Merge pull request #832 from llmware-ai/update-reqs-configs

updating requirements configuration

llmware/configs.py

@@ -97,7 +97,8 @@ class LLMWareConfig:
              "shared_lib_path": os.path.join(os.path.dirname(os.path.realpath(__file__)), "lib"),
              "logging_level": logging.WARNING,
              "logging_format": COLOR_WHITE + '%(levelname)-4s: %(message)s' + COLOR_RESET,
-             "logging_level_by_module": {"llmware.embeddings": 20, "llmware.models": 30, "llmware.agents":20},
+             "logging_level_by_module": {"llmware.embeddings": 20, "llmware.models": 30, "llmware.agents":20,
+                                         "llmware.prompts": 20},
              "agent_writer_mode": "screen",
              "agent_log_file": "agent_log.txt",
              "model_register": {"module": "llmware.models", "class": "register"},

llmware/prompts.py

@@ -40,6 +40,7 @@
 from llmware.configs import LLMWareConfig
 
 logger = logging.getLogger(__name__)
+logger.setLevel(level=LLMWareConfig().get_logging_level_by_module(__name__))
 
 
 class Prompt:
@@ -167,7 +168,7 @@ def __init__(self, llm_name=None, tokenizer=None, model_card=None, library=None,
             new_prompt_id = PromptState(self).issue_new_prompt_id()
             self.prompt_id = PromptState(self).initiate_new_state_session(new_prompt_id)
 
-            logger.info(f"update: creating new prompt id - {new_prompt_id}")
+            logger.debug(f"update: Prompt - creating new prompt id - {new_prompt_id}")
 
         self.save_prompt_state = save_state
 
@@ -322,7 +323,7 @@ def register_llm_inference (self, ai_dict, prompt_id=None, trx_dict=None):
                 ai_dict.update({key:value})
 
         # captures new interaction into the interaction history
-        logger.info(f"update: ai_dict getting registered - {ai_dict['event_type']}")
+        logger.debug(f"update: ai_dict getting registered - {ai_dict['event_type']}")
 
         PromptState(self).register_interaction(ai_dict)
         new_dialog = {"user": ai_dict["prompt"], "bot": ai_dict["llm_response"]}
@@ -435,7 +436,7 @@ def add_source_wikipedia(self, topic, article_count=3, query=None):
                 output = Utilities().fast_search_dicts(query, output, remove_stop_words=True)
 
         for i, entries in enumerate(output):
-            logger.info(f"update: source entries - {i} - {entries}")
+            logger.debug(f"update: source entries - {i} - {entries}")
 
         # step 2 - package wiki article results as source, loaded to prompt, and packaged as 'llm context'
         sources = Sources(self).package_source(output,aggregate_source=True)
@@ -455,7 +456,7 @@ def add_source_yahoo_finance(self, ticker=None, key_list=None):
 
         fin_info = YFinance().ticker(ticker).info
 
-        logger.info(f"update: fin_info - {fin_info}")
+        logger.debug(f"update: fin_info - {fin_info}")
 
         output = ""
         if key_list:
@@ -468,7 +469,7 @@ def add_source_yahoo_finance(self, ticker=None, key_list=None):
 
         results = {"file_source": "yfinance-" + str(ticker), "page_num": "na", "text": output}
 
-        logger.info(f"update: yfinance results - {results}")
+        logger.debug(f"update: yfinance results - {results}")
 
         # step 2 - package as source
         sources = Sources(self).package_source([results], aggregate_source=True)
@@ -636,7 +637,7 @@ def prompt_with_source(self, prompt, prompt_name=None, source_id_list=None, firs
 
         if temperature:
             self.temperature = temperature
-            
+
         #   this method assumes a 'closed context' with set of preloaded sources into the prompt
         # if len(self.source_materials) == 0:
         if not self.verify_source_materials_attached():
@@ -701,20 +702,13 @@ def prompt_with_source(self, prompt, prompt_name=None, source_id_list=None, firs
                     response_list.append(response_dict)
 
                 # log progress of iterations at info level
-                if not verbose:
-
-                    logger.info(f"update: prompt_with_sources - iterating through batch - {i} of total "
-                                f"{len(self.source_materials)} - {response_dict}")
-
-                    logger.info(f"update: usage stats - {response_dict['usage']}")
-
-                else:
-                    logger.info(f"update: iterating through source batches - {i} - {response_dict['llm_response']}")
+                if verbose:
+                    logger.info(f"update: prompt_with_sources - iterating through source batches - {i} - {response_dict['llm_response']}")
 
         # register inferences in state history, linked to prompt_id
         for l, llm_inference in enumerate(response_list):
 
-            logger.info (f"update: llm inference - {l} - {len(response_list)} - {llm_inference}")
+            logger.debug (f"update: llm inference - {l} - {len(response_list)} - {llm_inference}")
 
             self.register_llm_inference(llm_inference)
 
@@ -1074,6 +1068,7 @@ def summarize_document_fc(self, fp, fn, topic="key points", query=None, text_onl
             self.source_materials = self.source_materials[0:max_batch_cap]
 
         if real_time_update:
+
             logger.info(f"update: Prompt - summarize_document_fc - number of source batches - "
                         f"{len(self.source_materials)}")
 

llmware/requirements.txt

@@ -1,18 +1,13 @@
-boto3>=1.24.53  
+#   core requirements for main llmware operations and use cases
+#   please see also requirements_extras.txt
+boto3>=1.24.53
 numpy>=1.23.2
-openai>=1.0
 pymongo>=4.7.0
-torch>=1.13.1
-transformers>=4.36.0
-Wikipedia-API==0.6.0
 psycopg-binary==3.1.17
 psycopg==3.1.17
 pgvector==0.2.4  
 colorama==0.4.6
-einops==0.7.0
 librosa>=0.10.0
-
 tokenizers>=0.15.0
 huggingface-hub>=0.19.4  
-requests>=2.31.0
 

llmware/requirements_extras.txt

@@ -0,0 +1,14 @@
+#   requirements_extras - optional libraries most often used in conjunction with llmware
+#   this libraries will be installed with the welcome_to_llmware.sh script and are used in many examples
+
+torch>=1.13.1
+transformers>=4.36.0
+einops==0.7.0
+Wikipedia-API>=0.6.0
+openai>=1.0
+datasets>=2.15.0
+yfinance>=0.2.28
+pymilvus>=2.3.0
+chromadb>=0.4.22
+streamlit
+Flask

setup.py

@@ -56,27 +56,25 @@ def glob_fix(package_name, glob):
         'boto3>=1.24.53',
         'huggingface-hub>=0.19.4',
         'numpy>=1.23.2',
-        'openai>=1.0.0',
         'pymongo>=4.7.0',
         'tokenizers>=0.15.0',
-        'torch>=1.13.1',
-        'transformers>=4.36.0',
-        'Wikipedia-API==0.6.0',
         'psycopg-binary==3.1.17',
         'psycopg==3.1.17',
         'pgvector==0.2.4',
         'colorama==0.4.6',
-        'einops==0.7.0',
         'librosa>=0.10.0'
     ],
 
     extras_require={
         'milvus': ['pymilvus>=2.3.0'],
         'chromadb': ['chromadb>=0.4.22'],
         'pinecone': ['pinecone-client==3.0.0'],
-        'lancedb' :['lancedb==0.5.0'],
+        'lancedb': ['lancedb==0.5.0'],
         'qdrant': ['qdrant-client==1.7.0'],
         'redis': ['redis==5.0.1'],
-        'neo4j': ['neo4j==5.16.0']
+        'neo4j': ['neo4j==5.16.0'],
+        'full': ['torch>=1.13.1', 'transformers>=4.36.0', 'einops==0.7.0', 'Wikipedia-API>=0.6.0',
+                 'openai>=1.0', 'datasets>=2.15.0', 'yfinance>=0.2.28', 'pymilvus>=2.3.0',
+                 'chromadb>=0.4.22', 'streamlit', 'Flask']
     },
 )

welcome_to_llmware.sh

@@ -3,12 +3,11 @@
 # Welcome to LLMWare script - handles some basic setup for first-time cloning of the repo
 # Mac / Linux version
 
-# Install dependencies, including several useful libraries used in examples
+# Install core dependencies
 pip3 install -r ./llmware/requirements.txt
-pip3 install chromadb
-pip3 install datasets
-pip3 install yfinance
-pip3 install streamlit
+
+# # Note: this step is optional but adds many commonly-used optional dependencies (including in several examples)
+pip3 install -r ./llmware/requirements_extras.txt
 
 # Move selected examples into root path for easy execution from command line
 scp ./examples/Getting_Started/welcome_example.py .
@@ -62,4 +61,4 @@ echo ""
 
 # run welcome_example.py which serves as a test that the installation is successful
 echo "Running welcome_example.py"
-python3 welcome_example.py
+python3 welcome_example.py

welcome_to_llmware_windows.sh

@@ -3,12 +3,11 @@
 # Welcome to LLMWare script - handles some basic setup for first-time cloning of the repo
 # Windows version
 
-# Install dependencies, including several useful libraries used in examples
+# Install core dependencies
 pip3 install -r ./llmware/requirements.txt
-pip3 install chromadb
-pip3 install datasets
-pip3 install yfinance
-pip3 install streamlit
+
+# Note: this step is optional but adds many commonly-used optional dependencies (including in several examples)
+pip3 install -r ./llmware/requirements_extras.txt
 
 # Move selected examples into root path for easy execution from command line
 scp ./examples/Getting_Started/welcome_example.py .