9 Commits (27441555d0d583a11c2c00629defaef09905eb06)

Author	SHA1	Message	Date
Leonid Ganeline	3f6bf852ea	experimental: docstrings update (#18048) ... Added missed docstrings. Formatted docsctrings to the consistent format.	7 months ago
Erick Friis	ed789be8f4	docs, templates: update schema imports to core (#17885) ... - chat models, messages - documents - agentaction/finish - baseretriever,document - stroutputparser - more messages - basemessage - format_document - baseoutputparser --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	7 months ago
James Braza	24385a00de	core[minor], langchain[patch], experimental[patch]: Added missing `py.typed` to `langchain_core` (#14143) ... See PR title. From what I can see, `poetry` will auto-include this. Please let me know if I am missing something here. --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	10 months ago
Bagatur	c61e30632e	BUG: more core fixes (#13665) ... Fix some circular deps: - move PromptValue into top level module bc both PromptTemplates and OutputParsers import - move tracer context vars to `tracers.context` and import them in functions in `callbacks.manager` - add core import tests	10 months ago
Shorthills AI	25c98dbba9	Fixed some grammatical and Exception types issues (#12015) ... Fixed some grammatical issues and Exception types. @baskaryan , @eyurtsev --------- Co-authored-by: Sanskar Tanwar <142409040+SanskarTanwarShorthillsAI@users.noreply.github.com> Co-authored-by: UpneetShorthillsAI <144228282+UpneetShorthillsAI@users.noreply.github.com> Co-authored-by: HarshGuptaShorthillsAI <144897987+HarshGuptaShorthillsAI@users.noreply.github.com> Co-authored-by: AdityaKalraShorthillsAI <143726711+AdityaKalraShorthillsAI@users.noreply.github.com> Co-authored-by: SakshiShorthillsAI <144228183+SakshiShorthillsAI@users.noreply.github.com>	11 months ago
Nikhil Jha	dff24285ea	Comprehend Moderation 0.2 (#11730) ... This PR replaces the previous `Intent` check with the new `Prompt Safety` check. The logic and steps to enable chain moderation via the Amazon Comprehend service, allowing you to detect and redact PII, Toxic, and Prompt Safety information in the LLM prompt or answer remains unchanged. This implementation updates the code and configuration types with respect to `Prompt Safety`. ### Usage sample ```python from langchain_experimental.comprehend_moderation import (BaseModerationConfig, ModerationPromptSafetyConfig, ModerationPiiConfig, ModerationToxicityConfig ) pii_config = ModerationPiiConfig( labels=["SSN"], redact=True, mask_character="X" ) toxicity_config = ModerationToxicityConfig( threshold=0.5 ) prompt_safety_config = ModerationPromptSafetyConfig( threshold=0.5 ) moderation_config = BaseModerationConfig( filters=[pii_config, toxicity_config, prompt_safety_config] ) comp_moderation_with_config = AmazonComprehendModerationChain( moderation_config=moderation_config, #specify the configuration client=comprehend_client, #optionally pass the Boto3 Client verbose=True ) template = """Question: {question} Answer:""" prompt = PromptTemplate(template=template, input_variables=["question"]) responses = [ "Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.", "Final Answer: This is a really shitty way of constructing a birdhouse. This is fucking insane to think that any birds would actually create their motherfucking nests here." ] llm = FakeListLLM(responses=responses) llm_chain = LLMChain(prompt=prompt, llm=llm) chain = ( prompt | comp_moderation_with_config | {llm_chain.input_keys[0]: lambda x: x['output'] } | llm_chain | { "input": lambda x: x['text'] } | comp_moderation_with_config ) try: response = chain.invoke({"question": "A sample SSN number looks like this 123-456-7890. Can you give me some more samples?"}) except Exception as e: print(str(e)) else: print(response['output']) ``` ### Output ```python > Entering new AmazonComprehendModerationChain chain... Running AmazonComprehendModerationChain... Running pii Validation... Running toxicity Validation... Running prompt safety Validation... > Finished chain. > Entering new AmazonComprehendModerationChain chain... Running AmazonComprehendModerationChain... Running pii Validation... Running toxicity Validation... Running prompt safety Validation... > Finished chain. Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like XXXXXXXXXXXX John Doe's phone number is (999)253-9876. ``` --------- Co-authored-by: Jha <nikjha@amazon.com> Co-authored-by: Anjan Biswas <anjanavb@amazon.com> Co-authored-by: Anjan Biswas <84933469+anjanvb@users.noreply.github.com>	11 months ago
Predrag Gruevski	5d6b83d9cf	Make a copy of external data instead of mutating another object's attributes. (#11349) ... Fix for a bug surfaced as part of #11339. `mypy` caught this since the types didn't match up.	12 months ago
Harrison Chase	4abe85be57	Harrison/string inplace (#10153) ... Co-authored-by: Wrick Talukdar <wrick.talukdar@gmail.com> Co-authored-by: Anjan Biswas <anjanavb@amazon.com> Co-authored-by: Jha <nikjha@amazon.com> Co-authored-by: Lucky-Lance <77819606+Lucky-Lance@users.noreply.github.com> Co-authored-by: 陆徐东 <luxudong@MacBook-Pro.local>	1 year ago
nikhilkjha	d57d08fd01	Initial commit for comprehend moderator (#9665) ... This PR implements a custom chain that wraps Amazon Comprehend API calls. The custom chain is aimed to be used with LLM chains to provide moderation capability that let’s you detect and redact PII, Toxic and Intent content in the LLM prompt, or the LLM response. The implementation accepts a configuration object to control what checks will be performed on a LLM prompt and can be used in a variety of setups using the LangChain expression language to not only detect the configured info in chains, but also other constructs such as a retriever. The included sample notebook goes over the different configuration options and how to use it with other chains. ### Usage sample ```python from langchain_experimental.comprehend_moderation import BaseModerationActions, BaseModerationFilters moderation_config = { "filters":[ BaseModerationFilters.PII, BaseModerationFilters.TOXICITY, BaseModerationFilters.INTENT ], "pii":{ "action": BaseModerationActions.ALLOW, "threshold":0.5, "labels":["SSN"], "mask_character": "X" }, "toxicity":{ "action": BaseModerationActions.STOP, "threshold":0.5 }, "intent":{ "action": BaseModerationActions.STOP, "threshold":0.5 } } comp_moderation_with_config = AmazonComprehendModerationChain( moderation_config=moderation_config, #specify the configuration client=comprehend_client, #optionally pass the Boto3 Client verbose=True ) template = """Question: {question} Answer:""" prompt = PromptTemplate(template=template, input_variables=["question"]) responses = [ "Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.", "Final Answer: This is a really shitty way of constructing a birdhouse. This is fucking insane to think that any birds would actually create their motherfucking nests here." ] llm = FakeListLLM(responses=responses) llm_chain = LLMChain(prompt=prompt, llm=llm) chain = ( prompt | comp_moderation_with_config | {llm_chain.input_keys[0]: lambda x: x['output'] } | llm_chain | { "input": lambda x: x['text'] } | comp_moderation_with_config ) response = chain.invoke({"question": "A sample SSN number looks like this 123-456-7890. Can you give me some more samples?"}) print(response['output']) ``` ### Output ``` > Entering new AmazonComprehendModerationChain chain... Running AmazonComprehendModerationChain... Running pii validation... Found PII content..stopping.. The prompt contains PII entities and cannot be processed ``` --------- Co-authored-by: Piyush Jain <piyushjain@duck.com> Co-authored-by: Anjan Biswas <anjanavb@amazon.com> Co-authored-by: Jha <nikjha@amazon.com> Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago