
'embed' response. "
+ f"Response: {response}. Error: {e}"
+ ) from e
+ elif hasattr(self.client, "embeddings"):
+ response = self.client.embeddings(model=self.model, prompt=text)
+ try:
+ return list(response["embedding"])
+ except KeyError as e:
+ raise RuntimeError(
+ "Failed to extract embedding from Ollama client
'embeddings' response. "
+ f"Response: {response}. Error: {e}"
+ ) from e
Review Comment:
[nitpick] The nested try/except blocks could be refactored by first checking
the existence of keys in the response, which would improve clarity and reduce
reliance on exception handling.
```suggestion
# Check for the presence of "embedding" key in the response
if "embedding" in response:
return list(response["embedding"])
# Fallback for older batch-like response with "embeddings" key
elif "embeddings" in response and
isinstance(response["embeddings"], list) and len(response["embeddings"]) > 0:
return list(response["embeddings"][0])
else:
raise RuntimeError(
"Failed to extract embedding from Ollama client 'embed'
response. "
f"Response: {response}. Ensure the response structure is
correct."
)
elif hasattr(self.client, "embeddings"):
response = self.client.embeddings(model=self.model, prompt=text)
# Check for the presence of "embedding" key in the response
if "embedding" in response:
return list(response["embedding"])
else:
raise RuntimeError(
"Failed to extract embedding from Ollama client
'embeddings' response. "
f"Response: {response}. Ensure the response structure is
correct."
)
```
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