added openai type to all openai functions

ai_ta_backend/filtering_contexts.py

@@ -83,6 +83,7 @@ def run_replicate(prompt):
 
 def run_anyscale(prompt):
   print("in run anyscale")
+
   ret = openai.ChatCompletion.create(
           api_base = "https://api.endpoints.anyscale.com/v1",
           api_key=os.environ["ANYSCALE_ENDPOINT_TOKEN"],
@@ -126,9 +127,11 @@ def run(contexts, user_query, max_tokens_to_return=3000, max_time_before_return=
   langsmith_prompt_obj = hub.pull("kastanday/filter-unrelated-contexts-zephyr")
 
   print("Num jobs to run:", len(contexts))
+  #print("Context: ", contexts[0])
+  #exit()
 
   actor = AsyncActor.options(max_concurrency=max_concurrency).remote()
-  result_futures = [actor.filter_context.remote(c, user_query, langsmith_prompt_obj) for c in contexts]
+  result_futures = [actor.filter_context.remote(c['text'], user_query, langsmith_prompt_obj) for c in contexts]
   print("Num futures:", len(result_futures))
   #print("Result futures:", result_futures)
 

ai_ta_backend/nomic_logging.py

@@ -115,7 +115,7 @@ def log_convo_to_nomic(course_name: str, conversation) -> str:
       }]
 
       # create embeddings
-      embeddings_model = OpenAIEmbeddings() # type: ignore
+      embeddings_model = OpenAIEmbeddings(openai_api_type="azure") # type: ignore
       embeddings = embeddings_model.embed_documents(user_queries)
 
     # add embeddings to the project
@@ -279,7 +279,7 @@ def create_nomic_map(course_name: str, log_data: list):
       metadata.append(metadata_row)
 
     metadata = pd.DataFrame(metadata)
-    embeddings_model = OpenAIEmbeddings()  # type: ignore
+    embeddings_model = OpenAIEmbeddings(openai_api_type="azure")  # type: ignore
     embeddings = embeddings_model.embed_documents(user_queries)
 
     # create Atlas project

ai_ta_backend/vector_database.py

@@ -47,6 +47,7 @@
 
 
 MULTI_QUERY_PROMPT = hub.pull("langchain-ai/rag-fusion-query-generation")
+OPENAI_API_TYPE = "azure" # "openai" or "azure"
 
 class Ingest():
   """
@@ -68,7 +69,7 @@ def __init__(self):
     self.vectorstore = Qdrant(
         client=self.qdrant_client,
         collection_name=os.environ['QDRANT_COLLECTION_NAME'],
-        embeddings=OpenAIEmbeddings()) # type: ignore
+        embeddings=OpenAIEmbeddings(openai_api_type=OPENAI_API_TYPE)) # type: ignore
 
     # S3
     self.s3_client = boto3.client(
@@ -89,6 +90,7 @@ def __init__(self):
       openai_api_key=os.getenv('AZURE_OPENAI_KEY'), #type:ignore
       #openai_api_version=os.getenv('AZURE_OPENAI_API_VERSION'), #type:ignore
       openai_api_version=os.getenv('OPENAI_API_VERSION'), #type:ignore
+      openai_api_type=OPENAI_API_TYPE
       ) 
     # self.llm = OpenAI(temperature=0, openai_api_base='https://api.kastan.ai/v1')
     #self.llm = ChatOpenAI(temperature=0, model='gpt-3.5-turbo')
@@ -281,6 +283,7 @@ def _ingest_single_video(self, s3_path: str, course_name: str, **kwargs) -> str:
       # check for file extension
       file_ext = Path(s3_path).suffix
       openai.api_key = os.getenv('OPENAI_API_KEY')
+
       transcript_list = []
       with NamedTemporaryFile(suffix=file_ext) as video_tmpfile:
         # download from S3 into an video tmpfile
@@ -953,7 +956,7 @@ def getAll(
 
   def vector_search(self, search_query, course_name):
       top_n = 80
-      o = OpenAIEmbeddings() # type: ignore
+      o = OpenAIEmbeddings(openai_api_type=OPENAI_API_TYPE) # type: ignore
       user_query_embedding = o.embed_query(search_query)
       myfilter = models.Filter(
               must=[
@@ -996,7 +999,7 @@ def vector_search(self, search_query, course_name):
 
   def batch_vector_search(self, search_queries: List[str], course_name: str, top_n: int=20):
     from qdrant_client.http import models as rest
-    o = OpenAIEmbeddings()
+    o = OpenAIEmbeddings(openai_api_type=OPENAI_API_TYPE)
     # Prepare the filter for the course name
     myfilter = rest.Filter(
         must=[
@@ -1515,7 +1518,7 @@ def get_stuffed_prompt(self, search_query: str, course_name: str, token_limit: i
     try:
       top_n = 150
       start_time_overall = time.monotonic()
-      o = OpenAIEmbeddings() # type: ignore
+      o = OpenAIEmbeddings(openai_api_type=OPENAI_API_TYPE) # type: ignore
       user_query_embedding = o.embed_documents(search_query)[0] # type: ignore
       myfilter = models.Filter(
               must=[