From 880935dc6d88106ab4d8d1bd6175a0fa2b0a9ca8 Mon Sep 17 00:00:00 2001 From: Vineeth Voruganti <13438633+VVoruganti@users.noreply.github.com> Date: Tue, 12 Mar 2024 16:40:53 -0700 Subject: [PATCH] Add Built-in Langchain Utility function --- example/cli/main.py | 16 +- example/cli/poetry.lock | 677 ++++++++++-------- example/discord/honcho-dspy-personas/bot.py | 24 +- example/discord/honcho-dspy-personas/chain.py | 131 ++-- example/discord/honcho-fact-memory/bot.py | 3 +- example/discord/honcho-fact-memory/chain.py | 163 +++-- example/discord/simple-roast-bot/main.py | 11 +- sdk/honcho/ext/__init__.py | 0 sdk/honcho/ext/langchain.py | 29 + 9 files changed, 615 insertions(+), 439 deletions(-) create mode 100644 sdk/honcho/ext/__init__.py create mode 100644 sdk/honcho/ext/langchain.py diff --git a/example/cli/main.py b/example/cli/main.py index f1cd9e2..a44f0a6 100644 --- a/example/cli/main.py +++ b/example/cli/main.py @@ -2,14 +2,18 @@ from uuid import uuid4 from langchain.prompts import ChatPromptTemplate + from langchain.schema import AIMessage, HumanMessage, SystemMessage from langchain_community.chat_models.fake import FakeListChatModel from honcho import Honcho +from honcho.ext.langchain import langchain_message_converter app_name = str(uuid4()) -# honcho = Honcho(app_id=app_id, base_url="http://localhost:8000") # uncomment to use local +# honcho = Honcho( +# app_name=app_name, base_url="http://localhost:8000" +# ) # uncomment to use local honcho = Honcho(app_name=app_name) # uses demo server at https://demo.honcho.dev honcho.initialize() @@ -24,16 +28,6 @@ session = user.create_session() -def langchain_message_converter(messages: List): - new_messages = [] - for message in messages: - if message.is_user: - new_messages.append(HumanMessage(content=message.content)) - else: - new_messages.append(AIMessage(content=message.content)) - return new_messages - - def chat(): while True: user_input = input("User: ") diff --git a/example/cli/poetry.lock b/example/cli/poetry.lock index 0632c39..546d3c0 100644 --- a/example/cli/poetry.lock +++ b/example/cli/poetry.lock @@ -125,14 +125,14 @@ files = [ [[package]] name = "anyio" -version = "4.2.0" +version = "4.3.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" category = "main" optional = false python-versions = ">=3.8" files = [ - {file = "anyio-4.2.0-py3-none-any.whl", hash = "sha256:745843b39e829e108e518c489b31dc757de7d2131d53fac32bd8df268227bfee"}, - {file = "anyio-4.2.0.tar.gz", hash = "sha256:e1875bb4b4e2de1669f4bc7869b6d3f54231cdced71605e6e64c9be77e3be50f"}, + {file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"}, + {file = "anyio-4.3.0.tar.gz", hash = "sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6"}, ] [package.dependencies] @@ -166,14 +166,14 @@ tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "p [[package]] name = "certifi" -version = "2023.11.17" +version = "2024.2.2" description = "Python package for providing Mozilla's CA Bundle." category = "main" optional = false python-versions = ">=3.6" files = [ - {file = "certifi-2023.11.17-py3-none-any.whl", hash = "sha256:e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474"}, - {file = "certifi-2023.11.17.tar.gz", hash = "sha256:9b469f3a900bf28dc19b8cfbf8019bf47f7fdd1a65a1d4ffb98fc14166beb4d1"}, + {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"}, + {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"}, ] [[package]] @@ -278,14 +278,14 @@ files = [ [[package]] name = "dataclasses-json" -version = "0.6.3" +version = "0.6.4" description = "Easily serialize dataclasses to and from JSON." category = "dev" optional = false python-versions = ">=3.7,<4.0" files = [ - {file = 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sessions = list(user.get_sessions_generator(location_id, is_active=True, reverse=True)) + sessions = list( + user.get_sessions_generator(location_id, is_active=True, reverse=True) + ) if len(sessions) > 0: session = sessions[0] @@ -86,7 +88,9 @@ async def on_reaction_add(reaction, user): honcho_user = honcho.get_or_create_user(user_id) location_id = str(reaction.message.channel.id) - sessions = list(honcho_user.get_sessions_generator(location_id, is_active=True, reverse=True)) + sessions = list( + honcho_user.get_sessions_generator(location_id, is_active=True, reverse=True) + ) if len(sessions) > 0: session = sessions[0] else: @@ -100,25 +104,29 @@ async def on_reaction_add(reaction, user): user_response = user_responses[0] user_state_storage = dict(honcho_user.metadata) - user_state = list(session.get_metamessages_generator(metamessage_type="user_state", message=user_response, reverse=True))[0].content + user_state = list( + session.get_metamessages_generator( + metamessage_type="user_state", message=user_response, reverse=True + ) + )[0].content examples = user_state_storage[user_state]["examples"] # Check if the reaction is a thumbs up if str(reaction.emoji) == "👍": example = Example( - chat_input=user_response.content, + chat_input=user_response.content, response=ai_response, assessment_dimension=user_state, - label='yes' + label="yes", ).with_inputs("chat_input", "response", "assessment_dimension") examples.append(example.toDict()) # Check if the reaction is a thumbs down elif str(reaction.emoji) == "👎": example = Example( - chat_input=user_response.content, + chat_input=user_response.content, response=ai_response, assessment_dimension=user_state, - label='no' + label="no", ).with_inputs("chat_input", "response", "assessment_dimension") examples.append(example.toDict()) diff --git a/example/discord/honcho-dspy-personas/chain.py b/example/discord/honcho-dspy-personas/chain.py index eede40e..be92c21 100644 --- a/example/discord/honcho-dspy-personas/chain.py +++ b/example/discord/honcho-dspy-personas/chain.py @@ -2,7 +2,11 @@ from typing import List, Union from dotenv import load_dotenv from langchain_openai import ChatOpenAI -from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, load_prompt +from langchain_core.prompts import ( + ChatPromptTemplate, + SystemMessagePromptTemplate, + load_prompt, +) from langchain_core.messages import AIMessage, HumanMessage from honcho import Message @@ -10,84 +14,103 @@ load_dotenv() # langchain prompts -SYSTEM_STATE_COMMENTARY = load_prompt(os.path.join(os.path.dirname(__file__), 'langchain_prompts/state_commentary.yaml')) -SYSTEM_STATE_LABELING = load_prompt(os.path.join(os.path.dirname(__file__), 'langchain_prompts/state_labeling.yaml')) -SYSTEM_STATE_CHECK = load_prompt(os.path.join(os.path.dirname(__file__), 'langchain_prompts/state_check.yaml')) - -# quick utility function to convert messages from honcho to langchain -def langchain_message_converter(messages: List[Message]) -> List[Union[AIMessage, HumanMessage]]: - new_messages = [] - for message in messages: - if message.is_user: - new_messages.append(HumanMessage(content=message.content)) - else: - new_messages.append(AIMessage(content=message.content)) - return new_messages +SYSTEM_STATE_COMMENTARY = load_prompt( + os.path.join(os.path.dirname(__file__), "langchain_prompts/state_commentary.yaml") +) +SYSTEM_STATE_LABELING = load_prompt( + os.path.join(os.path.dirname(__file__), "langchain_prompts/state_labeling.yaml") +) +SYSTEM_STATE_CHECK = load_prompt( + os.path.join(os.path.dirname(__file__), "langchain_prompts/state_check.yaml") +) # convert chat history and user input into a string def format_chat_history(chat_history: List[Message], user_input=None): - messages = [("user: " + message.content if isinstance(message, HumanMessage) else "ai: " + message.content) for message in chat_history] + messages = [ + ( + "user: " + message.content + if isinstance(message, HumanMessage) + else "ai: " + message.content + ) + for message in chat_history + ] if user_input: messages.append(f"user: {user_input}") return "\n".join(messages) - class StateExtractor: """Wrapper class for all the DSPy and LangChain code for user state labeling and pipeline optimization""" - lc_gpt_4: ChatOpenAI = ChatOpenAI(model_name = "gpt-4") - lc_gpt_turbo: ChatOpenAI = ChatOpenAI(model_name = "gpt-3.5-turbo") - system_state_commentary: SystemMessagePromptTemplate = SystemMessagePromptTemplate(prompt=SYSTEM_STATE_COMMENTARY) - system_state_labeling: SystemMessagePromptTemplate = SystemMessagePromptTemplate(prompt=SYSTEM_STATE_LABELING) - system_state_check: SystemMessagePromptTemplate = SystemMessagePromptTemplate(prompt=SYSTEM_STATE_CHECK) + + lc_gpt_4: ChatOpenAI = ChatOpenAI(model_name="gpt-4") + lc_gpt_turbo: ChatOpenAI = ChatOpenAI(model_name="gpt-3.5-turbo") + system_state_commentary: SystemMessagePromptTemplate = SystemMessagePromptTemplate( + prompt=SYSTEM_STATE_COMMENTARY + ) + system_state_labeling: SystemMessagePromptTemplate = SystemMessagePromptTemplate( + prompt=SYSTEM_STATE_LABELING + ) + system_state_check: SystemMessagePromptTemplate = SystemMessagePromptTemplate( + prompt=SYSTEM_STATE_CHECK + ) def __init__(self) -> None: pass @classmethod - async def generate_state_commentary(cls, existing_states: List[str], chat_history: List[Message], input: str) -> str: + async def generate_state_commentary( + cls, existing_states: List[str], chat_history: List[Message], input: str + ) -> str: """Generate a commentary on the current state of the user""" # format existing states existing_states = "\n".join(existing_states) # format prompt - state_commentary = ChatPromptTemplate.from_messages([ - cls.system_state_commentary - ]) + state_commentary = ChatPromptTemplate.from_messages( + [cls.system_state_commentary] + ) # LCEL chain = state_commentary | cls.lc_gpt_4 # inference - response = await chain.ainvoke({ - "chat_history": chat_history, - "user_input": input, - "existing_states": existing_states, - }) + response = await chain.ainvoke( + { + "chat_history": chat_history, + "user_input": input, + "existing_states": existing_states, + } + ) # return output return response.content - + @classmethod - async def generate_state_label(cls, existing_states: List[str], state_commentary: str) -> str: + async def generate_state_label( + cls, existing_states: List[str], state_commentary: str + ) -> str: """Generate a state label from a commetary on the user's state""" # format existing states existing_states = "\n".join(existing_states) # format prompt - state_labeling = ChatPromptTemplate.from_messages([ - cls.system_state_labeling, - ]) + state_labeling = ChatPromptTemplate.from_messages( + [ + cls.system_state_labeling, + ] + ) # LCEL chain = state_labeling | cls.lc_gpt_4 # inference - response = await chain.ainvoke({ - "state_commentary": state_commentary, - "existing_states": existing_states, - }) + response = await chain.ainvoke( + { + "state_commentary": state_commentary, + "existing_states": existing_states, + } + ) # strip anything that's not letters - clean_response = ''.join(c for c in response.content if c.isalpha()) + clean_response = "".join(c for c in response.content if c.isalpha()) # return output return clean_response - + @classmethod async def check_state_exists(cls, existing_states: List[str], state: str): """Check if a user state is new or already is stored""" @@ -96,32 +119,36 @@ async def check_state_exists(cls, existing_states: List[str], state: str): existing_states = "\n".join(existing_states) # format prompt - state_check = ChatPromptTemplate.from_messages([ - cls.system_state_check - ]) + state_check = ChatPromptTemplate.from_messages([cls.system_state_check]) # LCEL chain = state_check | cls.lc_gpt_turbo # inference - response = await chain.ainvoke({ - "existing_states": existing_states, - "state": state, - }) + response = await chain.ainvoke( + { + "existing_states": existing_states, + "state": state, + } + ) # return output return response.content @classmethod - async def generate_state(cls, existing_states: List[str], chat_history: List[Message], input: str): - """"Determine the user's state from the current conversation state""" + async def generate_state( + cls, existing_states: List[str], chat_history: List[Message], input: str + ): + """ "Determine the user's state from the current conversation state""" # Generate label - state_commentary = await cls.generate_state_commentary(existing_states, chat_history, input) + state_commentary = await cls.generate_state_commentary( + existing_states, chat_history, input + ) state_label = await cls.generate_state_label(existing_states, state_commentary) # Determine if state is new # if True, it doesn't exist, state is new # if False, it does exist, state is not new, existing_state was returned existing_state = await cls.check_state_exists(existing_states, state_label) - is_state_new = existing_state == "None" + is_state_new = existing_state == "None" # return existing state if we found one if is_state_new: diff --git a/example/discord/honcho-fact-memory/bot.py b/example/discord/honcho-fact-memory/bot.py index a72e54a..aca2650 100644 --- a/example/discord/honcho-fact-memory/bot.py +++ b/example/discord/honcho-fact-memory/bot.py @@ -2,7 +2,8 @@ from uuid import uuid1 import discord from honcho import Honcho -from chain import langchain_message_converter, LMChain +from honcho.ext.langchain import langchain_message_converter +from chain import LMChain intents = discord.Intents.default() diff --git a/example/discord/honcho-fact-memory/chain.py b/example/discord/honcho-fact-memory/chain.py index a2b828b..0c528e1 100644 --- a/example/discord/honcho-fact-memory/chain.py +++ b/example/discord/honcho-fact-memory/chain.py @@ -2,7 +2,11 @@ from typing import List from dotenv import load_dotenv from langchain_openai import ChatOpenAI -from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, load_prompt +from langchain_core.prompts import ( + ChatPromptTemplate, + SystemMessagePromptTemplate, + load_prompt, +) from langchain_core.output_parsers import NumberedListOutputParser from langchain_core.messages import AIMessage, HumanMessage @@ -10,30 +14,37 @@ load_dotenv() -SYSTEM_DERIVE_FACTS = load_prompt(os.path.join(os.path.dirname(__file__), 'prompts/core/derive_facts.yaml')) -SYSTEM_INTROSPECTION = load_prompt(os.path.join(os.path.dirname(__file__), 'prompts/core/introspection.yaml')) -SYSTEM_RESPONSE = load_prompt(os.path.join(os.path.dirname(__file__), 'prompts/core/response.yaml')) -SYSTEM_CHECK_DUPS = load_prompt(os.path.join(os.path.dirname(__file__), 'prompts/utils/check_dup_facts.yaml')) - - -def langchain_message_converter(messages: List): - new_messages = [] - for message in messages: - if message.is_user: - new_messages.append(HumanMessage(content=message.content)) - else: - new_messages.append(AIMessage(content=message.content)) - return new_messages +SYSTEM_DERIVE_FACTS = load_prompt( + os.path.join(os.path.dirname(__file__), "prompts/core/derive_facts.yaml") +) +SYSTEM_INTROSPECTION = load_prompt( + os.path.join(os.path.dirname(__file__), "prompts/core/introspection.yaml") +) +SYSTEM_RESPONSE = load_prompt( + os.path.join(os.path.dirname(__file__), "prompts/core/response.yaml") +) +SYSTEM_CHECK_DUPS = load_prompt( + os.path.join(os.path.dirname(__file__), "prompts/utils/check_dup_facts.yaml") +) class LMChain: "Wrapper class for encapsulating the multiple different chains used" + output_parser = NumberedListOutputParser() - llm: ChatOpenAI = ChatOpenAI(model_name = "gpt-3.5-turbo") - system_derive_facts: SystemMessagePromptTemplate = SystemMessagePromptTemplate(prompt=SYSTEM_DERIVE_FACTS) - system_introspection: SystemMessagePromptTemplate = SystemMessagePromptTemplate(prompt=SYSTEM_INTROSPECTION) - system_response: SystemMessagePromptTemplate = SystemMessagePromptTemplate(prompt=SYSTEM_RESPONSE) - system_check_dups: SystemMessagePromptTemplate = SystemMessagePromptTemplate(prompt=SYSTEM_CHECK_DUPS) + llm: ChatOpenAI = ChatOpenAI(model_name="gpt-3.5-turbo") + system_derive_facts: SystemMessagePromptTemplate = SystemMessagePromptTemplate( + prompt=SYSTEM_DERIVE_FACTS + ) + system_introspection: SystemMessagePromptTemplate = SystemMessagePromptTemplate( + prompt=SYSTEM_INTROSPECTION + ) + system_response: SystemMessagePromptTemplate = SystemMessagePromptTemplate( + prompt=SYSTEM_RESPONSE + ) + system_check_dups: SystemMessagePromptTemplate = SystemMessagePromptTemplate( + prompt=SYSTEM_CHECK_DUPS + ) def __init__(self) -> None: pass @@ -43,18 +54,25 @@ async def derive_facts(cls, chat_history: List, input: str): """Derive facts from the user input""" # format prompt - fact_derivation = ChatPromptTemplate.from_messages([ - cls.system_derive_facts - ]) + fact_derivation = ChatPromptTemplate.from_messages([cls.system_derive_facts]) # LCEL chain = fact_derivation | cls.llm - + # inference - response = await chain.ainvoke({ - "chat_history": [("user: " + message.content if isinstance(message, HumanMessage) else "ai: " + message.content) for message in chat_history], - "user_input": input - }) + response = await chain.ainvoke( + { + "chat_history": [ + ( + "user: " + message.content + if isinstance(message, HumanMessage) + else "ai: " + message.content + ) + for message in chat_history + ], + "user_input": input, + } + ) # parse output facts = cls.output_parser.parse(response.content) @@ -62,28 +80,31 @@ async def derive_facts(cls, chat_history: List, input: str): print(f"DERIVED FACTS: {facts}") return facts - + @classmethod - async def check_dups(cls, user_message: Message, session: Session, collection: Collection, facts: List): + async def check_dups( + cls, + user_message: Message, + session: Session, + collection: Collection, + facts: List, + ): """Check that we're not storing duplicate facts""" # format prompt - check_duplication = ChatPromptTemplate.from_messages([ - cls.system_check_dups - ]) + check_duplication = ChatPromptTemplate.from_messages([cls.system_check_dups]) query = " ".join(facts) - result = collection.query(query=query, top_k=10) + result = collection.query(query=query, top_k=10) existing_facts = [document.content for document in result] # LCEL chain = check_duplication | cls.llm # inference - response = await chain.ainvoke({ - "existing_facts": existing_facts, - "facts": facts - }) + response = await chain.ainvoke( + {"existing_facts": existing_facts, "facts": facts} + ) # parse output new_facts = cls.output_parser.parse(response.content) @@ -96,28 +117,30 @@ async def check_dups(cls, user_message: Message, session: Session, collection: C # add facts as metamessages for fact in new_facts: - session.create_metamessage(message=user_message, metamessage_type="fact", content=fact) + session.create_metamessage( + message=user_message, metamessage_type="fact", content=fact + ) return - @classmethod - async def introspect(cls, user_message: Message, session: Session, chat_history: List, input:str): + async def introspect( + cls, user_message: Message, session: Session, chat_history: List, input: str + ): """Generate questions about the user to use as retrieval over the fact store""" # format prompt - introspection_prompt = ChatPromptTemplate.from_messages([ - cls.system_introspection - ]) + introspection_prompt = ChatPromptTemplate.from_messages( + [cls.system_introspection] + ) # LCEL chain = introspection_prompt | cls.llm # inference - response = await chain.ainvoke({ - "chat_history": chat_history, - "user_input": input - }) + response = await chain.ainvoke( + {"chat_history": chat_history, "user_input": input} + ) # parse output questions = cls.output_parser.parse(response.content) @@ -126,21 +149,22 @@ async def introspect(cls, user_message: Message, session: Session, chat_history: # write questions as metamessages for question in questions: - session.create_metamessage(message=user_message, metamessage_type="introspect", content=question) + session.create_metamessage( + message=user_message, metamessage_type="introspect", content=question + ) return questions - @classmethod - async def respond(cls, collection: Collection, chat_history: List, questions: List, input: str): + async def respond( + cls, collection: Collection, chat_history: List, questions: List, input: str + ): """Take the facts and chat history and generate a personalized response""" # format prompt - response_prompt = ChatPromptTemplate.from_messages([ - cls.system_response, - *chat_history, - HumanMessage(content=input) - ]) + response_prompt = ChatPromptTemplate.from_messages( + [cls.system_response, *chat_history, HumanMessage(content=input)] + ) retrieved_facts = collection.query(query=questions, top_k=10) retrieved_facts_content = [document.content for document in retrieved_facts] @@ -149,18 +173,29 @@ async def respond(cls, collection: Collection, chat_history: List, questions: Li chain = response_prompt | cls.llm # inference - response = await chain.ainvoke({ - "facts": retrieved_facts_content, - }) + response = await chain.ainvoke( + { + "facts": retrieved_facts_content, + } + ) return response.content - + @classmethod - async def chat(cls, chat_history: List, user_message: Message, session: Session, collection: Collection, input: str): + async def chat( + cls, + chat_history: List, + user_message: Message, + session: Session, + collection: Collection, + input: str, + ): """Chat with the model""" facts = await cls.derive_facts(chat_history, input) - await cls.check_dups(user_message, session, collection, facts) if facts is not None else None + await cls.check_dups( + user_message, session, collection, facts + ) if facts is not None else None # introspect questions = await cls.introspect(user_message, session, chat_history, input) @@ -169,7 +204,3 @@ async def chat(cls, chat_history: List, user_message: Message, session: Session, response = await cls.respond(collection, chat_history, questions, input) return response - - - - diff --git a/example/discord/simple-roast-bot/main.py b/example/discord/simple-roast-bot/main.py index 0f59931..2a7a4b2 100644 --- a/example/discord/simple-roast-bot/main.py +++ b/example/discord/simple-roast-bot/main.py @@ -11,6 +11,7 @@ from langchain_core.messages import AIMessage, HumanMessage from honcho import Honcho +from honcho.ext.langchain import langchain_message_converter load_dotenv() @@ -43,16 +44,6 @@ chain = prompt | model | output_parser -def langchain_message_converter(messages: List): - new_messages = [] - for message in messages: - if message.is_user: - new_messages.append(HumanMessage(content=message.content)) - else: - new_messages.append(AIMessage(content=message.content)) - return new_messages - - @bot.event async def on_ready(): print(f"We have logged in as {bot.user}") diff --git a/sdk/honcho/ext/__init__.py b/sdk/honcho/ext/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/sdk/honcho/ext/langchain.py b/sdk/honcho/ext/langchain.py new file mode 100644 index 0000000..c84a0be --- /dev/null +++ b/sdk/honcho/ext/langchain.py @@ -0,0 +1,29 @@ +import functools +import importlib +from typing import List + +from honcho.schemas import Message + + +def requires_langchain(func): + @functools.wraps(func) + def wrapper(*args, **kwargs): + if importlib.util.find_spec("langchain") is None: + raise ImportError("Langchain must be installed to use this feature") + # raise RuntimeError("langchain is not installed") + return func(*args, **kwargs) + + return wrapper + + +@requires_langchain +def langchain_message_converter(messages: List[Message]): + from langchain_core.messages import AIMessage, HumanMessage + + new_messages = [] + for message in messages: + if message.is_user: + new_messages.append(HumanMessage(content=message.content)) + else: + new_messages.append(AIMessage(content=message.content)) + return new_messages