Merge pull request #732 from tedcochran/issue-731-embeddings-not-call…

…able

Created a function for embeddings options

ingest.py

@@ -160,29 +160,29 @@ def main(device_type):
  their respective huggingface repository, project page or github repository.
  """
 
- if "instructor" in EMBEDDING_MODEL_NAME:
- return HuggingFaceInstructEmbeddings(
- model_name=EMBEDDING_MODEL_NAME,
- model_kwargs={"device": compute_device},
- embed_instruction='Represent the document for retrieval:',
- query_instruction='Represent the question for retrieving supporting documents:'
- )
-
- elif "bge" in EMBEDDING_MODEL_NAME:
- query_instruction = 'Represent this sentence for searching relevant passages:'
-
- return HuggingFaceBgeEmbeddings(
- model_name=EMBEDDING_MODEL_NAME,
- model_kwargs={"device": compute_device},
- query_instruction='Represent this sentence for searching relevant passages:'
- )
-
- else:
-
- return HuggingFaceEmbeddings(
- model_name=EMBEDDING_MODEL_NAME,
-  model_kwargs={"device": compute_device},
-  )
+ def get_embeddings():
+ if "instructor" in EMBEDDING_MODEL_NAME:
+ return HuggingFaceInstructEmbeddings(
+  model_name=EMBEDDING_MODEL_NAME,
+  model_kwargs={"device": compute_device},
+  embed_instruction='Represent the document for retrieval:',
+  query_instruction='Represent the question for retrieving supporting documents:'
+ )
+
+ elif "bge" in EMBEDDING_MODEL_NAME:
+ return HuggingFaceBgeEmbeddings(
+  model_name=EMBEDDING_MODEL_NAME,
+  model_kwargs={"device": compute_device},
+  query_instruction='Represent this sentence for searching relevant passages:'
+ )
+
+ else:
+  return HuggingFaceEmbeddings(
+  model_name=EMBEDDING_MODEL_NAME,
+  model_kwargs={"device": compute_device},
+ )
+ embeddings = get_embeddings()
+ logging.info(f"Loaded embeddings from {EMBEDDING_MODEL_NAME}")
 
  db = Chroma.from_documents(
  texts,

run_localGPT.py

@@ -126,27 +126,30 @@ def retrieval_qa_pipline(device_type, use_history, promptTemplate_type="llama"):
  their respective huggingface repository, project page or github repository.
  """
 
- if "instructor" in EMBEDDING_MODEL_NAME:
- return HuggingFaceInstructEmbeddings(
- model_name=EMBEDDING_MODEL_NAME,
- model_kwargs={"device": compute_device},
- embed_instruction='Represent the document for retrieval:',
- query_instruction='Represent the question for retrieving supporting documents:'
- )
-
- elif "bge" in EMBEDDING_MODEL_NAME:
- return HuggingFaceBgeEmbeddings(
- model_name=EMBEDDING_MODEL_NAME,
- model_kwargs={"device": compute_device},
- query_instruction='Represent this sentence for searching relevant passages:'
- )
-
- else:
- return HuggingFaceEmbeddings(
- model_name=EMBEDDING_MODEL_NAME,
- model_kwargs={"device": compute_device},
- )
+ def get_embeddings():
+ if "instructor" in EMBEDDING_MODEL_NAME:
+ return HuggingFaceInstructEmbeddings(
+ model_name=EMBEDDING_MODEL_NAME,
+ model_kwargs={"device": compute_device},
+ embed_instruction='Represent the document for retrieval:',
+ query_instruction='Represent the question for retrieving supporting documents:'
+ )
+
+ elif "bge" in EMBEDDING_MODEL_NAME:
+ return HuggingFaceBgeEmbeddings(
+ model_name=EMBEDDING_MODEL_NAME,
+ model_kwargs={"device": compute_device},
+ query_instruction='Represent this sentence for searching relevant passages:'
+ )
 
+ else:
+ return HuggingFaceEmbeddings(
+ model_name=EMBEDDING_MODEL_NAME,
+ model_kwargs={"device": compute_device},
+ )
+ embeddings = get_embeddings()
+ logging.info(f"Loaded embeddings from {EMBEDDING_MODEL_NAME}")
+
  # load the vectorstore
  db = Chroma(
  persist_directory=PERSIST_DIRECTORY,