#9 Add the ability to use Ollama Embeddings models

.pylintrc

@@ -70,7 +70,9 @@ disable=raw-checker-failed,
         use-symbolic-message-instead,
         # ACMI entries
         missing-docstring,
-        no-name-in-module
+        no-name-in-module,
+        duplicate-code,
+        import-error
 
 # Enable the message, report, category or checker with the given id(s). You can
 # either give multiple identifier separated by comma (,) or put this option

api/server.py

@@ -21,6 +21,7 @@
 from langchain.schema import format_document
 from langchain.schema.output_parser import StrOutputParser
 from langchain.schema.runnable import RunnableMap, RunnablePassthrough
+from langchain_community.embeddings import OllamaEmbeddings
 from langchain_community.llms import Ollama
 from langchain_community.vectorstores import Chroma
 from langchain_openai import ChatOpenAI, OpenAIEmbeddings
@@ -30,7 +31,8 @@
 DATABASE_PATH = os.getenv('DATABASE_PATH', '')
 COLLECTION_NAME = os.getenv('COLLECTION_NAME', 'works')
 PERSIST_DIRECTORY = os.getenv('PERSIST_DIRECTORY', 'works_db')
-MODEL = os.getenv('MODEL', 'gpt-4-turbo-2024-04-09')
+MODEL = os.getenv('MODEL', 'gpt-4o')
+EMBEDDINGS_MODEL = os.getenv('EMBEDDINGS_MODEL', 'nomic-embed-text')
 LLM_BASE_URL = os.getenv('LLM_BASE_URL', None)
 LANGCHAIN_TRACING_V2 = os.getenv('LANGCHAIN_TRACING_V2', 'false').lower() == 'true'
 
@@ -72,18 +74,21 @@ def _format_chat_history(chat_history: List[Tuple]) -> str:
     buffer = ""
     for dialogue_turn in chat_history:
         human = 'Human: ' + dialogue_turn[0]
-        ai = 'Assistant: ' + dialogue_turn[1]
-        buffer += '\n' + '\n'.join([human, ai])
+        assistant = 'Assistant: ' + dialogue_turn[1]
+        buffer += '\n' + '\n'.join([human, assistant])
     return buffer
 
 
-embeddings = OpenAIEmbeddings()
 if MODEL.startswith('gpt'):
     llm = ChatOpenAI(temperature=0, model=MODEL)
+    embeddings = OpenAIEmbeddings()
 else:
     llm = Ollama(model=MODEL)
+    embeddings = OllamaEmbeddings(model=EMBEDDINGS_MODEL)
     if LLM_BASE_URL:
         llm.base_url = LLM_BASE_URL
+        embeddings.base_url = LLM_BASE_URL
+
 docsearch = Chroma(
     collection_name=COLLECTION_NAME,
     embedding_function=embeddings,

chat.py

@@ -19,21 +19,34 @@
 from langchain.memory import ConversationBufferMemory
 from langchain.prompts import PromptTemplate
 from langchain_community.document_loaders import JSONLoader
+from langchain_community.embeddings import OllamaEmbeddings
+from langchain_community.llms import Ollama
 from langchain_community.vectorstores import Chroma
 from langchain_openai import ChatOpenAI, OpenAIEmbeddings
 
 DATABASE_PATH = os.getenv('DATABASE_PATH', '')
 COLLECTION_NAME = os.getenv('COLLECTION_NAME', 'works')
 PERSIST_DIRECTORY = os.getenv('PERSIST_DIRECTORY', 'works_db')
-MODEL = os.getenv('MODEL', 'gpt-4-turbo-2024-04-09')
+MODEL = os.getenv('MODEL', 'gpt-4o')
+EMBEDDINGS_MODEL = os.getenv('EMBEDDINGS_MODEL', 'nomic-embed-text')
+LLM_BASE_URL = os.getenv('LLM_BASE_URL', None)
 REBUILD = os.getenv('REBUILD', 'false').lower() == 'true'
 HISTORY = os.getenv('HISTORY', 'true').lower() == 'true'
 ALL = os.getenv('ALL', 'false').lower() == 'true'
 
 # Set true if you'd like langchain tracing via LangSmith https://smith.langchain.com
 os.environ['LANGCHAIN_TRACING_V2'] = 'false'
 
-embeddings = OpenAIEmbeddings()
+if MODEL.startswith('gpt'):
+    llm = ChatOpenAI(temperature=0, model=MODEL)
+    embeddings = OpenAIEmbeddings()
+else:
+    llm = Ollama(model=MODEL)
+    embeddings = OllamaEmbeddings(model=EMBEDDINGS_MODEL)
+    if LLM_BASE_URL:
+        llm.base_url = LLM_BASE_URL
+        embeddings.base_url = LLM_BASE_URL
+
 docsearch = Chroma(
     collection_name=COLLECTION_NAME,
     embedding_function=embeddings,
@@ -45,40 +58,47 @@
         'results': [],
     }
     params = {'page': ''}
-    if ALL:
-        print('Loading all of the works from the ACMI Public API')
-        while True:
-            page_data = requests.get(
-                'https://api.acmi.net.au/works/',
-                params=params,
-                timeout=10,
-            ).json()
-            json_data['results'].extend(page_data['results'])
-            if not page_data.get('next'):
-                break
-            params['page'] = furl(page_data.get('next')).args.get('page')
-            if len(json_data['results']) % 1000 == 0:
-                print(f'Downloaded {len(json_data["results"])}...')
+    TMP_FILE_PATH = 'data.json'
+
+    if os.path.isfile(TMP_FILE_PATH):
+        print('Loading works from the ACMI Public API data.json file you have already created...')
+        with open(TMP_FILE_PATH, 'r') as tmp_file:
+            json_data = json.load(tmp_file)
     else:
-        print('Loading the first ten pages of works from the ACMI Public API')
-        PAGES = 10
-        json_data = {
-            'results': [],
-        }
-        for index in range(1, (PAGES + 1)):
-            page_data = requests.get(
-                'https://api.acmi.net.au/works/',
-                params=params,
-                timeout=10,
-            )
-            json_data['results'].extend(page_data.json()['results'])
-            print(f'Downloaded {page_data.request.url}')
-            params['page'] = furl(page_data.json().get('next')).args.get('page')
-    print(f'Finished downloading {len(json_data["results"])} works.')
+        if ALL:
+            print('Loading all of the works from the ACMI Public API')
+            while True:
+                page_data = requests.get(
+                    'https://api.acmi.net.au/works/',
+                    params=params,
+                    timeout=10,
+                ).json()
+                json_data['results'].extend(page_data['results'])
+                if not page_data.get('next'):
+                    break
+                params['page'] = furl(page_data.get('next')).args.get('page')
+                if len(json_data['results']) % 1000 == 0:
+                    print(f'Downloaded {len(json_data["results"])}...')
+        else:
+            print('Loading the first ten pages of works from the ACMI Public API')
+            PAGES = 10
+            json_data = {
+                'results': [],
+            }
+            for index in range(1, (PAGES + 1)):
+                page_data = requests.get(
+                    'https://api.acmi.net.au/works/',
+                    params=params,
+                    timeout=10,
+                )
+                json_data['results'].extend(page_data.json()['results'])
+                print(f'Downloaded {page_data.request.url}')
+                params['page'] = furl(page_data.json().get('next')).args.get('page')
+        print(f'Finished downloading {len(json_data["results"])} works.')
+
+        with open(TMP_FILE_PATH, 'w', encoding='utf-8') as json_file:
+            json.dump(json_data, json_file)
 
-    TMP_FILE_PATH = 'data.json'
-    with open(TMP_FILE_PATH, 'w', encoding='utf-8') as json_file:
-        json.dump(json_data, json_file)
     json_loader = JSONLoader(
         file_path=TMP_FILE_PATH,
         jq_schema='.results[]',
@@ -103,14 +123,6 @@ def chunks(input_list, number_per_chunk):
         print(f'Added {len(sublist)} items to the database... total {(i + 1) * len(sublist)}')
     print(f'Finished adding {len(data)} items to the database')
 
-    docsearch = Chroma.from_documents(
-        data,
-        embeddings,
-        collection_name=COLLECTION_NAME,
-        persist_directory=PERSIST_DIRECTORY,
-    )
-
-llm = ChatOpenAI(temperature=0, model=MODEL)
 qa_chain = create_qa_with_sources_chain(llm)
 doc_prompt = PromptTemplate(
     template='Content: {page_content}\nSource: {source}',

config.tmpl.env

@@ -2,3 +2,4 @@ OPENAI_API_KEY=your-openai-api-key
 LANGCHAIN_API_KEY=your-langchain-api-key
 MODEL=
 LLM_BASE_URL=
+EMBEDDINGS_MODEL=