davidmezzetti
commented
4 months ago Hello,
Thanks for giving txtai a try. I had to modify the code a bit to get it to work. See below. The main thing is that the transform call passes an list of inputs vs a single input.
import ollama
import numpy as np
from txtai import Embeddings
def transform(inputs):
return np.array([ollama.embeddings(model="all-minilm", prompt=text)["embedding"] for text in inputs])
def test_stream(dataset):
for i, row in enumerate(dataset):
yield i,transform(row)
data = [
"US tops 5 million confirmed virus cases",
"Canada's last fully intact ice shelf has suddenly collapsed, " +
"forming a Manhattan-sized iceberg",
"Beijing mobilises invasion craft along coast as Taiwan tensions escalate",
"The National Park Service warns against sacrificing slower friends " +
"in a bear attack",
"Maine man wins $1M from $25 lottery ticket",
"Make huge profits without work, earn up to $100,000 a day"
]
embeddings = Embeddings({"transform": transform, "backend": "numpy", "content": True})
embeddings.index(data)
embeddings.search("feel good story", 1)
I am not sure if i am doing it correct but looking at the various examples i was able to get the following code to try and get ollama embeddings into txtai but i keep getting an error. Here is a minimal reproducible code. I ensured that Ollama server is running. Platform is Macos, also doesnt work on Windows with same error, txtai version 7.2.0.
Error
{ "name": "ResponseError", "message": "json: cannot unmarshal array into Go struct field EmbeddingRequest.prompt of type string", "stack": "--------------------------------------------------------------------------- ResponseError Traceback (most recent call last) Cell In[5], line 13 1 data = [ 2 \"US tops 5 million confirmed virus cases\", 3 \"Canada's last fully intact ice shelf has suddenly collapsed, \" + (...) 9 \"Make huge profits without work, earn up to $100,000 a day\" 10 ] 12 embeddings = Embeddings({\"transform\": transform, \"backend\": \"numpy\", \"content\": True}) ---> 13 embeddings.index(data) 14 embeddings.search(\"feel good story\", 1) File ~/Documents/LLM_test/.venv/lib/python3.12/site-packages/txtai/embeddings/base.py:120, in Embeddings.index(self, documents, reindex) 116 stream = Stream(self, Action.REINDEX if reindex else Action.INDEX) 118 with tempfile.NamedTemporaryFile(mode=\"wb\", suffix=\".npy\") as buffer: 119 # Load documents into database and transform to vectors --> 120 ids, dimensions, embeddings = transform(stream(documents), buffer) 121 if embeddings is not None: 122 # Build LSA model (if enabled). Remove principal components from embeddings. 123 if self.config.get(\"pca\"): File ~/Documents/LLM_test/.venv/lib/python3.12/site-packages/txtai/embeddings/index/transform.py:76, in Transform.__call__(self, documents, buffer) 73 ids, dimensions, embeddings = None, None, None 75 if self.model: ---> 76 ids, dimensions, embeddings = self.vectors(documents, buffer) 77 else: 78 ids = self.ids(documents) File ~/Documents/LLM_test/.venv/lib/python3.12/site-packages/txtai/embeddings/index/transform.py:95, in Transform.vectors(self, documents, buffer) 83 \"\"\" 84 Runs a vectors transform operation when dense indexing is enabled. 85 (...) 91 (document ids, dimensions, embeddings) 92 \"\"\" 94 # Consume stream and transform documents to vectors ---> 95 ids, dimensions, batches, stream = self.model.index(self.stream(documents), self.batch) 97 # Check that embeddings are available and load as a memmap 98 embeddings = None File ~/Documents/LLM_test/.venv/lib/python3.12/site-packages/txtai/vectors/base.py:136, in Vectors.index(self, documents, batchsize) 134 # Final batch 135 if batch: --> 136 uids, dimensions = self.batch(batch, output) 137 ids.extend(uids) 138 batches += 1 File ~/Documents/LLM_test/.venv/lib/python3.12/site-packages/txtai/vectors/base.py:195, in Vectors.batch(self, documents, output) 192 dimensions = None 194 # Build embeddings --> 195 embeddings = self.vectorize(documents) 196 if embeddings is not None: 197 dimensions = embeddings.shape[1] File ~/Documents/LLM_test/.venv/lib/python3.12/site-packages/txtai/vectors/base.py:241, in Vectors.vectorize(self, data) 225 \"\"\" 226 Runs data vectorization, which consists of the following steps. 227 (...) 237 embeddings vectors 238 \"\"\" 240 # Transform data into vectors --> 241 embeddings = self.encode(data) 243 if embeddings is not None: 244 # Truncate embeddings, if necessary 245 if self.dimensionality and self.dimensionality < embeddings.shape[1]: File ~/Documents/LLM_test/.venv/lib/python3.12/site-packages/txtai/vectors/external.py:31, in External.encode(self, data) 28 def encode(self, data): 29 # Call external transform function, if available and data not already an array 30 if self.transform and data and not isinstance(data[0], np.ndarray): ---> 31 data = self.transform(data) 33 # Cast to float32 34 return data.astype(np.float32) if isinstance(data, np.ndarray) else np.array(data, dtype=np.float32) Cell In[4], line 2, in transform(text) 1 def transform(text): ----> 2 n_array = ollama.embeddings(model=\"all-minilm\", prompt=text) 3 return np.array(n_array[\"embedding\"]) File ~/Documents/LLM_test/.venv/lib/python3.12/site-packages/ollama/_client.py:201, in Client.embeddings(self, model, prompt, options, keep_alive) 194 def embeddings( 195 self, 196 model: str = '', (...) 199 keep_alive: Optional[Union[float, str]] = None, 200 ) -> Mapping[str, Sequence[float]]: --> 201 return self._request( 202 'POST', 203 '/api/embeddings', 204 json={ 205 'model': model, 206 'prompt': prompt, 207 'options': options or {}, 208 'keep_alive': keep_alive, 209 }, 210 ).json() File ~/Documents/LLM_test/.venv/lib/python3.12/site-packages/ollama/_client.py:74, in Client._request(self, method, url, **kwargs) 72 response.raise_for_status() 73 except httpx.HTTPStatusError as e: ---> 74 raise ResponseError(e.response.text, e.response.status_code) from None 76 return response ResponseError: json: cannot unmarshal array into Go struct field EmbeddingRequest.prompt of type string" }