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Update embeddings integration for new batch embeddings interfaces (#669)

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* Fix vector extraction

* Fix embeddings integration
This commit is contained in:
cybermaggedon 2026-03-08 19:41:52 +00:00 • committed by GitHub
parent 0a2ce47a88
commit 919b760c05
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12 changed files with 55 additions and 56 deletions
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26
trustgraph-base/trustgraph/api/async_flow.py
View file

@ -613,8 +613,8 @@ class AsyncFlowInstance:
```
"""
# First convert text to embeddings vectors
emb_result = await self.embeddings(text=text)
vectors = emb_result.get("vectors", [])
emb_result = await self.embeddings(texts=[text])
vectors = emb_result.get("vectors", [[]])[0]
request_data = {
"vectors": vectors,
@ -626,20 +626,20 @@ class AsyncFlowInstance:
return await self.request("graph-embeddings", request_data)
async def embeddings(self, text: str, **kwargs: Any):
async def embeddings(self, texts: list, **kwargs: Any):
"""
Generate embeddings for input text.
Generate embeddings for input texts.
Converts text into a numerical vector representation using the flow's
Converts texts into numerical vector representations using the flow's
configured embedding model. Useful for semantic search and similarity
comparisons.
Args:
text: Input text to embed
texts: List of input texts to embed
**kwargs: Additional service-specific parameters
Returns:
dict: Response containing embedding vector and metadata
dict: Response containing embedding vectors
Example:
```python
@ -647,12 +647,12 @@ class AsyncFlowInstance:
flow = async_flow.id("default")
# Generate embeddings
result = await flow.embeddings(text="Sample text to embed")
vector = result.get("embedding")
print(f"Embedding dimension: {len(vector)}")
result = await flow.embeddings(texts=["Sample text to embed"])
vectors = result.get("vectors")
print(f"Embedding dimension: {len(vectors[0][0])}")
```
"""
request_data = {"text": text}
request_data = {"texts": texts}
request_data.update(kwargs)
return await self.request("embeddings", request_data)
@ -811,8 +811,8 @@ class AsyncFlowInstance:
```
"""
# First convert text to embeddings vectors
emb_result = await self.embeddings(text=text)
vectors = emb_result.get("vectors", [])
emb_result = await self.embeddings(texts=[text])
vectors = emb_result.get("vectors", [[]])[0]
request_data = {
"vectors": vectors,

12
trustgraph-base/trustgraph/api/async_socket_client.py
View file

@ -283,8 +283,8 @@ class AsyncSocketFlowInstance:
async def graph_embeddings_query(self, text: str, user: str, collection: str, limit: int = 10, **kwargs):
"""Query graph embeddings for semantic search"""
# First convert text to embeddings vectors
emb_result = await self.embeddings(text=text)
vectors = emb_result.get("vectors", [])
emb_result = await self.embeddings(texts=[text])
vectors = emb_result.get("vectors", [[]])[0]
request = {
"vectors": vectors,
@ -296,9 +296,9 @@ class AsyncSocketFlowInstance:
return await self.client._send_request("graph-embeddings", self.flow_id, request)
async def embeddings(self, text: str, **kwargs):
async def embeddings(self, texts: list, **kwargs):
"""Generate text embeddings"""
request = {"text": text}
request = {"texts": texts}
request.update(kwargs)
return await self.client._send_request("embeddings", self.flow_id, request)
@ -353,8 +353,8 @@ class AsyncSocketFlowInstance:
):
"""Query row embeddings for semantic search on structured data"""
# First convert text to embeddings vectors
emb_result = await self.embeddings(text=text)
vectors = emb_result.get("vectors", [])
emb_result = await self.embeddings(texts=[text])
vectors = emb_result.get("vectors", [[]])[0]
request = {
"vectors": vectors,

12
trustgraph-base/trustgraph/api/flow.py
View file

@ -603,8 +603,8 @@ class FlowInstance:
"""
# First convert text to embeddings vectors
emb_result = self.embeddings(text=text)
vectors = emb_result.get("vectors", [])
emb_result = self.embeddings(texts=[text])
vectors = emb_result.get("vectors", [[]])[0]
# Query graph embeddings for semantic search
input = {
@ -649,8 +649,8 @@ class FlowInstance:
"""
# First convert text to embeddings vectors
emb_result = self.embeddings(text=text)
vectors = emb_result.get("vectors", [])
emb_result = self.embeddings(texts=[text])
vectors = emb_result.get("vectors", [[]])[0]
# Query document embeddings for semantic search
input = {
@ -1363,8 +1363,8 @@ class FlowInstance:
"""
# First convert text to embeddings vectors
emb_result = self.embeddings(text=text)
vectors = emb_result.get("vectors", [])
emb_result = self.embeddings(texts=[text])
vectors = emb_result.get("vectors", [[]])[0]
# Query row embeddings for semantic search
input = {

12
trustgraph-base/trustgraph/api/socket_client.py
View file

@ -650,8 +650,8 @@ class SocketFlowInstance:
```
"""
# First convert text to embeddings vectors
emb_result = self.embeddings(text=text)
vectors = emb_result.get("vectors", [])
emb_result = self.embeddings(texts=[text])
vectors = emb_result.get("vectors", [[]])[0]
request = {
"vectors": vectors,
@ -699,8 +699,8 @@ class SocketFlowInstance:
```
"""
# First convert text to embeddings vectors
emb_result = self.embeddings(text=text)
vectors = emb_result.get("vectors", [])
emb_result = self.embeddings(texts=[text])
vectors = emb_result.get("vectors", [[]])[0]
request = {
"vectors": vectors,
@ -937,8 +937,8 @@ class SocketFlowInstance:
```
"""
# First convert text to embeddings vectors
emb_result = self.embeddings(text=text)
vectors = emb_result.get("vectors", [])
emb_result = self.embeddings(texts=[text])
vectors = emb_result.get("vectors", [[]])[0]
request = {
"vectors": vectors,